Precise quantification and localization of tracer-labeled neurons are essential for unraveling brain connectivity patterns and constructing a mesoscopic brain connectome atlas in macaques. However, methodological challenges and limitations in dataset development have impeded this scientific progress. This work introduced the Macaque Fluorescently Labeled Neurons (MFN) dataset, derived from retrograde tracing on three rhesus macaques. The dataset, meticulously annotated by six specialists, includes 1,600 images and 33,411 high-quality neuron annotations. Leveraging this dataset, we developed a Dense Convolutional Attention U-Net (DAUNet) cell counting model. By integrating Dense Convolutional blocks and a multi-scale attention module, the model exhibits robust feature extraction and representation capabilities while maintaining low complexity. On the MFN dataset, DAUNet achieved a Mean Absolute Error of 0.97 for cell counting and an F1-score of 96.29% for cell localization, outperforming several benchmark models. Extensive validation across four additional public datasets demonstrated the robust generalization ability of the model. Furthermore, the trained model was applied to quantify labeled neurons of a macaque brain, mapping the input connectivity patterns of two adjacent subregions in the lateral prefrontal cortex. This work provides a training dataset and algorithmic resource that advances mesoscopic brain connectivity research in macaques. The MFN dataset and source code are available at https://github.com/Gendwar/DAUnet.

Lysergic acid diethylamide (LSD) has shown remarkable potential in modulating brain functional organization and dynamics. However, the exact mechanisms underlying its effects remain unclear. In this study, we employed a data-driven approach to analyze recurrent functional connectivity patterns in resting-state fMRI data and developed a parameterized feedback inhibition model to characterize excitatory/inhibitory (E/I) balance. The findings demonstrate that LSD enhances global brain synchrony and dynamic complexity. This enhanced synchrony likely stems from LSD's preferential stabilization of a globally synchronized yet functionally non-modular brain state - a pattern showing higher occurrence probability and acts as an "attractor" that recruits transitions from cognitive control networks. Crucially, these phenomena appear underpinned by LSD-induced convergence of excitatory/inhibitory balance across cortical hierarchies, particularly through Sensorimotor (SOM) suppression coupled with transmodal potentiation, where the Sensorimotor cortices emerge as potential regulatory hubs driving this neurochemical rebalancing. These convergent effects are consistent with the emergence of a brain state characterized by weakened sensory anchoring and enhanced cognitive flexibility, where the typical separation between concrete perception and abstract cognition becomes blurred. This neurophysiological remodeling therefore suggests a potential mechanism that could contribute to LSD's hallucinatory effects and its therapeutic potential in mental disorders characterized by rigid thought patterns.

Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.

Motivation: It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects by modifying the data matrices, it is still difficult to remove the batch effects entirely. The remaining batch effect can cause artifacts in the detection of patterns in the data. Results: In this study, we consider the batch effect issue in the pattern detection among the samples, such as clustering, dimension reduction and construction of networks between subjects. Instead of adjusting the original data matrices, we design an adaptive method to directly adjust the dissimilarity matrix between samples. In simulation studies, the method achieved better results recovering true underlying clusters, compared to the leading batch effect adjustment method ComBat. In real data analysis, the method effectively corrected distance matrices and improved the performance of clustering algorithms. Availability and implementation: The R package is available at: https://github.com/tengfei-emory/QuantNorm. Supplementary information: Supplementary data are available at Bioinformatics online.