Biology subjects

Draganski, B.

Publications and source records attributed to Draganski, B..

16 recordsLinked to original sources

bioRxiv2026

Dose-dependent myelin g-ratio alterations in the white matter of 16p11.2 copy number variations

Copy number variations (CNVs) at the 16p11.2 locus are major genetic risk factors for neurodevelopmental conditions and drive reciprocal alterations in cerebral white matter structure and connectivity. However, whether these alterations reflect differences in the axonal or myelin compartments remains unresolved with conventional diffusion MRI. We combined diffusion MRI with magnetization transfer saturation mapping across two ComBat-harmonized cohorts comprising 80 controls, 26 16p11.2 deletion carriers, and 13 duplication carriers. We quantified axonal volume, myelin volume, and aggregate g-ratio of white matter tracts forming the structural connectome. Metrics were evaluated using normative modeling across tract length, resting-state functional networks, and cortical nodes. We additionally examined 29 individuals carrying other neurodevelopmental CNVs. 16p11.2 gene dosage was associated with regional reciprocal differences in tract axonal volume, with greater volume in deletion carriers and lower volume in duplication carriers. In contrast, myelin volume did not differ significantly from controls. This mismatch resulted in pervasive aggregate g-ratio alterations, with higher g-ratios (relatively thinner myelin sheaths) in deletion carriers and lower g-ratios (relatively thicker sheaths) in duplication carriers. Regional g-ratio alterations also extended to other neurodevelopmental CNVs. These findings resolve some of the biological ambiguity underlying white matter alterations in 16p11.2 CNV carriers, implicating the axonal compartment while revealing comparatively stable myelin volume. The disrupted axon-myelin scaling results in connectome-wide g-ratio alterations and may represent a shared microstructural hallmark of genetic neurodevelopmental risk.

neuroscience↗

bioRxiv2026

Multiscale reorganization of brain and behavior under large-scale electrical perturbation

Large-scale electrical perturbation of the human brain provides a unique model for understanding how multiscale biological constraints shape behaviorally relevant reorganization. Here, we integrate longitudinal neuroimaging coordinates from 148 experiments ({approx}2,300 subjects) with normative connectomics, chemoarchitecture, intrinsic electrophysiology, and transcriptomics to identify cross-scale principles governing human brain reconfiguration under strong perturbation. Convergent hubs of structural and functional plasticity embed within default-mode and salience systems and show complementary coupling to visual networks, linking perturbation-induced change to large-scale circuits supporting affective regulation, memory, interoception, and psychosis-relevant processes. These macroscopic patterns align with intrinsic cortical dynamics and chemoarchitectural gradients dominated by 5-HT1A receptors, with additional contributions from D2, -opioid and GABAA systems, and are enriched for astrocytic and microglial gene expression, implicating glial plasticity in systems-level reorganization. Finally, in a separate intervention dataset, regularized statistical-learning models demonstrate that this multiscale signature tracks behaviorally relevant symptom change specifically under strong electrical perturbation. Together, these results outline general organizing principles linking molecular, cellular and network-level constraints to human behavioral adaptation, providing a computational framework for understanding how large-scale perturbations reshape brain systems across levels of biological organization.

neuroscience↗

bioRxiv2025

Mid-level visual features engage V4 to support sparse object recognition under uncertainty

Cortical visual prostheses aim to restore sight by stimulating the visual cortex, yet current approaches remain limited. Implants targeting V1 typically evoke isolated phosphenes that do not support coherent object recognition. We asked whether mid-level visual features, specifically curve segments associated with area V4, could provide a more efficient substrate for artificial vision. Using fMRI, 24 participants performed an object recognition task with stimuli fragmented into either phosphene-like dots or curve segments at varying densities. Behaviorally, curve segments enabled recognition at lower density thresholds and produced steeper psychometric slopes. Neurally, both fragment types engaged V1, but only curve segments recruited V4, and correct recognition of segments relative to phosphenes evoked broader occipito-temporo-parietal responses. Multivariate decoding showed that successful recognition of phosphenes was predicted by early visual and cingulate activity, whereas recognition of segments was predicted only by V4, indicating a shift toward mid-level representational support. Mediation analyses further showed that phosphene-based recognition relied on anterior cingulate cortex under conditions of higher uncertainty, while segment-based recognition depended primarily on visual cortex. Together, these results show that mid-level fragments confer perceptual and neural advantages, reducing reliance on both dense stimulation and downstream uncertainty resolution, and identify V4 as a promising target for next-generation cortical prostheses.

neuroscience↗

bioRxiv2025

White Matter Microstructure Fingerprint of Cerebral Small Vessel Disease

BackgroundWhite matter hyperintensities (WMHs) are the imaging hallmark of cerebral small vessel disease (SVD), yet their microstructural composition, spatial heterogeneity, and relationship to diffuse normal-appearing white matter (NAWM) damage and cardiovascular risk remain incompletely defined. MethodsIn 363 community-dwelling adults from the BrainLaus cohort (mean age 55.5 years; range 19.8-89.4; 48.8% male), we combined quantitative relaxometry (MTsat, R1, R2*) and diffusion-derived metrics (FA, MD, NODDI, g-ratio). WMHs were automatically segmented on FLAIR and microstructure was quantified across lobes, white matter compartments, and geodesic layers extending from the WMH core into surrounding NAWM. Multivariate organisation was assessed using principal component analysis, and associations with cardiovascular risk factors were tested using partial least squares. ResultsOur analysis revealed demyelination, axonal loss, and extracellular fluid accumulation, particularly in periventricular regions. Layer-specific profiles showed a centrifugal gradient, with myelin loss and oedema at the core and axonal alteration in surrounding tissue. These signatures were associated with age-related cardiovascular risk factors, including higher blood pressure, bioimpedance, and lower hemoglobin levels. ConclusionWMHs index the endpoint of a broader, spatially structured white matter injury process that extends into NAWM, is regionally concentrated in periventricular tissue, and covaries with systemic vascular/metabolic factors. These findings support sustained vascular risk management to mitigate CSVD-related white matter degeneration.

neuroscience↗

bioRxiv2025

Beta-burst dynamics in the motor cortex are reshaped through sensorimotor refinement

Beta-band activity (15-30 Hz) in motor cortex is closely linked to movement-related processing, yet its transient burst dynamics during long-term learning remain poorly understood. We used High-density electroencephalography (HD-EEG) to examine how beta bursts change over nine sessions of a bimanual coordination task, under adaptive and non-adaptive training conditions. Both training conditions led to motor skill learning, with the adaptive group improving more during training, but the non-adaptive group showing better retention. With training, contralateral primary motor cortex showed stronger beta desynchronization during movement and greater synchronization after movement. These changes reflected underlying burst dynamics: post-movement bursts became more temporally confined and consistent, with increased probability and reduced timing variability across sessions. Only the adaptive group showed a session-related increase in burst amplitude. These results demonstrate that beta burst features reorganize with practice, providing a temporally precise neural readout of training progression and revealing how different learning conditions shape cortical dynamics over time.

neuroscience↗

bioRxiv2023

Is there a neural common factor for visual illusions?

It is tempting to map interindividual variability in human perception to variability in brain structure or neural activity. Indeed, it has been shown that susceptibility to size illusions correlates with the size of primary visual cortex V1. Yet contrary to common belief, illusions correlate only weakly at the perceptual level, raising the question of how they can correlate with a localized neural measure. In addition, mounting evidence suggests that there is substantial interindividual variability not only in neural function and anatomy but also in the mapping between the two, which further challenges the findings of a neural common factor for illusions. To better understand these questions, here, we re-evaluated previous studies by correlating illusion strengths in a battery of 13 illusions with the size of visual areas and population receptive field sizes. We did not find significant correlations either at the perceptual level or between illusion susceptibility and visual functional neuroanatomy.

neuroscience↗

bioRxiv2023

Using rare genetic mutations to revisit structural brain asymmetry

Asymmetry between the left and right brain is a key feature of brain organization. Hemispheric functional specialization underlies some of the most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variant studies, which typically exert small effects on brain phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We quantitatively dissected the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior mapping highlights the consequences of genetically controlled brain lateralization on human-defining cognitive traits.

neuroscience↗

bioRxiv2023

Statistical analyses of motion-corrupted MRI relaxometry data

Consistent noise variance across data points (i.e. homoscedasticity) is required to ensure the validity of statistical analyses of MRI data conducted using linear regression methods. However, head motion leads to degradation of image quality, introducing noise heteroscedasticity into ordinary-least square analyses. The recently introduced QUIQI method restores noise homoscedasticity by means of weighted least square analyses in which the weights, specific for each dataset of an analysis, are computed from an index of motion-induced image quality degradation. QUIQI was first demonstrated in the context of brain maps of the MRI parameter R2*, which were computed from a single set of images with variable echo time. Here, we extend this framework to quantitative maps of the MRI parameters R1, R2*, and MTsat, which are computed from multiple sets of images. QUIQI allows for optimization of the noise model by using metrics quantifying heteroscedasticity and free energy. QUIQI restores homoscedasticity more effectively than insertion of an image quality index in the analysis design and yields higher sensitivity than simply removing the datasets most corrupted by head motion from the analysis. In sum, QUIQI provides an optimal approach to group-wise analyses of a range of quantitative MRI parameter maps that is robust to inherent homoscedasticity.

neuroscience↗

bioRxiv2022

Rare CNVs and phenome-wide profiling: a tale of brain-structural divergence and phenotypical convergence

Copy number variations (CNVs) are rare genomic deletions and duplications that can exert profound effects on brain and behavior. Previous reports of pleiotropy in CNVs imply that they converge on shared mechanisms at some level of pathway cascades, from genes to large-scale neural circuits to the phenome. However, studies to date have primarily examined single CNV loci in small clinical cohorts. It remains unknown how distinct CNVs escalate the risk for the same developmental and psychiatric disorders. Here, we quantitatively dissect the impact on brain organization and behavioral differentiation across eight key CNVs. In 534 clinical CNV carriers from multiple sites, we explored CNV-specific brain morphology patterns. We extensively annotated these CNV-associated patterns with deep phenotyping assays through the UK Biobank resource. Although the eight CNVs cause disparate brain changes, they are tied to similar phenotypic profiles across [~]1000 lifestyle indicators. Our population-level investigation established brain structural divergences and phenotypical convergences of CNVs, with direct relevance to major brain disorders.

genetics↗

bioRxiv2021

Restoring statistical validity in group analyses of motion-corrupted MRI data

Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterize disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data-driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of brain MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.

neuroscience↗

bioRxiv2021

Mind the gap: performance metric evaluation in brain-age prediction

Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age based on neuroimaging data in two population-based datasets, and assessed the effects of age range, sample size, and age-bias correction on the model performance metrics r, R2, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R2 values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy - also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics in general indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.

neuroscience↗

bioRxiv2019

Leveraging biobank-scale rare and common variant analyses to identify ASPHD1 as the main driver of reproductive traits in the 16p11.2 locus

Whereas genome-wide association studies (GWAS) allowed identifying thousands of associations between variants and traits, their success rate in pinpointing causal genes has been disproportionately low. Here, we integrate biobank-scale phenotype data from carriers of a rare copy-number variant (CNV), Mendelian randomization and animal modeling to identify causative genes in a GWAS locus for age at menarche (AaM). We show that the dosage of the 16p11.2 BP4-BP5 interval is correlated positively with AaM in the UK and Estonian biobanks and 16p11.2 clinical cohorts, with a directionally consistent trend for pubertal onset in males. These correlations parallel an increase in reproductive tract disorders in both sexes. In support of these observations, 16p11.2 mouse models display perturbed pubertal onset and structurally altered reproductive organs that track with CNV dose. Further, we report a negative correlation between the 16p11.2 dosage and relative hypothalamic volume in both humans and mice, intimating a perturbation in the gonadotropin-releasing hormone (GnRH) axis. Two independent lines of evidence identified candidate causal genes for AaM; Mendelian randomization and agnostic dosage modulation of each 16p11.2 gene in zebrafish gnrh3:egfp models. ASPHD1, expressed predominantly in brain and pituitary gland, emerged as a major phenotype driver; and it is subject to modulation by KCTD13 to exacerbate GnRH neuron phenotype. Together, our data highlight the power of an interdisciplinary approach to elucidate disease etiologies underlying complex traits.

genomics↗

bioRxiv2018

Multiple Linear Regression: Bayesian Inference for Distributed and Big Data in the Medical Informatics Platform of the Human Brain Project

We propose a Multiple Linear Regression (MLR) methodology for the analysis of distributed and Big Data in the framework of the Medical Informatics Platform (MIP) of the Human Brain Project (HBP). MLR is a very versatile model, and is considered one of the workhorses for estimating dependences between clinical, neuropsychological and neurophysiological variables in the field of neuroimaging. One of the main concepts behind MIP is to federate data, which is stored locally in geographically distributed sites (hospitals, customized databases, etc.) around the world. We restrain from using a unique federation node for two main reasons: first the maintenance of data privacy, and second the efficiency in management of big volumes of data in terms of latency and storage resources needed in the federation node. Considering these conditions and the distributed nature of data, MLR cannot be estimated in the classical way, which raises the necessity of modifications of the standard algorithms. We use the Bayesian formalism that provides the armamentarium necessary to implement the MLR methodology for distributed Big Data. It allows us to account for the heterogeneity of the possible mechanisms that explain data sets across sites expressed through different models of explanatory variables. This approach enables the integration of highly heterogeneous data coming from different subjects and hospitals across the globe. Additionally, it offers general and sophisticated ways, which are extendable to other statistical models, to suit high-dimensional and distributed multimodal data. This work forms part of a series of papers related to the methodological developments embedded in the MIP.

neuroscience↗

bioRxiv2017

Simultaneous Estimation of Population Receptive Field and Hemodynamic Parameters from Single Point BOLD Responses using Metropolis-Hastings Sampling

We introduce a new approach to Bayesian pRF model estimation using Markov Chain Monte Carlo (MCMC) sampling for simultaneous estimation of pRF and hemodynamic parameters. To obtain high performance on commonly accessible hardware we present a novel heuristic consisting of interpolation between precomputed responses for predetermined stimuli and a large cross-section of receptive field parameters. We investigate the validity of the proposed approach with respect to MCMC convergence, tuning and biases. We compare different combinations of pRF - Compressive Spatial Summation (CSS), Dumoulin-Wandell (DW) and hemodynamic (5-parameter and 3-parameter Balloon-Windkessel) models within our framework with and without the usage of the new heuristic. We evaluate estimation consistency and log probability across models. We perform as well a comparison of one model with and without lookup table within the RStan framework using its No-U-Turn Sampler. We present accelerated computation of whole-ROI parameters for one subject. Finally, we discuss risks and limitations associated with the usage of the new heuristic as well as the means of resolving them. We found that the new algorithm is a valid sampling approach to joint pRF/hemodynamic parameter estimation and that it exhibits very high performance.

neuroscience↗

bioRxiv2017

Controlling motion artefact levels in MR images by suspending data acquisition during periods of head motion

Head movements are a major source of MRI artefacts that hamper radiological assessment and computer-based morphological and functional measures of the human brain. Prospective motion correction techniques continuously update the MRI scanner based on head position information provided by an external tracking system. While prospective motion correction significantly improves data quality, strong motion artefacts may remain with large head motions or when motion takes place at sensitive times of the acquisition. Here we present a framework that allows the suspension of data acquisition when head motion is predicted to have a strong negative impact on data quality. The predictor, calculated in real-time during the acquisition, accounts for the amplitude of the signal acquired at the time of the motion, thereby offering a re-acquisition strategy more efficient than relying on head speed alone. The suspension of data acquisition is governed by the trade-off between image degradation due to motion and prolonging the scan time. This trade-off can be tuned by the user according to the desired level of image quality and the participant s tolerability. We test the framework using two motion experiments and two head coils. Significant improvements in data quality are obtained with stringent threshold values for the suspension of acquisition. Substantial reductions in motion artefact levels are also achieved with minimal prolongation of scan time. However, high levels of motion artefacts occasionally remain despite stringent thresholds with the 64-channel head coil, an effect that might be attributed to head movement in the sharp sensitivity profile of this coil.

neuroscience↗

bioRxiv2017

Convex Optimized Population Receptive Field (CO-pRF) Mapping

Population receptive field (pRF) mapping represents an invaluable non-invasive tool for the study of sensory organization and plasticity within the human brain. Despite the very appealing result that fMRI derived pRF measures agree well with measurements made from other fields of neuroscience, current techniques often require very computationally expensive non-linear optimization procedures to fit the models to the data which are also vulnerable to bias due local minima issues. In this work we present a general framework for pRF model estimation termed Convex Optimized Population Receptive Field (CO-pRF) mapping and show how the pRF fitting problem can be linearized in order to be solved by extremely fast and efficient algorithms. The framework is general and can be readily applied to a variety of pRF models and measurement schemes. We provide an example of the CO-pRF methodology as applied to a computational neuroimaging approach used to map sensory processes in human visual cortex - the CSS-pRF model. Via simulation and in-vivo fMRI results we demonstrate that the CO-pRF approach achieves robust model fitting even in the presence of noise or reduced data, providing parameter estimates closer to the global optimum across 93% of in-vivo responses as compared to a typical nonlinear optimization procedure. Furthermore the example CO-pRF application substantially reduced model fitting times by a factor of 50. We hope that the availability of such highly accelerated and reliable pRF estimation algorithms will facilitate the spread of pRF techniques to larger imaging cohorts and the future study of neurological disorders and plasticity within the human brain.

neuroscience↗