Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes

Uncovering spatial representations from large-scale ensemble spike activity in specific brain circuits provides valuable feedback in closed-loop experiments. We develop a graphics processing unit (GPU)-powered population-decoding system for ultrafast reconstruction of spatial positions from rodents' unsorted spatiotemporal spiking patterns, during run behavior or sleep. In comparison with an optimized quad-core central processing unit (CPU) implementation, our approach achieves an ∼20- to 50-fold increase in speed in eight tested rat hippocampal, cortical, and thalamic ensemble recordings, with real-time decoding speed (approximately fraction of a millisecond per spike) and scalability up to thousands of channels. By accommodating parallel shuffling in real time (computation time <15 ms), our approach enables assessment of the statistical significance of online-decoded "memory replay" candidates during quiet wakefulness or sleep. This open-source software toolkit supports the decoding of spatial correlates or content-triggered experimental manipulation in closed-loop neuroscience experiments.status: Published onlin

Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes

Uncovering spatial representations from large-scale ensemble spike activity in specific brain circuits provides valuable feedback in closed-loop experiments. We develop a graphics processing unit (GPU)-powered population-decoding system for ultrafast reconstruction of spatial positions from rodents’ unsorted spatiotemporal spiking patterns, during run behavior or sleep. In comparison with an optimized quad-core central processing unit (CPU) implementation, our approach achieves an ∼20- to 50-fold increase in speed in eight tested rat hippocampal, cortical, and thalamic ensemble recordings, with real-time decoding speed (approximately fraction of a millisecond per spike) and scalability up to thousands of channels. By accommodating parallel shuffling in real time (computation time <15 ms), our approach enables assessment of the statistical significance of online-decoded “memory replay” candidates during quiet wakefulness or sleep. This open-source software toolkit supports the decoding of spatial correlates or content-triggered experimental manipulation in closed-loop neuroscience experiments. The hippocampal and neocortical neuronal ensembles encode rich spatial information in navigation. Hu et al. develop computational techniques that accommodate real-time decoding and assessment of large-scale unsorted neural ensemble place codes during running behavior and sleep. Keywords: neural decoding; population decoding; place codes; GPU; memory replay; spatiotemporal patternsNational Science Foundation (U.S.) (Grant IIS-130764)National Institutes of Health (U.S.) (Grant R01-MH118928)National Institutes of Health (U.S.) (Grant R01-MH092638)National Institutes of Health (U.S.) (Grant TR01-GM104948)National Institutes of Health (U.S.) (Grant R21-EY028381)National Science Foundation (U.S.) (Grant CCF-1231216

Rapid discrimination between tuberculosis and sarcoidosis using next-generation sequencing

Objectives: Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (MTB), has similar clinical, radiological, and histopathological characteristics to sarcoidosis (SA). Accurately distinguishing SA from TB remains a clinical challenge. Methods: A total of 44 TB patients and 47 SA patients who were clinically diagnosed using chest radiography, pathological examination, routine smear microscopy, and microbial culture were enrolled in this study. The MTB genome was captured and sequenced directly from tissue specimens obtained upon operation or biopsy, and the feasibility of next-generation sequencing (NGS) for the MTB genome in the differential diagnosis of TB from SA was evaluated. Results: Using a depth >10× and coverage >15% of the sequencing data, TB patients were identified via the NGS approach directly using operation or biopsy specimens without clinical pretreatment. The sensitivity, specificity, and concordance of the NGS method were 81.8% (36/44), 95.7% (45/47), and 89.0% (81/91), respectively (kappa = 0.78, 95% confidence interval 0.65–0.91; P < 0.001). Conclusions: This study established an improved NGS strategy for rapidly distinguishing patients with TB from those with SA and has potential clinical benefits

Lung tumor discrimination by deep neural network model CanDo via DNA methylation in bronchial lavage

Summary: Bronchoscopic-assisted discrimination of lung tumors presents challenges, especially in cases with contraindications or inaccessible lesions. Through meta-analysis and validation using the HumanMethylation450 database, this study identified methylation markers for molecular discrimination in lung tumors and designed a sequencing panel. DNA samples from 118 bronchial washing fluid (BWF) specimens underwent enrichment via multiplex PCR before targeted methylation sequencing. The Recursive Feature Elimination Cross-Validation and deep neural network algorithm established the CanDo classification model, which incorporated 11 methylation features (including 8 specific to the TBR1 gene), demonstrating a sensitivity of 98.6% and specificity of 97.8%. In contrast, bronchoscopic rapid on-site evaluation (bronchoscopic-ROSE) had lower sensitivity (87.7%) and specificity (80%). Further validation in 33 individuals confirmed CanDo’s discriminatory potential, particularly in challenging cases for bronchoscopic-ROSE due to pathological complexity. CanDo serves as a valuable complement to bronchoscopy for the discriminatory diagnosis and stratified management of lung tumors utilizing BWF specimens