21 papers · ranked by Valyu relevance
Bing Du, Xiaomu Cheng, Yiping Duan, Huansheng Ning + 5 more
'Andrea Luigi Guerra' 'Gabriele Baronio' 'Domenico Speranza' 'Luca Ulrich'] Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain-computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and…
Trung Quang Pham, Shota Nishiyama, Norihiro Sadato, Junichi Chikazoe
Multivoxel pattern analysis (MVPA) has become a standard tool for decoding mental states from brain activity patterns. Recent studies have demonstrated that MVPA can be applied to decode activity patterns of a certain region from those of the other regions. By applying a similar region-to-region decoding technique, we…
Zarina Rakhimberdina, Quentin Jodelet, Xin Liu, Tsuyoshi Murata
With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human brain. One of the most challenging brain decoding tasks is the accurate reconstruction of the perceived natural images from…
Yoichi Miyawaki, Kenshu Koiso, Daniel A Handwerker, Javier Gonzalez-Castillo + 5 more
High spatio-temporal resolution is crucial for neuroimaging techniques to improve our understanding of human brain function. While the fMRI signal is slow and shows a spread in latencies over space, the precision of hemodynamic response latency for each voxel is preserved and has been shown to be able to detect…
Wasserman, Navve, Cosarinsky, Matias + 10 more
Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have advanced our understanding of visual representations, yet brain signals remain large and complex, and the space of possible visual concepts…
Daniel Haenelt, Denis Chaimow, Marianna Elisa Schmidt, Shahin Nasr + 2 more
Multivariate pattern analysis (MVPA) methods are a versatile tool to retrieve information from neurophysiological data obtained with functional magnetic resonance imaging (fMRI) techniques. Since fMRI is based on measuring the hemodynamic response following neural activation, the spatial specificity of the fMRI signal…
Umur Yıldız, Burcu A. Urgen
Reconstructing natural images from brain activity represents one of the most compelling demonstrations of the synergy between modern neuroimaging and machine learning. However, the computational pipelines underlying these results remain scarcely accessible, difficult to reproduce, and offer limited opportunities for…
Xuelin Qian, Yikai Wang, Yanwei Fu, Xiangyang Xue + 1 more
The connection between brain activity and corresponding visual stimuli is crucial in comprehending the human brain. While deep generative models have exhibited advancement in recovering brain recordings by generating images conditioned on fMRI signals, accomplishing high-quality generation with consistent semantics…
Marc M. Himmelberg, Justin L. Gardner, Jonathan Winawer
In the domain of human neuroimaging, much attention has been paid to the question of whether and how the development of functional magnetic resonance imaging (fMRI) has advanced our scientific knowledge of the human brain. However, the opposite question is also important; how has our knowledge of the brain advanced our…
Medha Sharma, Marc N. Coutanche
There is great interest in understanding how different brain regions represent information across space and time. Information-based searchlight analyses systematically examine the information encoded within clusters of functional magnetic resonance imaging (fMRI) voxels across the brain. Significant searchlights…
Jonas Karolis Degutis, Denis Chaimow, Romy Lorenz
Laminar fMRI using GE-BOLD is vulnerable to spatial blurring from intracortical veins, while multivariate pattern analysis (MVPA) is often assumed to mitigate these biases. Yet, this assumption has not been systematically investigated. We thus developed a mechanistic laminar response model that simulates voxel-wise…
Yanchen Wang, Adam Turnbull, Tiange Xiang, Yunlong Xu + 5 more
Analysis Using fMRI Foundation Models Authors: ['Yanchen Wang' 'Adam Turnbull' 'Tiange Xiang' 'Yunlong Xu' 'Sa Zhou' 'Askari Masoud' 'Shekoofeh Azizi' 'Feng Lin' 'Ehsan Adeli'] Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive…
Jiahong Zhang, Jinning Zhao, Sijun Shen, Siyuan Xu + 2 more
Functional magnetic resonance imaging (fMRI)-based visual decoding aims to recover visual information from measured brain activity, commonly by mapping fMRI responses into latent visual features for downstream decoding tasks. Most existing methods learn mappings from fMRI responses to visual features extracted by…
Km Bhavna, Azman Akhter, Romi Banerjee, Dipanjan Roy
Decoding of brain tasks aims to identify individuals’ brain states and brain fingerprints to predict behavior. Deep learning provides an important platform for analyzing brain signals at different developmental stages to understand brain dynamics. Due to their internal architecture and feature extraction techniques…
Matteo Ferrante, Tommaso Boccato, Nicola Toschi
To-date, brain decoding literature has focused on singlesubject studies, i.e. reconstructing stimuli presented to a subject under fMRI acquisition from the fMRI activity of the same subject. The objective of this study is to introduce a generalization technique that enables the decoding of a subject's brain based on…
João F. Guassi Moreira, Jennifer A. Silvers
The current prevailing approaches to analyzing task fMRI data in developmental cognitive neuroscience are brain connectivity and mass univariate task-based analyses, used either in isolation or as part of a broader analytic framework (e.g., BWAS). While these are powerful tools, it is somewhat surprising that…
Yaoda Xu, Benjamin Swinchoski, JohnMark Taylor, Marvin Chun
Two defining visual features of a real-world object are its shape and color. While previous fMRI pattern decoding studies showed that these features are represented in a largely orthogonal and independent manner in the human occipitotemporal cortex (OTC) during visual perception, how they are coded together in visual…
Jun Kai Ho, Tomoyasu Horikawa, Kei Majima, Fan Cheng + 1 more
The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. Although anatomical and functional alignment methods have been proposed in functional magnetic…
Huzheng Yang, Jianbo Shi, James C. Gee
Brain encoding models aim to predict brain voxel-wise responses to stimuli images, replicating brain signals captured by neuroimaging techniques. There is a large volume of publicly available data, but training a comprehensive brain encoding model is challenging. The main difficulties stem from a) diversity within…
Colleen J. Gillon, Jason E. Pina, Jérôme A. Lecoq, Ruweida Ahmed + 19 more
'Yazan N. Billeh' 'Shiella Caldejon' 'Peter Groblewski' 'Timothy M. Henley' 'India Kato' 'Eric Lee' 'Jennifer Luviano' 'Kyla Mace' 'Chelsea Nayan' 'Thuyanh V. Nguyen' 'Kat North' 'Jed Perkins' 'Sam Seid' 'Matthew T. Valley' 'Ali Williford' 'Yoshua Bengio' 'Timothy P. Lillicrap' 'Blake A. Richards' 'Joel Zylberberg']…
Authors not listed
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and widespread neurovascular disturbances. Traditional fMRI provides valuable spatial maps of blood-oxygen-level-dependent (BOLD) changes but often lacks the capacity to quantify the interplay of cerebral blood flow…