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The characterization of gray matter morphology of individual brains is an important issue in neuroscience. Graph theory has been used to describe cortical morphology, with networks based on covariation of gray matter volume or thickness between cortical areas across people. Here, we extend this research by proposing a new method that describes the gray matter morphology of an individual cortex as a network. In these large-scale morphological networks, nodes represent small cortical regions, and edges connect regions that have a statistically similar structure. The method was applied to a healthy sample (n = 14, scanned at 2 different time points). For all networks, we described the spatial degree distribution, average minimum path length, average clustering coefficient, small world property, and betweenness centrality (BC). Finally, we studied the reproducibility of all these properties. The networks showed more clustering than random networks and a similar minimum path length, indicating that they were "small world." The spatial degree and BC distributions corresponded closely to those from group-derived networks. All network property values were reproducible over the 2 time points examined. Our results demonstrate that intracortical similarities can be used to provide a robust statistical description of individual gray matter morphology.

Grey matter atrophy is common in multiple sclerosis. However, in contrast with other neurodegenerative diseases, it is unclear whether grey matter atrophy in multiple sclerosis is a diffuse 'global' process or develops, instead, according to distinct anatomical patterns. Using source-based morphometry we searched for anatomical patterns of co-varying cortical thickness and assessed their relationships with white matter pathology, physical disability and cognitive functioning. Magnetic resonance imaging was performed at 3 T in 208 patients with long-standing multiple sclerosis (141 females; age = 53.7 ± 9.6 years; disease duration = 20.2 ± 7.1 years) and 60 age- and sex-matched healthy controls. Spatial independent component analysis was performed on cortical thickness maps derived from 3D T1-weighted images across all subjects to identify co-varying patterns. The loadings, which reflect the presence of each cortical thickness pattern in a subject, were compared between patients with multiple sclerosis and healthy controls with generalized linear models. Stepwise linear regression analyses were used to assess whether white matter pathology was associated with these loadings and to identify the cortical thickness patterns that predict measures of physical and cognitive dysfunction. Ten cortical thickness patterns were identified, of which six had significantly lower loadings in patients with multiple sclerosis than in controls: the largest loading differences corresponded to the pattern predominantly involving the bilateral temporal pole and entorhinal cortex, and the pattern involving the bilateral posterior cingulate cortex. In patients with multiple sclerosis, overall white matter lesion load was negatively associated with the loadings of these two patterns. The final model for physical dysfunction as measured with Expanded Disability Status Scale score (adjusted R(2) = 0.297; P < 0.001) included the predictors age, overall white matter lesion load, the loadings of two cortical thickness patterns (bilateral sensorimotor cortex and bilateral insula), and global cortical thickness. The final model predicting average cognition (adjusted R(2) = 0.469; P < 0.001) consisted of age, the loadings of two cortical thickness patterns (bilateral posterior cingulate cortex and bilateral temporal pole), overall white matter lesion load and normal-appearing white matter integrity. Although white matter pathology measures were part of the final clinical regression models, they explained limited incremental variance (to a maximum of 4%). Several cortical atrophy patterns relevant for multiple sclerosis were found. This suggests that cortical atrophy in multiple sclerosis occurs largely in a non-random manner and develops (at least partly) according to distinct anatomical patterns. In addition, these cortical atrophy patterns showed stronger associations with clinical (especially cognitive) dysfunction than global cortical atrophy.

We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal or dendritic tree, its actions of elongation, branching and turning are described in a stochastic, phenomenological manner. In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, can be generated. Synapses are formed as neurons grow out and axonal and dendritic branches come in close proximity of each other. NETMORPH is a flexible tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Research applications include studying the complex relationship between neuronal morphology and global patterns of synaptic connectivity. Possible future developments of NETMORPH are discussed.