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Resting-state brain network biomarkers of high cognitive resilience in Alzheimer's disease

Poster No:

109

Submission Type:

Abstract Submission

Authors:

Lin Liu¹, QianYu Yang², Wanwan Guo³, Feng Chen²

Institutions:

¹shenzhen bay labtorary, Shenzhen, Guangdong Sheng, ²Hainan General Hospital, Hainan Afliated Hospital of Hainan Medical University, Haikou, Hainan, ³Lab of Molecular Imaging and Medical Intelligence, Department of Radiology, Shenzhen, Guangdong

First Author:

Lin Liu
shenzhen bay labtorary
Shenzhen, Guangdong Sheng

Co-Author(s):

QianYu Yang
Hainan General Hospital, Hainan Afliated Hospital of Hainan Medical University
Haikou, Hainan

Wanwan Guo
Lab of Molecular Imaging and Medical Intelligence, Department of Radiology
Shenzhen, Guangdong

Feng Chen
Hainan General Hospital, Hainan Afliated Hospital of Hainan Medical University
Haikou, Hainan

Introduction:

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by cognitive decline and alterations in brain structure and function. The phenomenon referred to as cognitive resilience denotes the brain's capacity to sustain cognitive function in the face of pathological alterations. Recent progress in neuroimaging methodologies, especially resting-state functional magnetic resonance imaging (rs-fMRI), has opened up unprecedented possibilities for studying the neural foundation of cognitive resilience in individuals with AD. The ability of brain networks to flexibly reconfigure in response to cognitive demands may be a key factor in cognitive resilience. Therefore, this study aims to investigate the resting-state brain network characteristics associated with high cognitive resilience in AD patients. By identifying specific network configurations and dynamics that contribute to maintained cognitive function despite AD pathology, we hope to advance our understanding of cognitive resilience mechanisms and potentially inform the development of targeted therapeutic interventions.

Methods:

We analyzed 855 ADNI participants with Aβ PET (18F-florbetapir (FBP) or 18F-florbetaben (FBB)), CSF p-Tau181, and longitudinal residual hippocampal volume (rHCV) and cognitive assessments. A+/T+/N+ were defined as AD summary cortical standardized uptake value ratio (SUVR) for FBP ≥1.11 or FBB ≥1.08, CSF p-Tau≥23pg/ml, and rHCV-0.67 respectively. This study categorized 164 AD participants into reference group of 96 with cognitive unimpairments (CU) and CR group of 68 with high cognitive resilience. Preprocessing: Functional and anatomical data were preprocessed using a flexible preprocessing pipeline including realignment with correction of susceptibility distortion interactions, slice timing correction, outlier detection, direct segmentation and MNI-space normalization, and smoothing. ROI-to-ROI connectivity matrices (RRC) were estimated characterizing the patterns of functional connectivity with 164 HPC-ICA networks and Harvard-Oxford atlas ROIs. Group-level analyses were performed using a General Linear Model (GLM). Results were thresholded using a combination of a cluster-forming p < 0.001 voxel-level threshold, and a familywise corrected p-FDR < 0.05 cluster-size threshold.

Results:

Altered connectivity within the Default Mode Network (DMN) is a consistent finding in individuals with Alzheimer's disease (AD), notably showing reduced connectivity in the posterior cingulate cortex and precuneus. In contrast, those with high cognitive resilience exhibit maintained connectivity in the DMN, particularly in anterior regions.
Cognitively resilient Alzheimer's disease patients demonstrate enhanced local efficiency in crucial brain regions, preserved network hierarchy, and more stable network communities following network topology changes.
The significance of dynamic network reconfiguration in cognitive resilience is highlighted by Dynamic Connectivity Patterns, with individuals demonstrating higher resilience exhibiting more flexible network switching, better maintained network integration, and enhanced adaptive capacity.
Individuals with cognitive resilience display elevated connectivity in alternative networks, including enhanced frontal-parietal connectivity, strengthened salience network function, and improved cross-network integration.

·Figure 1

·Figure 2

Conclusions:

Resting-state brain network analysis has identified specific patterns associated with cognitive resilience in Alzheimer's disease, shedding light on potential protective mechanisms against cognitive decline and guiding the development of new therapeutic interventions. Continued exploration in this field, particularly through longitudinal studies and standardized approaches, is critical for advancing our knowledge of cognitive resilience in Alzheimer's disease.

Disorders of the Nervous System:

Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) ¹

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural) ²

fMRI Connectivity and Network Modeling

Keywords:

Cognition

FUNCTIONAL MRI

^1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

Resting state

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was this research conducted in the United States?

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

PET

Functional MRI

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

SPM

FSL

Free Surfer

Provide references using APA citation style.

Chumbley, J., Worsley, K., Flandin, G., & Friston, K. (2010). Topological FDR for neuroimaging. Neuroimage, 49(4), 3057-3064.

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