Elucidating the coupling between the structure and the function of the brain and its development across maturation has attracted a lot of interest in the field of network neuroscience in the last 15 years. Mounting evidence supports the hypothesis that the onset of certain brain disorders is linked with the interplay between the structural architecture of the brain and its functional processes, often accompanied with unusual connectivity features. This paper introduces a method called the network-based statistic–simultaneous node investigation (NBS-SNI) that integrates both representations into a single framework, and identifies connectivity abnormalities in case-control studies. With this method, significance is given to the properties of the nodes, as well as to their connections. This approach builds on the well-established network-based statistic (NBS) proposed in 2010. We uncover and identify the regimes in which NBS-SNI offers a gain in statistical resolution to identify a contrast of interest using synthetic data. We also apply our method on two real case-control studies, one consisting of individuals diagnosed with autism and the other consisting of individuals diagnosed with early psychosis. Using NBS-SNI and node properties such as the closeness centrality and local information dimension, we found hypo- and hyperconnected subnetworks and show that our method can offer a 9 percentage points gain in prediction power over the standard NBS. Author Summary We propose an extension to the well-known network-based statistic (NBS) dubbed NBS-SNI, where the extension SNI stands for simultaneous node investigation. The goal of this approach is to integrate nodal properties such as centrality measures into the statistical network-based framework of NBS to probe for abnormal connectivity between important nodes in case-control studies. We expose the regimes where NBS-SNI offers greater statistical resolution for identifying a contrast of interest using synthetic data and test the approach with a real autism-healthy dataset that contains both the structural ( DTI ) and functional (fMRI) brain networks of each individual. We also tested our approach on a second dataset of individuals diagnosed with early psychosis. In the second case, our framework is supplemented by incorporating the anatomically derived measures of intrinsic curvature index and gray matter volume directly as a node property, rather than the structural networks, thereby illustrating the versatility of our approach.

Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population ( n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines ( ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization. Author Summary The increasing use of diffusion MRI for mapping white matter connectivity has been matched by a similar increase in the number of ways to process the diffusion data. Here, we assess how diffusion processing affects hubs across 1,760 pipeline variations. Many processing pipelines do not show a high concentration of connectivity within hubs. When present, hub location and distribution vary based on processing choices. The choice of probabilistic or deterministic tractography has a major impact on hub location and strength. Finally, node strength in weighted networks can correlate highly with node size. Overall, our results illustrate the need for prudent decision-making when processing and interpreting diffusion MRI data.

Functional magnetic resonance imaging (fMRI) is widely used to investigate functional coupling (FC) disturbances in a range of clinical disorders. Most analyses performed to date have used group-based parcellations for defining regions of interest (ROIs), in which a single parcellation is applied to each brain. This approach neglects individual differences in brain functional organization and may inaccurately delineate the true borders of functional regions. These inaccuracies could inflate or underestimate group differences in case-control analyses. We investigated how individual differences in brain organization influence group comparisons of FC using psychosis as a case study, drawing on fMRI data in 121 early psychosis patients and 57 controls. We defined FC networks using either a group-based parcellation or an individually tailored variant of the same parcellation. Individualized parcellations yielded more functionally homogeneous ROIs than did group-based parcellations. At the level of individual connections, case-control FC differences were widespread, but the group-based parcellation identified approximately 7.7% more connections as dysfunctional than the individualized parcellation. When considering differences at the level of functional networks, the results from both parcellations converged. Our results suggest that a substantial fraction of dysconnectivity previously observed in psychosis may be driven by the parcellation method, rather than by a pathophysiological process related to psychosis. Author Summary Functional magnetic resonance imaging is widely used to map how brain network dysfunction is affected by diverse diseases. A fundamental step in this work involved defining specific brain regions, which act as network nodes in the analysis. Most research to date has used a one-size-fits-all approach, defining such regions on a template brain that is then applied to individual people, which neglects the potential for variability in regional borders and brain organization. Here, we show that using an individualized approach to region definition results in more valid area definitions and more conservative estimates of brain network dysfunction in people with psychosis, indicating that at least some of the group differences reported in the extant literature may be due to differences in regional definitions rather than a consequence of the illness itself.