Research Database
Curated neuroscience and AI research papers
Showing 20 of 110 papers
Large-scale foundation models and generative AI for BigData neuroscience.
Wang, Ran; Chen, Zhe Sage
Recent advances in machine learning have led to revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale...
A new era in cognitive neuroscience: the tidal wave of artificial intelligence (AI).
Chen, Zhiyi; Yadollahpour, Ali
Translating artificial intelligence techniques into the realm of cognitive neuroscience holds promise for significant breakthroughs in our ability to probe the intrinsic mechanisms of the brain. The...
Deep learning-based electroencephalography analysis: a systematic review.
Roy, Yannick; Banville, Hubert; Albuquerque, Isabela; Gramfort, Alexandre; Falk, Tiago H et al.
Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted....
Software tools for analysis and visualization of fMRI data.
Cox, R W; Hyde, J S
The tools needed for analysis and visualization of three-dimensional human brain functional magnetic resonance image results are outlined, covering the processing categories of data storage,...
Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review.
Ahmadzadeh, Maryam; Christie, Gregory J; Cosco, Theodore D; Arab, Ali; Mansouri, Mehrdad et al.
This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease...
Neural bases of atypical emotional face processing in autism: A meta-analysis of fMRI studies.
Aoki, Yuta; Cortese, Samuele; Tansella, Michele
We aim to outline the neural correlates of atypical emotional face processing in individuals with ASD.
AI-driven discovery of brain-penetrant Galectin-3 inhibitors for Alzheimer's disease therapy.
Liu, Xueyan; Xu, Jiexin; Zheng, Shuping; Yang, Yaoyao; Xie, Yuchong et al.
Galectin-3 (Gal-3) has emerged as a critical regulator of neuroinflammation and a promising therapeutic target for Alzheimer's disease (AD). Nevertheless, the development of brain-penetrant...
Optimization of rs-fMRI parameters in the Seed Correlation Analysis (SCA) in DPARSF toolbox: A preliminary study.
Karpiel, Ilona; Klose, Uwe; Drzazga, Zofia
There are a number of various methods of resting-state functional magnetic resonance imaging (rs-fMRI) analysis such as independent component analysis, multivariate autoregressive models, or seed...
The Neuroscience of Human and Artificial Intelligence Presence.
Harris, Lasana T
Two decades of social neuroscience and neuroeconomics research illustrate the brain mechanisms that are engaged when people consider human beings, often in comparison to considering artificial...
Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.
Wen, Junhao; Antoniades, Mathilde; Yang, Zhijian; Hwang, Gyujoon; Skampardoni, Ioanna et al.
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative...
EEG-Based Deep Learning Model for Hyper-Acute Large Vessel Occlusion Stroke Detection in Mice.
Zhang, Tan; Li, Xiaolin; Hu, Xinxin; Zhou, Zhiyong; Mu, Qingchun et al.
This study aims to develop a deep learning model for the early and accurate detection of hyper-acute large vessel occlusion (LVO) stroke using EEG data.
The effective connectivity analysis of fMRI based on asymmetric detection of transfer brain entropy.
Shi, Yuhu; Li, Yidan
It is important to explore causal relationships in functional magnetic resonance imaging study. However, the traditional effective connectivity analysis method is easy to produce false causality, and...
Brain-guided convolutional neural networks reveal task-specific representations in scene processing.
Hansen, Bruce C; Greene, Michelle R; Lewinsohn, Henry A S; Kris, Audrey E; Smyth, Sophie et al.
Scene categorization is the dominant proxy for visual understanding, yet humans can perform a large number of visual tasks within any scene. Consequently, we know little about how different tasks...
Rethinking Saliency Map: A Context-Aware Perturbation Method to Explain EEG-Based Deep Learning Model.
Wang, Hanqi; Zhu, Xiaoguang; Chen, Tao; Li, Chengfang; Song, Liang
Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically study how to explain EEG-based deep learning models. In this paper, we...
Deep Learning for EEG-Based Visual Classification and Reconstruction: Panorama, Trends, Challenges and Opportunities.
Li, Wei; Zhao, Penglu; Xu, Cheng; Hou, Yingting; Jiang, Wenhao et al.
Deep learning has significantly enhanced the research on the emerging issue of Electroencephalogram (EEG)-based visual classification and reconstruction, which has gained a growth of attention and...
EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.
Ahmad, Ijaz; Wang, Xin; Zhu, Mingxing; Wang, Cheng; Pi, Yao et al.
Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic...
Deep Learning for EEG Seizure Detection in Preterm Infants.
O'Shea, Alison; Ahmed, Rehan; Lightbody, Gordon; Pavlidis, Elena; Lloyd, Rhodri et al.
EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting...
Dual stream neural networks for brain signal classification.
Kuang, Dongyang; Michoski, Craig
. The primary objective of this work is to develop a neural nework classifier for arbitrary collections of functional neuroimaging signals to be used in brain-computer interfaces (BCIs).. We propose...
Deep reinforcement learning guided graph neural networks for brain network analysis.
Zhao, Xusheng; Wu, Jia; Peng, Hao; Beheshti, Amin; Monaghan, Jessica J M et al.
Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for...
Optogenetic fMRI for Brain-Wide Circuit Analysis of Sensory Processing.
Lee, Jeong-Yun; You, Taeyi; Woo, Choong-Wan; Kim, Seong-Gi
Sensory processing is a complex neurological process that receives, integrates, and responds to information from one's own body and environment, which is closely related to survival as well as...