Biology subjects

Libedinsky, I.

Publications and source records attributed to Libedinsky, I..

5 recordsLinked to original sources

bioRxiv2026

Evaluating the value and limits of null-models for Lesion Network Mapping

Lesion Network Mapping (LNM) is a method used to map symptom-related circuits in the human brain. Recent work has raised concerns about the method and the specificity of LNM-derived networks, which often show high spatial overlap across unrelated conditions. Null models have been proposed to address the raised limitations1. Useful statistical tools in network neuroscience, they are reference procedures; in LNM, the normative connectome matrix C remains the source of the derived networks also under null modelling. Their broader role as a general solution for LNM therefore warrants careful evaluation. Here, we examine the proposed null models step-by-step and test them empirically across multiple datasets. We show that the proposed null-model correction effectively only replaces C with a transformed matrix C' (e.g., C' ~ C - mean(C)), one that is directly derived from C and retains much of its low-dimensional structure. Consequently, null-model-corrected LNM maps remain shaped by the same standard set of connectome-derived patterns and continue to show repetition and non-specificity across unrelated conditions. We discuss the scope and limitations of null models for LNM, showing that they can statistically benchmark LNMs, but that their application does not resolve the core limitation of non-specificity of LNM networks.

neuroscience↗

bioRxiv2026

Assessing specificity testing in Lesion Network Mapping

Lesion Network Mapping (LNM) is a framework used for identifying symptom-related brain circuits by projecting lesion locations onto a normative connectome. Recent methodological investigations have raised concerns about the biological interpretation and specificity of the circuits derived using this method, with published LNM maps often showing high similarity across clinically unrelated conditions. Specificity testing has subsequently been put forward as the decisive step to ensure specificity to the symptom in question, accompanied by the argument that this step was not evaluated in the original methodological investigation. Yet, sensitivity testing, specificity testing, case-control LNM, permutation of group labels, and symptom-based LNM involve related operations on connectivity matrix C. We expand on specificity testing in LNM, clarify its relationship to other LNM steps and variants, and examine the persistent repetition among LNM specificity networks across studies. These considerations advance our understanding of the disease-specificity limitation of LNM and encourage the development of new methodological approaches for identifying brain circuits underlying psychiatric and neurological disorders.

neuroscience↗

bioRxiv2023

Quantifying brain connectivity signatures by means of polyconnectomic scoring

A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimers disease) and 14 studies encompassing [~]35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohens d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 x 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.

neuroscience↗

bioRxiv2023

Genetic timeline of human brain and cognitive traits

Human evolution is characterised by extensive changes of body and brain, with perhaps one of the core developments being the fast increase in cranial capacity and brain volume. Paleontological records are the most direct method to study such changes, but they can unfortunately provide a limited view of how soft traits such as brain function and cognitive abilities have evolved in humans. A potential complementary approach is to identify when particular genetic variants associated with human phenotypes (such as height, body mass index, intelligence, and also disease) have emerged in the 6-7 million years since we diverged from chimpanzees. In this study, we combine data from genome-wide association studies on human brain and cognitive traits with estimates of human genome dating. We systematically analyse the temporal emergence of genetic variants associated with modern-day human brain and cognitive phenotypes over the last five million years. Our analysis provides evidence that genetic variants related to neocortex structure (e.g., area, thickness; median evolutionary age = 400,170 years old), cognition (e.g., fluid intelligence; median age = 459,465), education (median age = 637,646), and psychiatric disorders (median age = 412,639) have emerged more recently in human evolution than expected by chance. In contrast, variants related to other physical traits, such as height (median age = 811,305) and body mass index (median age = 794,265), emerged relatively later. We further show that genes containing recent evolutionary modifications (from around 54,000 to 4,000 years ago) are linked to intelligence (P = 2 x 10-6) and neocortical surface area (P = 6.7 x 10-4), and that these genes tend to be highly expressed in cortical areas involved in language and speech (pars triangularis, P = 6.2 x 10-4). Elucidating the temporal dynamics of genetic variants associated with brain and cognition is another source of evidence to advance our understanding of human evolution.

genetics↗

bioRxiv2022

Reproducibility of neuroimaging studies of brain disorders with hundreds -not thousands- of participants

An important current question in neuroimaging concerns the sample sizes required for producing reliable and reproducible results. Recent findings suggest that brain-wide association studies (BWAS) linking neuroimaging features with behavioural phenotypes in the general population are characterised by (very) weak effects and consequently need large samples sizes of 3000+ to lead to reproducible findings. A second, important goal in neuroimaging is to study brain structure and function under disease conditions, where effects are likely much larger. This difference in effect size is important. We show by means of power calculations and empirical analysis that neuroimaging studies in clinical populations need hundreds -and not necessarily thousands-of participants to lead to reproducible findings.

neuroscience↗