AIMC Topic: Brain Mapping
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EEG-based image classification via a region-level stacked bi-directional deep learning framework.
BMC medical informatics and decision making
Dec 19, 2019
BACKGROUND: As a physiological signal, EEG data cannot be subjectively changed or hidden. Compared with other physiological signals, EEG signals are directly related to human cortical activities with excellent temporal resolution. After the rapid dev...
Hyperparameter-tuned prediction of somatic symptom disorder using functional near-infrared spectroscopy-based dynamic functional connectivity.
Journal of neural engineering
Dec 16, 2019
OBJECTIVE: Somatic symptom disorder (SSD) is a reflection of medically unexplained physical symptoms that lead to distress and impairment in social and occupational functioning. SSD is phenomenologically diagnosed and its neurobiology remains unsolve...
Decoding and mapping task states of the human brain via deep learning.
Human brain mapping
Dec 9, 2019
Support vector machine (SVM)-based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM-MVPA requir...
Robot-assisted versus manual navigated stereoelectroencephalography in adult medically-refractory epilepsy patients.
Epilepsy research
Dec 9, 2019
OBJECTIVE: Stereoelectroencephalography (SEEG) has experienced a recent growth in adoption for epileptogenic zone (EZ) localization. Advances in robotics have the potential to improve the efficiency and safety of this intracranial seizure monitoring ...
Utilizing wavelet deep learning network to classify different states of task-fMRI for verifying activation regions.
The International journal of neuroscience
Dec 4, 2019
We propose a convolutional neural network (CNN) based on wavelet for verifying the activation regions decided with statistical analysis. Because the functional magnetic resonance imaging (fMRI) data contains lots of noises, it is difficult to get th...
Integrating functional connectivity and MVPA through a multiple constraint network analysis.
NeuroImage
Nov 30, 2019
Traditional general linear model-based brain mapping efforts using functional neuroimaging are complemented by more recent multivariate pattern analyses (MVPA) that apply machine learning techniques to identify the cognitive states associated with re...
Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Nov 27, 2019
We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperinte...
A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory.
Medical image analysis
Nov 26, 2019
In this study, a deep neural network (DNN) is proposed to reduce the noise in task-based fMRI data without explicitly modeling noise. The DNN artificial neural network consists of one temporal convolutional layer, one long short-term memory (LSTM) la...
Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance.
Scientific reports
Nov 22, 2019
Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to class...
Humans
Algorithms
Electrophysiological Phenomena
Memory, Short-Term
Datasets as Topic
Task Performance and Analysis
Evoked Potentials
Verbal Behavior
Unsupervised Machine Learning
Brain
Electrocorticography
Brain Mapping
Retrospective Studies
Biomedical Engineering
Electroencephalography
Sensitivity and Specificity
Electrodes, Implanted
Epilepsy
Cognition
QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field.
NeuroImage
Nov 21, 2019
Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects th...
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