From Localization to Connectivity and ...

Lei Sheu1/11/2011

Research interests: To study the association of human behaviors and brain

functionality. Finding neural biomarkers of disease

What do we know Brain functionality depends on both structural and functional

characteristics. Neurons are genetically programmed but regulated and adjusted accordingly in response to environmental conditions (70% of the brain neurons are developed after birth).

Neurons act both as clusters, and networks. fMRI measures BOLD signal, an indirect measure of neuronal

activity. Measurements are subject to errors, which could be contributed

by 4M (machine, man, material, and method)

Structure Morphology Volumes, Cortical Thickness, Surface areas, etc.

(data: MRI structure)

Functional activation to stimuli.

Signal changes/ Contrasts: activation, deactivation(data: MRI BOLD)

Structure Connectivity

Morphological Correlations: Correlation of morphological descriptors in brain regions of interest. (data: MRI structure)

Anatomical Connectivity: White matter fiber connections among grey matter regions. (data: MRI Diffusion)

Functional ConnectivitySeed Based (functional

connectivity)ROIs/Network (effective

connectivity) (data: MRI BOLD)

Level 1 Localization

Level 2Integration

Level 3Complex Networks

(Graph Theoretical Analysis)

Structure Network(data: MRI structure and Diffusion)

Functional Network(data: MRI BOLDI)

Integrate Structure and Functional Networks

Methods: Data driven

Within Subject: voxel-wise general linear Model (GLM)

Group : Multiple regression, ANOVA

Measures: Signal change, Activation clusters

Methods: (Hypotheses driven)

Graph theory

Measures: Node degree, degree

distribution and assortativity

Clustering coefficient and motifs

Path length and efficiency

Connection or cost Hubs, centrality and

robustness Modularity

Level 1 Localization

Level 2Integration

Level 3Complex Networks

Methods: (Data driven/ Hypotheses driven)

Seed Based Functional connectivity: cross-correlationPPI: task associated connectivity

ROIs/Networks (Hypotheses driven)SEM, VAR (Granger Causality),

SVAR, DCM

Measures:Connectivity strengthConnectivity structural

Fair et. al. PLoS 2009

Functional Brain Networks Develop from a “Local to Distributed” Organization

To share the methods we used in fMRI connectivity analysis

Seed Based Analysis Functional Connectivity Psycophysiological Interaction (PPI)

Network Base Analysis Structural Equation Model (SEM) Vector Autoregressive Model (VAR) (Granger

Causality) Structural Vector Autoregressive Model (SVAR) Dynamic Causal Model (DCM)

(SPM)

(SPM)

(AFNI,R)

(R,Matlab

toolbox)

Reconstruct BOLD signals (Preprocessing and Level 1 analysis) Signal pre-whitening, filtering, and artifact correction Physiological noise correction Estimate contrast signals (activation/deactivation)

Determine Regions of Interest (ROIs) Anatomically defined regions Meta analysis results Sphere mask over the cluster shown association with the

psychophysiological or psychosocial variables of interest) Others

Extract BOLD time series Average over ROI Median within ROI Principle components among voxels within ROI

Remove effects of no interest Physiological noise Draft and aliasing (High pass filter) Series dependency (AR model) Movement Tasks of no interest Covariates (performance)

To examine how the brain regions synchronized with the activity in the seed regions.

Seed Based Exploratory Application: Resting State Model: GLM Output: Estimated brain statistical map (i.e.,

map) representing the strength of synchronization with the seed voxel-wise.

Some setup High passed filter of 100 second AR(1) for series dependency correction Covariate with a time series extracted from white

matter area Covariate with motion parameters

To examine task-specific connectivity. Estimate the changes of connectivity strength from a ‘baseline’ to a task of interest.

Seed based; exploratory.Model: GLM with interaction term. Be aware of the calculation of

interaction term in the GLM.

• GLM Model

ynx1 Xnxp px1 nx1

X seed task | seed | task | covariates |1

1: interaction effect on brain activity ( measure of connectivity difference for the two task conditions)

2 : mean seed effect on brain activity (measure of mean connectivity)

3 : task effect on brain activity (measure of activation difference for the two conditions)

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To validate or explore causal relationship within a ROI network.

GLM: X=AX+e; A: (aij)nxn

aij: path strength ij ; aij= 0 if no relationship between i and j

ROI1

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ROI2

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Prepare SEM inputs: Compute group summary time series for each ROI, e.g.,

eigentimeseries. Compute covariance/correlation matrix for the time series

in ROIs. Compute residual error variance for each ROI Calculate effective degree of freedom (adjusted for the

autocorrelation of the time series)

Construct network structure and estimate connection parameters Model validation: If the model is determined, then find aij ,

such that the covariance error is minimized. Test if each estimated connection parameter is significant different from 0.

Model search: Given model constrains to search for model that best fit the covariance.

• DCM allows you model brain activity at the neuronal level (which is not directly accessible in fMRI) taking into account the anatomical architecture of the system and the interactions within that architecture under different conditions of stimulus input and context.

• The modelled neuronal dynamics (z) are transformed into area-specific BOLD signals (y) by a hemodynamic forward model (λ).

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dMPFC vs VS

pACC vs OFC (L/R/M)

Gianaros et. al, Cerebral Cortex. 2010, Sep

Model estimation for each subject (parameters)

Model selection- Bayesian Model Selection (BMS) - Nonparametric method for paired comparison

Group analysis with the selected model

- Random effect analysis- Comparison of low and high PE groups

- Bayesian average

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Distributions of Model Comparison Result from 76 Subjects. Showing below are log of Bayes Factor (logBF(ij), or the diffence of log model evidences for each pair models i, j

Distributions of Model Comparison Result from 76 Subjects. Showing below are log of Bayes Factor (logBF(ij), or the diffence of log model evidences for each pair models i, j

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MRI Scanning DICOM

Sources of variationSources of variation::•Subject/Material•Machine•Man/Operator•Method

scanner

Image conversionData acquisition sequence

Subject’s position, physiological interferingState of mind

Experimental task design

Operator, environment

Preprocessing

Realignment, coregistration,Smoothing,reslice

Signal filtering, high pass filter, whitening

Templates

Smoothed images

Single subject GLM

Single subject GLM

Hemodynamic Response Function

Design matrix,Covariates

Group Analysis

Contrast maps

BP measurement

Threshold

Activation map

Effect/Correlation map

BP machine

Machine setup

Subject’s physiological reaction

Covariates