research methods for communication

ISBN:

9780195588033

Binding:

Paperback

Published:

21 Sep 2015

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Series:

• examine basic concepts of communication research;
• introduce specific data collection methods such as survey, content analysis, experimental design and qualitative methods;
• practice statistical techniques including sampling, descriptive and inferential analyses;
• oral and written presentation of research results. 

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Communication Research Methods

Comm 3160: communication research methods.

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Research methods in communication studies - An overview

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How do we Evaluate Programmes?

Types of Communication Research Differences Between Qualitative and Quantitative Research Methods Qualitative Research Methods Quasi-Quantitative Research Methods: Pretesting Messages and Materials Quantitative Research Methods Additional Research Methods

Types of Communication Research

Research into intended audiences� culture, lifestyle, behaviors and motivations, interests, and needs is a key component to a health communication program�s success. This section describes communication research methods commonly used throughout program planning. See the chart Types of Research and Evaluation for more detail about research conducted in each of the stages of health communication program planning.

Most programs use more than one research method. For example, conducting exploratory focus groups with an intended audience at the start of program planning can orient you to the types of approaches, messages, and channels that are most likely to be successful with a particular group. In some cases, focus groups might be augmented with in-depth interviews to learn more about intended audience members� motivations. Later, messages and materials might be pretested approximate how an individual would encounter them in "real life." Theater-style testing also approximates reality, using a simulated television-viewing environment. Clearly, some methods are better suited to specific purposes than others. Using multiple methods can help ensure that you get an accurate picture of your intended audience members and their likely responses to your program.

Differences Between Qualitative and Quantitative Research Methods

There are two basic types of research you might conduct with intended audiences: qualitative and quantitative. You will use methods from one of these two types depending upon what you want to learn. See the sidebar below, Qualitative Versus Quantitative Methods, for common distinctions between qualitative and quantitative research.

In this section, you will learn when to use each type of research, how to conduct research with members of your intended audience, and how you can use the data you collect to inform your project. Qualitative, quasi-quantitative, and quantitative research methods are discussed separately.

Qualitative Research

Use qualitative research methods when:

• You are planning a communication program and developing materials for it
• When the goal of your research is to explore a topic or idea
• When the goal of your research is to gain insights into an intended audience�s lifestyle, culture, motivations, behaviors, and preferences

Conduct qualitative research by:

• Selecting a small group of people chosen for particular characteristics
• Convening a discussion (i.e., a focus group or in-depth interview) or observing individuals� behaviors in schools, malls, supermarkets, etc.
• Keeping the discussion fairly unstructured, so that participants are free to make any response and are not required to choose from a list of possible responses
• Choosing which question to ask next based on your participants� previous responses

Qualitative research results cannot be:

• Quantified or subjected to statistical analysis
• Projected to the population from which the respondents were drawn because participants are not selected randomly (to be representative of the population as a whole) and because not all participants are asked precisely the same questions

Quantitative Research

Use quantitative research methods when:

• You are planning a communication program (e.g., to measure the prevalence of a particular behavior) or assessing a program already in place
• The goal of your research is measurement of particular variables Conduct quantitative research by:
• Selecting a large group or groups of people
• Using a structured questionnaire containing predominantly forced-choice or closed-ended questions

Quantitative research results can be:

• Analyzed using statistical techniques
• Considered representative of the population from which the respondents were drawn if each person in the population had an equal chance of being included

Qualitative Research Methods

Use qualitative research methods during the following parts of your program:

• Stage 1 �to find out more about your intended audiences and to learn what the priorities and approach should be for trying to influence their awareness, knowledge, attitudes, intentions, or behaviors
• Stage 2 �to determine whether your materials communicate the intended messages effectively and persuasively
• Stage 3 �to understand why the program is or isn�t working as expected
• Stage 4 �to learn more about what worked and what didn�t, and why certain outcomes occurred

Focus groups and in-depth interviews are the most common methods used in qualitative communication research. However, there are many innovative methods, some described here, that can help you learn about an audience. Because the methodologies for each are very similar, they are discussed together here, using instructions for focus groups as a guide.

About Focus Groups

Working from a discussion guide, a skilled moderator facilitates a 1- to 2-hour discussion among 6 to 10 participants, which can be conducted either in person or by telephone (ideally in person). The moderator keeps the session on track while participants talk freely and spontaneously. As new topics related to the material emerge, the moderator asks additional questions to learn more.

Common Uses

• Learning about feelings, motivators, and past experiences related to a health topic
• Exploring the feasibility of various potential actions (from the intended audience�s viewpoint)
• Identifying barriers to those actions
• Exploring what benefits the intended-audience members find compelling and what results they expect from taking a particular action
• Learning about the intended-audience�s use of settings, channels, and activities
• Capturing the language used by the intended audience to discuss issues
• Identifying concepts that do or do not resonate and understanding why
• Triggering the creative thinking of communication professionals
• Illustrating to others how the intended audience thinks and talks about a health issue
• Developing hypotheses (or broad issues) for quantitative studies and identifying the range of responses that should be included in closed-ended questionnaires
• Providing insights into the results of quantitative studies by obtaining in-depth information from individuals typical of the intended audience to help understand why individuals responded in certain ways
• Group interaction can help elicit in-depth thought and discussion.
• Group interaction can help with brainstorming because respondents can build off one another's ideas.
• Moderators have considerable opportunity to probe responses.
• Focus groups yield richer data than surveys about the complexities of an intended audience�s thinking and behavior.
• Findings are not generalizable to the population.
• Focus groups can be labor intensive and expensive, especially if sessions are conducted in multiple locations.
• Group responses do not necessarily reflect individuals�9 opinions because some individuals in the group may dominate the discussion or may influence others� opinions.
• Each person is limited to about 10 minutes of talking.

The process, benefits, and drawbacks of in-depth interviews are similar to those of focus groups, except that the interviewer speaks with one person at a time. In-depth interviews can take place at a central facility or at the participant�s home or place of project/programme purpose. As with focus groups, when individual interviews cannot be conducted in person, they can be conducted by phone or over the Internet. Although these interviews are more time intensive, one of their key benefits is that each respondent is isolated from other respondents and therefore not influenced by what others say.

How to Design and Conduct a Focus Group or In-Depth Interview Study

To design and conduct a qualitative research study, complete the following steps.

Plan the Study

Determine the following:

• What you want to learn. Determine the objectives of your study at the outset, and then check to make sure that the moderator�s/interviewer�s guide includes lines of questioning that will provide the answers.You may also use the objectives to help analyze the results of the discussions and to organize the focus group or in-depth interview report.
• When you need to have that information.
• How you will apply what you learn. It is important to decide how you will use your focus group or in-depth interview results before you conduct your study.
• Your budget.
• Typical of your intended audience (the same behavioral, demographic, and psychographic characteristics).You may want to conduct separate groups with "doers" (those who already engage in the desired behavior) and "nondoers" (those who do not) to help identify what actions the doers take, and why, so that those approaches can be explored with the nondoers.
• Not experts. Exclude market researchers and advertising professionals (because of their familiarity with the methodology) and those who have, or might be perceived by other group members as having, expertise in the subject matter (e.g., exclude health professionals from focus groups when the topic is a health issue).
• Relative newcomers to focus groups or interviews, so that their reactions will be spontaneous. This will help you avoid "professional" respondents (i.e., those who have participated in many previous focus groups or individual interviews before) who may lead or monopolize the discussion.
• Divide participants into different focus groups based on their gender, race, age, level of formal education, or any other variable likely to hinder their freedom of expression (e.g., teenage girls will be more comfortable discussing sexual activity if teenage boys or college-age women are not in the group).
• Conduct a minimum of two focus groups with each intended audience segment (e.g., if you are conducting separate groups with men and women, you will need at least four groups�two with men, two with women). If intended audience perceptions vary or the audience feedback is unclear, you may want to conduct additional groups with each segment, especially if you revise the discussion guide to more fully explore unresolved issues.
• If you are using in-depth interviews, conduct approximately 10 interviews per intended audience segment. If common themes do not emerge or the interviews, especially if you revise the interview guide in between interviews.

Choose the Location

You can convene focus group discussions or in-depth interviews in a variety of ways:

• Commercial focus group facilities can recruit participants for you (for both focus groups and interviews) and offer audio and video recording equipment as well as observation rooms with one-way mirrors for viewing. However, these facilities are usually available only in larger metropolitan areas.
• Teleconference services can set up telephone focus groups for you. Most allow observers to listen without being heard, and some provide remote viewing programs to allow the moderator to see a list of participant names (with a symbol next to the one currently speaking) or notes sent in by a technician from observers listening to the call. Some teleconference services can recruit participants; with others, you will have to recruit participants or contract with a recruiter separately.
• You can also conduct focus groups or in-depth interviews in meeting rooms at churches, office buildings, or other locations. If an observation room with a one-way mirror is not available, staff may still listen in through speakers hooked up in a nearby room or by audiotaping or videotaping the session. In some cases, one or two quiet observers may be allowed in the room to take notes.

See the sidebar Pros and Cons of Different Formats below for the advantages and disadvantages of different formats for focus group and in-depth interview research.

Draft a Recruitment Screener

A recruitment screener is a short questionnaire that is administered to potential participants, typically by telephone, to ensure that they meet the criteria you developed during step 1. Ideally, the screener should help you exclude participants who are already familiar with the specific subject of the sessions, or who know each other. If participants know the subject in advance, they may formulate ideas or may study to become more knowledgeable about the subject than the typical intended audience member. If participants know each other, they may speak less freely. See Appendix A for a sample screener.

Recruit Participants

Recruit participants by telephone one to three weeks in advance of the sessions. You can identify potential participants in different ways depending upon the type of people you are seeking and the resources you have available. Identify members of the public through focus group facility databases, by running an ad in a local publication, by working with community organizations , or by using the phone book (although the latter will be extremely time consuming if you have stringent recruitment criteria). Identify professionals through a relevant association or mailing list service or through a focus group facility�s recruiting databases. Depending on your budget and internal resources, you may choose to recruit in one of the following ways:

• Use your own organization�s staff to recruit participants.
• Hire a focus group facility or independent recruiter (to identify such facilities, consult a directory such as the American Marketing Association�s Green Book or the Marketing Research Association�s Blue Book ). If you have many facilities and recruiters to choose from, consider getting recommendations from local organizations or organizations that conduct qualitative research.
• Get help from a university marketing research or advertising class.
• Work through gatekeepers such as teachers (for students), health care providers (for patients, physicians, or nurses), religious institutions or community organizations (a small donation may encourage them to participate), and instructors of English as a second language.

Regardless of how the recruiting is done, ensure that the screener is followed carefully so that only individuals who qualify for participation will be included.

Getting People to Show Up

To ensure that enough people show up, offer an incentive (usually money) and recruit two or three more people than you actually need. If all show up, select those who best fit the screening criteria, thank the extra participants, give them the agreed-upon incentive, and dismiss them. Other ways to increase participation include:

• Scheduling sessions at times that are convenient for your potential participants (e.g., at lunch or after work)
• Choosing a safe and convenient site with easy parking
• Providing transportation (or reimbursement for agreed-upon transportation costs)
• Arranging for childcare, if necessary
• Letting them know you will be serving refreshments

Recruiting Patients and Their Families

Recruiting patients and their families requires special consideration. Contact clinics, hospitals, or local HMOs for help and make adequate plans to ensure that the participants and their family members are not inconvenienced or upset. Some organizations may require institutional review board (IRB) approval of your research. Gaining IRB approval is often a long process, so be sure you check with the organization early in the planning stage of your study to find out whether you will need IRB approval.

Recruiting for Telephone Interviews

If you are recruiting for in-depth interviews to be conducted on the telephone, create a spreadsheet that includes spaces for the following information about each potential participant:

• Time zone in which the person is located
• Date, time, and telephone number at which he or she should be called for the interview
• Disposition of each call (e.g., scheduled an interview, no answer, busy, refused)

Develop a Moderator�s Guide

The moderator�s guide tells the moderator what information you want from the groups and helps him or her keep the discussion on track and on time. It is only a guide, however. During the focus groups, experienced moderators flow with the conversation, asking questions in the prescribed language and sequence when possible but sometimes deviating from the guide to avoid awkward transitions or unnecessary back-and-forth between topics.

Before you draft the moderator's guide, answer the following questions:

• What do we want to learn from the focus group?
• How will we apply what we learn?
• What tools (e.g., descriptive information, message concepts, or other draft creative work) will we need to provide?

Then, write questions for the guide that relate to the purposes you have identified. Make most questions open-ended so that participants can provide more in-depth responses than just "yes" or "no," but make sure the questions are not leading. This will help you get answers that reflect participants� true feelings and not what they think you would like to hear. The amount of time and depth of questions devoted to each issue should reflect the value of the issue to the research. See Appendix A for an example of a moderator�s guide.

Do not include questions for group discussion when you need individual responses. However, you can have the moderator give self-administered questionnaires to each participant to be completed prior to conducting a focus group, or participants can be asked to individually rank items on paper�such as potential actions, benefits, or message concepts�during a group to obtain both individual and group reactions.

Conduct the Focus Groups

Focus groups typically begin with the moderator welcoming participants and briefing them on the process (e.g., all opinions welcome�there are no right or wrong answers; the presence of audio- and videotaping and observers; the importance of speaking one at a time; confidentiality). Participants introduce themselves to the group by first name, usually including some information relevant to the topic of discussion (e.g., number of years with glaucoma, amount/type of insulin used each day). Next, the moderator asks a few simple "ice-breaker" questions to help participants get used to the group process and to reduce participant anxiety. This also helps the moderator develop rapport with the participants.

Continuing to follow the moderator�s guide, the moderator manages the group and ensures that all topics are covered without overtly directing the discussion. Participants are encouraged to express their views and even disagree with each other about the discussion topics. The moderator does not simply accept what participants say but probes to learn more about participants� underlying thinking and attitudes. The moderator also seeks out opinions from all participants so that all are heard and a few do not dominate the discussion.

Near the end of the discussion, the moderator will often give participants an activity or simply excuse him- or herself from the room for a moment to check with the observers and obtain any additional questions. Alternatively or additionally, notes can be sent in to the moderator while the group is in process if the observers would like different questions asked or other changes made to the group.

One advantage of focus group methodology is that the moderator�s guide, and any materials presented, can be revised between groups if necessary.

Analyze Results

The easiest and most thorough way to analyze focus groups is by reviewing transcripts, although groups can also be analyzed (albeit less thoroughly) by reviewing notes taken during the discussion. In many analyses, the goal is to look for general trends and agreement on issues. At the same time, it is important to note divergent opinions. Don�t ignore individual comments that raise interesting ideas or concerns such as lack of cultural sensitivity or difficulty in comprehension. In some instances, the goal is to capture the range of opinions about an issue, rather than to look for evidence of agreement or consensus.

Avoid counting or quantifying types of responses (e.g., "75 percent of participants preferred concept A"). Attempting to quantify the results�or suggesting in other ways that they represent the opinions of the intended audience as a whole�is inappropriate for qualitative research.

Quasi-Quantitative Research Methods: Pretesting Messages and Materials

Some commonly used communication research methods, such as central-location intercept interviews and theater tests, are best termed quasi-quantitative. While these methods are used in situations in which the goal is measurement and typically involve a questionnaire with mostly forced-choice questions, the results cannot be projected to the population as a whole (as with true quantitative surveys) because of the way in which participants are selected. For centrallocation intercept interviews, the only people who have a chance to participate are those who go to the location where the interviews are being held and who go there during the times they are conducted; this is not a truly representative sample of the intended audience. For theater tests, the only people who have a chance to participate are those who are recruited for the test, and recruitment does not follow a truly representative sampling design.

Quasi-quantitative methods are most often used during Stage 2 to pretest messages and materials. If your intended audience is geographically dispersed or it is difficult for them to get to a central facility, you can use telephone interviews and send participants any materials in advance. This type of pretest typically resembles an in-depth interviewing project in price and number of interviews, although there may be more closed-ended questions and the question sequence may be adhered to more closely.

Central-Location Intercept Interviews

Central-location intercept interviews consist of stationing interviewers at a point frequented by individuals from your intended audience and asking the individuals to participate in a study. If they agree, they are asked specific screening questions to see whether they fit the study criteria. If so, the interviewer takes them to the interviewing station (a quiet spot at a shopping mall or other site), shows the pretest materials, and then administers the pretest questionnaire.

For intercept interviews to be effective, you must obtain results from a minimum of 60 to 100 respondents from each intended audience segment you want to test.

• You can connect with harder-to-reach respondents in locations convenient and comfortable for them.
• The interviews can be conducted quickly. (The interview should be no longer than 15 to 20 minutes.)
• The interviews are a cost-effective means of gathering data in a relatively short time.
• If you choose an appropriate location, you will increase your chance to interview respondents who are among your intended audience.
• You must train interviewers.
• Your results are not representative or generalizable.
• Intercept interviews are not appropriate for sensitive issues or potentially threatening questions.
• Intercept interviews do not allow you to probe easily for additional information.

Central-location intercept interviews should not be used if respondents must be interviewed in depth or on emotional or sensitive subjects. The intercept approach also may not be suitable if respondents are likely to be resistant to being interviewed on the spot. In cases in which central-location intercepts will not work well, schedule interviews with respondents instead.  

Unlike focus groups or in-depth interviews, the questionnaire used in central-location intercept pretesting is highly structured and contains primarily multiple choice or closedended questions to permit quick response. Open�ended questions, which allow free�flowing answers, should be kept to a minimum because they take too much time for the respondent to answer and for the interviewer to record. Questions that assess the intended audience's comprehension and perceptions of the pretest materials form the core of the questionnaire. A few additional questions, tailored to the specific item or items being tested ("Do you prefer this picture�or this one?"), may also be included to meet your program planners' particular needs. The questionnaire should be pretested before it is used in the field. See Appendix A for a sample questionnaire.

Interview Setup

A number of market research organizations throughout the country conduct central�location intercept interviews in shopping malls.You can also conduct these interviews in clinic waiting rooms, religious institutions, Social Security offices, schools, work sites, train stations, and other locations frequented by members of your intended audience. Be sure to obtain permission well in advance of the time you want to set up interviewing stations in these locations.

If you are using a market research organization to conduct the interviews, you will need to provide screening criteria, test materials, and the questionnaire. In some cases, market research organizations have offices in shopping malls, and you can watch the testing through a one-way mirror.

Participant Recruitment

If you or someone in your organization is recruiting the participants, you will need to develop a script and provide training in approaching members of the intended audience. For example, if you are recruiting participants in a clinic waiting room, the interviewer should be familiar with the screening criteria (e.g., women under 60 years of age) and approach only those people who appear to fit the criteria. When, after screening, individuals do not qualify to participate, the interviewer should thank them for their time and indicate that this study is not the right fit for them but that their willingness to participate is appreciated. If they do qualify, the interviewer can bring them to a designated location (e.g., another room or corner of the waiting room) and proceed with the study.

University and college departments of marketing, communication, or health education may be able to provide interviewer training or trained student interviewers. Pretesting is an excellent real�world project for a faculty member to adopt as a class project or for a master's student to adopt as a thesis project. However, this approach may mean that it takes longer to accomplish the research, and you could compromise the quality of the results if the individuals are not experienced in this type of research.

Theater-Style Tests

Theater testing is often used in the commercial arena to test advertisements for products and services. Theater testing can also be used to test the effectiveness of PSAs. In this methodology, participants are invited to a central location to respond to a pilot for a new television show; in the midst of viewing the TV pilot, they are shown your PSA or advertisement along with other ads. Participants complete a questionnaire following the presentation, first answering questions about the show and then answering questions about how effectively your message was communicated to them and what their overall reactions were.

Theater�style tests are most commonly used to test TV advertisements and PSAs. For theater�style tests to be effective, you must obtain results from 50 to 100 respondents from each segment you want to test.

• You can obtain responses from a large number of respondents at the same time.
• Theater�style tests more closely replicate what goes on in someone's home when they are watching TV, so you can accurately judge people's reactions to your message.
• Theater�style tests can be cost�effective if you use donated facilities and equipment.
• It is expensive to rent a facility and equipment (if necessary).
• Your results are not representative of the general population.

General Format

Individuals typical of your intended audience are invited to a conveniently located meeting room. The room should be set up for screening a television program. Participants should not be told the real purpose of the session, only that their reactions to a television program are being sought.

At the session, participants watch a television program. The program can be any entertaining, nonhealth video approximately 15 to 30 minutes in length. The videotape is interrupted about halfway through by a sequence of four commercials. Your message should be inserted between the second and third commercials. See Appendix A for a description of how to create a roughcut video for theater-testing your message.

At the end of the program, participants receive a questionnaire and answer questions designed to gauge their reactions, first to the program and then to the advertisements. Finally, your ad is played again and participants complete several questions about your ad. The majority of these questions should be closed-ended to enable an easy and accurate summary of participant responses.

In more sophisticated theater testing, participants use automated intended audience response systems to answer questions. Participants are provided with a small device that has response keys. Once a question is asked, they push a key to respond and the data are automatically tabulated.You have instant access to the numbers using this system. In addition, questions can be instantly added or deleted from the questionnaire based on the previous responses. Using an automated system is much more costly than using a standard paper-and-pencil questionnaire.

Other Media You Can Test

This methodology can also be used to test videos by asking participants to view a series of videos in which yours has been included. Examples of videos that might be tested include a 15- to 30-minute breast cancer awareness video that will be played in a clinic or a "how-to" video on administering epinephrine. These testing sessions will, of course, last longer than those testing ads. Participants evaluate the videos as described above.

Print advertisements can also be tested using a variation of this methodology. Several ads, including yours, are inserted into a magazine. Participants are given an adequate amount of time to read through the article, which includes your ad and others. After reading the article, participants receive a questionnaire and answer questions designed to gauge their reactions, first to the article and then to the advertisements. Finally, your ad is displayed alone and participants complete several additional questions.

Designing and Conducting a Theater-Style Pretest

The process for conducting a theater�style test includes the following steps:

• Planning the pretest
• Developing the questionnaire
• Recruiting respondents
• Preparing for the pretest
• Conducting the pretest
• Analyzing the pretest

You may find step 2 also useful for central�location intercept interviews.

Plan the Pretest

• The purpose of the study (e.g., what do you want to learn?)
• When you need to produce results
• What your budget is
• The type and number of people who should participate in the pretest
• The locations where the pretest will be conducted

To conduct theater testing, you must have a large enough space to accommodate all of your participants at the same time. You must also ensure that you have several video monitors so that all participants can adequately view the program. Space constraints may be overcome by seeking out low-cost facilities such as a school auditorium or church hall.You may be able to borrow the audiovisual equipment from these facilities as well. You can also rent space, such as a hotel ballroom, if you want to test a large number of people. Hotels often rent audiovisual equipment as well. Reserve facilities and equipment well in advance of your pretest.

Develop the Questionnaire

To gather useful information from the pretest, you must carefully construct the questionnaire. See the sidebar Components Used in Most Questionnaires on the next page for general guidelines. Once you have written your questionnaire, be sure to test and revise it before you use it with a large number of respondents.

Recruit Respondents

Participants may be recruited through a market research facility or through local community organizations . In either case, you will need to provide an incentive for participants. If using a market research facility, you will also incur recruiting expenses. If you are working with a community organization, you may choose to make a donation.

Prepare for the Pretest

• Is the pretesting videotape ready for use? Are the video and audio portions clear?
• Is the videotape equipment�recorder and television monitors�functioning properly?
• Is the facility set up? Is the room reserved? Are there enough chairs? Are extra chairs available in case more people show up than you expect? Do you need another monitor so that everyone will be able to see the program? Is the heat or air conditioning working properly? Do you know where the light switches are? If a microphone is needed, is it set up and functioning properly?
• Have you made all the necessary staffing arrangements? Are your assistants coming to the session? Do they have transportation and directions for getting there?
• Have you made enough copies of your pretest questionnaire (including some extras)? Is each questionnaire complete (with no pages missing)? Do you have pencils for participants? Will they need clipboards or pads?
• Has participant recruitment taken place as scheduled? Did you call and remind participants to attend? Do they have transportation and directions?
• Has the moderator rehearsed?

Conduct the Pretest

• Be friendly and courteous to participants from the moment they arrive until they leave (remember to say "thank you").
• Keep calm and cool-headed throughout the session.
• Anticipate problems in advance (conducting a rehearsal to make sure that both equipment and timing work is a good idea).

The test session should take no more than 1 hour and 15 minutes if you are organized and well prepared. Follow the steps below to conduct your test:

• Encourage participants to take a seat as they arrive. Close the doors no later than 10 minutes after the scheduled starting time.
• When everyone is seated, introduce yourself by your name only (assuming you are the host). Do not tell participants the name of your organization during the session because it might bias their responses to your test ad.
• Thank participants for coming and assure them that the evening should be enjoyable and that they will have a chance to give their views to the producers of "new" television program material. Discourage participants from talking to one another during the session. Tell them you are interested in their own individual views and that there are no right or wrong answers. Also, encourage them to write their answers clearly in the space provided on the questionnaire.
• After your introductory remarks, have your assistants hand out the questionnaires (see Appendix A for a sample), pencils, and clipboards (if needed). Instruct the participants not to open the questionnaire until you ask them to do so. Turn on the video recorder and monitor to begin the test session.
• Be attentive and watch for any problems with the sound or picture on the monitor. Be sure that the equipment is functioning properly throughout the program.
• Be prepared to stop the recorder when the television program has ended. Introduce the questions, and thank the participants for their help so far. Ask them to open their questionnaires and complete the questions on the first page.
• When the participants have finished Part I of the questionnaire, tell them that you would like to gather their reactions to the messages/PSAs that were shown during the program. Have them turn to Part II and instruct them to fill out the questions about the messages.When they have completed these questions, tell them that you want to obtain their reactions to one particular message in the series of messages they viewed.
• Start the video. (Note: To avoid an awkward pause in the session�s pace, be sure there is not too much lead tape before the message starts.) After your PSA/ad has been replayed, ask participants to turn to the next page of the questionnaire and complete the remaining questions. Encourage them to answer every question and to avoid giving more than one answer, except when this option is indicated on the questionnaire.
• Circulate through the room to monitor progress and to be sure participants are not discussing their responses. Collect the questionnaires as participants finish.
• Thank participants for their cooperation. If you have incentives or token gifts, distribute them to participants as they leave. If you have provided a donation to a group in lieu of payment to participants, mention that you hope the group will find the donation helpful.

Analyze the Pretest

At this point, look at the overall results:

• What did you learn from the pretest?
• Did your message receive a favorable and appropriate intended audience reaction?
• Did your message fulfill its communication objectives?
• What are your message's strengths? Weaknesses?
• Did answers to any particular question stand out?

Use your answers to these questions to decide whether your message is both effective and appropriate and whether you need to revise your message prior to program implementation.

Diaries and Activity Logs

Other tools you can use to evaluate your program are diaries and activity logs. If you plan to use these tools to gauge the quality of program planning or execution, be sure to start keeping the diaries and activity logs as soon as you begin program planning. For each activity, request information in a specific format from program managers or participants. This information may cover issues such as the quality of program components or track how your intended audience uses the components.

• Track program implementation
• Assess effectiveness of program implementation
• Monitor whether planned activities are being conducted on schedule and within budget
• Learn what questions program participants had
• Learn what technical assistance was needed by program staff
• Track intended audience exposure to program components
• Allow respondents flexibility in their responses
• Enable researchers to observe behavior over time, rather than only once
• Require considerable effort on respondents' parts (for this reason, consider offering incentives for completion of the diaries/logs)
• Require staff able to code voluminous and challenging incoming data
• Are not appropriate for respondents who have low literacy skills or who have poor writing skills or penmanship

Instituting Diary/Activity Log Use

Steps in instituting the keeping of diaries and activity logs are:

• Planning the study
• Identifying who will participate
• Developing and pretesting the form you will use
• Collecting data
• Analyzing results

Follow the steps below to institute the keeping of diaries and activity logs.

• What you want to learn
• How much information you need to collect
• How you will apply what you learn
• When you need the information
• Your criteria for who should participate

Identify Who Will Participate

The sample you select depends on the goals of your study. If you are focusing on program implementation, you will want the diaries/logs to be completed by program staff (e.g., nurses in a clinic). In this case, you may have some control over the quality of responses you receive.

When planning the study, you must obtain permission from a manager or supervisor on site for staff to complete the diaries/logs during the study.You should provide an estimate of the amount of time and effort participation will entail (e.g., 15 minutes per day, 1 hour per day). Share drafts of the diaries/logs and get input from the supervisor prior to the study. This will help to ensure cooperation during the study.

Before the study begins, you should train staff to complete the diaries/logs. Even if it seems obvious to you, it is essential that you explain exactly what you want recorded in the diary/log. (See the sample log in Appendix A .) In addition, you should provide detailed, written instructions for future reference. These instructions can be used in lieu of training if you cannot physically get to the study site.

If you are focusing on participant experience with a program, you will want the diaries/logs to be completed by people who were exposed to program components. In this case, you will have much less control over the quality and quantity of responses.

Obtaining cooperation from participants may also be more difficult in this situation. For example, people attending an educational program on nutrition might be recruited to complete a diary of what they eat for a week and send it back to the researchers.You will likely need to provide an incentive (e.g., a gift certificate upon receipt of the completed diary), and you may also need to remind participants to send back the diaries at the end of the study period.

Develop and Pretest the Form You Will Use

Create questions. Write questions that are specific to your study objectives.

Examples of the types of information you might collect include:

• What is the gender of the caller?
• Where did the caller get the number?
• Did the caller request any educational materials?
• What questions did the caller ask?
• Which module was used?
• Which of the suggested activities were completed?
• How long did it take to complete the suggested activities?
• Would you use these activities again?
• Why or why not?
• How many people passed by the display containing the new breast cancer information?
• How many stopped to look at the information?
• How many took a brochure?

Pretest the diary/log. Once you have created the draft diary/log, you must pretest it with individuals who represent your intended audience. Describe the scenario for them before the pretest. For example, in the case of a hotline, you might say, "You are an operator on a hotline. People will be calling in, and you will need to fill out this activity log as you complete each call." Sit together with them and ask them to read each question aloud and tell you what they think they are supposed to do. Do not correct them if they do not say what you intended. This probably means that your diary/log is unclear. Continue through the entire diary/log and then ask them if there was anything that they found confusing or unclear. Pretest the diary/log with everyone as planned before you make any changes.

Revise the diary/log. Revise questions that people found confusing during the pretest. If a question was confusing only to one person, use your judgment to decide whether to change the question. Ask yourself whether there is something you can easily fix that would have helped that one person understand the question (e.g., providing an example). If so, you may be able to make a simple change or addition to clarify the question. Also consider whether this respondent found many of the questions confusing while other respondents had no problem with them. If this is the case, you may not want to make changes.You will have to decide on a case-by-case basis. If you make substantial changes to the diary/log, you should conduct another pretest before finalizing the form.

Collect Data

Produce diaries/logs in sufficient quantities so that respondents have several extra forms in case they make errors or need more space. Deliver the diaries/logs to respondents, along with detailed written instructions, prior to training (if applicable) or at least 1 week before the study begins. If you are asking program participants rather than program staff to complete diaries/logs for you, you will have to distribute the materials on site. Give respondents a fixed time frame to complete the diaries/logs and provide them with a means (envelope/postage) to return the data to you. If your study is longer than a week or two, you may want to ask respondents to ship the first week of data to you so that you can review the logs for accuracy and completeness and even begin to tally some of the information.

In the planning phase, you determined what you wanted to learn from the study. Now you will have the chance to look through the diaries/logs to answer these questions. Diaries generally contain qualitative information (e.g., how food choices were made that day, evaluation of programs completed). Activity logs may contain several types of information�quantitative information you can tabulate easily (e.g., how many people called a hotline each day, whether people picked up a brochure) as well as qualitative information (e.g., reasons that students liked or participated in an activity).

Analyzing qualitative responses. The best way to analyze qualitative information is to read through the information, searching for similarities and differences between diaries. You will need to consider all of the questions that you determined were important in the planning phase. Once you have reviewed several diaries, you should be able to pull out general themes or patterns from the information. The best way to analyze these themes is to develop categories for the responses. For example, if you want to know why teachers thought their students liked or disliked a certain educational module in your program, you might group responses into categories such as "challenging," "fun," "too much work," "boring." Continue reading through the remaining diaries and see how many responses fall into these categories. As you go along, you may come up with additional categories or decide to collapse several categories together.You can certainly make inferences (e.g., "Teachers liked the module becau...") about diary information, but resist the temptation to quantify this information.

Analyzing quantitative responses. The easiest way to analyze these types of responses is to create a coding sheet for each quantitative question. Use a separate sheet for each question, writing the question at the top and creating columns for each possible response. For example, for a question about how many people picked up particular brochures, you could create columns for the following categories: 0, 1�5, 6�10, 11�15, 16�20, >20.

Use the following procedure to record the responses:

• Take the first activity log and record the response by making a check mark in the appropriate column.
• Repeat this procedure for every questionnaire.
• Tally the total number of check marks in each column and then calculate the percentage of participants who gave each type of response.

Quantitative Research Methods

Use quantitative research methods during the following parts of your program:

• Stage 1 �to obtain information on prevalence of relevant knowledge, attitudes, behaviors, and behavioral intentions
• Stage 3 �to monitor usage of materials and intended audience awareness of the communication program and its various tactics
• Stage 4 �to measure progress toward objectives

Two different quantitative research methods, surveying and readability testing, can be used.

Surveys are characterized by large numbers of respondents (100 or more) and questionnaires that contain predominantly forced-choice (closed-ended) questions.

Used in planning and assessment to obtain baseline and tracking information on knowledge, attitudes, behaviors, and behavioral intentions

• Provides generalizable results (to generalize to a broader population, you must have a statistically valid random sample)
• Can be anonymous (useful for sensitive topics)
• Can inorganizational visual material (e.g., can pretest prototype materials)
• Limited ability to probe answers
• Potential bias from possible respondent self-selection

Most surveys are custom studies that are designed to answer a specific set of research questions. Some surveys, however, are omnibus studies, in which you add questions about your topic to an already existing survey. A number of national and local public opinion polls offer this option.

Follow these steps to conduct a survey:

• Plan the study
• Determine how the sample will be obtained and contacted
• Develop and pretest the questionnaire
• Collect the data
• Analyze results

Sampling size and composition, questionnaire design, and analysis of quantitative data are complex topics beyond the scope of this book. If you are planning a quantitative study, see the reference list at the end of this book for additional information.

Additional Research Methods

Gatekeeper Reviews

Public and patient education materials are often routed to their intended audiences through health professionals or other individuals or organizations that can communicate with these audiences for you. These intermediaries act as gatekeepers, controlling the distribution channels that reach your intended audiences. Their approval or disapproval of materials can be a critical factor in your program�s success. If they do not like a poster or a booklet or do not believe it to be credible or scientifically accurate, it may never reach your intended audience.

Gatekeeper review of rough materials is important and should be considered part of the pretesting process, although it is not a substitute for pretesting materials with intended audience members. Neither is it a substitute for obtaining clearances or expert review for technical accuracy; these should be completed before pretesting is undertaken. Sometimes, telling gatekeepers that technical experts have reviewed the material for accuracy will reassure them and may speed their approval of your material.

Methodology

The methodology you should use for gatekeeper review depends upon your available resources, time, and budget. Common methods include:

• Self-administered questionnaires� Participants are sent the materials and the questionnaire at the same time.
• Interviewer-administered questionnaires� Typically, an appointment for the interview is scheduled with the gatekeeper, and the materials are sent for review prior to the interview.

Develop questionnaires that ask about overall reactions to the materials and for assessment of the information�s appropriateness and usefulness.

In some cases, you might not use a formal questionnaire (especially if you don�t think the reviewer will take the time to fill it out) but will instead schedule a telephone conversation or a meeting about the materials. If you are not using a questionnaire, consider in advance what kind of questions you want to ask in the meeting or interview and determine whether you need formal approval of the materials. A discussion with gatekeepers (e.g., a television public service director, the executive director of a medical society) at this point can also be used to solicit their involvement in a variety of ways that extend beyond materials development.

Readability Testing*

Readability formulas often are used to assess the reading level of materials. Fry, Flesch, FOG, and SMOG are among the most commonly used. Applying these formulas is a simple process that can be done manually or by using a computer software program. Each method takes only a few minutes.

Typically, readability formulas measure the difficulty of the vocabulary used and the average sentence length. In addition, computer software programs analyze a document�s grammar, style, word usage, and punctuation, and assign a reading level. These formulas, however, do not measure the reader�s level of comprehension.

Readability software programs are available at computer stores. Some software programs, such as Microsoft Word, include a readability-testing function. (Note: Mention of software products does not constitute an endorsement by the National Cancer Institute.)

Researchers James Pichert and Peggy Elam suggest three principles for using readability formulas effectively:

• Use readability formulas only in concert with other means of assessing the effectiveness of the material.
• Use a formula only when the text�s intended readers are similar to those on whom the formula was validated.
• Do not write a text with readability formulas in mind.

Before you choose a readability testing method, decide on an appropriate reading level for the materials you�ve written. Then use readability testing to determine whether your text corresponds to the reading level you want.

The term reading level refers to the number of years of education required for a reader to understand a written passage. Some experts suggest aiming for a level that is two to five grades lower than the highest average grade level of your intended audience to account for a probable decline in reading skills over time. Others note that a third- to fifth-grade level is frequently appropriate for low-literacy readers. Keep publications as simple as possible to increase reader comprehension of the material.

Readability Testing Methods

You can test readability easily using a formula such as Fry, Flesch, FOG, or SMOG. These tests can be done quickly to indicate any problems with the drafted text. They do not involve the intended audience.

To calculate the SMOG reading grade level of a written sample, begin with the entire written work that is being assessed, and follow these four steps:

• Count off 10 consecutive sentences each near the beginning, in the middle, and near the end of the text.
• From this sample of 30 sentences, circle all of the words containing three or more syllables (polysyllabic), including repetitions of the same word, and total the number of words circled.
• Estimate the square root of the total number of polysyllabic words counted. Do this by finding the nearest perfect square and taking its square root.
• Finally, add a constant of three to the square root. The resulting number is the SMOG grade or the reading grade level that a person must have reached to fully understand the text being assessed.

A few additional guidelines will help to clarify these instructions:

• A sentence is defined as a string of words punctuated with a period, an exclamation point, or a question mark.
• Hyphenated words are considered one word.
• Numbers that are written out should also be considered, and if in numeric form in the text, they should be pronounced to determine whether they are polysyllabic.
• Proper nouns, if polysyllabic, should be counted, too.
• Abbreviations should be read as unabbreviated to determine whether they are polysyllabic.

Not all pamphlets, fact sheets, or other printed materials contain 30 sentences. To test a text that has fewer than 30 sentences:

• Count all of the polysyllabic words in the text.
• Count the number of sentences.
• Find the average number of polysyllabic words per sentence as follows: Average = Total # of polysyllabic words                        Total # of sentences
• Subtract the total number of sentences from 30.
• Multiply that number by the average.
• Add that figure to the total number of polysyllabic words.
• Find the square root and add a constant of 3.

Perhaps the quickest way to administer the SMOG test is by using the SMOG conversion table. Simply count the number of polysyllabic words in 30 sentences (chains of 10 each from the beginning, middle, and end of the text) and look up the approximate grade level on the chart. See the sidebar Example Using the SMOG Readability Formula on the next page for an example of how to use the SMOG Readability Formula and the SMOG conversion table. In the sidebar, each of the 3 sets of 10 sentences is marked with brackets.

Readability Testing With the Intended Audience*

Other methods of evaluating reading levels and comprehension include having your intended audience pretest your materials. The WRAT or the Cloze technique can be used to do this. These types of testing are useful when you suspect that the intended audience may encounter difficulties with the material. Including pretest participants who have the same characteristics as the lowliteracy intended audience you are trying to reach is critical to the validity of your pretest results. Recruiting participants through groups or settings that include people with limited literacy skills is a logical starting point. But the only way to be sure your pretest volunteers read at the same level as your intended audience is to test their reading skills.

The Wide Range Achievement Test (WRAT) is used to measure reading levels, and the Cloze technique is used to measure comprehension. To avoid offending or causing discomfort to those whose reading ability you are testing, you can integrate a WRAT or a Cloze test into the pretest interview. For example, in a recent pretest conducted by the National Cancer Institute, the interviewers introduced the WRAT test as the last part of the pretest. They stated, "Thank you for helping with the questions on the chemotherapy booklet.We need your help with one last part�a word list. This will take only a few minutes. The word list will help us understand how difficult the words are in the chemotherapy booklet." This integrated approach spared participants the pressure or potential embarrassment of "failing a reading test."

The WRAT is based on word recognition and does not measure comprehension or vocabulary. The WRAT is an efficient way to determine reading levels and takes only a short time to administer.

The WRAT involves listening to the participant read from a prepared list of words, arranged in increasing order of difficulty. Pronouncing the word correctly shows that the reader recognizes the word. The WRAT focuses on recognition because, at the most basic level, if a person does not recognize a word, comprehension is impossible.

The test is over after the reader mispronounces 10 words. The test administrator notes the level at which the last mispronunciation occurred. The "stop" level equates to a grade level of reading skills.You can compare this level with the reading level of your intended audience to see if your pretest readers are a representative match for that audience.

The Cloze technique measures the reader�s ability to comprehend a written passage. Because it requires readers to process information, it may take up to 30 minutes to administer.

In a Cloze test, text appears with every fifth word omitted. The reader tries to fill in the blanks. This task demonstrates how well he or she understands the text. The reader�s ability to supply the correct word also reflects his or her familiarity with sentence structure.

While packaged Cloze tests are available, Leonard and Cecilia Doak�s Teaching Patients with Low Literacy Skills explains how to make up and score a Cloze test yourself, based on the materials you are pretesting. The book also discusses use of the WRAT to assess reading levels.

* Adapted from Clear and Simple: Developing Effective Print Materials for Low-Literate Readers (NIH Publication No. 95-3594), by the National Cancer Institute, 1994. Bethesda, MD. In the public domain.

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A Five-Finger Church Communication Strategy

Insights | Church Life & Ministry | Jul 23, 2024

Effective communication is a challenge for many church leaders. Do you need a new approach to punch up your church communication strategy?

By Bryan Rose

It’s common to think your church is the only one struggling with effective communication. Or that your website is possibly the worst church website ever created. Or that you’re probably the only one who loses hours to social media distraction when trying to post a simple announcement. But the truth is, these are shared challenges many church leaders face.

In this era of over-messaging and under-communicating, it’s a normal struggle to ensure church members and attendees know how to take the next step toward growth in Christ. This shared affliction underscores the reality that there is no silver-bullet solution. Effective church communication is an art, not a science.

Here’s one approach that emphasizes the need for a focused and holistic pattern of regularly connecting your church to what matters most: a five-finger church communication strategy. Each “finger” plays a crucial role, and active participation is vital in making this approach effective.

The thumb: email blasts

Your thumb is an instrumental part of your hand. The opposable thumb is a primary anatomical separator of humans from most animals. Thumbs allow us to use tools through grasping and gripping. However, you couldn’t do anything very effectively with only a thumb. Yet most churches rely solely on the weekly email blast or newsletter as their “opposable thumb” of church communication.

In today’s digital world, email communication is a must but useless by itself. Worse, when every department sends their own email blast every week, you suddenly have two hands full of thumbs. A weekly email update should, like the thumb on your hand, anchor your strategy and function to help members grasp what’s happening around the church—but only in cooperation with four other “fingers” of communication.

The pointer finger: stage announcements

Each Sunday, someone on your stage, platform, or pulpit points the church to critical events or the next steps in growing in Christ. This person either reads three minutes of announcements or carefully casts 180 seconds of visionary communication.

The difference between these two weekly moments is active engagement among the body versus continued inattention to anything more than Sunday morning worship attendance. A well-crafted and pointed video can also effectively direct someone to the right next step. Consider each Sunday morning your primary opportunity to point people to what matters most during announcement moments.

The middle finger: social media

What can you say about the middle finger that wasn’t completely obvious in middle school? On its own, the middle finger is all about making a statement. It’s often an emotional response and directly impacts a particular audience. The middle finger is more sender-centric than viewer-centered, resulting in consequences when the wrong person or group of people receives the message.

Similarly, social media posts say more about who’s posting them than who’s seeing them. Social media posts evoke emotion by design. Yet rarely is there a desired or immediate response, and honestly, when a response happens, it could be better. Think of your social media strategy as a powerful and emotional communication tool, but brace yourself if the wrong people get the message. With social media, it’s also essential to create a strategy that fits each specific platform (Facebook, Instagram, X).

The ring finger: family ministry

The wedding ring is one of the most powerful symbols in our culture today and every young pastor’s go-to baptism illustration. The ring finger stands for family, and the family is where our culture lives. Integration into the weekly rhythms of preschool, children, and student ministry is essential to any fully-formed communications strategy.

Take the extra time to craft take-home moments each week in every ministry that speak to what matters most at your church. Leverage parent meetings or milestone moments to communicate directly with parents during a season in which they are most attentive. If you want your parents’ attention, get their kids excited. Think of these moments as opportunities to engage parents and caregivers in the larger story of your church’s vision.

The pinky finger: church website

What exactly does the pinky finger do but, when extended, signify a special moment? The pinky is all about savoring small doses and a specialized approach to the finer things in life.

Studies suggest 80% or more of the average users of your church website are guests looking for basic information. The other 2-20% are typically church members trying to figure out when something starts. Unfortunately, most communication teams design websites that are inverse to these realities.

We bury essential information of service time and campus location, or we use language only insiders understand. Instead of considering small engagements and a unique audience (first-time guests), many church websites become burdened by bylaws, expired announcements, and labyrinthian navigation menus. Stop and think of your church website in small doses by asking who uses it and what’s most important.

Effective church communication can be as impactful as a closed fist, utilizing all four fingers and the thumb. Consider the challenges you face in your week-to-week church communication. How can you ensure your communication strategies are making a positive impact on your church community?

For permission to republish this article, contact  Marissa Postell Sullivan .

@thebryanrose

Bryan is the Chief Engagement Officer and a Senior Lead Navigator for Auxano.

Church Communications: Methods and Marketing

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• Nicholas V. Metcalf 3 ,
• Ravi V. Chacko 5 ,
• Caterina Gratton   ORCID: orcid.org/0000-0003-4607-7401 6 ,
• Christine Horan 7 ,
• Samuel R. Krimmel 3 ,
• Joshua S. Shimony 4 ,
• Julie A. Schweiger 1 ,
• Dean F. Wong   ORCID: orcid.org/0000-0001-9343-8367 4 ,
• David A. Bender 1 ,
• Kristen M. Scheidter 3 ,
• Forrest I. Whiting 3 ,
• Jonah A. Padawer-Curry 8 ,
• Russell T. Shinohara 9 , 10 , 11 ,
• Yong Chen 11 ,
• Julia Moser   ORCID: orcid.org/0000-0002-6219-415X 12 , 13 ,
• Essa Yacoub 14 ,
• Steven M. Nelson 12 , 15 ,
• Luca Vizioli 14 ,
• Damien A. Fair   ORCID: orcid.org/0000-0001-8602-393X 12 , 13 , 14 , 15 ,
• Eric J. Lenze 1 ,
• Robin Carhart-Harris   ORCID: orcid.org/0000-0002-6062-7150 16 , 17 ,
• Charles L. Raison 18 , 19 ,
• Marcus E. Raichle 3 , 4 , 8 , 20 , 21 ,
• Abraham Z. Snyder   ORCID: orcid.org/0000-0002-3379-9627 3 , 4 ,
• Ginger E. Nicol 1   na1 &
• Nico U. F. Dosenbach   ORCID: orcid.org/0000-0002-6876-7078 3 , 4 , 8 , 20 , 22   na1  

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• Consciousness
• Drug development
• Neural circuits
• Neuroscience

A single dose of psilocybin, a psychedelic that acutely causes distortions of space–time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials 1 , 2 , 3 , 4 . In animal models, psilocybin induces neuroplasticity in cortex and hippocampus 5 , 6 , 7 , 8 . It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6–12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.

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Psychedelic drugs can reliably induce powerful acute changes in the perception of self, time and space by agonism of the serotonin 2A (5-HT 2A ) receptor 9 , 10 . In clinical trials, a single high dose of psilocybin (25 mg) has demonstrated rapid and sustained symptom relief in depression 1 , 2 , 3 , 11 , 12 , 13 , 14 , addiction 4 , 15 and end-of-life anxiety 13 , 14 . Together, these observations indicate that psychedelics should induce potent acute (lasting roughly 6 hours) and persistent (24 hours to 21 days) neurobiological changes.

In rodent models, transient activation of the 5-HT 2A receptors by a psychedelic can alter neuronal communication in 5-HT 2A -rich regions (for example, the medial frontal lobe) and induce persistent plasticity-related phenomena 5 , 6 , 7 . Synaptogenesis in the medial frontal lobe and anterior hippocampus is thought to be key to the neurotrophic antidepressant effects of psilocybin 5 , 16 , 17 . Yet, inherent limitations of rodent models, and imperfect homology to the human 5-HT 2A receptor 18 , limit the generalizability of these assertions.

Understanding the effects of psychedelics on human brain networks is critical to unlocking their therapeutic mechanisms. In humans, during the roughly 6 hour duration of action, psilocybin increases glutamate signalling and glucose metabolism 19 , 20 , 21 , broadly decreases the power of electrophysiological signals 22 , reduces hemodynamic fluctuations 23 and decreases segregation between functional networks 24 . The drivers of these acute changes are poorly understood, particularly in the subcortex. Preliminary efforts to identify network changes in the weeks after psilocybin have yielded mixed results 25 , 26 , 27 . Persistent effects of psilocybin on clinically relevant circuits have yet to be characterized in humans.

The ventromedial prefrontal cortex and anterior and middle hippocampus are functionally connected to the default mode network (DMN) 28 , 29 . Increased FC between the hippocampus and DMN has been associated with depression symptoms 30 and decreased FC is associated with treatment 31 , 32 . These 5-HT 2A receptor rich 33 and depression associated default mode regions 34 , 35 , 36 are candidates for mediating the neurotrophic antidepressant effects of psychedelics.

Precision functional mapping uses dense repeated functional magnetic resonance imaging (fMRI) sampling 37 , 38 , 39 , 40 , 41 to reveal the time course of individual-specific intervention-driven brain changes 42 . This approach accounts for inter-individual variability in brain networks 37 and capitalizes on the high stability of networks within individuals from day to day 38 . Using precision functional mapping, we observed individual-specific acute and persistent brain changes following a single high dose of psilocybin.

Healthy young adults received 25 mg psilocybin and 40 mg methylphenidate (MTP, generic name Ritalin, dose-matched for arousal effects) 1–2 weeks apart and underwent regular MRI sessions (roughly 18 per participant) before, during, between and after the two drug doses (Extended Data Fig. 1 , Supplementary Table 1 and Supplementary Video  1 : data quality metrics for 129 total MRI visits). Dense predrug sampling familiarized participants with the scanner and established baseline variability.

Psilocybin disrupts brain connectivity

Psilocybin acutely caused profound and widespread brain FC changes (Fig. 1a ) across most of the cerebral cortex ( P  < 0.05 based on two-sided linear mixed-effects (LME) model and permutation testing), but most prominent in association networks (FC change mean (standard deviation, s.d.): association cortex 0.44 (0.03), primary cortex 0.36 (0.05)). In the subcortex the largest psilocybin-associated FC changes were seen in DMN connected parts of the thalamus, basal ganglia, cerebellum and hippocampus 29 , 43 , 44 (Fig. 1a and Extended Data Fig. 2 ). In the hippocampus, foci of strong FC disruption were located in the anterior hippocampus (Montreal Neurological Institute coordinates −24, −22, −16 and 24, −18, −16). Other large FC disruptions were seen in mediodorsal and paraventricular thalamus 45 and anteromedial caudate. In the cerebellum, the largest FC changes were seen in the DMN connected areas 44 (Fig. 1a ).

FC change (Euclidean distance) was calculated across the cortex and subcortical structures. Effects of drug condition were tested with an LME model in n  = 6 longitudinally sampled participants over ten sessions with psilocybin and six sessions with methylphenidate (MTP) ( a and b are thresholded at P  < 0.05 based on permutation testing with TFCE; see unthresholded statistical maps in Extended Data Fig. 2 ). a , Psilocybin-associated FC change, including in subcortex. a, anterior; p, posterior; L, left; R, right. b , MTP-associated FC change. c , Typical day-to-day variability as a control to the drug conditions (unthresholded: not included in LME model). d , Average FC change in individual-defined networks. Open circles represent individual participants. FC change is larger in DMN than other networks. Rotation-based null model (spin test 62 , 97 ): ten psilocybin doses, 1,000 permutations, one-sided P spin  < 0.001, ( P spin  > 0.05 for all other networks). ** P  < 0.001, uncorrected. e , Whole-brain FC change (Euclidean distance from baseline) for all rest scans across conditions. FC change for MTP, psilocybin and day-to-day are in comparison to same-participant baseline. White dots indicate median, vertical lines indicate quartiles. LME model predicting whole-brain FC change: ten psilocybin doses (275 observations), estimate (95% CI) = 15.83 (13.50, 18.15), t (266)  = 13.39, P  = 1.36 × 10 −31 , uncorrected. For the full FC distance matrix with session labels, see Extended Data Fig. 3 . f , g , Comparison of the differences in FC change to differences in psychedelic experiences. f , Individual FC change maps and MEQ30 scores for two exemplars (see Extended Data Fig. 4 for all drug sessions). g , The relationship between whole-brain FC change and mystical experience rating is plotted for all drug sessions (psilocybin and MTP). The LME model demonstrated a significant relationship: 16 drug doses (ten psilocybin, six MTP), estimate (95% CI) = 69.78 (50.15, 89.41), t (13)  = 7.68, P  = 3.5 × 10 −6 , uncorrected. h , The relationship between FC change and MEQ30 ( r 2 ) is mapped across the cortical surface.

By comparison, MTP-associated FC changes localized to sensorimotor systems (Fig. 1b and Extended Data Fig. 2 ) and paralleled the map of day-to-day variability (Fig. 1c ) probably due to arousal effects 39 . Psilocybin-associated FC change was largest in the DMN (Fig. 1d and Supplementary Fig. 1 ; averaged across all psilocybin sessions; spin test, 1,000 permutations, one-sided P spin  < 0.001; P spin  > 0.05 for all other networks). However, MTP-associated FC change was largest in motor and action networks ( P spin  = 0.002; P spin  > 0.05 for all other networks; Supplementary Fig. 1b ).

Despite MTP and psilocybin causing similar increases in heart rate (Supplementary Fig. 2 ), the effects of psilocybin on FC were more than threefold larger than the effects of MTP (Fig. 1e ; post hoc two-sided t -test; P  = 3.6 × 10 −6 , uncorrected). The psilocybin effects also dwarfed those of other control conditions (Fig. 1e ; day-to-day change (normalized) 1; task 1.22, MTP 1.10, high head motion 1.29, psilocybin 3.52, between person 3.53; Extended Data Fig. 3 ; these effects were robust to preprocessing choices: Supplementary Figs. 3 and 4 ). To put the effects of psilocybin into perspective, it helps to consider that the mean changes in brain organization caused by the drug were as large as the differences in brain organization between different people (Fig. 1e ).

The psychedelic experience

The large amount of data collected per participant, under the individual-specific imaging model, allowed us to move beyond group-analyses and compare the subjective psychedelic experience (30-item Mystical Experience Questionnaire, MEQ30) 46 to brain function data session-by-session (Fig. 1f ). The MEQ30 is a self-assessment instrument used to measure the intensity and quality of mystical experiences, including feelings of connectedness, transcendence of time and space, and a sense of awe, with a maximum score of 150 (ref. 46 ). Across psilocybin sessions and participants, FC change tracked with the intensity of the subjective experience (Fig. 1f and Extended Data Fig. 4 ). Correlating the whole-brain FC change ( x axis) against the MEQ30 scores ( y axis) for all drug sessions (Fig. 1g ) revealed an r 2  = 0.81 (LME model predicting MEQ30 score: effect of FC change, t (13)  = 7.68; P  = 3.5 × 10 −6 , uncorrected). Head motion was not significantly correlated with MEQ30 scores (effect of framewise displacement, t (13)  = −1.26, P  = 0.23, uncorrected). Projecting the relationship between someone’s mystical experience and the corresponding FC change onto the brain (Fig. 1h , vertex-wise) showed it to be driven by association cortex, relatively sparing primary motor and sensory regions. Of the four MEQ30 dimensions (mystical, positive mood, transcendence of time and space, and ineffability), the one most strongly correlating with brain change was transcendence (for example, ‘loss of your usual sense of time or space’, r 2  = 0.86, Supplementary Fig. 5 ), however, all dimensions were highly correlated ( r  > 0.8). Repeated sampling enabled us to determine that the inter-individual variability in the effects of psilocybin in the brain was more likely related to differences in drug effects than measurement error (likelihood ratio test of participant-specific response to psilocybin, P  = 0.00245, uncorrected) 47 , 48 .

The psychedelic dimension

To examine the latent dimensions of brain network changes we performed multidimensional scaling (MDS) on the parcellated FC matrices from every fMRI scan 38 . MDS is blind to session labels (for example, drug, participant). Yet, dimension 1, which explained the largest amount of variability, separated psilocybin from other scans (Fig. 2a ), apart from one session during which the participant (P5R) had emesis 30 minutes after taking psilocybin (dark red dots on the left of Fig. 2a ). The higher score on dimension 1 associated with psilocybin, corresponded to reduced segregation between the DMN and other networks (fronto-parietal 49 , dorsal attention 50 , salience 51 and action-mode 52 , 53 ) that are typically anticorrelated with it 54 (Fig. 2b and Extended Data Fig. 5 ). To determine whether this reflects a common effect of psilocybin that generalizes across datasets and psychedelics, we calculated dimension 1 scores for extant datasets from participants receiving intravenous (i.v.) psilocybin 55 and lysergic acid diethylamide (LSD) 56 . Psychedelic treatment increased dimension 1 in nearly every participant in the psilocybin and LSD datasets (Fig. 2c ), suggesting that this is a common effect across psychedelic drugs and individuals.

MDS blind to session labels was used to assess brain changes across conditions. a , In the scatter plots, each point represents whole-brain FC from a single 15 min scan, plotted in a multidimensional space on the basis of the similarity between scans. Dimensions 1 and 4 showed strong effects of psilocybin. The top shows scans coded on the basis of drug condition. Dark red denotes that the participant had an episode of emesis shortly after taking psilocybin. The bottom shows scans coloured on the basis of participant identity. Dimension 1 separates psilocybin from non-drug and MTP scans in most cases. See Extended Data Fig. 5 for the dimension 1–4 weight matrices. b , Visualization of dimension 1 weights. The top 1% of edges (connections) are projected onto the brain (green indicates connections that are increased by psilocybin). Cerebellar connections are included although the structure is not shown. c , Re-analysis of dimension 1 in extant datasets with intravenous psilocybin (left, ref. 55 , paired two-sided t -test of change in dimension 1 score, n  = 9, t (8)  = 2.97, P  = 0.0177, uncorrected) and LSD (right, ref. 56 , paired two-sided t -test: n  = 16, t (15)  = 4.58, P  = 3.63 × 10 −4 , uncorrected). * P  < 0.05, ** P  < 0.001, uncorrected. d , Average effects of psilocybin on network FC, shown separately for within-network integration (left) and between network segregation (right). For network integration (left), blue indicates a loss of FC (correlations) between regions within the same network. For network segregation (right), blue indicates a loss of FC (anticorrelations) to all other regions in different networks; see Extended Data Fig. 6 for a full correlation matrix. Dissolution of functional brain organization corresponds to decreased within-network integration and decreased between network segregation.

Subtraction of average FC (psilocybin minus baseline) revealed a pattern of FC change similar to dimension 1 (Fig. 2d and Extended Data Figs. 5 and 6 ). Consistent with previous psychedelics studies 24 , psilocybin increased FC between networks (particularly fronto-parietal, default mode and dorsal attention), whereas FC within networks was relatively less affected. A similar pattern of loss of segregation between brain networks is produced by nitrous oxide and ketamine 57 , suggesting that the psychedelic dimension observed here may generalize to psychedelic-like dissociative drugs.

By comparison, MTP decreased within-network FC in the sensory, motor and auditory regions (Extended Data Fig. 6 ), consistent with previous reports 58 and similar to the effects of caffeine 39 . To verify that observations in our sample ( n  = 6) were generalizable, we compared stimulant effects in our study to those in the Adolescent Brain Cognitive Development (ABCD) Study 59 ( n  = 487 taking stimulants). The effect of stimulant use in ABCD was consistent with MTP-associated FC changes in our dataset (Extended Data Fig. 6 ).

Desynchronization explains FC change

Multi-unit recording studies suggest that agonism of 5-HT 2A receptors by psychedelics desynchronizes populations of neurons that typically co-activate 60 . We proposed that this phenomenon, observed at a larger spatial scale, might account for psilocybin-associated FC change (Fig. 1 ). We observed that the typically stable spatial structure of resting fMRI fluctuations was disrupted and desynchronized by psilocybin (Supplementary Videos  2 – 7 : brain activity time courses during drug sessions for each participant). Therefore, we quantified brain signal synchrony using normalized global spatial complexity (NGSC): a measure of spatial entropy that is independent of the number of signals 61 . NGSC calculates cumulative variance explained by subsequent spatiotemporal patterns (Fig. 3a ). The lowest value of NGSC (0) means that the time course for every vertex and/or voxel is identical. The highest value of NGSC (equal to one) means that the time course for every vertex and/or voxel is independent, indicating maximal desynchronization (or spatial entropy).

a , NGSC captures the complexity of brain activity patterns. It is derived from the number of spatial principal components needed to explain the underlying structure. Higher entropy equals desynchronized activity. On the right is variance explained by subsequent principal components for psilocybin in red, MTP in blue and no drug in grey for P6. b , Whole-brain entropy (NGSC) is shown for every fMRI scan for a single participant (P6). At right, increases during psilocybin were present in all participants. Sample sizes are provided in Supplementary Table 1 . Grey bars indicate condition means. c , Parcel entropy (computed on individual-specific parcels) within functional brain areas shows similar psilocybin-driven increases as whole-brain entropy. d , Psilocybin-associated spatial entropy (individual-specific parcels, averaged across participants) is visualized on the cortical surface. Psilocybin-associated increases in entropy were largest in association cortex. e , LSD-associated increases in spatial entropy were similar to those induced by psilocybin (using data from ref. 56 ). f , Increases corresponded spatially to 5-HT 2A receptor density 33 . In b – d , n  = 6 participants, 272 observations (scans). For e , n  = 16 participants.

Psilocybin significantly increased NGSC acutely with values returning to predrug baseline by the following session (Fig. 3b,c ). The increase in NGSC was observed at the whole-brain level (Fig. 3b ; LME model, estimate (95% confidence interval (CI)) = 0.0510 (0.0343, 0.0676), t (265)  = 6.8, P  = 2.0 × 10 −6 , uncorrected) and correlated with the subjective experience (MEQ30: Extended Data Fig. 7 ; r  = 0.80, P  = 3.52 × 10 −4 , uncorrected, after single outlier removal), whereas nuisance variables did not. Increased NGSC was also observed for individual-defined brain areas 62 (Fig. 3c ; LME model, estimate (95% CI) = 0.0149 (0.0071, 0.0228), t (265)  = 3.74, P  = 2.30 × 10 −4 , uncorrected), with the largest increases in association cortex and minimal changes in primary cortex (Fig. 3d ). Global and local desynchronization replicated in an LSD dataset 56 (Fig. 3e ) and the distribution of these effects correlated with 5-HT 2A receptor density (Fig. 3f ; bivariate correlation NGSC psilocybin to Cimbi-36 binding, r  = 0.39, P  = 1.9 × 10 −13 ; NGSC LSD to Cimbi-36 binding, r  = 0.32, P  = 4.5 × 10 −9 , uncorrected) 33 , 63 .

Task engagement reduces desynchronization

To investigate how psilocybin-driven brain changes are influenced by task states, participants were asked to complete a simple auditory–visual matching task in the scanner ( Methods , perceptual fMRI task). Participants performed this task with more than 80% accuracy during drug sessions (Extended Data Fig. 8a–c ). Engagement in the task significantly decreased the magnitude of psilocybin-associated network disruption and desynchronization (Fig. 4 ; LME model interaction of task × psilocybin: FC change P  = 5.49 × 10 −5 , NGSC P  = 4.82 × 10 −8 , uncorrected). These results were robust to scan order effects (Supplementary Fig. 6 ) and regression of evoked responses (Supplementary Fig. 7 ).

a , Psilocybin-associated FC change from resting scans (left) and from task scans (right). b , Regional NGSC change (psilocybin minus baseline) from rest scans (left) and from task scans (right). Bar graphs on the bottom indicate the corresponding whole-brain FC change ( a ) and whole-brain NGSC values ( b ) during rest and task for baseline and drug conditions. LME models indicated an interaction of task × psilocybin on FC change ( n  = 7 with task data on psilocybin, estimate (95% CI) = −6.48 (−9.59, −3.37), t (265)  = −6.48, P  = 5.49 × 10 −5 , uncorrected) and an interaction of task × psilocybin on NGSC ( n  = 7 with task data on psilocybin, estimate (95% CI) = −0.042 (−0.056, −0.027), t (265)  = −5.62, P  = 4.82 × 10 −8 , uncorrected). Bars indicate mean and error bars indicate s.e.m.. ** P  < 0.001, uncorrected.

The reduction of psilocybin-driven brain changes during task performance seems to parallel the psychological principle of ‘grounding’: directing one’s attention externally as a means of alleviating intense or distressing thoughts or emotions. Grounding techniques are commonly used in psychedelic-associated psychotherapy to lessen overwhelming or distressing effects of psilocybin 64 . Task-related reductions in network desynchronization provide strong evidence for context-dependent effects of psilocybin on brain activity and FC 65 and fill an important gap between preclinical studies of context dependence 66 , 67 and clinical observations 68 .

Classical animal studies documented that psychedelics reduce optic tract responses to photic stimulation of the retina, indirectly reducing visual cortex activation 69 , 70 . We replicated these effects by documenting reduced task-evoked responses in primary visual cortex (Extended Data Fig. 8d–g ). To assess whether psilocybin affects the magnitude of hemodynamic responses elsewhere, we analysed evoked responses during the perceptual task in other task-related regions of interest (Extended Data Fig. 8f,g ). But the magnitudes of other evoked responses were not significantly changed by psilocybin (two-way analysis of variance of drug and participant; effect of drug: left V1 P  = 0.03, right V1 P  = 0.02, all other P  > 0.1, uncorrected).

Persistent decrease in hippocampal FC

To assess whether persistent neurotrophic and psychological effects of psychedelics might be associated with persistent FC changes after psilocybin, we compared FC changes 1–21 days post-psilocybin to pre-psilocybin. Whole-brain FC change scores were small (normalized FC change (range) of 1.05 (0.94, 1.27)), indicating that the brain’s network structure had mostly returned to baseline (Extended Data Fig. 2 ).

Atypical cortico-hippocampal connectivity has been associated with affective symptoms 30 and hippocampus neurogenesis is observed after psilocybin 6 . Further, acute decreases in hippocampal glutamate after psilocybin correlate with decreased DMN connectivity and ego dissolution 21 . Thus, we investigated whether the same region of the anterior hippocampus that showed strong acute FC change also showed persistent FC change. We observed significant FC change in the 3 week postdrug period (Fig. 5a,b ; LME mean change 0.095, P pre– post-psilocybin  = 0.0033, uncorrected). No persistent FC differences were observed post-MTP ( Methods , section ‘Persistent effects analysis’; LME ‘FC change’ 90% CI (−0.056, 0.080); equivalence δ  = ±0.086, P pre– post-MTP  = 0.77).

a , Hippocampus FC change maps (left hippocampus; unthresholded t -maps, as in Extended Data Fig. 2 ). Acute psilocybin FC change is shown on top and persistent FC change (3 weeks after psilocybin) on the bottom. b , Each dot represents the FC change score for the anterior hippocampus for a single scan before (left) and after (right) psilocybin for every participant (coloured as in Fig. 2 ). Participants showed a post-psilocybin increase in FC change in the anterior hippocampus (LME model, pre- versus post-psilocybin; n  = 6 participants, 186 observations, estimate (95% CI) = 0.095 (0.032, 0.168), t (182)  = 2.97, P  = 0.0033, uncorrected). c , Connectivity from an anterior hippocampus seed (Montreal Neurological Institute coordinates −24, −22, −16 and 24, −18, −16) pre-psilocybin (left), post-psilocybin (middle) and persistent change (post- minus pre-) for an exemplar participant (P3). The red border on the right-most brain outlines the individual-specific DMN. A decrease in hippocampal FC with parietal and frontal components of the DMN is seen. d , Time course of anterior hippocampus minus DMN for all participants and scans (participant colours as in b ). A moving average is shown in black. e , Schematic of hippocampal-cortical circuits, reproduced from ref. 29 , CC BY 4.0 .

FC between the anterior hippocampus and DMN was decreased postpsilocybin (Fig. 5c,d ). Time-course visualization, after aligning them so that psilocybin dose was day 0, suggests that connectivity is reduced for 3 weeks following a single psilocybin dose (Fig. 5d ; AntHip-DMN FC mean (95% CI): pre-psilocybin was 0.180 (0.169, 0.192); post-psilocybin was 0.163 (0.150, 0.176)). AntHip-DMN FC values returned to pre-psilocybin baseline by the replication visit 6–12 months later, however, the smaller replication sample ( n  = 4 with one pre-psilocybin visit each) was not statistically powered to detect small changes. This observation is compelling, as it localized to the anterior hippocampus, a brain region showing substantial synaptogenesis following psilocybin 6 . Reduced hippocampal-cortical FC may reflect increased plasticity of self-oriented hippocampal circuits 31 (Fig. 5e ).

From micro- to macro-scale psychedelic effects

The synchronized patterns of cofluctuations during the resting state are believed to reflect the brain’s perpetual task of modelling reality 71 . It follows that the stability of functional network organization across day, task, MTP and arousal levels (but not between individuals), reflects the subjective stability of waking consciousness. By contrast, the much larger changes induced by psilocybin fit with participants’ subjective reports of a radical change in consciousness. The large magnitude of effects of psilocybin, in comparison to the effects of MTP, suggests that observed changes are not merely due to increased arousal or non-specific effects of monoaminergic stimulation 72 .

Our observation that psychedelics desynchronize brain activity regionally and globally provides a bridge between previous findings at the micro- and macro-scales of neuroscience. Multi-unit recording studies suggest that agonism of 5-HT 2A receptors by psychedelics does not uniformly increase or decrease firing of pyramidal neurons, but rather serves to desynchronize pairs or populations of neurons that co-activate under typical conditions 60 . Meanwhile, previous resting fMRI studies have reported a range of acute changes following ingestion of psilocybin 55 , 63 , ayahuasca 73 and LSD 56 , 74 , which broadly converge on a loss of network connectivity and an increase in global integration 24 , 75 . Disruption of synchronized activity at several scales may explain the paradoxical observation that psychedelics produce an increase in metabolic activity 19 , 20 , a decrease in the power of local fluctuations 22 , 76 and a loss of the brain’s segregated network structure 23 , 56 . This desynchronization of neural activity has been described as an increase in entropy or randomness of brain activity in the psychedelic state 77 , 78 . Our results support the hypothesis that these changes underpin the cognitive and perceptual changes associated with psychedelics.

Desynchrony may drive persistent change

The dramatic departure from typical synchronized patterns of co-activity may be key to understanding the acute effects of psilocybin and also its persistent neurotrophic effects. Changes in resting activity are linked to shifts in glutamate-dependent signalling during psilocybin exposure 21 , 79 , 80 . This phenomenon, shared by ketamine and psychedelics, engages homeostatic plasticity mechanisms 81 , 82 , a neurobiological response to large deviations in typical network activity patterns 83 , 84 , 85 . This response to novelty includes rapid upregulation in expression of BDNF , M TOR , E EF2 and other plasticity-related immediate early genes 8 , 80 , which are thought to have a key role in the antidepressant response 86 . Consistent with this notion, psilocybin produced the largest changes in the DMN, frequently associated with neuropsychiatric disorders 34 , 35 , 87 , 88 , 89 , 90 , 91 , and in a region of the anterior and middle hippocampus associated with the self 29 , 92 and the present moment 93 .

Psychedelics rapidly induce synaptogenesis in the hippocampus and cortex, effects that seem to be necessary for rapid antidepressant-like effects in animal models 7 , 17 . However, understanding the underpinnings of the behavioural effects of psychedelics requires human studies. Advances in precision functional mapping 37 , 94 , 95 and individual-level characterization enabled us to identify desynchronization of resting-state fMRI signals, connect these changes with subjective psychedelic effects and localize these changes to depression-relevant circuits (DMN, hippocampus). These analyses rely on precise characterization of an individuals’ baseline brain organization (for example, individual definition of brain areas, networks and day-to-day variability) to understand how that organization is altered by an intervention. This precision drug mechanism study was conducted in non-depressed volunteers. Verification of the proposed antidepressant mechanism of psilocybin will require precision patient studies. New methods to measure neurotrophic markers in the human brain 96 will provide a critical link between mechanistic observations at the cellular, brain networks and psychological levels.

Regulatory approvals and registrations

Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki and procedures established by the Washington University in Saint Louis Institutional Review Board. All participants were compensated for their time. All aspects of this study were approved by the Washington University School of Medicine (WUSOM) Internal Review Board, the Washington University Human Research Protection Office (WU HRPO), the Federal Drug Administration (IND no. 202002165) and the Missouri Drug Enforcement Agency (DEA) under a federal DEA schedule 1 research licence and registered with ClinicalTrials.gov identifier NCT04501653. Psilocybin was supplied by Usona Institute through Almac Clinical Services.

Study design

Healthy young adults ( n  = 7, 18–45 years) were enrolled between April 2021 and March 2023 in a randomized cross-over precision functional brain mapping study at Washington University in Saint Louis (see  Supplementary Methods for inclusion and exclusion criteria). The purpose of the study was to evaluate differences in individual-level connectomics before, during and after psilocybin exposure. Participants underwent imaging during drug sessions (with MRI starting 1 h after drug ingestion) with 25 mg psilocybin or 40 mg MTP, as well as non-drug imaging sessions. Drug condition categories were (1) baseline, (2) drug 1 (MTP or psilocybin), (3) between, (4) drug 2 and (5) after. Randomization allocation was conducted using REDCap and generated by team members who prepared study materials including drug or placebo but otherwise had no contact with participants. A minimum of three non-drug imaging sessions were completed during each non-drug window: baseline, between and after drug sessions. The number of non-drug MRI sessions was dependent on availability of the participant, scanner and scanner support staff. Dosing day imaging sessions were conducted 60–180 min following drug administration during peak blood concentration 98 . One participant (P2) was not able tolerate fMRI while on psilocybin, and had trouble staying awake on numerous fMRI visits after psilocybin and was thus excluded from analysis (except for data quality metrics in Extended Data Fig. 1 ).

MTP was selected as the active control condition to simulate the cardiovascular effects and physiological arousal (that is, controlling for dopaminergic effects) associated with psilocybin 99 . Usona Institute, a US non-profit medical research organization, provided good manufacturing practices for psilocybin.

Drug sessions were facilitated by two clinical research staff who completed an approved in-person or online facilitator training programme provided by Usona Institute, as part of the phase 2 study (ClinicalTrials.gov identifier NCT03866174). The role of the study facilitators was to build a therapeutic alliance with the participant throughout the study, prepare them for their drug dosing days and to observe and maintain participant safety during dosing day visits 64 . The pair consisted of an experienced clinician (lead clinical facilitator) and a trainee (cofacilitator).

The predefined primary outcome measure was precision functional mapping (numerous visits, very long scans to produce individual connectomes) examining the effects of psilocybin on cortical and cortico- subcortical brain networks that could explain its rapid and sustained behavioural effects. Predefined secondary outcome measures included (1) assessment of hemodynamic response to evaluate how 5-HT 2A receptor agonism by psychedelics may alter neurovascular coupling, (2) assessment of acute psychological effects of psilocybin using the MEQ30 score ( Supplementary Methods ) and (3) assessment of personality change using the International Personality Item Pool-Five-Factor Model 100 . Changes in pulse rate and respiratory rate during psilocybin and placebo were later added as secondary outcome measures and personality change was abandoned because it was clear that we would not be powered to detect personality change.

Replication protocol

Participants were invited to return 6–12 months after completing the initial cross-over study for a replication protocol. This included 1–2 baseline fMRIs, a psilocybin session (identical to the initial session, except for lack of blinding) and 1–2 ‘after’ sessions within 4 days of the dose.

Participants

Healthy adults aged 18–45 years were recruited by campus-wide advertisement and colleague referral. Participants ( n  = 7) were enrolled from March 2021 to May 2023. Participants were required to have had at least one previous lifetime psychedelic exposure (for example, psilocybin, mescaline, ayahuasca, LSD), but no psychedelics exposure within the past 6 months. Individuals with psychiatric illness (depression, psychosis or addiction) based on the DSM-5 were excluded. Demographics and data summary details are provided in Supplementary Table 1 . One of the authors (N.U.F.D.) was a study participant.

Participants were scanned roughly every other day over the course of the experiment (Extended Data Fig. 1 ). Imaging was performed at a consistent time of day to minimize diurnal effects in FC 101 . Neuroimaging was performed on a Siemens Prisma scanner (Siemens) in the neuroimaging laboratories at the Washington University Medical Center.

Structural scans (T1w and T2w) were acquired for each participant at 0.9 mm isotropic resolution, with real-time motion correction. Structural scans from different sessions were averaged together for the purposes of Freesurfer segmentation and nonlinear atlas registrations.

To capture high-resolution images of blood oxygenation level-dependent (BOLD) signal, we used an echo-planar imaging sequence 102 with 2 mm isotropic voxels, multiband 6, multi-echo 5 (times to echo: 14.20, 38.93, 63.66, 88.39, 113.12 ms) 103 , repetition or relaxation time: 1,761 ms, flip angle of 68° and in-plane acceleration 104 (IPAT or grappa) of 2. This sequence acquired 72 axial slices (144 mm coverage). Each resting scan included 510 frames (lasting 15 min, 49 s) as well as three frames at the end used to provide estimate electronic noise.

Every session included two 15-min resting-state fMRI (rs-fMRI) scans, during which participants were instructed to hold still and look at a white fixation crosshair presented on a black background. Head motion was tracked in real time using Framewise Integrated Real-time MRI Monitoring software (FIRMM) 105 . An eye-tracking camera (EyeLink) was used to monitor participants for drowsiness.

Perceptual (matching) fMRI task

Participants also completed a previously validated event-related fMRI task. This was a suprathreshold auditory–visual matching task in which participants were presented with a naturalistic visual image (duration 500 ms) and coincident spoken English phrase, and were asked to respond with a button press to indicate whether the image and phrase were ‘congruent’ (for example, an image of a beach and the spoken word ‘beach’) or ‘incongruent’. Both accuracy and response time of button presses were recorded. Each trial was followed by a jittered inter-stimulus interval optimized for event-related designs. In a subset of imaging sessions, two task fMRI scans were completed following the two resting scans. Task fMRI scans used the same sequence used in resting fMRI, included 48 trials (24 congruent, 24 incongruent) and lasted a total of 410 s. In analyses, high motion frames were censored 106 and the two task scans were concatenated to better match the length of the rs-fMRI scans. Note the stimulus order in the two trials did not vary across session. The order of rest and task scans was not counterbalanced across sessions to avoid concern that task scans may influence subsequent rest scans.

Resting fMRI processing and resting-state network definition

Resting fMRI data were preprocessed using an in-house processing pipeline. In brief, this included removal of thermal noise using NORDIC denoising 107 , 108 , 109 , correction for slice timing and field distortions, alignment, optimal combination of many echoes by weighted summation 110 , normalization, nonlinear registration, bandpass filtering and scrubbing at a movement threshold of 0.3 mm to remove reduce the influence of confounds 111 . Tissue-based regressors were computed in volume (white matter, ventricles, extra-axial cerebrospinal fluid) 112 and applied following projection to surface. Task-based regressors were only applied when indicated. Details on rs-fMRI preprocessing are provided in  Supplementary Methods . Visualizations of motion, physiological traces and signal across the brain (‘grayplots’) before and after processing 113 are provided in Supplementary Video  1 .

Surface generation and brain areal parcellation

Surface generation and processing of functional data followed similar procedures to Glasser et al. 114 . To compare FC and resting-state networks across participants, we used a group-based surface parcellation and community assignments generated previously 62 .

For subcortical regions, we used a set of regions of interest 115 generated to achieve full coverage and optimal region homogeneity. A subcortical limbic network was defined on the basis of neuroanatomy: amygdala, anteromedial thalamus, nucleus accumbens, anterior hippocampus and posterior hippocampus 116 , 117 . These regions were expanded to cover anatomical structures (for example, anterior hippocampus) 31 .

To generate region-wise connectivity matrices, time courses of all surface vertices or subcortical voxels within a region were averaged. FC was then computed between each region timeseries using a bivariate correlation and then Fisher z -transformed for group comparison.

Individualized network and brain area mapping

We identified canonical large-scale networks using the individual-specific network matching approach described previously 43 , 44 , 62 . In brief, cortical surface and subcortical volume assignments were derived using the graph-theory-based Infomap algorithm 118 . In this approach, we calculated the correlation matrix from all cortical vertices and subcortical voxels, concatenated across all a participant’s scans. Correlations between vertices within 30 mm of each other were set to zero. The Infomap algorithm was applied to each participant’s correlation matrix thresholded at a range of edge densities spanning from 0.01 to 2%. At each threshold, the algorithm returned community identities for each vertex and voxel. Communities were labelled by matching them at each threshold to a set of independent group average networks described previously 62 . In each individual and in the average, a ‘consensus’ network assignment was derived by collapsing assignments across thresholds, giving each node the assignment it had at the sparsest possible threshold at which it was successfully assigned to one of the known group networks. See Extended Data Fig. 4 and Supplementary Fig. 1 for individual and group mode assignments, respectively. The following networks were included: the association networks including the DMN, fronto-parietal, dorsal attention, parietal memory, ventral attention, action-mode, salience and context networks; and the primary networks including the visual, somato-motor, somato-motor face and auditory networks.

To compute local (areal) desynchronization, we also defined brain areas at the individual level using a previously described areal parcellation approach 39 . In brief, for each participant, vertex-wise FC was averaged across all sessions to generate a dense connectome. Then, abrupt transitions in FC values across neighbouring vertices were used to identify boundaries between distinct functional areas.

To take advantage of the multilevel precision functional mapping study design, a LME model was used. Every scan was labelled on the following dimensions: participant identity (ID), MRI visit, task (task or rest), drug condition (prepsilocybin, psilocybin, MTP, postpsilocybin) and head motion (average framewise displacement). The rs-fMRI metrics (described below) were set as the dependent variable, drug (drug condition), task, framewise displacement (motion) and drug × task were defined as fixed effects, and participant ID and MRI session were random effects.

Let y ij be the rs-fMRI metric (for example, FC change score at a given vertex) for the j th observation (15 min fMRI scan) within the i th participant. The LME model can be written as:

β 0 is the intercept term.

β drug , β FD , β task and β task-by-drug are the coefficients for the fixed effects predictors.

drug ij , frame displacement ij (FD ij ) and task ij are the values of the fixed effects predictors for the j th observation within the i th group.

u 0 i represents the random intercept for the i th participant, accounting for individual-specific variability.

v 0 j represents the random intercept for the j th observation within the i th participant, capturing scan-specific variability.

ε ij is the error term representing unobserved random variation.

In MATLAB (Wilkinsonian notation), this model is expressed for every vertex Y (vertex) = fitlme(groupd, FC_Change(vertex) ~ drug + framewise displacement + task + task-by-drug + (1 |SubID) + (1 |session)).

To compensate for the implementations of this LME model on many rs-fMRI-related dependent variables, differences were highlighted when P  < 0.001. All P values reported are not corrected for multiple comparisons.

Vertex-wise FC change

FC change (‘distance’) was calculated at the vertex level to generate FC change maps and a LME model (equation ( 1 )) was used in combination with wild bootstrapping 119 , 120 and threshold-free cluster enhancement (TFCE) 95 , 121 to estimate P values for t -statistic maps resulting from the model (Figs. 1a–d and  4 ). Wild bootstrapping is an approach to permutation testing that was designed for models that are not independent and identically distributed, and are heteroscedastic.

First, a FC change map was generated for every scan by computing, for each vertex, the average distance between its FC seedmap and the FC seedmap for each of that participant’s baseline scans. As each participant had several baseline visits, FC change was computed for baseline scans by computing distance from all other baseline scans (excluding scans within the same visit). This provided a measure of day-to-day variability. Second, the distance value was used as the dependent variable y ij in the LME model to generate a t -statistic. Third, a wild bootstrapping procedure was implemented as follows. Several bootstrap samples ( B  = 1,000) were generated using the Rademacher procedure 120 , in which the residuals were randomly inverted. Specifically, a Rademacher vector was generated by randomly assigning −1 or 1 values with equal probability to the residual of each observation. By element-wise multiplication of the original residuals with the Rademacher vector, bootstrap samples were created to capture the variability in the data.

For the observed t -statistic-map and each bootstrap sample, the TFCE algorithm was applied to enhance the sensitivity to clusters of significant voxels or regions while controlling for multiple comparisons. The value of the enhanced cluster statistic derived from the bootstrap samples was used to create a null distribution under the null hypothesis. By comparing the original observed cluster statistic with the null distribution, P values were derived to quantify the statistical significance of the observed effect. The P values were obtained on the basis of the proportion of bootstrap samples that produced a maximum cluster statistic exceeding the observed cluster statistic.

The combined approach of wild bootstrapping with the Rademacher procedure and TFCE provided the method to estimate P values for our multilevel (drug condition, participant, session, task) design. This methodology accounted for the complex correlation structure, effectively controlled for multiple comparisons and accommodated potential autocorrelation in the residuals through the Rademacher procedure. By incorporating these techniques, association with psilocybin and other conditions was reliably identified amid noise and spatial dependencies.

Whole-brain FC change

For analyses in Figs. 1e,g , 2 and 4a (bottom), Extended Data Fig. 3 and Supplementary Figs. 3 , 4 and 6 , distance calculations were computed on the FC matrix using z -transformed bivariate correlation of time courses from parcellated brain areas 62 . The effects of day-to-day, drug condition, task and framewise displacement and drug × task were directly examined by calculating the distance between functional network matrices generated from each scan. Root-mean-squared Euclidean distance was computed between the linearized upper triangles of the parcellated FC matrix between each pair of 15 min fMRI scans, creating a second-order distance matrix (Extended Data Fig. 3 ). Subsequently, the average distance (reported as ‘whole-brain FC change’) was examined for FC matrices that were from the same individual within a single session, from the same individual across days (‘day-to-day’), from the same participant between drug and baseline (for example, psilocybin), from the same individual but different tasks (‘task:rest’), from the same individual between highest motion scans and baseline (‘hi:lo motion’), from different individuals (‘between person’). In the ‘high head motion’ comparison (‘hi:lo motion’ in Supplementary Fig. 3 ), the two non-drug scans with the highest average framewise displacement were labelled and compared against all other baseline scans.

A LME model (equation ( 1 )) and post hoc t -tests were used to assess statistical differences between drug conditions. A related approach using z -transformed bivariate correlation (‘similarity’ rather than distance) was also taken and results were unchanged (Supplementary Fig. 3c ).

Likelihood ratio test of participant-specific response

To test whether variability in participant-specific response to psilocybin was larger than would be expected by chance, we used a likelihood ratio test for variance of random slopes for a participant-specific response to psilocybin 48 . The difference in log likelihood ratios was compared to a null distribution of 1 million draws from a mixture of chi-squared distributions with degrees of freedom 1 and 2. We note that the likelihood ratio test of variance components is a non-standard problem 47 as the covariance matrix of the random effects is positive definite and the variances of random effects are non-negative. Finally, the test statistic for the likelihood ratio in this LME model was compared against a 50/50 mixture of two independent chi-squared distributions, each with one and two degrees of freedom, respectively.

Assessing subjective experience

Subjective experience was assessed for drug sessions using the MEQ30 46  ( Supplementary Methods ). The MEQ30 is designed to capture the core domains of the subjective effects of psychedelics (as compared to the altered states of consciousness rating scales that more broadly assess effects of psychoactive drugs 122 ) and is related to the therapeutic benefits of psychedelics. We applied a LME model across all drug sessions, similar to the one described above, but with MEQ30 total score as the dependent variable. Whole-brain FC change and framewise displacement were modelled as fixed effects, and participant was modelled as a random effect. The same model was solved using FC change from every vertex to generate a vertex-wise map of the FC change versus MEQ30.

Normalized FC change

The conditions above were compared by calculating normalized FC change scores using the following procedure: we (1) determined FC change for each condition compared to baseline as described above, (2) subtracted within-session distance for all conditions (such that within-session FC change was 0), (3) divided all conditions by day-to-day distance (such that day-to-day FC change was equal to 1). Thus, normalized whole-brain FC change values (for example, psilocybin versus base was 3.52) could be thought of as proportional to day-to-day variability.

Data-driven MDS

We used a classical MDS approach to cluster parcellated connectomes across fMRI scans, as previously described 38 . This data-driven approach was used to identify how different parameters (for example, task, drug, individual) affect similarity and/or distance between networks. MDS places data in multidimensional space on the basis of the dissimilarity (Euclidean distance) among data points, which in this case means a data point represents the linearized upper triangle of a FC matrix. Every matrix was entered into the classical MDS algorithm (implemented using MATLAB 2019, cmdscale.m). Many dimensions of the data were explored. The eigenvectors were multiplied by the original FC matrices to generate a matrix of eigenweights that corresponded to each dimension. These eigenweights were also applied to other rs-fMRI psychedelics datasets to generate dimensions scores (section ‘Other datasets’).

Rotation-based null model (spin test) for network specificity

To assess network specificity of FC change values, we calculated average FC change of matched null networks consisting of randomly rotated networks with preserved size, shape and relative position to each other 62 , 97 . To create matched random networks, we rotated each hemisphere of the original networks a random amount around the x , y and z axes on the spherical expansion of the cortical surface 62 . This procedure randomly relocated each network while maintaining networks’ sizes, shapes and relative positions to each other. Random rotation followed by computation of network-average FC change score was repeated 1,000 times to generate null distributions of FC change scores. Vertices rotated into the medial wall were not included in the calculation. Actual psilocybin FC change was then compared to null rotation permutations to generate a P value for the 12 networks that were consistently present across every participant’s Infomap parcellation. For bar graph visualization (Fig. 1 and Supplementary Fig. 1b ), networks with greater change ( P  < 0.05 based on null rotation permutations) are shown in their respective colour and other networks are shown in grey.

We used an approach previously validated to assess spatial complexity (termed entropy) or neural signals 61 . Temporal principal component analysis was conducted on the full BOLD dense timeseries, which yielded m principal components ( m roughly 80 K surface vertices and subcortical voxels) and associated eigenvalues. The normalized eigenvalue of the i th principal component was calculated as

where m is the number of principal components, and λ i and λ ′ i represent the eigenvalue and the normalized eigenvalue of the i th principal component, respectively. Last, the NGSC, defined as the normalized entropy of normalized eigenvalues, was computed using the equation:

The NGSC computed above attains values from the interval 0 to 1. The lowest value NGSC = 0 would mean the brain-wide BOLD signal consisted of exactly one principal component or spatial mode, and there is maximum global FC between all vertices. The highest value NGSC = 1 would mean the total data variance is uniformly distributed across all m principal components, and a maximum spatial complexity or a lowest FC is found.

NGSC was additionally calculated at the ‘parcel level’. To respect areal boundaries, this was done by first generating a set of individual-specific parcels in every participant (on all available resting fMRI sessions concatenated) using procedures described oreviously 39 , 62 .

NGSC maps were compared to PET-based 5-HT 2A receptor binding maps published in ref. 33 . Similarity was assessed by computing the bivariate correlation between NGSC values and 5-HT 2A binding across 324 cortical parcels from the Gordon–Laumann parcellation.

Persistent effects analysis

To assess the persistent effects of psilocybin, we compared FC changes 1–21 days postpsilocybin to predrug baseline. The FC change analysis (described above) indicated that connectivity at the whole-brain level did not change following psilocybin (Supplementary Fig. 1 ). A screen was conducted with P  < 0.05 threshold to identify brain networks or areas showing persistent effects. This analysis identified the anterior hippocampus as a candidate region of interest for persistent FC change (section ‘Baseline/after psilocybin FC change analysis’ in  Supplementary Methods ).

We assessed change in anterior hippocampus ‘FC change’ pre- versus postpsilocybin using the LME model described previously. In this model, all sessions before psilocybin (irrespective or cross-over order) were labelled as prepsilocybin and all sessions within 21 days after psilocybin were labelled as postpsilocybin.

As a control, we tested anterior hippocampus FC change pre- versus post-MTP using both the LME model, and an equivalence test. To control for potential persistent psilocybin effects, only the block of scans immediately before and after MTP were used (for example, if a participant took MTP as drug 1, then all baseline scans were labelled as ‘pre-MTP’ and all scans between drugs 1 and 2 were labelled ‘post-MTP’).

Equivalence testing (to conclude no change in anterior hippocampus after MTP) was accomplished by setting δ  = 0.5 standard deviation of FC change across pre-MTP sessions. We computed the 90% CI of change in FC change between pre- and post-MTP sessions. If the bounds of the 90% CI were within ± δ , then equivalence was determined 123 .

Other datasets

Raw fMRI and structural data published previously 55 , 56 were run through our in-house registration and processing pipeline described above. These datasets were used for replication, external validation and generalization to another classic psychedelic (that is, LSD) for the measures described above (for example, NGSC and the MDS-derived psilocybin FC dimension, dimension 1).

Using the data from ref. 55 : n  = 15 healthy adults (five women, mean age 34.1 years, s.d. 8.2) completed two scanning sessions (psilocybin and saline) that included an eyes-closed resting-state BOLD scan for 6 min before and following i.v. infusion of drug. fMRI data were acquired using a gradient-echo-planar imaging sequence, TR and TE of 3,000 and 35 ms, field-of-view 192 mm, 64 × 64 acquisition matrix, parallel acceleration factor of 2 and 90° flip angle.

Using the data from ref. 56 : healthy adults completed two scanning sessions (LSD and saline), which included an eyes-closed resting-state BOLD scan acquired for 22 min following i.v. drug infusion lasting 12 min. n  = 20 participants completed the protocol, but data were used for n  = 15 (four women; mean age 30.5, standard deviation 8.0) deemed suitable for BOLD analyses. fMRI data were acquired using a gradient-echo-planar imaging sequence, TR and TE of 2,000 and 35 ms, field-of-view 220 mm, 64 × 64 acquisition matrix, parallel acceleration factor of 2, 90° flip angle and 3.4 mm isotropic voxels.

The ABCD database resting-state functional MRI 59 (annual release v.2.0, https://doi.org/10.15154/1503209 ) was used to replicate the effects of stimulant use on FC. Preprocessing included framewise censoring with a criterion of frame displacement less than or equal to 0.2 mm in addition the standard predefined preprocessing procedures 124 . Participants with fewer than 600 frames (equivalent to 8 min of data after censoring) were excluded from the analysis. Parcel-wise group-averaged FC matrices were constructed for each participant as described above for 385 regions on inter-test in the brain.

Use of a stimulant (for example, MTP, amphetamine salts, lisdexamfetamine) in the last 24 h was assessed by parental report. Participants with missing data were excluded. Regression analysis was used to assess the relationship between FC (edges) and stimulant use in the last 24 h. Framewise displacement (averaged over frames remaining after censoring) was used as a covariate to account for motion-related effects. The t -values that reflect the relationship between stimulant use and FC were visualized on a colour scale from −5 to +5 to provide a qualitative information about effect of stimulant use on FC.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All data from individual participants P1–P7 are available at https://wustl.box.com/v/PsilocybinPFM , with a password available on completion of a data use agreement. The ABCD data used in this report came from ABCD the Annual Release 2.0, https://doi.org/10.15154/1503209 . The ABCD data repository grows and changes over time ( https://nda.nih.gov/abcd ). The Imperial College London psilocybin and LSD datasets are available upon request.

Code availability

Data processing code for the psilocybin precision functional mapping data can be found at https://wustl.box.com/s/dmj5s3h9pxt9bcw9mm3ai9c15y756o79 . Code specific to analyses can be found at https://gitlab.com/siegelandthebrain1/Psilocybin_PFM/ . Data processing code for the ABCD data can be found at https://github.com/DCAN-Labs/abcd-hcp-pipeline . Matching task stimuli are available at https://gitlab.com/siegelandthebrain1/Psilocybin_PFM/-/blob/main/image_task_clean.zip . Software packages incorporated into the above pipelines for data analysis included: MATLAB R2019b, https://www.mathworks.com/ (including Psychtoolbox v.2.0 and Statistics and Machine Learning Toolbox v.11.6); Connectome Workbench v.1.5; http://www.humanconnectome.org/software/connectome-workbench.html ; Freesurfer v.6.2, https://surfer.nmr.mgh.harvard.edu/ ; FSL v.6.0, https://fsl.fmrib.ox.ac.uk/fsl/fslwiki ; 4dfp tools, https://4dfp.readthedocs.io/en/latest/ ; Infomap, https://www.mapequation.org ; Cifti MATLAB utilities (including spin test): https://github.com/MidnightScanClub/SCAN and 4dfp tools, https://4dfp.readthedocs.io/en/latest/ . MRI pulse sequences used to acquire the data are provided at https://gitlab.com/siegelandthebrain1/Psilocybin_PFM/-/blob/main/NP1161_MRI_sequence.pdf .

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Acknowledgements

This work was supported by the Taylor Family Institute Fund for Innovative Psychiatric Research (J. S. Siegel, T.O.L., G.E.N.); the McDonnell Center for Systems Neuroscience (J. S. Siegel, G.E.N.); the Institute of Clinical and Translational Science (G.E.N.); National Institutes of Health (NIH) grants MH112473 (J. S. Siegel, S.S., D.A.B., C.H., G.E.N.), T32 DA007261 (J. S. Siegel), NS123345 (B.P.K.), MH129616 (T.O.L.), MH121276 (N.U.F.D., E.M.G., D.A.F.), MH118370 (C.G.), NS124738 (C.G.), MH096773 (D.A.F., N.U.F.D.), MH122066 (D.A.F., E.M.G., N.U.F.D.), MH124567 (D.A.F., E.M.G., N.U.F.D.), NS129521 (E.M.G., D.A.F., N.U.F.D.) and NS088590 (N.U.F.D.); the National Spasmodic Dysphonia Association (E.M.G.); the Ralph Metzner Professorship and the Tianqiao and Chrissy Chen Institute (R.C.-H.); the Intellectual and Developmental Disabilities Research Center (N.U.F.D.); by the Kiwanis Foundation (N.U.F.D.); the Washington University Hope Center for Neurological Disorders (E.M.G., N.U.F.D.) and by Mallinckrodt Institute of Radiology pilot funding (E.M.G., N.U.F.D.). Furthermore, this study used data from the ABCD study, supported by National Institutes of Health grant no. U01DA041120. We give a special thanks to our study participants, who completed a demanding protocol with grace for the benefit of scientific inquiry. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study ( https://abcdstudy.org ), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html . A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/ . ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Author information

These authors contributed equally: Ginger E. Nicol, Nico U. F. Dosenbach

Authors and Affiliations

Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA

Joshua S. Siegel, Demetrius Perry, Timothy O. Laumann, Julie A. Schweiger, David A. Bender, Eric J. Lenze & Ginger E. Nicol

Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA

Subha Subramanian

Department of Neurology, Washington University School of Medicine, St Louis, MO, USA

Benjamin P. Kay, Nicholas V. Metcalf, Samuel R. Krimmel, Kristen M. Scheidter, Forrest I. Whiting, Marcus E. Raichle, Abraham Z. Snyder & Nico U. F. Dosenbach

Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA

Evan M. Gordon, T. Rick Reneau, Joshua S. Shimony, Dean F. Wong, Marcus E. Raichle, Abraham Z. Snyder & Nico U. F. Dosenbach

Department of Emergency Medicine, Advocate Christ Health Care, Oak Lawn, IL, USA

Ravi V. Chacko

Department of Psychology, Florida State University, Tallahassee, FL, USA

Caterina Gratton

Miami VA Medical Center, Miami, FL, USA

Christine Horan

Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA

Jonah A. Padawer-Curry, Marcus E. Raichle & Nico U. F. Dosenbach

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA

Russell T. Shinohara

Penn Statistics in Imaging and Visualization Endeavor, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Russell T. Shinohara & Yong Chen

Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA

Julia Moser, Steven M. Nelson & Damien A. Fair

Institute of Child Development, University of Minnesota, Minneapolis, MN, USA

Julia Moser & Damien A. Fair

Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA

Essa Yacoub, Luca Vizioli & Damien A. Fair

Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA

Steven M. Nelson & Damien A. Fair

Department of Neurology, University of California, San Francisco, CA, USA

Robin Carhart-Harris

Centre for Psychedelic Research, Imperial College London, London, UK

Usona Institute, Fitchburg, WI, USA

Charles L. Raison

Department of Psychiatry, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA

Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA

Marcus E. Raichle & Nico U. F. Dosenbach

Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA

Marcus E. Raichle

Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA

Nico U. F. Dosenbach

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Contributions

The concept came from J. S. Siegel and G.E.N. The study was designed by J. S. Siegel, S.S., T.O.L., C.L.R., E.J.L., A.Z.S. and G.E.N. Data acquisition and processing were done by J. S. Siegel, S.S., T.R.R., D.P., C.H., J. S. Shimony, J.A.S., D.A.B., K.M.S., F.I.W., J.M., E.Y., S.M.N., L.V., D.A.F. and A.Z.S.. Data analysis and interpretation were carried out by J. S. Siegel, B.P.K., E.M.G., T.O.L., N.V.M., C.G., R.V.C., S.R.K., D.F.W., J.A.P.-C., R.T.S., Y.C., R.C.-H., M.E.R., G.E.N. and N.U.F.D. The paper was written and revised by J. S. Siegel, S.S., M.E.R., A.Z.S., G.E.N. and N.U.F.D. Participant 7 was author N.U.F.D.

Corresponding author

Correspondence to Joshua S. Siegel .

Ethics declarations

Competing interests.

Within the past year, J. S. Siegel has been an employee of Sumitomo Pharma America and received consulting fees from Longitude Capital. J. S. Siegel, N.U.F.D., T.O.L. and E.M.G. have submitted a provisional patent (patent no. 020949/US 15060-1787) for the use of precision functional mapping for measuring target engagement by experimental therapeutics. R.T.S. has received consulting compensation from Octave Bioscience and compensation for reviewership duties from the American Medical Association. C.L.R. serves as a consultant to Usona Institute and Novartis and receives research support from the Tiny Blue Dot Foundation. G.E.N. has received research support from Usona Institute (drug only). She has served as a paid consultant for Carelon, Alkermes, Inc., Sunovion Pharmaceuticals, Inc. and Novartis Pharmaceuticals Corp. T.O.L. holds a patent for taskless mapping of brain activity licenced to Sora Neurosciences and a patent for optimizing targets for neuromodulation, implant localization and ablation is pending. J. S. Siegel is a consultant and received stock options in Sora Neuroscience, and company that focuses on resting-state analysis. D.A.F. and N.U.F.D. are cofounders of Turing Medical Inc, have financial interest, may benefit financially if the company is successful in marketing FIRMM motion monitoring software products, may receive royalty income based on FIRMM technology developed at WUSOM and licenced to Turing Medical Inc. S.M.N., E.M.G. and T.O.L. have received consulting fees from Turing Medical Inc. D.F.W. is a consultant for Engrail Therapeutics and receives contract funds for WUSOM research studies from Eisai, Anavex and Roche. These potential conflicts of interest have been reviewed and are managed by WUSOM. The other authors declare no competing interests. All authors report no financial interest in psychedelics companies.

Peer review

Peer review information.

Nature thanks Charles Lynch, Petros Petridis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended data fig. 1 quantifying psilocybin effects with precision functional mapping: design..

a) Schematic illustrating the study protocol of the individual-specific precision functional mapping study of acute and persistent effects of psilocybin (single dose: 25 mg). Repeated longitudinal study visits enabled high-fidelity individual brain mapping, measurement of day-to-day variance, and acclimation to the scanner. The open label replication protocol 6-12 months later included one or two scans each of baseline, psilocybin, and after drug. b) Timeline of imaging visits for 7 participants. c) Head motion comparisons across psychedelics studies 55 , 56 . Average head motion (FD, framewise displacement, in mm) off and on drug compared between our dataset and prior psychedelic fMRI studies. Unpaired two-sided t -test: n PSIL 2012  = 15, n LSD,2016  = 20, n PSIL(PFM)  = 7; off drug PSIL2012-PSIL(PFM) t (274)  = −4.57, P uncorr  = 7.33 × 10 −5 ; off drug LSD2016-PSIL(PFM) t (286)  = −4.03, P uncorr  = 7.34 × 10 −6 ; on drug PSIL2012-PSIL(PFM) t (46)  = −1.80, P uncorr  = 0.079; on drug LSD2016-PSIL(PFM) t (88)  = −0.73, P uncorr  = 0.46. Dotted line at FD of 0.2 mm. Dark gray bars indicate quartiles, light gray violins indicate distribution. * P uncorr  < 0.05, + P  < 0.1. d) Timeline for an example participant (P1). e) Participants reported significantly higher scores on all dimensions of the mystical experience questionnaire during psilocybin (red) than placebo (40 mg methylphenidate; blue). Paired two-tailed t -test, n  = 7; Mystical t (6)  = −3.64, P uncorr  = 0.011; Positive Mood t (6)  = −5.44, P uncorr  = 0.0016; Transcendence t (6)  = −4.98, P uncorr  = 0.0025; Ineffability t (6)  = −2.54, P uncorr  = 0.044. Error bars indicate SEM.

Extended Data Fig. 2 Unthresholded vertex-wise FC change maps.

T-statistic maps, resulting from the linear mixed effects (LME) model based on vertex-wise FC change (Euclidean distance from baseline scans) across the cortex and subcortical structures for every scan. Higher t values indicate a larger change from baseline (pre-drug) scans. Effects of drug condition (baseline, psilocybin, methylphenidate, post-psilocybin, post-methylphenidate), were modeled as fixed effects. For example, if drug 1 was psilocybin and drug 2 was methylphenidate, then scans between drug visits were labeled post-psilocybin and scans after drug 2 were labeled post-methylphenidate.

Extended Data Fig. 3 Functional connectivity (FC) distance and condition matrices for all fMRI scans.

Following Gratton et al. 38 , we compared FC matrices between rs-fMRI sessions to quantify contributors to variability in whole-brain FC. Under this approach, the effects of group, individual, session, and drug (as well as their interactions) are examined by first calculating the Euclidean distance among every pair of FC matrices (i.e., distance among the linearized upper triangles). a) In the resulting second-order distance matrix, each row and column show whole-brain FC from a single study visit. The colours in the matrix indicate distance between functional networks for a pair of visits (i.e., Euclidean distance between the linearized upper triangles of two FC matrices). Panels b and c demonstrate how the distance matrix was subdivided to compare different conditions. b) Black triangles represent distinct individuals. Replication protocol visits are listed at the end. c ) Task and rest scans are shown in white and orange, respectively. Note that psilocybin scans (black arrows pointing to P1 psilocybin scans in panel a are very dissimilar to no-drug scans from the same individual (left arrow; in a ) but have heightened similarity to psilocybin scans from other individuals (right arrow in a ).

Extended Data Fig. 4 Participant-specific FC change maps for drug sessions.

Individual participant methylphenidate (MTP) and psilocybin (PSIL) FC change maps. Left most column shows individuals’ functional networks. Right 3 columns show FC change maps, generated by calculating Euclidean distance from baseline seedmaps for each vertex. For each session the total score on the Mystical Experience Questionnaire (MEQ30: out of a maximum of 150) is given in the upper right corner. *P5 had an episode of emesis 30 minutes after drug ingestion during PSIL2.

Extended Data Fig. 5 Multi-dimensional scaling, dimension edge weights.

a) Group parcellation (324 cortical and 61 subcortical parcels) 31 b) Weights from the first 4 dimensions generated by multi-dimensional scaling of the full dataset. The color of each pixel in the plot represents the weight of a given edge. Dimension 1 captures the loss of network integration (on diagonal boxes) and segregation (off diagonal boxes) of psilocybin. Dimensions 2 and 3 primarily explain individual differences and do not show network patterns as clearly. Dimension 4 captures shared effects of psilocybin (PSIL) and methylphenidate (MTP) on sensorimotor systems (suspected arousal effects).

Extended Data Fig. 6 Average functional connectivity (FC) matrices by condition.

a) Group parcellation (324 cortical and 61 subcortical parcels) 31 . b) Average FC matrices and condition differences. Top left shows the group average FC adjacency matrix. Bottom left shows the effect of psilocybin, e.g. increased correlation between dorsal attention, fronto-parietal, and default mode network to each other and to other cortical, limbic, and cerebellar systems. Top right shows effect of methylphenidate. For comparison and validation, we compared methylphenidate to the main effect of stimulant use within the last 24 hours (bottom right, n  = 487 yes, n  = 7992 no) in ABCD rs-fMRI data (bottom right).

Extended Data Fig. 7 Correlations with mystical experience scores.

Comparison of MEQ30 score (y-axes) to global desynchronization (top left; NGSC change, drug minus baseline), head motion (bottom left; framewise displacement (FD) in mm), heart rate change (top right; drug minus baseline), and respiratory rate change (bottom right; drug minus baseline), for all drug sessions. Statistics ( rho , P ) are based on bivariate correlation, two-sided, uncorrected. In the case of Δ NGSC, statistics are reported before and after the removal of an outlier point (> 2 SD lower than mean, indicated by the gray arrow).

Extended Data Fig. 8 Auditory-visual matching fMRI task.

a) Schematic of auditory/visual matching task design. b) Comparison of performance (‘No Drug’ and psilocybin conditions are at ceiling). Lines indicate means and standard deviation across sessions. Number of task sessions are indicated in Supplementary Table 1 . c) Comparison of reaction time (RT). Lines indicate mean and standard deviation across all trials (48 trials per session). d) Task fMRI activation maps (beta weights) and e) contrasts (simple subtraction) using the canonical hemodynamic response function (HRF). f) Eight a priori regions of interest for timecourse analyses. g) Average timecourses from the regions of interest shown in panel f , calculated using finite impulse response model over 13 TR x 1.761 s/TR = 22.89 seconds, for all trials. Shaded area around each line indicates SEM. ANOVAN of Condition x HRF Beta (Main effect of all trials) magnitude testing effect of drug, two-sided: Left V1, F (2,40)  = 3.91, P  = 0.030; Right V1, F (2,40)  = 4.40, P  = 0.020; Left M1 hand, F (2,40)  = 0.40, P  = 0.68; Left Auditory A1, F (2,40)  = 0.22, P  = 0.81; Right Auditory A1, F (2,40)  = 0.77, P  = 0.47; Left Language, F (2,40)  = 0.025, P  = 0.98; Left DMN, F (2,40)  = 1.15, P  = 0.33; Right DMN, F (2,40)  = 0.14, P  = 0.87. * P  < 0.05. P-values are uncorrected for multiple comparisons.

Supplementary information

Supplementary information.

This file contains Supplementary Table 1, Figs. 1–7 and Methods.

Reporting Summary

Peer review file, supplementary video 1.

Quality control plots for every fMRI scan. For each participant (P1, P3–P7, concatenated) the quality control plots are concatenated in the order that the scans were acquired (Extended Data Fig. 1). The top plot shows head position (frame-by-frame, relative to frame 1) separated into x , y , z translation and x , y , z rotation (six parameters). The second plot from the top shows DVARS, which index the rate of change of fMRI signal across the entire brain at each frame of data. The D refers to the temporal derivative of time courses, and VARS refers to the root-mean-square variance over voxels. The third plot shows head motion measured as FD (framewise displacement) in mm. Underneath in the fourth row, the time course for the whole-brain grayordinates (cortex on top, subcortex on the bottom) are shown before preprocessing (known as ‘grayplot’ or ‘carpet plot’). The fifth row shows the same grayordinates, but after preprocessing (bandpass filtering, removal of nuisance signals by regression, and smoothing at 4 mm full-width at half-maximum). The vertical black lines or bars in the grayplots indicate these data frames that were censored due to excessive head motion. At the end, quality control plots are compared to physiology (heart rate, respiratory rate) plots for every session in which physiological monitoring data were acquired.

Supplementary Video 2

Time series of fully preprocessed resting-state fMRI (rs-fMRI) data (roughly 9 min), taken from the first resting scan of the MRI session. Frame-by-frame rs-fMRI data, excluding high head motion frames (FD > 0.3), are shown for the drug scans (psilocybin, MTP) for each participant (P1, P3–P7).

Supplementary Video 3

Supplementary video 4, supplementary video 5, supplementary video 6, supplementary video 7, rights and permissions.

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Siegel, J.S., Subramanian, S., Perry, D. et al. Psilocybin desynchronizes the human brain. Nature (2024). https://doi.org/10.1038/s41586-024-07624-5

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Received : 24 October 2023

Accepted : 29 May 2024

Published : 17 July 2024

DOI : https://doi.org/10.1038/s41586-024-07624-5

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