Objective This study aimed to investigate the morphometric differences in the corpus callosum between patients with major depressive disorder (MDD) and healthy controls and analyze their relationship to gray matter changes.Methods Twenty female MDD patients and 21 healthy controls (HCs) were included in the study. To identify the difference in the regional gray matter concentration (GMC), VBM was performed with T1 magnetic resonance imaging. The shape analysis of the corpus callosum was processed. Diffusion tensor imaging (DTI) fiber-tracking was performed to identify the regional tract pathways in the damaged corpus callosal areas.Results In the shape analysis, regional shape contractions in the rostrum and splenium were found in the MDD patients. VBM analysis showed a significantly lower white matter concentration in the genu and splenium, and a significantly lower GMC in the frontal, limbic, insular, and temporal regions of the MDD patients compared to the HCs. In DTI fiber-tracking, the fibers crossing the damaged areas of the genu, rostrum, and splenium were anatomically connected to the areas of lower GMC in MDD patients.Conclusion These findings support that major depressive disorder may be due to disturbances in multiple neuronal circuits, especially those associated with the corpus callosum.

An ischemic cerebral stroke results from the interruption of blood flow to the brain, triggering rapid and complex cascades of excitotoxicity, oxidative stress, and inflammation. Current reperfusion therapies, including intravenous thrombolysis and mechanical thrombectomy, cause further brain injury due to reperfusion-induced cytotoxicity. To date, novel cytoprotective therapies that could address these challenges have yet to be developed, likely due to the limitations of targeting a single pathologic mechanism. To address these unmet clinical needs, we investigated a synthetic verbenone derivative, SP-8356, as a potential multi-target cytoprotective agent for acute ischemic strokes. In transient middle cerebral artery occlusion (MCAO) rats, SP-8356 significantly reduced brain infarct and edema volumes while improving acute neurological deficits in a dose-dependent manner. Furthermore, SP-8356 improved long-term outcomes, particularly by reducing mortality. These potent cytoprotective effects of SP-8356 were achieved by suppressing the excessive production of free radicals and pro-inflammatory cytokines, reducing the infiltration of inflammatory cells, and mitigating increases in blood–brain barrier permeability. Additional research is needed to determine whether co-administration of SP-8356 can extend the therapeutic time window of reperfusion therapies by mitigating ischemia/reperfusion injury.

Background: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Methods: Data came from 30 original studies in PubMed with the following search terms: “neuroimaging” (title) together with “machine learning” (title) or ”deep learning” (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and/or variable importance; the publication year of 2019 or later; and the publication language of English. Results: The performance outcomes reported were within 58–96 for accuracy (%), 66–97 for sensitivity (%), 76–98 for specificity (%), and 70–98 for the AUC (%). The support vector machine and the convolutional neural network registered the best performance (AUC 98%) for the classifications of low- vs. high-grade glioma and brain conditions, respectively. Likewise, the random forest delivered the best performance (root mean square error 1) for the regression of brain conditions. The following factors were discovered to be major predictors of brain image or associated disease: (demographic) age, education, sex; (health-related) alpha desynchronization, Alzheimer’s disease stage, CD4, depression, distress, mild behavioral impairment, RNA sequencing; (neuroimaging) abnormal amyloid-β, amplitude of low-frequency fluctuation, cortical thickness, functional connectivity, fractal dimension measure, gray matter volume, left amygdala activity, left hippocampal volume, plasma neurofilament light, right cerebellum, regional homogeneity, right middle occipital gyrus, surface area, sub-cortical volume. Conclusion: Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications.

Cerebellar mutism (CM) is a rare neurological condition characterized by lack of speech due to cerebellar lesions. CM is often reported in children. We describe a rare case of CM after spontaneous cerebellar hemorrhage. The patient showed mutism, irritability, decreased spontaneous movements and oropharyngeal apraxia. Diffusion tensor imaging revealed significant volume reduction of medial frontal projection fibers from the corpus callosum. In Tracts Constrained by UnderLying Anatomy (TRACULA) analysis, forceps major and minor and bilateral cingulum-angular bundles were not visualized. Cerebello-frontal pathway reconstructed from the FMRIB Software Library showed continuity of fibers, with decreased number of fibers on qualitative analysis. These results suggest that cerebello-frontal disconnection may be a neuroanatomical mechanism of CM. Damage of brain network between occipital lobe, cingulate and cerebellum caused by hemorrhage may also have role in the mechanism of CM in our case.