20 papers · ranked by Valyu relevance
Tijl Grootswagers, Susan G. Wardle, Thomas A. Carlson
Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analysing fMRI data. Although decoding methods have been extensively applied in Brain Computing Interfaces (BCI), these methods have only recently been applied to timeseries neuroimaging data such as MEG and EEG to address…
Stefan Bode, Anna Hanxi He, Chun Siong Soon, Robert Trampel + 3 more
'Robert Turner' 'John-Dylan Haynes' 'Sam Gilbert'] Recently, we demonstrated using functional magnetic resonance imaging (fMRI) that the outcome of free decisions can be decoded from brain activity several seconds before reaching conscious awareness. Activity patterns in anterior frontopolar cortex (BA 10) were…
J Brendan Ritchie, David Michael Kaplan, Colin Klein
Since its introduction, multivariate pattern analysis (MVPA), or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence…
J. Brendan Ritchie, David Michael Kaplan, Colin Klein
Since its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence…
Arjen Alink, Alexandra Krugliak, Alexander Walther, Nikolaus Kriegeskorte
'Nikolaus Kriegeskorte'] The orientation of a large grating can be decoded from V1 functional magnetic resonance imaging (fMRI) data, even at low resolution (3-mm isotropic voxels). This finding has suggested that columnar-level neuronal information might be accessible to fMRI at 3T. However, orientation decodability…
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…
Martin Wegrzyn, Joana Aust, Larissa Barnstorf, Magdalena Gippert + 14 more
Cognitive processes, such as the generation of language, can be mapped onto the brain using fMRI. These maps can in turn be used for decoding the respective processes from the brain activation patterns. Given individual variations in brain anatomy and organization, analyzes on the level of the single person are…
Philip A. Kragel, R. McKell Carter, Scott A. Huettel
Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of…
Martin Wegrzyn, Joana Aust, Larissa Barnstorf, Magdalena Gippert + 15 more
'Mareike Harms' 'Antonia Hautum' 'Shanna Heidel' 'Friederike Herold' 'Sarah M. Hommel' 'Anna-Katharina Knigge' 'Dominik Neu' 'Diana Peters' 'Marius Schaefer' 'Julia Schneider' 'Ria Vormbrock' 'Sabrina M. Zimmer' 'Friedrich G. Woermann' 'Kirsten Labudda' 'Satoru Hayasaka'] Cognitive processes, such as the generation of…
K. Seeliger, U. Güçlü, L. Ambrogioni, Y. Güçlütürk + 1 more
We explore a method for reconstructing visual stimuli from brain activity. Using large databases of natural images we trained a deep convolutional generative adversarial network capable of generating gray scale photos, similar to stimUli presented during two functional magnetic resonance imaging experiments. Using a…
Ruben S. van Bergen, Janneke F. M. Jehee
Brain decoding algorithms form an important part of the arsenal of analysis tools available to neuroscientists, allowing for a more detailed study of the kind of information represented in patterns of cortical activity. While most current decoding algorithms focus on estimating a single, most likely stimulus from the…
R. Jabakhanji, A.D. Vigotsky, J. Bielefeld, L. Huang + 3 more
High-profile studies claim to assess mental states across individuals using multi-voxel decoders of brain activity. The fixed, fine-grained, multi-voxel patterns in these “optimized” decoders are purportedly necessary for discriminating between, and accurately identifying, mental states. Here, we present compelling…
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…
Hongming Li, Yong Fan
Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies. Promising performance has been achieved using brain functional connectivity or brain activation signatures for a variety of brain…
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…
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…
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…
Osama Hourani, Nasrollah Moghadam Charkari, Saeed Jalili
Visual stimulus decoding is an increasingly important challenge in neuroscience. The goal is to classify the activity patterns from the human brain; during the sighting of visual objects. The inputs are fMRI data, and the stimuli names are the outputs. One of the crucial problems in the brain decoder is the selecting…
Tomoyasu Horikawa, Yukiyasu Kamitani
Object recognition is a key function in both human and machine vision. While recent studies have achieved fMRI decoding ofseen and imagined contents, the prediction is limited to training examples. We present a decoding approach for arbitrary objects, using the machine vision principle that an object category is…
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…