The Development of Individual Differences in Adolescent Brain Structure and Risk
青少年大脑结构和风险的个体差异的发展
基本信息
- 批准号:10412438
- 负责人:
- 金额:$ 28.67万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-04-01 至 2026-03-31
- 项目状态:未结题
- 来源:
- 关键词:10 year old16 year oldAdolescenceAdolescentAgeAnxietyAreaAttentionBehaviorBehavior assessmentBirthBrainChildCognitionCognitiveDataData SetDatabasesDevelopmentEnrollmentFailureFiberGenerationsImageIndividual DifferencesLifeLongitudinal StudiesMachine LearningMagnetic Resonance ImagingMeasuresMental disordersMethodsModelingNational Institute of Mental HealthNetwork-basedParticipantPhenotypeProcessPropertyPubertyReadinessResearch Domain CriteriaResearch PersonnelResourcesRiskSample SizeStructureStudy SubjectSurfaceThickTimeTrainingVisitbasebehavioral outcomecognitive testingcohortconvolutional neural networkdata archivedeep learningdetection methoddisorder riskearly adolescenceexecutive functionimage processingimage reconstructionimaging modalityimprovedinnovationmachine learning algorithmmachine learning methodmultimodalitypostnatal periodpredictive modelingpredictive testprenatalrepositoryvisual processingwhite matter
项目摘要
Rescuing Missed Longitudinal MRI visits in the UNC Early Brain Development
Studies Database
PROJECT ABSTRACT
In our ongoing R01 (MH123747-01A1) “The Development of Individual Differences in Adolescent Brain
Structure and Risk”) project, we aim to characterize the portion of individual differences in brain structure in the
early adolescent brain is already present in the earlier years of life. Early adolescence and puberty is a major
period of postnatal brain development, characterized by dynamic structural and functional brain maturation and
reorganization, and emerging risk for psychiatric disorders, though it is not known how this period of development
contributes to individual differences in brain structure and risk. The UNC Early Brain Development Study (EBDS)
is a unique and innovative longitudinal study that has followed children, enrolled prenatally, with imaging and
cognitive/behavioral assessments at birth, 1, 2, 4, 6, 8, and 10 years. 482 children from this cohort are now
reaching adolescence, and we are following these children at 12, 14, and 16 years of age via MRI, cognitive and
behavioral assessments, with a focus on the phenotypes of executive function, attention, and anxiety, consistent
with RDoC constructs important for psychiatric disorder risk. One particular aim is to investigate the use of
machine learning (ML) for the predictive analysis of early brain development to cognitive and behavioral
outcomes in adolescence and to risk for subsequent psychiatric disorders. Yet, most machine learning (ML)
algorithms applied to longitudinal data do not perform well (or at all) when data points are missing, as ML
methods need both complete data and large sample sizes. As longitudinal studies suffer commonly from
significant missing data at different time points due to acquisition failure as well as participant attrition, even a
rich database like the UNC EBDS is reduced to a significantly lower sample size by selecting only complete
datasets to apply predictive ML (less than a third of the datasets of EBDS data from age 1 – 10 years is complete).
Here, we propose to rescue missing EBDS timepoints (at ages 1 - 10 yrs) of structural MR image data via
multi-modal, multi-timepoints image predictions. This image data imputation includes cross-modality image
generation (generating missing MRI data from existing MRI data at the same time), where available, as well as
multi-timepoints imputation of longitudinal data (generating missing MRI data from existing MRI data at different
time points). We will then apply our out-of-distribution model to provide additional information on the
appropriateness of the imputed data. Subsequently, the same image processing that was applied to the original
EBDS MRI data will be applied to the imputed/generated MRI data to compute missing information of
morphometric measures (regional volumes, cortical thickness, surface area, and white matter fiber tract
properties). This imputed data will be a highly significant resource for longitudinal ML/AI studies of brain
development performed on the EBDS dataset, as it would allow for an increase in training data of over 200%.
The original MR images, the imputed MR images, and the morphometric measures will all be shared via NDA,
alongside the trained imputation network for use by others.
在UNC早期脑发育中抢救错过的纵向MRI检查
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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JOHN Horace GILMORE其他文献
JOHN Horace GILMORE的其他文献
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{{ truncateString('JOHN Horace GILMORE', 18)}}的其他基金
The Development of Individual Differences in Adolescent Brain Structure and Risk
青少年大脑结构和风险的个体差异的发展
- 批准号:
10596162 - 财政年份:2021
- 资助金额:
$ 28.67万 - 项目类别:
The Development of Individual Differences in Adolescent Brain Structure and Risk
青少年大脑结构和风险的个体差异的发展
- 批准号:
10376251 - 财政年份:2021
- 资助金额:
$ 28.67万 - 项目类别:
The Development of Individual Differences in Adolescent Brain Structure and Risk
青少年大脑结构和风险的个体差异的发展
- 批准号:
10206731 - 财政年份:2021
- 资助金额:
$ 28.67万 - 项目类别:
1/5, HEAL Consortium: Establishing Innovative Approaches for the HEALthy Brain and Child Development Study
1/5,HEAL 联盟:建立健康大脑和儿童发展研究的创新方法
- 批准号:
10018225 - 财政年份:2019
- 资助金额:
$ 28.67万 - 项目类别:
1/5, HEAL Consortium: Establishing Innovative Approaches for the HEALthy Brain and Child Development Study
1/5,HEAL 联盟:建立健康大脑和儿童发展研究的创新方法
- 批准号:
9900350 - 财政年份:2019
- 资助金额:
$ 28.67万 - 项目类别:
The Origins of Preadolescent Risk for Psychiatric Disorders in Early Childhood Brain Development
儿童早期大脑发育中青春期前精神疾病风险的根源
- 批准号:
10176261 - 财政年份:2017
- 资助金额:
$ 28.67万 - 项目类别:
The Origins of Preadolescent Risk for Psychiatric Disorders in Early Childhood Brain Development
儿童早期大脑发育中青春期前精神疾病风险的根源
- 批准号:
9383608 - 财政年份:2017
- 资助金额:
$ 28.67万 - 项目类别:
Prospective Studies of the Pathogenesis of Schizophrenia
精神分裂症发病机制的前瞻性研究
- 批准号:
8061034 - 财政年份:2010
- 资助金额:
$ 28.67万 - 项目类别:
PROSPECTIVE STUDIES OF THE PATHOGENESIS OF SCHIZOPHRENIA
精神分裂症发病机制的前瞻性研究
- 批准号:
8171047 - 财政年份:2010
- 资助金额:
$ 28.67万 - 项目类别:
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