Graph theoretical analysis has become an important tool in the examination of brain dysconnectivity in neurological and psychiatric brain disorders. A common analysis step in the construction of the ...functional graph or network involves “thresholding” of the connectivity matrix, selecting the set of edges that together form the graph on which network organization is evaluated. To avoid systematic differences in absolute number of edges, studies have argued against the use of an “absolute threshold” in case-control studies and have proposed the use of “proportional thresholding” instead, in which a pre-defined number of strongest connections are selected as network edges, ensuring equal network density across datasets. Here, we systematically studied the effect of proportional thresholding on the construction of functional matrices and subsequent graph analysis in patient-control functional connectome studies. In a few simple experiments we show that differences in overall strength of functional connectivity (FC) – as often observed between patients and controls – can have predictable consequences for between-group differences in network organization. In individual networks with lower overall FC the proportional thresholding algorithm has to select more edges based on lower correlations, which have (on average) a higher probability of being spurious, and thus introduces a higher degree of randomness in the resulting network. We show across both empirical and artificial patient-control datasets that lower levels of overall FC in either the patient or control group will most often lead to differences in network efficiency and clustering, suggesting that differences in FC across subjects will be artificially inflated or translated into differences in network organization. Based on the presented case-control findings we inform about the caveats of proportional thresholding in patient-control studies in which groups show a between-group difference in overall FC. We make recommendations on how to examine, report and to take into account overall FC effects in future patient-control functional connectome studies.
•Proportional thresholding is a commonly used analysis step in the reconstruction of functional brain networks to ensure equal density across patient and control samples.•Proportional thresholding may result in the inclusion of more spurious connections in datasets based on low overall functional connectivity (FC).•When graph analysis is applied to these networks low overall FC may translate into more random network characterization.•Systematic differences in overall FC between patients and controls can artificially inflate differences in network organization.•We recommend to test and control for differences in overall FC in functional disease connectome studies.
Cognitive brain networks such as the default-mode network (DMN), frontoparietal network, and salience network, are key functional networks of the human brain. Here we show that the rapid evolutionary ...cortical expansion of cognitive networks in the human brain, and most pronounced the DMN, runs parallel with high expression of human-accelerated genes (HAR genes). Using comparative transcriptomics analysis, we present that HAR genes are differentially more expressed in higher-order cognitive networks in humans compared to chimpanzees and macaques and that genes with high expression in the DMN are involved in synapse and dendrite formation. Moreover, HAR and DMN genes show significant associations with individual variations in DMN functional activity, intelligence, sociability, and mental conditions such as schizophrenia and autism. Our results suggest that the expansion of higher-order functional networks subserving increasing cognitive properties has been an important locus of genetic changes in recent human brain evolution.
•CATO is a toolbox for reconstructing structural and functional connectivity.•Multimodal reconstructions enable integrative connectome analyses.•Reconstruction performance was assessed using ...simulated data and test-retest data.•CATO is shared as an open-source MATLAB toolbox and as a stand-alone application.
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 data from the ITC2015 challenge and 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.
•Digitized versions of Campbell, Smith, Brodmann, von Economo, flechsig and kleist cortical atlases.•Atlases based on microscale histological observations including cytoarchitecture, ...myeloarchitecture, myelogenesis and loss-of-function lesion mappings.•Cortical atlases digitized in standardized MRI space.•Surface- and volume-based digital atlases available.
The parcellation of the brain's cortical surface into anatomically and/or functionally distinct areas is a topic of ongoing investigation and interest. We provide digital versions of six classical human brain atlases in common MRI space. The cortical atlases represent a range of modalities, including cyto- and myeloarchitecture (Campbell, Smith, Brodmann and Von Economo), myelogenesis (Flechsig), and mappings of symptomatic information in relation to the spatial location of brain lesions (Kleist). Digital reconstructions of these important cortical atlases widen the range of modalities for which cortex-wide imaging atlases are currently available and offer the opportunity to compare and combine microstructural and lesion-based functional atlases with in-vivo imaging-based atlases.
Multiscale integration of gene transcriptomic and neuroimaging data is becoming a widely used approach for exploring the molecular underpinnings of large‐scale brain organization in health and ...disease. Proper statistical evaluation of determined 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, nonspecific effects. Recent approaches have shown the importance of statistical models that can correct for spatial autocorrelation effects in the data to avoid inflation of reported statistics. Here, we discuss the need for examination of a second category of statistical models in transcriptomic‐neuroimaging analyses, namely those that can provide “gene specificity.” By means of a couple of simple examples of commonly performed transcriptomic‐neuroimaging analyses, we illustrate some of the potentials and challenges of transcriptomic‐imaging analyses, showing that providing gene specificity on observed transcriptomic‐neuroimaging effects is of high importance to avoid reports of nonspecific effects. Through means of simulations we show that the rate of reported nonspecific effects (i.e., effects that cannot be specifically linked to a specific gene or gene‐set) can run as high as 60%, with only less than 5% of transcriptomic‐neuroimaging associations observed through ordinary linear regression analyses showing both spatial and gene specificity. We provide a discussion, a tutorial, and an easy‐to‐use toolbox for the different options of null models in transcriptomic‐neuroimaging analyses.
We highlight the need of using null models to provide ‘gene specificity’ when examining associations between the spatial patterns of gene transcriptomic profiles and imaging‐derived brain traits.
Objective
Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging‐based ...biomarkers in ALS have been shown to detect ALS‐associated pathology in vivo, although anatomical patterns of disease spread are poorly characterized. The objective of this study is to simulate disease propagation using network analyses of cerebral magnetic resonance imaging (MRI) data to predict disease progression.
Methods
Using brain networks of ALS patients (n = 208) and matched controls across longitudinal time points, network‐based statistics unraveled progressive network degeneration originating from the motor cortex and expanding in a spatiotemporal manner. We applied a computational model to the MRI scan of patients to simulate this progressive network degeneration. Simulated aggregation levels at the group and individual level were validated with empirical impairment observed at later time points of white matter and clinical decline using both internal and external datasets.
Results
We observe that computer‐simulated aggregation levels mimic true disease patterns in ALS patients. Simulated patterns of involvement across cortical areas show significant overlap with the patterns of empirically impaired brain regions on later scans, at both group and individual levels. These findings are validated using an external longitudinal dataset of 30 patients.
Interpretation
Our results are in accordance with established pathological staging systems and may have implications for patient stratification in future clinical trials. Our results demonstrate the utility of computational models in ALS to predict disease progression and underscore their potential as a prognostic biomarker. ANN NEUROL 2020;87:725–738
Major depressive disorder (MDD) is associated to affected brain wiring. Little is known whether these changes are stable over time and hence might represent a biological predisposition, or whether ...these are state markers of current disease severity and recovery after a depressive episode. Human white matter network ("connectome") analysis via network science is a suitable tool to investigate the association between affected brain connectivity and MDD. This study examines structural connectome topology in 464 MDD patients (mean age: 36.6 years) and 432 healthy controls (35.6 years). MDD patients were stratified categorially by current disease status (acute vs. partial remission vs. full remission) based on DSM-IV criteria. Current symptom severity was assessed continuously via the Hamilton Depression Rating Scale (HAMD). Connectome matrices were created via a combination of T1-weighted magnetic resonance imaging (MRI) and tractography methods based on diffusion-weighted imaging. Global tract-based metrics were not found to show significant differences between disease status groups, suggesting conserved global brain connectivity in MDD. In contrast, reduced global fractional anisotropy (FA) was observed specifically in acute depressed patients compared to fully remitted patients and healthy controls. Within the MDD patients, FA in a subnetwork including frontal, temporal, insular, and parietal nodes was negatively associated with HAMD, an effect remaining when correcting for lifetime disease severity. Therefore, our findings provide new evidence of MDD to be associated with structural, yet dynamic, state-dependent connectome alterations, which covary with current disease severity and remission status after a depressive episode.
An MRI Von Economo – Koskinas atlas Scholtens, Lianne H.; de Reus, Marcel A.; de Lange, Siemon C. ...
NeuroImage (Orlando, Fla.),
04/2018, Letnik:
170
Journal Article
Recenzirano
The cerebral cortex displays substantial variation in cellular architecture, a regional patterning that has been of great interest to anatomists for centuries. In 1925, Constantin von Economo and ...George Koskinas published a detailed atlas of the human cerebral cortex, describing a cytoarchitectonic division of the cortical mantle into over 40 distinct areas. Von Economo and Koskinas accompanied their seminal work with large photomicrographic plates of their histological slides, together with tables containing for each described region detailed morphological layer-specific information on neuronal count, neuron size and thickness of the cortical mantle. Here, we aimed to make this legacy data accessible and relatable to in vivo neuroimaging data by constructing a digital Von Economo – Koskinas atlas compatible with the widely used FreeSurfer software suite. In this technical note we describe the procedures used for manual segmentation of the Von Economo – Koskinas atlas onto individual T1 scans and the subsequent construction of the digital atlas. We provide the files needed to run the atlas on new FreeSurfer data, together with some simple code of how to apply the atlas to T1 scans within the FreeSurfer software suite. The digital Von Economo – Koskinas atlas is easily applicable to modern day anatomical MRI data and is made publicly available online.
•In 1925 Von Economo and Koskinas published their atlas of the human brain.•We digitized the 1925 atlas, making the data accessible to the neuroimaging community.•The resulting FreeSurfer compatible atlas is made publicly available online.
Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions that can involve symptoms of psychosis and cognitive dysfunction. The 2 conditions share symptomatology and genetic ...etiology and are regularly hypothesized to share underlying neuropathology. Here, we examined how genetic liability to SCZ and BD shapes normative variations in brain connectivity.
We examined the effect of the combined genetic liability for SCZ and BD on brain connectivity from two perspectives. First, we examined the association between polygenic scores for SCZ and BD for 19,778 healthy subjects from the UK Biobank and individual variation in brain structural connectivity reconstructed by means of diffusion weighted imaging data. Second, we conducted genome-wide association studies using genotypic and imaging data from the UK Biobank, taking SCZ-/BD-involved brain circuits as phenotypes of interest.
Our findings showed brain circuits of superior parietal and posterior cingulate regions to be associated with polygenic liability for SCZ and BD, circuitry that overlaps with brain networks involved in disease conditions (r = 0.239, p < .001). Genome-wide association study analysis showed 9 significant genomic loci associated with SCZ-involved circuits and 14 loci associated with BD-involved circuits. Genes related to SCZ-/BD-involved circuits were significantly enriched in gene sets previously reported in genome-wide association studies for SCZ and BD.
Our findings suggest that polygenic liability of SCZ and BD is associated with normative individual variation in brain circuitry.
The genetic basis and human-specific character of schizophrenia has led to the hypothesis that human brain evolution may have played a role in the development of 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 and chimpanzees, one of our closest living evolutionary relatives and a species with which we share a very recent common ancestor. We contrasted the connectome layout between the chimpanzee and human brain and compared differences with the pattern of schizophrenia-related changes in brain connectivity as observed in patients. We show evidence of evolutionary modifications of human brain connectivity to significantly overlap with the cortical pattern of schizophrenia-related dysconnectivity (P < 0.001, permutation testing). We validated these effects in three additional, independent schizophrenia datasets. We further assessed the specificity of effects by examining brain dysconnectivity patterns in seven other psychiatric and neurological brain disorders (including, among others, major depressive disorder and obsessive-compulsive disorder, arguably characterized by behavioural symptoms that are less specific to humans), which showed no such associations with modifications of human brain connectivity. Comparisons of brain connectivity across humans, chimpanzee and macaques further suggest that features of connectivity that evolved in the human lineage showed the strongest association to the disorder, that is, brain circuits potentially related to human evolutionary specializations. Taken together, our findings suggest that human-specific features of connectome organization may be enriched for changes in brain connectivity related to schizophrenia. Modifications in human brain connectivity in service of higher order brain functions may have potentially also rendered the brain vulnerable to brain dysfunction.
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