M
Mohammad R. Arbabshirani
Researcher at Geisinger Health System
Publications - 31
Citations - 2528
Mohammad R. Arbabshirani is an academic researcher from Geisinger Health System. The author has contributed to research in topics: Resting state fMRI & Deep learning. The author has an hindex of 19, co-authored 31 publications receiving 1913 citations. Previous affiliations of Mohammad R. Arbabshirani include The Mind Research Network & Lovelace Respiratory Research Institute.
Papers
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Journal ArticleDOI
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
TL;DR: There is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders, however, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper.
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Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity.
Barnaly Rashid,Mohammad R. Arbabshirani,Eswar Damaraju,Eswar Damaraju,Mustafa S. Çetin,Robyn L. Miller,Godfrey D. Pearlson,Vince D. Calhoun +7 more
TL;DR: This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features, and compares cross-validated classification performance between static andynamic FNC.
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Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration.
Mohammad R. Arbabshirani,Brandon K. Fornwalt,Gino J. Mongelluzzo,Jonathan D. Suever,Brandon D. Geise,Aalpen A. Patel,Gregory J. Moore +6 more
TL;DR: An artificial intelligence algorithm can prioritize radiology worklists to reduce time to diagnosis of new outpatient ICH by 96% and may also identify subtle ICH overlooked by radiologists, demonstrating the positive impact of advanced machine learning in radiology workflow optimization.
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Classification of schizophrenia patients based on resting-state functional network connectivity
Mohammad R. Arbabshirani,Mohammad R. Arbabshirani,Kent A. Kiehl,Kent A. Kiehl,Godfrey D. Pearlson,Vince D. Calhoun,Vince D. Calhoun,Vince D. Calhoun +7 more
TL;DR: These effects represent the first use of resting-state functional network connectivity (FNC) features to classify schizophrenia and show that classification with high accuracy is achievable using simple non-linear discriminative methods such as k-nearest neighbors (KNNs) which is very promising.
Journal ArticleDOI
Brain connectivity networks in schizophrenia underlying resting state functional magnetic resonance imaging
Qingbao Yu,Elena A. Allen,Elena A. Allen,Jing Sui,Mohammad R. Arbabshirani,Mohammad R. Arbabshirani,Godfrey D. Pearlson,Vince D. Calhoun +7 more
TL;DR: Overall, the resting-state findings regarding brain networks deficits have advanced the understanding of the underlying pathology of SZ, and future challenges are discussed.