Thursday, November 21, 2024
11/21/2024
Abstract/Summary: Attention deficit (AD) is a reported concern across mental health and neurological disorders. It exists as an extreme condition of a continuously distributed trait in the general population. AD as a key component of ADHD is often associated with impairments in multiple neurocognitive domains, particularly in attention/vigilance, working memory, processing speed, and response variability. To date, most investigations on AD focus on frontal-parietal circuity, and less is known about the frontal-thalamo-cerebellar circuitry (FCC) relates to AD. To extend our knowledge on neural mechanisms of AD, this study aims to delineate FCC alteration in relation to neurocognition and AD symptoms, leveraging longitudinal brain imaging data, genomics, neurocognition, environmental data in ABCD cohort. First, in Aim 1 we will apply advanced deep learning algorithms to model the relationship between multimodal brain image data in FCC (gray matter, white matter, rest state fMRI functional connectivity, and emotional N-back task fMRI activation) and neurocognitive measures in the four domains (attention/vigilance, working memory, processing speed, and response variability) at baseline. And then we will apply the transfer learning techniques to the latent neuroimaging features underlying neurocognition to estimate AD. Then, in Aim 2 we will focus on the relation between longitudinal changes of FCC neuroimaging features and the changes in neurocognition and AD in two years. We will apply advanced machine learning methods just as in Aim 1 to identify FCC dynamic features underlying longitudinal changes in neurocognition, and then transfer to AD. AD symptoms and symptom changes are also affected by genetic profiles and environmental factors. In Aim 3 we will apply multivariate data mining algorithms to extract genetic factors associated with FCC neuroimaging features, and build a prediction model for AD and changes of AD using genetic factors extracted, social demographic and environmental factors, in addition to FCC multimodal neuroimaging features. Lastly, in Aim 4 we will validate the FCC-genetic-environmental-AD model using Year 4 follow up data in ABCD cohort and validate the FCC-genetic-AD model using an independent PNC cohort. The findings from this study will specify alterations in crucial regions of FCC underlying each neurocognition domain and contribution of each neurocognition domain to AD symptoms mediated by FCC neuronal features. Brain, gene, and environmental model of AD will help identify a subpopulation with risk for AD due to FCC alterations in the general population, and help specify patients across the boundaries of mental disorders who are risk for worsening AD due to FCC abnormalities.
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