Investigating neural correlates in non-prodromal individuals at familial high-risk for psychotic and bipolar disorders: A multimodal MRI approach
Psychiatry Research - Neuroimaging, vol.360, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 360
- Publication Date: 2026
- Doi Number: 10.1016/j.pscychresns.2026.112228
- Journal Name: Psychiatry Research - Neuroimaging
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, MEDLINE, Psycinfo
- Keywords: Bipolar disorder, Familial high-risk, Graph theory, Multimodal MRI, Psychotic disorders, white matter
- Dokuz Eylül University Affiliated: Yes
Abstract
Neuroimaging studies in familial high-risk (FHR) individuals are vital for identifying vulnerability markers independent of overt illness. However, research on purely non-prodromal FHR cohorts using comparative multimodal approaches remains limited. This study addresses this gap through multimodal MRI analysis—including cortical morphometry, white matter microstructure, tractography, and functional connectivity—in non-prodromal FHR for psychosis (FHR-P, n = 18), bipolar disorder (FHR-BD, n = 19), and healthy controls (HC, n = 25). FHR-BD showed increased right inferior parietal surface area and right middle temporal volume compared to HC. Conversely, FHR-P exhibited reduced right superior frontal cortical thickness compared to FHR-BD and decreased left pallidum volume compared to HC. White matter analysis revealed significantly lower fractional anisotropy in FHR-P compared to both FHR-BD and HC. FHR-BD showed higher axial diffusivity than HC in the forceps minor, uncinate fasciculus, and right-fronto-occipital fasciculus. No significant differences were found in network-based statistics or graph theoretical measures. These findings reveal shared and distinct neurobiological alterations in non-prodromal FHR-P and FHR-BD, suggesting that grey and white matter disruptions constitute endophenotypes even without clinical symptoms. The lack of network-level findings may reflect the modest sample size, requiring further investigation in larger cohorts.