Scientists have discovered regional differences in the functional brain networks and circuits typical of different mental illnesses by analyzing thousands of brain samples.
This international collaborative effort, spearheaded by researchers from Monash University in Australia and Radboud University in the Netherlands, challenges widely held beliefs that certain disorders stem from the dysfunction of hallmark neural systems specifically targeted by the disease process. The results highlight the necessity of considering the network context of pathophysiology-related disorder markers and show that the impact of primary pathology on distant, linked systems also plays a role in the clinical manifestation of disease.
The article, “Regional, circuit, and network heterogeneity of brain abnormalities in psychiatric disorders,” was published in Nature Neuroscience.
Numerous neuroimaging studies have revealed links between brain changes and psychiatric diagnoses. In addition, meta-analyses have identified the brain regions most consistently affected by each condition, showing both diagnosis-specific and cross-diagnosis effects. Nevertheless, pathophysiological mechanisms are still poorly understood, and no biomarkers are useful in clinical settings. Extreme regional variation in individual brain deviations is consistent with the well-documented clinical heterogeneity frequently linked to particular psychiatric diagnoses. Still, it also warrants a key question: if there is minimal anatomical overlap in the locations of gray matter volume (GMV) deviations, how can phenotypic similarities between individuals with the same diagnosis be explained?
Lead author Ashlea Segal, a research assistant at the Turner Institute for Brain and Mental Health at Monash University, and her colleagues performed a multiscale characterization of neural heterogeneity in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder, and schizophrenia) and 1,465 matched controls.
According to their report, a common characteristic of mental illness is individual heterogeneity in regional GMV deviations; however, these regionally heterogeneous loci are frequently integrated into shared functional circuits and networks. Regional heterogeneity can thus account for the well-documented clinical heterogeneity observed in psychiatric disorders, while circuit- and network-level aggregation of deviations may represent a putative neural substrate for phenotypic similarities among individuals with the same diagnosis.
Additionally, the authors showed that a large portion of the increased overlap seen at the circuit and network levels can be attributed to the total deviation burden. This finding casts doubt on popular models that postulate that particular disorders result from the malfunction of distinctive neural systems that the disease process has chosen to target.
“This result is expected since the failure so far of psychiatric neuroimaging to identify robust diagnostic biomarkers of illness indicates that any disease-related brain changes are likely to be subtle and complex,” the authors wrote in Nature Neuroscience. “We should, therefore, be circumspect about our ability to identify strong neurobiological signatures of psychiatric disorders.”
A significant area of future research will be accurately describing the connection between interindividual variations in symptom expression and GMV deviations. Subsequent research endeavors could focus on creating transdiagnostic, multisite, harmonized clinical protocols that apply to various disorders.
As the kind and extent of case-control differences can change throughout a person’s lifetime, it is also important to consider the precise age at which the measurements are taken. With the help of patient-specific deviation maps that transcend conventional diagnostic boundaries, this method could make data-driven strategies for identifying biological subtypes easier to implement.
