Structure-function subsystem model and computational lesions of the central nervous system's rostral sector (forebrain and midbrain).

The craniote central nervous system has been divided into rostral, intermediate, and caudal sectors, with the rostral sector containing the vertebrate forebrain and midbrain. Here, network science tools were used to create and analyze a rat hierarchical structure-function subsystem model of intrarostral sector neural connectivity between gray matter regions. The hierarchy has 109 bottom-level subsystems and three upper-level subsystems corresponding to voluntary behavior control, cognition, and affect; instinctive survival behaviors and homeostasis; and oculomotor control. As in previous work, subsystems identified based on their coclassification as network communities are revealed as functionally related. We carried out focal perturbations of neural structural connectivity comprehensively by computationally lesioning each region of the network, and the resulting effects on the network's modular (subsystem) organization were systematically mapped and measured. The pattern of changes was found to be correlated with three structural attributes of the lesioned region: region centrality (degree, strength, and betweenness), region position in the hierarchy, and subsystem distribution of region neural outputs and inputs. As expected, greater region centrality results, on average, in stronger lesion impact and more distributed lesion effects. In addition, our analysis suggests that strongly functionally related regions, belonging to the same bottom-level subsystem, exhibit similar effects after lesioning. These similarities account for coherent patterns of disturbances that align with subsystem boundaries and propagate through the network. These systematic lesion effects and their similarity across functionally related regions are of potential interest for theoretical, experimental, and clinical studies.

Subsystem macroarchitecture of the intrinsic midbrain neural network and its tectal and tegmental subnetworks.

The midbrain is the smallest of three primary vertebrate brain divisions. Here we use network science tools to reveal the global organizing principles of intramidbrain axonal circuitry before adding extrinsic connections with the remaining nervous system. Curating the experimental neuroanatomical literature yielded 17,248 connection reports for 8,742 possible connections between the 94 gray matter regions forming the right and left midbrain. Evidence for the existence of 1,676 connections suggests a 19.2% connection density for this network, similar to that for the intraforebrain network [L. W. Swanson et al., Proc. Natl. Acad. Sci. U.S.A. 117, 31470-31481 (2020)]. Multiresolution consensus cluster analysis parceled this network into a hierarchy with 6 top-level and 30 bottom-level subsystems. A structure-function model of the hierarchy identifies midbrain subsystems that play specific functional roles in sensory-motor mechanisms, motivation and reward, regulating complex reproductive and agonistic behaviors, and behavioral state control. The intramidbrain network also contains four bilateral region pairs designated putative hubs. One pair contains the superior colliculi of the tectum, well known for participation in visual sensory-motor mechanisms, and the other three pairs form spatially compact right and left units (the ventral tegmental area, retrorubral area, and midbrain reticular nucleus) in the tegmentum that are implicated in motivation and reward mechanisms. Based on the core hypothesis that subsystems form functionally cohesive units, the results provide a theoretical framework for hypothesis-driven experimental analysis of neural circuit mechanisms underlying behavioral responses mediated in part by the midbrain.

Italian, European, and international neuroinformatics efforts: An overview.

Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.

Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors.

Neurons and glia are the two main cell classes in the nervous systems of most animals. Although functionally distinct, neurons and glia are both characterized by multiple branching arbors stemming from the cell bodies. Glial processes are generally known to form smaller trees than neuronal dendrites. However, the full extent of morphological differences between neurons and glia in multiple species and brain regions has not yet been characterized, nor is it known whether these cells can be reliably distinguished based on geometric features alone. Here, we show that multiple supervised learning algorithms deployed on a large database of morphological reconstructions can systematically classify neuronal and glial arbors with nearly perfect accuracy and precision. Moreover, we report multiple morphometric properties, both size related and size independent, that differ substantially between these cell types. In particular, we newly identify an individual morphometric measurement, Average Branch Euclidean Length that can robustly separate neurons from glia across multiple animal models, a broad diversity of experimental conditions, and anatomical areas, with the notable exception of the cerebellum. We discuss the practical utility and physiological interpretation of this discovery.

Structure-function subsystem models of female and male forebrain networks integrating cognition, affect, behavior, and bodily functions.

The forebrain is the first of three primary vertebrate brain subdivisions. Macrolevel network analysis in a mammal (rat) revealed that the 466 gray matter regions composing the right and left sides of the forebrain are interconnected by 35,738 axonal connections forming a large set of overlapping, hierarchically arranged subsystems. This hierarchy is bilaterally symmetrical and sexually dimorphic, and it was used to create a structure-function conceptual model of intraforebrain network organization. Two mirror image top-level subsystems are presumably the most fundamental ontogenetically and phylogenetically. They essentially form the right and left forebrain halves and are relatively weakly interconnected. Each top-level subsystem in turn has two second-level subsystems. A ventromedial subsystem includes the medial forebrain bundle, functionally coordinating instinctive survival behaviors with appropriate physiological responses and affect. This subsystem has 26/24 (female/male) lowest-level subsystems, all using a combination of glutamate and GABA as neurotransmitters. In contrast, a dorsolateral subsystem includes the lateral forebrain bundle, functionally mediating voluntary behavior and cognition. This subsystem has 20 lowest-level subsystems, and all but 4 use glutamate exclusively for their macroconnections; no forebrain subsystems are exclusively GABAergic. Bottom-up subsystem analysis is a powerful engine for generating testable hypotheses about mechanistic explanations of brain function, behavior, and mind based on underlying circuit organization. Targeted computational (virtual) lesioning of specific regions of interest associated with Alzheimer's disease, clinical depression, and other disorders may begin to clarify how the effects spread through the entire forebrain network model.

Quantification of neuron types in the rodent hippocampal formation by data mining and numerical optimization.

Quantifying the population sizes of distinct neuron types in different anatomical regions is an essential step towards establishing a brain cell census. Although estimates exist for the total neuronal populations in different species, the number and definition of each specific neuron type are still intensively investigated. Hippocampome.org is an open-source knowledge base with morphological, physiological and molecular information for 122 neuron types in the rodent hippocampal formation. While such framework identifies all known neuron types in this system, their relative abundances remain largely unknown. This work quantitatively estimates the counts of all Hippocampome.org neuron types by literature mining and numerical optimization. We report the number of neurons in each type identified by main neurotransmitter (glutamate or GABA) and axonal-dendritic patterns throughout 26 subregions and layers of the dentate gyrus, Ammon's horn, subiculum and entorhinal cortex. We produce by sensitivity analysis reliable numerical ranges for each type and summarize the amounts across broad neuronal families defined by biomarkers expression and firing dynamics. Study of density distributions indicates that the number of dendritic-targeting interneurons, but not of other neuronal classes, is independent of anatomical volumes. All extracted values, experimental evidence and related software code are released on Hippocampome.org.

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Macroscale intrinsic network architecture of the hypothalamus.

Control of multiple life-critical physiological and behavioral functions requires the hypothalamus. Here, we provide a comprehensive description and rigorous analysis of mammalian intrahypothalamic network architecture. To achieve this at the gray matter region (macroscale) level, macroscale connection (macroconnection) data for the rat hypothalamus were extracted from the primary literature. The dataset indicated the existence of 7,982 (of 16,770 possible) intrahypothalamic macroconnections. Network analysis revealed that the intrahypothalamic macroconnection network (its macroscale subconnectome) is divided into two identical top-level subsystems (or subnetworks), each composed of two nested second-level subsystems. At the top-level, this suggests a deeply integrated network; however, regional grouping of the two second-level subsystems suggested a partial separation between control of physiological functions and behavioral functions. Furthermore, inclusion of four candidate hubs (dominant network nodes) in the second-level subsystem that is associated prominently with physiological control suggests network primacy with respect to this function. In addition, comparison of network analysis with expression of gene markers associated with inhibitory (GAD65) and excitatory (VGLUT2) neurotransmission revealed a significant positive correlation between measures of network centrality (dominance) and the inhibitory marker. We discuss these results in relation to previous understandings of hypothalamic organization and provide, and selectively interrogate, an updated hypothalamus structure-function network model to encourage future hypothesis-driven investigations of identified hypothalamic subsystems.

The network organization of rat intrathalamic macroconnections and a comparison with other forebrain divisions.

The thalamus is 1 of 4 major divisions of the forebrain and is usually subdivided into epithalamus, dorsal thalamus, and ventral thalamus. The 39 gray matter regions comprising the large dorsal thalamus project topographically to the cerebral cortex, whereas the much smaller epithalamus (2 regions) and ventral thalamus (5 regions) characteristically project subcortically. Before analyzing extrinsic inputs and outputs of the thalamus, here, the intrinsic connections among all 46 gray matter regions of the rat thalamus on each side of the brain were expertly collated and subjected to network analysis. Experimental axonal pathway-tracing evidence was found in the neuroanatomical literature for the presence or absence of 99% of 2,070 possible ipsilateral connections and 97% of 2,116 possible contralateral connections; the connection density of ipsilateral connections was 17%, and that of contralateral connections 5%. One hub, the reticular thalamic nucleus (of the ventral thalamus), was found in this network, whereas no high-degree rich club or clear small-world features were detected. The reticular thalamic nucleus was found to be primarily responsible for conferring the property of complete connectedness to the intrathalamic network in the sense that there is, at least, one path of finite length between any 2 regions or nodes in the network. Direct comparison with previous investigations using the same methodology shows that each division of the forebrain (cerebral cortex, cerebral nuclei, thalamus, hypothalamus) has distinct intrinsic network topological organization. A future goal is to analyze the network organization of connections within and among these 4 divisions of the forebrain.

The network architecture of rat intrinsic interbrain (diencephalic) macroconnections.

The endbrain and interbrain form 2 great vertebrate forebrain divisions, and the interbrain is subdivided into the hypothalamus ventrally and thalamus dorsally. General organizing principles of intrainterbrain axonal circuitry were examined here at the level of gray matter regions using network analysis tools in a mammal with the most complete available dataset-before examining interbrain input-output relationships with other nervous system parts. The dataset was curated expertly from the neuroanatomical literature using experimental axonal pathway-tracing methods, and evidence from 74,242 connection reports indicates the existence of 10,836 macroconnections of the possible 49,062 macroconnections between the 222 gray matter regions forming the right and left halves of the interbrain. Two identical sets of 6 putative hubs were identified in the intrainterbrain network and form a continuous tissue mass in a part of the right and left medial hypothalamus associated functionally with physiological mechanisms controlling bodily functions. The intrainterbrain network shows only weak evidence of small-world attributes, rich club organization is absent, and multiresolution consensus cluster analysis indicates a solution with only 3 top-level subsystems or modules. In contrast, a previous analysis employing the same methodology to the significantly denser 244-node intraendbrain network revealed 2 identical sets of 13 hubs, small-world and rich club attributes, and 4 top-level subsystems. These differences in intrinsic network architecture across subdivisions suggest that intrinsic connections shape regional functional specialization to a varying extent, in part driven by differences in density and centrality, with extrinsic input-output connectivity playing a greater role in subdivisions that are sparser and less centralized.

Biological databases and tools for neurological disorders.

Computational approach to study of neuronal impairment is rapidly evolving, as experiments and intuition alone could not explain the complexity of brain system. The increase in an overwhelming amount of new data from both theory and computational modeling necessitate the development of databases and tools for analysis, visualization, and interpretation of neuroscience data. To ensure the sustainability of this development, consistent update and training of young professionals are imperative. For this purpose, relevant articles, chapters, and modules are essential to keep abreast of developments. Therefore, this article seeks to outline the biological databases and analytical tools along with their applications. It's envisaged that knowledge along this line would be a "training recipe" for young talents and guide for professionals and researchers in neuroscience.

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.

The classification of biological neuron types and networks poses challenges to the full understanding of the human brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain-machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain's structure.

ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification.

Pandemics influence people negatively and people experience fear and disappointment. With the global outspread of COVID-19, the sentiments of the general public are substantially influenced, and analyzing their sentiments could help to devise corresponding policies to alleviate negative sentiments. Often the data collected from social media platforms is unstructured leading to low classification accuracy. This study brings forward an ensemble model where the benefits of handcrafted features and automatic feature extraction are combined by machine learning and deep learning models. Unstructured data is obtained, preprocessed, and annotated using TextBlob and VADER before training machine learning models. Similarly, the efficiency of Word2Vec, TF, and TF-IDF features is also analyzed. Results reveal the better performance of the extra tree classifier when trained with TF-IDF features from TextBlob annotated data. Overall, machine learning models perform better with TF-IDF and TextBlob. The proposed model obtains superior performance using both annotation techniques with 0.97 and 0.95 scores of accuracy using TextBlob and VADER respectively with Word2Vec features. Results reveal that use of machine learning and deep learning models together with a voting criterion tends to yield better results than other machine learning models. Analysis of sentiments indicates that predominantly people possess negative sentiments regarding COVID-19.

Trainingspace: Neuroeducation Without Borders.

Advancements in the field of neuroinformatics have resulted in a massive explosion of raw data of many varieties, yet many traditional neuroscience training programs have not changed their curricula to reflect the urgent need for improved computational skills that would enable trainees to handle, organize, and interrogate such large, multimodal datasets. Thus, the objective of this project was to build an open access hub of neuroscience educational resources to fill the gap between current neuroscience curricula and the computationally focused skillset required to work with big data. To achieve this aim, we invited representatives from the world's leading neuroscience societies and large-scale brain initiatives to form the INCF Training and Education Committee that would provide oversight over the content and capabilities of the online hub. As a result, we developed TrainingSpace (https://training.incf.org/), an open access hub of nearly 500 multimedia courses, lectures, and tool tutorials covering the subspecialisms of neuroscience and neuroinformatics, as well as computer science, data science, and ethics. In addition to course content, TrainingSpace also provides users with access to publicly available datasets through KnowledgeSpace, a discoverability portal and community encyclopedia for neuroscience, as well as a question and answer forum, Neurostars.org. Since its launch in 2019, TrainingSpace has steadily increased in popularity with both trainees and trainers alike. It has also become popular with content providers that want to make their training materials available to the neuroscience community-at-large, as well as integrate their content into the larger TrainingSpace ecosystem.

Biophysics of Evolution of Intellectual Systems.

The first work on noogenesis as evolution of intellect was published 150 years ago. However, it was not until the 21st century that quantitation became possible for certain parameters that contribute to the understanding of the evolution of intellectual systems in natural sciences, the progress being due to basic achievements in physics, biology, medicine, and interdisciplinary fields. Analyses of the parameters of intellectual systems, patterns of their emergence and evolution, distinctive features, and the constants and limits of their structures and functions made it possible to measure and compare the capacity of communications (~100 to 300 million m/s), to quantify the number of components in intellectual systems (10-100 billion components), and to calculate the number of successful links responsible for cooperation (from 150 to 1 trillion links). Prognostic models can be developed by studying the phenomenon of the origin and evolution of the brain as a population of neurons within the biological evolution of Homo sapiens and the advent of cognition; by studying the brain of an individual throughout individual anatomic and physiological development, including the development of creativity, thinking, consciousness, idea, insight, intuition, and eureka; and by studying and "noo" in the context of the hypothesis of the morphological and functional evolution of the human population.

Cingulate networks associated with gray matter loss in Parkinson's disease show high expression of cholinergic genes in the healthy brain.

Structural covariance networks are able to identify functionally organized brain regions by gray matter volume covariance across a population. We examined the transcriptomic signature of such anatomical networks in the healthy brain using postmortem microarray data from the Allen Human Brain Atlas. A previous study revealed that a posterior cingulate network and anterior cingulate network showed decreased gray matter in brains of Parkinson's disease patients. Therefore, we examined these two anatomical networks to understand the underlying molecular processes that may be involved in Parkinson's disease. Whole brain transcriptomics from the healthy brain revealed upregulation of genes associated with serotonin, GPCR, GABA, glutamate, and RAS-signaling pathways. Our results also suggest involvement of the cholinergic circuit, in which genes NPPA, SOSTDC1, and TYRP1 may play a functional role. Finally, both networks were enriched for genes associated with neuropsychiatric disorders that overlap with Parkinson's disease symptoms. The identified genes and pathways contribute to healthy functions of the posterior and anterior cingulate networks and disruptions to these functions may in turn contribute to the pathological and clinical events observed in Parkinson's disease.

Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT: Bases for Biologically Realistic Models.

The cerebral cortex of primates encompasses multiple anatomically and physiologically distinct areas processing visual information. Areas V1, V2, and V5/MT are conserved across mammals and are central for visual behavior. To facilitate the generation of biologically accurate computational models of primate early visual processing, here we provide an overview of over 350 published studies of these three areas in the genus Macaca, whose visual system provides the closest model for human vision. The literature reports 14 anatomical connection types from the lateral geniculate nucleus of the thalamus to V1 having distinct layers of origin or termination, and 194 connection types between V1, V2, and V5, forming multiple parallel and interacting visual processing streams. Moreover, within V1, there are reports of 286 and 120 types of intrinsic excitatory and inhibitory connections, respectively. Physiologically, tuning of neuronal responses to 11 types of visual stimulus parameters has been consistently reported. Overall, the optimal spatial frequency (SF) of constituent neurons decreases with cortical hierarchy. Moreover, V5 neurons are distinct from neurons in other areas for their higher direction selectivity, higher contrast sensitivity, higher temporal frequency tuning, and wider SF bandwidth. We also discuss currently unavailable data that could be useful for biologically accurate models.

Subsystem organization of axonal connections within and between the right and left cerebral cortex and cerebral nuclei (endbrain).

The endbrain (telencephalon) is at the rostral end of the central nervous system and is primarily responsible for supporting cognition and affect. Structurally, it consists of right and left cerebral hemispheres, each parceled into multiple cortical and nuclear gray matter regions. The global network organization of axonal macroconnections between the 244 regions forming the endbrain was analyzed with a multiresolution consensus clustering (MRCC) method that provides a hierarchical description of community clustering (modules or subsystems) within the network. Experimental evidence was collated from the neuroanatomical literature for the existence of 10,002 of a possible 59,292 connections within the network, and they cluster into four top-level subsystems and 60 bottom-level subsystems arranged in a 50-level hierarchy. Two top-level subsystems are bihemispheric: One deals with auditory and visual information, and the other corresponds broadly to the default mode network. The other two top-level subsystems are bilaterally symmetrical, and each deals broadly with somatic and visceral information. Because the entire endbrain connection matrix was assembled from multiple subconnectomes, it was easy to show that the status of a region as a connectivity hub is not absolute but, instead, depends on the size and coverage of its anatomical neighborhood. It was also shown numerically that creating an ultradense connection matrix by converting all "absent" connections to a "very weak" connection weight has virtually no effect on the clustering hierarchy. The next logical step in this project is to complete the forebrain connectome by adding the thalamus and hypothalamus (together, the interbrain) to the endbrain analysis.

Modeling brain dynamics after tumor resection using The Virtual Brain.

Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first "virtual neurosurgery", mimicking patient's actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome. We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation.

Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies.

Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the joint analysis of raw fMRI data across studies (mega-analysis), with the hope of achieving more detailed insights. However, it has not been clear if such analyses in the EEG field are possible or equally fruitful. Here we present the results of a large-scale EEG mega-analysis using 18 studies from six sites representing several different experimental paradigms. We demonstrate that when meta-data are consistent across studies, both channel-level and source-level EEG mega-analysis are possible and can provide insights unavailable in single studies. The analysis uses a fully-automated processing pipeline to reduce line noise, interpolate noisy channels, perform robust referencing, remove eye-activity, and further identify outlier signals. We define several robust measures based on channel amplitude and dispersion to assess the comparability of data across studies and observe the effect of various processing steps on these measures. Using ICA-based dipolar sources, we also observe consistent differences in overall frequency baseline amplitudes across brain areas. For example, we observe higher alpha in posterior vs anterior regions and higher beta in temporal regions. We also detect consistent differences in the slope of the aperiodic portion of the EEG spectrum across brain areas. In a companion paper, we apply mega-analysis to assess commonalities in event-related EEG features across studies. The continuous raw and preprocessed data used in this analysis are available through the DataCatalog at https://cancta.net.