Temporal stability of functional network configurations and its relationship to cognition and psychopathology in early psychoses
Theis N, Rubin J, O'Rourke E, Bahuguna J, Cape J, Ouyang B, Iyengar S, Prasad K.
Neuropsychopharmacology, 2026
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From microstates to macroscales: A critical review of maximum entropy modeling and energy landscape analysis in functional MRI
Prasad K, Ouyang B, Theis N
Neuroscience & Biobehavioral Reviews, 2026
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Independence-based causal discovery analysis reveals statistically non-significant regions to be functionally significant
M. Lewis, S. Eack, N. Theis, M. S. Keshavan and K. M. Prasad
bioRxiv (under peer review), 2025
FHR and HC directed and undirected connections with α-centrality regions. Blue indicates a silent region, green indicates
an active region, red arrow is a causal connection, black line is an undirected connection
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Leveraging fMRI non-stationarity for deep learning classifier training and feature detection to improve schizophrenia diagnosis
Madison Lewis, Nicholas, Bowei Ouyang, Konasale Prasad.
medRxiv (under peer review), 2025
Brain regions that have high "Class Activation Mapping" (CAM) values are informative for AI-assisted diagnosis.
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Determining the Atlas Correspondence of Desikan-Killiany-Tourville and Glasser MMP1 Atlases across Magnetic Field Strengths
Madison Lewis, Nicholas Theis, Nidhi Girish, Konasale Prasad
Journal of Neuroscience Methods, 2025
Volume Coefficient of Variation: Regional volume coefficient of variation (CoV) across subjects shown spatially for 1.5 T (left), 3 T (middle), and 7 T (right) samples. A. the Glasser atlas and B. the DKT atlas. Red indicates high CoV and dark blue indicates low CoV in each region.
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Energy of functional brain states correlates with cognition in adolescent-onset schizophrenia and healthy persons
Nicholas Theis, Jyotika Bahuguna, Jonathan E. Rubin, Shubha Sankar Banerjee, Brendan Muldoon, and Konasale M. Prasad
Human Brain Mapping, 2025
A schematic outline of the experimental design. One sample of nodes is pictured, with only four nodes. (A) Four example components from GICA are randomly selected. (B) An example time series for these nodes for a single subject. (C) The four regions are binarized and concatenated across all subjects. Next, an MEM is fit to the data, using the binarized components as the nodal timeseries used to optimize the model parameters. (D) The MEM assigns energy values to each brain state, and these values are summed per individual and compared performance on the PCET. The relationship between total energy and cognitive performance across subjects is the main variable of interest compared across node samples. (E) For one sample, an energy landscape analysis is performed to study differences between the group-level trajectories on the population level MEM.
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Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes
Madison Lewis, Wenlong Jiang, Nicholas D. Theis, Joshua Cape, and Konasale M. Prasad
Neural Networks, 2025
Pipeline used to classify preprocessed structural and images; GCNs were used to classify psychosis from controls.
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Leveraging ultra-high field (7T) MRI in psychiatric research
Finnegan J Calabro, Ashley C Parr, Valerie J Sydnor, Hoby Hetherington, Konasale M Prasad, Tamer S Ibrahim, Deepak K Sarpal, Alyssa Famalette, Piya Verma, Beatriz Luna
Neuropsychopharmacology, 2024
An image showing the improved resolution associated with 7T.
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Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses
Madison Lewis, Tales Santini, Nicholas Theis, Brendan Muldoon, Katherine Dash, Jonathan Rubin, Matcheri Keshavan, Konasale Prasad
Scientific Reports, 2023
Results of simulated attacks on the nodes based on BC ranking.
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Multipronged investigation of morphometry and connectivity of hippocampal network in relation to risk for psychosis using ultrahigh field MRI
Konasale M Prasad, Brendan Muldoon, Nicholas Theis, Satish Iyengar, Matcheri S Keshavan
Schizophrenia Research, 2023
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Threshold Selection for Brain Connectomes
Nicholas Theis, Jonathan Rubin, Joshua Cape, Satish Iyengar, Konasale M Prasad
Brain Connectivity, 2023
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Structural covariance networks in schizophrenia: A systematic review Part I
Konasale Prasad, Jonathan Rubin, Anirban Mitra, Madison Lewis, Nicholas Theis, Brendan Muldoon, Satish Iyengar, Joshua Cape
Schizophrenia Research, 2022
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Structural covariance networks in schizophrenia: A systematic review Part II
Konasale Prasad, Jonathan Rubin, Anirban Mitra, Madison Lewis, Nicholas Theis, Brendan Muldoon, Satish Iyengar, Joshua Cape
Schizophrenia Research, 2022
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Heritable anisotropy associated with cognitive impairments among patients with schizophrenia and their non-psychotic relatives in multiplex families
K M Prasad, J Gertler, S Tollefson, J A Wood, D Roalf, R C Gur, R E Gur, L Almasy, M F Pogue-Geile, V L Nimgaonkar
Psychological Medicine, 2022
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Neuroimaging in Schizophrenia
Matcheri S Keshavan, Guusje Collin, Synthia Guimond, Sinead Kelly, Konasale M Prasad, Paulo Lizano
Neuroimaging Clinics of North America, 2020
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For a full list of Dr. Prasad's publications, visit his PubMed page.