Mapping And Modeling Brain Network Deterioration Using Fmri And Meg

Abstract

Background:
Alzheimer’s, Parkinson’s, and Epilepsy are neurodegenerative disorders which result in the neural circuits progressively collapsing, causing a cognitive, motor, or sensory dysfunctions. The circuit degeneration due to pathologic progression of all these diseases is what makes it a focal area of concern. How neural circuits collapse and how computational models help in studying these changes can provide answers toward formulating therapeutic targets within the disease mechanisms. This study aims to determine the extent to which computational models of neural circuits collapse circuits can be created for neurodegenerative diseases step with ever evolving changes in these models for treatment frameworks.

Objective:

The focus of this study is to assess the extent to which computational models aid to understand the neural circuit collapse in Alzheimer’s, Parkinson’s and Epilepsy. The objectives include measuring how these models will be able to simulate the disease progression, outcome measure predictions of the intervention simulations versus the expected results, and opposition in executing treatment within the framework of a defined therapeutic boundary. This research investigates whether the validation of computational models for neurodegenerative diseases will shift the designed plan to a focused strategy target upon intervention for treating those diseases.

Methods:

An open-ended online survey was sent to 167 participants with some affiliation to neuroscience, computational modeling, healthcare, or lay people.The survey captured information on participants' awareness and understanding of computation models, their regard of the usefulness of the models in regard to disease research, and the difficulties encountered in their real-world use. Quantitative data were analyzed using descriptive statistics while qualitative data was analyzed thematically. Thematic analyses of the qualitative data focused on trends and insights around the use of computational models in studying neural circuit collapse.

Results:
Though most participants have some knowledge of neural circuits and computational modeling, still a sizeable fraction (28%) appear to be less versed with them as tools. A greater percentage of participants (62%) consider computational models appropriate for studying the collapse of neural circuits, especially in Alzheimer's and Parkinson’s diseases. However, data sufficiency (32%), the intricate nature of neural systems (25%), and the computational capacity (20%) of a given model were reported as fundamental obstacles to the application of these models. Notwithstanding these obstacles, a decisive proportion of respondents (70%) reported confidence regarding the role of the models in understanding the diseases and the therapy approaches in the models’ future prospects. 

Conclusion: 

This study emphasizes the potential usefulness of computational models in studying the collapse of neural circuits in neurodegenerative diseases. While the models aim to predict outcomes associated with disease progression and treatment, issues pertaining to the availability of data, model complexity, and limited computational resources present hurdles. Findings advocate that further the refinement of computational models concerning data barriers, accessibility, and innovative modeling approaches should be the focus. More sustained cross-disciplinary focus along with robust spending on computational resources will improve the effectiveness of these models in the clinic and research.