Literature Review

Brain volume patterns vary across psychiatric disorders



A large brain imaging study of adults with six different psychiatric illnesses shows that heterogeneity in regional gray matter volume deviations is a general feature of psychiatric illness, but that these regionally heterogeneous areas are often embedded within common functional circuits and networks.

The findings suggest that “targeting brain circuits, rather than specific brain regions, may be a more effective way of developing new treatments,” study investigator Ashlea Segal said in an email.

The findings also suggest that it’s “unlikely that a single cause or mechanism of a given disorder exists, and that a ‘one-size-fits-all’ approach to treatment is likely only appropriate for a small subset of individuals. In fact, one size doesn’t fit all. It probably doesn’t even fit most,” said Ms. Segal, a PhD candidate with the Turner Institute for Brain and Mental Health’s Neural Systems and Behaviour Lab at Monash University in Melbourne.

“Focusing on brain alterations at an individual level allows us to develop more personally tailored treatments,” Ms. Segal added.

Regional heterogeneity, the authors write, “thus offers a plausible explanation for the well-described clinical heterogeneity observed in psychiatric disorders, while circuit- and network-level aggregation of deviations is a putative neural substrate for phenotypic similarities between patients assigned the same diagnosis.”

The study was published online in Nature Neuroscience

Beyond group averages

For decades, researchers have mapped brain areas showing reduced gray matter volume (GMV) in people diagnosed with a variety of mental illnesses, but these maps have only been generated at the level of group averages, Ms. Segal explained.

“This means that we understand how the brains of people with, say, schizophrenia, differ from those without schizophrenia on average, but we can’t really say much about individual people,” Ms. Segal said.

For their study, the researchers used new statistical techniques developed by Andre Marquand, PhD, who co-led the project, to characterize the heterogeneity of GMV differences in 1,294 individuals diagnosed with one of six psychiatric conditions and 1,465 matched controls. Dr. Marquand is affiliated with the Donders Institute for Brain, Cognition, and Behavior in Nijmegen, the Netherlands.

These techniques “allow us to benchmark the size of over 1,000 different brain regions in any given person relative to what we should expect to see in the general population. In this way, we can identify, for any person, brain regions showing unusually small or large volumes, given that person’s age and sex,” Ms. Segal told this news organization.

The clinical sample included 202 individuals with autism spectrum disorder, 153 with attention-deficit/hyperactivity disorder (ADHD), 228 with bipolar disorder, 161 with major depressive disorder, 167 with obsessive-compulsive disorder, and 383 individuals with schizophrenia.

Confirming earlier findings, those with psychiatric illness showed more GMV deviations than healthy controls, the researchers found.

However, at the individual level, deviations from population expectations for regional gray matter volumes were “highly heterogeneous,” affecting the same area in less than 7% of people with the same diagnosis, they note. “This result means that it is difficult to pinpoint treatment targets or causal mechanisms by focusing on group averages alone,” Alex Fornito, PhD, of Monash University, who led the research team, said in a statement.

“It may also explain why people with the same diagnosis show wide variability in their symptom profiles and treatment outcomes,” Dr. Fornito added.

Yet, despite considerable heterogeneity at the regional level across different diagnoses, these deviations were embedded within common functional circuits and networks in up to 56% of cases.

The salience-ventral attention network, for example, which plays a central role in cognitive control, interoceptive awareness, and switching between internally and externally focused attention, was implicated across diagnoses, with other neural networks selectively involved in depression, bipolar disorder, schizophrenia, and ADHD.

The researchers say the approach they developed opens new opportunities for mapping brain changes in mental illness.

“The framework we have developed allows us to understand the diversity of brain changes in people with mental illness at different levels, from individual regions through to more widespread brain circuits and networks, offering a deeper insight into how the brain is affected in individual people,” Dr. Fornito said in a statement.

The study had no commercial funding. Ms. Segal, Dr. Fornito, and Dr. Marquand report no relevant financial relationships.

A version of this article first appeared on

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