3. Stable variations in human brain network architecture: applications to psychopathology

COEX 

While human brain networks follow a canonical organization, every individual measured so far differs from this archetypal pattern. A central question in clinical neuroscience is the extent to which these individual differences are informative regarding neuropathological traits versus less temporally stable features of psychiatric disorders. In past work, we have used a combination of precision and big-N datasets to investigate the properties of locations that differ most strongly between individuals and the canonical group-average brain network organization. In our work, we refer to these locations with idiosyncratic brain connectivity profiles as network “variants”. In neurotypical samples, variants appear most frequently in frontal regions and near the temporo-parietal junction, with a higher prevalence in the right hemisphere (d = 0.57; p<0.001; a pattern that reproduces across datasets). With sufficient individual-level data, variants are highly reliable (test-retest r > 0.8) and stable over years (average r = 0.79 longitudinally). When examined across task conditions, variants show some state-dependent features, but are largely trait-like. In a preliminary investigation, we also show that variants have potential for revealing trait-like features of psychopathology, as variants differ in individuals with schizophrenia, with a higher frequency of variants in the dorsolateral prefrontal cortex, relative to matched controls (p < 0.01, d = 0.64). Jointly, this work suggests that network variants are stable, systematic elements of brain networks in individuals, with potential for revealing clinically-relevant neural targets. We close by considering experimental designs that may be useful for untangling trait and state like features of brain networks in clinical neuroscience.