Chen, T.-L. | bioRxiv Science

bioRxiv2023

The Complexity of Functional Connectivity Profiles of the Subgenual Anterior Cingulate Cortex and Dorsal Lateral Prefrontal Cortex in Major Depressive Disorder: a DIRECT Consortium Study

BackgroundThe subgenual anterior cingulate cortex (sgACC) plays a central role in the pathophysiology of major depressive disorder (MDD), and its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) is associated with transcranial magnetic stimulation (TMS) treatment outcomes. Nevertheless, previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power. Furthermore, calculating sgACC-FC to target TMS individually is challenging. MethodsLeveraging a large multi-site cross-sectional sample (1660 MDD patients vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT), we systematically delineated case-control difference maps of sgACC-FC. Then, we explored the potential impact of such group-level abnormality profiles on the TMS target localization and clinical efficacy. Next, we developed an MDD big data-guided individualized TMS targeting algorithm to integrate group-level statistical maps with individual-level brain activity to localize TMS targets individually. ResultsWe found an enhanced sgACC-DLPFC FC in MDD patients compared to healthy controls (HC). Such group differences altered the position of the sgACC anti-correlation peak in the left DLPFC. In two independent clinical samples, we showed that the magnitude of TMS targets case-control differences in sgACC FC was related to clinical improvement. The MDD big data-guided individualized TMS targeting algorithm may generate individualized TMS targets that are clinically superior to group-level targets. InterpretationWe reliably delineated MDD-related abnormalities of sgACC-FC profiles in a large, independently ascertained sample and demonstrated the potential impact of such case-control differences on FC-guided localization of TMS targets. FundingMinistry of Science and Technology of the Peoples Republic of China, National Natural Science Foundation of China, and Chinese Academy of Sciences

neuroscience↗

bioRxiv2022

RE2DC: a robust and efficient 2D classifier with visualization for processing massive and heterogeneous cryo-EM data

Single-particle cryo-electron microscopy (cryo-EM) increasingly generates millions of particle images, yet two-dimensional (2D) classification remains a major bottleneck because existing approaches balance computational efficiency against robustness to noise, outliers and structural heterogeneity. We introduce RE2DC (Robust and Efficient 2D Classifier), an algorithmic framework that resolves this trade-off through dynamic linear-time clustering, dimension-reduction multi-reference alignment, and offers real-time interactive t-SNE visualization. Rather than relying primarily on hardware acceleration, RE2DC reduces the computational cost of robust clustering and employs de-noised images for alignment, enabling efficient execution on standard multi-core CPUs. Across diverse benchmark datasets, RE2DC achieves class homogeneity comparable to ISAC while processing datasets three- to ten-fold faster per classification round than RELION. Notably, RE2DC resolves rare, structurally coherent particle populations, enabling detection of transient conformational intermediates and supporting near real-time cryo-EM analysis. By addressing algorithmic complexity, RE2DC establishes a general framework for robust, scalable analysis of massive and heterogeneous image datasets.

bioinformatics↗