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The idea that complex systems have a hierarchical modular organization originated in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules”) decomposition of human brain functional networks, measured using functional magnetic resonance imaging in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I = 0.63. The largest five modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.

These results provide new evidence of exposure-related structural abnormalities in the hippocampus and amygdala in long-term heavy cannabis users and corroborate similar findings in the animal literature. These findings indicate that heavy daily cannabis use across protracted periods exerts harmful effects on brain tissue and mental health.

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The anterior cingulate cortex (ACC) is a functionally heterogeneous region involved in diverse cognitive and emotional processes that support goal-directed behaviour. Structural magnetic resonance imaging (MRI) and neuropathological findings over the past two decades have converged to suggest abnormalities in the region may represent a neurobiological basis for many of the clinical manifestations of schizophrenia. However, while each approach offers complimentary information that can provide clues regarding underlying patholophysiological processes, the findings from these 2 fields are seldom integrated. In this article, we review structural neuroimaging and neuropathological studies of the ACC, focusing on the unique information they provide. The available imaging data suggest grey matter reductions in the ACC precede psychosis onset in some categories of high-risk individuals, show sub-regional specificity, and may progress with illness duration. The available post-mortem findings indicate these imaging-related changes are accompanied by reductions in neuronal, synaptic, and dendritic density, as well as increased afferent input, suggesting the grey matter differences observed with MRI arise from alterations in both neuronal and non-neuronal tissue compartments. We discuss the potential mechanisms that might facilitate integration of these findings and consider strategies for future research.