Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

3.8K
The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor cortex....
3.8K
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.3K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.3K
Somatosensory, Motor, and Association Cortex01:24

Somatosensory, Motor, and Association Cortex

495
The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...
495

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Capturing attitudes towards research and data sharing in down syndrome (CARDS-DS): Piloting a novel parent-report measure.

Social sciences & humanities open·2026
Same author

Multiscale characterization of the human claustrum from histology to MRI.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

CIVET-Chimp: An automated pipeline for MRI-based cortical surface extraction in chimpanzees.

Neuroimage. Reports·2026
Same author

Organization, fine structure, and stereotaxic maps of the human Bed nucleus of the Stria terminalis.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Subclinical neuropsychiatric trait variation in parents of children with autism spectrum disorder: a cohort study.

Molecular autism·2026
Same author

Comprehensive large-scale analyses reveal association between brain structure and cognitive ability during adolescence.

Communications biology·2026
Same journal

ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images.

Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.)·2024
Same journal

Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models.

Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.)·2024
Same journal

Particle-Based Shape Modeling for Arbitrary Regions-of-Interest.

Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.)·2024
Same journal

SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction.

Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.)·2024
Same journal

Geodesic Logistic Analysis of Lumbar Spine Intervertebral Disc Shapes in Supine and Standing Positions.

Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.)·2024
Same journal

Modeling Longitudinal Optical Coherence Tomography Images for Monitoring and Analysis of Glaucoma Progression.

Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.)·2024
查看所有相关文章

相关实验视频

Updated: Jul 1, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K

IcoConv:用于ASD分类的可解释的大脑皮质表面分析

Ugo Rodriguez1, Tahya Deddah1, Sun Hyung Kim1

  • 1University of North Carolina, Chapel Hill, NC.

Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.)
|March 1, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用2D卷积神经网络 (CNN) 进行自闭症谱系障碍 (ASD) 研究的新3D形状分析方法. 它有助于识别高风险个体的大脑差异,有助于理解ASD.

关键词:
在ASD中,使用的是ASD.形状分析 形状分析大脑大脑大脑的大脑大脑

更多相关视频

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

相关实验视频

Last Updated: Jul 1, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

科学领域:

  • 神经科学是一个神经科学.
  • 计算机视觉 计算机视觉
  • 医疗成像医学成像

背景情况:

  • 自闭症谱系障碍 (ASD) 诊断依赖于行为观察,具有有限的客观生物标志物.
  • 分析复杂的3D神经成像数据带来了计算方面的挑战.

研究的目的:

  • 开发和验证一种用于分析3D大脑形状的新型计算方法.
  • 应用这种方法来区分患ASD高风险的个体.

主要方法:

  • 一种新的方法,使用修改的2D卷积神经网络 (CNN) 分析3D大脑形状的2D视角.
  • 使用一个二元象面卷积运算符来有效地聚合多视图数据.
  • 应用于将大脑属性 (皮层厚度,表面积,脑脊液体积) 映射到球体上.

主要成果:

  • 该方法成功地进行了二元分类,以区分高风险的阳性和阴性ASD病例.
  • 在大脑表面上可视化生成基于梯度的可解释性地图.
  • 这些发现与已知的ASD受影响的大脑区域一致.

结论:

  • 新的3D形状分析方法在神经科学研究中是有效的,特别是在ASD中.
  • 可解释性地图提供了与现有的ASD知识相一致的见解.
  • 这种方法有可能发现ASD的新方面.