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<p>Resting-state functional magnetic resonance imaging (rs-fMRI) has offered insights into the neural mechanisms underlying psychosis, particularly when associated with clinically relevant features. 102 individuals at ultra-high risk for psychosis (UHR) and 105 matched healthy controls (HC) aged 18–40 underwent clinical and cognitive assessments and rs-fMRI at baseline. Using a recently developed prediction-based extension of the network-based statistics (NBS-predict), incorporating nested cross-validation, we tested the predictive power of functional connectivity estimated from rs-fMRI data, investigating diagnostic classification and prediction of level of functioning, estimated IQ, and UHR-symptoms. Hyper-connectivity predicted group with a classification accuracy of 0.58, <i>p</i> = 0.043, and hypo-connectivity predicted group with a classification accuracy of 0.59, <i>p</i> = 0.018. Hyper-connectivity in UHR-individuals was observed primarily in interhemispheric and cortico-thalamic connections, within networks that predicted poorer levels of functioning across groups. Hypo-connectivity in UHR-individuals was observed mainly in thalamic connections with posterior cingulate cortex, frontal medial, and precuneus, within networks that predicted higher level of functioning across groups. Post hoc analyses identified a significant groupwise interaction effect on the association between functional connectivity and level of functioning (<i>ρ</i> = 0.34, <i>p</i> &lt; 0.001), with main nodes in the frontal medial regions connected across hemispheres. Within-group, no connections predicted level of functioning or UHR-symptoms. Whole-brain functional connectivity predicted UHR-status in hyper- and hypo-connected networks, with thalamus as a central integrative hub across networks. Connections that predicted level of functioning across groups were equivalent to the connections predicting UHR-status, hence capturing a neural correlate to a key clinical component of the UHR-status.</p>

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Whole-brain functional connectivity predicts ultra-high risk for psychosis status and level of functioning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月6日

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  • Karen S. Ambrosen,
  • Tina D. Kristensen,
  • Louise B. Glenthøj,
  • Merete Nordentoft,
  • Birte Y. Glenthøj,
  • Anita D. Barber,
  • Bjørn H. Ebdrup

摘要

Resting-state functional magnetic resonance imaging (rs-fMRI) has offered insights into the neural mechanisms underlying psychosis, particularly when associated with clinically relevant features. 102 individuals at ultra-high risk for psychosis (UHR) and 105 matched healthy controls (HC) aged 18–40 underwent clinical and cognitive assessments and rs-fMRI at baseline. Using a recently developed prediction-based extension of the network-based statistics (NBS-predict), incorporating nested cross-validation, we tested the predictive power of functional connectivity estimated from rs-fMRI data, investigating diagnostic classification and prediction of level of functioning, estimated IQ, and UHR-symptoms. Hyper-connectivity predicted group with a classification accuracy of 0.58, p = 0.043, and hypo-connectivity predicted group with a classification accuracy of 0.59, p = 0.018. Hyper-connectivity in UHR-individuals was observed primarily in interhemispheric and cortico-thalamic connections, within networks that predicted poorer levels of functioning across groups. Hypo-connectivity in UHR-individuals was observed mainly in thalamic connections with posterior cingulate cortex, frontal medial, and precuneus, within networks that predicted higher level of functioning across groups. Post hoc analyses identified a significant groupwise interaction effect on the association between functional connectivity and level of functioning (ρ = 0.34, p < 0.001), with main nodes in the frontal medial regions connected across hemispheres. Within-group, no connections predicted level of functioning or UHR-symptoms. Whole-brain functional connectivity predicted UHR-status in hyper- and hypo-connected networks, with thalamus as a central integrative hub across networks. Connections that predicted level of functioning across groups were equivalent to the connections predicting UHR-status, hence capturing a neural correlate to a key clinical component of the UHR-status.