PowerPoint Presentation

Computational approaches for mapping the human connectome R. Cameron Craddock, PhDDirector, Computational Neuroimaging LabNathan S. Kline Institute for Psychiatric ResearchDirector of Imaging, Center for the Developing BrainChild Mind Institute

March 30, 2016

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Functional Magnetic Resonance Imaging (fMRI)An fMRI time-course is formed by the rapid acquisition of MR images which are sensitive to the blood oxygen level dependent (BOLD) contrast

Hemoglobin, the protein which transports oxygen in blood, contains four heme molecules, each with an atom of ironDeoxy-hemoglobin is paramagnetic and creates a magnetic gradient that dephases the MRI signalOxy-hemoglobin is diamagnetic and does not affect the MRI signal

Hemodynamic ResponseInitially neuronal oxygen consumption increases the amount of deoxy-hemoglobin and the MR signal decreasesBlood flow increases, bringing more oxygenated blood to the area than is required, resulting in a signal increaseWhen neuronal activity ceases, the signal returns to baseline after a brief undershoot

Brain Mapping with fMRI

Resting State Functional Connectivity

Biswal et al. MRM 1995Intrinsic activity is ongoing neural and metabolic activity which is not directly associated with subjects performance of a task-Raichle TICS 2010

Intrinsic Connectivity Networks

De Luca et al, 2006Independent Component Analysis

Kelly et al, 2007Functional Connectivity Seeding

Functional Connectivity AnalysisData are preprocessedIndividual level FC maps are generated by ROI correlation with rest of brain, ICA, cross-correlation between several ROIs, or other methodFC maps are compared between groups feature-by-feature (voxel-by-voxel) using t-tests

Data Driven ROI AtlasCraddock et al. Human Brain Mapping 2012.

Functional Connectivity Graphs

Courtesy of Dr. Xinian Zuo

The Human ConnectomeThe sum total of all of the brains connectionsStructural connections: synapses and fibersDiffusion MRI Functional connections: synchronized physiological activityResting state functional MRI

Nodes are brain areasEdges are connections

Craddock et al. Nature Methods, 2013.

Discovery science of human brain functionCharacterizing inter-individual variation in connectomes (Kelly et al. 2012)

Identifying biomarkers of disease state, severity, and prognosis (Craddock 2009)

Re-defining mental health in terms of neurophenotypes, e.g. RDOC (Castellanos 2013)

MVPA ClassificationGiven a training set of observations and corresponding labels (categorical) objective is to find a linear hyperplane capable of discriminating observations For linearly separable data there are an infinite number of hyper-planes that meet this requirementSVM finds the unique hyperplane that maximizes the perpendicular distance from the hyperplane to the nearest observations of a class (margin)

Train vs. TestTraining or learning is the process of optimizing MVPA modelRequires: Data + LabelResults: ModelTesting involves applying MVPA model to a datasetRequires: Data + ModelResults: Predicted LabelsTraining and Testing dataset should be independent to avoid bias

Diagnosing DepressionSVC successfully learned patterns of functional connectivity capable of predicting MDD from HCUncovered differences not discovered by t-test analysisFeature selection substantially improved the prediction accuracy of SVCMethods that incorporate reliability performed the bestMethod requires selecting and localizing ROIsProblematic if there is no previous research, introduces experimenter bias/error

Predicting Intrinsic Brain ActivityMultivariate model of brain activity

Underdetermined problem: solved using support vector regression or other regularized regression / dimensionality reduction methodCraddock et al. NeuroImage 2013.

Nonparametric prediction, activation, influence and reproducibility resampling

Prediction AccuracyMeasure of the generalization ability of a modelCan be interpreted as a measure of the information content in the model about the region being modeled

ReproducibilityMeasures the Signal-to-Noise ratio of the model

Strother, S. C. et al. NeuroImage 2003

Predicting Intrinsic Brain Function

Intra-individual variation

Intra-individual variation

Inter-subject prediction 480 subjects69 DZ twin pairs80 MZ twin pairs200 Non-siblings

Train on one individual, test with anotherIntra individualBetween siblings (MZ, DZ)Age and sex matched non-siblings

Global prediction accuracy

Regional Differences

SVR Training

Tracking Intrinsic Connectivity Networks

Amount of Training

Predicting the Future

RT Neurofeedback of the Default Mode Network (DMN)

ICN Competition

Fox MD PNAS 2005

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Exp. Design

Class Training Labels

Training run

Time-LabeledScans

Image Recon and SVM Classification

Image Data

Data AcquisitionStimulus Presentation

StimulusConventional FMRI

Test Data Classifier OutputTesting Run

Real-Time Tracking RSNsLaConte, et al. (2007) Hum Brain Mapp. 28: 1033-1044Stephen LaConte August 19, 2009

Stimulus seen by volunteerUpdated fMRI resultsMotion tracking and correctionIntensity (brightness) of a single voxel, changing during stimulus conditionsController interface for display parameters

RT Neurofeedback of DMNTest hypothesis of DMN dysregulation in depression, ADHD, aging, etc

Preprocessing

Online preprocessing can be performed in ~ 5 minutes, most of which can occur in parallel with acquisition

Online DenoisingfMRI activity is confounded by intensity modulations induced by head motion, physiological noise, scanner drift, Implemented RT denoising in AFNI to remove contributions of confoundsNth order polynomialGlobal meanMask average time series (i.e. WM, CSF)Motion parameters (6 or 24 regressor models)Spatial smoothingAdds ~ 5 ms of delay

DMN Modulation Task

Modulating the DMN

Results

Accuracy was measured from Pearsons correlation between task paradigm and DMN activity extracted after post-processing.

Behavioral Correlates

Measures that were significantly associated with DN regulation include (p 0.2m% Voxels with FD > 0.2m

http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/

Quality Assessment Protocol (2)Implemented in pythonNormative datasets to help learn thresholds for quality controlABIDECoRR

http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/

Pledges from 25 different research institutions to share neuroimaging data from over 3000 individuals, 500 of which suffer from autism.

Ten new analysis software toolsets have been released from 9 institutions including MIT, the Beijing Normal University, Linkping University (Sweden), Columbia University, and the Mind Research Network (New Mexico).

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High Res Anatomical

Diffusion Imaging

AcknowledgmentsCMI/NKIMichael Milham, MD, PHDZarrar ShehzadStan Colcombe, PhDVirginia Tech Carilion Research InstituteStephen LaConte, PhDJonathan Lisinski, MSSiemens MedicalKeith Heberlein, PhDChris Glielmi, PhDResearch Funded in part by a NARSAD Young Investigator Award and NIMH R01MH101555

Thank You!

Why MVPA?

Multi-voxel pattern analysis

Current state-of-the-art functional connectivity analyses are based on mass univariate techniques that ignore the interactions between featuresMultivariate Techniques

Null hypothesis testing evaluates p(feature|NULL), for biomarker we are interested in p(disease state|pattern) or p(brain state|pattern)Predictive Modeling

MVPA RegressionSimilar to classification, except label is now a continuous variableFind a function of the features capable of accurately producing the labels, within a specified error

Disease State PredictionTo learn a pattern of resting state functional connectivity (FC) capable of predicting the presence or absence of a disease state (biomarker)Apply support vector classification (SVC) to FC maps Performed in the context of a study of major depressive disorder (MDD)

To investigate the role of feature selection in improving pattern prediction accuracy and interpretationIncorporate reliability into feature selection

Subject DataData acquired on 3T Siemens TRIO Tim20 Healthy Controls scanned with CP head coil(HC; 12F, mean age 28.9 +/- 7.2)20 depressed scanned with 8-channel matrix(MDD; 12 F, mean age 43.2 +/- 10.8)Resting State ScanEyes open and fixatingZ-Saga sequence (20 4-mm thick axial slices, FOV = 24 cm, Matrix = 64x64, FA/TE/TR = 90/30 ms/2.02 s)210 Volumes (7 min, 20 s)

Preprocessing / ROI extractionPreprocessing performed in SPM5Motion correction, slice timing correction, written into MNI space at 4 mm x 4 mm x 4 mm resolution, smoothed with 6-mm FWHM GaussianTime course cleaning, ROI extraction performed in AFNIRegress out residual motion, global mean, white matter, and CSF time coursesBandpass filter (0.009 Hz < f < 0.08 Hz)ROI time courses summarized as first eigenvariate from SVDFunctional connectivity maps constructed by cross-correlating ROI time coursesFisher Transform

Regions of Interest15 6-mm radius spherical ROIs chosen based on their relevance to MDD:

Chosen from previous PET and fMRI measures of activity, not functional connectivity

SVC of Resting State FC

Filter FS(N/TF/RF)Data Seti=0i=i+1PEii < NCalculate LOOCV PENoWrapper FS (N/RFE/RRFE)SVCYesExclude ith observation

Observations ->Features ->

Reliability Feature Selection

T-test FilterBetween group t-test for each feature and retain those that pass a liberal threshold (p < 0.05, uncorrected)Univariate criterionReliability FilterEstimate bootstrap confidence and exclude features whose 95% CI include zero

Prediction AccuracyMethodLOOCV PEAVG # FeatAVG # SVsRF5%1110.8TF17.5%1110.3None37.5%10530.4

Discriminating Features8 features selected by both3 features selected by RF only due to multivariate interactions3 features selected by TF only filtered out by reliability

TFRF

Normalized Cut (ncut) clusteringSpectral clustering methodUses spectral (eigen) decomposition of connectivity matrix to determine clustersInterprets connectivity matrix as a graphGraph is partitioned into a number of subsets by removing edges from the graphSimilarity within subsets is high Similarity between subsets is lowNumber of clusters must be specified

ncut Cost functionNormalized cut normalizes cut cost by the number of edges within a subsetLess sensitive to outliers than other graph cut methods

Clustering Results