AIMC Topic: Cerebral Cortex

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Integrated functional neuronal network analysis of 3D silk-collagen scaffold-based mouse cortical culture.

STAR protocols
Bioengineered 3D tunable neuronal constructs are a versatile platform for studying neuronal network functions, offering numerous advantages over existing technologies and providing for the discovery of new biological insights. Functional neural netwo...

Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions.

NeuroImage
Head motion during MRI acquisition presents significant challenges for neuroimaging analyses. In this work, we present a retrospective motion correction framework built on a Fourier domain motion simulation model combined with established 3D convolut...

Insights on the role of external globus pallidus in controlling absence seizures.

Neural networks : the official journal of the International Neural Network Society
Absence epilepsy, characterized by transient loss of awareness and bilaterally synchronous 2-4 Hz spike and wave discharges (SWDs) on electroencephalography (EEG) during absence seizures, is generally believed to arise from abnormal interactions betw...

Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks.

Nature neuroscience
Cortical neurons process information on multiple timescales, and areas important for working memory (WM) contain neurons capable of integrating information over a long timescale. However, the underlying mechanisms for the emergence of neuronal timesc...

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites.

PloS one
Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy ...

DNN-assisted statistical analysis of a model of local cortical circuits.

Scientific reports
In neuroscience, computational modeling is an effective way to gain insight into cortical mechanisms, yet the construction and analysis of large-scale network models-not to mention the extraction of underlying principles-are themselves challenging ta...

A Computational Model of the Brain Cortex and Its Synchronization.

BioMed research international
Obtaining the computational models for the functioning of the brain gives us a chance to understand the brain functionality thoroughly. This would help the development of better treatments for neurological illnesses and disorders. We created a cortic...

Predicting memory from study-related brain activity.

Journal of neurophysiology
To isolate brain activity that may reflect effective cognitive processes during the study phase of a memory task, cognitive neuroscientists commonly contrast brain activity during study of later-remembered versus later-forgotten items. This "subseque...

Predicting alcohol dependence from multi-site brain structural measures.

Human brain mapping
To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored ...

Reconstructing lost BOLD signal in individual participants using deep machine learning.

Nature communications
Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles gov...