Biology subjects

de Lange, S. C.

Publications and source records attributed to de Lange, S. C..

10 recordsLinked to original sources

bioRxiv2024

Multimodal brain imaging of insomnia, depression and anxiety symptoms: transdiagnostic commonalities and differences

Insomnia disorder, major depressive disorder and anxiety disorders are the most common mental health conditions, with high comorbidity and genetic overlap suggesting shared brain mechanisms. Studies on brain correlates of these disorders have not fully addressed this overlap. Aiming to distinguish shared from specific brain structural and functional properties associated with symptoms of these disorders, this study analyzed multimodal brain imaging data from over 40,000 UK Biobank participants. Functional enrichment analyses were conducted to understand the cognitive-emotional and neurotransmission implications of the identified brain regions and connections. Results showed that smaller cortical surfaces, smaller thalamic volumes, and weaker functional connectivity were linked to more severe symptoms across all symptom types. Several symptom-specific associations were revealed, most commonly in different parts of the amygdala-hippocampal-medial prefrontal circuit. These findings revealed both transdiagnostically shared and unique brain properties that could lead to more directed treatment targets for insomnia, depression, and anxiety.

neuroscience↗

bioRxiv2022

Specificity and overlap in the genetic architectures of functional and structural connectivity within cerebral resting-state networks

The functional connectivity and dynamics of resting-state networks (RSN-FC) are vital for cognitive functioning. RSN-FC is heritable and partially translates to the anatomical architecture of white matter, but the genetic component of structural connections of RSNs (RSN-SC) and their potential genetic overlap with RSN-FC remains unknown. Here we perform genome-wide association studies (Ndiscovery=24,336; Nreplication=3,412) and in silico annotation on RSN-SC and RSN-FC. We identify the first genes for visual network-SC, that are involved in axon guidance and synaptic functioning and show that genetic variation in RSN-FC impacts biological processes related to brain disorders that have previously been associated with FC alterations in those same RSNs. Correlations of the genetic components of RSNs are mostly observed within the functional domain, whereas less overlap is observed within the structural domain and between the functional and structural domains. This study advances the understanding of the complex functional organization of the brain and its structural underpinnings from a genetics viewpoint.

genetics↗

bioRxiv2021

Genome-wide association study of cerebellar volume

Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. A genome-wide associations study for cerebellar volume was performed in a sample of 27,486 individuals from UK Biobank, resulting in 29 genome-wide significant loci and a SNP heritability of 39.82%. We pinpoint variants that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types or developmental stages. Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinsons disease, Alzheimers disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health.

genetics↗

bioRxiv2021

Common micro- and macroscale principles of connectivity in the human brain

The brain requires efficient information transfer between neurons and between large-scale brain regions. Brain connectivity follows predictable organizational principles: at the cellular level, larger supragranular pyramidal neurons have larger dendritic trees, more synapses, more complex branching and perform more complex neuronal computations; at the macro-scale, region-to-region connections are suggested to display a diverse architecture with highly connected hub-areas facilitating complex information integration and computation. Here, we explore the hypothesis that the branching structure of large-scale region-to-region connectivity follows similar organizational principles as known for the neuronal scale. We examine microscale connectivity of basal dendritic trees of supragranular pyramidal neurons (300+) across ten cortical areas in five human donor brains (1M/4F). Dendritic complexity was quantified as the number of branch points, tree length, spine count, spine density and overall branching complexity. High-resolution diffusion-weighted MRI was used to construct white matter trees of cortico-cortical wiring. Examining the complexity of the resulting white matter trees using the same measures as for dendritic trees shows multimodal association areas to have larger, more complexly branched white matter trees than primary areas (all p<0.0001) and regional macroscale complexity to run in parallel with microscale measures, in terms of number of inputs (r=0.677, p=0.032), branch points (r=0.790, p=0.006), total tree length (r=0.664, p=0.036) and branching complexity (r=0.724, p=0.018). Our findings support the integrative theory that brain connectivity is structured following similar principles of connectivity at the neuronal and macroscale level, and provide a framework to study connectivity changes in brain conditions at multiple levels of brain organization.

neuroscience↗

bioRxiv2021

Scaling principles of white matter brain connectivity

Brains come in many shapes and sizes. Nature has endowed big-brained primate species like humans with a proportionally large cerebral cortex. White matter connectivity - the brains infrastructure for long-range communication - might not always scale at the same pace as the cortex. We investigated the consequences of this allometric scaling for white matter brain network connectivity. Structural T1 and diffusion MRI data were collated across fourteen primate species, describing a comprehensive 350-fold range in brain volume. We report volumetric scaling relationships that point towards a restriction in macroscale connectivity in larger brains. Building on previous findings, we show cortical surface to outpace white matter volume and the corpus callosum, suggesting the emergence of a white matter bottleneck of lower levels of connectedness through the corpus callosum in larger brains. At the network level, we find a potential consequence of this bottleneck in shaping connectivity patterns, with homologous regions in the left and right hemisphere showing more divergent connectivity in larger brains. Our findings show conserved scaling relationships of major brain components and their consequence for macroscale brain circuitry, providing a comparative framework for expected connectivity architecture in larger brains such as the human brain.

neuroscience↗

bioRxiv2021

Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox

We describe a Connectivity Analysis TOolbox (CATO) for the reconstruction of structural and functional brain connectivity based on diffusion weighted imaging and resting-state functional MRI data. CATO is a multimodal software package that enables researchers to run end-to-end reconstructions from MRI data to structural and functional connectome maps, customize their analyses and utilize various software packages to preprocess data. Structural and functional connectome maps can be reconstructed with respect to user-defined (sub)cortical atlases providing aligned connectivity matrices for integrative multimodal analyses. We outline the implementation and usage of the structural and functional processing pipelines in CATO. Performance was calibrated with respect to simulated diffusion weighted imaging from the ITC2015 challenge, test-retest diffusion weighted imaging data and resting-state functional MRI data from the Human Connectome Project. CATO is open-source software distributed under the MIT License and available as a MATLAB toolbox and as a stand-alone application at www.dutchconnectomelab.nl/CATO.

neuroscience↗

bioRxiv2021

Statistical testing and annotation of gene transcriptomic-neuroimaging associations

Multiscale integration of gene transcriptomic and neuroimaging data is becoming a widely used approach for exploring the molecular underpinnings of large-scale brain structure and function. Proper statistical evaluation of computed associations between imaging-based phenotypic and transcriptomic data is key in these explorations, in particular to establish whether observed associations exceed chance level of random, non-specific effects. Recent approaches have shown the importance of spatial null models to test for spatial specificity of effects to avoid serious inflation of reported statistics. Here, we discuss the need for examination of the second category of specificity in transcriptomic-neuroimaging analyses, namely that of gene specificity, examined using null models built upon effects that occur from sets of random genes. Through simple examples of commonly performed transcriptomic-neuroimaging analyses, we show that providing additional gene specificity on observed transcriptomic-neuroimaging effects is of high importance to avoid non-specific (potentially false-positive) effects. Through simulations we further show that the rate of reported non-specific effects (i.e., effects that are generally observed and cannot be specifically linked to a gene-set of interest) can run as high as 60%, with only less than 5% of transcriptomic-neuroimaging associations observed through ordinary linear regression analyses showing spatial and gene specificity. We explain that using proper null models that test for both spatial specificity and gene specificity is warranted.

neuroscience↗

bioRxiv2019

Genetic correlates of evolutionary adaptations in cognitive functional brain networks and their relationship to human cognitive functioning and disease

Cognitive functional networks such as the default-mode network (DMN), frontal-parietal network (FPN), and salience network (SN), are key networks of the human brain. Here, we show that the distinct rapid evolutionary cortical expansion of cognitive networks in the human brain, and most pronounced the DMN, runs parallel with high expression of genes important for human evolution (so-called HAR genes). Comparative gene expression examination then shows that HAR genes are more differentially expressed in cognitive networks in humans compared to the chimpanzee and macaque. Genes with distinct high expression in the DMN display broad involvement in the formation of synapses and dendrites. Next, we performed a genome-wide association analysis on functional MRI data, and show that HAR genes are associated with individual variations in DMN functional connectivity in todays human population. Finally, gene-set analysis suggests associations of HAR genes with intelligence, social cognition, and mental conditions such as schizophrenia and autism. Taken together, our results indicate that the expansion of higher-order functional networks and their cognitive properties have been an important locus of change in recent human brain evolution.

neuroscience↗

bioRxiv2018

Evolutionarily developed connections compromised in schizophrenia

The genetic basis and uniquely human character of schizophrenia has led to the notion of human brain evolution to have resulted in vulnerability to the disorder. We examined schizophrenia-related changes in brain connectivity in the context of evolutionary changes in human brain wiring by comparing in-vivo neuroimaging data from humans, chimpanzees and macaque monkeys. We find that evolutionary changes in human connectome organization overlap with the pattern of schizophrenia-related changes in brain connectivity, with connections evolutionary enhanced in the human brain showing significantly more involvement in schizophrenia pathology than connections shared between humans and non-human primates (effects shown in three independent patient-control datasets). Our findings suggest that the evolution of brain wiring in support of complex brain function in humans may have come at the cost of an increased vulnerability to brain dysfunction in disease.

neuroscience↗

bioRxiv2018

Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders

Macroscale white matter pathways form the infrastructure for large-scale communication in the human brain, a prerequisite for healthy brain function. Conversely, disruptions in the brains connectivity architecture are thought to play an important role in a wide range of psychiatric and neurological brain disorders. Here we show that especially connections important for global communication and network integration are involved in a wide range of brain disorders. We report on a meta-analytic connectome study comprising in total 895 patients and 1,016 controls across twelve neurological and psychiatric disorders. We extracted disorder connectome fingerprints for each of these twelve disorders, which were then combined into a cross-disorder disconnectivity involvement map, representing the involvement of each brain pathway across brain disorders. Our findings show connections central to the brains infrastructure are disproportionally involved across a wide range of disorders. Connections critical for global network communication and integration display high disturbance across disorders, suggesting a general cross-disorder involvement and importance of these pathways in normal function. Taken together, our cross-disorder study suggests a convergence of disconnectivity across disorders to a partially shared disconnectivity substrate of central connections.

neuroscience↗