Biology subjects

Leonardsen, E. H.

Publications and source records attributed to Leonardsen, E. H..

4 recordsLinked to original sources

bioRxiv2026

Latent neural network representations of the brain reflect broad-scale adolescent phenotypic variation

The adolescent brain is attuned to social and environmental exploration, allowing behavioral adaptation as experiences shape lasting patterns of morphological organization. Using a convolutional neural network on longitudinal structural MRI data, we assess the early part of this developmental window and derive latent brain representations reflecting patterns of structural variability linked to personal, social, and neighborhood conditions in adolescence. These representations offer a flexible framework for mapping brain-trait associations in adolescence and beyond.

neuroscience↗

bioRxiv2025

Cortical thickness changes precede high levels of amyloid by at least seven years

Alzheimers disease (AD) is now defined based on its underlying brain pathology1, with the presence of amyloid (A{beta}) plaques at high enough levels sufficient to warrant a diagnosis in the absence of cognitive symptoms. High levels of PET-detectable A{beta} are widely thought to be the first imaging marker, with structural brain changes detectable on MRI scans thought to occur later. We combined 4570 longitudinal MRIs and 1684 A{beta} PET scans from three cognitively healthy cohorts to test the difference in cortical thickness and its change between those that subsequently converted to be A{beta}-positive or stayed A{beta}-negative, using MRIs acquired exclusively in the years before conversion. We found those that subsequently developed elevated A{beta} levels show both thicker cortex and less cortical thinning, even when the last MRI used to estimate their thickness trajectories was acquired at least seven years before conversion. Many effects remained when accounting for quantitative A{beta} levels, suggesting some cortical thickness effects may be partly independent of A{beta}. Differences in cortical thickness and its change between converters and A{beta}-negative individuals showed moderate alignment with patterns of A{beta} deposition, and the timing of thickness changes tracked the temporal progression of A{beta} accumulation. Thus, if amyloid is AD1, we show that high levels of PET-detectable amyloid are not the first imaging marker of AD, as cortical thickness changes can be traced years before pathological amyloid. This has implications for understanding the sequence of events leading up to the earliest stages of AD.

neuroscience↗

bioRxiv2025

Identifying discriminative EEG features of Unsuccessful and Successful stopping during the Stop Signal Task

The stop-signal task is often used to study inhibitory control. When combined with electrophysiological recordings, the N2 and P3 event-related potentials (ERPs) are regularly observed. Numerous studies link both amplitude and latency differences of the N2 and P3 to failed versus successful stopping. A slower N2-P3 complex when stopping fails has repeatedly been reported across many studies and found to correlate moderately with behavioral stopping speed. However, most studies rely on averaging across trials, thereby limiting the examination of trial-by-trial dynamics. In the present study, we employed different machine learning-approaches to classify successful from failed stop trials based on time-frequency single-trial EEG data. We also tested whether attenuating the slowing effect would alter classification performance. To preserve interpretability, we first identified five group-level EEG components time-locked to stopping and then used the time-frequency representation as features in different models. Our findings suggest that regularized logistic regression can reliably classify successful from failed stopping with an AUC = 0.72. Correcting for ERP latency differences did not markedly reduce overall classification (i.e., AUC = 0.71), but the model had to compensate by leveraging subtler, broadly distributed time-frequency features. Our feature importance measure indicated that a component closely resembling the N2-P3 complex contributed largely to the classification performance, producing a sparse model. Once the slowing effect was attenuated in the data, the model still retained predictive performance but had to rely on 15 times as many time-frequency features across the five components. Thus, it is likely that multiple overlapping processes unfold during stopping that influence response inhibition in addition to the N2-P3 complex. While the N2-P3 complex is consistently evoked during stopping and carry large discriminative ability, considering additional auxiliary processes might further our understanding into mechanisms underlying response inhibition.

neuroscience↗

bioRxiv2023

Accelerated brain change in healthy adults is associated with genetic risk for Alzheimer's disease and uncovers adult lifespan memory decline

Across healthy adult life our brains undergo gradual structural change in a pattern of atrophy that resembles accelerated brain changes in Alzheimers disease (AD). Here, using four polygenic risk scores for AD (PRS-AD) in a longitudinal adult lifespan sample aged 30 to 89 years (2-7 timepoints), we show that healthy individuals who lose brain volume faster than expected for their age, have a higher genetic AD risk. We first demonstrate PRS-AD associations with change in early Braak regions, namely hippocampus, entorhinal cortex, and amygdala, and find evidence these extend beyond that predicted by APOE genotype. Next, following the hypothesis that brain changes in ageing and AD are largely shared, we performed machine learning classification on brain change trajectories conditional on age in longitudinal AD patient-control data, to obtain a list of AD-accelerated features and model change in these in adult lifespan data. We found PRS-AD was associated with a multivariate marker of accelerated change in many of these features in healthy adults, and that most individuals above [~]50 years of age are on an accelerated change trajectory in AD-accelerated brain regions. Finally, high PRS-AD individuals also high on a multivariate marker of change showed more adult lifespan memory decline, compared to high PRS-AD individuals with less brain change. Our results support a dimensional account linking normal brain ageing with AD, suggesting AD risk genes speed up the shared pattern of ageing- and AD-related neurodegeneration that starts early, occurs along a continuum, and tracks memory change in healthy adults.

neuroscience↗