Self-Evaluation Across Adolescence: A Longitudinal fmri Study. Dani Cosme

Size: px
Start display at page:

Download "Self-Evaluation Across Adolescence: A Longitudinal fmri Study. Dani Cosme"

Transcription

1 Self-Evaluation Across Adolescence: A Longitudinal fmri Study Dani Cosme

2 Background Adolescence is a formative period for the development of identity and self-concept (Pfeifer & Peake, 2012) Self-referential processing is associated with activity in cortical midline structures (Northoff et al., 2006) Unclear how self-referential processing and the underlying neural networks that support it develop over time 2

3 Developmental Trajectories Linear Increase Early Adolescent Specific Adolescent Emergent 3

4 Study Design 6 year longitudinal fmri study 10 years old 13 years old 16 years old T1 N = females T2 N = females T3 N = females All 3 Ts N = females Included N = females Included participants ages: T1 M = 10.1, SD = 0.35 T2 M = 13.0, SD = 0.32 T3 M = 16.3, SD =

5 Task Design Self-evaluation paradigm 2 x 2 within subjects factorial block design Stimuli presented auditorily SOCIAL ACADEMIC SELF I am popular I make many spelling mistakes HARRY POTTER Harry gets left out at school Harry reads very quickly Rest (21 s) Self (75 s) Rest (21 s) Harry (75 s) Rest (21 s) Self (75 s) Rest (21 s) Harry (75 s) Rest (21 s) 5

6 Data Acquisition & Analysis Siemens 3T Allegra scanner at UCLA Preprocessed & analyzed using SPM 6 Brain extraction, realignment, coregistration, reorientation, normalization to age-appropriate templates, smoothing Participants with excessive motion and/or unsatisfactory preprocessing were excluded Only participants with complete data were included in the analysis Flexible factorial model was used at the RX level Time (T1, T2, T3) x Target (self, other) x Domain (social, academic)

7 Self > Other z = -18 Main effects at the whole-brain level p <.005, k = 76 (AlphaSim corrected FDR p <.05) right lateral OFC BA 47 t = 3.73, k = t left VS BA 25 t = 4.18, k = 146 right VS BA 25 t = 4.06, k = 90 x = -4 racc / vmpfc (BA 24 / 32 / 10) t = 5.35, k = 888 y = 10 7

8 Self > Other 8

9 Early Adolescent Specific Trends Quadratic effects at the whole-brain level p <.005, k = 76 (AlphaSim corrected FDR p <.05) t 4 2 x = 2 racc BA 24 / 32 t = 4.31, k = 133 dacc BA 24 t = 3.07, k = 92 Precuneus BA 7 t = 3.70, k = 288 9

10 Early Adolescent Specific Trends Quadratic effects at the whole-brain level p <.005, k = 76 (AlphaSim corrected FDR p <.05) t 4 2 x = 2 racc BA 24 / 32 t = 4.31, k = 133 dacc BA 24 t = 3.07, k = 92 Precuneus BA 7 t = 3.70, k =

11 Adolescent Emergent Trends Asymptotic effects using ventromedial ROI mask p <.005, k = 37 (AlphaSim corrected FDR p <.05) 3.5 t 1.5 x = 4 racc / mofc BA 24 / 32 / 12 t = 3.72, k = 307 vmpfc BA 10 t = 3.54, k = 65 11

12 Adolescent Emergent Trends Asymptotic effects using ventromedial ROI mask p <.005, k = 37 (AlphaSim corrected FDR p <.05) 3.5 t 1.5 x = 4 racc / mofc BA 24 / 32 / 12 t = 3.72, k = 307 vmpfc BA 10 t = 3.54, k = 65 12

13 Summary 13

14 Future Directions Do these trajectories continue into late adolescence? What are the possible explanations for these developmental trajectories? Deeper processing and integration at T2? Greater variability at T1/T3? Does this represent a shift from subcortical to cortical processing over time? How does pubertal status relate to these trajectories? How do these functional trajectories relate to structural trajectories? What roles do subregions of racc / vmpfc play in supporting self-evaluation? 14

15 15 Friday Poster Session, #P-1-103

16 Thank you! University of Oregon Jennifer Pfeifer Jordan Livingston John Flournoy Will Moore 16 University of California Los Angeles Mirella Dapretto John Mazziotta Matthew Lieberman Carrie Masten

17 17 Additional Slides

18 Self > Other Main effects at the whole-brain level p <.005, k = 76 (AlphaSim corrected FDR p <.05) racc, vmpfc 18

19 Self > Other Main effects at the whole-brain level p <.005, k = 76 (AlphaSim corrected FDR p <.05) left VS right VS 19

20 Self > Other Main effects at the whole-brain level p <.005, k = 76 (AlphaSim corrected FDR p <.05) right lateral OFC 20

21 Self > Other Simple Effects T1 T2 T3 left VS t = 4.19, k = 65 y =? racc, vmpfc x = 0 racc, vmpfc x = 0 *p <.005, k = 65 t = 6.73, k = 710 t = 5.89, k = 378 dacc t = 5.79, k = 101 MCC p <.005, k = t = 7.38, k = 466 Precuneus t = 3.97, k = 103 left VS t = 4.47, k = 206

22 Self > Other x=2 racc precuneus BA 7 t = 3.70, k = 288 x = -10 BA 24 / 32 t = 4.31, k = 133 dacc BA 24 t = 3.07, k = 92 left lat. precuneus t BA 7 t = 3.70, k = 119 Quadratic effects at the whole-brain level p <.005, k = 76 (AlphaSim corrected FDR p <.05)

23 Other > Self Main effects at the whole-brain level p <.005, k = 76 (AlphaSim corrected FDR p <.05) x = -2 x = 2 right PCC (BA 31 / 23) t = 4.33, k = 188 dmpfc (BA 6 / 8) t = 4.16, k = 216 precuneus (BA 7) t = 3.57, k = 100 x = 52 mofc (BA 11) t = 4.41, k = 118 angular gyrus (BA 39) t = 4.23, k = t left anterior temporal cortex (BA 20) t = 4.69, k = 155

24 Other > Self Quadratic effects at the whole-brain level p <.005, k = 76 (AlphaSim corrected FDR p <.05) z = -8 x = 32 left FFG (BA 19/37) t = 4.04, k = 148 right FFG (BA 19/37) t = 4.17, k = 229 right anterior insula (BA 13) t = 3.69, k = t

25 Intraclass Correlations Main Effects % Variation racc 18.9 Left VS 9.1 Right VS 4.2 Right lofc 26.9 Quadratic Effects % Variation racc 23.9 dacc 12.5 Precuneus 47.6 Left PCC 27.2 Asymptotic Effects % Variation racc / mofc 16.1 vmpfc

RS-fMRI analysis in healthy subjects confirms gender based differences

RS-fMRI analysis in healthy subjects confirms gender based differences RS-fMRI analysis in healthy subjects confirms gender based differences Alberto A. Vergani (aavergani@uninsubria.it) PhD student in Computer Science and Computational Mathematics University of Insubria

More information

Tracking whole-brain connectivity dynamics in the resting-state

Tracking whole-brain connectivity dynamics in the resting-state Tracking whole-brain connectivity dynamics in the resting-state Supplementary Table. Peak Coordinates of ICNs ICN regions BA t max Peak (mm) (continued) BA t max Peak (mm) X Y Z X Y Z Subcortical networks

More information

Experimental design of fmri studies

Experimental design of fmri studies Experimental design of fmri studies Sandra Iglesias Translational Neuromodeling Unit University of Zurich & ETH Zurich With many thanks for slides & images to: Klaas Enno Stephan, FIL Methods group, Christian

More information

Experimental design of fmri studies & Resting-State fmri

Experimental design of fmri studies & Resting-State fmri Methods & Models for fmri Analysis 2016 Experimental design of fmri studies & Resting-State fmri Sandra Iglesias With many thanks for slides & images to: Klaas Enno Stephan, FIL Methods group, Christian

More information

Experimental design of fmri studies

Experimental design of fmri studies Experimental design of fmri studies Zurich SPM Course 2016 Sandra Iglesias Translational Neuromodeling Unit (TNU) Institute for Biomedical Engineering (IBT) University and ETH Zürich With many thanks for

More information

Experimental design of fmri studies

Experimental design of fmri studies Methods & Models for fmri Analysis 2017 Experimental design of fmri studies Sara Tomiello With many thanks for slides & images to: Sandra Iglesias, Klaas Enno Stephan, FIL Methods group, Christian Ruff

More information

Experimental design of fmri studies

Experimental design of fmri studies Experimental design of fmri studies Sandra Iglesias With many thanks for slides & images to: Klaas Enno Stephan, FIL Methods group, Christian Ruff SPM Course 2015 Overview of SPM Image time-series Kernel

More information

Reporting Checklist for Nature Neuroscience

Reporting Checklist for Nature Neuroscience Corresponding Author: Manuscript Number: Manuscript Type: Junichi Chikazoe, Adam K. Anderson NNA47510A Article Reporting Checklist for Nature Neuroscience # Main Figures: 6 # lementary Figures: 8 # lementary

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION 1. Supplementary Tables 2. Supplementary Figures 1/12 Supplementary tables TABLE S1 Response to Expected Value: Anatomical locations of regions correlating with the expected value

More information

Modelling Time- varying effec3ve Brain Connec3vity using Mul3regression Dynamic Models. Thomas Nichols University of Warwick

Modelling Time- varying effec3ve Brain Connec3vity using Mul3regression Dynamic Models. Thomas Nichols University of Warwick Modelling Time- varying effec3ve Brain Connec3vity using Mul3regression Dynamic Models Thomas Nichols University of Warwick Dynamic Linear Model Bayesian 3me series model Predictors {X 1,,X p } Exogenous

More information

Functional Causal Mediation Analysis with an Application to Brain Connectivity. Martin Lindquist Department of Biostatistics Johns Hopkins University

Functional Causal Mediation Analysis with an Application to Brain Connectivity. Martin Lindquist Department of Biostatistics Johns Hopkins University Functional Causal Mediation Analysis with an Application to Brain Connectivity Martin Lindquist Department of Biostatistics Johns Hopkins University Introduction Functional data analysis (FDA) and causal

More information

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks www.nature.com/scientificreports Received: 4 August 2016 Accepted: 20 June 2017 Published online: 26 July 2017 OPEN Contextual connectivity: A framework for understanding the intrinsic dynamic architecture

More information

Supplementary Information. Brain networks involved in tactile speed classification of moving dot patterns: the. effects of speed and dot periodicity

Supplementary Information. Brain networks involved in tactile speed classification of moving dot patterns: the. effects of speed and dot periodicity Supplementary Information Brain networks involved in tactile speed classification of moving dot patterns: the effects of speed and dot periodicity Jiajia Yang, Ryo Kitada *, Takanori Kochiyama, Yinghua

More information

Unravelling the Intrinsic Functional Organization of the Human Lateral Frontal Cortex: A Parcellation Scheme Based on Resting State fmri

Unravelling the Intrinsic Functional Organization of the Human Lateral Frontal Cortex: A Parcellation Scheme Based on Resting State fmri 10238 The Journal of Neuroscience, July 25, 2012 32(30):10238 10252 Behavioral/Systems/Cognitive Unravelling the Intrinsic Functional Organization of the Human Lateral Frontal Cortex: A Parcellation Scheme

More information

The General Linear Model Ivo Dinov

The General Linear Model Ivo Dinov Stats 33 Statistical Methods for Biomedical Data The General Linear Model Ivo Dinov dinov@stat.ucla.edu http://www.stat.ucla.edu/~dinov Slide 1 Problems with t-tests and correlations 1) How do we evaluate

More information

Discovering the Human Connectome

Discovering the Human Connectome Networks and Complex Systems 2012 Discovering the Human Connectome Olaf Sporns Department of Psychological and Brain Sciences Indiana University, Bloomington, IN 47405 http://www.indiana.edu/~cortex, osporns@indiana.edu

More information

Neuroimage Processing

Neuroimage Processing Neuroimage Processing Instructor: Moo K. Chung mkchung@wisc.edu Lecture 2. General Linear Models (GLM) Multivariate General Linear Models (MGLM) September 11, 2009 Research Projects If you have your own

More information

Functional Connectivity and Network Methods

Functional Connectivity and Network Methods 18/Sep/2013" Functional Connectivity and Network Methods with functional magnetic resonance imaging" Enrico Glerean (MSc), Brain & Mind Lab, BECS, Aalto University" www.glerean.com @eglerean becs.aalto.fi/bml

More information

The General Linear Model. Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London

The General Linear Model. Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London The General Linear Model Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London SPM Course Lausanne, April 2012 Image time-series Spatial filter Design matrix Statistical Parametric

More information

Integrative Methods for Functional and Structural Connectivity

Integrative Methods for Functional and Structural Connectivity Integrative Methods for Functional and Structural Connectivity F. DuBois Bowman Department of Biostatistics Columbia University SAMSI Challenges in Functional Connectivity Modeling and Analysis Research

More information

What is NIRS? First-Level Statistical Models 5/18/18

What is NIRS? First-Level Statistical Models 5/18/18 First-Level Statistical Models Theodore Huppert, PhD (huppertt@upmc.edu) University of Pittsburgh Departments of Radiology and Bioengineering What is NIRS? Light Intensity SO 2 and Heart Rate 2 1 5/18/18

More information

Finding a Basis for the Neural State

Finding a Basis for the Neural State Finding a Basis for the Neural State Chris Cueva ccueva@stanford.edu I. INTRODUCTION How is information represented in the brain? For example, consider arm movement. Neurons in dorsal premotor cortex (PMd)

More information

Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging

Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging Vladimir Koltchinskii Department of Mathematics and Statistics University of New Mexico Albuquerque, NM, 87131

More information

Functional brain imaging has provided an important window

Functional brain imaging has provided an important window Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus Yvette I. Sheline a,b,c,1, Joseph L. Price d, Zhizi Yan b, and Mark A. Mintun a,b Departments

More information

Contents. Introduction The General Linear Model. General Linear Linear Model Model. The General Linear Model, Part I. «Take home» message

Contents. Introduction The General Linear Model. General Linear Linear Model Model. The General Linear Model, Part I. «Take home» message DISCOS SPM course, CRC, Liège, 2009 Contents The General Linear Model, Part I Introduction The General Linear Model Data & model Design matrix Parameter estimates & interpretation Simple contrast «Take

More information

A longitudinal Bayesian model for spectral analysis of neuroimaging time series data. Joint work with Ning Dai

A longitudinal Bayesian model for spectral analysis of neuroimaging time series data. Joint work with Ning Dai A longitudinal Bayesian model for spectral analysis of neuroimaging time series data Joint work with Ning Dai Resting-state fmri is popular for functional connectivity studies. Another analysis approach

More information

Jean-Baptiste Poline

Jean-Baptiste Poline Edinburgh course Avril 2010 Linear Models Contrasts Variance components Jean-Baptiste Poline Neurospin, I2BM, CEA Saclay, France Credits: Will Penny, G. Flandin, SPM course authors Outline Part I: Linear

More information

Basic MRI physics and Functional MRI

Basic MRI physics and Functional MRI Basic MRI physics and Functional MRI Gregory R. Lee, Ph.D Assistant Professor, Department of Radiology June 24, 2013 Pediatric Neuroimaging Research Consortium Objectives Neuroimaging Overview MR Physics

More information

One patient and one control were unable to complete the TOMM. An additional HC had abnormally low scores

One patient and one control were unable to complete the TOMM. An additional HC had abnormally low scores Ling et al. 2013-1 Supplemental Methods Participants One patient and one control were unable to complete the TOMM. An additional HC had abnormally low scores on the TOMM only (T-score < 0), and was not

More information

ROI analysis of pharmafmri data: an adaptive approach for global testing

ROI analysis of pharmafmri data: an adaptive approach for global testing ROI analysis of pharmafmri data: an adaptive approach for global testing Giorgos Minas, John A.D. Aston, Thomas E. Nichols and Nigel Stallard Abstract Pharmacological fmri (pharmafmri) is a new highly

More information

Signal Processing for Functional Brain Imaging: General Linear Model (2)

Signal Processing for Functional Brain Imaging: General Linear Model (2) Signal Processing for Functional Brain Imaging: General Linear Model (2) Maria Giulia Preti, Dimitri Van De Ville Medical Image Processing Lab, EPFL/UniGE http://miplab.epfl.ch/teaching/micro-513/ March

More information

Statistical models for neural encoding

Statistical models for neural encoding Statistical models for neural encoding Part 1: discrete-time models Liam Paninski Gatsby Computational Neuroscience Unit University College London http://www.gatsby.ucl.ac.uk/ liam liam@gatsby.ucl.ac.uk

More information

Semi-supervised subspace analysis of human functional magnetic resonance imaging data

Semi-supervised subspace analysis of human functional magnetic resonance imaging data Max Planck Institut für biologische Kybernetik Max Planck Institute for Biological Cybernetics Technical Report No. 185 Semi-supervised subspace analysis of human functional magnetic resonance imaging

More information

MULTISCALE MODULARITY IN BRAIN SYSTEMS

MULTISCALE MODULARITY IN BRAIN SYSTEMS MULTISCALE MODULARITY IN BRAIN SYSTEMS Danielle S. Bassett University of California Santa Barbara Department of Physics The Brain: A Multiscale System Spatial Hierarchy: Temporal-Spatial Hierarchy: http://www.idac.tohoku.ac.jp/en/frontiers/column_070327/figi-i.gif

More information

Dynamic Causal Modelling for fmri

Dynamic Causal Modelling for fmri Dynamic Causal Modelling for fmri André Marreiros Friday 22 nd Oct. 2 SPM fmri course Wellcome Trust Centre for Neuroimaging London Overview Brain connectivity: types & definitions Anatomical connectivity

More information

Event-related fmri. Christian Ruff. Laboratory for Social and Neural Systems Research Department of Economics University of Zurich

Event-related fmri. Christian Ruff. Laboratory for Social and Neural Systems Research Department of Economics University of Zurich Event-related fmri Christian Ruff Laboratory for Social and Neural Systems Research Department of Economics University of Zurich Institute of Neurology University College London With thanks to the FIL

More information

Jan 16: The Visual System

Jan 16: The Visual System Geometry of Neuroscience Matilde Marcolli & Doris Tsao Jan 16: The Visual System References for this lecture 1977 Hubel, D. H., Wiesel, T. N., Ferrier lecture 2010 Freiwald, W., Tsao, DY. Functional compartmentalization

More information

Beyond Univariate Analyses: Multivariate Modeling of Functional Neuroimaging Data

Beyond Univariate Analyses: Multivariate Modeling of Functional Neuroimaging Data Beyond Univariate Analyses: Multivariate Modeling of Functional Neuroimaging Data F. DuBois Bowman Department of Biostatistics and Bioinformatics Center for Biomedical Imaging Statistics Emory University,

More information

Morphometry. John Ashburner. Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK. Voxel-Based Morphometry

Morphometry. John Ashburner. Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK. Voxel-Based Morphometry Morphometry John Ashburner Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK. Overview Voxel-Based Morphometry Morphometry in general Volumetrics VBM preprocessing followed by SPM Tissue

More information

Morphometrics with SPM12

Morphometrics with SPM12 Morphometrics with SPM12 John Ashburner Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK. What kind of differences are we looking for? Usually, we try to localise regions of difference.

More information

Experimental Design. Rik Henson. With thanks to: Karl Friston, Andrew Holmes

Experimental Design. Rik Henson. With thanks to: Karl Friston, Andrew Holmes Experimental Design Rik Henson With thanks to: Karl Friston, Andrew Holmes Overview 1. A Taxonomy of Designs 2. Epoch vs Event-related 3. Mixed Epoch/Event Designs A taxonomy of design Categorical designs

More information

Revealing Interactions Among Brain Systems With Nonlinear PCA

Revealing Interactions Among Brain Systems With Nonlinear PCA Human Brain Mapping 8:92 97(1999) Revealing Interactions Among Brain Systems With Nonlinear PCA Karl Friston,* Jacquie Phillips, Dave Chawla, and Christian Büchel The Wellcome Department of Cognitive Neurology,

More information

From Pixels to Brain Networks: Modeling Brain Connectivity and Its Changes in Disease. Polina Golland

From Pixels to Brain Networks: Modeling Brain Connectivity and Its Changes in Disease. Polina Golland From Pixels to Brain Networks: Modeling Brain Connectivity and Its Changes in Disease Polina Golland MIT Computer Science and Artificial Intelligence Laboratory Joint work with Archana Venkataraman C.-F.

More information

The General Linear Model (GLM)

The General Linear Model (GLM) The General Linear Model (GLM) Dr. Frederike Petzschner Translational Neuromodeling Unit (TNU) Institute for Biomedical Engineering, University of Zurich & ETH Zurich With many thanks for slides & images

More information

Hierarchical Dirichlet Processes with Random Effects

Hierarchical Dirichlet Processes with Random Effects Hierarchical Dirichlet Processes with Random Effects Seyoung Kim Department of Computer Science University of California, Irvine Irvine, CA 92697-34 sykim@ics.uci.edu Padhraic Smyth Department of Computer

More information

Introduction to Event History Analysis. Hsueh-Sheng Wu CFDR Workshop Series June 20, 2016

Introduction to Event History Analysis. Hsueh-Sheng Wu CFDR Workshop Series June 20, 2016 Introduction to Event History Analysis Hsueh-Sheng Wu CFDR Workshop Series June 20, 2016 1 What is event history analysis Event history analysis steps Outline Create data for event history analysis Data

More information

Figure 1-figure supplement 1

Figure 1-figure supplement 1 Figure 1-figure supplement 1 a 1 Stroop Task b 1 Reading Task 8 8 Percent Correct 6 4 2 Percent Correct 6 4 2 c 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 Subject Number d 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 Subject

More information

Introduction to functional MRI in humans. Michael Hallquist University of Pittsburgh

Introduction to functional MRI in humans. Michael Hallquist University of Pittsburgh Introduction to functional MRI in humans Michael Hallquist University of Pittsburgh Goals of human neuroimaging Localization of brain function (mapping) Understanding large-scale functional integration

More information

Collective Dynamics in Human and Monkey Sensorimotor Cortex: Predicting Single Neuron Spikes

Collective Dynamics in Human and Monkey Sensorimotor Cortex: Predicting Single Neuron Spikes Collective Dynamics in Human and Monkey Sensorimotor Cortex: Predicting Single Neuron Spikes Supplementary Information Wilson Truccolo 1,2,5, Leigh R. Hochberg 2-6 and John P. Donoghue 4,1,2 1 Department

More information

The Specification of Causal Models with Tetrad IV: A Review

The Specification of Causal Models with Tetrad IV: A Review Structural Equation Modeling, 17:703 711, 2010 Copyright Taylor & Francis Group, LLC ISSN: 1070-5511 print/1532-8007 online DOI: 10.1080/10705511.2010.510074 SOFTWARE REVIEW The Specification of Causal

More information

Data Analysis I: Single Subject

Data Analysis I: Single Subject Data Analysis I: Single Subject ON OFF he General Linear Model (GLM) y= X fmri Signal = Design Matrix our data = what we CAN explain x β x Betas + + how much x of it we CAN + explain ε Residuals what

More information

Group analysis. Jean Daunizeau Wellcome Trust Centre for Neuroimaging University College London. SPM Course Edinburgh, April 2010

Group analysis. Jean Daunizeau Wellcome Trust Centre for Neuroimaging University College London. SPM Course Edinburgh, April 2010 Group analysis Jean Daunizeau Wellcome Trust Centre for Neuroimaging University College London SPM Course Edinburgh, April 2010 Image time-series Spatial filter Design matrix Statistical Parametric Map

More information

Selected Topics in Statistics for fmri Data Analysis

Selected Topics in Statistics for fmri Data Analysis p. HST.583: Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Harvard-MIT Division of Health Sciences and Technology Course Instructor: Dr. Mark Vangel. Selected Topics in Statistics

More information

Cortical Shape Analysis using the Anisotropic Global Point Signature

Cortical Shape Analysis using the Anisotropic Global Point Signature Cortical Shape Analysis using the Anisotropic Global Point Signature Anand A Joshi 1,3, Syed Ashrafulla 1, David W Shattuck 2, Hanna Damasio 3 and Richard M Leahy 1 1 Signal and Image Processing Institute,

More information

- Supplementary information -

- Supplementary information - Influence of FKBP5 polymorphism and DNA methylation on structural changes of the brain in major depressive disorder - Supplementary information - Kyu-Man Han MD a*, Eunsoo Won, MD, PhD a*, Youngbo Sim,

More information

Contents. design. Experimental design Introduction & recap Experimental design «Take home» message. N εˆ. DISCOS SPM course, CRC, Liège, 2009

Contents. design. Experimental design Introduction & recap Experimental design «Take home» message. N εˆ. DISCOS SPM course, CRC, Liège, 2009 DISCOS SPM course, CRC, Liège, 2009 Contents Experimental design Introduction & recap Experimental design «Take home» message C. Phillips, Centre de Recherches du Cyclotron, ULg, Belgium Based on slides

More information

The University of Warwick. Department of Statistics. Dissertation. Controls for Multiplicity in Resting State Functional Connectivity Analysis

The University of Warwick. Department of Statistics. Dissertation. Controls for Multiplicity in Resting State Functional Connectivity Analysis The University of Warwick Department of Statistics Master of Science in Statistics Academic Year 2014/2015 Dissertation Controls for Multiplicity in Resting State Functional Connectivity Analysis Candidate:

More information

Testing Stationarity of Brain Functional Connectivity Using Change-Point Detection in fmri Data

Testing Stationarity of Brain Functional Connectivity Using Change-Point Detection in fmri Data Testing Stationarity of Brain Functional Connectivity Using Change-Point Detection in fmri Data Mengyu Dai 1, Zhengwu Zhang 2 and Anuj Srivastava 1 1 Department of Statistics, Florida State University,

More information

Longitudinal growth analysis of early childhood brain using deformation based morphometry

Longitudinal growth analysis of early childhood brain using deformation based morphometry Longitudinal growth analysis of early childhood brain using deformation based morphometry Junki Lee 1, Yasser Ad-Dab'bagh 2, Vladimir Fonov 1, Alan C. Evans 1 and the Brain Development Cooperative Group

More information

Resampling-Based Information Criteria for Adaptive Linear Model Selection

Resampling-Based Information Criteria for Adaptive Linear Model Selection Resampling-Based Information Criteria for Adaptive Linear Model Selection Phil Reiss January 5, 2010 Joint work with Joe Cavanaugh, Lei Huang, and Amy Krain Roy Outline Motivating application: amygdala

More information

Functional Magnetic Resonance Imaging (http://www.pallier.org)

Functional Magnetic Resonance Imaging (http://www.pallier.org) Functional Magnetic Resonance Imaging Christophe@pallier.org (http://www.pallier.org) 1) Practical Aspects : a typical scanning session, the scanner hardware, risks, costs,... 2) Rudiments of Nuclear Magnetic

More information

Master of Science Thesis. Development of a phantom for optimisation and quality control in functional MRI (fmri) Anders Nilsson

Master of Science Thesis. Development of a phantom for optimisation and quality control in functional MRI (fmri) Anders Nilsson Master of Science Thesis Development of a phantom for optimisation and quality control in functional MRI (fmri) Anders Nilsson Supervisor: Johan Olsrud, PhD Medical Radiation Physics Clinical Sciences,

More information

ALZHEIMER S disease (AD) is the most common type of

ALZHEIMER S disease (AD) is the most common type of 576 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 2, FEBRUARY 2014 Integration of Network Topological and Connectivity Properties for Neuroimaging Classification Biao Jie, Daoqiang Zhang, Wei

More information

Part I: SEM & Introduction to Time Series Models A brief introduction to SEM with an eating disorder example A fmri time series study + AR and MV

Part I: SEM & Introduction to Time Series Models A brief introduction to SEM with an eating disorder example A fmri time series study + AR and MV Structural Equation Modeling with New Development for Mixed Designs Plus Introductions to Time Series Modeling, Bootstrap Resampling, & Partial Correlation Network Analysis (PCNA) Outline Part I: SEM &

More information

Statistical Analysis Aspects of Resting State Functional Connectivity

Statistical Analysis Aspects of Resting State Functional Connectivity Statistical Analysis Aspects of Resting State Functional Connectivity Biswal s result (1995) Correlations between RS Fluctuations of left and right motor areas Why studying resting state? Human Brain =

More information

RESTING STATE FMRI FUNCTIONAL CONNECTIVITY ANALYSIS USING SOFT COMPETITIVE LEARNING ALGORITHMS

RESTING STATE FMRI FUNCTIONAL CONNECTIVITY ANALYSIS USING SOFT COMPETITIVE LEARNING ALGORITHMS 15 th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3 rd Conference on Imaging and Visualization CMBBE 2018 P. R. Fernandes and J. M. Tavares (Editors) RESTING

More information

Reproducibility and Power

Reproducibility and Power Reproducibility and Power Thomas Nichols Department of Sta;s;cs & WMG University of Warwick Reproducible Neuroimaging Educa;onal Course OHBM 2015 slides & posters @ http://warwick.ac.uk/tenichols/ohbm

More information

MEG and fmri for nonlinear estimation of neural activity

MEG and fmri for nonlinear estimation of neural activity Copyright 2009 SS&C. Published in the Proceedings of the Asilomar Conference on Signals, Systems, and Computers, November 1-4, 2009, Pacific Grove, California, USA MEG and fmri for nonlinear estimation

More information

Evidence for Hubs in Human Functional Brain Networks

Evidence for Hubs in Human Functional Brain Networks Article Evidence for Hubs in Human Functional Brain Networks Jonathan D. Power, 1, * Bradley L. Schlaggar, 1,2,3,4 Christina N. Lessov-Schlaggar, 5 and Steven E. Petersen 1,2,4,6,7,8 1 Department of Neurology

More information

Overview of SPM. Overview. Making the group inferences we want. Non-sphericity Beyond Ordinary Least Squares. Model estimation A word on power

Overview of SPM. Overview. Making the group inferences we want. Non-sphericity Beyond Ordinary Least Squares. Model estimation A word on power Group Inference, Non-sphericity & Covariance Components in SPM Alexa Morcom Edinburgh SPM course, April 011 Centre for Cognitive & Neural Systems/ Department of Psychology University of Edinburgh Overview

More information

Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration

Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration Panayiota Poirazi* Biomedical Engineering Department University of Southern California Los Angeles, CA 90089 poirazi@sc/. usc. edu

More information

Group Analysis. Lexicon. Hierarchical models Mixed effect models Random effect (RFX) models Components of variance

Group Analysis. Lexicon. Hierarchical models Mixed effect models Random effect (RFX) models Components of variance Group Analysis J. Daunizeau Institute of Empirical Research in Economics, Zurich, Switzerland Brain and Spine Institute, Paris, France SPM Course Edinburgh, April 2011 Image time-series Spatial filter

More information

FUNCTIONAL CONNECTIVITY ANALYSIS OF FMRI TIME-SERIES DATA

FUNCTIONAL CONNECTIVITY ANALYSIS OF FMRI TIME-SERIES DATA FUNCTIONAL CONNECTIVITY ANALYSIS OF FMRI TIME-SERIES DATA by Dongli Zhou B.S. in Statistics, Beijing Normal University, China, 25 M.A. in Statistics, University of Pittsburgh, 27 Submitted to the Graduate

More information

A nonparametric test for path dependence in discrete panel data

A nonparametric test for path dependence in discrete panel data A nonparametric test for path dependence in discrete panel data Maximilian Kasy Department of Economics, University of California - Los Angeles, 8283 Bunche Hall, Mail Stop: 147703, Los Angeles, CA 90095,

More information

PROTO-EXPERIENCES AND SUBJECTIVE EXPERIENCES: CLASSICAL AND QUANTUM CONCEPTS

PROTO-EXPERIENCES AND SUBJECTIVE EXPERIENCES: CLASSICAL AND QUANTUM CONCEPTS Journal of Integrative Neuroscience, Vol. 7, No. 1 (2008) 49 73 c Imperial College Press PROTO-EXPERIENCES AND SUBJECTIVE EXPERIENCES: CLASSICAL AND QUANTUM CONCEPTS Research Report RAM LAKHAN PANDEY VIMAL

More information

High-dimensional geometry of cortical population activity. Marius Pachitariu University College London

High-dimensional geometry of cortical population activity. Marius Pachitariu University College London High-dimensional geometry of cortical population activity Marius Pachitariu University College London Part I: introduction to the brave new world of large-scale neuroscience Part II: large-scale data preprocessing

More information

A pairwise maximum entropy model accurately describes resting-state human brain networks

A pairwise maximum entropy model accurately describes resting-state human brain networks Received 5 May 22 Accepted Dec 22 Published 22 Jan 23 DOI:.3/ncomms23 OPEN A pairwise maximum entropy model accurately describes resting-state human brain networks Takamitsu Watanabe, Satoshi Hirose, Hiroyuki

More information

FUNCTIONAL CONNECTIVITY MODELLING IN FMRI BASED ON CAUSAL NETWORKS

FUNCTIONAL CONNECTIVITY MODELLING IN FMRI BASED ON CAUSAL NETWORKS FUNCTIONAL CONNECTIVITY MODELLING IN FMRI BASED ON CAUSAL NETWORKS F.F. Deleus, P.A. De Mazière, & M.M. Van Hulle Laboratorium voor Neuro- en Psychofysiologie Katholieke Universiteit Leuven Campus Gasthuisberg

More information

First Technical Course, European Centre for Soft Computing, Mieres, Spain. 4th July 2011

First Technical Course, European Centre for Soft Computing, Mieres, Spain. 4th July 2011 First Technical Course, European Centre for Soft Computing, Mieres, Spain. 4th July 2011 Linear Given probabilities p(a), p(b), and the joint probability p(a, B), we can write the conditional probabilities

More information

Induced Magnetic Force in Human Heads Exposed to 4 T MRI

Induced Magnetic Force in Human Heads Exposed to 4 T MRI JOURNAL OF MAGNETIC RESONANCE IMAGING 31:815 820 (2010) Original Research Induced Magnetic Force in Human Heads Exposed to 4 T MRI Ruiliang Wang, PhD, 1 * Gene-Jack Wang, MD, 1 Rita Z. Goldstein, PhD,

More information

Scaling in Neurosciences: State-of-the-art

Scaling in Neurosciences: State-of-the-art Spatially regularized multifractal analysis for fmri Data Motivation Scaling in Neurosciences: State-of-the-art Motivation Multifractal Multifractal Princeton Experiment Princeton Experimen Van Gogh P.

More information

Neuroscience Introduction

Neuroscience Introduction Neuroscience Introduction The brain As humans, we can identify galaxies light years away, we can study particles smaller than an atom. But we still haven t unlocked the mystery of the three pounds of matter

More information

Graph Frequency Analysis of Brain Signals

Graph Frequency Analysis of Brain Signals 1 Graph Frequency Analysis of Brain Signals Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett and Alejandro Ribeiro arxiv:1512.37v1 [q-bio.nc] 2 Nov 215 Abstract This

More information

Field trip: Tuesday, Feb 5th

Field trip: Tuesday, Feb 5th Pulse Sequences Field trip: Tuesday, Feb 5th Hardware tour of VUIIIS Philips 3T Meet here at regular class time (11.15) Complete MRI screening form! Chuck Nockowski Philips Service Engineer Reminder: Project/Presentation

More information

Survival Analysis. Hsueh-Sheng Wu CFDR Workshop Series Spring 2010

Survival Analysis. Hsueh-Sheng Wu CFDR Workshop Series Spring 2010 Survival Analysis Hsueh-Sheng Wu CFDR Workshop Series Spring 2010 1 Outline Survival analysis steps Create data for survival analysis Data for different analyses The dependent variable in Life Table analysis

More information

Overview. Experimental Design. A categorical analysis. A taxonomy of design. A taxonomy of design. A taxonomy of design. 1. A Taxonomy of Designs

Overview. Experimental Design. A categorical analysis. A taxonomy of design. A taxonomy of design. A taxonomy of design. 1. A Taxonomy of Designs Experimental Design Overview Rik Henson With thanks to: Karl Friston, Andrew Holmes 1. A Taxonomy of Designs 2. Epoch vs Event-related 3. Mixed Epoch/Event Designs designs designs - and nonlinear interactions

More information

Group Study of Simulated Driving fmri Data by Multiset Canonical Correlation Analysis

Group Study of Simulated Driving fmri Data by Multiset Canonical Correlation Analysis DOI 0.007/s265-00-0572-8 Group Study of Simulated Driving fmri Data by Multiset Canonical Correlation Analysis Yi-Ou Li Tom Eichele Vince D. Calhoun Tulay Adali Received: 8 August 200 / Revised: 5 November

More information

Teaching Research Methods: Resources for HE Social Sciences Practitioners. Sampling

Teaching Research Methods: Resources for HE Social Sciences Practitioners. Sampling Sampling Session Objectives By the end of the session you will be able to: Explain what sampling means in research List the different sampling methods available Have had an introduction to confidence levels

More information

A Multivariate Time-Frequency Based Phase Synchrony Measure for Quantifying Functional Connectivity in the Brain

A Multivariate Time-Frequency Based Phase Synchrony Measure for Quantifying Functional Connectivity in the Brain A Multivariate Time-Frequency Based Phase Synchrony Measure for Quantifying Functional Connectivity in the Brain Dr. Ali Yener Mutlu Department of Electrical and Electronics Engineering, Izmir Katip Celebi

More information

Human! Brain! Networks!

Human! Brain! Networks! Human! Brain! Networks! estimated with functional connectivity" Enrico Glerean (MSc), Brain & Mind Lab, BECS, Aalto University" www.glerean.com @eglerean becs.aalto.fi/bml enrico.glerean@aalto.fi" Why?"

More information

Computational Brain Anatomy

Computational Brain Anatomy Computational Brain Anatomy John Ashburner Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK. Overview Voxel-Based Morphometry Morphometry in general Volumetrics VBM preprocessing followed

More information

CSE 473: Artificial Intelligence Spring 2014

CSE 473: Artificial Intelligence Spring 2014 CSE 473: Artificial Intelligence Spring 2014 Hidden Markov Models Hanna Hajishirzi Many slides adapted from Dan Weld, Pieter Abbeel, Dan Klein, Stuart Russell, Andrew Moore & Luke Zettlemoyer 1 Outline

More information

New Machine Learning Methods for Neuroimaging

New Machine Learning Methods for Neuroimaging New Machine Learning Methods for Neuroimaging Gatsby Computational Neuroscience Unit University College London, UK Dept of Computer Science University of Helsinki, Finland Outline Resting-state networks

More information

A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches

A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches Bo Luan 1, Peter Sörös 2, Ervin Sejdić 1 * 1 Department of Electrical and Computer Engineering, Swanson School of

More information

Longitudinal Data Analysis of Health Outcomes

Longitudinal Data Analysis of Health Outcomes Longitudinal Data Analysis of Health Outcomes Longitudinal Data Analysis Workshop Running Example: Days 2 and 3 University of Georgia: Institute for Interdisciplinary Research in Education and Human Development

More information

Functional Neuroimaging with PET

Functional Neuroimaging with PET Functional Neuroimaging with PET Terry Oakes troakes@wisc.edu W.M.Keck Lab for Functional Brain Imaging and Behavior Seeing the Brain Just look at it! Anatomic Images (MRI) Functional Images PET fmri (Just

More information

Modelling temporal structure (in noise and signal)

Modelling temporal structure (in noise and signal) Modelling temporal structure (in noise and signal) Mark Woolrich, Christian Beckmann*, Salima Makni & Steve Smith FMRIB, Oxford *Imperial/FMRIB temporal noise: modelling temporal autocorrelation temporal

More information

Comparing Adaptive Interventions Using Data Arising from a SMART: With Application to Autism, ADHD, and Mood Disorders

Comparing Adaptive Interventions Using Data Arising from a SMART: With Application to Autism, ADHD, and Mood Disorders Comparing Adaptive Interventions Using Data Arising from a SMART: With Application to Autism, ADHD, and Mood Disorders Daniel Almirall, Xi Lu, Connie Kasari, Inbal N-Shani, Univ. of Michigan, Univ. of

More information

Bayesian probability theory and generative models

Bayesian probability theory and generative models Bayesian probability theory and generative models Bruno A. Olshausen November 8, 2006 Abstract Bayesian probability theory provides a mathematical framework for peforming inference, or reasoning, using

More information

Spatial Source Filtering. Outline EEG/ERP. ERPs) Event-related Potentials (ERPs( EEG

Spatial Source Filtering. Outline EEG/ERP. ERPs) Event-related Potentials (ERPs( EEG Integration of /MEG/fMRI Vince D. Calhoun, Ph.D. Director, Image Analysis & MR Research The MIND Institute Outline fmri/ data Three Approaches to integration/fusion Prediction Constraints 2 nd Level Fusion

More information