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Age- and Alzheimer’s-related changes in functional connectivity align with organizational gradients

Poster No:

Submission Type:

Abstract Submission

Authors:

Jonathan Rittmo¹, Nicolai Franzmeier², Olof Strandberg³, Theodore Satterthwaite⁴^,5, Laura Wisse⁶, Nicola Spotorno³, Hamid Behjat³, Danielle van Westen⁶, Toomas Anijärv³, Sebastian Palmqvist³^,7, Shorena Janelidze³, Erik Stomrud³^,7, Rik Ossenkoppele³^,8^,9, Niklas Mattsson-Carlgren³^,10^,11, Oskar Hansson³^,10, Jacob Vogel¹

Institutions:

¹Department of Clinical Sciences Malmö, Faculty of Medicine, SciLifeLab, Lund University, Lund, Sweden, ²Institute for Stroke and Dementia Research, Klinikum der Ludwig-Maximilians Universität München, Munich, Germany, ³Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ, Lund, Sweden, ⁴Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, ⁵Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, ⁶Department of Diagnostic Radiology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden, ⁷Memory Clinic, Skåne University Hospital,, Malmö, Sweden, ⁸Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands, ⁹Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands, ¹⁰Memory Clinic, Skåne University Hospital, Malmö, Sweden, ¹¹Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden

First Author:

Jonathan Rittmo
Department of Clinical Sciences Malmö, Faculty of Medicine, SciLifeLab, Lund University
Lund, Sweden

Co-Author(s):

Nicolai Franzmeier
Institute for Stroke and Dementia Research, Klinikum der Ludwig-Maximilians Universität München
Munich, Germany

Olof Strandberg
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Sweden

Theodore Satterthwaite, MD
Penn Lifespan Informatics and Neuroimaging Center (PennLINC)|Department of Psychiatry, University of Pennsylvania Perelman School of Medicine
Philadelphia, PA|Philadelphia, PA

Laura Wisse
Department of Diagnostic Radiology, Department of Clinical Sciences Lund, Lund University
Lund, Sweden

Nicola Spotorno
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Sweden

Harry Behjat
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Sweden

Danielle van Westen
Department of Diagnostic Radiology, Department of Clinical Sciences Lund, Lund University
Lund, Sweden

Toomas Anijärv
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Sweden

Sebastian Palmqvist
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ|Memory Clinic, Skåne University Hospital,
Lund, Sweden|Malmö, Sweden

Shorena Janelidze
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Sweden

Erik Stomrud
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ|Memory Clinic, Skåne University Hospital,
Lund, Sweden|Malmö, Sweden

Rik Ossenkoppele
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ|Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC|Amsterdam Neuroscience, Neurodegeneration
Lund, Sweden|Amsterdam, Netherlands|Amsterdam, Netherlands

Niklas Mattsson-Carlgren
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ|Memory Clinic, Skåne University Hospital|Wallenberg Center for Molecular Medicine, Lund University
Lund, Sweden|Malmö, Sweden|Lund, Sweden

Oskar Hansson
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ|Memory Clinic, Skåne University Hospital
Lund, Sweden|Malmö, Sweden

Jacob Vogel
Department of Clinical Sciences Malmö, Faculty of Medicine, SciLifeLab, Lund University
Lund, Sweden

Introduction:

Aging and Alzheimer's disease (AD) both alter resting state functional brain connectivity (FC), yet the nature of these changes – whether compensatory, pathological, or both – remains unclear (Corriveau-Lecavalier et al. 2024). Studies focusing on regional increases or decreases in FC have often reported mixed or conflicting patterns of hyper- and hypoconnectivity that fail to offer a unified understanding of complex functional brain changes in aging (Badhwar et al. 2017). Variance components of connectivity matrices – or 'gradients' of brain organization (Margulies et al. 2016) – offer a promising framework for understanding large-scale changes in connectivity. This study leverages group-level gradients to examine the spatial distribution of regional FC changes related to aging and AD pathology, how these changes progress, and how they relate to cognition.

Methods:

We used data from the BioFINDER-2 study (655 cognitively unimpaired controls; 319 impaired amyloid-β positive patients). Each participant received a pathology score (0-1) based on a non-linear mapping of their cerebrospinal fluid Aβ40/42 ratio, and tau PET SUVR from Braak regions (see Fig. 2B1). Longitudinal analyses used tau PET only. Cognition was assessed using the modified Preclinical Alzheimer Cognitive Composite (mPACC). Resting-state functional MRI images were acquired on a 3T scanner (TR=1), preprocessed using a modified CPAC pipeline (Craddock et al., 2013) and parcellated with the Schaefer 1000 atlas (Schaefer et al., 2018). Connectivity matrices were generated as pairwise Pearson correlation between parcels, and FC was quantified via nodal affinity by averaging the cosine similarity of thresholded (at 25%) rows, yielding one value per parcel and subject. Parcel-wise linear regression, linear mixed-effects and generalized additive models were used with FC as the outcome, covaried for sex and motion, with age, AD pathology and cognition as independent variables. T-values from each model term were correlated with component scores of the three primary organizational gradients, derived from cognitively unimpaired, Aβ- individuals under 60 years. As effect patterns were only associated with Gradients 1 and 3, we focus on these. Main findings were replicated in a subset of the ADNI cohort (89 cognitively unimpaired and 40 impaired), see e.g. Franzmeier et al., (2020) for details.

Results:

AD pathology-related FC decreases were found in sensorimotor areas and FC increases in associative cortices, aligning with Gradient 1 (Fig. 1A). Age showed decreases in non-executive areas and increases in executive areas, aligning with Gradient 3. These findings were observed longitudinally and replicated in ADNI (Fig. 1B,C). Gradient 1-aligned changes were linked to (tau driven) pathology even in cognitively unimpaired Aβ-, APOE ε4 non-carriers, but were diminished in clinically impaired Aβ+ patients (Fig. 1D,E). Nonlinear analyses confirmed this finding, with peak Gradient 1 alignment of FC changes during the early phase of AD (Fig. 2). For age, peaks of alignment with Gradient 3 were observed at ages 55 and 65 (Fig. 2B2). Interestingly, in the clinically impaired group, FC changes related to worse cognition showed a pattern resembling Gradient 1, even when adjusting for AD pathology (Fig. 1E), which was also seen for older individuals without clinical impairment (Fig. 1D, interaction term).

Conclusions:

In sum, we find that age- and AD-related FC alterations align with large-scale cortical gradients, clarifying discrepancies in the literature. These alignments vary along the age and AD pathology continuum, and are associated with cognition independent of AD pathology. Rather than reflecting purely compensatory or detrimental changes, these gradient-aligned patterns may represent a general neural response to cognitive strain or system stress, emerging in older individuals, those with subtle AD pathology, and clinically impaired patients alike where cognitive demands may outpace available neural resources.

Disorders of the Nervous System:

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

Lifespan Development:

Aging

Modeling and Analysis Methods:

fMRI Connectivity and Network Modeling ²

Novel Imaging Acquisition Methods:

BOLD fMRI

Keywords:

Aging

Cognition

FUNCTIONAL MRI

Other - Alzheimer's disease, Brain organization

^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

Neuropsychological testing

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

3.0T

Which processing packages did you use for your study?

Free Surfer

Provide references using APA citation style.

Badhwar, A., et al. (2017). Resting-state network dysfunction in alzheimer’s disease: A systematic review and meta-analysis. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 8, 73–85. https://doi.org/10.1016/j.dadm.2017.03.007
Corriveau-Lecavalier, N., et al. (2024). Cerebral hyperactivation across the alzheimer’s disease pathological cascade. Brain Communications, fcae376. https://doi.org/10.1093/braincomms/fcae376
Craddock, C., et al. (2013). Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (c-pac). Frontiers in Neuroinformatics, 42(10.3389).
Franzmeier, N., et al. (2020). Functional brain architecture is associated with the rate of tau accumulation in Alzheimer’s disease. Nature Communications, 11(1), 347. https://doi.org/10.1038/s41467-019-14159-1
Margulies, D. S., et al. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences, 113(44), 12574–12579. https://doi.org/10.1073/pnas.1608282113
Schaefer, A., et al. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex (New York, NY), 28(9), 3095–3114. https://doi.org/10.1093/cercor/bhx179

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