22 papers · ranked by Valyu relevance
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…
Itır Önal Ertuğrul, Mete Özay, Fatoş T. Yarman Vural
In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated Descriptors (VLAD) and Bag-of-Words (BoW) methods. We first obtain local descriptors, called Mesh Arc Descriptors (MADs) from fMRI data, by forming local meshes around…
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…
Liting Wang, Huan Liu, Xin Zhang, Shijie Zhao + 3 more
By integrating hierarchical feature modeling of auditory information using deep neural networks (DNNs), recent functional magnetic resonance imaging (fMRI) encoding studies have revealed the hierarchical neural auditory representation in the superior temporal gyrus (STG). Most of these studies adopted supervised DNNs…
Kuan Han, Haiguang Wen, Junxing Shi, Kun-Han Lu + 2 more
Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with…
Yuanyuan Jiang, Patricia Pais‐Roldán, Rolf Pohmann, Xin Yu
High-field preclinical functional MRI (fMRI) is enabled the high spatial resolution mapping of vessel-specific hemodynamic responses, that is single-vessel fMRI. In contrast to investigating the neuronal sources of the fMRI signal, single-vessel fMRI focuses on elucidating its vascular origin, which can be readily…
Seungkyu Nam, Dae-Shik Kim
Recent advances in functional magnetic resonance imaging (fMRI) have been used to reconstruct cognitive states based on brain activity evoked by sensory or cognitive stimuli. To date, such decoding paradigms were mostly used for visual modalities. On the other hand, reconstructing functional brain activity in motor…
Cedric Foucault, Tiffany Bounmy, Sébastien Demortain, Bertrand Thirion + 2 more
Assessing probabilities and predicting future events are fundamental for perception and adaptive behavior, yet the neural representations of probability remain elusive. While previous studies have shown that neural activity in several brain regions correlates with probability-related factors such as surprise and…
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…
Authors not listed
Neural encoding models aim to predict fMRI-measured brain responses to natural images. fMRI data is acquired as a 3D volume of voxels, where each voxel has a defined spatial location in the brain. However, conventional encoding models often flatten this volume into a 1D vector and treat voxel responses as independent…
Meenakshi Khosla, Gia H. Ngo, K. A. Jamison, Amy Kuceyeski + 1 more
'Mert R. Sabuncu'] > Abstract. The increasing popularity of naturalistic paradigms in fMRI (such as movie watching) demands novel strategies for multi-subject data analysis, such as use of neural encoding models. In the present study, we propose a shared convolutional neural encoding method that accounts for…
Aditya R. Vaidya, Richard J. Antonello, Alexander G. Huth
Neuroscientists have recently turned to intracranial brain recording methods, like electrocorticography (ECoG), for human experiments because of the fine spatial and temporal resolution that they afford. Models trained on this data, however, are fundamentally restricted by the patient populations that can receive the…
Tomoya Nakai, Charlotte Constant-Varlet, Jérôme Prado
Cognitive computational neuroscience has received broad attention in recent years as an emerging area integrating cognitive science, neuroscience, and artificial intelligence. At the heart of this field, approaches using encoding models allow for explaining brain activity from latent and high-dimensional features…
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
The success of fMRI places constraints on the nature of the neural code. The fact that re-searchers can infer similarities between neural representations, despite limitations in what fMRI measures, implies that certain neural coding schemes are more likely than others. For fMRI to be successful given its low temporal…
Zi-Jian Cheng, Wen-Hao Zhang, Ru-Yuan Zhang
Two sensory neurons usually display trial-by-trial response correlations given the repeated representations of an identical stimulus. The effects of such response correlations on population-level sensory coding have been the focal contention in computational neuroscience over the past few years. In the meantime…
Subba Reddy Oota, Manish Gupta, Raju S. Bapi, Gaël Jobard + 2 more
'Frédéric Alexandre' 'Xavier Hinaut'] How does the brain represent different modes of information? Can we design a system that automatically understands what the user is thinking? Such questions can be answered by studying brain recordings like functional magnetic resonance imaging (fMRI). As a first step, 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…
Hajime Tamaki, Yoh Matsuki
Fast magic-angle spinning (MAS) solid-state NMR spectroscopy is a powerful tool for gaining structural and dynamics in-formation on solid proteins. To access such information site-specifically, the signal assignment process is unavoidable. In the assignment process, Cα and Cβ chemical shifts are of paramount importance…
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…