Characteristic indicators for mild-cognitive-impairment obtained from dimension reduction of brain networks
Xinmeng Weng1, Minghuan Xu1, Zhanxiong Wu1
1School of Electronic Information, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China.
Abstract:
Alzheimer's Disease (AD) is irreversible. Mild cognitive impairment (MCI) is the first symptomatic stage of AD. Distinguishing MCI patients from healthy controls (HCs) through appropriate techniques is critical for early therapeutic interventions and prolonging patients' health. In this study, we explored characteristic indicators for MCI through dimension reduction of brain networks. After the brains (100 HCs and 100 MCIs from ADNI dataset) were partitioned into 360 parcels, one-dimension time series was extracted from diffusion and resting-state functional magnetic resonance imaging (MRI) data using network dimension-reduction techniques. Power-spectrum was then employed to transform the time series into frequency domain, to find characteristic indicators for MCI. Statistical tests indicate that the indicators (mean square frequency, and center frequency) estimated with brain network reductions could differentiate MCIs from HCs more significantly, compared with those of BOLD time series of specific AD-related subregions (hippocampus, and parieto-temporal subregions). Power-spectrum of one-dimension time series extracted with network reductions might be a viable method for distinguishing MCI progression stages. This approach could potentially facilitate earlier and more precise differentiation between MCIs and HCs, showing future clinical applicability.
Insights
Researchers identified key brain network indicators to differentiate mild cognitive impairment (MCI) from healthy controls. This method offers a promising approach for early Alzheimer's Disease (AD) detection and intervention.
Area of Science:
- Neuroscience
- Medical Imaging
- Biostatistics
Background:
- Alzheimer's Disease (AD) is an irreversible neurodegenerative condition.
- Mild cognitive impairment (MCI) represents the earliest symptomatic stage of AD.
- Early and accurate differentiation between MCI and healthy controls (HCs) is crucial for timely therapeutic interventions.
Purpose of the Study:
- To explore characteristic brain network indicators for distinguishing MCI patients from HCs.
- To investigate the efficacy of dimension reduction techniques combined with power-spectrum analysis for MCI detection.
- To assess the potential of this novel approach for early diagnosis and monitoring of MCI progression.
Main Methods:
- Utilized diffusion and resting-state functional MRI data from 100 HCs and 100 MCIs (ADNI dataset).
- Partitioned brain data into 360 parcels and extracted one-dimension time series using network dimension-reduction techniques.
- Applied power-spectrum analysis to frequency-domain transformation of time series for indicator identification.
Main Results:
- Brain network indicators (mean square frequency, center frequency) derived from dimension reduction significantly differentiated MCIs from HCs.
- This method showed greater significance compared to analyses of BOLD time series from specific AD-related subregions (hippocampus, parieto-temporal).
- The power-spectrum of dimension-reduced time series shows potential as a viable method for distinguishing MCI stages.
Conclusions:
- Brain network dimension reduction coupled with power-spectrum analysis offers a robust method for differentiating MCI from HCs.
- This approach facilitates earlier and more precise differentiation, potentially aiding clinical applicability in diagnosing MCI.
- The findings suggest a promising avenue for early detection and management strategies in Alzheimer's Disease progression.
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