Biology subjects

Pijnenburg, R.

Publications and source records attributed to Pijnenburg, R..

3 recordsLinked to original sources

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

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↗

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↗