The Most Common Lesions Detected by Neuroimaging as Causes of Epilepsy

Epilepsy is a common neurological disorder characterized by chronic, unprovoked and recurrent seizures, which are the result of rapid and excessive bioelectric discharges in nerve cells. Neuroimaging is used to detect underlying structural abnormalities which may be associated with epilepsy. This paper reviews the most common abnormalities, such as hippocampal sclerosis, malformations of cortical development and vascular malformation, detected by neuroimaging in patients with epilepsy to help understand the correlation between these changes and the course, treatment and prognosis of epilepsy. Magnetic resonance imaging (MRI) reveals structural changes in the brain which are described in this review. Recent studies indicate the usefulness of additional imaging techniques. The use of fluorodeoxyglucose positron emission tomography (FDG-PET) improves surgical outcomes in MRI-negative cases of focal cortical dysplasia. Some techniques, such as quantitative image analysis, magnetic resonance spectroscopy (MRS), functional MRI (fMRI), diffusion tensor imaging (DTI) and fibre tract reconstruction, can detect small malformations—which means that some of the epilepsies can be treated surgically. Quantitative susceptibility mapping may become the method of choice in vascular malformations. Neuroimaging determines appropriate diagnosis and treatment and helps to predict prognosis.

Neural signatures of pathological hyperdirect pathway activity in Parkinson’s disease

Parkinson’s disease (PD) is characterised by the emergence of pathological patterns of oscillatory synchronisation across the cortico-basal-ganglia circuit. The relationship between anatomical connectivity and oscillatory synchronisation within this system remains poorly understood. We address this by integrating evidence from invasive electrophysiology, magnetoencephalography, tractography and computational modelling in patients. Coupling between supplementary motor area (SMA) and subthalamic nucleus (STN) within the high beta frequency (21-30 Hz) range correlated with fibre tract densities between these two structures. Additionally within the STN, non-linear waveform features suggestive of cortical synchronisation correlated with cortico-STN fibre densities. Finally, computational modelling revealed that exaggerated hyperdirect cortical inputs to the STN in the upper beta frequency range can provoke the generation of widespread pathological synchrony at lower beta (13-20 Hz) frequencies. These observations reveal a spectral signature of the hyperdirect pathway at high beta frequencies and provide evidence for its pathophysiological role in oscillatory network dysfunction in PD.One sentence summarySignatures of the hyperdirect pathway and its likely role in pathological network disruption in Parkinson’s disease are identified.

Long-term development of white matter fibre density and morphology up to 13 years after preterm birth

BackgroundIt is well documented that infants born very preterm (VP) are at risk of brain injury and altered brain development in the neonatal period, however there is a lack of long-term, longitudinal studies on the effects of VP birth on white matter development over childhood. Most previous studies were based on voxel-averaged, non-fibre-specific diffusion magnetic resonance imaging (MRI) measures, such as fractional anisotropy. In contrast, the novel diffusion MRI analysis framework, fixel-based analysis (FBA), enables whole-brain analysis of microstructural and macrostructural properties of individual fibre populations at a sub-voxel level. We applied FBA to investigate the long-term implications of VP birth and associated perinatal risk factors on fibre development in childhood and adolescence.MethodsDiffusion images were acquired for a cohort of VP (born <30 weeks’ gestation) and full-term (FT, ≥37 weeks’ gestation) children at two ages: mean (SD) 7.6 (0.2) years (n=138 VP and 32 FT children) and 13.3 (0.4) years (n=130 VP and 45 FT children). 103 VP and 21 FT children had images at both ages for longitudinal analysis. At every fixel (individual fibre population within an image voxel) across the white matter, we compared FBA metrics (fibre density (FD), cross-section (FC) and a combination of these properties (FDC)) between VP and FT groups cross-sectionally at each age, and longitudinally between ages. We also examined associations between perinatal risk factors and FBA metrics in the VP group.ResultsCompared with FT children, VP children had lower FD, FC and FDC throughout the white matter, particularly in the corpus callosum, tapetum, inferior fronto-occipital fasciculus, fornix and cingulum at ages 7 and 13 years, as well as the motor pathways at age 13 years. VP children also had slower FDC development in the corpus callosum and corticospinal tract between ages 7 and 13 years compared with FT children. Within VP children, earlier gestational age at birth, lower birth weight z-score, and neonatal brain abnormalities were associated with lower FD, FC and FDC throughout the white matter at both ages.ConclusionsVP birth and concomitant perinatal risk factors are associated with fibre tract-specific alterations to axonal development in childhood and adolescence.

Nonlinear growth: an origin of hub organization in complex networks

Many real-world networks contain highly connected nodes called hubs. Hubs are often crucial for network function and spreading dynamics. However, classical models of how hubs originate during network development unrealistically assume that new nodes attain information about the connectivity (for example the degree) of existing nodes. Here, we introduce hub formation through nonlinear growth where the number of nodes generated at each stage increases over time and new nodes form connections independent of target node features. Our model reproduces variation in number of connections, hub occurrence time, and rich-club organization of networks ranging from protein–protein, neuronal and fibre tract brain networks to airline networks. Moreover, nonlinear growth gives a more generic representation of these networks compared with previous preferential attachment or duplication–divergence models. Overall, hub creation through nonlinear network expansion can serve as a benchmark model for studying the development of many real-world networks.

Different neural substrates for executive functions in youths with ADHD: a diffusion spectrum imaging tractography study

BackgroundThe relationship between white-matter tracts and executive functions (EF) in attention deficit hyperactivity disorder (ADHD) has not been well studied and previous studies mainly focused on frontostriatal (FS) tracts. The authors explored the microstructural property of several fibre tracts hypothesized to be involved in EF, to correlate their microstructural property with EF, and to explore whether such associations differ between ADHD and typically developing (TD) youths.MethodWe assessed 45 youths with ADHD and 45 individually matched TD youths with a computerized test battery for multiple dimensions of EF. From magnetic resonance imaging, FS tract, superior longitudinal fasciculus (SLF), arcuate fasciculus (AF) and cingulum bundle (CB) were reconstructed by diffusion spectrum imaging tractography. The generalized fractional anisotropy (GFA) values of white-matter tracts were computed to present microstructural property of each tract.ResultsWe found lower GFA in the left FS tract, left SLF, left AF and right CB, and poorer performance in set-shifting, sustained attention, cognitive inhibition and visuospatial planning in ADHD than TD. The ADHD and TD groups demonstrated different association patterns between EF and fibre tract microstructural property. Most of the EF were associated with microstructural integrity of the FS tract and CB in TD youths, while with that of the FS tract, SLF and AF in youths with ADHD.ConclusionsOur findings support that the SLF, AF and CB also involve in a wide range of EF and that the main fibre tracts involved in EF are different in youths with ADHD.

From Caenorhabditis elegans to the human connectome: a specific modular organization increases metabolic, functional and developmental efficiency

The connectome, or the entire connectivity of a neural system represented by a network, ranges across various scales from synaptic connections between individual neurons to fibre tract connections between brain regions. Although the modularity they commonly show has been extensively studied, it is unclear whether the connection specificity of such networks can already be fully explained by the modularity alone. To answer this question, we study two networks, the neuronal network of Caenorhabditis elegans and the fibre tract network of human brains obtained through diffusion spectrum imaging. We compare them to their respective benchmark networks with varying modularities, which are generated by link swapping to have desired modularity values. We find several network properties that are specific to the neural networks and cannot be fully explained by the modularity alone. First, the clustering coefficient and the characteristic path length of both C. elegans and human connectomes are higher than those of the benchmark networks with similar modularity. High clustering coefficient indicates efficient local information distribution, and high characteristic path length suggests reduced global integration. Second, the total wiring length is smaller than for the alternative configurations with similar modularity. This is due to lower dispersion of connections, which means each neuron in the C. elegans connectome or each region of interest in the human connectome reaches fewer ganglia or cortical areas, respectively. Third, both neural networks show lower algorithmic entropy compared with the alternative arrangements. This implies that fewer genes are needed to encode for the organization of neural systems. While the first two findings show that the neural topologies are efficient in information processing, this suggests that they are also efficient from a developmental point of view. Together, these results show that neural systems are organized in such a way as to yield efficient features beyond those given by their modularity alone.