The mammalian brain is composed of densely connected and interacting regions, which form structural and functional networks. An improved understanding of the structure–function relation is crucial to understand the structural underpinnings of brain function and brain plasticity after injury. It is currently unclear how functional connectivity strength relates to structural connectivity strength. We obtained an overview of recent papers that report on correspondences between quantitative functional and structural connectivity measures in the mammalian brain. We included network studies in which functional connectivity was measured with resting-state fMRI, and structural connectivity with either diffusion-weighted MRI or neuronal tract tracers. Twenty-seven of the 28 included studies showed a positive structure–function relationship. Large inter-study variations were found comparing functional connectivity strength with either quantitative diffusion-based (correlation coefficient (r) ranges: 0.18–0.82) or neuronal tracer-based structural connectivity measures (r = 0.24–0.74). Two functional datasets demonstrated lower structure–function correlations with neuronal tracer-based (r = 0.22 and r = 0.30) than with diffusion-based measures (r = 0.49 and r = 0.65). The robust positive quantitative structure–function relationship supports the hypothesis that structural connectivity provides the hardware from which functional connectivity emerges. However, methodological differences between the included studies complicate the comparison across studies, which emphasize the need for validation and standardization in brain structure–function studies.

Diffusion MRI (dMRI)-based tractography offers unique abilities to map whole-brain structural connections in human and animal brains. However, dMRI-based tractography indirectly measures white matter tracts, with suboptimal accuracy and reliability. Recently, sophisticated methods including constrained spherical deconvolution (CSD) and global tractography have been developed to improve tract reconstructions through modeling of more complex fiber orientations. Our study aimed to determine the accuracy of connectome reconstruction for three dMRI-based tractography approaches: diffusion tensor (DT)-based, CSD-based and global tractography. Therefore, we validated whole brain structural connectome reconstructions based on ten ultrahigh-resolution dMRI rat brain scans and 106 cortical regions, from which varying tractography parameters were compared against standardized neuronal tracer data. All tested tractography methods generated considerable numbers of false positive and false negative connections. There was a parameter range trade-off between sensitivity: 0.06–0.63 interhemispherically and 0.22–0.86 intrahemispherically; and specificity: 0.99–0.60 interhemispherically and 0.99–0.23 intrahemispherically. Furthermore, performance of all tractography methods decreased with increasing spatial distance between connected regions. Similar patterns and trade-offs were found, when we applied spherical deconvolution informed filtering of tractograms, streamline thresholding and group-based average network thresholding. Despite the potential of CSD-based and global tractography to handle complex fiber orientations at voxel level, reconstruction accuracy, especially for long-distance connections, remains a challenge. Hence, connectome reconstruction benefits from varying parameter settings and combination of tractography methods to account for anatomical variation of neuronal pathways.Electronic supplementary materialThe online version of this article (10.1007/s00429-018-1628-y) contains supplementary material, which is available to authorized users.

ObjectiveSince the introduction of diffusion tensor imaging, white matter abnormalities in epilepsy have been studied extensively. However, the affected areas reported, the extent of abnormalities and the association with relevant clinical parameters are highly variable. We aimed to obtain a more consistent estimate of white matter abnormalities and their association with clinical parameters in different epilepsy types.MethodsWe systematically searched for differences in white matter fractional anisotropy and mean diffusivity, at regional and voxel level, between people with epilepsy and healthy controls. Meta-analyses were used to quantify the directionality and extent of these differences. Correlations between white matter differences and age of epilepsy onset, duration of epilepsy and sex were assessed with meta-regressions.ResultsForty-two studies, with 1027 people with epilepsy and 1122 controls, were included with regional data. Sixteen voxel-based studies were also included. People with temporal or frontal lobe epilepsy had significantly decreased fractional anisotropy (Δ –0.021, 95% confidence interval –0.026 to –0.016) and increased mean diffusivity (Δ0.026 × 10–3 mm2/s, 0.012 to 0.039) in the commissural, association and projection white matter fibers. White matter was much less affected in generalized epilepsy. White matter changes in people with focal epilepsy correlated with age at onset, epilepsy duration and sex.SignificanceThis study provides a better estimation of white matter changes in different epilepsies. Effects are particularly found in people with focal epilepsy. Correlations with the duration of focal epilepsy support the hypothesis that these changes are, at least partly, a consequence of seizures and may warrant early surgery. Future studies need to guarantee adequate group sizes, as white matter differences in epilepsy are small.

a b s t r a c t a r t i c l e i n f oDescriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have limitations for inter-subject or between-group comparisons in which network sizes and edge densities may differ, such as in studies on neurodevelopment or brain diseases. Furthermore, descriptive neural network analyses lack an appropriate generic null model and a unifying framework. These issues may be solved with an alternative framework based on a Bayesian generative modeling approach, i.e. Bayesian exponential random graph modeling (ERGM), which explains an observed network by the joint contribution of local network structures or features (for which we chose neurobiologically meaningful constructs such as connectedness, local clustering or global efficiency). We aimed to identify how these local network structures (or features) are evolving across the life-span, and how sensitive these features are to random and targeted lesions. To that aim we applied Bayesian exponential random graph modeling on structural networks derived from whole-brain diffusion tensor imaging-based tractography of 382 healthy adult subjects (age range: 20.2-86.2 years), with and without lesion simulations. Networks were successfully generated from four local network structures that resulted in excellent goodness-of-fit, i.e. measures of connectedness, local clustering, global efficiency and intrahemispheric connectivity. We found that local structures (i.e. connectedness, local clustering and global efficiency), which give rise to the global network topology, were stable even after lesion simulations across the lifespan, in contrast to overall descriptive network changes -e.g. lower network density and higher clustering -during aging, and despite clear effects of hub damage on network topologies. Our study demonstrates the potential of Bayesian generative modeling to characterize the underlying network structures that drive the brain's global network topology at different developmental stages and/or under pathological conditions.

This study demonstrates that sufficient power in clinical trials is still problematic, although the situation is slowly improving. Our data encourage further efforts to increase statistical power in clinical trials to guarantee rigorous and reproducible evidence-based medicine.

Measuring lifetime MDD with an online instrument was feasible. Sensitivity and specificity were adequate. The instrument gave a prevalence of lifetime MDD in line with reported population prevalences. LIDAS is a promising tool for rapid determination of lifetime MDD status in large samples, such as needed for genomics studies.

Prolonged auditory sensory deprivation leads to brain reorganization, indicated by functional enhancement in remaining sensory systems, a phenomenon known as cross-modal plasticity.In this study we investigated differences in functional brain network shifts from eyes-closed to eyes-open conditions between deaf and hearing people. Electroencephalography activity was recorded in deaf (N = 71) and hearing people (N = 122) living in rural Africa, which yielded a unique data-set of congenital, pre-lingual and post-lingual deaf people, with a divergent experience in American Sign Language. Functional networks were determined from the synchronization of electroencephalography signals between fourteen electrodes distributed over the scalp. We studied the synchronization between the auditory and visual cortex and performed whole-brain minimum spanning tree analysis based on the phase lag index of functional connectivity. This tree analysis accounts for variations in global network density and allows unbiased characterization of functional network backbones. We found increased functional connectivity between the auditory and visual cortex in deaf people during the eyesclosed condition in both the alpha and beta bands. Furthermore, we found functional network backbone shifts both in deaf and healthy people as they went from eyes-closed to eyes-open conditions. In both the alpha and beta band the deafs' brain showed larger functional backbone-shifts in node strength compared to controls. In the alpha band this shift in network strength differed among deaf participants and depended on type of deafness: congenital, prelingual or post-lingual deafness. In addition, a correlation was found between functional backbone characteristics and experience of sign language. Our study revealed more insights in functional network reorganization specifically due to prolonged lack of auditory input, but might also be helpful for sensory deprivation and cross-modal plasticity in general. Global cortical network reorganization in deaf people supports the plastic capacities of the young brain. The differences between type of deafness stresses that etiology affects functional .

Functional outcome after stroke depends on the local site of ischemic injury and on remote effects within connected networks, frequently extending into the contralesional hemisphere. However, the pattern of large-scale contralesional network remodeling remains largely unresolved. In this study, we applied diffusion-based tractography and graph-based network analysis to measure structural connectivity in the contralesional hemisphere chronically after experimental stroke in rats. We used the minimum spanning tree method, which accounts for variations in network density, for unbiased characterization of network backbones that form the strongest connections in a network. Ultrahigh-resolution diffusion MRI scans of eight post-mortem rat brains collected 70 days after right-sided stroke were compared against scans from 10 control brains. Structural network backbones of the left (contralesional) hemisphere, derived from 42 atlas-based anatomical regions, were found to be relatively stable across stroke and control animals. However, several sensorimotor regions showed increased connection strength after stroke. Sensorimotor function correlated with specific contralesional sensorimotor network backbone measures of global integration and efficiency. Our findings point toward post-stroke adaptive reorganization of the contralesional sensorimotor network with recruitment of distinct sensorimotor regions, possibly through strengthening of connections, which may contribute to functional recovery.