AIMC Topic: Brain

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Topics and trends in artificial intelligence assisted human brain research.

PloS one
Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous gro...

Predicting postmortem interval based on microbial community sequences and machine learning algorithms.

Environmental microbiology
Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decompositi...

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning.

Computational intelligence and neuroscience
Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagn...

An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy.

Multiview learning for understanding functional multiomics.

PLoS computational biology
The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification ...

Kernel Granger Causality Based on Back Propagation Neural Network Fuzzy Inference System on fMRI Data.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Granger causality (GC) is one of the most popular measures to investigate causality influence among brain regions and has been achieved significant results for exploring brain networks based on functional magnetic resonance imaging (fMRI). However, t...

Machine translation of cortical activity to text with an encoder-decoder framework.

Nature neuroscience
A decade after speech was first decoded from human brain signals, accuracy and speed remain far below that of natural speech. Here we show how to decode the electrocorticogram with high accuracy and at natural-speech rates. Taking a cue from recent a...

Brain MRI analysis using a deep learning based evolutionary approach.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear understanding of why they perform so well, or what they have learned. In this paper, a three-dimensional...

Learning in the machine: To share or not to share?

Neural networks : the official journal of the International Neural Network Society
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sh...

Brain tumor classification using modified local binary patterns (LBP) feature extraction methods.

Medical hypotheses
Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient's survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tum...