Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies brain activity by measuring blood oxygenation levels, aiming to understand brain function and its relation to cognition and behavior. Current research heavily utilizes deep learning, including transformer networks, autoencoders, and diffusion models, to analyze high-dimensional fMRI data, improve spatial and temporal resolution, and decode cognitive states or even reconstruct visual imagery from brain activity. These advancements are improving diagnostic accuracy for neurological disorders like autism and Alzheimer's disease, and enabling novel applications such as personalized brain-computer interfaces and the development of more brain-like artificial intelligence models.

Papers

October 31, 2022

Emotional Brain State Classification on fMRI Data Using Deep Residual and Convolutional Networks
Maxime Tchibozo, Donggeun Kim, Zijing Wang, Xiaofu He
Convolutional Neural Network Functional Magnetic Resonance Imaging Residual Image Group Level Emotion

October 23, 2022

fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships
Alexander Kovalev, Ilia Mikheev, Alexei Ossadtchi
Deep Learning Digital Twin Greater Public Use Functional Magnetic Resonance Imaging Multi Channel EEG Wearable Brain Computer Interface

October 17, 2022

Sparse Kronecker Product Decomposition: A General Framework of Signal Region Detection in Image Regression
Sanyou Wu, Long Feng
Functional Magnetic Resonance Imaging General Framework Kronecker Product Image Regression Signal Detection

October 11, 2022

Multi-site Diagnostic Classification Of Schizophrenia Using 3D CNN On Aggregated Task-based fMRI Data
Vigneshwaran Shankaran, Bhaskaran V
Deep Learning Functional Magnetic Resonance Imaging 3d CNN Schizophrenia Diagnosis Disease Detection Model Task Based

October 8, 2022

Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network
Ziyuan Ye, Youzhi Qu, Zhichao Liang, Mo Wang, Quanying Liu
Functional Magnetic Resonance Imaging Graph Convolution Spatial Domain Brain Decoding Human Connectome Project

September 30, 2022

Mind Reader: Reconstructing complex images from brain activities
Sikun Lin, Thomas Sprague, Ambuj K Singh
Functional Magnetic Resonance Imaging Brain Activity Image Complexity Brain Encoding FMRI Encoding

September 27, 2022

DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning
Alexander Campbell, Antonio Giuliano Zippo, Luca Passamonti, Nicola Toschi, Pietro Lio
Functional Magnetic Resonance Imaging Dynamic Graph Learning Brain Graph Brain Predictive

September 23, 2022

Multivariate Wasserstein Functional Connectivity for Autism Screening
Oleg Kachan, Alexander Bernstein
Functional Magnetic Resonance Imaging Autism Spectrum Disorder Functional Connectivity Sparse Connectivity

August 17, 2022

"Task-relevant autoencoding" enhances machine learning for human neuroscience
Seyedmehdi Orouji, Vincent Taschereau-Dumouchel, Aurelio Cortese, Brian Odegaard, Cody Cushing, Mouslim Cherkaoui, Mitsuo Kawato, Hakwan Lau, Megan A. K. Peters
New Machine Functional Magnetic Resonance Imaging Neuroscience Research Low Dimensional Representation Task Specific Embeddings Task Induced Representation

August 8, 2022

fMRI-S4: learning short- and long-range dynamic fMRI dependencies using 1D Convolutions and State Space Models
Ahmed El-Gazzar, Rajat Mani Thomas, Guido Van Wingen
State Space Model Functional Magnetic Resonance Imaging Resting State fMRI Time Series 1D Convolution

July 3, 2022

Facial Image Reconstruction from Functional Magnetic Resonance Imaging via GAN Inversion with Improved Attribute Consistency
Pei-Chun Chang, Yan-Yu Tien, Chia-Lin Chen, Li-Fen Chen, Yong-Sheng Chen, Hui-Ling Chan
Generative Adversarial Network Functional Magnetic Resonance Imaging GAN Inversion Latent Code Brain Encoding Level Consistency

June 17, 2022

Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
Joseph Stember, Danielle Stember, Luca Pasquini, Jenabi Merhnaz, Andrei Holodny, Hrithwik Shalu
Deep Reinforcement Learning Functional Magnetic Resonance Imaging Autism Spectrum Disorder

June 7, 2022

May 26, 2022

Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data
Eloy Geenjaar, Amrit Kashyap, Noah Lewis, Robyn Miller, Vince Calhoun
Application Proficiency Functional Magnetic Resonance Imaging Independent Component Analysis Independent Latent

May 25, 2022

FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang
Functional Magnetic Resonance Imaging Brain Network Functional Connectivity Task Aware fMRI Analysis

May 24, 2022

Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs
Christopher Sims
Time Matter Functional Magnetic Resonance Imaging fMRI Analysis 3D Generative Adversarial Network FMRI Classification

May 23, 2022

BolT: Fused Window Transformers for fMRI Time Series Analysis
Hasan Atakan Bedel, Irmak Şıvgın, Onat Dalmaz, Salman Ul Hassan Dar, Tolga Çukur
Deep Learning High Dimensional Functional Magnetic Resonance Imaging fMRI Time Series Bolt Rotation

May 20, 2022

DEMAND: Deep Matrix Approximately Nonlinear Decomposition to Identify Meta, Canonical, and Sub-Spatial Pattern of functional Magnetic Resonance Imaging in the Human Brain
Wei Zhang, Yu Bao
Matrix Factorization Functional Magnetic Resonance Imaging Human Brain fMRI Signal Spatiotemporal Pattern Nonlinear Matrix Decomposition

May 8, 2022

Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants
Yunsong Luo, Wenyu Chen, Jiang Qiu, Tao Jia
Functional Magnetic Resonance Imaging Evidence Piece Brain Age Participant State Brain Aging Major Depressive Disorder

Functional Magnetic Resonance Imaging

Papers

Emotional Brain State Classification on fMRI Data Using Deep Residual and Convolutional Networks

fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships

Sparse Kronecker Product Decomposition: A General Framework of Signal Region Detection in Image Regression

Multi-site Diagnostic Classification Of Schizophrenia Using 3D CNN On Aggregated Task-based fMRI Data

Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network

Mind Reader: Reconstructing complex images from brain activities

DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning

Multivariate Wasserstein Functional Connectivity for Autism Screening

"Task-relevant autoencoding" enhances machine learning for human neuroscience

fMRI-S4: learning short- and long-range dynamic fMRI dependencies using 1D Convolutions and State Space Models

Facial Image Reconstruction from Functional Magnetic Resonance Imaging via GAN Inversion with Improved Attribute Consistency

Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder

Improving the Diagnosis of Psychiatric Disorders with Self-Supervised Graph State Space Models

Transfer learning to decode brain states reflecting the relationship between cognitive tasks

Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data

FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation

Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs

BolT: Fused Window Transformers for fMRI Time Series Analysis

DEMAND: Deep Matrix Approximately Nonlinear Decomposition to Identify Meta, Canonical, and Sub-Spatial Pattern of functional Magnetic Resonance Imaging in the Human Brain

Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants