Thalamocortical Connectivity in Preclinical Alzheimer’s Disease
Poster No:
124
Submission Type:
Abstract Submission
Authors:
Samuel Maddox1, Shinwon Park2, Koen Haak3, Jacob Newman1, Michal Mackiewicz1, Saber Sami1
Institutions:
1University of East Anglia, Norwich, Norfolk, 2Autism Center, Child Mind Institute, New York, NY, 3Department of Cognitive Science and Artificial Intelligence, Tilburg University,, Tilburg, Tilburg
First Author:
Co-Author(s):
Koen Haak
Department of Cognitive Science and Artificial Intelligence, Tilburg University,
Tilburg, Tilburg
Department of Cognitive Science and Artificial Intelligence, Tilburg University,
Tilburg, Tilburg
Introduction:
Early pathological alterations in brain function are challenging to detect due to their subtle and variable effects across individuals. While much of the existing research focuses on cortical brain regions, advances in brain mapping methodologies now allow for the analysis of interactions between deep brain hubs, and broader cortical networks [1]. Among these hubs, the thalamus is notably affected in the early stages of neurodegeneration and maintains complex, integrative connections with the wider cortex [2]. These thalamocortical connections offer a more complete picture of emerging disease processes and potential role in neurodegeneration.
In this study, we investigate the resting-state thalamocortical connectivity in individuals at risk of Alzheimer's Disease (AD). By applying gradient based methods to measure subtle alterations in connectivity, we aim to uncover new insights into the earliest stages of cognitive decline.
In this study, we investigate the resting-state thalamocortical connectivity in individuals at risk of Alzheimer's Disease (AD). By applying gradient based methods to measure subtle alterations in connectivity, we aim to uncover new insights into the earliest stages of cognitive decline.
Methods:
Longitudinal data from 197 individuals across three timepoints (12 months separated) was obtained from the PREVENT-AD dataset [3]. From this cohort, 27 participants who developed probable mild cognitive impairment (MCI) an average of 4.6 years after initial imaging were selected. An equal sample of 27 age and education matched healthy control individuals (CN) with no cognitive decline were also included. Each participant contributed functional and structural MRI data across three timepoints.
Data preprocessing included fMRIprep [4] with FreeSurfer reconstruction [5], followed by XCP_D post-processing for despiking, bandpass filtering, and confound regression [6], without global signal regression. Functional and anatomical outputs were processed with Ciftify incorporating smoothing, dilation, and registration [7]. Thalamocortical connectopic gradients were then computed using the 'Congrads' tool [8] with the left and right hemisphere thalamus and cortex as inputs. Participants with poor limbic region coverage were excluded, leaving 21 in MCI and 24 in CN.
Individual projection maps were aligned to a normalised group connectopic template using MATLAB procrustes function to ensure consistent gradients across individuals [1]. This template was derived from the entire cohort of 197 participants, processed using the same pipeline. Aligned data was analysed to identify differences in thalamic projections across networks using two-sample t-tests at each timepoint. Mean vertex values from the 7 functional brain networks in the Schaefer400 atlas were used as inputs. A statistical threshold of p < 0.05 was applied, with Bonferroni correction. Analyses focused on the first two gradients.
Data preprocessing included fMRIprep [4] with FreeSurfer reconstruction [5], followed by XCP_D post-processing for despiking, bandpass filtering, and confound regression [6], without global signal regression. Functional and anatomical outputs were processed with Ciftify incorporating smoothing, dilation, and registration [7]. Thalamocortical connectopic gradients were then computed using the 'Congrads' tool [8] with the left and right hemisphere thalamus and cortex as inputs. Participants with poor limbic region coverage were excluded, leaving 21 in MCI and 24 in CN.
Individual projection maps were aligned to a normalised group connectopic template using MATLAB procrustes function to ensure consistent gradients across individuals [1]. This template was derived from the entire cohort of 197 participants, processed using the same pipeline. Aligned data was analysed to identify differences in thalamic projections across networks using two-sample t-tests at each timepoint. Mean vertex values from the 7 functional brain networks in the Schaefer400 atlas were used as inputs. A statistical threshold of p < 0.05 was applied, with Bonferroni correction. Analyses focused on the first two gradients.
Results:
Connectopic gradients reveal distinct patterns along each axis of the thalamus, with the first two gradients showing particularly prominent effects (see Figure 1a). Within the MCI and CN groups, clear trends emerge across networks: the visual network consistently receives the lowest thalamic projection map scores, while the frontoparietal and salience networks score higher (see Figure 1b). Examination of projection scores within the first 12 months shows no group differences. However, by the 24-month mark, significant differences in the second thalamocortical projection are observed in the default mode network (see Figure 1c).
Conclusions:
Our findings highlight distinct alterations in networks known to be involved in AD, detectable around two and a half years before symptoms become apparent. Notable changes within the default mode network suggest a functional reorganization linked to thalamocortical connectivity. These results align with previous studies identifying early predictive markers of neurodegeneration in this resting-state network [9].
Our approach could play a key role in the early diagnosis of individuals at risk for Alzheimer's Disease (AD), facilitating the monitoring of their disease trajectories and supporting the development of personalised treatment strategies.
Our approach could play a key role in the early diagnosis of individuals at risk for Alzheimer's Disease (AD), facilitating the monitoring of their disease trajectories and supporting the development of personalised treatment strategies.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
Aging
Cortex
Data analysis
Degenerative Disease
Memory
Other - Thalamocortical
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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