AIMC Topic: Brain

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Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

NeuroImage
Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the succes...

q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans.

IEEE transactions on medical imaging
Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines. An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive microstructure assessment method with a prominent application in neuroima...

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease.

International journal of neural systems
Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the constr...

Classifying Response Correctness across Different Task Sets: A Machine Learning Approach.

PloS one
Erroneous behavior usually elicits a distinct pattern in neural waveforms. In particular, inspection of the concurrent recorded electroencephalograms (EEG) typically reveals a negative potential at fronto-central electrodes shortly following a respon...

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network.

IEEE transactions on medical imaging
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes u...

Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia.

NeuroImage. Clinical
Meta-analytic studies suggest that dyslexia is characterized by subtle and spatially distributed variations in brain anatomy, although many variations failed to be significant after corrections of multiple comparisons. To circumvent issues of signifi...

Deep models for brain EM image segmentation: novel insights and improved performance.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these mo...

Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

Bio Systems
Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefor...

Can Lionel Messi's brain slow down time passing?

Chronobiology international
It seems that seeing others in slow-motion by heroes does not belong only to movies. When Lionel Messi plays football, you can hardly see anything from him that other players cannot do. Then why he is not stoppable really? It seems the answer may be ...

The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA.

NeuroImage
Resting state functional network connectivity (rsFNC) derived from functional magnetic resonance (fMRI) imaging is emerging as a possible biomarker to identify several brain disorders. Recently it has been pointed out that methods used to preprocess ...