SUPPLEMENTARY INFORMATION
|
|
- Daniel Hood
- 6 years ago
- Views:
Transcription
1 SUPPLEMENTARY INFORMATION 1. Supplementary Tables 2. Supplementary Figures 1/12
2 Supplementary tables TABLE S1 Response to Expected Value: Anatomical locations of regions correlating with the expected value on each trial, having accounted for mean changes in activity due to the target level. Data are thresholded at p<0.005, uncorrected. We report areas reaching a peak voxel-level significance p and cluster size of >5 voxels only. Area L/R MNI coordinates T value Extent x y z (voxels) mid-orbital gyrus - ventromedial orbitofrontal cortex R fusiform gyrus L ventral striatum - nucleus accumbens R superior medial gyrus L /12
3 TABLE S2 Response to Target Level: Anatomical locations of regions correlating with the target level. We report areas reaching a peak voxel-level significance p and cluster size of >5 voxels only. Area L/R MNI coordinates T value Extent x y z middle frontal gyrus, BA6 R anterior cingulate cortex R middle occipital gyrus L superior frontal gyrus L paracentral lobule/ supplementary motor area, BA6 L L precentral gyrus L postcentral gyrs L middle occipital gyrus R /12
4 TABLE S3 Response to Variance: Anatomical locations of regions correlating with the expected variance on each trial, having accounted for mean changes in activity due to the target level and expected value. Data are thresholded at p<=0.005, uncorrected. We report areas reaching a peak voxel-level significance p and cluster size of >5 voxels only. Area L/R MNI coordinates T value Extent x y z (voxels) Putamen R middle frontal gyrus R inferior frontal gyrus R superior orbital gyrus R inferior parietal lobe L inferior temporal gyrus R ventral striatum L postcentral gyrus, BA6 R anterior cingulate cortex R anterior insula L anterior insula R middle frontal gyrus L inferior frontal gyrus / anterior insula R /12
5 TABLE S4 Response to Skewness: Anatomical locations of regions correlating with the expected skewness on each trial, having accounted for mean changes in activity due to the target level, expected value, and variance. Data are thresholded at p<0.005, uncorrected. We report areas reaching a peak voxel-level significance p and cluster size of >5 voxels only. Area L/R MNI coordinates T value Extent x y z postcentral gyrus, BA 1 L superior parietal lobe, BA2 L mid-orbital gyrus, medial orbitofrontal cortex L inferior frontal gyrus L medial temporal pole R superior frontal gyrus L middle temporal gyrus L precentral gyrus, BA 6 R /12
6 TABLE S5 Response to Integrated Utility: Anatomical locations of regions correlating with the overall utility of choice on each trial. We report areas reaching a peak voxellevel significance p and cluster size of >5 voxels only. Area L/R MNI coordinates T value Extent x y z corpus callosum / mid-cingulate gyrus R primary somatosensory cortex R prmary motor cortex L medial prefrontal cortex L R Thalamus L Thalamus L pre-supplementary motor area L anterior insula L /12
7 Previous studies identifying regions of interest for expected value and variance-related activity TABLE S6 EXPECTED VALUE Study reference Description Regions MNI coordinate [x, y, z] - peak Knutson 2005 EV Ventral striatum + 11,11,-3-8,-11,-2 mofc + -4,51,3 Yacubian 2006 Gain related EV Ventral striatum -12, 9, -3 12, 9, -3 R OFC 36, 63, 0 Probability* R mofc 3, 51, -6 Ventral striatum -12, 15, -3 15, 15, -6 Abler 2006 Probability** Ventral striatum 9, 0, -12-9, 9, -12 Plassmann 2007 Willlingness to pay*** mofc 6, 30, -17 Elliot 2008 Relative value mofc 6, 48, -12 Rolls 2008 EV mofc 19, 51, -3 EV and magnitude mofc 2, 38, -14 De Martino 2009 Willingness to pay*** mofc -4, 40, indicates that coordinates have been transformed from reported Talairach to MNI space for comparison (using tal2mni (Brett 2001)) *reward probability was collinear with relative gain-related EV, as there were only two magnitudes of gain (high or low), and these were cued by an image of a coin or a banknote, which sets the context similarly to our target manipulation **collinear with EV as fixed value of outcome ***subjective value. EV expected value; mofc medial orbitofrontal cortex Mean coordinates of interest based on average coordinates from above studies: Ventral striatum: right: [12, 9, -6], left: [-10, 6, -5] mofc: right: [12, 47, -9], left [-4, 46, -7] 7/12
8 TABLE S7 VARIANCE Study reference Description Regions MNI coordinate [x y z] - peak Critchley 2001 Risk* Anterior cingulate 8, 22, 28-6, 28, 20 latofc / Anterior insula 30, 24, , 14, -4 Huettel 2005 Uncertainty*** Anterior insula 35, 26, 4-30,20,5 IFG 55, 10, 35 MFG -44, -4, 36 IPL IPS , -77, 48 Preuschoff 2006 Variance** Anterior insula + -32, 17, 2 34, 13, 2 PHG + -17, -29, , -22, -18 TTG + 58, -14, 10-53, -8, 3 Dreher 2006 Variance Putamen 23, 8, , 0, -11 Tobler 2007 Variance latofc -42, 30, -20 Rolls 2008 Variance Anterior insula 46, 16, 6 Preuschoff 2009 Variance** Anterior insula , 15, -2 Posterior insula + 49, -12, 6 IPL Angular gyrus + 52, -55, 24 IFG + 48, 17, 18 STG + -38, -8, 22 + indicates that coordinates have been transformed from reported Talairach to MNI space for comparison (using tal2mni (Brett 2001)) *risk expressed as probability of most likely outcome in a task with binary outcomes, which is highly correlated with expected variance. **immediate response to variance. ***uncertainty expressed as the 1-p(corr), where p(corr) is the probability of being correct in a decision task with binary outcomes. This correlates with outcome variance latofc lateral orbitofronal cortex; IFG inferior frontal gyrus; MFG middle frontal gyrus; IPL inferior parietal lobe; IPS inferior parietal sulcus; STG superior temporal gyrus; TTG; transverse temporal gyrus; PHG parahippocampal gyrus. Mean coordinates of interest based on above average coordinates of regions reported more than once in above studies: Anterior insula: right: [35, 19, -2] left: [-31, 17, 0] Inferior frontal gyrus: [52, 14, 27] Inferior parietal lobe: [51, -17, 33] 8/12
9 9/12
10 10/12
11 11/12
12 Figure S3 Legend Model comparison including 3 utility models. EUT expected utility, MVS meanvariance-skewness, PT prospect theory-type. ACV average continuation value, OCV optimal continuation value, SCV sure continuation value. To illustrate relative model fits, we plot the inverse of the average criterion function (mean D) -1, where D is the criterion value (the distance between simulated and actual choice frequencies)). Larger values for D -1 indicate a better model fit, however the 3 utility models are not directly comparable as they have different numbers of parameters. Power utility (EUT) was specified as: V h n, t ( θ ) = V h n, t ( j) h 1 B = Ε n, t ( ρ) 1 ρ ρ h Where B ( ) indexes the jth outcome from the set of discrete outcomes B, from strategy n, t j s n, and ρ reflects the curvature of the power utility function, and hence risk aversion. Prospectic utility (PT) was specified as : V h n, t ( θ ) = V h n, t Δ ( ρ) = Ε h ( ) ( ), } n, t j j m R h B { B n, t 1 ρ R 1 ρ Where Δ {A} is a step function (=1 if A is true, = -1 if A is false), R is the reference point (we set R = 10, the summed expected value of the five lottery proposals within a block), and ρ reflects the curvature of the utility function. In this simplified version of prospect theory, we have no probability weighting and risk seeking for losses is of the same level as risk aversion for gains. 12/12
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 informationTracking 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 informationSUPPLEMENTARY MATERIAL. Patient Group Hem. Lesion Description Etiology FRONTAL LOBE GROUP 1 FL-LAT right SFG (pos), SFS, Central sulcus
1 SUPPLEMENTARY MATERIAL Table S1: Lesion locations of the Frontal Lobe (FL), Basal Ganglia (BG) and Parietal-temporal (PTL) clinical groups. Lesion site was determined by (T1 and T2 weighted) anatomical
More informationOne 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 informationSupplementary Material & Data. Younger vs. Older Subjects. For further analysis, subjects were split into a younger adult or older adult group.
1 1 Supplementary Material & Data 2 Supplemental Methods 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Younger vs. Older Subjects For further analysis, subjects were split into a younger adult
More informationNature Neuroscience: doi: /nn Supplementary Figure 1. Localization of responses
Supplementary Figure 1 Localization of responses a. For each subject, we classified neural activity using an electrode s response to a localizer task (see Experimental Procedures). Auditory (green), indicates
More 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 informationFigure 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 informationModelling 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 informationIntegrative 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 informationMorphometrics 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 informationResampling-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 informationROI 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 informationHierarchical Clustering Identifies Hub Nodes in a Model of Resting-State Brain Activity
WCCI 22 IEEE World Congress on Computational Intelligence June, -5, 22 - Brisbane, Australia IJCNN Hierarchical Clustering Identifies Hub Nodes in a Model of Resting-State Brain Activity Mark Wildie and
More informationPart 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 informationNeuropsychologia 51 (2013) Contents lists available at SciVerse ScienceDirect. Neuropsychologia
Neuropsychologia 51 (2013) 67 78 Contents lists available at SciVerse ScienceDirect Neuropsychologia journal homepage: www.elsevier.com/locate/neuropsychologia Distinct subdivisions of the cingulum bundle
More informationJan 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 informationNeuroImage. Diffeomorphic registration using geodesic shooting and Gauss Newton optimisation. John Ashburner, Karl J. Friston.
NeuroImage 55 (2011) 954 967 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg Technical Note Diffeomorphic registration using geodesic shooting and Gauss
More informationUnravelling 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 informationVisualization and Labeling
Visualization and Labeling Tools for Visualization and Labeling SPM MRIcron MRIcroGL xjview MARINA FreeSurfer Talairach Daemon SPM Anatomy Toolbox WFU Pickatlas SPM Visualization Gesture imitation with
More informationFrom 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 informationREVEALING THE INFLUENCE OF DEFINITE BRAIN REGIONS UPON THE EMERGENCE OF SPATIAL AND TEMPORAL PATTERNS IN THE RESTING-STATE BRAIN ACTIVITY
REVEALING THE INFLUENCE OF DEFINITE BRAIN REGIONS UPON THE EMERGENCE OF SPATIAL AND TEMPORAL PATTERNS IN THE RESTING-STATE BRAIN ACTIVITY By Sergei D. Verzilin THESIS Submitted in partial fulfillment of
More informationA 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腦網路連結分析 A Course of MRI
本週課程內容 腦網路連結 (connectivity & network) 結構性與功能性連結關係 (structural/functional connectivity) 腦網路連結分析 A Course of MRI 盧家鋒助理教授國立陽明大學物理治療暨輔助科技學系 alvin01@ym.edu.tw 複雜網路 : 圖學理論 (graph theory) Brain networks Larger
More informationFundamentals of Computational Neuroscience 2e
Fundamentals of Computational Neuroscience 2e Thomas Trappenberg March 21, 2009 Chapter 9: Modular networks, motor control, and reinforcement learning Mixture of experts Expert 1 Input Expert 2 Integration
More informationUse Case. Interoperating between ontology and rules for identifying brain anatomical structures
W3C Workshop Rule Interoperability Use Case Interoperating between ontology and rules for identifying brain anatomical structures Christine Golbreich 1, Olivier Bierlaire 1 2, Olivier Dameron 3 2, Bernard
More informationSelf-Evaluation Across Adolescence: A Longitudinal fmri Study. Dani Cosme
Self-Evaluation Across Adolescence: A Longitudinal fmri Study Dani Cosme Background Adolescence is a formative period for the development of identity and self-concept (Pfeifer & Peake, 2012) Self-referential
More informationNew Procedures for False Discovery Control
New Procedures for False Discovery Control Christopher R. Genovese Department of Statistics Carnegie Mellon University http://www.stat.cmu.edu/ ~ genovese/ Elisha Merriam Department of Neuroscience University
More informationA 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 information3D Moment Invariant Based Morphometry
3D Moment Invariant Based Morphometry J.-F. Mangin, F. Poupon, D. Rivière, A. Cachia, D. L. Collins, A. C. Evans, and J. Régis Service Hospitalier Frédéric Joliot, CEA, 91401 Orsay, France mangin@shfj.cea.fr,
More informationSupplementary Figure 1. Structural MRIs.
Supplementary Figure 1 Structural MRIs. Coronal and transverse sections of pre-electrode insertion T1 weighted MRIs, illustrating radiologically normal amygdala in the 10 patients for which ierps are presented.
More informationExperimental 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 informationExperimental 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 informationA framework for systematic promoter motif discovery and expression profiling from high dimensional brain transcriptome data. Jeremy A.
A framework for systematic promoter motif discovery and expression profiling from high dimensional brain transcriptome data Jeremy A. Lieberman Submitted in partial fulfillment of the requirements for
More informationTract-Specific Analysis for DTI of Brain White Matter
Tract-Specific Analysis for DTI of Brain White Matter Paul Yushkevich, Hui Zhang, James Gee Penn Image Computing & Science Lab Department of Radiology University of Pennsylvania IPAM Summer School July
More informationInternally generated preactivation of single neurons in human medial frontal cortex predicts volition
Internally generated preactivation of single neurons in human medial frontal cortex predicts volition Itzhak Fried, Roy Mukamel, Gabriel Kreiman List of supplementary material Supplementary Tables (2)
More informationDevelopment of image processing tools and procedures for analyzing multi-site longitudinal diffusion-weighted imaging studies
University of Iowa Iowa Research Online Theses and Dissertations 2014 Development of image processing tools and procedures for analyzing multi-site longitudinal diffusion-weighted imaging studies Joy Tamiko
More informationPiotr Majer Risk Patterns and Correlated Brain Activities
Alena My²i ková Piotr Majer Song Song Alena Myšičková Peter N. C. Mohr Peter N. C. Mohr Wolfgang K. Härdle Song Song Hauke R. Heekeren Wolfgang K. Härdle Hauke R. Heekeren C.A.S.E. Centre C.A.S.E. for
More informationReinforcement learning
einforcement learning How to learn to make decisions in sequential problems (like: chess, a maze) Why is this difficult? Temporal credit assignment Prediction can help Further reading For modeling: Chapter
More informationTHE MYELOARCHITECTONICS OF THE DORSOMEDIAL THALAMIC NUCLEUS OF THE DOG
Acta Neurobiol. Exp. 1970. 30: 145-156 THE MYELOARCHITECTONICS OF THE DORSOMEDIAL THALAMIC NUCLEUS OF THE DOG BARBARA SYCHOWA Department of Neurophysiology, Nencki Institute of Experimental Biology, Warsaw,
More informationExperimental 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 informationExperimental 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 informationInduced 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 informationNeuroimage 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 informationEncoding of Financial Signals in the Human Brain
Encoding of Financial Signals in the Human Brain Thesis by Antoine Jean Bruguier In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy California Institute of Technology Pasadena,
More informationUndirected graphs of frequency-dependent functional connectivity in whole brain networks
36, 937 946 doi:198/rstb.25.1645 Published online 29 May 25 Undirected graphs of frequency-dependent functional connectivity in whole brain networks Raymond Salvador 1, John Suckling 1, Christian Schwarzbauer
More informationOutlines: (June 11, 1996) Instructor:
Magnetic Resonance Imaging (June 11, 1996) Instructor: Tai-huang Huang Institute of Biomedical Sciences Academia Sinica Tel. (02) 2652-3036; Fax. (02) 2788-7641 E. mail: bmthh@ibms.sinica.edu.tw Reference:
More informationIntroduction 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 informationDecision theory. 1 We may also consider randomized decision rules, where δ maps observed data D to a probability distribution over
Point estimation Suppose we are interested in the value of a parameter θ, for example the unknown bias of a coin. We have already seen how one may use the Bayesian method to reason about θ; namely, we
More information12/2/15. G Perception. Bayesian Decision Theory. Laurence T. Maloney. Perceptual Tasks. Testing hypotheses. Estimation
G89.2223 Perception Bayesian Decision Theory Laurence T. Maloney Perceptual Tasks Testing hypotheses signal detection theory psychometric function Estimation previous lecture Selection of actions this
More informationLongitudinal 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 informationKernel Granger Causality Mapping Effective Connectivity on fmri Data Wei Liao, Daniele Marinazzo, Zhengyong Pan, Qiyong Gong, and Huafu Chen*
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 28, NO. 11, NOVEMBER 2009 1825 Kernel Granger Causality Mapping Effective Connectivity on fmri Data Wei Liao, Daniele Marinazzo, Zhengyong Pan, Qiyong Gong, and
More informationNeuroimage Processing
Neuroimage Processing Instructor: Moo K. Chung mkchung@wisc.edu Lecture 10-11. Deformation-based morphometry (DBM) Tensor-based morphometry (TBM) November 13, 2009 Image Registration Process of transforming
More informationMeasuring Brain Variability by Extrapolating Sparse Tensor Fields Measured on Sulcal Lines
Measuring Brain Variability by Extrapolating Sparse Tensor Fields Measured on Sulcal Lines Pierre Fillard 1, Vincent Arsigny 1, Xavier Pennec 1, Kiralee M. Hayashi 2, Paul M. Thompson 2, and Nicholas Ayache
More informationLateral Prefrontal Cortex is Organized into Parallel Dorsal and Ventral Streams Along the Rostro-Caudal Axis
Cerebral Cortex October 2013;23:2457 2466 doi:10.1093/cercor/bhs223 Advance Access publication August 9, 2012 Lateral Prefrontal Cortex is Organized into Parallel Dorsal and Ventral Streams Along the Rostro-Caudal
More informationLarge brain effective network from EEG/MEG data and dmr information
Large brain effective network from EEG/MEG data and dmr information Brahim Belaoucha, Théodore Papadopoulo To cite this version: Brahim Belaoucha, Théodore Papadopoulo Large brain effective network from
More informationAnnouncements: Test4: Wednesday on: week4 material CH5 CH6 & NIA CAPE Evaluations please do them for me!! ask questions...discuss listen learn.
Announcements: Test4: Wednesday on: week4 material CH5 CH6 & NIA CAPE Evaluations please do them for me!! ask questions...discuss listen learn. The Chemical Senses: Olfaction Mary ET Boyle, Ph.D. Department
More informationBios 6648: Design & conduct of clinical research
Bios 6648: Design & conduct of clinical research Section 2 - Formulating the scientific and statistical design designs 2.5(b) Binary (a) Time-to-event (revisited) (b) Binary (revisited) (c) Skewed (d)
More informationFinding 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 informationMedical Image Analysis
Medical Image Analysis Instructor: Moo K. Chung mchung@stat.wisc.edu Lecture 3. Deformation-based Morphometry (DBM) January 30, 2007 Deformation based Morphometry (DBM) It uses deformation fields obtained
More informationWill Penny. 21st April The Macroscopic Brain. Will Penny. Cortical Unit. Spectral Responses. Macroscopic Models. Steady-State Responses
The The 21st April 2011 Jansen and Rit (1995), building on the work of Lopes Da Sliva and others, developed a biologically inspired model of EEG activity. It was originally developed to explain alpha activity
More informationIntertemporal Risk Aversion, Stationarity, and Discounting
Traeger, CES ifo 10 p. 1 Intertemporal Risk Aversion, Stationarity, and Discounting Christian Traeger Department of Agricultural & Resource Economics, UC Berkeley Introduce a more general preference representation
More informationGroup 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 informationJournal of Neuroscience Methods
Journal of Neuroscience Methods 199 (2011) 108 118 Contents lists available at ScienceDirect Journal of Neuroscience Methods j o ur nal homep age: www.elsevier.com/locate/jneumeth Detecting overlapped
More informationDoing Cosmology with Balls and Envelopes
Doing Cosmology with Balls and Envelopes Christopher R. Genovese Department of Statistics Carnegie Mellon University http://www.stat.cmu.edu/ ~ genovese/ Larry Wasserman Department of Statistics Carnegie
More informationSample Size and Power I: Binary Outcomes. James Ware, PhD Harvard School of Public Health Boston, MA
Sample Size and Power I: Binary Outcomes James Ware, PhD Harvard School of Public Health Boston, MA Sample Size and Power Principles: Sample size calculations are an essential part of study design Consider
More informationDiffusion imaging of the brain: technical considerations and practical applications
Diffusion imaging of the brain: technical considerations and practical applications David G. Norris FC Donders Centre for Cognitive Neuroimaging Nijmegen Sustaining the physiologist in measuring the atomic
More informationSingapore Institute for Neurotechnology & Memory Network Programme, National University of Singapore, Singapore
SUPPLEMENTARY INFORMATION: Gene expression links functional networks across cortex and striatum Kevin M Anderson 1, Fenna M Krienen 2, Eun Young Choi 3, Jenna M Reinen 1, B T Thomas Yeo 4,5, Avram J Holmes
More informationNew Approaches to False Discovery Control
New Approaches to False Discovery Control Christopher R. Genovese Department of Statistics Carnegie Mellon University http://www.stat.cmu.edu/ ~ genovese/ Larry Wasserman Department of Statistics Carnegie
More information#A offered. #B offered. Firing rate. Value of X
15 12 #A offered 9 6 3 2 1 0 0 1 2 3 4 5 6 8 10 15 20 25 30 35 40 45 #B offered Figure S1. The x-axis and y-axis represent, respectively, the quantities of juices B and A offered in any given trial, and
More informationNeural Network. Eung Je Woo Department of Biomedical Engineering Impedance Imaging Research Center (IIRC) Kyung Hee University Korea
Neural Network Eung Je Woo Department of Biomedical Engineering Impedance Imaging Research Center (IIRC) Kyung Hee University Korea ejwoo@khu.ac.kr Neuron and Nervous System 2 Neuron (Excitable Cell) and
More informationCortical 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 informationDYNAMIC FUNCTIONAL CONNECTIVITY USING HEAT KERNEL
DYNAMIC FUNCTIONAL CONNECTIVITY USING HEAT KERNEL Shih-Gu Huang Moo K. Chung Ian C. Carroll H. Hill Goldsmith Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, USA
More informationDiffusion Weighted MRI. Zanqi Liang & Hendrik Poernama
Diffusion Weighted MRI Zanqi Liang & Hendrik Poernama 1 Outline MRI Quick Review What is Diffusion MRI? Detecting Diffusion Stroke and Tumor Detection Presenting Diffusion Anisotropy and Diffusion Tensor
More informationSummary of part I: prediction and RL
Summary of part I: prediction and RL Prediction is important for action selection The problem: prediction of future reward The algorithm: temporal difference learning Neural implementation: dopamine dependent
More information9.01 Introduction to Neuroscience Fall 2007
MIT OpenCourseWare http://ocw.mit.edu 9.01 Introduction to Neuroscience Fall 2007 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Complex cell receptive
More informationLearning Structural Equation Models for fmri
Learning Structural Equation Models for fmri Amos J. Storkey School of Informatics Enrico Simonotto Division of Psychiatry Heather Whalley Division of Psychiatry Stephen Lawrie Division of Psychiatry Lawrence
More informationConnectomics analysis and parcellation of the brain based on diffusion-weighted fiber tractography
Connectomics analysis and parcellation of the brain based on diffusion-weighted fiber tractography Alfred Anwander Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany What is the
More information18. Which body system is needed for the exchange of oxygen and carbon dioxide? A. Respiratory B. Integumentary C. Digestive D. Urinary 19.
1 Student: 1. Which of the following is NOT a part of the study of anatomy? A. The structure of body parts B. Predicting the body's responses to stimuli C. Microscopic organization D. The relationship
More informationReporting 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 informationReporting Checklist for Nature Neuroscience
Corresponding Author: Manuscript Number: Manuscript Type: Geoffrey Schoenbaum NNA771T Article Reporting Checklist for Nature Neuroscience # Main Figures: # Supplementary Figures: # Supplementary s: # Supplementary
More information+ + ( + ) = Linear recurrent networks. Simpler, much more amenable to analytic treatment E.g. by choosing
Linear recurrent networks Simpler, much more amenable to analytic treatment E.g. by choosing + ( + ) = Firing rates can be negative Approximates dynamics around fixed point Approximation often reasonable
More informationTesting 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 informationIntroduction to Embryology. He who sees things grow from the beginning will have the finest view of them.
He who sees things grow from the beginning will have the finest view of them. Aristotle 384 322 B.C. Introduction to Embryology This lecture will introduce you to the science of developmental biology or
More informationConcerns of the Psychophysicist. Three methods for measuring perception. Yes/no method of constant stimuli. Detection / discrimination.
Three methods for measuring perception Concerns of the Psychophysicist. Magnitude estimation 2. Matching 3. Detection/discrimination Bias/ Attentiveness Strategy/Artifactual Cues History of stimulation
More informationEvidence 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 informationPDF hosted at the Radboud Repository of the Radboud University Nijmegen
PDF hosted at the Radboud Repository of the Radboud University Nijmegen The following full text is a publisher's version. For additional information about this publication click this link. http://hdl.handle.net/2066/71144
More informationQ-ball imaging of macaque white matter architecture
36, 869 879 doi:1.198/rstb.25.1651 Published online 29 May 25 Q-ball imaging of macaque white matter architecture David S. Tuch 1,2, *, Jonathan J. Wisco 1, Mark H. Khachaturian 1,3, Leeland B. Ekstrom
More informationMultiple Orderings of Events in Disease Progression
Multiple Orderings of Events in Disease Progression Alexandra L Young 1, Neil P Oxtoby 1, Jonathan Huang 2, Razvan V Marinescu 1, Pankaj Daga 1, David M Cash 1,3, Nick C Fox 3, Sebastien Ourselin 1,3,
More informationarxiv: v1 [q-bio.nc] 27 Jan 2016 Received: date / Accepted: date
Noname manuscript No. (will be inserted by the editor) Hierarchical organization of functional connectivity in the mouse brain: a complex network approach Giampiero Bardella, Angelo Bifone, Andrea Gabrielli,
More informationALZHEIMER 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 informationTECHNICAL REPORT NO January 1, Tensor-Based Surface Morphometry
DEPARTMENT OF STATISTICS University of Wisconsin 1210 West Dayton St. Madison, WI 53706 TECHNICAL REPORT NO. 1049 January 1, 2002 Tensor-Based Surface Morphometry Moo K. Chung 1 Department of Statistics,
More informationClicker Question. Discussion Question
Connectomics Networks and Graph Theory A network is a set of nodes connected by edges The field of graph theory has developed a number of measures to analyze networks Mean shortest path length: the average
More informationCollective 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 informationA 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 informationNon-Inferiority Tests for the Ratio of Two Proportions in a Cluster- Randomized Design
Chapter 236 Non-Inferiority Tests for the Ratio of Two Proportions in a Cluster- Randomized Design Introduction This module provides power analysis and sample size calculation for non-inferiority tests
More informationConfidence Thresholds and False Discovery Control
Confidence Thresholds and False Discovery Control Christopher R. Genovese Department of Statistics Carnegie Mellon University http://www.stat.cmu.edu/ ~ genovese/ Larry Wasserman Department of Statistics
More informationHigher Order Cartesian Tensor Representation of Orientation Distribution Functions (ODFs)
Higher Order Cartesian Tensor Representation of Orientation Distribution Functions (ODFs) Yonas T. Weldeselassie (Ph.D. Candidate) Medical Image Computing and Analysis Lab, CS, SFU DT-MR Imaging Introduction
More informationQuantitative Introduction ro Risk and Uncertainty in Business Module 5: Hypothesis Testing
Quantitative Introduction ro Risk and Uncertainty in Business Module 5: Hypothesis Testing M. Vidyasagar Cecil & Ida Green Chair The University of Texas at Dallas Email: M.Vidyasagar@utdallas.edu October
More informationNon-parametric Bayesian graph models reveal community structure in resting state fmri
Non-parametric Bayesian graph models reveal community structure in resting state fmri Kasper Winther Andersen a,b,, Kristoffer H. Madsen b, Hartwig Roman Siebner b,c,d, Mikkel N. Schmidt a, Morten Mørup
More information