Application of Resting-State fMRI in Auxiliary Diagnosis of Dissociative Disorders: Advances in Cerebral Biomarker Research and Clinical Implications
Transactions on Materials, Biotechnology and Life Sciences October 11, 2025 DOI: 10.62051/7gbv0a19 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Review Peer reviewed |
|---|---|
| Key findings | Argues that resting-state fMRI shows promise for identifying neurobiological markers of dissociative disorders, including default mode network dysregulation, regional connectivity alterations, and compensatory mechanisms, with potential applications in differential diagnosis, therapeutic target identification, and prognosis. Contends that progress is limited by sample heterogeneity, methodological variability, and insufficient causal inference, and proposes multimodal data integration, genetic-epigenetic correlates, and interventional validation as solutions. |
Abstract
Dissociative disorders (DDs), a group of trauma-related psychiatric conditions, are characterized by disruptions in consciousness, memory integration, identity coherence, and perceptual continuity. These disorders present significant diagnostic challenges due to symptom overlap with other psychiatric conditions and the lack of objective biomarkers. Resting-state functional magnetic resonance imaging (rs-fMRI), a non-invasive neuroimaging modality, has emerged as a transformative tool for investigating the neurobiological underpinnings of DDs. By capturing low-frequency oscillations (0.01–0.1 Hz) in spontaneous neural activity, rs-fMRI enables the identification of functional network abnormalities, including default mode network (DMN) dysregulation, regional connectivity alterations, and compensatory neural mechanisms. This review synthesizes recent advancements in rs-fMRI biomarker research, highlighting its applications in differential diagnosis, therapeutic target identification, and prognostic evaluation. Critical limitations—such as sample heterogeneity, methodological variability, and insufficient causal inference—are discussed, with proposed solutions emphasizing multimodal data integration, genetic-epigenetic correlates, and interventional validation. The review concludes with a roadmap for advancing precision psychiatry in DDs management through innovative neuroimaging frameworks.