YEARS

2005-2011

AUTHORS

Dimitrios Samaras

TITLE

Machine Learning for Analysis of functional MRI of Underlying Inhibitory Control

ABSTRACT

DESCRIPTION (provided by applicant): Compromised inhibitory control is a hallmark neuropsychological deficit underlying complex disorders and behaviors as drug addiction and aggression. Individuals with compromised mechanisms of control are difficult to identify unless they are subjected to challenging conditions directly. This is possibly due to the subtle ways in which poor inhibitory control is expressed in their baseline functioning, making classification and most importantly, prediction of future behavior, extremely challenging. We propose novel computational techniques to analyze brain-behavior relationships underlying mechanisms of inhibitory control, focusing on performing classification of hard-to-categorize groups of subjects based on brain activation response patterns to behavioral challenges of inhibitory control using functional magnetic resonance imaging (fMRl). These classification methods are applied on two distinct datasets: one of substance dependent individuals and the other of individuals with a particular genotype conferring vulnerability to poor inhibitory control. We hypothesize that unique patterns of variability in brain function can assist in identification of brain mechanisms rooted in compromised inhibitory control. Such patterns will increase our understanding of brain connectivity and circuitry as we move iteratively between a-priori and exploratory means of describing circuits of inhibitory control. Machine Learning techniques have been shown to be successful in discovering optimal features and patterns in complex high dimensional datasets. The diversity of the underlying questions when studying inhibitory control and the subtlety of the effects that can be used for classification, motivate us to propose an integrated machine learning framework for the joint exploration of spatial, temporal and functional information for the analysis of fMRI signals, thus allowing the testing of hypotheses and development of applications that are not supported by traditional analysis methods. We hypothesize that: 1) A differential spatial brain pattern will indicate a diagnosis of drug addiction and a membership in one or another level ol MAOA genotype. Spatial information from static 3D contrast maps will be input to PCA-based and Voxel-based methods, Adaboosting and Learning with Side information 2) A temporally accounted intrasubject pattern of response to the inhibitory control challenge conditions in the fMRI paradigms will reveal group membership in both data sets. Temporal fMRl information wilt be used for Hidden Markov Models, Conditional Random Fields, etc. 3) A connectivity map corresponding to brain circuits functionally subserving inhibitory control wilt be revealed with indications of directionality of influence between brain regions by analyzing functional information with Dynamic Bayesian Networks, Dynamically Multi-Linked HMMs, etc.

FUNDED PUBLICATIONS

  • Multi-voxel pattern analysis of selective representation of visual working memory in ventral temporal and occipital regions.
  • Can a single brain region predict a disorder?
  • Oral methylphenidate normalizes cingulate activity in cocaine addiction during a salient cognitive task.
  • Simultaneous cast shadows, illumination and geometry inference using hypergraphs.
  • How to use: Click on a object to move its position. Double click to open its homepage. Right click to preview its contents.

    Download the RDF metadata as:   json-ld nt turtle xml License info


    25 TRIPLES      17 PREDICATES      26 URIs      9 LITERALS

    Subject Predicate Object
    1 grants:c5ec67572a51fd769f75e9030adfef52 sg:abstract DESCRIPTION (provided by applicant): Compromised inhibitory control is a hallmark neuropsychological deficit underlying complex disorders and behaviors as drug addiction and aggression. Individuals with compromised mechanisms of control are difficult to identify unless they are subjected to challenging conditions directly. This is possibly due to the subtle ways in which poor inhibitory control is expressed in their baseline functioning, making classification and most importantly, prediction of future behavior, extremely challenging. We propose novel computational techniques to analyze brain-behavior relationships underlying mechanisms of inhibitory control, focusing on performing classification of hard-to-categorize groups of subjects based on brain activation response patterns to behavioral challenges of inhibitory control using functional magnetic resonance imaging (fMRl). These classification methods are applied on two distinct datasets: one of substance dependent individuals and the other of individuals with a particular genotype conferring vulnerability to poor inhibitory control. We hypothesize that unique patterns of variability in brain function can assist in identification of brain mechanisms rooted in compromised inhibitory control. Such patterns will increase our understanding of brain connectivity and circuitry as we move iteratively between a-priori and exploratory means of describing circuits of inhibitory control. Machine Learning techniques have been shown to be successful in discovering optimal features and patterns in complex high dimensional datasets. The diversity of the underlying questions when studying inhibitory control and the subtlety of the effects that can be used for classification, motivate us to propose an integrated machine learning framework for the joint exploration of spatial, temporal and functional information for the analysis of fMRI signals, thus allowing the testing of hypotheses and development of applications that are not supported by traditional analysis methods. We hypothesize that: 1) A differential spatial brain pattern will indicate a diagnosis of drug addiction and a membership in one or another level ol MAOA genotype. Spatial information from static 3D contrast maps will be input to PCA-based and Voxel-based methods, Adaboosting and Learning with Side information 2) A temporally accounted intrasubject pattern of response to the inhibitory control challenge conditions in the fMRI paradigms will reveal group membership in both data sets. Temporal fMRl information wilt be used for Hidden Markov Models, Conditional Random Fields, etc. 3) A connectivity map corresponding to brain circuits functionally subserving inhibitory control wilt be revealed with indications of directionality of influence between brain regions by analyzing functional information with Dynamic Bayesian Networks, Dynamically Multi-Linked HMMs, etc.
    2 sg:endYear 2011
    3 sg:fundingAmount 1120973.0
    4 sg:fundingCurrency USD
    5 sg:hasContribution contributions:0de1adcf951c32e682a452cd989d091a
    6 sg:hasFieldOfResearchCode anzsrc-for:08
    7 anzsrc-for:0801
    8 anzsrc-for:11
    9 anzsrc-for:1109
    10 anzsrc-for:17
    11 anzsrc-for:1701
    12 sg:hasFundedPublication articles:28098d923f28b4c2aef43bd26d407ecf
    13 articles:9e27a4016e40eea0e1a317579231bda4
    14 articles:dacf950dea11f83acc39a876ce188433
    15 articles:e85cf9a95b5e1d0416c91b941445f82f
    16 sg:hasFundingOrganization grid-institutes:grid.420090.f
    17 sg:hasRecipientOrganization grid-institutes:grid.36425.36
    18 sg:language English
    19 sg:license http://scigraph.springernature.com/explorer/license/
    20 sg:scigraphId c5ec67572a51fd769f75e9030adfef52
    21 sg:startYear 2005
    22 sg:title Machine Learning for Analysis of functional MRI of Underlying Inhibitory Control
    23 sg:webpage http://projectreporter.nih.gov/project_info_description.cfm?aid=7761665
    24 rdf:type sg:Grant
    25 rdfs:label Grant: Machine Learning for Analysis of functional MRI of Underlying Inhibitory Control
    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular JSON format for linked data.

    curl -H 'Accept: application/ld+json' 'http://scigraph.springernature.com/things/grants/c5ec67572a51fd769f75e9030adfef52'

    N-Triples is a line-based linked data format ideal for batch operations .

    curl -H 'Accept: application/n-triples' 'http://scigraph.springernature.com/things/grants/c5ec67572a51fd769f75e9030adfef52'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/grants/c5ec67572a51fd769f75e9030adfef52'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/grants/c5ec67572a51fd769f75e9030adfef52'






    Preview window. Press ESC to close (or click here)


    ...