AIMC Topic: Brain

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Decoding with confidence: Statistical control on decoder maps.

NeuroImage
In brain imaging, decoding is widely used to infer relationships between brain and cognition, or to craft brain-imaging biomarkers of pathologies. Yet, standard decoding procedures do not come with statistical guarantees, and thus do not give confide...

Age estimates from brain magnetic resonance images of children younger than two years of age using deep learning.

Magnetic resonance imaging
The accuracy of brain age estimates from magnetic resonance (MR) images has improved with the advent of deep learning artificial intelligence (AI) models. However, most previous studies on predicting age emphasized aging from childhood to adulthood a...

Excitatory-inhibitory tone shapes decision strategies in a hierarchical neural network model of multi-attribute choice.

PLoS computational biology
We are constantly faced with decisions between alternatives defined by multiple attributes, necessitating an evaluation and integration of different information sources. Time-varying signals in multiple brain areas are implicated in decision-making; ...

Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alzheimer's Disease (AD) is associated with neuronal damage and decrease. Micro-Optical Sectioning Tomography (MOST) provides an approach to acquire high-resolution images for neuron analysis in the whole-brain. Application ...

Attenuation correction using deep learning for brain perfusion SPECT images.

Annals of nuclear medicine
OBJECTIVE: Non-uniform attenuation correction using computed tomography (CT) improves the image quality and quantification of single-photon emission computed tomography (SPECT). However, it is not widely used because it requires a SPECT/CT scanner. T...

Deep learning-based enhancement of epigenomics data with AtacWorks.

Nature communications
ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks...

TractLearn: A geodesic learning framework for quantitative analysis of brain bundles.

NeuroImage
Deep learning-based convolutional neural networks have recently proved their efficiency in providing fast segmentation of major brain fascicles structures, based on diffusion-weighted imaging. The quantitative analysis of brain fascicles then relies ...

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.

Journal of neural engineering
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning technique...

Frameless stereotactic brain biopsy: A prospective study on robot-assisted brain biopsies performed on 32 patients by using the RONNA G4 system.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: We present a novel robotic neuronavigation system (RONNA G4), used for precise preoperative planning and frameless neuronavigation, developed by a research group from the University of Zagreb and neurosurgeons from the University Hospital...

Machine learning-based classification of mitochondrial morphology in primary neurons and brain.

Scientific reports
The mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeos...