AIMC Topic: Brain

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VacQuant: a tool to quantify neurodegeneration and associated vacuolation in brain tissue.

Fly
Neurodegenerative diseases are devastating conditions characterized by progressive cognitive decline with few available treatments. Neurodegeneration can be quantified in vertebrate and invertebrate models of disease by analysis of vacuolation - the ...

Progress, challenges and future of linguistic neural decoding with deep learning.

Communications biology
Language is the primary medium through which humans achieve information transfer and exchange. It enables the conveyance of ideas, concepts, and messages, thereby playing an indispensable role in social interaction and knowledge dissemination. Lingui...

Online reinforcement learning of state representation in recurrent network supported by the power of random feedback and biological constraints.

eLife
Representation of external and internal states in the brain plays a critical role in enabling suitable behavior. Recent studies suggest that state representation and state value can be simultaneously learned through Temporal-Difference-Reinforcement-...

A novel hybrid deep learning model for segmentation and uzzy Res-LeNet based classification for Alzheimer's disease.

Neurogenetics
Alzheimer's disease (AD) is a progressive illness that can cause behavioural abnormalities, personality changes, and memory loss. Early detection helps with future planning for both the affected person and caregivers. Thus, an innovative hybrid Deep ...

Defining individualized theta frequency for memory modulation: A machine learning approach across brain states and regions.

NeuroImage
Recent transcranial alternating current stimulation (tACS) studies suggest that theta-frequency stimulation can modulate memory performance, with evidence highlighting individual variability in optimal stimulation frequency. However, it remains uncle...

Linking dynamic connectivity states to cognitive decline and anatomical changes in Alzheimer's disease.

NeuroImage
Alterations in brain connectivity provide early indications of neurodegenerative diseases like Alzheimer's disease (AD). Here, we present a novel framework that integrates a Hidden Markov Model (HMM) within the architecture of a convolutional neural ...

Learning and spiking dynamics in brain-like nanoscale networks.

Nanoscale horizons
Neuromorphic approaches to computation are driven by both the low-power operation of the biological brain and ever-increasing energy consumption of modern computing systems. Percolating networks of nanoparticles are promising candidates for self-asse...

CQ-CNN: A lightweight hybrid classical-quantum convolutional neural network for Alzheimer's disease detection using 3D structural brain MRI.

PloS one
The automatic detection of Alzheimer's disease (AD) using 3D volumetric MRI data is a complex, multi-domain challenge that has traditionally been addressed by training classical convolutional neural networks (CNNs). With the rise of quantum computing...

AI-Validated Brain Targeted mRNA Lipid Nanoparticles with Neuronal Tropism.

ACS nano
Targeting therapeutic nanoparticles to the brain poses a challenge due to the restrictive nature of the blood-brain barrier (BBB). Here we report the development of mRNA-loaded lipid nanoparticles (LNPs) functionalized with BBB-interacting small mole...

From perceiving words to reading: Neural multivariate representations of sublexical vs. lexico-semantic processing during word-reading.

NeuroImage
While the neural underpinnings of semantic cognition have been extensively studied, the brain mechanisms that allow the extraction of meaning from the initially perceptual visual linguistic input are less understood. These mechanisms have typically b...