Neurodegenerative disorders: Stereotyped relationships between pathological metabolic topographies and the healthy default mode network
Abstract
Background and Objectives: Neurodegenerative disorders are associated with disease-specific networks on metabolic brain imaging. Depending on the disease, the associated pathological topography overlaps to varying degrees with the healthy default mode network (DMN). In this study, we examined the relationships of these networks in various neurodegenerative disorders and in prodromal syndromes. Methods: We analyzed FDG PET scans from 1,117 patients with neurodegenerative disorders manifest by dementia (Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (bvFTD), and dementia with Lewy bodies (DLB)), parkinsonism (Parkinson’s disease (PD) and PD dementia (PDD)), or by mixed/atypical syndromes (progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal degeneration (CBD), and X-linked dystonia-parkinsonism (XDP)). We also studied scan data from 120 individuals with prodromal syndromes. Topographic relationships between the disease-specific networks and the healthy DMN were assessed by computing spatial similarity indices and voxel-wise correlations. We also computed the expression of the two networks in each patient and in 499 healthy subjects. In each patient group, the resulting values were correlated with each other and with corresponding MMSE scores. Results: The disorders fell into one of three categories based on the topographic relationship of the disease-specific and healthy networks. Category 1 (bvFTD, PSP, CBD, and XDP) was characterized by substantial spatial overlap of the two metabolic networks, tight correlations between their respective expression levels, and significant DMN reductions. Category 2 (PD, AD, DLB, and their associated prodromal syndromes) was characterized by less pronounced overlap, correspondingly weak correlations, and smaller DMN reductions, particularly at prodromal stages. In this group, correlations with MMSE were greater for the difference between disease network and DMN scores than for the disease network alone. Category 3 (MSA) had no discernible network overlap and no loss of DMN expression. Discussion: Category 1 disorders were associated with close relationships between the pathological and healthy topographies, indicating primary DMN involvement. Category 2 disorders were characterized by weaker network relationships, consistent with secondary DMN involvement. Category 3 disorders showed no evidence of DMN involvement. The degree to which disease-specific networks overlap with DMN provides unique insight into the underlying neurodegenerative process.
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BibTeX
@misc{perovnik2026neurodegenerative,
title = {{Neurodegenerative disorders: Stereotyped relationships between pathological metabolic topographies and the healthy default mode network}},
author = {Matej Perovnik and David Eidelberg},
howpublished = {Research Square},
year = {2026},
month = oct,
doi = {10.21203/rs.3.rs-11320531/v1},
url = {https://doi.org/10.21203/rs.3.rs-11320531/v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Neurodegenerative disorders: Stereotyped relationships between pathological metabolic topographies and the healthy default mode network
AU - Matej Perovnik
AU - David Eidelberg
PY - 2026
DA - 2026/10/13
PB - Research Square
DO - 10.21203/rs.3.rs-11320531/v1
UR - https://doi.org/10.21203/rs.3.rs-11320531/v1
AB - Background and Objectives: Neurodegenerative disorders are associated with disease-specific networks on metabolic brain imaging. Depending on the disease, the associated pathological topography overlaps to varying degrees with the healthy default mode network (DMN). In this study, we examined the relationships of these networks in various neurodegenerative disorders and in prodromal syndromes. Methods: We analyzed FDG PET scans from 1,117 patients with neurodegenerative disorders manifest by dementia (Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (bvFTD), and dementia with Lewy bodies (DLB)), parkinsonism (Parkinson’s disease (PD) and PD dementia (PDD)), or by mixed/atypical syndromes (progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal degeneration (CBD), and X-linked dystonia-parkinsonism (XDP)). We also studied scan data from 120 individuals with prodromal syndromes. Topographic relationships between the disease-specific networks and the healthy DMN were assessed by computing spatial similarity indices and voxel-wise correlations. We also computed the expression of the two networks in each patient and in 499 healthy subjects. In each patient group, the resulting values were correlated with each other and with corresponding MMSE scores. Results: The disorders fell into one of three categories based on the topographic relationship of the disease-specific and healthy networks. Category 1 (bvFTD, PSP, CBD, and XDP) was characterized by substantial spatial overlap of the two metabolic networks, tight correlations between their respective expression levels, and significant DMN reductions. Category 2 (PD, AD, DLB, and their associated prodromal syndromes) was characterized by less pronounced overlap, correspondingly weak correlations, and smaller DMN reductions, particularly at prodromal stages. In this group, correlations with MMSE were greater for the difference between disease network and DMN scores than for the disease network alone. Category 3 (MSA) had no discernible network overlap and no loss of DMN expression. Discussion: Category 1 disorders were associated with close relationships between the pathological and healthy topographies, indicating primary DMN involvement. Category 2 disorders were characterized by weaker network relationships, consistent with secondary DMN involvement. Category 3 disorders showed no evidence of DMN involvement. The degree to which disease-specific networks overlap with DMN provides unique insight into the underlying neurodegenerative process.
SN - 2693-5015
ER -