AIMC Topic: Cerebral Cortex

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A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease.

Human brain mapping Oct 22, 2021
Structural magnetic resonance imaging (sMRI) can capture the spatial patterns of brain atrophy in Alzheimer's disease (AD) and incipient dementia. Recently, many sMRI-based deep learning methods have been developed for AD diagnosis. Some of these met...
Neuroimaging Cerebral Cortex Humans Prognosis Deep Learning Aged Cognitive Dysfunction Disease Progression Magnetic Resonance Imaging Alzheimer Disease
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MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning.

Nature communications Oct 13, 2021
Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires...
Algorithms Animals Male Image Processing, Computer-Assisted Machine Learning Nerve Net Stereotaxic Techniques Mice, Transgenic Atlases as Topic Mice Brain Mapping Cerebral Cortex Optical Imaging
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Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT.

NeuroImage Sep 25, 2021
Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more wide...
Humans Neural Networks, Computer Aged Male Deep Learning Magnetic Resonance Imaging Algorithms Brain Tomography, X-Ray Computed Female Gray Matter Cerebral Cortex White Matter Birth Cohort Benchmarking Image Interpretation, Computer-Assisted Neuroimaging
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Adaptive Dropout Method Based on Biological Principles.

IEEE transactions on neural networks and learning systems Aug 31, 2021
Dropout is one of the most widely used methods to avoid overfitting neural networks. However, it rigidly and randomly activates neurons according to a fixed probability, which is not consistent with the activation mode of neurons in the human cerebra...
Neurons Cerebral Cortex Neural Networks, Computer Algorithms Deep Learning Models, Neurological Humans
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Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.

Human brain mapping Aug 13, 2021
Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing thre...
Neuroimaging Aged Cerebral Cortex Adult Depressive Disorder, Major Electroconvulsive Therapy Humans Male Magnetic Resonance Imaging Middle Aged Outcome Assessment, Health Care Machine Learning Female
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Identifying resting state differences salient for resilience to chronic pain based on machine learning multivariate pattern analysis.

Psychophysiology Aug 12, 2021
Studies have documented behavior differences between more versus less resilient adults with chronic pain (CP), but the presence and nature of underlying neurophysiological differences have received scant attention. In this study, we attempted to iden...
Machine Learning Female Adult Male Middle Aged Resilience, Psychological Magnetic Resonance Imaging Reward Cerebral Cortex Humans Pain Perception Connectome Executive Function Nerve Net Emotional Regulation Chronic Pain Pattern Recognition, Automated Musculoskeletal Pain Multivariate Analysis
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Single cortical neurons as deep artificial neural networks.

Neuron Aug 10, 2021
Utilizing recent advances in machine learning, we introduce a systematic approach to characterize neurons' input/output (I/O) mapping complexity. Deep neural networks (DNNs) were trained to faithfully replicate the I/O function of various biophysical...
Pyramidal Cells Dendrites Humans Deep Learning Models, Neurological Receptors, N-Methyl-D-Aspartate Cerebral Cortex
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Long-Tailed Characteristic of Spiking Pattern Alternation Induced by Log-Normal Excitatory Synaptic Distribution.

IEEE transactions on neural networks and learning systems Aug 3, 2021
Studies of structural connectivity at the synaptic level show that in synaptic connections of the cerebral cortex, the excitatory postsynaptic potential (EPSP) in most synapses exhibits sub-mV values, while a small number of synapses exhibit large EP...
Synaptic Transmission Excitatory Postsynaptic Potentials Cerebral Cortex Nerve Net Entropy Humans Neural Networks, Computer Algorithms Models, Neurological Nonlinear Dynamics Synapses
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The neural representation of abstract words may arise through grounding word meaning in language itself.

Human brain mapping Jul 15, 2021
In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of b...
Cerebral Cortex Humans Young Adult Psycholinguistics Concept Formation Models, Statistical Machine Learning Adult Reading Brain Mapping Female Male Magnetoencephalography Semantics Adolescent
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Automatic recognition of specific local cortical folding patterns.

NeuroImage Jun 2, 2021
The study of local cortical folding patterns showed links with psychiatric illnesses as well as cognitive functions. Despite the tools now available to visualize cortical folds in 3D, manually classifying local sulcal patterns is a time-consuming and...
Support Vector Machine Magnetic Resonance Imaging Humans Pattern Recognition, Automated Machine Learning Cerebral Cortex Databases, Factual
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