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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Mar 12, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

BrainConnect:处理大脑连接和空间转录组学数据,用于整合性分析.

Chenglong Sang¹, Cheng Peng¹

¹Yunnan Key Laboratory of Cell Metabolism and Disease, and Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China.

Bioinformatics (Oxford, England)

|March 11, 2026

概括

此摘要是机器生成的。

这项研究介绍了一种新的软件,用于整合小鼠大脑连接和空间转录学数据. 开发的框架准确地预测大脑连接的强度,并识别了参与神经电路调节的关键基因.

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Last Updated: Mar 12, 2026

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Published on: September 5, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

科学领域:

神经科学是一个神经科学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

了解大脑神经电路需要表征神经连接体,但目前的方法缺乏分子信息.
全脑空间转录学为预测大脑连接提供了潜力,但缺乏用于整合性分析的标准化处理方法.

研究的目的:

开发一个计算框架来处理各种小鼠大脑连接和空间转录学数据.
用空间转录组学数据预测大脑连接的强度,并识别相关的基因.

主要方法:

开发了一个软件工具来协调不同的小鼠大脑连接数据集与空间转录学数据.
在数据框架内利用长短期内存 (LSTM) 网络,从转录数据中预测连接强度.

主要成果:

该软件能够对大脑连接和空间转录数据进行一致的处理.
该LSTM模型准确地预测了连接强度,并确定了调节大脑连接的潜在基因.

结论:

开发的数据框架和模型促进了多模式大脑数据的整合.
这种方法有助于理解大脑连接和神经回路的遗传基础.