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Science Bulletin 2024年第11期69卷 1738-1747页

作者：李远宁杨虎铮顾实 School of Biomedical Engineering&State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech UniversityShanghai 201210China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Computer and Information Science University of PennsylvaniaPhiladelphiaPA 19104USA Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China

Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks(DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.

关键词： Neural encoding Visual perception Artificial neural networks functional neuroimaging Naturalistic stimuli

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IN VIVO MAPPING BRAIN MICROCIRCULATION BY LASER SPECKLE CONTRAST IMAGING:A MAGNETIC RESONANCE PERSPECTIVE OF THEORETICAL FRAMEWORK

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Journal of Innovative Optical Health Sciences 2008年第2期1卷 239-256页

作者： ZHENG WANG Center for functional and Metabolic Mapping Robarts Research Institute Department of Medical Biophysics The University of Western Ontario LondonCanada

The fundamental limitations of most vascular-based functional neuroimaging techniques are placed by the fact how fine the brain regulates the blood supply system.In vivo mapping of the cerebral microcirculation with high resolution and sensitivity hence becomes unprecedentedly compelling.This paper reviews the theoretical background of the laser speckle contrast imaging(LSCI)technique and attempts to present a complete framework stemming from a simple biophysical model.Through the sensitivity analysis,more insights into the tool optimization are attained for in vivo applications.Open questions of the technical aspects are discussed within this unified framework.Finally,it concludes with a brief perspective of future research in a way analogous to the magnetic resonance imaging(MRI)technique.Such exploration could catalyze their development and initiate a technological fusion for precise assessment of blood flow across various spatial scales.

关键词： Laser speckle contrast imaging brain microcirculation functional neuroimaging magnetic resonance imaging

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