Rutgers researchers found that changes in brain network dynamics were more closely linked to individual symptom profiles than traditional psychiatric diagnoses.

Brain network dynamics may provide a more precise way of understanding individual psychiatric symptoms than traditional diagnostic categories, according to research from Rutgers University.

The study, published in Nature Communications, found that people with psychiatric conditions showed reduced flexibility in how their brain networks reconfigured over time. The researchers also found that patterns of dynamic brain function were more strongly associated with individual symptom profiles than with conventional diagnostic labels.

The findings could support more personalised approaches to understanding and assessing mental health by focusing on how symptoms manifest in individuals rather than relying primarily on whether a person meets the criteria for a particular psychiatric diagnosis.

The study was led by Carrisa Cocuzza, a postdoctoral fellow, and Avram Holmes, associate professor of psychiatry at Robert Wood Johnson Medical School and core faculty member of the Center for Advanced Human Brain Imaging Research within the Rutgers Brain Health Institute.

The researchers analysed data from 219 people, including individuals with 11 psychiatric diagnoses, alongside extensive behavioural, cognitive and clinical assessments.

Participants underwent magnetic resonance imaging scans both while at rest and while performing tasks. This allowed the researchers to examine how patterns of brain connectivity changed across six different cognitive states.

The team then characterised how brain networks reconfigured over time and grouped behavioural and clinical measurements to create individual symptom profiles, described by the researchers as “symptom fingerprints”.

Individuals with psychiatric conditions showed reduced flexibility in their brain network dynamics compared with healthy participants.

“Their brain networks were less able to shift between different configurations across cognitive states compared to healthy individuals,” Cocuzza said.

The researchers found that these less flexible patterns of brain network activity could distinguish people with psychiatric conditions from healthy participants and could also help identify specific diagnostic categories.

However, the study’s findings suggested that the relationship between dynamic brain function and individual symptoms may extend beyond traditional diagnostic boundaries.

Patterns of brain network dynamics were more strongly related to individual symptom fingerprints than to conventional psychiatric diagnoses. This suggests that dynamic features of brain function may capture differences in how mental health symptoms are experienced and expressed across individuals, even when those individuals have different diagnoses.

“This work addresses a central challenge in clinical neuroscience, linking changes in brain function to the diverse symptoms experienced across psychiatric disorders,” Holmes said.

He added: “The findings indicate that impairments in time-varying brain processes may underlie differences in cognitive and behavioral functioning across individuals.”

The researchers said the findings support a move towards more personalised, biology-informed models of mental health that consider individual symptom patterns alongside traditional diagnostic categories.

The study does not establish that reduced brain network flexibility causes psychiatric conditions or determine why these dynamic changes occur. Instead, it identifies an association between altered brain network dynamics and psychiatric conditions and suggests that these measures may be useful for understanding variation in symptoms between individuals.

Future research will examine how brain network dynamics change over time and in response to interventions, with the aim of developing more precise and individualised approaches to assessing and supporting mental health.