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相关概念视频

Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Functions of Connective Tissues01:17

Functions of Connective Tissues

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Connective tissues perform a broad range of functions in the body. Their primary function is to connect and link different tissues in the body and act as packaging material between tissues. The areolar tissue, a connective tissue prototype, commonly cements various tissue types in diverse body organs. In contrast, adipose tissue cushions internal organs while insulating the body from heat loss.
Hard connective tissues, such as bones and cartilage, provide structure and support to the body.
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Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

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Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
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Functional Brain Systems: Limbic System01:15

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The limbic system, often called the "emotional brain," is a complex set of structures located deep within the brain. The intricate network of the limbic system supports a wide range of psychological functions, from emotional regulation to memory formation and sensory processing. This functional brain region encompasses specific parts of the diencephalon and the cerebrum, integrating the higher mental functions of the cerebral cortex with the primitive emotional responses of the deep brain...
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Higher Mental Functions of the Brain: Language01:10

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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In biological systems, most metabolic pathways are interconnected. The cellular respiration processes that convert glucose to ATP—such as glycolysis, pyruvate oxidation, and the citric acid cycle—tie into those that break down other organic compounds. As a result, various foods—from apples to cheese to guacamole—end up as ATP. In addition to carbohydrates, food also contains proteins and lipids—such as cholesterol and fats. All of these organic compounds are used...
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相关实验视频

Updated: Feb 7, 2026

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging
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TMN:学习多时间尺度的功能连接,用于识别大脑疾病.

Jinlong Hu1, Jianmiao Luo1, Jiatong Huang1

  • 1Guangdong Key Lab of Multimodal Big Data Intelligent Analysis, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.

Psychiatry research. Neuroimaging
|February 5, 2026
PubMed
概括
此摘要是机器生成的。

这项研究使用多时间尺度功能连接 (mFC) 和新型深度学习模型 (TMN) 识别了大脑疾病. 研究结果显示,mFC在诊断任务中表现优于静态和动态方法.

关键词:
脑部疾病 脑部疾病深度多个实例学习多时间尺度的功能连接性.

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Immunohistochemical Visualization of Hippocampal Neuron Activity After Spatial Learning in a Mouse Model of Neurodevelopmental Disorders
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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相关实验视频

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Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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科学领域:

  • 神经科学是一个神经科学.
  • 计算精神病学是一种计算精神病学.
  • 机器学习 机器学习

背景情况:

  • 功能连接 (FC) 对于识别大脑疾病至关重要.
  • 现有的方法通常在单个时间尺度上分析FC,可能缺少复杂的神经动态.

研究的目的:

  • 通过分析跨多个时间尺度的功能连接来识别大脑疾病.
  • 开发和评估一个深度学习模型,以捕捉多时尺度功能连接 (mFC) 中的时空模式.

主要方法:

  • 休息状态fMRI数据被细分为计算大脑区域之间的mFC.
  • 开发了一种深度多实例学习 (MIL) 方法,即双阶段多流网络 (TMN),用于分析mFC.
  • 在ABIDE I和REST-Meta-MDD数据集上验证了TMN模型.

主要成果:

  • 使用mFC的TMN模型在识别大脑疾病方面取得了优异的表现,与静态FC和静态/动态FC组合方法相比.
  • 使用输入Xgrad的模型解释强调了FC在所有时间范围内对疾病识别的贡献.
  • 确定了与大脑疾病密切相关的特定重要的FC.

结论:

  • 拟议的TMN与mFC有效识别大脑疾病.
  • 功能连接的多时间尺度分析为了解大脑疾病提供了更全面的方法.
  • 需要对更大的队列和各种大脑疾病进行进一步的验证.