A growing number of studies approach the brain as a complex network, the so-called ‘connectome’. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network ‘robustness’—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer’s disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research.

Aim: To better understand the neural and performance factors that may underlie developmental coordination disorder (DCD), and implications for a multi‐component account.Method: A systematic review of the experimental literature published between June 2011 and September 2016 was conducted using a modified PICOS (population, intervention, comparison, outcomes, and study type) framework. A total of 106 studies were included.Results: Behavioural data from 91 studies showed a broad cluster of deficits in the anticipatory control of movement, basic processes of motor learning, and cognitive control. Importantly, however, performance issues in DCD were often shown to be moderated by task type and difficulty. As well, we saw new evidence of compensatory processes and strategies in several studies. Neuroimaging data (15 studies, including electroencephalography) showed reduced cortical thickness in the right medial orbitofrontal cortex and altered brain activation patterns across functional networks involving prefrontal, parietal, and cerebellar regions in children with DCD than those in comparison groups. Data from diffusion‐weighted magnetic resonance imaging suggested reduced white matter organization involving sensorimotor structures and altered structural connectivity across the whole brain network.Interpretation: Taken together, results support the hypothesis that children with DCD show differences in brain structure and function compared with typically developing children. Behaviourally, these differences may affect anticipatory planning and reduce automatization of movement skill, prompting greater reliance on slower feedback‐based control and compensatory strategies. Implications for future research, theory development, and clinical practice are discussed.

Virtual-reality based rehabilitation (VR) shows potential as an engaging and effective way to improve upper-limb function and cognitive abilities following a stroke. However, an updated synthesis of the literature is needed to capture growth in recent research and address gaps in our understanding of factors that may optimize training parameters and treatment effects. Published randomized controlled trials comparing VR to conventional therapy were retrieved from seven electronic databases. Treatment effects (Hedge’s g) were estimated using a random effects model, with motor and functional outcomes between different protocols compared at the Body Structure/Function, Activity, and Participation levels of the International Classification of Functioning. Thirty-three studies were identified, including 971 participants (492 VR participants). VR produced small to medium overall effects (g = 0.46; 95% CI: 0.33–0.59, p < 0.01), above and beyond conventional therapies. Small to medium effects were observed on Body Structure/Function (g = 0.41; 95% CI: 0.28–0.55; p < 0.01) and Activity outcomes (g = 0.47; 95% CI: 0.34–0.60, p < 0.01), while Participation outcomes failed to reach significance (g = 0.38; 95% CI: -0.29-1.04, p = 0.27). Superior benefits for Body Structure/Function (g = 0.56) and Activity outcomes (g = 0.62) were observed when examining outcomes only from purpose-designed VR systems. Preliminary results (k = 4) suggested small to medium effects for cognitive outcomes (g = 0.41; 95% CI: 0.28–0.55; p < 0.01). Moderator analysis found no advantage for higher doses of VR, massed practice training schedules, or greater time since injury. VR can effect significant gains on Body Structure/Function and Activity level outcomes, including improvements in cognitive function, for individuals who have sustained a stroke. The evidence supports the use of VR as an adjunct for stroke rehabilitation, with effectiveness evident for a variety of platforms, training parameters, and stages of recovery.

Graph theory (GT) is a powerful framework for quantifying topological features of neuroimaging-derived functional and structural networks. However, false positive (FP) connections arise frequently and influence the inferred topology of networks. Thresholding is often used to overcome this problem, but an appropriate threshold often relies on a priori assumptions, which will alter inferred network topologies. Four common network metrics (global efficiency, mean clustering coefficient, mean betweenness and smallworldness) were tested using a model tractography dataset. It was found that all four network metrics were significantly affected even by just one FP. Results also show that thresholding effectively dampens the impact of FPs, but at the expense of adding significant bias to network metrics. In a larger number (n=248) of tractography datasets, statistics were computed across random group permutations for a range of thresholds, revealing that statistics for network metrics varied significantly more than for non-network metrics (i.e., number of streamlines and number of edges). Varying degrees of network atrophy were introduced artificially to half the datasets, to test sensitivity to genuine group differences. For some network metrics, this atrophy was detected as significant (p<0.05, determined using permutation testing) only across a limited range of thresholds. We propose a multi-threshold permutation correction (MTPC) method, based on the cluster-enhanced permutation correction approach, to identify sustained significant effects across clusters of thresholds. This approach minimises requirements to determine a single threshold a priori. We demonstrate improved sensitivity of MTPC-corrected metrics to genuine group effects compared to an existing approach and demonstrate the use of MTPC on a previously published network analysis of tractography data derived from a clinical population. In conclusion, we show that there are large biases and instability induced by thresholding, making statistical comparisons of network metrics difficult. However, by testing for effects across multiple thresholds using MTPC, true group differences can be robustly identified.

Objective: To examine the relation between measures of whole-brain white matter connectivity and cognitive performance in patients with early Alzheimer disease (AD) using a network-based approach and to assess whether network parameters provide information that is complementary to conventional MRI markers of AD. Methods: Fifty patients (mean age 78.8 ± 7.1 years) with early AD were recruited via a memory clinic. In addition, 15 age-, sex-, and education-matched control participants were used as a reference group. All participants underwent a 3-T MRI scan and cognitive assessment. Diffusion tensor imaging–based tractography was used to reconstruct the brain network of each individual, followed by graph theoretical analyses. Overall network efficiency was assessed by measures of local (clustering coefficient, local efficiency) and global (path length, global efficiency) connectivity. Age-, sex-, and education-adjusted cognitive scores were related to network measures and to conventional MRI parameters (i.e., degree of cerebral atrophy and small-vessel disease). Results: The structural brain network of patients showed reduced local efficiency compared to controls. Within the patient group, worse performance in memory and executive functioning was related to decreased local efficiency (r = 0.434; p = 0.002), increased path length (r = −0.538; p < 0.001), and decreased global efficiency (r = 0.431; p = 0.005). Measures of network efficiency explained up to 27% of the variance in cognitive functioning on top of conventional MRI markers (p < 0.01). Conclusion: This study shows that network-based analysis of brain white matter connections provides a novel way to reveal the structural basis of cognitive dysfunction in AD.