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

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Year : 2025 | Volume : 15 | Issue : 02 | Page : –
    By

    Manisha Agrahari,

  • Raghuvendra Solanki,

  1. Assistant Professor, Department of Nursing, St. Stephene’s College of Nursing, Supaul, Pipra, Bihar, India
  2. Sr. Manager, Operations & Marketing, Department of Nursing, Felix Hospital, Noida, Utter Pradesh, India

Abstract

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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: UK Biobank, Spectral dynamic, FMRI, microglia, astrocytes, vasculature

[This article belongs to Research and Reviews: A Journal of Neuroscience ]

How to cite this article:
Manisha Agrahari, Raghuvendra Solanki. An Overview of Artificially Generated Neural Networks Inside the Brain’s Structure in an Alzheimer’s Disease Patient. Research and Reviews: A Journal of Neuroscience. 2025; 15(02):-.
How to cite this URL:
Manisha Agrahari, Raghuvendra Solanki. An Overview of Artificially Generated Neural Networks Inside the Brain’s Structure in an Alzheimer’s Disease Patient. Research and Reviews: A Journal of Neuroscience. 2025; 15(02):-. Available from: https://journals.stmjournals.com/rrjons/article=2025/view=0


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Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 31/01/2025
Accepted 25/03/2025
Published 07/06/2025
Publication Time 127 Days
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