STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews: A Journal of Neuroscience Review Article

An Overview of Artificially Generated Neural Networks Inside the Brain’s Structure in an Alzheimer’s Disease Patient

  1. Manisha Agrahari Department of Nursing, St. Stephene’s College of Nursing, Supaul, Pipra
  2. Raghuvendra Solanki Department of Nursing, Felix Hospital, Noida

Abstract

Alzheimer’s disease produces significant neuronal loss, while the precise mechanisms and timing are yet unknown. Other types of cell death, such necroptosis, parthanatosis, ferroptosis, and cuproptosis, need further investigation. Based on brain images of people with mild cognitive impairment, this study assesses artificial neural networks (ANNs) used to diagnose and predict Alzheimer’s disease (AD). This research was conducted considering growing recognition among researchers and medical professionals regarding the importance of early identification of AD. The amyloid hypothesis of Alzheimer’s disease (AD) is based on a neuron-centric, linear cascade that is initiated by Aβ and ends in dementia. This direct relationship is not supported by clinical findings. We investigate the findings suggesting a long-lasting complex cellular phase composed of feedback and feedforward interactions from microglia, astrocytes, and vasculature. Because of deep learning’s unparalleled performance in general image processing, its application to neuroimaging data has grown. The discussed methodologies detect subtle neuroanatomical and functional brain alterations, aiding in identifying abnormalities, predicting disease progression, and classifying disorders. A new method assessed gene expression in over 1.3 million cells across 70+ cell types from six brain areas of 48 donors – 26 with Alzheimer’s and 22 without. By analyzing brain cell activity based on cell type, region, pathology, and cognitive assessments, the study provides a comprehensive insight into Alzheimer’s disease. Using spectral dynamic causal modeling on resting-state fMRI data from the UK Biobank (1,030 controls, 81 future dementia cases), dysconnectivity predicted dementia incidence (AUC = 0.82) and diagnosis delay (R = 0.53), surpassing anatomical and functional connectivity models.

Keywords

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