Bioinformatics for post-traumatic stress
Bioinformatics for post-traumatic stress
批准号:
10412074
负责人:
Erich Kummerfeld
金额:
$50.2万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-08-10 至 2024-05-31
关键词:
BioinformaticsBiologicalBiological MarkersCategoriesClinicalClinical DataClinical ResearchClinical TrialsClinical Trials DatabaseComplexDataData AnalysesData SetDatabasesDevelopmentDiagnosticDiagnostic testsDiagnostics ResearchDimensionsDiseaseExposure toGenomicsGrowthImageLaboratoriesLinear ModelsLinear RegressionsLogisticsMachine LearningMapsMeasuresMedical HistoryMental HealthMental disordersMetadataMethodsModelingNational Institute of Mental HealthNervous System TraumaNeurocognitiveObservational StudyOutcomePathologyPatientsPatternPersonsPhenotypePopulationPrecision therapeuticsPrediction of Response to TherapyPredictive AnalyticsPrincipal Component AnalysisPsychiatryPsychopathologyRecoveryReproducibilityResearchResearch Domain CriteriaResearch Project GrantsSeverity of illnessSourceStatistical MethodsSupervisionSymptomsSyndromeTechniquesTestingTherapeuticTraumaTrauma ResearchTrauma patientTrauma recoveryTraumatic Brain InjuryValidationVeteransWorkaccurate diagnosisanalytical toolbasebiobehaviorcombat veterancomputational platformdata archivedata complexitydata miningdata repositorydata sharingdemographicsdiagnostic criteriadiverse datafeature selectionfederated computingguided inquiryhands-on learningheterogenous datain silicoindexinginnovationinsightinterestlarge datasetsmachine learning methodmultidimensional datamultimodalitypatient populationpatient subsetspost-traumatic stresspost-traumatic symptomsprecision medicinepredictive modelingpredictive testpsychologicresearch and developmentresearch studyresponsestatisticsstress related disordersupervised learningsymptomatologytooltrauma exposuretraumatic eventtreatment planningtreatment responderstreatment responseunsupervised learningvector
中文摘要
项目总结/文摘
英文摘要
Project Summary/Abstract
Maladaptive complications following trauma, including post-traumatic stress (PTS), are highly prevalent in both
veterans and civilians, and have been difficult to accurately diagnose, manage and treat. Debate regarding
diagnostic criteria and the need to represent the full spectrum of inter-connected features contributing to
psychopathology has spawned the development of the Research Domain Criteria (RDoC) by the National
Institute of Mental Health (NIMH). RDoC is a developing framework to help guide the discovery and validation
of new dimensions of mental health disorders and their relationships to underlying biological mechanisms.
NIMH now has a rich federated database that currently houses raw data from RDoC-sponsored clinical
research, and clinical trial data from the National Database of Clinical Trials (NDCT) with information that may
help to unlock the complex and overlapping relationships between symptoms of PTS and the underlying
biomarkers to fuel improvements on diagnostic and therapeutic frameworks for trauma recovery. The
proposed project will apply bioinformatics and machine learning analytical tools to these large, heterogeneous
datasets to identify and validate new research dimensions of trauma-related psychopathology and treatment
response trajectories and their predictors. Aim 1 will develop an in silico trauma patient population by
integrating data from diverse sources, including cross-sectional and observational longitudinal clinical studies
housed within available data repositories for trauma and other related mental health research. Data will include
medical history, demographics, diagnostic tests, clinical outcomes, psychological assessments, genomics,
imaging, and other relevant study and meta-data. Aim 2 will identify multiple dimensions of PTS diagnostic
criteria, using a combination of unsupervised dimension-reduction statistical methods, internal and external
cross-validation, and supervised hypothesis testing of predictive models to understand the heterogeneous
subtypes of PTS. Aim 3 will deploy unsupervised machine learning methods, such as topological data analysis
and hierarchical clustering, to identify unique clusters of patients based on symptomatology to develop
clustering methods for precision mapping of PTS patients based on disease severity. Aim 4 will use supervised
machine learning techniques for targeted predictive analytics focused on identifying treatment responders from
the NDCT, and identification of latent variables that predict treatment response. The results of the proposed
research project will greatly enrich the field of computational psychiatry research to identify conserved
dimensions associated with the complex relationships of psychopathology and precision treatment planning
following exposure to traumatic events.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1016/j.psychres.2023.115164
发表时间:
2023-05
期刊:
PSYCHIATRY RESEARCH
影响因子:
11.3
作者:
[Reinke, Michael, Falke, Chloe, Cohen, Ken, Anderson, David, Cullen, Kathryn R., Nielson, Jessica L.]
通讯作者:
Nielson, Jessica L.
Causal discovery replicates symptomatic and functional interrelations of posttraumatic stress across five patient populations.
因果发现在五名患者人群中复制创伤后压力的症状和功能相互关系。
DOI:
10.3389/fpsyt.2022.1018111
发表时间:
2022
期刊:
FRONTIERS IN PSYCHIATRY
影响因子:
4.7
作者:
[Pierce, Benjamin, Kirsh, Thomas, Ferguson, Adam R., Neylan, Thomas C., Ma, Sisi, Kummerfeld, Erich, Cohen, Beth E., Nielson, Jessica L.]
通讯作者:
Nielson, Jessica L.
Resource Core
-
批准号:10672631
-
项目类别:
-
资助金额:$19.06万
-
财政年份:2023
-
负责人:Erich Kummerfeld
-
依托单位:
海外基金