Disrupted topological properties of brain white matter networks in left temporal lobe epilepsy: a diffusion tensor imaging study

Abnormal white matter structural network topological property in patients with temporal lobe epilepsy

BACKGROUND

Diffusion tensor imaging (DTI) studies have demonstrated white matter (WM) abnormalities in patients with temporal lobe epilepsy (TLE). However, alterations in the topological properties of the WM structural network in patients with TLE remain unclear. Graph theoretical analysis provides a new perspective for evaluating the connectivity of WM structural networks.

METHODS

DTI was used to map the structural networks of 18 patients with TLE (10 males and 8 females) and 29 (17 males and 12 females) age- and gender-matched normal controls (NC). Graph theory was used to analyze the whole-brain networks and their topological properties between the two groups. Finally, partial correlation analyses were performed on the weighted network properties and clinical characteristics, namely, duration of epilepsy, verbal intelligence quotient (IQ), and performance IQ.

RESULTS

Patients with TLE exhibited reduced global efficiency and increased characteristic path length. A total of 31 regions with nodal efficiency alterations were detected in the fractional anisotropy_ weighted network of the patients. Communication hubs, such as the middle temporal gyrus, right inferior temporal gyrus, left calcarine, and right superior parietal gyrus, were also differently distributed in the patients compared with the NC. Several node regions showed close relationships with duration of epilepsy, verbal IQ, and performance IQ.

CONCLUSIONS

Our results demonstrate the disruption of the WM structural network in TLE patients. This study may contribute to the further understanding of the pathological mechanism of TLE.

Structural networking of the developing brain: from maturation to neurosurgical implications

Modern neuroscience agrees that neurological processing emerges from the multimodal interaction among multiple cortical and subcortical neuronal hubs, connected at short and long distance by white matter, to form a largely integrated and dynamic network, called the brain "connectome." The final architecture of these circuits results from a complex, continuous, and highly protracted development process of several axonal pathways that constitute the anatomical substrate of neuronal interactions. Awareness of the network organization of the central nervous system is crucial not only to understand the basis of children's neurological development, but also it may be of special interest to improve the quality of neurosurgical treatments of many pediatric diseases. Although there are a flourishing number of neuroimaging studies of the connectome, a comprehensive vision linking this research to neurosurgical practice is still lacking in the current pediatric literature. The goal of this review is to contribute to bridging this gap. In the first part, we summarize the main current knowledge concerning brain network maturation and its involvement in different aspects of normal neurocognitive development as well as in the pathophysiology of specific diseases. The final section is devoted to identifying possible implications of this knowledge in the neurosurgical field, especially in epilepsy and tumor surgery, and to discuss promising perspectives for future investigations.

Metabolic connectivity is associated with seizure outcome in surgically treated temporal lobe epilepsies: A 18F-FDG PET seed correlation analysis

¹⁸F-FDG PET provides high sensitivity for the pre-surgical assessment of drug-resistant temporal lobe epilepsy (TLE). However, little is known about the metabolic connectivity of epileptogenic networks involved. This study therefore aimed to evaluate the association between metabolic connectivity and seizure outcome in surgically treated TLE.

METHODS

The study included 107 right-handed patients that had undergone a presurgical interictal ¹⁸F-FDG PET assessment followed by an anterior temporal lobectomy and were classified according to seizure outcome 2 years after surgery. Metabolic connectivity was evaluated by seed correlation analysis in left and right epilepsy patients with a Class Engel IA or > IA outcome and compared to age-, sex- and handedness-matched healthy controls.

RESULTS

Increased metabolic connectivity was observed in the >IA compared to the IA group within the operated temporal lobe (respective clusters of 7.5 vs 3.3 cm³ and 2.6 cm³ vs 2.2 cm³ in left and right TLE), and to a lower extent with the contralateral temporal lobe (1.2 vs 0.7 cm³ and 1.7 cm³ vs 0.7 cm³ in left and right TLE). Seed correlations provided added value for the estimated individual performance of seizure outcome over the group comparisons in left TLE (AUC of 0.74 vs 0.67).

CONCLUSION

Metabolic connectivity is associated with outcome in surgically treated TLE with a strengthened epileptogenic connectome in patients with non-free-seizure outcomes. The added value of seed correlation analysis in left TLE underlines the importance of evaluating metabolic connectivity in network related diseases.

An updated systematic review and meta-analysis of brain network organization in focal epilepsy: Looking back and forth

Abnormalities of the brain network organization in focal epilepsy have been extensively quantified. However, the extent and directionality of abnormalities are highly variable and subtype insensitive. We conducted meta-analyses to obtain a more accurate and epilepsy type-specific quantification of the interictal global brain network organization in focal epilepsy. By using random-effects models, we estimated differences in average clustering coefficient, average path length, and modularity between patients with focal epilepsy and controls, based on 45 studies with a total sample size of 1,468 patients and 1,021 controls. Structural networks had a significant lower level of integration in patients with epilepsy as compared to controls, with a standardized mean difference of -0.334 (95 % confidence interval -0.631 to -0.038; p-value 0.027). Functional networks did not differ between patients and controls, except for the beta band clustering coefficient. Our meta-analyses show that differences in the brain network organization are not as well defined as individual studies often propose. We discuss potential pitfalls and suggestions to enhance the yield and clinical value of network studies.

The role of preoperative diffusion tensor imaging in predicting and improving functional outcome in pediatric patients undergoing epilepsy surgery: a systematic review

OBJECTIVE

Diffusion tensor imaging (DTI) is a useful neuroimaging technique for surgical planning in adult patients. However, no systematic review has been conducted to determine its utility for pre-operative analysis and planning of Pediatric Epilepsy surgery. We sought to determine the benefit of pre-operative DTI in predicting and improving neurological functional outcome after epilepsy surgery in children with intractable epilepsy.

METHODS

A systematic review of articles in English using PubMed, EMBASE and Scopus databases, from inception to January 10, 2020 was conducted. All studies that used DTI as either predictor or direct influencer of functional neurological outcome (motor, sensory, language and/or visual) in pediatric epilepsy surgical candidates were included. Data extraction was performed by two blinded reviewers. Risk of bias of each study was determined using the QUADAS 2 Scoring System.

RESULTS

13 studies were included (6 case reports/series, 5 retrospective cohorts, and 2 prospective cohorts) with a total of 229 patients. Seven studies reported motor outcome; three reported motor outcome prediction with a sensitivity and specificity ranging from 80 to 85.7 and 69.6 to 100%, respectively; four studies reported visual outcome. In general, the use of DTI was associated with a high degree of favorable neurological outcomes after epilepsy surgery.

CONCLUSION

Multiple studies show that DTI helps to create a tailored plan that results in improved functional outcome. However, more studies are required in order to fully assess its utility in pediatric patients. This is a desirable field of study because DTI offers a non-invasive technique more suitable for children.

ADVANCES IN KNOWLEDGE

This systematic review analyses, exclusively, studies of pediatric patients with drug-resistant epilepsy and provides an update of the evidence regarding the role of DTI, as part of the pre-operative armamentarium, in improving post-surgical neurological sequels and its potential for outcome prediction.

Abnormalities in the thalamo-cortical network in patients with functional constipation

Functional constipation (FCon) is a common functional gastrointestinal disorder (FGID); neuroimaging studies have shown brain functional abnormalities in thalamo-cortical regions in patients with FGID. However, association between FCon and topological characteristics of brain networks remains largely unknown. We employed resting-state functional magnetic resonance imaging (RS-fMRI) and graph theory approach to investigate functional brain topological organization in 42 patients with FCon and 41 healthy controls (HC) from perspectives of global, regional and modular levels. Results showed patients with FCon had a significantly lower normalized clustering coefficient and small-worldness, implying decreased brain functional connectivity. Regions showed altered nodal degree and efficiency mainly located in the thalamus, rostral anterior cingulate cortex (rACC), and supplementary motor area (SMA), which are involved in somatic/sensory, emotional processing and motor-control. For the modular analysis, thalamus, rACC and SMA had an aberrant within-module nodal degree and nodal efficiency, and thalamus-related network exhibited abnormal interaction with the limbic network (amygdala and hippocampal gyrus). Nodal degree in the thalamus was negatively correlated with difficulty of defecation, and nodal degree in the rACC was negatively correlated with sensation of incomplete evacuation. These findings indicated that FCon was associated with abnormalities in the thalamo-cortical network.

A Purinergic P2 Receptor Family-Mediated Increase in Thrombospondin-1 Bolsters Synaptic Density and Epileptic Seizure Activity in the Amygdala-Kindling Rat Model

Previous studies suggested that the thrombospondin-1/transforming growth factor-β1 (TSP-1/TGF-β1) pathway might be critical in synaptogenesis during development and that the purinergic P2 receptor family could regulate synaptogenesis by modulating TSP-1 signaling. However, it is unclear whether this pathway plays a role in synaptogenesis during epileptic progression. This study was designed to investigate this question by analyzing the dynamic changes and effects of TSP-1 levels on the density of synaptic markers that are related to epileptic seizure activity. In addition, we evaluated whether P2-type receptors could regulate these effects. We generated a rat seizure model via amygdala kindling and inhibited TSP-1 activity using small interfering RNA (siRNA) interference and pharmacological inhibition. We treated the rats with antagonists of P2 or P2Y receptors, pyridoxalphosphate-6-azophenyl-2’,4’-disulfonic (PPADS) or Reactive Blue 2. Following this, we quantified TSP-1 and TGF-β1 immunoreactivity (IR), the density of synaptic markers, and seizure activity. There were significantly more synapses/excitatory synapses in several brain regions, such as the hippocampus, which were associated with progressing epileptic discharges after kindling. These were associated with increased TSP-1 and TGF-β1-IR. Genetic or pharmacologic inhibition of TSP-1 significantly reduced the density of synaptic/excitatory synaptic markers and inhibited the generalization of focal epilepsy. The administration of PPADS or Reactive Blue 2 attenuated the increase in TSP-1-IR and the increase in the density of synaptic markers that follows kindling and abolished most of the epileptic seizure activity. Altogether, our results indicate that the TSP-1/TGF-β1 pathway and its regulation by P2, particularly P2Y-type receptors, may be a critical promoter of synaptogenesis during the progression of epilepsy. Therefore, components of this pathway may be targets for novel antiepileptic drug development.

Aberrant brain functional connectome in patients with obstructive sleep apnea

OBJECTIVE

Obstructive sleep apnea (OSA) is accompanied by widespread abnormal spontaneous regional activity related to cognitive deficits. However, little is known about the topological properties of the functional brain connectome of patients with OSA. This study aimed to use the graph theory approaches to investigate the topological properties and functional connectivity (FC) of the functional connectome in patients with OSA, based on resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

Forty-five male patients with newly diagnosed untreated severe OSA and 45 male good sleepers (GSs) underwent a polysomnography (PSG), clinical evaluations, and rs-fMRI scans. The automated anatomical labeling (AAL) atlas was used to construct the functional brain connectome. The topological organization and FC of brain functional networks in patients with OSA were characterized using graph theory methods and investigated the relationship between functional network topology and clinical variables.

RESULTS

Both the patients with OSA and the GSs exhibited high-efficiency "small-world" network attributes. However, the patients with OSA exhibited decreased σ, γ, Eglob; increased Lp, λ; and abnormal nodal centralities in several default-mode network (DMN), salience network (SN), and central executive network (CEN) regions. However, the patients with OSA exhibited abnormal functional connections between the DMN, SN, and CEN. The disrupted FC was significantly positive correlations with the global network metrics γ and σ. The global network metrics were significantly correlated with the Epworth Sleepiness Scale (ESS) score, Montreal Cognitive Assessment (MoCA) score, and oxygen desaturation index.

CONCLUSION

The findings suggest that the functional connectome of patients with OSA exhibited disrupted functional integration and segregation, and functional disconnections of the DMN, SN, and CEN. The aberrant topological attributes may be associated with disrupted FC and cognitive functions. These topological abnormalities and disconnections might be potential biomarkers of cognitive impairments in patients with OSA.

Progressive topological disorganization of brain network in focal epilepsy

OBJECTIVE

Increasing evidence has suggested that epilepsy is a network disease. Graph theory is a mathematical tool that allows for the analysis and quantification of the brain network. We aimed to evaluate the influences of duration of epilepsy on the topological organization of brain network in focal epilepsy patients with normal MRI using the graph theoretical analysis based on diffusion tenor imaging.

METHODS

We prospectively enrolled 66 patients with focal epilepsy (18/66 patients were newly diagnosed) and 84 healthy subjects. All of the patients with epilepsy had normal MRI on visual inspection. All of the subjects underwent diffusion tensor imaging that was analyzed using graph theory to obtain network measures.

RESULTS

The measures of characteristic path length and small-worldness in the patients with focal epilepsy were significantly decreased, even after multiple corrections (P < .01). Moreover, the measures including mean clustering coefficient and global efficiency in the patients with epilepsy had strong tendency to decrease compared to those in healthy subjects (P = .0153 and P = .0138, respectively). When comparing the measures among the patients with newly diagnosed/chronic epilepsy and healthy subjects using ANOVA, the characteristic path length (P = .006), small-worldness (P = .032), and global efficiency (P = .004) were significantly different. In addition, the duration of epilepsy was negatively correlated with global efficiency (r = -.249, P = .0454).

CONCLUSIONS

We newly found a progressive topological disorganization of the brain network in focal epilepsy. In addition, we demonstrated disrupted topological organization in focal epilepsy, shifting toward a more random state.

Structural and effective connectivity in focal epilepsy

Patients with medically-refractory focal epilepsy may be candidates for neurosurgery and some may require placement of intracranial EEG electrodes to localise seizure onset. Assessing cerebral responses to single pulse electrical stimulation (SPES) may give diagnostically useful data. SPES produces cortico-cortical evoked potentials (CCEPs), which infer effective brain connectivity. Diffusion-weighted images and tractography may be used to estimate structural brain connectivity. This combination provides the opportunity to observe seizure onset and its propagation throughout the brain, spreading contiguously along the cortex explored with electrodes, or non-contiguously. We analysed CCEPs and diffusion tractography in seven focal epilepsy patients and reconstructed the effective and structural brain networks. We aimed to assess the inter-modal similarity of the networks at a large scale across the cortex, the effective and structural connectivity of the ictal-onset zone, and investigate potential mechanisms of non-contiguous seizure spread. We found a significant overlap between structural and effective networks. Effective network CCEP amplitude, baseline variation, and outward connectivity was higher at ictal-onset zones, while structural connection strength within the ictal-onset zone tended to be higher. These findings support the concept of hyperexcitable cortex being associated with seizure generation. The high prevalence of structural and effective connections from the ictal-onset zone to sites of non-contiguous spread suggests that macroscopic structural and effective connections are plausible routes for non-contiguous seizure spread.

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Inter-modal network agreement was higher than by chance and correlation was low.

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High CCEP amplitude, baseline variation and outdegree at the ictal-onset zone.

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Streamline density tended to be higher within the ictal-onset zone.

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High ictal-onset zone connectivity to early and late seizure spread sites.

Fibre Dissection and Sectional Study of the Major Porcine Cerebral White Matter Tracts

White matter anatomy is the basis for numerous applications in neurology, neurosurgery and fundamental neuroscience. Although the porcine brain is frequently used as experimental model in these fields of research, the description of its white matter is not as thorough as in the human brain or other species. Thus, the aim of this study is to describe the porcine white matter tracts in a complex manner. Two stepwise dissection protocols adapted from human anatomy were performed on six adult pig brain hemispheres prepared according to the Klingler method. Other four hemispheres were sectioned along section planes that were chosen similar to the Talairach coordinate system. As a result, three commissural tracts, seven association tracts and one projection tract were identified: corpus callosum, fornix, commissura rostralis, the short-association tracts, fasciculus longitudinalis superior, fasciculus uncinatus, fasciculus longitudinalis inferior, fasciculus occipitofrontalis inferior, cingulum, tractus mamillothalamicus and capsula interna. They were described and illustrated from multiple points of view, focusing on their trajectory, position, dimensions and anatomical relations. All in all, we achieved a three-dimensional understanding of the major tracts. The results are ready to be applied in future imagistic or experimental studies.

Disrupted small-world brain functional network topology in male patients with severe obstructive sleep apnea revealed by resting-state fMRI

PURPOSE

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that can damage cognitive function. However, the functional network organization remains poorly understood. The aim of this study was to investigate the topological properties of OSA patients using a graph theoretical analysis.

PATIENTS AND METHODS

A total of 30 male patients with untreated severe OSA and 25 male education- and age-matched good sleepers (GSs) underwent functional magnetic resonance imaging (MRI) examinations. Clinical and cognitive evaluations were conducted by an experienced psychologist. GRETNA (a toolbox for topological analysis of imaging connectomics) was used to construct the brain functional network and calculate the small-world properties (γ, λ, σ, E_glob, and E_loc). Relationships between these small-world properties and clinical and neuropsychological assessments were investigated in OSA patients.

RESULTS

The networks of both OSA patients and GSs exhibited efficient small-world topology over the sparsity range of 0.05-0.40. Compared with GSs, the OSA group had significantly decreased γ, but significantly increased λ and σ. The OSA group's brain network showed significantly decreased E_glob (P<0.05) over the sparsity range of 0.09-0.15, but significantly increased E_loc over the sparsity range of 0.23-0.40. In OSA patients, γ was significantly negatively correlated with apnea-hypopnea index (AHI; r=-0.326, P=0.015) and Epworth Sleepiness Scale (ESS; r=-0.274, P=0.043), λ was significantly positively correlated with AHI (r=0.373, P=0.005) and ESS (r=0.269, P=0.047), and σ was significantly negatively correlated with AHI (r=-0.363, P=0.007) and ESS (r=-0.295, P=0.029).

CONCLUSION

Our results suggest that the high degree of local integration and integrity of the brain connections in OSA patients may be disrupted. The topological alterations of small-world properties may be the mechanism of cognitive impairment in OSA patients. In addition, σ, γ, and λ could be used as a quantitative physiological index for auxiliary clinical diagnoses.

Structural connectivity within neural ganglia: A default small-world network

Diffusion tensor imaging (DTI) provides a unique contrast based on the restricted directionality of water movement in an anisotropic environment. As such, DTI-based tractography can be used to characterize and quantify the structural connectivity within neural tissue. Here, DTI-based connectivity within isolated abdominal ganglia of Aplysia californica (ABG) is analyzed using network theory. In addition to quantifying the regional physical proprieties of the fractional anisotropy and apparent diffusion coefficient, DTI tractography was used to probe inner-connections of local communities, yielding unweighted, undirected graphs that represent community structures. Local and global efficiency, characteristic path lengths and clustering analysis are performed on both experimental and simulated data. The relevant intensity by which these specific nodes communicate is probed through weighted clustering coefficient measurements. Both small-worldness and novel small-world metrics were used as tools to verify the small-world properties for the experimental results. The aim of this manuscript is to categorize the properties exhibited by structural networks in a model neural tissue to derive unique mean field information that quantitatively describe macroscopic connectivity. For ABG, findings demonstrate a default structural network with preferential specific small-world properties when compared to simulated lattice and random networks that are equivalent in order and degree.

Organization and control of epileptic circuits in temporal lobe epilepsy

When studying the pathological mechanisms of epilepsy, there are a seemingly endless number of approaches from the ultrastructural level-receptor expression by EM-to the behavioral level-comorbid depression in behaving animals. Epilepsy is characterized as a disorder of recurrent seizures, which are defined as "a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain" (Fisher et al., 2005). Such abnormal activity typically does not occur in a single isolated neuron; rather, it results from pathological activity in large groups-or circuits-of neurons. Here we choose to focus on two aspects of aberrant circuits in temporal lobe epilepsy: their organization and potential mechanisms to control these pathological circuits. We also look at two scales: microcircuits, ie, the relationship between individual neurons or small groups of similar neurons, and macrocircuits, ie, the organization of large-scale brain regions. We begin by summarizing the large body of literature that describes the stereotypical anatomical changes in the temporal lobe-ie, the anatomical basis of alterations in microcircuitry. We then offer a brief introduction to graph theory and describe how this type of mathematical analysis, in combination with computational neuroscience techniques and using parameters obtained from experimental data, can be used to postulate how microcircuit alterations may lead to seizures. We then zoom out and look at the changes which are seen over large whole-brain networks in patients and animal models, and finally we look to the future.

Structural Brain Network: What is the Effect of LiFE Optimization of Whole Brain Tractography?

Structural brain networks constructed based on diffusion-weighted MRI (dMRI) have provided a systems perspective to explore the organization of the human brain. Some redundant and nonexistent fibers, however, are inevitably generated in whole brain tractography. We propose to add one critical step while constructing the networks to remove these fibers using the linear fascicle evaluation (LiFE) method, and study the differences between the networks with and without LiFE optimization. For a cohort of nine healthy adults and for 9 out of the 35 subjects from Human Connectome Project, the T 1-weighted images and dMRI data are analyzed. Each brain is parcellated into 90 regions-of-interest, whilst a probabilistic tractography algorithm is applied to generate the original connectome. The elimination of redundant and nonexistent fibers from the original connectome by LiFE creates the optimized connectome, and the random selection of the same number of fibers as the optimized connectome creates the non-optimized connectome. The combination of parcellations and these connectomes leads to the optimized and non-optimized networks, respectively. The optimized networks are constructed with six weighting schemes, and the correlations of different weighting methods are analyzed. The fiber length distributions of the non-optimized and optimized connectomes are compared. The optimized and non-optimized networks are compared with regard to edges, nodes and networks, within a sparsity range of 0.75-0.95. It has been found that relatively more short fibers exist in the optimized connectome. About 24.0% edges of the optimized network are significantly different from those in the non-optimized network at a sparsity of 0.75. About 13.2% of edges are classified as false positives or the possible missing edges. The strength and betweenness centrality of some nodes are significantly different for the non-optimized and optimized networks, but not the node efficiency. The normalized clustering coefficient, the normalized characteristic path length and the small-worldness are higher in the optimized network weighted by the fiber number than in the non-optimized network. These observed differences suggest that LiFE optimization can be a crucial step for the construction of more reasonable and more accurate structural brain networks.

Diffusion tensor tractography imaging in pediatric epilepsy - A systematic review

PURPOSE

Recent years brought several experimental and clinical reports applying diffusion tensor tractography imaging (DTI) of the brain in epilepsy. This study was aimed to evaluate current evidence for adding the DTI sequence to the standard diagnostic magnetic resonance imaging (MRI) protocol in pediatric epilepsy.

MATERIAL AND METHODS

Rapid and qualitative systematic review (RAE, Rapid Evidence Assessment), aggregating relevant studies from the recent 7 years. The PubMed database was hand searched for records containing terms "tractography AND epilepsy." Only studies referring to children were included; studies were rated using "final quality of evidence."

RESULTS

Out of 144 screened records, relevant 101 were aggregated and reviewed. The synthesis was based on 73 studies. Case-control clinical studies were the majority of the material and comprised 43.8% of the material. Low 'confirmability' and low 'applicability' referred to 18 and 17 articles (29.5% and 27.9%), respectively. The sufficient quality of evidence supported performing DTI in temporal lobe epilepsy, malformations of cortical development and prior to a neurosurgery of epilepsy.

CONCLUSIONS

The qualitative RAE provides an interim estimate of the clinical relevance of quickly developing diagnostic methods. Based on the critical appraisal of current knowledge, adding the DTI sequence to the standard MRI protocol may be clinically beneficial in selected patient groups with childhood temporal lobe epilepsy or as a part of planning for an epilepsy surgery.

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

BACKGROUND AND PURPOSE

We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to determine lateralization of epileptogenicity in temporal lobe epilepsy (TLE).

MATERIALS AND METHODS

We analyzed diffusion tensor and 3-dimensional (3D) T1-weighted images of 44 patients with TLE (right, 15, left, 29; mean age, 33.0 ± 11.6 years) and 14 age-matched controls. We constructed a whole brain structural connectome for each subject, calculated graph theoretical network measures, and used a support vector machine (SVM) for classification among 3 groups (right TLE versus controls, left TLE versus controls, and right TLE versus left TLE) following a feature reduction process with sparse linear regression.

RESULTS

In left TLE, we found a significant decrease in local efficiency and the clustering coefficient in several brain regions, including the left posterior cingulate gyrus, left cuneus, and both hippocampi. In right TLE, the right hippocampus showed reduced nodal degree, clustering coefficient, and local efficiency. With use of the leave-one-out cross-validation strategy, the SVM classifier achieved accuracy of 75.9 to 89.7% for right TLE versus controls, 74.4 to 86.0% for left TLE versus controls, and 72.7 to 86.4% for left TLE versus right TLE.

CONCLUSION

Machine learning of graph theoretical measures from the DTI structural connectome may give support to lateralization of the TLE focus. The present good discrimination between left and right TLE suggests that, with further refinement, the classifier should improve presurgical diagnostic confidence.