Combination of MRI-based prediction and CRISPR/Cas12a-based detection for IDH genotyping in glioma.

Early identification of IDH mutation status is of great significance in clinical therapeutic decision-making in the treatment of glioma. We demonstrate a technological solution to improve the accuracy and reliability of IDH mutation detection by combining MRI-based prediction and a CRISPR-based automatic integrated gene detection system (AIGS). A model was constructed to predict the IDH mutation status using whole slices in MRI scans with a Transformer neural network, and the predictive model achieved accuracies of 0.93, 0.87, and 0.84 using the internal and two external test sets, respectively. Additionally, CRISPR/Cas12a-based AIGS was constructed, and AIGS achieved 100% diagnostic accuracy in terms of IDH detection using both frozen tissue and FFPE samples in one hour. Moreover, the feature attribution of our predictive model was assessed using GradCAM, and the highest correlations with tumor cell percentages in enhancing and IDH-wildtype gliomas were found to have GradCAM importance (0.65 and 0.5, respectively). This MRI-based predictive model could, therefore, guide biopsy for tumor-enriched, which would ensure the veracity and stability of the rapid detection results. The combination of our predictive model and AIGS improved the early determination of IDH mutation status in glioma patients. This combined system of MRI-based prediction and CRISPR/Cas12a-based detection can be used to guide biopsy, resection, and radiation for glioma patients to improve patient outcomes.

Structural network topologies are associated with deep brain stimulation outcomes in Meige syndrome.

Deep brain stimulation (DBS) is an effective therapy for Meige syndrome (MS). However, the DBS efficacy varies across MS patients and the factors contributing to the variable responses remain enigmatic. We aim to explain the difference in DBS efficacy from a network perspective. We collected preoperative T1-weighted MRI images of 76 MS patients who received DBS in our center. According to the symptomatic improvement rates, all MS patients were divided into two groups: the high improvement group (HIG) and the low improvement group (LIG). We constructed group-level structural covariance networks in each group and compared the graph-based topological properties and interregional connections between groups. Subsequent functional annotation and correlation analyses were also conducted. The results indicated that HIG showed a higher clustering coefficient, longer characteristic path length, lower small-world index, and lower global efficiency compared with LIG. Different nodal betweennesses and degrees between groups were mainly identified in the precuneus, sensorimotor cortex, and subcortical nuclei, among which the gray matter volume of the left precentral gyrus and left thalamus were positively correlated with the symptomatic improvement rates. Moreover, HIG had enhanced interregional connections within the somatomotor network and between the somatomotor network and default-mode network relative to LIG. We concluded that the high and low DBS responders have notable differences in large-scale network architectures. Our study sheds light on the structural network underpinnings of varying DBS responses in MS patients.

Dynamic functional connectivity changes associated with psychiatric traits and cognitive deficits in Cushing's disease.

Cushing's disease is a rare neuroendocrine disorder with excessive endogenous cortisol, impaired cognition, and psychiatric symptoms. Evidence from resting-state fMRI revealed the abnormalities of static brain connectivity in patients with Cushing's disease (CD patients). However, it is unknown whether the CD patients' dynamic functional connectivity would be abnormal and whether the dynamic features are associated with deficits in cognition and psychopathological symptoms. Here, we evaluated 50 patients with Cushing's disease and 57 healthy participants by using resting-state fMRI and dynamic functional connectivity (dFNC) approach. We focused on the dynamic features of default mode network (DMN), salience network (SN), and central executive network (CEN) because these are binding sites for the cognitive-affective process, as well as vital in understanding the pathophysiology of psychiatric disorders. The dFNC was further clustered into four states by k-mean clustering. CD patients showed more dwell time in State 1 but less time in State 4. Intriguingly, group differences in dwell time in these two states can explain the cognitive deficits of CD patients. Moreover, the inter-network connections between DMN and SN and the engagement time in State 4 negatively correlated with anxiety and depression but positively correlated with cognitive performance. Finally, the classifier trained by the dynamic features of these networks successfully classified CD patients from healthy participants. Together, our study revealed the dynamic features of CD patients' brains and found their associations with impaired cognition and emotional symptoms, which may open new avenues for understanding the cognitive and affective deficits induced by Cushing's disease.

Rapid detection of isocitrate dehydrogenase 1 mutation status in glioma based on Crispr-Cas12a.

The aim is to use Crispr-Cas12a for the rapid detection of the single nucleotide polymorphism (SNP) of isocitrate dehydrogenase 1 (IDH1)-R132H locus and explore the effectiveness and consistency of this method with direct sequencing method for detecting IDH1-R132H of glioma tissue samples. 58 previous frozen tissue and 46 recent fresh tissue samples of adult diffuse glioma were selected to detect IDH1-R132H using Crispr-Cas12a. The results of immunohistochemistry (IHC) and direct sequencing methods were analyzed. We calculated the efficiency index of Crispr-Cas12a and IHC, and analyzed the consistency among Crispr-Cas12a, IHC and direct sequencing method using paired Chi-sequare test and Kappa identity test. We accomplished the rapid detection of IDH1-R132H in 60 min using Crispr-Cas12a. Regarding direct sequencing method as the gold standard, the sensitivity, specificity and consistency rate of Crispr-Cas12a was 91.4%, 95.7% and 93.1% in the frozen sample group, while 96.1%, 89.7% and 92.0% in the fresh sample group, respectively. Kappa test showed good consistency between the two methods (k = 0.858). Crispr-Cas12a can quickly and accurately detect IDH1-R132H and has good stability. It is a promising method to detect IDH1 mutation status intraoperatively.

Cerebellar gray matter alterations predict deep brain stimulation outcomes in Meige syndrome.

BACKGROUND: The physiopathologic mechanism of Meige syndrome (MS) has not been clarified, and neuroimaging studies centering on cerebellar changes in MS are scarce. Moreover, even though deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been recognized as an effective surgical treatment for MS, there has been no reliable biomarker to predict its efficacy. OBJECTIVE: To characterize the volumetric alterations of gray matter (GM) in the cerebellum in MS and to identify GM measurements related to a good STN-DBS outcome. METHODS: We used voxel-based morphometry and lobule-based morphometry to compare the regional and lobular GM differences in the cerebellum between 47 MS patients and 52 normal human controls (HCs), as well as between 31 DBS responders and 10 DBS non-responders. Both volumetric analyses were achieved using the Spatially Unbiased Infratentorial Toolbox (SUIT). Further, we performed partial correlation analyses to probe the relationship between the cerebellar GM changes and clinical scores. Finally, we plotted the receiver operating characteristic (ROC) curve to select biomarkers for MS diagnosis and DBS outcomes prediction. RESULTS: Compared to HCs, MS patients had GM atrophy in lobule Crus I, lobule VI, lobule VIIb, lobule VIIIa, and lobule VIIIb. Compared to DBS responders, DBS non-responders had lower GM volume in the left lobule VIIIb. Moreover, partial correlation analyses revealed a positive relationship between the GM volume of the significant regions/lobules and the symptom improvement rate after DBS surgery. ROC analyses demonstrated that the GM volume of the significant cluster in the left lobule VIIIb could not only distinguish MS patients from HCs but also predict the outcomes of STN-DBS surgery with high accuracy. CONCLUSION: MS patients display bilateral GM shrinkage in the cerebellum relative to HCs. Regional GM volume of the left lobule VIIIb can be a reliable biomarker for MS diagnosis and DBS outcomes prediction.

One-pass deep brain stimulation of subthalamic nucleus and ventral intermediate nucleus for levodopa-resistant tremor-dominant Parkinson's disease.

Objective: Tremor-dominant Parkinson's disease (TD-PD) can be further separated into levodopa-responsive and levodopa-resistant types, the latter being considered to have a different pathogenesis. Previous studies indicated that deep brain stimulation (DBS) of the subthalamic nucleus (STN) or the globus pallidus internus (GPi) individually was not sufficient for tremor control, especially for the levodopa-resistant TD-PD (LRTD-PD). The thalamic ventral intermediate nucleus (VIM) has been regarded as a potent DBS target for different kinds of tremors. Therefore, we focused on the LRTD-PD subgroup and performed one-pass combined DBSs of STN and VIM to treat refractory tremors, aiming to investigate the safety and effectiveness of this one-trajectory dual-target DBS scheme. Methods: We retrospectively collected five LRTD-PD patients who underwent a one-pass combined DBS of STN and VIM via a trans-frontal approach. The targeting of VIM was achieved by probabilistic tractography. Changes in severity of symptoms (measured by the Unified Parkinson Disease Rating Scale part III, UPDRS-III), levodopa equivalent daily doses (LEDD), and disease-specific quality of life (measured by the 39-item Parkinson's Disease Questionnaire, PDQ-39) were evaluated. Results: Three-dimensional reconstruction of electrodes illustrated that all leads were successfully implanted into predefined positions. The mean improvement rates (%) were 53 ± 6.2 (UPDRS-III), 82.6 ± 11.4 (tremor-related items of UPDRS), and 52.1 ± 11.4 (PDQ-39), respectively, with a mean follow-up of 11.4 months. Conclusion: One-pass combined DBS of STN and VIM via the trans-frontal approach is an effective and safe strategy to alleviate symptoms for LRTD-PD patients.

Perfusion magnetic resonance imaging in the differentiation between glioma recurrence and pseudoprogression: a systematic review, meta-analysis and meta-regression.

Background: Tumor recurrence and pseudoprogression (PsP) have similar imaging manifestations in conventional magnetic resonance imaging (MRI), although the subsequent treatments are completely different. This study aimed to evaluate the value of perfusion-weighted imaging (PWI) in differentiating PsP from glioma recurrence. Methods: A comprehensive literature search was performed to evaluate clinical studies focused on differentiating recurrent glioma from PsP using PWI, including dynamic susceptibility contrast MRI (DSC-MRI), dynamic contrast enhanced MRI (DCE-MRI), and arterial spin labeling (ASL). Study selection and data extraction were independently completed by two reviewers. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was applied to evaluate the quality of the included studies. The software Stata 16.0 and Meta-Disc 1.4 were used for the meta-analysis. Meta-regression and subgroup analyses were applied to identify the sources of heterogeneity in the studies. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) prior to initiation (CRD42022304404). Results: A total of 40 studies were included, including 27 English studies and 13 Chinese studies. There were 1,341 patients with glioma recurrence and 876 patients with PsP. The pooled sensitivity and specificity of DSC-MRI for differentiating glioma recurrence from PsP were 0.82 [95% confidence interval (CI): 0.78 to 0.86] and 0.87 (95% CI: 0.80 to 0.92), respectively. The pooled sensitivity and specificity of DCE-MRI were 0.83 (95% CI: 0.76 to 0.89) and 0.83 (95% CI: 0.78 to 0.87), respectively. The pooled sensitivity and specificity of ASL were 0.80 (95% CI: 0.73 to 0.86) and 0.86 (95% CI: 0.76 to 0.92), respectively. Discussion: The DSC-MRI, DCE-MRI, and ASL perfusion techniques displayed high accuracy in distinguishing glioma recurrence from PsP, and DSC-MRI had a higher diagnostic performance than the other two techniques. However, due to the diversity of the parameters and threshold differences, further investigation and standardization are needed.