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Mapping Neural Circuit Dysfunction in AD: Pathology to Effective Connectivity in Memory Encoding

Poster No:

1380

Submission Type:

Abstract Submission

Authors:

Yanin Suksangkharn¹^,2, Björn Schott³^,4, Peter Zeidman⁵, Niklas Vockert², René Lattmann², Renat Yakupov¹^,2, Oliver Peter⁶^,7, Josef Priller⁶^,8^,9^,10, Anja Schneider¹¹^,12, Jens Wiltfang³^,13^,14, Frank Jessen¹¹^,15^,16, Stefan Teipel¹⁷^,18, Christoph Laske¹⁹^,20, Annika Spottke¹¹^,21, Frederic Brosseron¹¹, Falk Lüsebrink², Stefan Hetzer²², Peter Dechent²³, Klaus Scheffler²⁴, Anne Maass², Emrah Düzel¹^,2, Gabriel Ziegler¹^,2

Institutions:

¹Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany, ²German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany, ³German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany, ⁴Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany, ⁵Wellcome Centre for Human Neuroimaging, London, England, ⁶German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany, ⁷Charité – Universitätsmedizin Berlin, Institute of Psychiatry and Psychotherapy, Berlin, Germany, ⁸Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany, ⁹University of Edinburgh and UK DRI, Edinburgh, United Kingdom, ¹⁰Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, and German Center for Mental Health (DZPG), Munich, Germany, ¹¹German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, ¹²Department of Old Age Psychiatry and Cognitive Disorders, University Hospital Bonn and University of Bonn, Bonn, Germany, ¹³Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany, ¹⁴Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal, ¹⁵Department of Psychiatry, University of Cologne, Cologne, Germany, ¹⁶Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany, ¹⁷German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany, ¹⁸Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany, ¹⁹German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany, ²⁰Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany, ²¹Department of Neurology, University of Bonn, Bonn, Germany, ²²Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin Berlin, Berlin, Germany, ²³MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University, Göttingen, Germany, ²⁴Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany

First Author:

Yanin Suksangkharn
Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University|German Center for Neurodegenerative Diseases (DZNE)
Magdeburg, Germany|Magdeburg, Germany

Co-Author(s):

Björn Schott
German Center for Neurodegenerative Diseases (DZNE)|Department of Psychiatry and Psychotherapy, University Medical Center Göttingen
Göttingen, Germany|Göttingen, Germany

Peter Zeidman
Wellcome Centre for Human Neuroimaging
London, England

Niklas Vockert
German Center for Neurodegenerative Diseases (DZNE)
Magdeburg, Germany

René Lattmann
German Center for Neurodegenerative Diseases (DZNE)
Magdeburg, Germany

Renat Yakupov
Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University|German Center for Neurodegenerative Diseases (DZNE)
Magdeburg, Germany|Magdeburg, Germany

Oliver Peter
German Center for Neurodegenerative Diseases (DZNE)|Charité – Universitätsmedizin Berlin, Institute of Psychiatry and Psychotherapy
Berlin, Germany|Berlin, Germany

Josef Priller
German Center for Neurodegenerative Diseases (DZNE)|Department of Psychiatry and Psychotherapy, Charité|University of Edinburgh and UK DRI|Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, and German Center for Mental Health (DZPG)
Berlin, Germany|Berlin, Germany|Edinburgh, United Kingdom|Munich, Germany

Anja Schneider
German Center for Neurodegenerative Diseases (DZNE)|Department of Old Age Psychiatry and Cognitive Disorders, University Hospital Bonn and University of Bonn
Bonn, Germany|Bonn, Germany

Jens Wiltfang
German Center for Neurodegenerative Diseases (DZNE)|Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen|Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro
Göttingen, Germany|Göttingen, Germany|Aveiro, Portugal

Frank Jessen
German Center for Neurodegenerative Diseases (DZNE)|Department of Psychiatry, University of Cologne|Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne
Bonn, Germany|Cologne, Germany|Cologne, Germany

Stefan Teipel
German Center for Neurodegenerative Diseases (DZNE)|Department of Psychosomatic Medicine, Rostock University Medical Center
Rostock, Germany|Rostock, Germany

Christoph Laske
German Center for Neurodegenerative Diseases (DZNE)|Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen
Tübingen, Germany|Tübingen, Germany

Annika Spottke
German Center for Neurodegenerative Diseases (DZNE)|Department of Neurology, University of Bonn
Bonn, Germany|Bonn, Germany

Frederic Brosseron
German Center for Neurodegenerative Diseases (DZNE)
Bonn, Germany

Falk Lüsebrink
German Center for Neurodegenerative Diseases (DZNE)
Magdeburg, Germany

Stefan Hetzer
Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin Berlin
Berlin, Germany

Peter Dechent
MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University
Göttingen, Germany

Klaus Scheffler
Department for Biomedical Magnetic Resonance, University of Tübingen
Tübingen, Germany

Anne Maass
German Center for Neurodegenerative Diseases (DZNE)
Magdeburg, Germany

Emrah Düzel
Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University|German Center for Neurodegenerative Diseases (DZNE)
Magdeburg, Germany|Magdeburg, Germany

Gabriel Ziegler
Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University|German Center for Neurodegenerative Diseases (DZNE)
Magdeburg, Germany|Magdeburg, Germany

Introduction:

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by beta-amyloid (Aβ) and tau protein accumulation, leading to neurodegeneration and cognitive decline (Jack et al., 2018). AD disrupts synaptic communication through complex spatiotemporal interactions (Tzioras et al., 2023), making it challenging to map its effects at the circuit level. This study aimed to disentangle the impact of AD pathology on memory circuit connectivity and identify alterations linked to memory performance. We employed Dynamic Causal Modelling (DCM) of a memory encoding network to quantify effective connectivity (EC) and further studied effects of AD pathology accounting for non-linear and interaction effects using generalized additive models (GAMs).

Methods:

We analyzed data from the DZNE DELCODE study, including 235 participants: 92 cognitively normal, 95 with subjective cognitive decline (SCD), 34 with mild cognitive impairment (MCI), and 14 with mild dementia due to AD. Participants performed a visual memory-encoding fMRI task with novel and pre-familiarized images, followed by a recognition memory test for novel images to measure encoding performance. EC was assessed using DCM (Zeidman et al., 2019) in a memory-encoding network comprising the parahippocampal place area (PPA), hippocampus (HC), and precuneus (PCU) (Schott et al., 2023). The model included interregional and auto-inhibitory connectivity, modulated by individual recognition scores. Processing of novel images by the PPA was defined as driving input to the DCM. First, Spearman's correlations (|ρ| > 0.2, FDR-corrected) identified EC patterns associated with memory performance. We then evaluated the effects of ATN AD biomarkers (Aβ42/40 ratio, p-tau, hippocampal volume) on these ECs using pyGAM (Servén et al., 2018). Models included spline terms for Aβ42/40 and p-tau, their interaction, hippocampal volume, and covariates like age, sex, and education. Corrected Akaike Information Criterion (AICc; Hurvich & Tsai, 1989) guided model selection.

Results:

Four ECs were associated with memory performance: PPA to HC (ρ = 0.22, p < 0.001), PCU to PPA (ρ = 0.26, p < 0.001), PPA to PCU (ρ = −0.25, p < 0.001), and HC to PPA (ρ = −0.24, p < 0.001). The EC from PPA to HC was best explained by (spline terms of) Aβ42/40, p-tau, and hippocampal volume, with only p-tau contributing significantly (p = 0.002). Increased p-tau was associated with diminished excitatory activity of this EC (Figure 1). The EC from PPA to PCU was best explained Aβ42/40, p-tau, and their interaction, with p-tau (p = 0.0005) and the interaction (p = 0.003) being significant. Specifically, the interdependent progression of Aβ42/40 and p-tau was associated with less inhibitory activity (Figure 2).

·Figure 1 Estimated ATN pathology effects on model-based bottom-up connectivity from PPA to HC.

·Figure 2 Amyloid and tau effects on the connectivity from PPA to PCU.

Conclusions:

Disruptions of EC within the memory-encoding network were associated to AD pathology in a region-specific manner. The bottom-up connectivity from PPA to HC and PPA to PCU showed key synaptic alterations linking molecular pathology to memory impairment. EC differences within the MTL (PPA to HC) were primarily associated with tau pathology, whereas MTL to parietal connectivity reflected both p-tau and interactions with amyloid. These findings underscore the nuanced and region-specific effects of molecular pathology on synaptic connectivity captured by the techniques.

Disorders of the Nervous System:

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

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural)

fMRI Connectivity and Network Modeling ¹

Multivariate Approaches ²

Keywords:

Degenerative Disease

FUNCTIONAL MRI

Memory

Modeling

Multivariate

^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.

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

Patients

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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.

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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.

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Please indicate which methods were used in your research:

Functional MRI

Computational modeling

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

3.0T

Which processing packages did you use for your study?

SPM

Provide references using APA citation style.

Hurvich, C. M., & Tsai, C.-L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297–307. https://doi.org/10.1093/biomet/76.2.297
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., Liu, E., Molinuevo, J. L., Montine, T., Phelps, C., Rankin, K. P., Rowe, C. C., Scheltens, P., Siemers, E., Snyder, H. M., … Silverberg, N. (2018). NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia : The Journal of the Alzheimer’s Association, 14(4), 535–562. https://doi.org/10.1016/j.jalz.2018.02.018
Schott, B. H., Soch, J., Kizilirmak, J. M., Schütze, H., Assmann, A., Maass, A., Ziegler, G., Sauvage, M., & Richter, A. (2023). Inhibitory temporo-parietal effective connectivity is associated with explicit memory performance in older adults. iScience, 26(10), 107765. https://doi.org/10.1016/j.isci.2023.107765
Servén, D., Brummitt, C., Abedi, H., & Hlink. (2018). dswah/pyGAM: V0.8.0 (Version v0.8.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.1476122
Tzioras, M., McGeachan, R. I., Durrant, C. S., & Spires-Jones, T. L. (2023). Synaptic degeneration in Alzheimer disease. Nature Reviews Neurology, 19(1), 19–38. https://doi.org/10.1038/s41582-022-00749-z
Zeidman, P., Jafarian, A., Corbin, N., Seghier, M. L., Razi, A., Price, C. J., & Friston, K. J. (2019). A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI. NeuroImage, 200, 174–190. https://doi.org/10.1016/j.neuroimage.2019.06.031

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