|Replicability of time-varying connectivity patterns in large resting state fMRI samples|
A Abrol, E Damaraju, RL Miller, JM Stephen, ED Claus, AR Mayer, ...
Neuroimage 163, 160-176, 2017
|NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders|
Y Du, Z Fu, J Sui, S Gao, Y Xing, D Lin, M Salman, A Abrol, MA Rahaman, ...
NeuroImage: Clinical 28, 102375, 2020
|Deep residual learning for neuroimaging: an application to predict progression to Alzheimer’s disease|
A Abrol, M Bhattarai, A Fedorov, Y Du, S Plis, V Calhoun, ...
Journal of neuroscience methods 339, 108701, 2020
|Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning|
A Abrol, Z Fu, M Salman, R Silva, Y Du, S Plis, V Calhoun
Nature communications 12 (1), 1-17, 2021
|The chronnectome: Evaluating replicability of dynamic connectivity patterns in 7500 resting fMRI datasets|
A Abrol, C Chaze, E Damaraju, VD Calhoun
2016 38th Annual International Conference of the IEEE Engineering in …, 2016
|Resting-state fMRI dynamics and null models: perspectives, sampling variability, and simulations|
RL Miller, A Abrol, T Adali, Y Levin-Schwarz, VD Calhoun
Frontiers in neuroscience 12, 551, 2018
|Determining the number of states in dynamic functional connectivity using cluster validity indexes|
VM Vergara, M Salman, A Abrol, FA Espinoza, VD Calhoun
Journal of neuroscience methods 337, 108651, 2020
|An average sliding window correlation method for dynamic functional connectivity|
VM Vergara, A Abrol, VD Calhoun
Human brain mapping 40 (7), 2089-2103, 2019
|Schizophrenia shows disrupted links between brain volume and dynamic functional connectivity|
A Abrol, B Rashid, S Rachakonda, E Damaraju, VD Calhoun
Frontiers in neuroscience 11, 624, 2017
|Age‐related structural and functional variations in 5,967 individuals across the adult lifespan|
N Luo, J Sui, A Abrol, D Lin, J Chen, VM Vergara, Z Fu, Y Du, E Damaraju, ...
Human brain mapping 41 (7), 1725-1737, 2020
|Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer’s Disease Progression *|
A Abrol, Z Fu, Y Du, VD Calhoun
2019 41st Annual International Conference of the IEEE Engineering in …, 2019
|Prediction of progression to Alzheimer's disease with deep infomax|
A Fedorov, RD Hjelm, A Abrol, Z Fu, Y Du, S Plis, VD Calhoun
2019 IEEE EMBS International conference on biomedical & health informatics …, 2019
|Structural brain architectures match intrinsic functional networks and vary across domains: a study from 15 000+ individuals|
N Luo, J Sui, A Abrol, J Chen, JA Turner, E Damaraju, Z Fu, L Fan, D Lin, ...
Cerebral Cortex 30 (10), 5460-5470, 2020
|Decentralized temporal independent component analysis: leveraging fMRI data in collaborative settings|
BT Baker, A Abrol, RF Silva, E Damaraju, AD Sarwate, VD Calhoun, ...
NeuroImage 186, 557-569, 2019
|A classification-based approach to estimate the number of resting functional magnetic resonance imaging dynamic functional connectivity states|
DK Saha, E Damaraju, B Rashid, A Abrol, SM Plis, VD Calhoun
Brain connectivity 11 (2), 132-145, 2021
|Addressing inaccurate nosology in mental health: A multilabel data cleansing approach for detecting label noise from structural magnetic resonance imaging data in mood and …|
H Rokham, G Pearlson, A Abrol, H Falakshahi, S Plis, VD Calhoun
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 5 (8), 819-832, 2020
|A phase II study repurposing atomoxetine for neuroprotection in mild cognitive impairment|
AI Levey, D Qiu, L Zhao, WT Hu, DM Duong, L Higginbotham, ...
Brain, doi.org/10.1093/brain/awab452, 2021
|Diagnostic and Prognostic Classification of Brain Disorders Using Residual Learning on Structural MRI Data*|
A Abrol, H Rokham, VD Calhoun
2019 41St annual international conference of the IEEE engineering in …, 2019
|Classification as a criterion to select model order for dynamic functional connectivity states in rest-fMRI data|
DK Saha, A Abrol, E Damaraju, B Rashid, SM Plis, VD Calhoun
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019
|Deep learning in neuroimaging: Promises and challenges|
W Yan, G Qu, W Hu, A Abrol, B Cai, C Qiao, SM Plis, YP Wang, J Sui, ...
IEEE Signal Processing Magazine 39 (2), 87-98, 2022