Dhivya Srinivasan
Overview
Explore the profile of Dhivya Srinivasan including associated specialties, affiliations and a list of published articles.
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Articles
26
Citations
705
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Recent Articles
1.
Antoniades M, Srinivasan D, Wen J, Erus G, Abdulkadir A, Mamourian E, et al.
EBioMedicine
. 2024 Oct;
109():105399.
PMID: 39437659
Background: Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals,...
2.
Wen J, Yang Z, Nasrallah I, Cui Y, Erus G, Srinivasan D, et al.
Transl Psychiatry
. 2024 Oct;
14(1):420.
PMID: 39368996
Alzheimer's disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy...
3.
Yang Z, Wen J, Erus G, Govindarajan S, Melhem R, Mamourian E, et al.
Nat Med
. 2024 Aug;
30(10):3015-3026.
PMID: 39147830
Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been...
4.
Skampardoni I, Nasrallah I, Abdulkadir A, Wen J, Melhem R, Mamourian E, et al.
JAMA Psychiatry
. 2024 Feb;
81(5):456-467.
PMID: 38353984
Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases....
5.
Yang Z, Wen J, Erus G, Govindarajan S, Melhem R, Mamourian E, et al.
medRxiv
. 2024 Jan;
PMID: 38234857
Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have...
6.
Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, et al.
Nat Commun
. 2024 Jan;
15(1):354.
PMID: 38191573
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting...
7.
Wen J, Nasrallah I, Abdulkadir A, Satterthwaite T, Yang Z, Erus G, et al.
Proc Natl Acad Sci U S A
. 2023 Dec;
120(52):e2300842120.
PMID: 38127979
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases....
8.
Dwyer D, Chand G, Pigoni A, Khuntia A, Wen J, Antoniades M, et al.
Mol Psychiatry
. 2023 May;
28(5):2008-2017.
PMID: 37147389
Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with...
9.
Hwang G, Wen J, Sotardi S, Brodkin E, Chand G, Dwyer D, et al.
JAMA Psychiatry
. 2023 Apr;
80(5):498-507.
PMID: 37017948
Importance: Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. Objective: To assess distinct neuroanatomical dimensions of ASD using novel...
10.
Zhou Z, Srinivasan D, Li H, Abdulkadir A, Shou H, Davatzikos C, et al.
Proc SPIE Int Soc Opt Eng
. 2023 Feb;
12036.
PMID: 36845412
Brain age prediction based on functional magnetic resonance imaging (fMRI) data has the potential to serve as a biomarker for quantifying brain health. To predict the brain age based on...