20 papers · ranked by Valyu relevance
Yun Wang, Tjeerd Olde Scheper
Background/Objectives: Neural decoding methods are often limited by the performance of brain encoders, which map complex brain signals into a latent representation space of perception information. These brain encoders are constrained by the limited amount of paired brain and stimuli data available for training, making…
Yueh-Po Peng, Vincent K. M. Cheung, Li Su
— A fundamental challenge in neuroscience is to decode mental states from brain activity. While functional magnetic resonance imaging (fMRI) offers a non-invasive approach to capture brain-wide neural dynamics with high spatial precision, decoding from fMRI data—particularly from task-evoked activity—remains…
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…
Lee de-Wit, David Alexander, Vebjørn Ekroll, Johan Wagemans
Psychology moved beyond the stimulus response mapping of behaviorism by adopting an information processing framework. This shift from behavioral to cognitive science was partly inspired by work demonstrating that the concept of information could be defined and quantified (Shannon, [69]). This transition developed…
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…
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…
Gael Varoquaux, Bertrand Thirion
Functional brain images are rich and noisy data that can capture indirect signatures of neural activity underlying cognition in a given experimental setting. Can data mining leverage them to build models of cognition? Only if it is applied to well-posed questions, crafted to reveal cognitive mechanisms. Here we review…
Hamid Karimi-Rouzbahani, Anina N. Rich, Alexandra Woolgar
The multiple-demand network (MDN), a set of highly interconnected, domain-general regions active across a wide variety of cognitively demanding tasks, is thought to support cognitive functions by integrating distinct types of information depending on the task. However, the spatiotemporal characteristics with which each…
Nicolas Affolter, Béni Egressy, Damián Pascual, Roger Wattenhofer
Brain decoding, understood as the process of mapping brain activities to the stimuli that generated them, has been an active research area in the last years. In the case of language stimuli, recent studies have shown that it is possible to decode fMRI scans into an embedding of the word a subject is reading. However…
Richard Antonello, Nihita Sarma, Jerry Tang, Jiaru Song + 1 more
'Alexander G. Huth'] Brain-computer interfaces have promising medical and scientific applications for aiding speech and studying the brain. In this work, we propose an informationbased evaluation metric for brain-to-text decoders. Using this metric, we examine two methods to augment existing state-of-the-art continuous…
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…
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…
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…
Vencislav Popov, Markus Ostarek, Caitlin Tenison
A key challenge for cognitive neuroscience is to decipher the representational schemes of the brain. A recent class of decoding algorithms for fMRI data, stimulus-feature-based encoding models, is becoming increasingly popular for inferring the dimensions of neural representational spaces from stimulus-feature spaces.…
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…
Olivia Guest, Bradley C Love, Russell Poldrack
The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI’s limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution…
Martin N. Hebart, Chris I. Baker
Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the…
Nikolaus Kriegeskorte, Pamela K. Douglas
Encoding and decoding models are widely used in systems, cognitive, and computational neuroscience to make sense of brain-activity data. However, the interpretation of their results requires care. Decoding models can help reveal whether particular information is present in a brain region in a format the decoder can…
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…
Charles Eads
This report describes and illustrates a set of automatable multicomponent exponential relaxation analysis protocols that are model-agnostic and suited to extracting information under circumstances when little prior knowledge about the underlying system is used. Methods are illustrated and mathematical and physical…