Daniel H. Mathalon

OBJECTIVE: To model in vivo the dynamic interrelations of head size, gray matter, white matter, and cerebrospinal fluid (CSF) volumes from infancy to old age using magnetic resonance imaging (MRI). DESIGN: Cross-sectional, between-subjects using an age-regression model. SETTING: A Veterans Affairs medical center and community hospitals. PARTICIPANTS: There were 88 male and female subjects aged 3 months to 30 years whose clinical MRI film had been read as normal and 73 healthy male volunteers aged 21 to 70 years who had an MRI performed specifically for this study. MAIN OUTCOME MEASURES: These MRI data were quantified using a semiautomated computer technique for segmenting images into gray matter, white matter, and CSF compartments. The cortex was defined geometrically as the outer 45% on each analyzed slice, and the volumes of cortical white matter, gray matter, and CSF were computed. Subcortical (ventricular) CSF volume was computed for the inner 55% of each analyzed slice. RESULTS: In the younger sample, intracranial volume increased by about 300 mL from 3 months to 10 years. The same patterns of change in volume of each compartment across the age range were seen in both sexes: cortical gray matter volume peaked around age 4 years and decreased thereafter; cortical white matter volume increased steadily until about age 20 years; cortical and ventricular CSF volumes remained constant. In the older sample, brain volumes were statistically adjusted for normal variation in head size through a regression procedure and revealed the following pattern: cortical gray matter volume decreased curvilin-early, showing an average volume loss of 0.7 mL/y, while cortical white matter volume remained constant during the five decades; complementary to the cortical gray matter decrease, cortical CSF volume increased by 0.6 mL/y and ventricular volumes increased by 0.3 mL/y. CONCLUSIONS: These patterns of growth and change seen in vivo with MRI are largely consistent with neuropathological studies, as well as animal models of development, and may reflect neuronal progressive and regressive processes, including cell growth, myelination, cell death, and atrophy.

The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen's d=-0.46), amygdala (d=-0.31), thalamus (d=-0.31), accumbens (d=-0.25) and intracranial volumes (d=-0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical-subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.