Biology subjects

Scheffler, K.

Publications and source records attributed to Scheffler, K..

At least 19 recordsLinked to original sources

bioRxiv2026

The Genome In A Bottle HG002 assembly-based variant benchmark set enables comprehensive benchmarking of small and structural variants.

High-accuracy, high-resolution benchmark data are fundamental to improving the accuracy and generalizability of sequencing technologies and associated algorithms. Most existing benchmarks for DNA sequencing rely on mapping to a single linear reference, omitting inaccessible and highly repetitive complex genomic regions. Here, we present the Genome in a Bottle (GIAB) Consortium's HG002 v5.0q benchmark set, a more comprehensive variant benchmark derived from a fully phased, highly curated, and accurate diploid assembly of HG002. Our approach leverages recent advances in long-read sequencing and assembly to provide confident small- and structural-variant calls across previously inaccessible regions, including segmental duplications, homopolymers, tandem repeats, and complex immune loci. The v5.0 benchmark significantly expands the callable genome relative to v4.2.1, adding x200 Mbp of sequence and increasing variant detection by 18 % for small variants and 3-fold for structural variants compared to previous benchmarks. The benchmark regions include more variants in repetitive regions, including 1.2 x segmental duplications, 1.8 x tandem repeats, and 2.0 x homopolymers, compared to v4.2.1. Separate and combined small- and structural-variant benchmarks are available for three versions of the human reference genome (GRCh37, GRCh38, and CHM13). We validate the reliability of this benchmark set by comparing multiple sequencing technologies and variant calling pipelines and curating differences. The v5.0q benchmark provides a critical resource for the development and validation of next-generation sequencing technologies and analysis methods.

genomics↗

bioRxiv2026

Multi-Parametric Phase-Cycled Balanced Steady-State Free Precession Brain Tissue Characterization in Relapsing-Remitting Multiple Sclerosis

Quantitative MRI enables the detection of subtle microstructural alterations in normal-appearing white matter (NAWM) associated with pathological conditions such as multiple sclerosis. Quantitative metrics including R1, R2, and the bSSFP asymmetry index (AI) were evaluated in the WM of 20 relapsing-remitting multiple sclerosis (RRMS) patients and 10 healthy controls (HC). A multi-parametric frame-work based on a phase-cycled balanced steady-state free precession (pc-bSSFP) sequence was used. Diffusion tensor imaging-derived measures, including fiber-to-field angle, number of fiber orientations, and fractional anisotropy, were incorporated to assess parameter anisotropy. Statistical analysis was performed using linear mixed-effects models to test for group, ROI, and group-by-ROI effects for each metric, with ROI-specific group comparisons derived from the model. Significant main effects of group, ROI, and group-by-ROI interaction were observed for both R1 and R2, whereas for AI only the ROI effect reached significance (group p= 0.462; group-by-ROI p = 0.786). Fourteen of sixteen ROIs demonstrated significantly lower R1 and R2 values in RRMS compared with HC. No ROI showed significant differences in AI. In conclusion, pc-bSSFP-based relaxometry reveals predominantly white matter alterations in RRMS, while enabling a comprehensive whole-brain assessment that also encompasses gray matter.

neuroscience↗

bioRxiv2026

Characterizing Physicochemical Selection in Protein Evolution with Property-Informed Models (PRIME)

AbstractStandard probabilistic models of coding sequence evolution effectively identify where and when selection acts but remain agnostic to the mechanistic realization of these forces. We introduce PRIME (PRoperty Informed Models of Evolution), a framework of codon-level maximum likelihood methods--including global (G-PRIME), episodic (E-PRIME), and site-specific (S-PRIME) implementations--that explicitly model amino acid exchange-ability as a function of physicochemical properties. By parameterizing attributes such as molecular volume, hydropathy, and secondary structure propensities, PRIME resolves the biophysical basis of selective constraint across both the sequence and the phylogeny. At the site level, S-PRIME leverages an explicit biophysical taxon-omy to precisely categorize residues as conserved, neutral, or changing for specific properties, resolving selective signals that remain invisible to traditional rate-based metrics. Our analysis of a benchmark of 24 diverse datasets and a genome-wide screen of 18,944 mammalian genes demonstrates that biophysical realism yields substantial improvements in model fit, acting synergistically with rate variation to explain complex evolutionary patterns. We find that power to detect physicochemical constraints at individual sites is fundamentally governed by simple informational redundancy (substitutions per unique amino acid; AUC = 0.91), with sensitivity exceeding 90% in data-rich alignments. E-PRIME reveals a distinct biophysical hierarchy: while core packing and beta-sheet scaffolds are rigidly conserved, alpha-helix propensity and surface electrostatics serve as the primary substrates for adaptive tuning. Furthermore, PRIME importance weights align with aspects of the primary semantic axes of deep learning representations (ESM-2) and capture key features of experimental fitness landscapes. By trans-forming abstract evolutionary rates into interpretable biophysical rules, PRIME provides a useful framework for characterizing the mechanistic drivers of protein diversity.

evolutionary biology↗

bioRxiv2025

Direct encoding of new visual concepts in early visual cortex

Neocortical memories are assumed to rely on slow systems consolidation through hippocampal offline reactivation. However, recent studies show rapid neocortical integration of new memories for information associated with prior knowledge. Testing the limits of rapid neocortical learning, we tracked memory formation across 24h with functional and diffusion-weighted MRI in subjects acquiring either conceptual or detailed knowledge of novel objects from identical visual stimulation. Concept learning-induced rapid, 24h-stable changes in strength and pattern of functional early visual cortex responses. These were co-localized with microstructural changes, correlated positively with categorization performance and negatively with context recognition. Detailed item-context learning elicited analogous changes in posterior parietal areas. Results show that the neocortex can rapidly learn without prior knowledge and that concept learning can occur in early sensory regions that process corresponding visual features. Thus, we substantiate the possibility of direct or parallel neocortical encoding without offline abstraction from individual episodes.

neuroscience↗

bioRxiv2025

Disrupted Forward Connectivity in Parieto-temporal Network Impairs Memory Performance in Alzheimer's Disease

Alzheimers disease (AD) is characterised by the accumulation of beta-amyloid (A{beta}) and tau proteins, leading to neurodegeneration and cognitive decline. While A{beta} and tau are known to disrupt synaptic function, the mechanisms linking these molecular pathologies to network-level dysfunction and memory impairment remain poorly understood. Here we investigated the effects of A{beta} and tau pathology (CSF A{beta}42/40 ratio and tau phosphorylated at position 181, p-tau-181, respectively) on effective connectivity (EC) related to memory encoding, which may constitute a link between synaptic pathology and cognitive outcomes. Functional magnetic resonance imaging (fMRI) during visual memory encoding was acquired from participants of the multicentric DZNE Longitudinal Cognitive Impairment and Dementia Study (DELCODE), including 203 cognitively normal older participants (CN) as well as individuals with subjective cognitive decline (SCD; N = 204), mild cognitive impairment (MCI; N = 65), and early dementia due to AD (DAT; N = 21). EC was assessed by applying Dynamic causal modelling (DCM) to the fMRI data, using brain regions previously implicated in memory-encoding: the parahippocampal place area (PPA), the hippocampus (HC) and the precuneus (PCU). Disruptions in forward connectivity from the PPA to the HC and PCU were associated with both memory impairment and indices of AD pathology. Specifically, reduced excitatory EC from the PPA to the HC was associated with higher p-tau-181 levels and correlated with poorer memory performance. Diminished inhibitory EC from the PPA to the PCU was driven by both tau and amyloid pathology and was likewise linked to memory decline. Our findings suggest that disrupted forward connectivity within the temporo-parietal memory network constitutes a candidate mechanism mediating the relationship between molecular pathology and cognitive dysfunction.

neuroscience↗

bioRxiv2025

Beyond ROIs: Laminar fMRI Mapping with Cylarim

Laminar fMRI offers a powerful approach to investigate brain function by resolving neural activity across the distinct layers of the cortical gray matter. However, current laminar fMRI analysis methods often rely on predefined regions of interest (ROIs), which introduce subjectivity and bias due to arbitrary spatial delineation. To address this limitation, we present "Cylarim", a novel software package that eliminates dependence on rigid ROI definitions. Cylarim employs small, overlapping cylinders that systematically traverse the cortical ribbon, enabling unbiased, large-scale mapping of laminar-specific hemodynamic activity. This approach supports comprehensive sampling of cortical depth-resolved signals across extensive regions. By circumventing the need for manual or atlas-driven ROI selection, Cylarim facilitates the study of feedforward and feedback pathways in diverse cortical areas without compromising spatial coverage. Preliminary results demonstrate Cylarims ability to accurately localize layer-dependent responses, providing a robust framework for advancing ultrahigh-resolution fMRI research in neuroscience.

neuroscience↗

bioRxiv2025

Functional connectivity of thalamic nuclei during sensorimotor task-based fMRI at 9.4 Tesla

The thalamus is the brains central communication hub, playing a key role in processing and relaying sensorimotor and cognitive information between the cerebral cortex and other brain regions. It consists of specific and non-specific nuclei, each with a different role. Specific thalamic nuclei relay sensory and motor information to specific cortical and subcortical regions to ensure precise communication. In contrast, non-specific thalamic nuclei are involved in general functions such as attention or consciousness through broader and less targeted connections. In the present study, we aimed to investigate the functional connectivity patterns of the thalamic nuclei identified in our previous study as being involved in motor (finger-tapping) and sensory (finger-touch) tasks. The results of this study show that thalamic nuclei are not static hubs with a predefined role in neural signal processing, as they show different task-specific functional connectivity patterns in the anterior, middle, lateral, and posterior thalamic nuclei. Instead, they are all functional hubs that can flexibly change their connections to other brain regions in response to task demands. This work has important implications for understanding task-dependent functional connectivity between thalamic nuclei and different brain regions using task-based fMRI at 9.4 Tesla.

neuroscience↗

bioRxiv2024

SMAS: Structural MRI-Based AD Score using Bayesian VAE

This study introduces the Structural MRI-based Alzheimers Disease Score (SMAS), a novel index intended to quantify Alzheimers Disease (AD)-related morphometric patterns using a deep learning Bayesian-supervised Variational Autoencoder (Bayesian-SVAE). SMAS index was constructed using baseline structural MRI data from the DELCODE study and evaluated longitudinally in two independent cohorts: DEL-CODE (n=415) and ADNI (n=190). Our findings indicate that SMAS has strong associations with cognitive performance (DELCODE: r=-0.83; ADNI: r=-0.62), age (DEL-CODE: r=0.50; ADNI: r=0.28), hippocampal volume (DEL-CODE: r=-0.44; ADNI: r=-0.66), and total grey matter volume (DELCODE: r=-0.42; ADNI: r=-0.47), suggesting its potential as a biomarker for AD-related brain atrophy. Moreover, our longitudinal studies suggest that SMAS may be useful for early identification and tracking of AD. The model demonstrated significant predictive accuracy in distinguishing cognitively healthy individuals from those with AD (DELCODE: AUC=0.971 at baseline, 0.833 at 36 months; ADNI: AUC=0.817 at baseline, improving to 0.903 at 24 months). Notably, over a 36-month period, SMAS index outperformed existing measures such as SPARE-AD and hippocampal volume. Relevance map analysis revealed significant morphological changes in key AD-related brain regions--including the hippocampus, posterior cingulate cortex, precuneus, and lateral parietal cortex--highlighting that SMAS is a sensitive and interpretable biomarker of brain atrophy, suitable for early AD detection and longitudinal monitoring of disease progression.

neuroscience↗

bioRxiv2024

Dysfunction of the episodic memory network in the Alzheimer's disease cascade

Alzheimers disease (AD) is a major cause of dementia and cognitive decline. Here we assessed how episodic memory circuit dysfunction, a hallmark of AD, is related to the longitudinal cascade of AD biomarkers, neurodegeneration and cognition using data from the DZNE Longitudinal Cognitive Impairment and Dementia study. This data set is unique by including over 1000 longitudinal functional magnetic resonance imaging (fMRI) measurements during episodic memory encoding. We leveraged a disease progression model (DPM) to obtain AD progression scores. Voxel-wise analyses revealed widespread loss of deactivation (hyperactivation) and activation (hypoactivation) with increasing disease stage. Hyperactivation trajectories were nonlinear and visually preceded trajectories of cognition. Overall, hyperactivation was independently associated with co-occurrence of amyloid- and tau-positivity and neurodegeneration, suggesting synaptic dysfunction and neurodegeneration as two independent drives of cognitive decline. Our results therefore provide evidence for a critical time window in which pharmacological treatments targeting the synapse may improve cognition.

neuroscience↗

bioRxiv2024

Macrovascular contributions to resting-state fMRI signals: A comparison between EPI and bSSFP at 9.4 Tesla

The draining-vein bias of T2*-weighted sequences, like gradient echo echo-planar imaging (GRE-EPI), can limit the spatial specificity of functional MRI (fMRI). The underlying extravascular signal changes increase with field strength (B0) and the perpendicularity of draining veins to the main axis of B0, and are therefore particularly problematic at ultra-high field (UHF). In contrast, simulations showed that T2-weighted sequences are less affected by the draining-vein bias, depending on the amount of rephasing of extravascular signal. As large pial veins on the cortical surface follow the cortical folding tightly, their orientation can be approximated by the cortical orientation to [Formula]. In our work, we compare the influence of the cortical orientation to [Formula] on the resting-state fMRI signal of three sequences aiming to understand their macrovascular contribution. While 2D GRE-EPI and 3D GRE-EPI (both T2*-weighted) showed a high dependence on the cortical orientation to [Formula], especially on the cortical surface, this was not the case for 3D balanced steady-state free precession (bSSFP) (T2/T1-weighted). Here, a slight increase of orientation dependence was shown in depths closest to WM. And while orientation dependence decreased with increased distance to the veins for both EPI sequences, no change in orientation dependence was observed in bSSFP. This indicates the low macrovascular contribution to the bSSFP signal, making it a promising sequence for layer fMRI at UHF.

neuroscience↗

bioRxiv2024

UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentations

The UltraCortex repository (https://www.ultracortex.org) houses magnetic resonance imaging data of the human brain obtained at an ultra-high field strength of 9.4 T. It contains 86 structural MR images with spatial resolutions ranging from 0.6 to 0.8 mm. Additionally, the repository includes segmentations of 12 brains into gray and white matter compartments. These segmentations have been independently validated by two expert neuroradiologists, thus establishing them as a reliable gold standard. This resource provides researchers with access to high-quality brain imaging data and validated segmentations, facilitating neuroimaging studies and advancing our understanding of brain structure and function. Existing repositories do not accommodate field strengths beyond 7 T, nor do they offer validated segmentations, underscoring the significance of this new resource.

neuroscience↗

bioRxiv2024

DNA glycosylases Ogg1 and Mutyh mediate gene expression of PRC2 target genes important for neuronal processes underlying memory formation

Base excision repair (BER) initiated by DNA glycosylases is known to preserve genomic integrity by removing damaged bases. Recently, several DNA glycosylases were identified as potential readers of epigenetic modifications and proteins involved in BER have been associated with active DNA demethylation. DNA glycosylases Ogg1 and Mutyh were shown to alter the hippocampal transcriptome associated with cognitive function and independent of global DNA damage accumulation. However, the mechanism of DNA glycosylases in regulating cognition and their role in epigenetic remodeling in the brain remains elusive. Here we report that the combined deficiency of Ogg1 and Mutyh impairs spatial but not associative long-term memory. We demonstrate that Ogg1 or Mutyh modulate DNA methylation at gene regulatory regions of polycomb repressive complex 2 (PRC2) target genes in the adult hippocampus. Moreover, we find that the distribution of the PRC2 complex and histone modifications associated with PRC2 activity changes in both hippocampal neurons and glia depend on Ogg1 and Mutyh. Epigenetic alterations correlated with cell-type specific gene expression changes which were associated with pathways important for neuronal function and cognition. Our results provide a novel role for Ogg1 and Mutyh beyond DNA repair in modulating the epigenome to control transcriptional responses in the brain important for memory formation.

neuroscience↗

bioRxiv2024

Genomic 8-oxoguanine modulates gene transcription independent of its repair by DNA glycosylases OGG1 and MUTYH

8-oxo-7,8-dihydroguanine (OG) is one of the most abundant oxidative lesions in the genome and associated with genome instability. Its mutagenic potential is counteracted by a concerted action of 8-oxoguanine DNA glycosylase (OGG1) and mutY homolog DNA glycosylase (MUTYH). It has been suggested that OG and its repair has epigenetic-like properties and mediates transcription, but genome-wide evidence of this interdependence is lacking. Here, we applied an improved OG-sequencing approach reducing artificial background oxidation and RNA-sequencing to correlate genome-wide distribution of OG with gene transcription in OGG1 and/or MUTYH-deficient cells. Our data identified moderate enrichment of OG in the genome that is mainly dependent on the genomic context and not affected by DNA glycosylase-initiated repair. Interestingly, no association was found between genomic OG deposition and gene expression changes upon loss of OGG1 and MUTYH. Regardless of DNA glycosylase activity, OG in promoter regions correlated with expression of genes related to metabolic processes and damage response pathways indicating that OG functions as a cellular stress sensor to regulate transcription. Our work provides novel insights into the mechanism underlying transcriptional regulation by OG and DNA glycosylases OGG1 and MUTYH and suggests that oxidative DNA damage accumulation and its repair utilize different pathways.

genomics↗

bioRxiv2023

Diagnostic images for mild cognitive impairment reveal biomarker status and abnormal scene processing

Research on the impairment of episodic memory in Alzheimers disease often focuses on the processes of memory rather than the content of the specific images being remembered. We recently showed that patients with mild cognitive impairment (MCI), Stage 3 of Alzheimers disease, can memorize certain images quite well, suggesting that episodic memory is not uniformly impaired. Certain images, on the other hand, could not be memorized by MCI patients and were instead diagnostic for distinguishing MCI from healthy older adults. In this study, we investigate whether poor memory for diagnostic images is related to impaired neural processing in specific brain regions due to Alzheimers biomarker pathology. 64 healthy controls and 48 MCI participants in the DELCODE dataset performed a visual scene memory task during fMRI, with CSF Alzheimers disease biomarker data collected (i.e., amyloid and tau biomarkers). We found that diagnostic images have larger behavior-biomarker correlations for total tau, phospho-tau, A{beta}42/A{beta}40, A{beta}42/phospho-tau compared to non-diagnostic images, suggesting that memory for these specific images are more affected by Alzheimers disease pathology. The fMRI data revealed an interaction effect between group membership (healthy control / MCI) and image diagnosticity (diagnostic / non-diagnostic scene images), with MCI participants having higher activation in scene processing regions (parahippocampal place area, retrosplenial cortex and occipital place area) for diagnostic images than non-diagnostic images. In contrast, healthy controls showed no differences in processing between diagnostic and non-diagnostic images. These results suggest that MCI individuals may engage in inefficiently heightened encoding activation for these diagnostic images. Our results show that special "diagnostic" images exist that can reveal amyloid and tau pathology and differences in neural activity in scene regions.

neuroscience↗

bioRxiv2023

Protocol for 3D Virtual Histology of Unstained Human Brain Tissue using Synchrotron Radiation Phase-Contrast Microtomography

X-ray phase-contrast micro computed tomography using synchrotron radiation (SR PhC-{micro}CT) offers unique 3D imaging capabilities for visualizing microstructure of the human brain. Its applicability for unstained soft tissue is an area of active research. Acquiring images from a tissue block without needing to section it into thin slices, as required in routine histology, allows for investigating the microstructure in its natural 3D space. This paper presents a detailed step-by-step guideline for imaging unstained human brain tissue at resolutions of a few micrometers with SR PhC-{micro}CT implemented at SYRMEP, the hard X-ray imaging beamline of Elettra, the Italian synchrotron facility. We present examples of how blood vessels and neurons appear in the images acquired with isotropic 5 {micro}m and 1 {micro}m voxel sizes. Furthermore, the proposed protocol can be used to investigate important biological substrates such as neuromelanin or corpora amylacea. Their spatial distribution can be studied using specifically tailored segmentation tools that are validated by classical histology methods. In conclusion, SR PhC-{micro}CT using the proposed protocols, including data acquisition and image processing, offers viable means of obtaining information about the anatomy of the human brain at the cellular level in 3D.

neuroscience↗

bioRxiv2023

Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning

Predicting neuromarkers for cognitive abilities using fMRI has been a major focus of research in the past few years. However, it has recently been reported that many thousands of participants are required to obtain reproducible results (Marek et al (2022)). This appears to be a major impediment to obtaining neuromarkers from fMRI because large sample sizes are typically not available in neuroimaging studies. Here we show that the out-of-sample prediction accuracy can be dramatically improved by supplementing fMRI with readily available non-imaging information so that reliable predictive modeling becomes feasible even for small sample sizes. Specifically, we introduce a novel machine learning method that predicts intelligence from resting-state fMRI data, leveraging educational level as supplementary information. We refer to our approach as "semi-blind machine learning (SML)" because it operates under the assumption that supplementary information, such as educational level, is available for subjects in both the training and test sets. This setup closely mirrors real-world scenarios, especially in clinical contexts, where patient background information typically exists and can be utilized to boost prediction accuracy. However, guarding against bias is crucial. Subjects should not be categorized as more intelligent simply based on their higher education levels. Therefore, our approach contains a component explicitly designed for bias control. We have applied our method to three different data collections and observed marked improvements in prediction accuracies across a wide range of sample sizes. We anticipate that semi-blind machine learning provides a promising approach to fMRI-based predictive modelling with the potential for a wide range of future applications.

neuroscience↗

bioRxiv2023

Cognitive Reserve Against Alzheimer's Pathology Is Linked to Brain Activity During Memory Formation

The cognitive reserve (CR) hypothesis posits that individuals can differ in how their brain function is disrupted by pathology associated with aging and neurodegeneration. Here, we tested this hypothesis in the Alzheimers disease continuum using longitudinal data from 490 participants of the DELCODE multicentric observational study. Brain function was measured using task fMRI of visual memory encoding. Using a multivariate moderation analysis we identified a CR-related activity pattern underlying successful memory encoding that moderated the detrimental effect of AD pathological load on cognitive performance. CR was mainly represented by a more pronounced expression of the task-active network encompassing the default mode network (DMN), anterior cingulate cortex (ACC) and inferior temporal regions including the fusiform gyrus. We devised personalized fMRI-based CR scores that moderated the impact of AD pathology on cognitive performance and were positively associated with years of education. Furthermore, higher CR scores were associated with slower cognitive decline over time. Our findings suggest maintenance of core cognitive circuits including the DMN and ACC as the primary mechanism of CR. Individual brain activity levels of these areas during memory encoding have prognostic value for future cognitive decline.

neuroscience↗

bioRxiv2023

Alpha-180 spin-echo based line-scanning method for high resolution laminar-specific fMRI

Laminar-specific functional magnetic resonance imaging (fMRI) has been widely used to study circuit-specific neuronal activity by mapping spatiotemporal fMRI response patterns across cortical layers. Hemodynamic responses reflect indirect neuronal activity given limit of spatial and temporal resolution. Previous gradient-echo based line-scanning fMRI (GELINE) method was proposed with high temporal (50 ms) and spatial (50 {micro}m) resolution to better characterize the fMRI onset time across cortical layers by employing 2 saturation RF pulses. However, the imperfect RF saturation performance led to poor boundary definition of the reduced region of interest (ROI) and aliasing problems outside of the ROI. Here, we propose (alpha)-180 spin-echo-based line-scanning fMRI (SELINE) method to resolve this issue by employing a refocusing 180{degrees} RF pulse perpendicular to the excitation slice. In contrast to GELINE signals peaked at the superficial layer, we detected varied peaks of laminar-specific BOLD signals across deeper cortical layers with the SELINE method, indicating the well-defined exclusion of the large drain-vein effect with the spin-echo sequence. Furthermore, we applied the SELINE method with 200 ms TR to sample the fast hemodynamic changes across cortical layers with a less draining vein effect. In summary, this SELINE method provides a novel acquisition scheme to identify microvascular-sensitive laminar-specific BOLD responses across cortical depth.

neuroscience↗