Multiplex Nodal Modularity: A novel network metric for the regional analysis of amnestic mild cognitive impairment during a working memory binding task (2501.09805v2)

Abstract: Modularity is a well-established concept for assessing community structures in various single and multi-layer networks, including those in biological and social domains. Brain networks are known to exhibit community structure at local, meso, and global scale. However, modularity is limited as a metric to a global scale describing the overall strength of community structure, overlooking important variations in community structure at node level. To address this limitation, we extended modularity to individual nodes. This novel measure of nodal modularity (nQ) captures both mesoscale and local-scale changes in modularity. We hypothesized that nQ would illuminate granular changes in the brain due to diseases such as Alzheimer's disease (AD), which are known to disrupt the brain's modular structure. We explored nQ in multiplex networks of a visual short-term memory binding task in fMRI and DTI data in the early stages of AD. While limited by sample size, changes in nQ for individual regions of interest (ROIs) in our fMRI networks were predominantly observed in visual, limbic, and paralimbic systems in the brain, aligning with known AD trajectories and linked to amyloid-$\beta$ and tau deposition. Furthermore, observed changes in white-matter microstructure in our DTI networks in parietal and frontal regions may compliment studies of white-matter integrity in poor memory binders. Additionally, nQ clearly differentiated MCI from MCI converters indicating that nQ may be sensitive to this key turning point of AD. Our findings demonstrate the utility of nQ as a measure of localized group structure, providing novel insights into task and disease-related variability at the node level. Given the widespread application of modularity as a global measure, nQ represents a significant advancement, providing a granular measure of network organization applicable to a wide range of disciplines.

• The paper introduces multiplex nodal modularity to assess region-specific network changes during working memory binding tasks in aMCI.
• The study integrates fMRI and DTI data, providing a detailed analysis of functional and structural connectivity impairments.
• Results indicate increased nodal disruptions in memory-related regions, distinguishing MCI subtypes and aiding early Alzheimer’s diagnosis.

Multiplex Nodal Modularity in Analyzing Amnestic Mild Cognitive Impairment

The paper under discussion introduces a novel network measure, termed as multiplex nodal modularity, designed for regional analysis of amnestic mild cognitive impairments (aMCI) during working memory tasks. This paper is situated in the context of Alzheimer’s disease progression, where Mild Cognitive Impairment (MCI), particularly the amnestic subtype, serves as a precursor to Alzheimer’s disease (AD).

Background and Objectives

The concept of modularity has been a cornerstone in neuroscience for evaluating community structures in networks, particularly evident in brain network studies. Previous analyses predominantly considered modularity at a global scale, often overlooking granular, regional changes. The main objective of this research is to push beyond those limitations by introducing nodal modularity ($nQ$) that captures changes in modular structure relevant to individual nodes within multiplex networks derived from fMRI and DTI data. The hypothesis is that $nQ$ could reveal more localized disturbances in brain structure due to neurodegenerative changes associated with Alzheimer's.

Methods

The paper utilizes data from visual short-term memory binding tasks, using both fMRI (to capture functional connectivity) and DTI (to evaluate structural connectivity) data. Subjects included in the paper were divided into control, early MCI, MCI, and MCI converters. These multiplex networks were examined both at a layer level (encoding and recall phases) and as an interconnected multi-layer system. The innovative aspect here was using the introduced $nQ$ to capture variations at the nodal level.

Results

The findings illustrate that $nQ$ provides a nuanced understanding of how aMCI impacts the brain, particularly in functional and structural networks. As AD progresses from early MCI through to conversion, the number and specificity of nodes with altered modularity increase significantly. In functional networks, abnormalities were prevalent in regions associated with visual, limbic, and paralimbic systems, areas known for their involvement in memory processing and disease progression in AD.

Interestingly, this granular view showed specific regions with changes aligning with known AD pathophysiologies, such as increased amyloid-beta and tau deposition in poor memory binders during key memory tasks. Moreover, $nQ$ demonstrated a capability to distinctly categorize MCI from MCI converters, suggesting its potential utility as a diagnostic tool.

Discussion

The introduction of multiplex nodal modularity signifies an advancement in the ability to capture dynamic region-specific alterations in brain networks due to aMCI. By highlighting localized changes, this approach offers a crucial improvement over previous global modularity assessments. The findings align with the expected trajectory of Alzheimer's, revealing compensatory mechanisms in brain networks that adapt to the disease's progression.

Implications and Future Directions

This paper's insights underscore the importance of network-based approaches in unraveling the complexities of Alzheimer's disease. By utilizing multiplex frameworks and nodal analysis, $nQ$ could facilitate early detection and enhance understanding of the disease's neural substrates. Future work could expand upon these findings by integrating additional layers, such as genetic and clinical data, to refine diagnostic capabilities and develop targeted interventions.

In conclusion, multiplex nodal modularity represents a promising tool in the landscape of neuroimaging and neuroscience, offering potential for more precise diagnostic strategies in the context of neurodegenerative diseases like Alzheimer’s.