In the past three decades, neuroimaging has provided important insights into structure-function relationships in the human brain. Recently, however, the methods for analyzing functional magnetic resonance imaging (fMRI) data have come under scrutiny, with studies questioning cross-software comparability, the validity of statistical inference and interpretation, and the influence of the spatial filter size on neuroimaging analyses. As most fMRI studies only use a single filter for analysis, much information on the size and shape of the BOLD signal in Gaussian scale space remains hidden and constrains the interpretation of fMRI studies. To investigate the influence of the spatial observation scale on fMRI analysis, we use a spatial multiscale analysis with a range of Gaussian filters from 1-20 mm (full width at half maximum) to analyze fMRI data from a speech repetition paradigm in 25 healthy subjects. We show that analyzing the fMRI data over a range of Gaussian filter kernels reveals substantial variability in the neuroanatomical localization and the average signal strength and size of suprathreshold clusters depending on the filter size. We also demonstrate how small spatial filters bias the results towards subcortical and cerebellar clusters. Furthermore, we describe substantially different scale-dependent cluster size dynamics between cortical and cerebellar clusters. We discuss how spatial multiscale analysis may substantially improve the interpretation of fMRI data. We propose to further develop a spatial multiscale analysis to fully explore the deep structure of the BOLD signal in Gaussian scale space.
Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech
Facebook AI Research（Facebook AI研究院）；Université de Paris（巴黎大学）；CoML, ENS/CNRS/EHESS/INRIA/PSL University（计算语言学实验室，巴黎高等师范学院/法国国家科学研究中心/法国社会科学高等研究院/法国国家信息与自动化研究所/巴黎文理研究大学）；CRI, Département Frontières du Vivant et de l’Apprendre, IIFR, Université de Paris（认知研究所，生命与学习前沿系，法国医学研究院，巴黎大学）；École normale supérieure, PSL University, CNRS（巴黎高等师范学院，巴黎文理研究大学，法国国家科学研究中心）
Toward a realistic model of speech processing in the brain with self-supervised learning
Ecole Normale Supérieure, PSL University（巴黎高等师范学院，巴黎文理研究大学）；Université de Paris cité（巴黎城市大学）；Université Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；University of Toronto（多伦多大学）；INSERM（法国国家健康与医学研究院）；Meta AI
Longitudinal Acoustic Speech Tracking Following Pediatric Traumatic Brain Injury
Stanford University（斯坦福大学）；MIT Lincoln Laboratory（麻省理工学院林肯实验室）；Worcester Polytechnic Institute（伍斯特理工学院）；Mass General Hospital（麻省总医院）；University of Texas at Dallas（德克萨斯大学达拉斯分校）
Towards unified brain-to-text decoding across speech production and perception
Computer Science and Technology（计算机科学与技术）；Zhejiang University（浙江大学）；Shanghai Institute of Microsystem and Information Technology（上海微系统与信息工程研究所）；Chinese Academy of Sciences（中国科学院）；Department of Neurosurgery（神经外科部门）；Huashan Hospital, Fudan University（复旦大学华山医院）
本文提出一种统一的脑-句子解码框架，用于 Mandarin Chinese 的语音生成与感知，通过单字符数据训练实现句子级解码，并通过多模态神经动态比较提升解码性能。
EEG Entrainment to Non-Speech Rhythmic Patterns Associated with Dimensions of Language Performance
University of Granada（格拉纳达大学）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究所）；Research Institute in Information and Communications Technology (CITIC-UGR)（信息与通信技术研究所）；University of Málaga（马拉加大学）
Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals
Peking University（北京大学）；Chinese Institute for Brain Research（中国脑科学研究院）；Beijing Normal University（北京师范大学）；Shanghai Jiao Tong University（上海交通大学）；Capital Medical University（首都医科大学）；Xuanwu Hospital（宣武医院）
Condition-Invariant fMRI Decoding of Speech Intelligibility with Deep State Space Model
1Academia Sinica, Taiwan 2Keio University, Japan 3Graduate Institute of Communication Engineering, National Taiwan University, Taiwan 4Taiwan International Graduate Program in Interdisciplinary Neuroscience, National Taiwan University, Taiwan
