Although it is generally assumed that brain damage predominantly affects only the function of the damaged region, here we show that focal damage to critical locations causes disruption of network organization throughout the brain. Using resting state fMRI, we assessed whole-brain network structure in patients with focal brain lesions. Only damage to those brain regions important for communication between subnetworks (e.g., “connectors”)—but not to those brain regions important for communication within sub-networks (e.g., “hubs”)—led to decreases in modularity, a measure of the integrity of network organization. Critically, this network dysfunction extended into the structurally intact hemisphere. Thus, focal brain damage can have a widespread, nonlocal impact on brain network organization when there is damage to regions important for the communication between networks. These findings fundamentally revise our understanding of the remote effects of focal brain damage and may explain numerous puzzling cases of functional deficits that are observed following brain injury.

An emerging theory of the neurobiology of category learning postulates that there are separate neural systems supporting the learning of categories based on verbalizeable rules (RB) or through implicit information integration (II). The medial temporal lobe (MTL) is thought to play a crucial role in successful RB categorization, whereas the posterior regions of the caudate are hypothesized to support II categorization. Functional neuroimaging was used to assess activity in these systems during category-learning tasks with category structures designed to afford either RB or II learning. Successful RB categorization was associated with relatively increased activity in the anterior MTL. Successful II categorization was associated with increased activity in the caudate body. The dissociation observed with neuroimaging is consistent with the roles of these systems in memory and dissociations reported in patient populations. Convergent evidence from these approaches consistently reinforces the idea of multiple neural systems supporting category learning.

Neuroimaging studies of cognitive control have identified two distinct networks with dissociable resting state connectivity patterns. This study, in patients with heterogeneous damage to these networks, demonstrates network independence through a double dissociation of lesion location on two different measures of network integrity: functional correlations among network nodes and within-node graph theory network properties. The degree of network damage correlates with a decrease in functional connectivity within that network while sparing the nonlesioned network. Graph theory properties of intact nodes within the damaged network show evidence of dysfunction compared with the undamaged network. The effect of anatomical damage thus extends beyond the lesioned area, but remains within the bounds of the existing network connections. Together this evidence suggests that networks defined by their role in cognitive control processes exhibit independence in resting data.functional MRI | functional connectivity | graph theory | resting state | stroke C ognitive control is required in everyday life to coordinate our thoughts and actions to achieve internal goals while still allowing the flexibility to adjust these goals with changing task demands. Although previous studies have attributed cognitive control to various prefrontal cortical regions (1, 2), recently it has been proposed that a dual-network architecture exists in the human brain in which cognitive control depends on regions that extend beyond the frontal cortex (3). In a recent cross-task analysis, Dosenbach et al. (4) identified a number of regions active during different stages of cognitive control tasks. Given the difficulty in isolating cognitive control networks that are simultaneously active during task performance, the investigators took advantage of the recent advent of resting state functional MRI (rs-fMRI) for detecting spontaneous fluctuations between coherent brain regions. In a follow-up study, these predefined regions of interest (ROIs) obtained from the task data served as seeds in a correlation analysis of rs-fMRI data (3) in which graph theory and hierarchical clustering were applied to the correlation matrices. These analyses identified two distinct networks labeled as fronto-parietal (FP) and cinguloopercular (CO) (Fig. 1A). Based on their role in cognitive tasks, the FP network consists of nodes proposed to provide signals that act on a rapid time scale to initiate and adjust control, whereas the CO network nodes act to provide signals that allow set maintenance over a longer time scale (3,5).Numerous studies using rs-fMRI have shown that neuronal activity is characterized by temporal correlations in blood oxygen level-dependent signal across disparate brain regions (6, 7). These fluctuations seem highly consistent over time and reflect the presence of intrinsic functional (8) and structural (9) connectivity. Among these fluctuations, different networks can be distinguished, many of which show remarkable resemblance to task-related networks (...

Objective: We tested the value of measuring modularity, a graph theory metric indexing the relative extent of integration and segregation of distributed functional brain networks, for predicting individual differences in response to cognitive training in patients with brain injury.Methods: Patients with acquired brain injury (n 5 11) participated in 5 weeks of cognitive training and a comparison condition (brief education) in a crossover intervention study design. We quantified the measure of functional brain network organization, modularity, from functional connectivity networks during a state of tonic attention regulation measured during fMRI scanning before the intervention conditions. We examined the relationship of baseline modularity with pre-to posttraining changes in neuropsychological measures of attention and executive control.Results: The modularity of brain network organization at baseline predicted improvement in attention and executive function after cognitive training, but not after the comparison intervention. Individuals with higher baseline modularity exhibited greater improvements with cognitive training, suggesting that a more modular baseline network state may contribute to greater adaptation in response to cognitive training. Conclusions:Brain network properties such as modularity provide valuable information for understanding mechanisms that influence rehabilitation of cognitive function after brain injury, and may contribute to the discovery of clinically relevant biomarkers that could guide rehabilitation efforts.