This document outlines an educational workshop on studying individual differences in functional brain networks and their relationship to behavior. It discusses why studying individual differences is important for basic neuroscience and potential clinical applications. The workshop covers topics like how to acquire high-quality brain and behavioral data, how to relate brain connectivity to behaviors, and choosing appropriate behaviors to study. It recommends longer scan durations, considering task-based scans in addition to rest, and selecting stable, trait-like behaviors that show variability in the population when studying individual differences.
Estimating Functional Connectomes: Sparsity’s Strength and LimitationsGael Varoquaux
Talk given at the OHBM 2017 education course.
I present the challenges and techniques to estimating meaningful brain functional connectomes from fMRI: why sparsity in inverse covariance leads to models that can interpreted as interactions between regions.
Then I discuss the limitations of sparse estimators and introduce shrinkage as an alternative. Finally, I discuss how to compare multiple functional connectomes.
Generalizability in fMRI, fast and slowTal Yarkoni
- The document discusses issues around generalizability in fMRI research and proposes strategies for "fast" versus "slow" generalization.
- It notes that most findings in neuroimaging are only interesting if they generalize broadly, but that statistical models must support desired inferences about new stimuli or subjects.
- The document advocates treating factors like subjects and stimuli as random effects to support generalization, and using large stimulus samples to avoid overgeneralizing findings. It suggests both modeling approaches (e.g. random effects) and study design (e.g. more stimuli/subjects) can help researchers generalize more appropriately.
The document outlines the steps of the PPDAC statistical inquiry cycle which includes problem, plan, data, analysis, and conclusion. It provides descriptions of the key elements and activities that occur at each step such as formulating a question, developing a plan to collect and measure data, analyzing and interpreting results, and drawing conclusions. Examples are also provided of elements that would be classified under each step of the cycle to illustrate how to apply the framework to an investigation.
VU Library: Evidence-based practice tutorialIshbel Leggat
This presentation was designed for use in Library tutorials with Nursing & Paramedic Science students. The workshop introduces students to the basic concepts of evidence-based practice; asking answerable clinical questions using PICO; levels of evidence and how to search Library databases to find appropriate evidence.
sience 2.0 : an illustration of good research practices in a real studywolf vanpaemel
a presentation explaining the what, how and why of some of the features of science 2.0 (replication, registration, high power, bayesian statistics, estimation, co-pilot multi-software approach, distinction between confirmatory and exploratory analyses, and open science) using steegen et al. (2014) as a running example.
The document discusses research methods used in psychology, including experiments, observations, questionnaires, interviews, and case studies. It provides examples of how to identify independent and dependent variables, operationalize variables, and design observational studies. Key terms are defined, such as aims, hypotheses, reliability, and ethics. Research methods each have advantages and disadvantages for different types of studies.
This document discusses various topics related to forensic science, including whether forensic science is truly a science, cognitive biases that can affect forensic examiners, efforts to introduce quantitative methods and reduce bias, and developments in audio forensics. Specifically, it addresses the scientific method, cognitive biases, likelihood ratios as a quantitative measure, blind testing techniques, and the analysis of voiceprints in audio forensics cases.
Crisis of confidence, p-hacking and the future of psychologyMatti Heino
The document discusses issues with statistical analysis and interpretation in research. It notes that traditional null hypothesis significance testing can lead to problems like publication bias. Bayesian statistics are presented as an alternative that considers the probability of the data under both the null and alternative hypotheses. However, Bayesian methods still require transparent reporting and are not a panacea. Overall statistical power in many fields remains low, and selective reporting can still undermine reliability regardless of the statistical approach. Transparency in analysis, open sharing of data and materials, and preregistration of hypotheses are emphasized as ways to improve the credibility of research findings.
Estimating Functional Connectomes: Sparsity’s Strength and LimitationsGael Varoquaux
Talk given at the OHBM 2017 education course.
I present the challenges and techniques to estimating meaningful brain functional connectomes from fMRI: why sparsity in inverse covariance leads to models that can interpreted as interactions between regions.
Then I discuss the limitations of sparse estimators and introduce shrinkage as an alternative. Finally, I discuss how to compare multiple functional connectomes.
Generalizability in fMRI, fast and slowTal Yarkoni
- The document discusses issues around generalizability in fMRI research and proposes strategies for "fast" versus "slow" generalization.
- It notes that most findings in neuroimaging are only interesting if they generalize broadly, but that statistical models must support desired inferences about new stimuli or subjects.
- The document advocates treating factors like subjects and stimuli as random effects to support generalization, and using large stimulus samples to avoid overgeneralizing findings. It suggests both modeling approaches (e.g. random effects) and study design (e.g. more stimuli/subjects) can help researchers generalize more appropriately.
The document outlines the steps of the PPDAC statistical inquiry cycle which includes problem, plan, data, analysis, and conclusion. It provides descriptions of the key elements and activities that occur at each step such as formulating a question, developing a plan to collect and measure data, analyzing and interpreting results, and drawing conclusions. Examples are also provided of elements that would be classified under each step of the cycle to illustrate how to apply the framework to an investigation.
VU Library: Evidence-based practice tutorialIshbel Leggat
This presentation was designed for use in Library tutorials with Nursing & Paramedic Science students. The workshop introduces students to the basic concepts of evidence-based practice; asking answerable clinical questions using PICO; levels of evidence and how to search Library databases to find appropriate evidence.
sience 2.0 : an illustration of good research practices in a real studywolf vanpaemel
a presentation explaining the what, how and why of some of the features of science 2.0 (replication, registration, high power, bayesian statistics, estimation, co-pilot multi-software approach, distinction between confirmatory and exploratory analyses, and open science) using steegen et al. (2014) as a running example.
The document discusses research methods used in psychology, including experiments, observations, questionnaires, interviews, and case studies. It provides examples of how to identify independent and dependent variables, operationalize variables, and design observational studies. Key terms are defined, such as aims, hypotheses, reliability, and ethics. Research methods each have advantages and disadvantages for different types of studies.
This document discusses various topics related to forensic science, including whether forensic science is truly a science, cognitive biases that can affect forensic examiners, efforts to introduce quantitative methods and reduce bias, and developments in audio forensics. Specifically, it addresses the scientific method, cognitive biases, likelihood ratios as a quantitative measure, blind testing techniques, and the analysis of voiceprints in audio forensics cases.
Crisis of confidence, p-hacking and the future of psychologyMatti Heino
The document discusses issues with statistical analysis and interpretation in research. It notes that traditional null hypothesis significance testing can lead to problems like publication bias. Bayesian statistics are presented as an alternative that considers the probability of the data under both the null and alternative hypotheses. However, Bayesian methods still require transparent reporting and are not a panacea. Overall statistical power in many fields remains low, and selective reporting can still undermine reliability regardless of the statistical approach. Transparency in analysis, open sharing of data and materials, and preregistration of hypotheses are emphasized as ways to improve the credibility of research findings.
Redevelop 2019 - Debugging our biases and intuition in software developmentDave Hulbert
We program algorithms with shortcuts known as heuristics. These allow us to get a good enough answer to a problem with less CPU and memory usage. Brains attempt to take shortcuts too, using intuition and biases to figure things out with less thinking or knowledge. Heuristics are valuable but they're not perfect. We can evaluate best and worst cases for code but how do we do the same with our own decision making process?
In this talk, we'll go through the ups and downs of heuristics and biases that exist in a developer's world. We'll look at ways to reduce any resulting fallacies, whilst still taking advantage of the performance improvement.
This document discusses several key methods of science: naturalistic observation which involves non-intrusively observing populations without influencing them; correlational approaches which relate variables mathematically without implying causation; experimental methods which test hypotheses using experimental and control groups; and operational definitions which clearly define variables. It also discusses independent and dependent variables, confounds, validity, reliability, and the scientific attitude which values falsification, testability, skepticism, and parsimony.
This document discusses finding and evaluating evidence to answer clinical questions. It emphasizes that well-constructed clinical questions help focus the search for relevant evidence. The PICO (Patient/Problem, Intervention, Comparison, Outcome) framework is presented as a tool to help formulate answerable clinical questions. Examples of different types of clinical questions that can be answered through evidence-based medicine resources are provided, including questions of therapy, prognosis, diagnosis, and harm. Key hunting tools for searching evidence-based literature are described, including PubMed and its Clinical Queries feature.
Data collection methods to improve reproducibilityDigital Science
"Reproducibility, data collection, and laboratory management technologies" - Louis Culot, CEO of Biodata
Slides from Shaking It Up: Challenges and Solutions in Scholarly Information Management, San Francisco, April 22, 2015
(2017/06)Practical points of deep learning for medical imagingKyuhwan Jung
This document provides an overview of deep learning and its applications in medical imaging. It discusses key topics such as the definition of artificial intelligence, a brief history of neural networks and machine learning, and how deep learning is driving breakthroughs in tasks like visual and speech recognition. The document also addresses challenges in medical data analysis using deep learning, such as how to handle limited data or annotations. It provides examples of techniques used to address these challenges, such as data augmentation, transfer learning, and weakly supervised learning.
RecSys 2016 Talk: Feature Selection For Human RecommendersKatherine Livins
The document discusses human computation at Stitch Fix and how to shape what human workers are processing to improve recommendations. It suggests 1) determining what workers are currently attending to and using, 2) analyzing which features produce the best recommendations, and 3) changing how information is displayed or providing training to influence what workers process. Feature drop out studies and eye tracking can be used to evaluate the impact of different features on performance. A controlled lab study or A/B test in Stitch Fix's "Styling Lab" could then shape what information workers see.
This document discusses finding evidence to answer a PICO question. It begins by reviewing PICO questions and different types of studies. Patient-oriented evidence (POE) focuses on outcomes like mortality and quality of life, while disease-oriented evidence (DOE) examines pathophysiology and etiology. The best studies are randomized controlled trials at the top of the evidence pyramid. Other primary sources include case reports, case series, case-control and cohort studies. Secondary sources like systematic reviews and meta-analyses synthesize multiple primary studies. Searching requires considering relevance, validity and effort required. The document outlines strategies and resources for efficiently finding the best evidence.
The document discusses critical analysis, including defining it as looking at topics from different perspectives, considering context, and not making immediate judgments without evidence. It provides examples of applying critical thinking skills like evaluation, synthesis, and analysis to understand a photograph related to protests at Heathrow Airport. The document emphasizes that critical thinking is an important skill for university work and various academic activities require applying critical analysis.
This document provides guidance on searching the literature for health information. It recommends planning searches by focusing questions, breaking them into basic terms, and choosing appropriate research methods and databases. Searches can be refined using Boolean operators like AND, OR and NOT to combine search terms. Higher levels of evidence include systematic reviews and randomized controlled trials. The document encourages contacting librarians for assistance if searches take more than 10 minutes.
This document discusses searching for evidence to practice evidence-based dentistry. It describes primary sources like original research articles and secondary sources like systematic reviews, synopses, guidelines, and evidence summaries. Popular secondary sources mentioned include Cochrane Library, DARE, EBD journal, CATs, textbooks like UpToDate, and clinical guidelines from NICE and NGC. The document emphasizes appraising the quality of evidence from different sources and searching efficiently using keywords and databases like PubMed. It notes that absence of evidence found does not mean absence of evidence overall.
This document summarizes different types of psychological research methods: descriptive research simply observes and measures behaviors; correlational research tests relationships between variables but cannot prove causation; experimental research tests causal hypotheses by manipulating an independent variable and measuring its effects on a dependent variable with a control group for comparison. Biases that can influence results are also discussed.
The document provides information about conducting nursing research and evidence-based practice. It discusses key topics such as the importance of research for nurses, different types of research, overcoming challenges in doing research, and communicating research findings. The document aims to help nurses better understand research methods and utilization of evidence to improve patient care.
The document discusses correlation versus causality in experimental design. It provides examples of different types of experimental designs including randomized controlled trials, natural experiments, before-after designs, and differences-in-differences designs. It emphasizes the importance of randomness, control groups, and understanding the outcome variable when analyzing experimental data. Key considerations include whether the outcome is continuous or categorical and choosing the appropriate statistical tests accordingly. The document also discusses examples of experiments in various contexts like economics, policy, and online settings.
Psychometric assessment of older adults oct 21 to 26 2013 winter workshopDr. Rakesh Tripathi
This document discusses psychological assessment of older adults. It provides an overview of concepts related to psychometrics and psychological assessment. It describes the purpose and steps of psychological assessment for older adults, including screening and detailed assessment. Several commonly used cognitive screening tools are described, including the Mini-Mental State Examination, Hindi Mental State Examination, and a newly developed Hindi Cognitive Screening Test. The document emphasizes the importance of using culturally appropriate assessment tools for accurate evaluation of older adults.
The Reproducibility Crisis in Psychological Science: One Year LaterJimGrange
The document discusses the reproducibility crisis in psychological science. It notes several cases from 2011 that called into question research practices. An open science collaboration attempted to replicate 100 psychology studies and found that only 36% replicated. The document recommends promoting open science practices like replication, transparent statistics and data sharing, and teaching/rewarding rigor over quantity or novelty. It argues the field needs to change incentives to prioritize accuracy over publication. Overall, the document analyzes issues compromising reproducibility and proposes open science solutions to improve research integrity in psychology.
Study notesSome of the information below may be repetitive of wh.docxhanneloremccaffery
Study notes
Some of the information below may be repetitive of what you have read in Creswell. In chapter 10, Singleton addressed field research, which overlaps with some qualitative designs, but for Singleton it is different from qualitative research because field research often involves quantification and more than simply observation. (Sometimes qualitative research also involves quantification) What Singleton addressed as field research is out the traditions of sociology and anthropology. Field research is probably more like ethnography than it is like other qualitative designs.
In a previous unit, we mentioned the use of existing data for research. Sometimes using data that are available lessens the data gathering task because you do not have to be dependent on others to return a survey or agree to an interview. Here is a good example of the use of existing data in a causal-comparative design. A former Princeton student who was in the Education program and is an assistant principal did her dissertation using existing data. She wanted to know if the reading scores on a standardized test (ITBS) were different after a new approach to teaching reading than before the new approach began. She went back to 1991 and recorded scores of 1st and 2nd graders for a five-year period before the intervention in 1996. Then she obtained scores of 1st and 2nd graders for five years after the new program and then did a number of statistical comparisons. She found significant differences on the comparisons so it would appear that the new approach to reading was effective. She could have set up a quasi-experimental design, but unless she did it for a number of years, she would not have had nearly as much data. This is a case in which it was not feasible to do an experimental design, but she obtained useful data.
Not all research using available data is causal-comparative. Much is descriptive. Probably the use of available data for research is among the top three types of designs used. Think of all the studies that come from the U.S. Census every ten years. You may have some good data stored at your place of employment. One researcher in Arizona has studied the trash/garbage of people for 25 years to find out how they live. Can you imagine sifting through someone's trash for 25 years? He has, however, learned a great deal about how the people whose trash he has swiped in the Tucson area live.
Moving back now to Chapter 10 in Singleton. While qualitative research is simply not acceptable to some researchers, in many ways, it can be more valuable than quantitative research when specificity and correctness are not necessary. Probably about 40% of Princeton students do some type of qualitative research for their dissertations.
Singleton refers to qualitative research as field research. He simply uses a broad category to cover various kinds because qualitative research is done in the real world (field).
One primary difference between quantitative and quali.
This document summarizes research on consciousness and attention. It discusses how attention is not necessary for consciousness and presents evidence that some visual stimuli like gender can be consciously perceived with little attention. The document also discusses how metacognition can be used to measure consciousness independently of task performance. Dual-task experiments show gender perception without attention is associated with conscious insight, even with minimal training. While some visual stimuli may attract attention, faces can be discriminated without relying on bottom-up attention.
Do you have responses to open-ended questions or want to use qualitative data to evaluate CE/QI interventions? Qualitative Analysis Boot Camp at the ACEHP 2013 meeting in San Francisco on 1 February has tools to get you started.
This document provides an overview of a qualitative analysis boot camp session covering topics such as qualitative research introduction, data collection, coding and analysis, reporting, and resources. The session includes a coding practice exercise and time for questions. Presenters will discuss qualitative vs quantitative research, applications in health education and promotion, sample methodologies like interviews and focus groups, online data collection methods, grounded theory, coding with software assistance, visualizing data, and reporting trends and themes from qualitative analysis.
Does preregistration improve the interpretability and credibility of research...Mark Rubin
Rubin, M. (2022, March). Does preregistration improve the interpretability and credibility of research findings? In Research transparency: From preregistration to open access. Erasmus Research Institute of Management Research Transparency Campaign, Erasmus University Rotterdam. [Video recording: https://www.youtube.com/watch?v=xsEoLhQrKNQ&t=1s]
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
BIRDS DIVERSITY OF SOOTEA BISWANATH ASSAM.ppt.pptxgoluk9330
Ahota Beel, nestled in Sootea Biswanath Assam , is celebrated for its extraordinary diversity of bird species. This wetland sanctuary supports a myriad of avian residents and migrants alike. Visitors can admire the elegant flights of migratory species such as the Northern Pintail and Eurasian Wigeon, alongside resident birds including the Asian Openbill and Pheasant-tailed Jacana. With its tranquil scenery and varied habitats, Ahota Beel offers a perfect haven for birdwatchers to appreciate and study the vibrant birdlife that thrives in this natural refuge.
Redevelop 2019 - Debugging our biases and intuition in software developmentDave Hulbert
We program algorithms with shortcuts known as heuristics. These allow us to get a good enough answer to a problem with less CPU and memory usage. Brains attempt to take shortcuts too, using intuition and biases to figure things out with less thinking or knowledge. Heuristics are valuable but they're not perfect. We can evaluate best and worst cases for code but how do we do the same with our own decision making process?
In this talk, we'll go through the ups and downs of heuristics and biases that exist in a developer's world. We'll look at ways to reduce any resulting fallacies, whilst still taking advantage of the performance improvement.
This document discusses several key methods of science: naturalistic observation which involves non-intrusively observing populations without influencing them; correlational approaches which relate variables mathematically without implying causation; experimental methods which test hypotheses using experimental and control groups; and operational definitions which clearly define variables. It also discusses independent and dependent variables, confounds, validity, reliability, and the scientific attitude which values falsification, testability, skepticism, and parsimony.
This document discusses finding and evaluating evidence to answer clinical questions. It emphasizes that well-constructed clinical questions help focus the search for relevant evidence. The PICO (Patient/Problem, Intervention, Comparison, Outcome) framework is presented as a tool to help formulate answerable clinical questions. Examples of different types of clinical questions that can be answered through evidence-based medicine resources are provided, including questions of therapy, prognosis, diagnosis, and harm. Key hunting tools for searching evidence-based literature are described, including PubMed and its Clinical Queries feature.
Data collection methods to improve reproducibilityDigital Science
"Reproducibility, data collection, and laboratory management technologies" - Louis Culot, CEO of Biodata
Slides from Shaking It Up: Challenges and Solutions in Scholarly Information Management, San Francisco, April 22, 2015
(2017/06)Practical points of deep learning for medical imagingKyuhwan Jung
This document provides an overview of deep learning and its applications in medical imaging. It discusses key topics such as the definition of artificial intelligence, a brief history of neural networks and machine learning, and how deep learning is driving breakthroughs in tasks like visual and speech recognition. The document also addresses challenges in medical data analysis using deep learning, such as how to handle limited data or annotations. It provides examples of techniques used to address these challenges, such as data augmentation, transfer learning, and weakly supervised learning.
RecSys 2016 Talk: Feature Selection For Human RecommendersKatherine Livins
The document discusses human computation at Stitch Fix and how to shape what human workers are processing to improve recommendations. It suggests 1) determining what workers are currently attending to and using, 2) analyzing which features produce the best recommendations, and 3) changing how information is displayed or providing training to influence what workers process. Feature drop out studies and eye tracking can be used to evaluate the impact of different features on performance. A controlled lab study or A/B test in Stitch Fix's "Styling Lab" could then shape what information workers see.
This document discusses finding evidence to answer a PICO question. It begins by reviewing PICO questions and different types of studies. Patient-oriented evidence (POE) focuses on outcomes like mortality and quality of life, while disease-oriented evidence (DOE) examines pathophysiology and etiology. The best studies are randomized controlled trials at the top of the evidence pyramid. Other primary sources include case reports, case series, case-control and cohort studies. Secondary sources like systematic reviews and meta-analyses synthesize multiple primary studies. Searching requires considering relevance, validity and effort required. The document outlines strategies and resources for efficiently finding the best evidence.
The document discusses critical analysis, including defining it as looking at topics from different perspectives, considering context, and not making immediate judgments without evidence. It provides examples of applying critical thinking skills like evaluation, synthesis, and analysis to understand a photograph related to protests at Heathrow Airport. The document emphasizes that critical thinking is an important skill for university work and various academic activities require applying critical analysis.
This document provides guidance on searching the literature for health information. It recommends planning searches by focusing questions, breaking them into basic terms, and choosing appropriate research methods and databases. Searches can be refined using Boolean operators like AND, OR and NOT to combine search terms. Higher levels of evidence include systematic reviews and randomized controlled trials. The document encourages contacting librarians for assistance if searches take more than 10 minutes.
This document discusses searching for evidence to practice evidence-based dentistry. It describes primary sources like original research articles and secondary sources like systematic reviews, synopses, guidelines, and evidence summaries. Popular secondary sources mentioned include Cochrane Library, DARE, EBD journal, CATs, textbooks like UpToDate, and clinical guidelines from NICE and NGC. The document emphasizes appraising the quality of evidence from different sources and searching efficiently using keywords and databases like PubMed. It notes that absence of evidence found does not mean absence of evidence overall.
This document summarizes different types of psychological research methods: descriptive research simply observes and measures behaviors; correlational research tests relationships between variables but cannot prove causation; experimental research tests causal hypotheses by manipulating an independent variable and measuring its effects on a dependent variable with a control group for comparison. Biases that can influence results are also discussed.
The document provides information about conducting nursing research and evidence-based practice. It discusses key topics such as the importance of research for nurses, different types of research, overcoming challenges in doing research, and communicating research findings. The document aims to help nurses better understand research methods and utilization of evidence to improve patient care.
The document discusses correlation versus causality in experimental design. It provides examples of different types of experimental designs including randomized controlled trials, natural experiments, before-after designs, and differences-in-differences designs. It emphasizes the importance of randomness, control groups, and understanding the outcome variable when analyzing experimental data. Key considerations include whether the outcome is continuous or categorical and choosing the appropriate statistical tests accordingly. The document also discusses examples of experiments in various contexts like economics, policy, and online settings.
Psychometric assessment of older adults oct 21 to 26 2013 winter workshopDr. Rakesh Tripathi
This document discusses psychological assessment of older adults. It provides an overview of concepts related to psychometrics and psychological assessment. It describes the purpose and steps of psychological assessment for older adults, including screening and detailed assessment. Several commonly used cognitive screening tools are described, including the Mini-Mental State Examination, Hindi Mental State Examination, and a newly developed Hindi Cognitive Screening Test. The document emphasizes the importance of using culturally appropriate assessment tools for accurate evaluation of older adults.
The Reproducibility Crisis in Psychological Science: One Year LaterJimGrange
The document discusses the reproducibility crisis in psychological science. It notes several cases from 2011 that called into question research practices. An open science collaboration attempted to replicate 100 psychology studies and found that only 36% replicated. The document recommends promoting open science practices like replication, transparent statistics and data sharing, and teaching/rewarding rigor over quantity or novelty. It argues the field needs to change incentives to prioritize accuracy over publication. Overall, the document analyzes issues compromising reproducibility and proposes open science solutions to improve research integrity in psychology.
Study notesSome of the information below may be repetitive of wh.docxhanneloremccaffery
Study notes
Some of the information below may be repetitive of what you have read in Creswell. In chapter 10, Singleton addressed field research, which overlaps with some qualitative designs, but for Singleton it is different from qualitative research because field research often involves quantification and more than simply observation. (Sometimes qualitative research also involves quantification) What Singleton addressed as field research is out the traditions of sociology and anthropology. Field research is probably more like ethnography than it is like other qualitative designs.
In a previous unit, we mentioned the use of existing data for research. Sometimes using data that are available lessens the data gathering task because you do not have to be dependent on others to return a survey or agree to an interview. Here is a good example of the use of existing data in a causal-comparative design. A former Princeton student who was in the Education program and is an assistant principal did her dissertation using existing data. She wanted to know if the reading scores on a standardized test (ITBS) were different after a new approach to teaching reading than before the new approach began. She went back to 1991 and recorded scores of 1st and 2nd graders for a five-year period before the intervention in 1996. Then she obtained scores of 1st and 2nd graders for five years after the new program and then did a number of statistical comparisons. She found significant differences on the comparisons so it would appear that the new approach to reading was effective. She could have set up a quasi-experimental design, but unless she did it for a number of years, she would not have had nearly as much data. This is a case in which it was not feasible to do an experimental design, but she obtained useful data.
Not all research using available data is causal-comparative. Much is descriptive. Probably the use of available data for research is among the top three types of designs used. Think of all the studies that come from the U.S. Census every ten years. You may have some good data stored at your place of employment. One researcher in Arizona has studied the trash/garbage of people for 25 years to find out how they live. Can you imagine sifting through someone's trash for 25 years? He has, however, learned a great deal about how the people whose trash he has swiped in the Tucson area live.
Moving back now to Chapter 10 in Singleton. While qualitative research is simply not acceptable to some researchers, in many ways, it can be more valuable than quantitative research when specificity and correctness are not necessary. Probably about 40% of Princeton students do some type of qualitative research for their dissertations.
Singleton refers to qualitative research as field research. He simply uses a broad category to cover various kinds because qualitative research is done in the real world (field).
One primary difference between quantitative and quali.
This document summarizes research on consciousness and attention. It discusses how attention is not necessary for consciousness and presents evidence that some visual stimuli like gender can be consciously perceived with little attention. The document also discusses how metacognition can be used to measure consciousness independently of task performance. Dual-task experiments show gender perception without attention is associated with conscious insight, even with minimal training. While some visual stimuli may attract attention, faces can be discriminated without relying on bottom-up attention.
Do you have responses to open-ended questions or want to use qualitative data to evaluate CE/QI interventions? Qualitative Analysis Boot Camp at the ACEHP 2013 meeting in San Francisco on 1 February has tools to get you started.
This document provides an overview of a qualitative analysis boot camp session covering topics such as qualitative research introduction, data collection, coding and analysis, reporting, and resources. The session includes a coding practice exercise and time for questions. Presenters will discuss qualitative vs quantitative research, applications in health education and promotion, sample methodologies like interviews and focus groups, online data collection methods, grounded theory, coding with software assistance, visualizing data, and reporting trends and themes from qualitative analysis.
Does preregistration improve the interpretability and credibility of research...Mark Rubin
Rubin, M. (2022, March). Does preregistration improve the interpretability and credibility of research findings? In Research transparency: From preregistration to open access. Erasmus Research Institute of Management Research Transparency Campaign, Erasmus University Rotterdam. [Video recording: https://www.youtube.com/watch?v=xsEoLhQrKNQ&t=1s]
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
BIRDS DIVERSITY OF SOOTEA BISWANATH ASSAM.ppt.pptxgoluk9330
Ahota Beel, nestled in Sootea Biswanath Assam , is celebrated for its extraordinary diversity of bird species. This wetland sanctuary supports a myriad of avian residents and migrants alike. Visitors can admire the elegant flights of migratory species such as the Northern Pintail and Eurasian Wigeon, alongside resident birds including the Asian Openbill and Pheasant-tailed Jacana. With its tranquil scenery and varied habitats, Ahota Beel offers a perfect haven for birdwatchers to appreciate and study the vibrant birdlife that thrives in this natural refuge.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Mechanisms and Applications of Antiviral Neutralizing Antibodies - Creative B...Creative-Biolabs
Neutralizing antibodies, pivotal in immune defense, specifically bind and inhibit viral pathogens, thereby playing a crucial role in protecting against and mitigating infectious diseases. In this slide, we will introduce what antibodies and neutralizing antibodies are, the production and regulation of neutralizing antibodies, their mechanisms of action, classification and applications, as well as the challenges they face.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...Sérgio Sacani
We present the JWST discovery of SN 2023adsy, a transient object located in a host galaxy JADES-GS
+
53.13485
−
27.82088
with a host spectroscopic redshift of
2.903
±
0.007
. The transient was identified in deep James Webb Space Telescope (JWST)/NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) program. Photometric and spectroscopic followup with NIRCam and NIRSpec, respectively, confirm the redshift and yield UV-NIR light-curve, NIR color, and spectroscopic information all consistent with a Type Ia classification. Despite its classification as a likely SN Ia, SN 2023adsy is both fairly red (
�
(
�
−
�
)
∼
0.9
) despite a host galaxy with low-extinction and has a high Ca II velocity (
19
,
000
±
2
,
000
km/s) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low-
�
Ca-rich population. Although such an object is too red for any low-
�
cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
≲
1
�
) with
Λ
CDM. Therefore unlike low-
�
Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
�
truly diverge from their low-
�
counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...Sérgio Sacani
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆Sérgio Sacani
Context. The early-type galaxy SDSS J133519.91+072807.4 (hereafter SDSS1335+0728), which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). This variability behaviour, coupled with the host-galaxy properties, suggests that SDSS1335+0728 hosts a ∼ 106M⊙ black hole (BH) that is currently in the process of ‘turning on’. Aims. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. Methods. We used archival photometry (from WISE, 2MASS, SDSS, GALEX, eROSITA) and spectroscopic data (from SDSS and LAMOST) to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We analysed the variability of SDSS1335+0728 in the X-ray/UV/optical/mid-infrared range, modelled its spectral energy distribution prior to and after December 2019, and studied the evolution of its UV/optical spectra. Results. From our multi-wavelength photometric analysis, we find that: (a) since 2021, the UV flux (from Swift/UVOT observations) is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1−W2 WISE colour has become redder; and (c) since February 2024, the source has begun showing X-ray emission. From our spectroscopic follow-up, we see that (i) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (ii) broad emission lines are not detected; and (iii) the [OIII] line increased its flux ∼ 3.6 years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. Conclusions. We conclude that the variations observed in SDSS1335+0728 could be either explained by a ∼ 106M⊙ AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGNobserved in the process of activating. If the latter were found to be the case, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour. Key words. galaxies: active– accretion, accretion discs– galaxies: individual: SDSS J133519.91+072807.4
TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptxshubhijain836
Centrifugation is a powerful technique used in laboratories to separate components of a heterogeneous mixture based on their density. This process utilizes centrifugal force to rapidly spin samples, causing denser particles to migrate outward more quickly than lighter ones. As a result, distinct layers form within the sample tube, allowing for easy isolation and purification of target substances.
TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptx
Finn ohbm2017 ed_workshop
1. Reliable individual
functional networks
and their relationship
to behavior
Emily S. Finn, PhD
Section on Functional Imaging Methods
Laboratory of Brain & Cognition, NIMH
emily.finn@nih.gov
Taking Connectivity to a Skeptical Future
Educational Workshop | OHBM Annual Meeting
June 25, 2017
@esfinn
19. Why study individual differences?
0.93 0.84 0.63
0.72 0.79 0.67
0.64 0.60 0.54
R1
WM
Mt
R2 Lg Em
Database
Target
ID rate
0.5 1.0
Finn et al., Nat Neurosci (2015)
20. Why study individual differences?
You always look most like yourself,
regardless of what you’re doing
0.93 0.84 0.63
0.72 0.79 0.67
0.64 0.60 0.54
R1
WM
Mt
R2 Lg Em
Database
Target
ID rate
0.5 1.0
Finn et al., Nat Neurosci (2015)
21. Why study individual differences?
You always look most like yourself,
regardless of what you’re doing
0.93 0.84 0.63
0.72 0.79 0.67
0.64 0.60 0.54
R1
WM
Mt
R2 Lg Em
Database
Target
ID rate
0.5 1.0
Finn et al., Nat Neurosci (2015)
22. Why study individual differences?
You always look most like yourself,
regardless of what you’re doing
0.93 0.84 0.63
0.72 0.79 0.67
0.64 0.60 0.54
R1
WM
Mt
R2 Lg Em
Database
Target
ID rate
0.5 1.0
Individual differences in FC predict
individual differences in behavior
Finn et al., Nat Neurosci (2015)
27. Q. Do you need HCP-quality data?
A. Not really
28. Q. Do you need HCP-quality data?
A. Not really
ID is fairly robust even at more standard spatial & temporal resolutions:
29. Q. Do you need HCP-quality data?
A. Not really
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
30. Q. Do you need HCP-quality data?
A. Not really
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Courtesy of Jason Druzgal
31. Q. Do you need HCP-quality data?
A. Not really
• More nodes —> higher identification rate
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Courtesy of Jason Druzgal
32. Q. Do you need HCP-quality data?
A. Not really
• More nodes —> higher identification rate
‣ Parcellation method (random vs. functional) did not matter
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Courtesy of Jason Druzgal
33. Q. Do you need HCP-quality data?
A. Not really
• More nodes —> higher identification rate
‣ Parcellation method (random vs. functional) did not matter
‣ Caution: Higher resolution may amplify effects of anatomical diffs/registration error
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Courtesy of Jason Druzgal
34. Q. Do you need HCP-quality data?
A. Not really
• More nodes —> higher identification rate
‣ Parcellation method (random vs. functional) did not matter
‣ Caution: Higher resolution may amplify effects of anatomical diffs/registration error
‣ Parcellations in the 200-300 node range seem like a good compromise
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Courtesy of Jason Druzgal
37. Q. What about amount of data?
A. Scan duration matters!
38. Q. What about amount of data?
A. Scan duration matters!
Longer acquisitions are better:
39. Q. What about amount of data?
A. Scan duration matters!
Longer acquisitions are better:
• higher reliability within subjects
Birn et al., NeuroImage (2013)
40. Q. What about amount of data?
A. Scan duration matters!
Finn et al., Nat Neurosci (2015)
• higher identifiability across subjects
Longer acquisitions are better:
• higher reliability within subjects
Birn et al., NeuroImage (2013)
41. Q. What about amount of data?
A. Scan duration matters!
Finn et al., Nat Neurosci (2015)
• higher identifiability across subjects
‣ higher sampling rate (shorter TR) cannot
make up for shorter scan duration
Longer acquisitions are better:
• higher reliability within subjects
Birn et al., NeuroImage (2013)
42. Q. What about amount of data?
A. Scan duration matters!
Finn et al., Nat Neurosci (2015)
• higher identifiability across subjects
‣ higher sampling rate (shorter TR) cannot
make up for shorter scan duration
Airan et al., Hum Brain Mapp (2016)
Longer acquisitions are better:
• higher reliability within subjects
Birn et al., NeuroImage (2013)
43. Q. What about amount of data?
A. Scan duration matters!
Finn et al., Nat Neurosci (2015)
• higher identifiability across subjects
‣ higher sampling rate (shorter TR) cannot
make up for shorter scan duration
Airan et al., Hum Brain Mapp (2016)
Shah et al., Brain & Behav (2016)
Longer acquisitions are better:
• higher reliability within subjects
Birn et al., NeuroImage (2013)
47. Q. Does scan condition matter?
A. Yes!
Rest has become the default condition for FC & individual differences,
but tasks may increase signal-to-noise
48. Q. Does scan condition matter?
A. Yes!
Rest has become the default condition for FC & individual differences,
but tasks may increase signal-to-noise
0.93 0.84 0.63
0.72 0.79 0.67
0.64 0.60 0.54
R1
WM
Mt
R2 Lg Em
Database
Target
ID rate
0.5 1.0
49. Q. Does scan condition matter?
A. Yes!
Rest has become the default condition for FC & individual differences,
but tasks may increase signal-to-noise
50. Q. Does scan condition matter?
A. Yes!
Rest has become the default condition for FC & individual differences,
but tasks may increase signal-to-noise
Finn et al., NeuroImage (2017)
51. Q. Does scan condition matter?
A. Yes!
Rest has become the default condition for FC & individual differences,
but tasks may increase signal-to-noise
Finn et al., NeuroImage (2017)
52. Q. Does scan condition matter?
A. Yes!
Rest has become the default condition for FC & individual differences,
but tasks may increase signal-to-noise
Finn et al., NeuroImage (2017)
53. Q. Does scan condition matter?
A. Yes!
Rest has become the default condition for FC & individual differences,
but tasks may increase signal-to-noise
Finn et al., NeuroImage (2017)
54. Q. Does scan condition matter?
A. Yes!
Rest has become the default condition for FC & individual differences,
but tasks may increase signal-to-noise
‣ for a set scan duration, tasks may be more reliable than rest
‣ tasks may converge faster on a subject’s “true” profile
Finn et al., NeuroImage (2017)
58. Q. Is rest best?
A. Probably not
Tasks may stabilize individuals’ functional architecture, increase SNR:
59. Q. Is rest best?
A. Probably not
‣ some task pairs give better ID than the two rest sessions
Tasks may stabilize individuals’ functional architecture, increase SNR:
60. Q. Is rest best?
A. Probably not
‣ some task pairs give better ID than the two rest sessions
Target
Database
Day 1 Day 2
IDrate
Day1Day2
Tasks may stabilize individuals’ functional architecture, increase SNR:
Finn et al., NeuroImage (2017)
61. Q. Is rest best?
A. Probably not
‣ some task pairs give better ID than the two rest sessions
Target
Database
Day 1 Day 2
IDrate
Day1Day2
‣ consider a combination of rest and task
Tasks may stabilize individuals’ functional architecture, increase SNR:
Finn et al., NeuroImage (2017)
62. Q. Is rest best?
A. Probably not
‣ some task pairs give better ID than the two rest sessions
Target
Database
Day 1 Day 2
IDrate
Day1Day2
‣ consider a combination of rest and task
Tasks may stabilize individuals’ functional architecture, increase SNR:
Finn et al., NeuroImage (2017)
Finn et al., Nat Neurosci (2015)
64. Q. Is rest best?
A. Probably not
Consider naturalistic tasks:
65. Q. Is rest best?
A. Probably not
Consider naturalistic tasks:
Inscapes: Vanderwal et al., NeuroImage 2015
headspacestudios.org
66. Q. Is rest best?
A. Probably not
Consider naturalistic tasks:
67. Q. Is rest best?
A. Probably not
Consider naturalistic tasks:
‣ ID rate is just as good as (if not better than) rest
68. Q. Is rest best?
A. Probably not
Consider naturalistic tasks:
Session 1
Rest Inscapes Ocean’s 11
Session 2
‣ ID rate is just as good as (if not better than) rest
69. Q. Is rest best?
A. Probably not
Consider naturalistic tasks:
Session 1
Rest Inscapes Ocean’s 11
Session 2
‣ ID rate is just as good as (if not better than) rest
Vanderwal et al., NeuroImage (2017)
70. Q. Is rest best?
A. Probably not
Consider naturalistic tasks:
Session 1
Rest Inscapes Ocean’s 11
Session 2
‣ ID rate is just as good as (if not better than) rest
Vanderwal et al., NeuroImage (2017)
72. Q. Is rest best?
A. Probably not
Tasks also have purely practical advantages:
73. ‣ increase subject compliance (i.e., decrease head motion), especially in certain populations
Q. Is rest best?
A. Probably not
Tasks also have purely practical advantages:
74. ‣ increase subject compliance (i.e., decrease head motion), especially in certain populations
Huijbers et al., NeuroImage (2017)
Q. Is rest best?
A. Probably not
Tasks also have purely practical advantages:
75. ‣ increase subject compliance (i.e., decrease head motion), especially in certain populations
ChildrenAdults
Vanderwal et al., NeuroImage (2015)Huijbers et al., NeuroImage (2017)
Q. Is rest best?
A. Probably not
Tasks also have purely practical advantages:
80. How to choose behavior
Is it stable?
• Trait vs. state
• State variables may be better
suited to within-subject analysis
81. How to choose behavior
Is it stable?
• Trait vs. state
• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
82. How to choose behavior
Is it stable?
• Trait vs. state
• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
83. How to choose behavior
Is it stable?
• Trait vs. state
• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
84. How to choose behavior
Is it stable?
Does it show a good distribution
in your population?
• Trait vs. state
• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
85. How to choose behavior
Is it stable?
Does it show a good distribution
in your population?
• Trait vs. state
• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
86. How to choose behavior
Is it stable?
Does it show a good distribution
in your population?
• Trait vs. state
• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
87. How to choose behavior
Is it stable?
Does it show a good distribution
in your population?
• Trait vs. state
• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
88. How to choose behavior
Is it stable?
Does it show a good distribution
in your population?
• Trait vs. state
• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
89. How to choose behavior
Is it stable?
Does it show a good distribution
in your population?
Is it something you expect to be
reflected in brain function?
• Trait vs. state
• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
92. Behavior: Mitigating confounds
Many behaviors/phenotypes are correlated with head motion!
Siegel et al., Cerebral Cortex (2016)
Negatively: Positively:
93. Behavior: Mitigating confounds
Many behaviors/phenotypes are correlated with head motion!
Siegel et al., Cerebral Cortex (2016)
Negatively: Positively:
Geerligs et al., Hum Brain Mapp (2017)
Age, vascular health:
96. Behavior: Mitigating confounds
Many behaviors/phenotypes are correlated with head motion!
‣ Check for correlation in your sample
‣ Consider excluding particularly high-motion subjects
97. Behavior: Mitigating confounds
Many behaviors/phenotypes are correlated with head motion!
‣ Check for correlation in your sample
‣ Consider excluding particularly high-motion subjects
‣ Choose appropriate preprocessing techniques
98. Behavior: Mitigating confounds
Many behaviors/phenotypes are correlated with head motion!
‣ Check for correlation in your sample
‣ Consider excluding particularly high-motion subjects
‣ Choose appropriate preprocessing techniques
Ciric et al., NeuroImage (2017)
99. Behavior: Mitigating confounds
Many behaviors/phenotypes are correlated with head motion!
‣ Check for correlation in your sample
‣ Consider excluding particularly high-motion subjects
‣ Choose appropriate preprocessing techniques
‣ Use motion as an explicit covariate
Ciric et al., NeuroImage (2017)
104. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
105. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
2
7.5
4
6.3
106. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
2
7.5
4
6.3
107. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
108. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
109. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
110. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
111. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
112. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
113. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
114. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
115. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
116. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
‣ even better: cross-dataset
Sustained attention model
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
Rosenberg et al., Nature Neuroscience (2016)
Rosenberg et al., Trends in Cog Sci (2017)
117. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
‣ even better: cross-dataset
Sustained attention model
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
Rosenberg et al., Nature Neuroscience (2016)
Rosenberg et al., Trends in Cog Sci (2017)
Observed behav
Predictedbehav
n = 25 adults
118. Brain behavior: Cross-validate!
Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out
(within dataset)
‣ even better: cross-dataset
Sustained attention model
Connectome-based Predictive Modeling
Shen et al., Nature Protocols (2017)
Predictedbehav
Observed behav
2
7.5
4
6.3
Rosenberg et al., Nature Neuroscience (2016)
Rosenberg et al., Trends in Cog Sci (2017)
Observed behav
Predictedbehav
n = 25 adults
Observed ADHD score
n = 113 children
PredictedADHDscore
121. Q. What is the best brain state?
A. Maybe it depends on your behavior
122. Q. What is the best brain state?
A. Maybe it depends on your behavior
Certain task conditions generate better predictions of behavior:
123. Q. What is the best brain state?
A. Maybe it depends on your behavior
n = 716, 10-fold cross-validation
Connectome-based Predictive Modeling (CPM; Shen et al., Nat Protocols 2017)
Modelinputdata
Target behavior
Certain task conditions generate better predictions of behavior:
124. Q. What is the best brain state?
A. Maybe it depends on your behavior
n = 716, 10-fold cross-validation
Connectome-based Predictive Modeling (CPM; Shen et al., Nat Protocols 2017)
Modelinputdata
Target behavior
Certain task conditions generate better predictions of behavior:
Consider tailoring scan condition
to behavior of interest
➡“Stress test”?
129. Take-home points
Introduction:
Why study
individual
differences?
1 How to get good
brain data
2 How to get good
behavioral data
3
How to relate
brain to behavior
4
Learn more5
• Data quality ≠ critical
• Longer scans are better!
• Consider using tasks
‣ improve compliance
130. Take-home points
Introduction:
Why study
individual
differences?
1 How to get good
brain data
2 How to get good
behavioral data
3
How to relate
brain to behavior
4
Learn more5
• Data quality ≠ critical
• Longer scans are better!
• Consider using tasks
‣ improve compliance
‣ increase sensitivity
131. Take-home points
Introduction:
Why study
individual
differences?
1 How to get good
brain data
2 How to get good
behavioral data
3
How to relate
brain to behavior
4
Learn more5
• Data quality ≠ critical
• Longer scans are better!
• Consider using tasks
‣ improve compliance
‣ increase sensitivity
• Choose an interesting
behavior with a good
distribution
132. Take-home points
Introduction:
Why study
individual
differences?
1 How to get good
brain data
2 How to get good
behavioral data
3
How to relate
brain to behavior
4
Learn more5
• Data quality ≠ critical
• Longer scans are better!
• Consider using tasks
‣ improve compliance
‣ increase sensitivity
• Choose an interesting
behavior with a good
distribution
• Consider potential
confounds and take
steps to mitigate them
133. Take-home points
Introduction:
Why study
individual
differences?
1 How to get good
brain data
2 How to get good
behavioral data
3
How to relate
brain to behavior
4
Learn more5
• Data quality ≠ critical
• Longer scans are better!
• Consider using tasks
‣ improve compliance
‣ increase sensitivity
• Choose an interesting
behavior with a good
distribution
• Consider potential
confounds and take
steps to mitigate them
• Cross-validate whenever
possible
134. Take-home points
Introduction:
Why study
individual
differences?
1 How to get good
brain data
2 How to get good
behavioral data
3
How to relate
brain to behavior
4
Learn more5
• Data quality ≠ critical
• Longer scans are better!
• Consider using tasks
‣ improve compliance
‣ increase sensitivity
• Choose an interesting
behavior with a good
distribution
• Consider potential
confounds and take
steps to mitigate them
• Cross-validate whenever
possible
• Consider tailoring your
scan condition to
behavior of interest
135. Take-home points
Introduction:
Why study
individual
differences?
1 How to get good
brain data
2 How to get good
behavioral data
3
How to relate
brain to behavior
4
Learn more5
• Data quality ≠ critical
• Longer scans are better!
• Consider using tasks
‣ improve compliance
‣ increase sensitivity
• Choose an interesting
behavior with a good
distribution
• Consider potential
confounds and take
steps to mitigate them
• Cross-validate whenever
possible
• Consider tailoring your
scan condition to
behavior of interest
136. Open data sets with brain & behavior
➡ Use these on their own or in combination with your own data
to generate or test hypotheses, see if a finding generalizes, etc
Philadelphia Neurodevelopmental
Cohort
137. Further reading
Building a science of individual differences from fMRI
Dubois & Adolphs, Trends in Cognitive Sciences (2016)
From regions to connections and networks: new bridges
between brain and behavior
Misic & Sporns, Current Opinion in Neurobiology (2016)
Can brain state be manipulated to emphasize individual
differences in functional connectivity?
Finn et al., NeuroImage (2017)
Prediction as a humanitarian and pragmatic contribution from
human cognitive neuroscience
Gabrieli, Ghosh & Gabrieli, Neuron (2015)
Selected reviews:
138. Learn more at OHBM 2017
Symposium:
Collect your thoughts: Individual differences in the networks underlying intelligence
Tues 8:00-9:15am
Symposium:
Relating connectivity to inter- and intra-individual differences in attention and cognition
Weds 8:00-9:15am
Poster #4042: Can brain state be manipulated to emphasize individual differences in
functional connectivity? Finn et al.
Poster #4040: Large-scale functional connectivity networks predict attention
fluctuations Rosenberg et al.
Poster #2110: Functional connectivity-based predictors of naturalistic reading
comprehension Jangraw et al.
Poster #4029: FMRI connectivity is differentially associated with performance
across tasks in a multi-task study Topolski et al.
@esfinn