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Updated: Jul 16, 2025

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia
Published on: December 2, 2015
Abnormal temporal variability of rich-club organization in three major psychiatric conditions
Meng Niu1,2,3, Hanning Guo4, Zhe Zhang5,6
1Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China.
Introduction:
Convergent evidence has demonstrated a shared rich-club reorganization across multiple major psychiatric conditions. However, previous studies assessing altered functional couplings between rich-club regions have typically focused on the mean time series from entire functional magnetic resonance imaging (fMRI) scanning session, neglecting their time-varying properties.
Methods:
In this study, we aim to explore the common and/or unique alterations in the temporal variability of rich-club organization among schizophrenia (SZ), bipolar disorder (BD), and attention deficit/hyperactivity disorder (ADHD). We employed a temporal rich-club (TRC) approach to quantitatively assess the propensity of well-connected nodes to form simultaneous and stable structures in a temporal network derived from resting-state fMRI data of 156 patients with major psychiatric disorders (SZ/BD/ADHD = 71/45/40) and 172 healthy controls. We executed the TRC workflow at both whole-brain and subnetwork scales across varying network sparsity, sliding window strategies, lengths and steps of sliding windows, and durations of TRC coefficients.
Results:
The SZ and BD groups displayed significantly decreased TRC coefficients compared to corresponding HC groups at the whole-brain scale and in most subnetworks. In contrast, the ADHD group exhibited reduced TRC coefficients in longer durations, as opposed to shorter durations, which markedly differs from the SZ and BD groups. These findings reveal both transdiagnostic and illness-specific patterns in temporal variability of rich-club organization across SZ, BD, and ADHD.
Discussion:
TRC may serve as an effective metric for detecting brain network disruptions in particular states, offering novel insights and potential biomarkers into the neurobiological basis underpinning the behavioral and cognitive deficits observed in these disorders.
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