AIMC Topic: Brain

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Sequential temporal anticipation characterized by neural power modulation and in recurrent neural networks.

eLife
Relevant prospective moments arise intermittently, while most of the time is filled with irrelevant events, or noise, that constantly bombard our sensory systems. Thus, anticipating a few key moments necessitates disregarding what lies between the pr...

Forewarned Is Forearmed: The Single- and Dual-Brain Mechanisms in Detectors from Dyads of Varying Social Distance during Deceptive Outcome Evaluation.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Preventing deception requires understanding how lie detectors process social information across social distance. Although the outcomes of such information are crucial, how detectors evaluate gains or losses from close versus distant others remains un...

Effects of iron repletion on brain iron content, myelination, neural network activation, and cognition.

JCI insight
BACKGROUNDBlood donation increases the risk of iron deficiency, but its effect on brain iron, myelination, and neurocognition remains unclear.METHODSThis ancillary study enrolled 67 iron-deficient blood donors, 19-73 years of age, participating in a ...

Aging as an active player in Alzheimer's disease classification: Insights from feature selection in BrainAge models.

NeuroImage
BACKGROUND: BrainAge models estimate the biological age of the brain using neuroimaging or clinical features, making them promising tools for studying neurodegenerative diseases like Alzheimer's disease. However, the reliance of BrainAge models on ne...

Descattering and image restoration with a transformer-based neural network in deep tissue imaging.

Proceedings of the National Academy of Sciences of the United States of America
Imaging biological structures deep inside tissues is crucial but challenging due to common light scattering. This study proposes a multiattention network that directly maps degraded scattering two-photon excitation fluorescence (TPEF) images to high-...

Exploring the impact mechanisms of EEG signals and emotional intelligence levels on language learning efficiency.

Scientific reports
Improving language learning through a better understanding of how brain activity and emotional intelligence interact is a promising research direction with practical value in education. Traditional methods in this area often use static models, which ...

A lightweight network for brain MRI segmentation.

Scientific reports
Brain MRI segmentation plays a crucial role in medical imaging, aiding in the identification and monitoring of brain diseases. This research presents a novel deep learning-based framework designed to achieve high segmentation accuracy while maintaini...

An Explainable 3D-Deep Learning Model for EEG Decoding in Brain-Computer Interface Applications.

International journal of neural systems
Decoding electroencephalographic (EEG) signals is of key importance in the development of brain-computer interface (BCI) systems. However, high inter-subject variability in EEG signals requires user-specific calibration, which can be time-consuming a...

Hierarchical dynamic coding coordinates speech comprehension in the human brain.

Proceedings of the National Academy of Sciences of the United States of America
Speech comprehension involves transforming an acoustic waveform into meaning. To do so, the human brain generates a hierarchy of features that converts the sensory input into increasingly abstract language properties. However, little is known about h...

Early subtypes and progressions of progressive supranuclear palsy: a data-driven brain bank study.

Journal of neurology
BACKGROUND: Progressive supranuclear palsy (PSP) is typically characterized by vertical supranuclear gaze palsy and early falls, referred to as Richardson's syndrome (PSP-RS). Other presentations include postural instability (PSP-PI), Parkinsonism (P...