Search · four archives
Search · four archives
24 papers · ranked by Valyu relevance
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…
Kai Schreiber, Bart Krekelberg, Essa Yacoub
The goal of multi-voxel pattern analysis (MVPA) in BOLD imaging is to determine whether patterns of activation across multiple voxels change with experimental conditions. MVPA is a powerful technique, its use is rapidly growing, but it poses serious statistical challenges. For instance, it is well-known that the slow…
Heidi M. Bonnici, Martin J. Chadwick, Dharshan Kumaran, Demis Hassabis + 2 more
'Demis Hassabis' 'Nikolaus Weiskopf' 'Eleanor A. Maguire'] A complete understanding of the hippocampus depends on elucidating the representations and computations that exist in its anatomically distinct subfields. High-resolution structural and functional MRI scanning is starting to permit insights into hippocampal…
Carsten Allefeld, John–Dylan Haynes
Multi-voxel pattern analysis (MVPA) is a fruitful and increasingly popular complement to traditional univariate methods of analyzing neuroimaging data. We propose to replace the standard 'decoding' approach to searchlight-based MVPA, measuring the performance of a classifier by its accuracy, with a method based on the…
Marc N. Coutanche, Sharon L. Thompson-Schill
The fluctuations in a brain region's activation levels over a functional magnetic resonance imaging (fMRI) time-course are used in functional connectivity (FC) to identify networks with synchronous responses. It is increasingly recognized that multi-voxel activity patterns contain information that cannot be extracted…
Rui Xu, Zonglei Zhen, Jia Liu, Olaf Sporns
Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies…
Karen F. LaRocque, Tyler H. Davis, Jeanette A. Mumford, Russell A. Poldrack + 1 more
Pattern similarity analysis, which uses correlation to examine similarities between neural activation patterns evoked by different trials or conditions, are often leveraged to test hypotheses not easily answerable with univariate comparisons, such as how events are represented or processed and the relationships between…
Xinpei Ma, Chun-An Chou, Hiroki Sayama, Wanpracha Art Chaovalitwongse
'Wanpracha Art Chaovalitwongse'] Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI…
Mete Özay, Ilke Öztekin, Uygar Öztekin, Fatoş T. Yarman-Vural
A relatively recent advance in cognitive neuroscience has been multi-voxel pattern analysis (MVPA), which enables researchers to decode brain states and/or the type of information represented in the brain during a cognitive operation. MVPA methods utilize machine learning algorithms to distinguish among types of…
Nima Asadi, Yin Wang, Ingrid Olson, Zoran Obradovic
Detecting the most relevant brain regions for explaining the distinction between cognitive conditions is one of the most sought after objectives in neuroimaging research. A popular approach for achieving this goal is the multivariate pattern analysis (MVPA) which is commonly conducted through the searchlight procedure…
Muhammad Yousefnezhad, Daoqiang Zhang
A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity…
Itır Önal, Mete Özay, Eda Mızrak, Ilke Öztekin + 1 more
'Fatoş T. Yarman Vural'] Abstract—We represent the sequence of fMRI (Functional Magnetic Resonance Imaging) brain volumes recorded during a cognitive stimulus by a graph which consists of a set of local meshes. The corresponding cognitive process, encoded in the brain, is then represented by these meshes each of which…
Mansooreh Pakravan, Ali Ghazizadeh
Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can…
Muhammad Yousefnezhad, Daoqiang Zhang
A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity…
Muhammad Yousefnezhad, Daoqiang Zhang
Background: A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing…
Leyla Tarhan, Talia Konkle
While functional magnetic resonance imaging (fMRI) studies typically measure responses across the whole brain, not all regions are likely to be informative for a given study. Which voxels should be considered? Here we propose a method for voxel selection based on the reliability of the data. This method isolates voxels…
Xuelei Wang, Jana Zweerings, Michael Lührs, Fengyu Cong + 5 more
Identifying informative voxels is a critical, yet challenging step in functional magnetic resonance imaging (fMRI), particularly for multivariate analyses involving multiple related conditions. Existing approaches often rely on predefined regions of interest (ROIs) or activation-based criteria, which may be…
Xiangyang He, Yubo Tao, Qirui Wang, Hai Lin
In complex multivariate data sets, different features usually include diverse associations with different variables, and different variables are associated within different regions. Therefore, exploring the associations between variables and voxels locally becomes necessary to better understand the underlying…
Qiang Li, Dinghong Gong, Jie Shen, Chang Rao + 2 more
Compared with traditional volume space-based multivariate pattern analysis (MVPA), surface space-based MVPA has many advantages and has received increasing attention. However, surface space-based MVPA requires considerable programming and is therefore difficult for people without a programming foundation. To address…
Muhammad Yousefnezhad, Daoqiang Zhang
Multivariate Pattern (MVP) classification holds enormous potential for decoding visual stimuli in the human brain by employing task-based fMRI data sets. There is a wide range of challenges in the MVP techniques, i.e. decreasing noise and sparsity, defining effective regions of interest (ROIs), visualizing results, and…
Muhammad Yousefnezhad, Daoqiang Zhang
Multivariate Pattern (MVP) classification can map different cognitive states to the brain tasks. One of the main challenges in MVP analysis is validating the generated results across subjects. However, analyzing multi-subject fMRI data requires accurate functional alignments between neuronal activities of different…
Matheus Müller Pereira da Silva, Isabella Alvim Guedes, Fábio Lima Custódio, Laurent Emmanuel Dardenne
A critical aspect of successful deep learning (DL) modelling in computer-aided drug discovery (CADD) is the representation of biomolecular data. Voxel grid representations have emerged as a straightforward method for depicting 3D molecular structures of protein-ligand complexes. Proper structural preparation of these…
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…
Authors not listed
A thorough understanding of drug pharmacokinetics and target interaction within complex biological environments is critical for successful drug discovery. Quantitative matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI; qMSI) presents a promising avenue for such investigations. While…