AIMC Topic: Brain

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EIQ: EEG based IQ test using wavelet packet transform and hierarchical extreme learning machine.

Journal of neuroscience methods
BACKGROUND: The use of electroencephalography has been perpetually incrementing and has numerous applications such as clinical and psychiatric studies, social interactions, brain computer interface etc. Intelligence has baffled us for centuries, and ...

Deep learning only by normal brain PET identify unheralded brain anomalies.

EBioMedicine
BACKGROUND: Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a mo...

Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks.

IEEE transactions on medical imaging
Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampl...

Assaying neural activity of children during video game play in public spaces: a deep learning approach.

Journal of neural engineering
OBJECTIVE: Understanding neural activity patterns in the developing brain remains one of the grand challenges in neuroscience. Developing neural networks are likely to be endowed with functionally important variability associated with the environment...

PatcherBot: a single-cell electrophysiology robot for adherent cells and brain slices.

Journal of neural engineering
OBJECTIVE: Intracellular patch-clamp electrophysiology, one of the most ubiquitous, high-fidelity techniques in biophysics, remains laborious and low-throughput. While previous efforts have succeeded at automating some steps of the technique, here we...

Confidence resets reveal hierarchical adaptive learning in humans.

PLoS computational biology
Hierarchical processing is pervasive in the brain, but its computational significance for learning under uncertainty is disputed. On the one hand, hierarchical models provide an optimal framework and are becoming increasingly popular to study cogniti...

DynMat, a network that can learn after learning.

Neural networks : the official journal of the International Neural Network Society
To survive in the dynamically-evolving world, we accumulate knowledge and improve our skills based on experience. In the process, gaining new knowledge does not disrupt our vigilance to external stimuli. In other words, our learning process is 'accum...

Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network.

Magnetic resonance imaging
For quantitative neuroimaging studies using multi-echo gradient echo (mGRE) images, additional T-weighted magnetization prepared rapid gradient echo (MPRAGE) images are often acquired to supplement the insufficient morphometric information of mGRE fo...

Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain F-FDG PET.

Physics in medicine and biology
Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-body PET systems, which is important to support PET neuroimaging and particularly useful for the diag...

Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach.

NeuroImage. Clinical
BACKGROUND: It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogen...