Objective: Deep brain stimulation (DBS) has shown promising outcomes as new therapeutic opportunities for patients with treatment-resistant depression (TRD) who do not respond adequately to several consecutive treatments. This study aims to systematically review and conduct a meta-analysis on the efficacy and safety of DBS for TRD.Method: The literature was comprehensively reviewed using Medline, Google scholar, Cochrane library, Embase, and World Health Organization International Clinical Trials Registry Platform until January 2019. The studied outcomes included response, remission, recurrence, and adverse events (AEs) rates, and were reported as the rate ratio (RR) or pooled estimate with a 95% confidence interval (95% CI). Heterogeneity was measured by an I-square test and a sensitive analysis.Results: A total of 17 studies involving 7 DBS targets were included. For efficacy, DBS treatment was statistically beneficial for TRD, and the response, remission, and recurrence rates were 56% (ranging from 43 to 69%), 35% (ranging from 27 to 44%), and 14% (ranging from 4 to 25%), respectively. However, only two randomized-controlled trials (RCTs) considered the invalidity of DBS (RR = 1.45, 95% CI = 0.50–4.21). For safety, the AEs rate was 67% (ranging from 54 to 80%). The AEs were common and moderate, but the problems related to suicide and suicidal ideation should not be underestimated.Conclusion: These findings suggest that DBS for TRD is considered promising, which should be confirmed by well-designed and large sample studies. Future basic research and comprehensive clinical trials are needed to reach better understanding on the mechanisms of action and optimal targeted structure.

Objective: To report clinical experience with presurgical evaluation in patients with insulo‐opercular epilepsy. Quantitative analysis on PET imaging and stereoelectroencephalography (SEEG) signals was used to summarize their electroclinical features.Methods: Twenty‐two patients with focal epilepsy arising from the insular and/or opercular cortex according to SEEG were retrospectively analyzed. Presurgical noninvasive data were analyzed in detail. Interictal PET data of patients were then statistically compared with those of healthy controls to identify the interictal hypometabolic network. The epileptogenicity index (EI) of ictal SEEG signal was computed to identify areas of spread at the beginning of seizure onset.Results: Focal tonic seizures of the face and/or neck (16/22, 73%) were the most prevalent early objective signs. Epileptic discharges in the interictal and ictal scalp‐EEG mostly showed an ipsilateral perisylvian distribution. Statistical analysis of interictal PET showed significant hypometabolism in the insular lobe, central operculum, supplementary motor area, middle cingulate cortex, bilateral caudate nuclei, and putamen. According to the EI analysis, insulo‐opercular epilepsy could be classified as insulo‐opercular epilepsy (50%), opercular epilepsy (41%), and insular cortex epilepsy (9%).Significance: Clinical diagnosis of insulo‐opercular epilepsy is challenging because of its complex seizure semiology and nonlocalizing discharges on scalp‐EEG. A common hypometabolic network involving the insulo‐opercular cortex, mesial frontal cortex and subcortical nuclei may be involved in the organization of the insulo‐opercular epilepsy network. Furthermore, quantified SEEG analysis suggested that pure insular epilepsy is rare, and the close connection between insular and opercular cortex necessitates SEEG implantation to define the epileptogenic zone.

Significance: Direct electrical stimulation and intracranial recordings from the human brain during cognitive experiments revealed unique information about the temporal order of signal processing within and across intrinsic resting-state networks. Pairs of regions located within the same intrinsic networks communicate faster and coactivate closer in time than those located across different intrinsic networks.

Objective: To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and to assess its clinical value. Methods: Neuroimaging data and clinical information for 74 participants (40 with histologically proven FCD type II) was retrospectively included. The morphology, intensity and function-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface and fed to an artificial neural network. The classifier performance was quantitatively and qualitatively assessed by performing statistical analysis and conventional visual analysis. Results: The accuracy, sensitivity, specificity of the neural network classifier based on multimodal surface-based features were 70.5%, 70.0%, and 69.9%, respectively, which outperformed the unimodal classifier. There was no significant difference in the detection rate of FCD subtypes (Pearson’s Chi-Square = 0.001, p = 0.970). Cohen’s kappa score between automated detection outcomes and post-surgical resection region was 0.385 (considered as fair). Conclusion: Automated machine learning with multimodal surface features can provide objective and intelligent detection of FCD lesion in pre-surgical evaluation and can assist the surgical strategy. Furthermore, the optimal parameters, appropriate surface features and efficient algorithm are worth exploring.