Raymont, V. | Search bioRxiv

bioRxiv2026

NTAD: A longitudinal neuroimaging dataset of Alzheimer's disease pathology

We introduce the New Therapeutics in Alzheimer's Disease (NTAD) dataset, a multi-modal and longitudinal imaging dataset from 70 biomarker-positive people with early dementia or Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD), assessed over 2.5 years, and 35 healthy controls assessed at baseline. The data include simultaneous magneto- and electro-encephalography (M/EEG), 3-Tesla structural and functional magnetic resonance imaging (MRI), Amyloid-beta; positron emission tomography (PET), fluid proteomic biomarkers in cerebrospinal fluid and blood, and a battery of clinical and neuropsychological assessments. 11C UCB-J PET was undertaken for a subset of participants. For people with AD, assessments were repeated at approximately three weeks, 15 months, and 28 months after baseline. The data can be accessed through the Dementias Platform UK (DPUK), including pre-processed M/EEG and T1 MRI in pseudonymised BIDS format. With the pressing need to develop treatments for slowing, reversing, and preventing AD, these data provide an opportunity for identifying and validating novel biomarkers of Alzheimer's disease neurophysiology, as well as understanding its treatable pathology.

neuroscience↗

bioRxiv2023

Reliability of dynamic causal modelling of resting state magnetoencephalography

This study assesses the reliability of resting-state dynamic causal modelling (DCM) of magneto-electroencephalography under conductance-based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting state magneto-electroencephalography (MEG) data from two sessions, acquired two weeks apart, from a cohort with high between-subject variance arising from Alzheimers disease. Our focus is not on the effect of disease, but on the predictive validity of the methods implicit in their reliability, which is crucial for future studies of disease progression and drug intervention. To assess the predictive validity of first-level DCMs, we compare model evidence associated with the covariance among subject-specific free energies (i.e., the quality of the models) with vs. without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the predictive validity of DCM parameters at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within-subject, within-session, between-epochs; (ii) within-subject between-session and (iii) within-site between-subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of reliability and the conditional dependency among inferred model parameters. Our analyses confirm the predictive validity and reliability of the conductance-based DCMs for resting-state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/562379v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@135c964org.highwire.dtl.DTLVardef@38e4a7org.highwire.dtl.DTLVardef@12bc358org.highwire.dtl.DTLVardef@25f110_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗