Depression as a disease of network disruption: learning from multiple sclerosis
抑郁症是一种网络中断疾病:从多发性硬化症中学习
基本信息
- 批准号:10643057
- 负责人:
- 金额:$ 19.49万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-07-01 至 2028-06-30
- 项目状态:未结题
- 来源:
- 关键词:AccelerationAdultAffectAgeAtrophicAttentionBiological MarkersBrainBrain regionChronic DiseaseClinicalClinical TrialsClinical assessmentsCognitionComputational TechniqueComputerized Medical RecordComputing MethodologiesDataData SetDiagnosisDimensionsDiseaseDorsalExclusionFascicleFemaleFiberFoundationsFunctional disorderHeterogeneityImageImmuneImpairmentKnowledgeLearningLesionLocationMagnetic Resonance ImagingMapsMeasuresMediatingMediationMedicalMental DepressionMentored Patient-Oriented Research Career Development AwardMentorshipMimosaModelingMultiple SclerosisNeurocognitiveParticipantPatientsPatternPennsylvaniaPerformancePersonsPhenotypePrevalenceProcessPsychiatryPublic HealthRecording of previous eventsReproducibilityResearchSamplingSeveritiesSex DifferencesSocietiesStrokeStructureSymptomsSystemTechniquesTestingTrainingUnited StatesUniversity resourcesVisitWhite Matter Diseasealgorithmic methodologiesautomated segmentationbiotypesclinical carecognitive functioncognitive testingcomorbid depressioncomorbiditycostdepressive symptomsexperiencegray matterindividual variationinstrumentmachine learning algorithmmachine learning methodmalenetwork dysfunctionneuropsychiatric symptompredictive modelingprogramsprospectivepsychiatric symptomrecruitsupervised learningsymptomatologytooltreatment stratificationwhite matter
项目摘要
PROJECT ABSTRACT/SUMMARY
Multiplesclerosis (MS) is an immune-mediatedneurological disorder that affects one million people in
the United States. Up to 50% of patients with MS experience depression, yet the mechanisms of depression in
MS remain under-investigated. MS is characterized by white matter lesions, suggesting that brain network
disruption may underly depression symptoms. Studies of medically healthy participants with depression have
described associations between white matter variability and depressive symptoms, but frequently exclude
participants with medical comorbidities and thus cannot be extrapolated to people with intracranial diseases.
Previous research using lesion network mapping, a technique for mapping heterogeneous gray matter lesions
to neuropsychiatric symptoms, has demonstrated that strokes in gray matter associated with depression disrupt
a reproducible depression network. However, such techniques have never been applied to white matter disease
or MS. Studying white matter lesions associated with depression in MS may provide a way to understand both
the pathophysiology of depression in MS and general network mechanisms of depression more broadly. The
purpose of this current study is to investigate how brain network disruption underlies depression by learning from
the example of multiple sclerosis. In Aim 1, I will delineate how depression in adults with MS is associated with
white matter lesion location and burden in a retrospective sample of 1,554 MS patients with research-grade 3T
MRIs acquired as part of clinical care. Depression and MS diagnoses will be obtained from the electronic medical
record. While this sample provides an ideal dataset for developing a model, the electronic medical record does
not contain granular depression measures. In Aim 2, I will obtain structured clinical and cognitive assessments
for MS patients and prospectively evaluate white matter integrity as a predictor of dimensional depressive
symptoms. However, it is possible that symptoms of depression may reflect heterogenous brain network
disruption patterns. Therefore, in Aim 3, I will use advanced semi-supervised machine learning methods to parse
heterogeneity in MS white matter lesion burden in the retrospective sample and test whether this model predicts
phenotypic heterogeneity in our deeply-phenotyped prospective sample. The support of the K23 award will
provide the applicant with the training necessary to achieve these aims. The training objectives will be
accomplished with the support of an outstanding mentorship team, Drs. Satterthwaite, Shinohara, Bassett, Bar-
Or, Fox, McCoy, and the world class resources of the University of Pennsylvania. Together, the proposed
scientific aims and training objectives will form the foundation for an independent research program that will use
techniques from computational psychiatry to understand depression in patients with medical comorbidities.
项目摘要/总结
项目成果
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