The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortical parcellation was applied to localize the SOZ in cortical nodes of the epileptogenic hemisphere. At each node, the laminar surface analysis was followed to sample 1) the relative intensity of gray matter and white matter in multi-modal MRI and 2) the neighboring white matter connectivity using diffusion tractography edge strengths. A cross-validation was employed to train and test all layers of a multi-scale residual neural network (msResNet) that can classify SOZ node in an end-to-end fashion. A prediction probability of a given node belonging to the SOZ class was proposed as a non-invasive MRI marker of seizure onset likelihood. In an independent validation cohort, the proposed MRI marker provided a very large effect size of Cohen's d = 1.21 between SOZ and non-SOZ, and classified SOZ with a balanced accuracy of 0.75 in lesional and 0.67 in non-lesional MRI groups. The subsequent multi-variate logistic regression found the incorporation of the proposed MRI marker into interictal intracranial EEG (iEEG) markers further improves the differentiation between the epileptogenic focus (defined as SOZ resected during surgery) and non-epileptogenic sites ( i.e. , non-SOZ sites preserved during surgery) up to 15 $\%$ in non-lesional MRI group, suggesting that the proposed MRI marker could improve the localization of epileptogenic foci for successful pediatric epilepsy surgery.

Objective: Structural characteristics of the brain can be analyzed based on structural brain networks constructed by diffusion tensor imaging (DTI). When a brain network is constructed by the existing parcellation method, the structure of the network changes depending on the scale of parcellation and arbitrary thresholding. To overcome these issues, we propose to construct brain networks using the improved μ-neighbor construction method, which is a parcellation free technique. Methods: We acquired DTI data from 14 control subjects and 15 subjects with autism. We examined the differences in topological properties of the brain networks constructed using the proposed method and existing parcellation between the two groups. Results: As the number of nodes increased, the connectedness of the network decreased in the parcellation method. However, for brain networks constructed using our proposed method, connectedness remained at a high level even with an increase in the number of nodes. We found significant differences in several topological properties of brain networks constructed using the proposed method, whereas topological properties were not significantly different for the parcellation method. Conclusion: The brain networks constructed using the proposed method are considered as more realistic than a parcellation method with respect to the stability of connectedness. We found that subjects with autism showed the abnormal characteristics in the brain networks. These results demonstrate that the proposed method may provide new insights to analysis in the structural brain network. Significance: We proposed the novel brain network construction method to overcome the shortcoming in the existing parcellation method.

Hemiparetic stroke is a common motor network disorder that affects a wide range of functional movements due to cortical and subcortical network lesions in stroke patients. Conventional magnetic resonance imaging (MRI) has been used to examine structural brain damage, but the integrity and connectivity of the whole brain are poorly understood. Hence, advanced neuroimaging with diffusion tensor imaging (DTI) has been developed to better localize fiber architecture and connectivity in the motor network or pathways that are responsible for motor impairments in hemiparetic stroke. To ascertain motor network connectivity between the involved and non-involved hemispheres in stroke patients, we analyzed the DTI data from all right hemiparetic stroke patients using fractional anisotropy (FA) and network parameters, including node degree and edge betweenness centrality (EBC). The FA values were substantially lower in the left hemisphere than the right hemisphere. Similarly, the node degree and EBC were significantly lower in the left hemisphere than the right hemisphere. The present brain network analysis may provide a useful neuropathway marker for accurate diagnosis and therapeutic intervention.

An acute stress response is a complex process that activates the neuroendocrine and metabolic systems for homeostasis. A study on acute stress is important to understand how an organism adapts to stress for survival. However, most studies have focused on chronic stress, and there are few studies on acute stress. They have analyzed the metabolic alterations in the brain at a particular time after acute stress. This study explored the temporal variations of the brain metabolites in the hippocampus after acute restraint stress using proton magnetic resonance spectroscopy. All mice in the acute stress group were physically restrained for two hours in a 50 mL conical tube. A 9.4 T animal MRI and MRS scanner was used with point-resolved spectroscopy technique for data acquisition, which was repeated four times without interscan interval. Metabolites were quantified from the data using LCModel with a simulated basis set. Based on the change in concentration of metabolites, the data were statistically analyzed using two-way repeated-measures analysis of variance between groups and a support vector machine for all time points and Student’s t-test with FDR correction for each time point. The present study found that the differences between groups are significantly ( ) presented in alanine and glutamate. The effect of time of the two metabolites significantly exists ( ): the first, second, and third time points in alanine and the first and second time points in glutamate. A combination of stress-specific metabolites (alanine, glutamate, N-acetyl-aspartate) that best reflect the influence of acute stress was determined using a support vector machine. These findings may indicate the importance of the timing of analysis after acute stress and provide new insights into a deeper understanding of acute stress response, including the molecular mechanism of stress-related disorders and stress resilience or vulnerability.

Background/objectives: The acute stress response affects brain metabolites closely linked to the tricarboxylic acid (TCA) cycle. This response involves time-dependent changes in hormones and neurotransmitters, which contribute to resilience and the ability to adapt to acute stress while maintaining homeostasis. This physiological mechanism of metabolic dynamics, combined with time-series analysis, has prompted the development of new methods to observe the relationship between TCA cycle-related brain metabolites. This study aimed to observe the acute stress response through metabolic interactions using time-series proton magnetic resonance spectroscopy (1H-MRS) in the left hippocampus of mice. Methods: In this study, 4-week-old male C57BL/6N mice (n = 24) were divided into control (n = 12) and acute stress groups (n = 12). Acute stress was induced through a 2 h restraint protocol. Time-series 1H-MRS data were obtained on the left hippocampus of both groups using a 9.4 T 1H-MRS scanner. Time-series MRS data were quantified using LCModel, and significant metabolic interactions were identified through Spearman correlation analysis, a one-tailed sign test, and false discovery rate correction. Results: No significant metabolic correlation coefficient was observed in the control group. However, in the acute stress group, glutathione (GSH) and N-acetyl aspartate (NAA) showed a significant positive correlation over time, with a high correlation coefficient exceeding 0.5. Conclusions: Temporal measurement of GSH and NAA, combined with correlation analysis, offers a comprehensive understanding for the metabolic dynamics during acute stress. This approach emphasizes their distinct roles and interdependence in the progression of oxidative stress, mitochondrial function, and the maintenance of physiological homeostasis.