Summary: New Rutgers research shows that reduced dynamic flexibility of brain networks—what the authors call “flattened” network dynamics—not only differentiates individuals with psychiatric conditions from healthy controls, but also aligns far better with each person’s unique symptom profile than with conventional diagnostic categories. The results support a move toward personalized, biology-informed approaches in clinical neuroscience and precision psychiatry.
Key Facts
- Flattened network dynamics: People with psychiatric conditions exhibited markedly reduced flexibility in functional brain networks, indicating a diminished capacity for networks to reconfigure across changing cognitive demands.
- Symptom fingerprints outperform labels: Time-varying patterns of network reconfiguration correlated more strongly with individualized “symptom fingerprints” — multidimensional behavioral, cognitive, and clinical profiles — than with traditional categorical diagnoses.
- Transdiagnostic sample: The study analyzed 219 participants spanning 11 diagnostic categories, supporting RDoC-style efforts to map mental health across continua of biology and behavior.
- Six cognitive states measured: fMRI was collected during rest and multiple task conditions, enabling the team to track network reconfiguration across six distinct cognitive states rather than relying on static resting-state snapshots.
- Personalized biomarkers: Time-varying network dynamics emerged as a promising neurobiological marker for distinguishing clinical populations and guiding individualized interventions.
Source: Rutgers University
Reduced flexibility in brain network dynamics is linked to individual psychiatric symptom profiles, according to a new Rutgers study.
The human brain organizes into interacting networks that dynamically reconfigure to meet shifting cognitive, emotional, and environmental demands. This ongoing flexibility supports attention, problem solving, emotional regulation, and adaptive behavior. When those dynamic processes are disrupted, symptoms across psychiatric conditions may follow.
Published in Nature Communications, the study was led by Carrisa Cocuzza and Avram Holmes from Rutgers’ Robert Wood Johnson Medical School and the Rutgers Brain Health Institute. The team used a densely phenotyped, transdiagnostic dataset of 219 participants (134 with psychiatric diagnoses and 85 without) that included individuals across 11 diagnostic categories, paired with extensive behavioral, cognitive, and clinical assessments.
Participants completed both resting-state and task-based fMRI, permitting analysis of how functional connectomes reconfigured across six cognitive states. The researchers decomposed these connectomes to capture time-varying network organization, then used hierarchical clustering of 110 clinical, behavioral, and cognitive measures to construct participant-specific symptom profiles—or “symptom fingerprints.” That clustering revealed four core dimensions of functioning: internalizing, externalizing, cognitive, and social/reward.
Across these analyses, the team found that individuals with psychiatric conditions showed a consistent reduction in network flexibility. In other words, their brain networks tended to remain in more similar configurations across different tasks and resting states, reflecting a flattening of normally dynamic behavior.
Importantly, patterns of brain network dynamics were more closely aligned with each person’s multidimensional symptom fingerprint than with their categorical diagnosis or simple case/control status. This suggests that dynamic measures of brain function capture clinically meaningful variation in symptom expression that conventional diagnostic labels can miss.
Model comparisons highlighted a key role for interactions between frontoparietal control systems and inhibitory cognitive control processes in shaping these dynamics. The authors argue that assessing time-varying network behavior can therefore provide mechanistic insight into how cognitive control and network interactions contribute to symptom expression.
The findings have clear implications for the development of precision psychiatry. By tracking individualized neural dynamics, clinicians and researchers could better tailor interventions—behavioral, pharmacological, or neuromodulatory—to restore neural flexibility and monitor treatment effects at the single-person level.
Future research will be needed to determine how network dynamics change with longitudinal course and treatment, and how best to translate these biomarkers into clinical tools for assessment, prognosis, and intervention.
Key Questions Answered
A: In healthy brains, communication pathways among networks change depending on the task—resting, problem-solving, or regulating emotion. Reduced flexibility means these networks remain relatively rigid or stuck, failing to reconfigure appropriately across changing mental states and demands.
A: Categorical diagnoses group diverse individuals under broad labels that obscure important differences. Symptom fingerprints combine many behavioral and clinical measures into individualized profiles. The study shows brain dynamics align more closely with those personalized profiles than with diagnostic names, offering finer-grained and clinically relevant biological mapping.
A: If dynamic network measures reliably track symptom variation and respond to treatment, they can guide precision interventions—targeting specific network dysfunctions, monitoring recovery of neural flexibility, and personalizing treatment plans to each individual’s neural and behavioral profile.
Editorial Notes
- This article was edited by a Neuroscience News editor.
- The journal paper was reviewed in full by editorial staff.
- Additional context was added by the editorial team to clarify implications for clinical neuroscience.
About this research
Author: Tongyue Zhang
Source: Rutgers University
Contact: Tongyue Zhang – Rutgers
Image credit: Neuroscience News
Original research: Open access. “Brain network dynamics reflect psychiatric illness status and transdiagnostic symptom profiles across health and disease” by Carrisa V. Cocuzza, Sidhant Chopra, Ashlea Segal, Loïc Labache, Rowena Chin, Kaley Joss & Avram J. Holmes. Nature Communications. DOI: 10.1038/s41467-026-75585-6
Abstract
Brain network dynamics reflect psychiatric illness status and transdiagnostic symptom profiles across health and disease
The human brain’s network organization dynamically reconfigures in response to changing environmental demands — an adaptive process that may be disrupted in ways that relate to psychiatric symptoms. In a transdiagnostic sample including 134 participants with psychiatric diagnoses and 85 without, intrinsic and task-evoked fMRI connectomes were analyzed across six cognitive states. Hierarchical clustering of 110 clinical, behavioral, and cognitive measures produced participant-specific symptom profiles, revealing four core dimensions: internalizing, externalizing, cognitive, and social/reward. Individuals with psychiatric illness exhibited flattened network dynamics across cognitive states. These dynamic features differentiated dimensional symptom profiles more robustly than simple case/control status or primary diagnostic categories, with frontal-parietal control and inhibitory cognitive control interactions playing a central role. The results indicate that time-varying brain network dynamics can accurately reflect the degree to which psychiatrically relevant functional dimensions are expressed across health and disease.