Biology subjects

Naessens, M.

Publications and source records attributed to Naessens, M..

4 recordsLinked to original sources

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↗

bioRxiv2026

From group-level neurophysiological biomarkers to individual-level phenotyping to identify mechanisms of neurodegenerative disease

Precision medicine approaches require physiological effects to be informative when translated to the individual. Reliable group differences may have limited individual value when distributions overlap, mechanisms are heterogeneous, or patients differ in phenotype. A feature can differ between group means and still overlap heavily across individuals Moreover, combinations of physiological abnormalities may reveal subject-level structure that is not captured by any single marker at the group level. We evaluate the translation between group- and individual-level features derived from resting-state magnetoencephalography (MEG) using behavioural-variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy (PSP) as demonstrator conditions. These illnesses have distinct molecular pathology but share the pressing need for therapeutic development and have partially overlapping clinical and physiological features. We combine clinical phenotype, power spectral density (PSD), and latent network mechanisms inferred by dynamic causal modelling (DCM) to compare groups and to access their individuals mapping translations. We used classification as an exploratory probe of how group-defined physiological features from 74 healthy controls, 45 participants with bvFTD and 55 participants with PSP support individual assignment. Clinical scores showed strong individual-level translation, consistent with clinical diagnostic definitions (e.g, controls-vs-PSP accuracy 0.94; bvFTD-vs-PSP accuracy 0.83). MEG spectra feature classified individuals weakly (accuracy ~0.6), despite group level re-organization (e.g., reduced beta-band and increased fronto-insular alpha-phase/beta-amplitude coupling in bvFTD). Inferred biophysical parameters gave intermediate performance (e.g. PSP-vs-control accuracy 0.70; bvFTD-vs-PSP accuracy 0.74) while retaining mechanistic interpretability. Using this approach, experimental medicine studies of neurological disease can formally trade between mechanistic insight and classification performance, in using biomarkers that can account for individual heterogeneity within and between diagnostic groups.

bioengineering↗

bioRxiv2025

Apathy as a Loss of Prior Precision on Action Outcomes

Apathy is common in neurological disease, associated with poor prognosis and limited treatments. Current models posit that goal-directed actions are reduced because costs or effort outweigh the expected reward. We highlight an alternative account of apathy, based on the reduction in precision of prior beliefs about action outcomes. In this preregistered study, we test the hypothesis that precision is encoded in the GABAergic gain of prefrontal superficial pyramidal neurons. Fifty healthy adults undertook a goal-directed task during magnetoencephalography. Estimates of synaptic efficacy or gain were obtained by dynamic causal modelling of induced responses. There was strong evidence of a negative correlation between prior precision and apathy (Bayes Factor=12, p<0.01), and that prior precision was associated with gain in prefrontal and premotor neuronal populations (Posterior probability>0.99). The importance of prior precision and GABAergic gain for goal-directed actions opens new avenues to advance the understanding and treatment of apathy.

neuroscience↗

bioRxiv2022

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

We present a hierarchical and empirical Bayesian framework for testing hypotheses about synaptic neurotransmission, based on the integration of ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals neurophysiological observations. At the second level, individuals 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare models of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. We perform cross-validation using split-sampling of the MEG dataset. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.

neuroscience↗