Relationship Between Seizure Frequency and Functional Abnormalities in Limbic Network of Medial Temporal Lobe Epilepsy

Background: We compared resting-state functional connectivity (RSFC) among limbic and temporal lobe regions between patients with medial temporal lobe epilepsy (mTLE) and healthy control subjects to identify imaging evidence of functional networks related to seizure frequency, age of seizure onset, and duration of epilepsy.Methods: Twelve patients with drug-resistant, unilateral medial temporal lobe epilepsy and 12 healthy control subjects matched for age, sex, and handedness participated in the imaging experiments. We used network-based statistics to compare functional connectivity graphs in patients with mTLE and healthy controls to investigate the relationship between functional connectivity abnormalities and seizure frequency.Results: Among mTLE patients, we found functional network abnormalities throughout the limbic system, but primarily in the hemisphere ipsilateral to the seizure focus. The RSFCs between ipsilateral hypothalamus and ventral anterior cingulate cortex and between ipsilateral subiculum and contralateral posterior cingulate cortex were highly correlated with seizure frequency.Discussion: These findings suggest that in mTLE, changes in limbic networks ipsilateral to the epileptic focus are common. The pathological changes in connectivity between cingulate cortex, hypothalamus and subiculum ipsilateral to the seizure focus were correlated with increased seizure frequency

The effect of meditation on brain structure: cortical thickness mapping and diffusion tensor imaging

A convergent line of neuroscientific evidence suggests that meditation alters the functional and structural plasticity of distributed neural processes underlying attention and emotion. The purpose of this study was to examine the brain structural differences between a well-matched sample of long-term meditators and controls. We employed whole-brain cortical thickness analysis based on magnetic resonance imaging, and diffusion tensor imaging to quantify white matter integrity in the brains of 46 experienced meditators compared with 46 matched meditation-naïve volunteers. Meditators, compared with controls, showed significantly greater cortical thickness in the anterior regions of the brain, located in frontal and temporal areas, including the medial prefrontal cortex, superior frontal cortex, temporal pole and the middle and interior temporal cortices. Significantly thinner cortical thickness was found in the posterior regions of the brain, located in the parietal and occipital areas, including the postcentral cortex, inferior parietal cortex, middle occipital cortex and posterior cingulate cortex. Moreover, in the region adjacent to the medial prefrontal cortex, both higher fractional anisotropy values and greater cortical thickness were observed. Our findings suggest that long-term meditators have structural differences in both gray and white matter

Right Hemisphere Lateralization in Neural Connectivity Within Fronto-Parietal Networks in Non-human Primates During a Visual Reaching Task

A fronto-parietal network, comprised of the posterior parietal cortex (PPC) and the dorsal premotor cortex (PMd) has been proposed to be involved in planning and guiding movement. However, the issue of how the network is expressed across the bilateral cortical area according to the effector's side remains unclear. In this study, we tested these questions using electrocorticographic (ECoG) recordings in non-human primates and using a simple visual guided reaching task that induced a left or right hand response based on relevant cues provided for the task. The findings indicate that right hemisphere lateralized network patterns in which the right PMd was strongly coordinated with bilateral PPC immediately after presentation of the movement cue occurred, while the coherence with the left PMd was not enhanced. No difference was found in the coherence pattern between the effector's side (left hand or right hand), but the strength of coherence was different, in that animals showed a higher coherence in the right hand response compared to the left. Our data support that right lateralization in long-range phase synchrony in the 10–20 Hz low beta band is involved in motor preparation stage, irrespective of the upcoming effector's side

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex

Altered Amygdala Resting-State Functional Connectivity and Hemispheric Asymmetry in Patients With Social Anxiety Disorder

Background: The amygdala plays a key role in emotional hyperreactivity in response to social threat in patients with social anxiety disorder (SAD). We investigated resting-state functional connectivity (rs-FCN) of the left and right amygdala with various brain regions and functional lateralization in patients with SAD.Methods: A total of 36 patients with SAD and 42 matched healthy controls underwent functional magnetic resonance imaging (fMRI) at rest. Using the left and right amygdala as seed regions, we compared the strength of the rs-FCN in the patient and control groups. Furthermore, we investigated group differences in the hemispheric asymmetry of the functional connectivity maps of the left and right amygdala.Results: Compared with healthy controls, the rs-FCN between the left amygdala and the dorsolateral prefrontal cortex was reduced in patients with SAD, whereas left amygdala connectivity with the fusiform gyrus, anterior insula, supramarginal gyrus, and precuneus was increased or positively deflected in the patient group. Additionally, the strength rs-FCN between the left amygdala and anterior insula was positively associated with the severity of the fear of negative evaluation in patients with SAD (r = 0.338, p = 0.044). The rs-FCN between the right amygdala and medial frontal gyrus was decreased in patients with SAD compared with healthy controls, whereas connectivity with the parahippocampal gyrus was greater in the patient group than in the control group. The hemispheric asymmetry patterns in the anterior insula, intraparietal sulcus (IPS), and inferior frontal gyrus of the patient group were opposite those of the control group, and functional lateralization of the connectivity between the amygdala and the IPS was associated with the severity of social anxiety symptoms (r = 0.365, p = 0.037).Conclusion: Our findings suggest that in addition to impaired fronto-amygdala communication, the functional lateralization of amygdala function plays a central role in the pathophysiology of SAD

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

Multimodal features of structural and functional magnetic resonance imaging (MRI) of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE) and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001) accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function) support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis