AIMC Topic: Brain

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Limits to visual representational correspondence between convolutional neural networks and the human brain.

Nature communications
Convolutional neural networks (CNNs) are increasingly used to model human vision due to their high object categorization capabilities and general correspondence with human brain responses. Here we evaluate the performance of 14 different CNNs compare...

Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience.

eLife
In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by ...

Longitudinal self-supervised learning.

Medical image analysis
Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscien...

Deep learning-based attenuation correction for brain PET with various radiotracers.

Annals of nuclear medicine
OBJECTIVES: Attenuation correction (AC) is crucial for ensuring the quantitative accuracy of positron emission tomography (PET) imaging. However, obtaining accurate μ-maps from brain-dedicated PET scanners without AC acquisition mechanism is challeng...

Neurophysiological brain mapping of human sleep-wake states.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018). The latter has been expanded to inclu...

Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks.

Scientific reports
The study of cellular complexity in the nervous system based on anatomy has shown more practical and objective advantages in morphology than other perspectives on molecular, physiological, and evolutionary aspects. However, morphology-based neuron ty...

3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.

eLife
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We develope...

U-net model for brain extraction: Trained on humans for transfer to non-human primates.

NeuroImage
Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed...

Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach.

Sensors (Basel, Switzerland)
Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functional neuroimaging studies. However, susceptibility artifacts, which cause misinterpretations of brain functions, are unavoidable distortions in EPI. T...

[Artificial intelligence in psychiatry: predictive value of characteristics on MR imaging of the brain].

Nederlands tijdschrift voor geneeskunde
The clinical application of neuroimaging for psychological complaints has so far been limited to the exclusion of somatic pathology. Radiological assessment of brain scans usually does not explain the psychological symptoms. However, that does not me...