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Resting-State Network Alterations Differ between Alzheimer's Disease Atrophy Subtypes.

ABSTRACT: Several Alzheimer's disease (AD) atrophy subtypes were identified, but their brain network properties are unclear. We analyzed data from two independent datasets, including 166 participants (103 AD/63 controls) from the DZNE-longitudinal cognitive impairment and dementia study and 151 participants (121 AD/30 controls) from the AD neuroimaging initiative cohorts, aiming to identify differences between AD atrophy subtypes in resting-state functional magnetic resonance imaging intra-network connectivity (INC) and global and nodal network properties. Using a data-driven clustering approach, we identified four AD atrophy subtypes with differences in functional connectivity, accompanied by clinical and biomarker alterations, including a medio-temporal-predominant (S-MT), a limbic-predominant (S-L), a diffuse (S-D), and a mild-atrophy (S-MA) subtype. S-MT and S-D showed INC reduction in the default mode, dorsal attention, visual and limbic network, and a pronounced reduction of "global efficiency" and decrease of the "clustering coefficient" in parietal and temporal lobes. Despite severe atrophy in limbic areas, the S-L exhibited only marginal global network but substantial nodal network failure. S-MA, in contrast, showed limited impairment in clinical and cognitive scores but pronounced global network failure. Our results contribute toward a better understanding of heterogeneity in AD with the detection of distinct differences in functional connectivity networks accompanied by CSF biomarker and cognitive differences in AD subtypes.

SUBMITTER: Rauchmann BS

PROVIDER: S-EPMC8491689 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Resting-State Network Alterations Differ between Alzheimer's Disease Atrophy Subtypes.

Rauchmann Boris-Stephan BS Ersoezlue Ersin E Stoecklein Sophia S Keeser Daniel D Brosseron Frederic F Buerger Katharina K Dechent Peter P Dobisch Laura L Ertl-Wagner Birgit B Fliessbach Klaus K Haynes John Dylan JD Heneka Michael T MT Incesoy Enise I EI Janowitz Daniel D Kilimann Ingo I Laske Christoph C Metzger Coraline D CD Munk Matthias H MH Peters Oliver O Priller Josef J Ramirez Alfredo A Roeske Sandra S Roy Nina N Scheffler Klaus K Schneider Anja A Spottke Annika A Spruth Eike Jakob EJ Teipel Stefan S Tscheuschler Maike M Vukovich Ruth R Wagner Michael M Wiltfang Jens J Yakupov Renat R Duezel Emrah E Jessen Frank F Perneczky Robert R

Cerebral cortex (New York, N.Y. : 1991) 20211001 11

Several Alzheimer's disease (AD) atrophy subtypes were identified, but their brain network properties are unclear. We analyzed data from two independent datasets, including 166 participants (103 AD/63 controls) from the DZNE-longitudinal cognitive impairment and dementia study and 151 participants (121 AD/30 controls) from the AD neuroimaging initiative cohorts, aiming to identify differences between AD atrophy subtypes in resting-state functional magnetic resonance imaging intra-network connect ...[more]

PMID: 34080613

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Project description:ObjectiveWe evaluated the value of resting-state EEG source biomarkers to characterize mild cognitive impairment (MCI) subjects with an Alzheimer's disease (AD)-like cerebrospinal fluid (CSF) profile and to track neurodegeneration throughout the AD continuum. We further applied a resting-state functional MRI (fMRI)-driven model of source reconstruction and tested its advantage in terms of AD diagnostic accuracy.MethodsThirty-nine consecutive patients with AD dementia (ADD), 86 amnestic MCI, and 33 healthy subjects enter the EEG study. All ADD subjects, 37 out of 86 MCI patients and a distinct group of 53 healthy controls further entered the fMRI study. MCI subjects were divided according to the CSF phosphorylated tau/β amyloid-42 ratio (MCIpos: ≥ 0.13, MCIneg: < 0.13). Using Exact low-resolution brain electromagnetic tomography (eLORETA), EEG lobar current densities were estimated at fixed frequencies and analyzed. To combine the two imaging techniques, networks mostly affected by AD pathology were identified using Independent Component Analysis applied to fMRI data of ADD subjects. Current density EEG analysis within ICA-based networks at selected frequency bands was performed. Afterwards, graph analysis was applied to EEG and fMRI data at ICA-based network level.ResultsADD patients showed a widespread slowing of spectral density. At a lobar level, MCIpos subjects showed a widespread higher theta density than MCIneg and healthy subjects; a lower beta2 density than healthy subjects was also found in parietal and occipital lobes. Evaluating EEG sources within the ICA-based networks, alpha2 band distinguished MCIpos from MCIneg, ADD and healthy subjects with good accuracy. Graph analysis on EEG data showed an alteration of connectome configuration at theta frequency in ADD and MCIpos patients and a progressive disruption of connectivity at alpha2 frequency throughout the AD continuum.ConclusionsTheta frequency is the earliest and most sensitive EEG marker of AD pathology. Furthermore, EEG/fMRI integration highlighted the role of alpha2 band as potential neurodegeneration biomarker.

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Resting-State Network Alterations Differ between Alzheimer's Disease Atrophy Subtypes.

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Resting-State Network Alterations Differ between Alzheimer's Disease Atrophy Subtypes.

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