statistical modeling of neuroimaging data: targeting...

Spatial modeling Multimodal PD Biomarkers Future Directions

Statistical Modeling of Neuroimaging Data:Targeting Activation, Task-Related Connectivity,

and Prediction

F. DuBois Bowman

Department of Biostatistics and BioinformaticsEmory University, Atlanta, GA, 30322

CBIS Five-Year Anniversary Symposium

February 8, 2013

F. D. Bowman (Emory University) Spatial Modeling CBIS Symposium 1 / 25


Acknowledgements

Research Team

Lijun Zhang, PhD, Emory, Biostatistics and BioinformaticsJian Kang, PhD, Emory, Biostatistics and BioinformaticsYing Guo, PhD, Emory, Biostatistics and BioinformaticsGordana Derado, PhD, CDCShuo Chen, PhD, Univ. of Maryland, Epidemiology and BiostatisticsWenqiong Xue, MS, Emory, Biostatistics and BioinformaticsAnthony Pileggi, Emory, Biostatistics and BioinformaticsDaniel Huddleston, MD, Kaiser Permanente GeorgiaXiaoping Hu, PhD, Emory/GA Tech, Biomedical Engineering



Outline

1 Spatial Modeling for Activation and Connectivity

2 Determining Multimodal Imaging Biomarkers for Parkinson’s Disease

3 Future Directions



Data Example

Working Memory in Schizophrenia Patients

N=28 subjects: 15 schizophrenia patients and 13 healthy controlsfMRI Tasks: Serial Item Recognition Paradigm (SIRP)

Encoding set: Subjects asked to memorize 1, 3, or 5 target digits.Probing set: Subjects sequentially shown single digit probes and askedto press a button:

with their index finger, if the probe matchedwith their middle finger, if not.

6 runs per subject: (177 scans per run for each subject)3 runs of working memory tasks on each of 2 days

Objective: Compare working memory-related brain activity between

patients and controls

Data from the Biomedical Informatics Research Network (BIRN) [1]: Potkin et al. (2002), Proc. 41st Annu. Meeting Am.

College Neuropsychopharm.



fMRI Data Characteristics

Massive Data SetsRoughly 300,000 brain voxels for each scan at time of analysisS = 177 scans per run, 3 runs each day, 2 days (sessions)Over 300 million spatio-temporal data points per subject! Over 5billion for all subjects!!

Temporal correlationsScan to scan correlationsBetween session correlations

Complex spatial correlationsCorrelations between neighboring voxelsLong-range correlations between different brain regionsRoughly 45 billion voxel pairs



Spatial Correlations

Distances

Functional

Physical (Geometric)

Anatomical

The complexneuroanatomy andneurophysiology makebasic assumptions ofmany spatial methodsquestionable forneuroimaging

(a) Functional (b) Geometric

Figure: Alternative measures ofdistance

Bowman (2007), JASA.



Common Activation Analysis Framework

Two-stage Model

First, fit a linear model separately for each subject (at each voxel)

Temporal correlations between scans: AR models (+ white noise)

Second, fit linear model that combines subject-specific estimates

For Inference: Compute t-statistics at each voxel and threshold

Consider a multiple testing adjustment (Bonferonni-type, FDR, RFT)

Properties

Two-stage (random effects) model

Simplifies computationsSacrifices efficiency

Assumes independence between different brain locations

Targets activation analyses, disregarding functional connectivity



Spatial Modeling Framework

Bayesian Spatial Model for Activation and Connectivity (BSMac):

Stage I: Individual level - addresses temporal correlations

Stage II: Group levelDefine brain regions usingneuroanatomic parcellation (e.g.Brodmann or AAL)

Spatial correlations

Within regionsBetween regions

Inferences

Voxel-levelRegional

Bowman et al. (2008), NeuroImage

Zhang et al. (2011), Journal of Neuroscience Methods



BSMacStage II: Bayesian Spatial Model for Activation and Connectivity (BSMac):

Yigj | µgj , αigj , σ2gj ∼ Normal(µgj + 1αigj , σ

2gj I)

µgj | λ2gj ∼ Normal(1µ0gj , λ2gj I)

σ−2gj ∼ Gamma(a0, b0)

αij | Γj ∼ Normal(0,Γj)

λ−2gj ∼ Gamma(c0, d0)

Γ−1j ∼ Wishart{

(h0H0j)−1, h0

}Yigj = (Yigj1, . . . ,YigjVg )′,µgj = (µgj1, . . . , µgjVg )′, and

αij = (αi1j , . . . , αiGj)′

Bowman et al. (2008), NeuroImage

Zhang et al. (2011), Journal of Neuroscience Methods



BSMac MATLAB Toolbox

GUI Interface: Available at www.sph.emory.edu/bios/CBIS/




Interactive Activation Maps




Task-Related Connectivity Maps: Schizophrenia Patients, WM Load 5




Task-Related Connectivity Maps: Healthy Controls, WM Load 5



BSMac Properties

BSMac framework

Considers activation objectives and task-related FC

Models spatial correlations in brain activity

Within and between defined neuroanatomic regions

Yields easily interpretable posterior probabilities of activation

Permits voxel-level and region-level inferences

Can apply FDR-like concepts

Limitations

Does not account for temporal dependence between multiple sessions

Fairly simple intra-regional correlation model



Extensions: Spatial Modeling

Model Correlations

Between scanning sessions [Derado et al. (2010), Biometrics]

E.g. between days or treatment periodsDoes NOT model between-region spatial correlations

Between sessions, between regions, and locally between voxels (withinregions) [Derado et al. (2012), Statistical Methods in Medical Research]

Use imaging data to predict future neural responses

Forecast neural representations of disease progressionPredict neural responses to various treatments

*Between brain regions, between subregions (within regions), betweenvoxels locally within subregions [Xue et al. (2013), in progress]

Use imaging data to predict/classify patient characteristics

Forecast clinical diagnosisPredict clinical response to various treatmentsPermits multimodal imaging data



Determining Multimodal Biomarkers for PD

Data from Multimodal Imaging Studies

The data will include 81 subjectsacross three studies

38 Parkinson’s disease patients32 Healthy control subjects11 Alzheimers disease patients

Potential Biomarkers

Imaging (MRI, fMRI, DTI,NM-MRI, CSI)GeneticNeurocognitive TestingQuestionnaire-Derived ScoresClinicalCSF NeuroinflammationCSF Catecholamine Metabolites



Determining Multimodal Biomarkers for PD

Project in the NINDS Parkinson’s Disease Biomarker Program

Aim 1: To develop new statistical techniques to reveal multimodal

biomarkers for PD including imaging, clinical, and biologic variables.

Develop statistical models for high-dimensional data, which poolpredictive strength across multiple data modalities for classifying PDversus HC.

Joint multimodal probability modelsPenalized likelihood approaches, with variable selection usingmodality-specific penalties`p(β, λ) = −`(β) +

∑k λkPk(βk)

`(β) =∑

i [yi logπi + (1− yi )log(1− πi ]Model development, training, testing, and validation



Objectives

Multimodal biomarker detection

Numerous findings suggest links between PDand single genetic, imaging, and biologicfactors

Many of these are non-specific or insensitiveSingle modality biomarkers may not fullyaddress the complexity of PD

We regard PD as a complex, systems-level,multi-dimensional disorder with discrete, butfunctionally integrated manifestations

We will develop methods to definemultimodal PD biomarkers from a massivenumber of hypothesis driven and exploratorycandidate markers



Exploratory Analysis

Selected features from multiple imaging modalities

[Blue represents PD patients and red represents HC subjects.Networks: FC; Local activity: ALFF; Volumetric: VBM;Chemical shift imaging: RLC and LLC]

[NM-MRI Estimated Locus Coeruleus Volume.Controls: N=6; PD: N=9]



Bayesian Spatial Prediction Model

We predict (or classify according to) patientcharacteristics from multimodal imaging data (andother patient information)

Two-level parcellation: AAL Regions → Subregions

Spatial correlations:

Between AAL regions: Unstructured covariancematrixBorrow strength between subregions within eachAAL region: CAR modelBetween voxels within each subregion:Exchangeable structure




Example of modelling framework using ALFF and VBM data

Let Di ∈ {0, 1} represent disease status (e.g. 1=PD, 0=HC)

Let Xilg (v) and Zilg (v) respectively denote the ALFF and VBM for

subject i at voxel v in subregion l (in region g), and

Bi = [Xilg (v),Zilg (v)]

Construct a Bayesian joint (multimodal) probability model

f (Xilg (v),Zilg (v)|Di ) ∝ f1(Xilg (v)|Zilg(v),Di )× f2(Zilg (v)|Di )

Generate predictions for a new subject n + 1 based on

Pr(Dn+1 = k |Bn+1, {Bi ,Di}ni=1)




Generates whole brain, region-level, and voxel-level predictions

Region- and voxel-level predictions reveal brain regions with strongdiscriminatory power

Use leave-one-out cross validation to assess accuracy

Whole-brain prediction achieves 100% accuracy for distinguishing PDpatients from healthy controls based on ALFF and VBM data




Regional accuracies:



Future Directions

Expand the imaging modalities used to determine PD biomarkers

MRI, fMRI, DTI, NM-MRI, CSISome provide whole-brain assessmentsSome focus on specific regions of interest: locus coeruleus (LC) andsubstantia nigra (SN)

Expand our joint Bayesian multimodal probability model

Consider penalized likelihood approaches:

`p(β, λ) = −`(β) +∑

k λkPk(βk)

`(β) =∑

i [yi logπi + (1− yi )log(1− πi ]

Validation of identified biomarkers



Acknowledgements

Thank you!


statistical modeling of neuroimaging data: targeting...

Documents