To design network-based treatments, it is critical to understand dynamic changes in brain networks over time. We comprehensively examined network structure using regional activation-only, pairwise-coactivation-only, and systems-level maximum entropy models (MEM) applied to the Human Connectome Project-Early Psychosis (HCP-EP) resting fMRI data (patients = 109, controls = 56). The MEM integrates regional activation levels and pairwise correlation-based functional connectivity (FC), providing the potential for better characterization of clinically meaningful brain states and how they change over time. Using the HCP/Glasser atlas to define brain regions within the default mode (DMN) and dorsal attention (DAN) networks, group differences in regional activation, graph measures of FC, and MEM features such as transition rates between minima in an energy landscape along with correlations with cognitive and psychopathological measures were examined. Psychosis was associated with reduced activation in several regions and multiple FC graph metric alterations. MEM demonstrated a wider variety of DMN and DAN activation/deactivation configurations, with more frequent switching between them, more pronounced network configuration differences and higher basin transition rates with reduced basin dwell times suggesting temporally unstable network configurations, meaning the brain cannot rely on any particular configuration for cognitive processing. Activation of only three regions correlated with working memory, but none of the FC metrics did. MEM features, in particular basin transitions and total energy, correlated negatively with working memory. Our findings suggest that MEM provides unique information, specifically attenuated inter-network connectivity with reduced stability of DMN and DAN brain states, to characterize networks as targets and their features as markers for novel treatment development.
This study investigates resting-state EEG alpha-band connectivity in first-episode psychosis (FEP) using a novel dynamic connectivity pipeline and examines its relationship with cognitive functioning and psychopathological scores. Data from 78 individuals with FEP and 60 healthy controls (CTR) were analyzed. Source est...
Romain Aubonnet, Mahmoud Hassan, Paolo Gargiulo et al.· Brain Topography· 0 citations
It is suggested that subtle differences in higher-order functional brain network organization associated with metabolic syndrome can be detected during midlife and are related to cognitive functioning, highlighting the importance of prevention and intervention of cardiometabolic risk in midlife to preserve network inte...
Isabelle Gallagher, Makenna B. McGill, David M. Schnyer et al.· Frontiers in Aging Neuroscie...· 0 citations
BACKGROUND
Although structural and functional alterations in borderline personality disorder (BPD) have been widely reported in neuroimaging studies, the findings remain heterogeneous. Emerging consensus suggests that these distributed alterations may converge on a common pathological network associated with BPD, provi...
Le-Yi Zhang, Yi-Ding Han, Y. Ou et al.· Journal of Affective Disorde...· 0 citations
These findings show that psychiatric disorders are characterized by instability in brain network dynamics, with both shared and disorder-specific features, and align with frameworks emphasizing that psychopathology arises from graded disruptions in interacting neural systems rather than discrete diagnostic categories.
E. Agoalikum, B. Klugah-Brown, Hong-Zhou Wu et al.· BMC Psychiatry· 0 citations
It is demonstrated that nonlinear FC enhances sensitivity to PD-related network alterations, while spatial features highlight clinically relevant biomarkers and contributes to the methodological development of resting-state fMRI and its translational application to PD.
Charles Okanda Nyatega, Qiang Li, Wei-Zhi Nie et al.· Brain Topography· 0 citations
Increased functional alpha connectivity, simultaneous with elevated heart rate during FDS, suggests that FDSs involve altered brain network dynamics rather than simple alpha power suppression by voluntary motor activity.
T. Dubček, Meret Küenzler, M. Schmutz et al.· Epilepsy & Behavior· 0 citations
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