Cross-sectional and longitudinal spectral differences in MEG brain networks of Alzheimer’s patients
Poster No:
146
Submission Type:
Abstract Submission
Authors:
Mats Van Es1, Andrew Quinn2, Jemma Pitt3, Tony Thayanandan4, Cara Alcock4, Ece Kocagoncu5, Juliette Lanksey5, Marlou Perquin5, Chetan Gohil6, Vanessa Raymont4, James Rowe5, Anna Nobre7, Mark Woolrich8
Institutions:
1University of Oxford, Dpt of Psychiatry, Oxford, United Kingdom, 2University of Birmingham, Birmingham, United Kingdom, 3University of Oxford, Oxford, Oxfordshire, 4University of Oxford, Oxford, United Kingdom, 5University of Cambridge, Cambridge, United Kingdom, 6University of Sydney, Sydney, NSW, 7Yale University, New Haven, CT, 8University of Oxford, Oxford, Oxon
First Author:
Co-Author(s):
Introduction:
Current diagnosis of Alzheimer's Disease (AD) is based on the presence of Amyloid-beta and Tau-protein in the brain, which requires invasive and costly examination. To develop treatments, it is essential to find other biomarkers that are reliable and predictive of disease progression. Here, we used MEG to study spectral activity of patients' amyloid pathology.
Methods:
We used resting-state (eyes closed) MEG data from the New Therapeutics in Alzheimer's Disease (NTAD) (Lanksey et al., 2022). Participants were aged 50-85, including patients with mild cognitive impairment or early AD (n=100) and matched controls with normal cognition (n=30). Subjects' amyloid status was determined on the basis of amyloid PET or a lumbar puncture. In order to model confounds, we further used the Cam-CAN resting state eyes closed data of all subjects over the age of 50 (N=350) (Taylor et al., 2017).
First, we investigated static spectral power. Spectral power of the MEG data was estimated in the 1-120 Hz range, and confounding variables (age, sex, scanner type) were regressed out using a general linear model (GLM).
Next, we used Hidden-Markov Modelling (HMM) (Vidaurre et al., 2018) with 10 networks to investigate brain network dynamics as a potentially more sensitive biomarker. We assessed differences between amyloid groups (i.e., cross-sectional), differences between baseline and annual follow-up within the amyloid positive group (i.e., longitudinal). Furthermore, we assessed two-week test-retest reliability using the intraclass correlation coefficient (ICC).
First, we investigated static spectral power. Spectral power of the MEG data was estimated in the 1-120 Hz range, and confounding variables (age, sex, scanner type) were regressed out using a general linear model (GLM).
Next, we used Hidden-Markov Modelling (HMM) (Vidaurre et al., 2018) with 10 networks to investigate brain network dynamics as a potentially more sensitive biomarker. We assessed differences between amyloid groups (i.e., cross-sectional), differences between baseline and annual follow-up within the amyloid positive group (i.e., longitudinal). Furthermore, we assessed two-week test-retest reliability using the intraclass correlation coefficient (ICC).
Results:
Cross-sectionally, we replicated previous findings of a slowing of static oscillatory activity in the amyloid positive group. In particular, we observed significant increases in delta (1-4 Hz)/ theta (4-8 Hz) power and decreases in alpha (8-13 Hz) / beta (13-30 Hz) power. These differences were also widespread in the brain network spectra, though some networks showed only narrow band changes (Figure 1). Aside from one network, the network spectra showed high (>0.7) reliability.
Longitudinally, we found that a fronto-temporal network, predominantly characterized by low frequency activity, showed a significant increase in high gamma (73-85 Hz) power at the annual follow-up visit (following correction for multiple comparisons), see Figure 2. This effect was not present in the static spectra.
Longitudinally, we found that a fronto-temporal network, predominantly characterized by low frequency activity, showed a significant increase in high gamma (73-85 Hz) power at the annual follow-up visit (following correction for multiple comparisons), see Figure 2. This effect was not present in the static spectra.
·Cross-sectional (Amyloid+ vs. Amyloid-) differences in brain network power. For each state, the mean spectral power the Amyloid+ (solid) and Amyloid- (dotted) groups are shown.
·Longitudinal (Annual vs. Baseline) differences in brain network power. For each state, the mean spectral power for the Annual (solid) and Baseline (dotted) visit are shown.
Conclusions:
These results indicate brain network dynamics contain information sensitive to amyloid status and potentially, disease progression, whilst retaining high reliability. Therefore, this may provide a promising avenue as a biomarker tool. Future work will investigate correlations with cognitive scores, and other biomarkers, as well as multivariate prediction.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Task-Independent and Resting-State Analysis
Keywords:
Degenerative Disease
DISORDERS
MEG
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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.
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.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Taylor, J. R., Williams, N., Cusack, R., Auer, T., Shafto, M. A., Dixon, M., ... & Henson, R. N. (2017). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. neuroimage, 144, 262-269.
Vidaurre, D., Hunt, L. T., Quinn, A. J., Hunt, B. A., Brookes, M. J., Nobre, A. C., & Woolrich, M. W. (2018). Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks. Nature communications, 9(1), 2987.