Biology subjects

Kern, S.

Publications and source records attributed to Kern, S..

6 recordsLinked to original sources

bioRxiv2026

FASTIMAGES: Validating replay detection methods in human Neuroimaging using a combined MEG and fMRI dataset

Studies in rodents and humans using invasive electrophysiology have established that neural replay is a ubiquitous phenomenon in the brain that is associated with a wide range of cognitive functions, including memory, planning and decision making. Yet, invasively recording in humans remains difficult, and hence knowledge about replay in humans remains scarce. Hence, to comprehensively understand replay in humans, we need reliable approaches that can detect it non-invasively. Several main non-invasive approaches have been proposed, but we lack a full comparative validation against known ground truth signals. In this study, we present FASTIMAGES, a benchmark dataset from seventy participants with parallel fMRI (n = 40, previously published) and MEG (n=30) recordings containing known neural sequences evoked by fast visual stimulation as well as functional localizer trials. The neural sequences were elicited by five different visual stimuli shown in sequences at speeds of 132, 164, 228 and 612 milliseconds onset-to-onset intervals. Using this dataset, we investigate two existing statistical methods for sequence detection, namely Temporally Delayed Linear Modelling (TDLM, developed for MEG by Liu et al., 2021) and Slope Order Dynamic Analysis (SODA, developed for fMRI by Wittkuhn & Schuck, 2021). We examine the underlying assumptions of each method, analyse their resulting strengths and weaknesses in application to MEG and fMRI. We demonstrate that both approaches excel in their native modality (TDLM for MEG and SODA for fMRI), with comparable effect sizes given idealized conditions in this benchmark. Cross-modality transfer remains challenging. Finally, the FASTIMAGES dataset provides data with known and clearly expressed sequences and can be used to benchmark and validate future sequence detection methods under idealized conditions.

neuroscience↗

bioRxiv2025

Challenges in Replay Detection by TDLM in Post-Encoding Resting State

Using temporally delayed linear modelling (TDLM) and magnetoencephalography (MEG), we investigated whether items associated with an underlying graph structure are replayed during a post-learning resting state. In these same data, we previously provided evidence for replay during on-line (non-rest) memory retrieval. Despite successful decoding of brain activity during a localizer task, and contrary to predictions, we found no evidence for replay during a post-learning resting state. To better understand this, we performed a hybrid simulation analysis in which we inserted synthetic replay events into a control resting state recorded prior to the actual experiment. This simulation revealed that replay detection using our current pipeline requires an extremely high replay density to reach significance (>1 replay sequence per second, with "replay" defined as a sequence of reactivations within a certain time lag). Furthermore, when scaling the number of replay events with a behavioural measure, we were unable to induce a strong correlation between sequenceness and this measure. We infer that even if replay was present at plausible rates in our resting state dataset, we would lack statistical power to detect it with TDLM. Finally, contrasting our novel hybrid simulation to existing purely synthetic simulations indicated that the latter approaches overestimate the sensitivity of TDLM. We discuss approaches that might optimize the analytic methodology, including identifying boundary conditions under which TDLM can be expected to detect replay. We conclude that solving these methodological constraints will be crucial for optimizing the non-invasive measurement of human replay using MEG.

neuroscience↗

bioRxiv2025

Global brain maintenance predicts well-preserved cognitive function: A pooled analysis of three longitudinal population-based Swedish cohorts

Substantial heterogeneity in cognitive ageing is well documented. Such heterogeneity has been attributed to individual differences in brain maintenance - i.e., the relative preservation of neural resources in ageing. However, large-scale longitudinal evidence is lacking. We pooled data from three population-based Swedish cohorts (Betula, N = 196; SNAC-K, N = 472; H70, N = 688; aged 60-93 years at baseline, follow-up duration up to 7 years) to assess whether global brain maintenance is associated with better preserved cognition in ageing, and to identify lifestyle predictors of brain maintenance. In each cohort, global brain integrity was indexed by the volume of the lateral ventricles (adjusted for total intracranial volume), and general cognitive function based on a principal component analysis of four age-sensitive cognitive domains. Participants were classified into subgroups of low (i.e., aged) versus high (i.e., youth-like) brain integrity based on ventricular volume estimates available for a younger reference sample in one of the cohorts (Betula, 25-55 years, N = 60). Subgroup differences in cognition at baseline and over the follow-up were assessed with ANCOVAs and linear mixed effects models. Logistic regressions were used to examine lifestyle predictors of brain maintenance. Across cohorts, 881 individuals (64.97%) were classified into the high brain integrity subgroup at baseline and 409 individuals (49.82%) over the follow-up. Maintenance of more youth-like brain integrity was associated with better baseline cognition (p < .001) and less cognitive decline longitudinally (p < .001). Moreover, lower cardiovascular disease (CVD) risk and the absence of diabetes predicted brain maintenance at baseline (CVD risk, OR = 0.80, 95% CI [0.68, 0.93]; diabetes, OR = 0.39, 95% CI [0.26, 0.59]) and over the follow-up (CVD risk, OR = 0.79, 95% CI [0.64, 0.96]; diabetes, OR = 0.53, 95% CI [0.29, 0.94]). These findings underscore brain maintenance as a key determinant of cognitive ageing and highlight the importance of managing cardiovascular and metabolic disease risk factors for promotion of brain and cognitive health in later life.

neuroscience↗

bioRxiv2023

Reactivation strength during cued recall is modulated by graph distance within cognitive maps

Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alternatively, replayed sequentially as temporally separate instances. In the current study, participants learned associations between images that were embedded in a directed graph network and retained this information over a brief 8-minute consolidation period. During a subsequent cued recall session, participants retrieved the learned information while undergoing magnetoencephalographic (MEG) recording. Using a trained stimulus decoder, we found evidence for clustered reactivation of learned material. Reactivation strength of individual items during clustered reactivation decreased as a function of increasing graph distance, an ordering present solely for successful retrieval but not for retrieval failure. In line with previous research, we found evidence that sequential replay was dependent on retrieval performance and was most evident in low performers. The results provide evidence for distinct performance-dependent retrieval mechanisms with graded clustered reactivation emerging as a plausible mechanism to search within abstract cognitive maps.

neuroscience↗

bioRxiv2020

Larger organ size caused by obesity is a mechanism for higher cancer risk.

Obesity increases significantly cancer risk in various organs. Although this has been recognized for decades, the mechanism through which this happens has never been explained. Here, we show that obese people (BMI [≥]30) have on average 55% (95%CI: 46%-66%), 68% (95%CI: 59%-76%), and 39% (95%CI: 29%-49%) larger kidneys, liver, and pancreas, respectively. We also find a significant linear relationship between the increase in organ volume and the increase in cancer risk (P-value<10-12). These results provide a mechanism explaining why obese individuals have higher cancer risk in several organs: the larger the organ volume the more cells at risk of becoming cancerous. These findings are important for a better understanding of the effects that obesity has on cancer risk and, more generally, for the development of better preventive strategies to limit the mortality caused by obesity.

cancer biology↗

bioRxiv2019

ADSoluble aggregates present in cerebrospinal fluid change in size and mechanism of toxicity during Alzheimer's disease progression

Soluble aggregates of amyloid-{beta} (A{beta}) have been associated with neuronal and synaptic loss in Alzheimers disease (AD). However, despite significant recent progress, the mechanisms by which these aggregated species contribute to disease progression are not fully determined. As the analysis of human cerebrospinal fluid (CSF) provides an accessible window into the molecular changes associated with the disease progression, we studied the soluble A{beta} aggregates present in CSF samples from individuals with AD, mild cognitive impairment (MCI) and healthy controls. We found that these aggregates vary structurally and in their mechanisms of toxicity. More small aggregates of A{beta} that can cause membrane permeabilization already found in MCI; in established AD, the aggregates were larger and more prone to elicit a pro-inflammatory response in glial cells. These results suggest that different neurotoxic mechanisms are prevalent at different stages of AD.

neuroscience↗