Research Database
Curated neuroscience and AI research papers
Showing 20 of 100 papers
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...
A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders.
Philip, Ruth C M; Dauvermann, Maria R; Whalley, Heather C; Baynham, Katie; Lawrie, Stephen M et al.
Recent years have seen a rapid increase in the investigation of autism spectrum disorders (ASD) through the use of functional magnetic resonance imaging (fMRI). We carried out a systematic review and...
Human neuroimaging: fMRI.
Wall, Matthew B; Carhart-Harris, Robin L
Human neuroimaging with functional Magnetic Resonance Imaging has been a key feature of the current wave of psychedelic research, in both healthy and clinical populations. The available data has...
Pitfalls in FMRI.
Haller, Sven; Bartsch, Andreas J
Several different techniques allow a functional assessment of neuronal activations by magnetic resonance imaging (fMRI). The by far most influential fMRI technique is based on a local T2*-sensitive...
Brain-Computer Interface: Advancement and Challenges.
Mridha, M F; Das, Sujoy Chandra; Kabir, Muhammad Mohsin; Lima, Aklima Akter; Islam, Md Rashedul et al.
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several...
Brain-Computer Interface, Neuromodulation, and Neurorehabilitation Strategies for Spinal Cord Injury.
Cajigas, Iahn; Vedantam, Aditya
As neural bypass interfacing, neuromodulation, and neurorehabilitation continue to evolve, there is growing recognition that combination therapies may achieve superior results. This article briefly...
Translational machine learning for psychiatric neuroimaging.
Walter, Martin; Alizadeh, Sarah; Jamalabadi, Hamidreza; Lueken, Ulrike; Dannlowski, Udo et al.
Despite its initial promise, neuroimaging has not been widely translated into clinical psychiatry to assist in the prediction of diagnoses, prognoses, and optimal therapeutic strategies. Machine...
How Machine Learning is Powering Neuroimaging to Improve Brain Health.
Singh, Nalini M; Harrod, Jordan B; Subramanian, Sandya; Robinson, Mitchell; Chang, Ken et al.
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that...