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Peer-reviewedCC BY 4.0PLOS One

Energy landscape and dynamic state modeling for schizophrenia biomarker extraction in task-based fMRI

  • Janerra D. Allen1
  • Elliot Hong2
  • Fow-Sen Choa3
  1. 1Johns Hopkins University Applied Physics Lab, Laurel, Maryland, United States of America
  2. 2Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland, United States of America
  3. 3Department of Psychiatry and Behavioral Sciences, UT Health, Houston, Texas, United States of America
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Abstract

Task-based functional magnetic resonance imaging (fMRI) examines how the brain dynamically responds to cognitive and perceptual demands, offering complementary insight beyond traditional activation-based analyses in schizophrenia. Prior task-based fMRI studies have identified reduced functional connectivity within auditory and associated cortical areas. In this study, we investigated task-evoked functional connectivity and brain state dynamics in 25 healthy controls, 23 patients with schizophrenia experiencing auditory verbal hallucinations (AVH+), and 23 patients without hallucinations (AVH-). Participants completed multiple auditory paradigms, including word lists, sentence lists, and reverse speech. The most robust connectivity differences emerged during the word list task, where cluster-level analyses revealed distinct network interaction patterns differentiating controls, AVH + , and AVH-patients. Energy landscape modeling further demonstrated altered stability and organization of brain states in both patient groups, with the largest deviations observed in AVH+ individuals. These alterations point to dysregulated neural dynamics linked to hallucination vulnerability. Overall, the results show that task-based fMRI sensitively captures disruptions in functional connectivity and brain state stability relevant to auditory hallucinations, underscoring its value for characterizing network-level mechanisms underlying perceptual disturbances in schizophrenia.

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Cite

BibTeXRIS
BibTeX
@article{allen2026energy,
  title = {{Energy landscape and dynamic state modeling for schizophrenia biomarker extraction in task-based fMRI}},
  author = {Janerra D. Allen and Elliot Hong and Fow-Sen Choa},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0339756},
  url = {https://doi.org/10.1371/journal.pone.0339756},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Energy landscape and dynamic state modeling for schizophrenia biomarker extraction in task-based fMRI
AU  - Janerra D. Allen
AU  - Elliot Hong
AU  - Fow-Sen Choa
PY  - 2026
DA  - 2026/10/08
JO  - PLOS One
DO  - 10.1371/journal.pone.0339756
UR  - https://doi.org/10.1371/journal.pone.0339756
AB  - Task-based functional magnetic resonance imaging (fMRI) examines how the brain dynamically responds to cognitive and perceptual demands, offering complementary insight beyond traditional activation-based analyses in schizophrenia. Prior task-based fMRI studies have identified reduced functional connectivity within auditory and associated cortical areas. In this study, we investigated task-evoked functional connectivity and brain state dynamics in 25 healthy controls, 23 patients with schizophrenia experiencing auditory verbal hallucinations (AVH+), and 23 patients without hallucinations (AVH-). Participants completed multiple auditory paradigms, including word lists, sentence lists, and reverse speech. The most robust connectivity differences emerged during the word list task, where cluster-level analyses revealed distinct network interaction patterns differentiating controls, AVH + , and AVH-patients. Energy landscape modeling further demonstrated altered stability and organization of brain states in both patient groups, with the largest deviations observed in AVH+ individuals. These alterations point to dysregulated neural dynamics linked to hallucination vulnerability. Overall, the results show that task-based fMRI sensitively captures disruptions in functional connectivity and brain state stability relevant to auditory hallucinations, underscoring its value for characterizing network-level mechanisms underlying perceptual disturbances in schizophrenia.
SN  - 1932-6203
ER  -