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We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

Résumé La segmentation des faisceaux de matière blanche à l'aide de la tractographie des fibres IRM de diffusion est devenue la méthode de choix pour identifier les voies des fibres de matière blanche in vivo dans le cerveau humain. Cependant, comme d'autres analyses de données complexes, il existe une variabilité considérable dans les protocoles et les techniques de segmentation. Cela peut entraîner différentes reconstructions des mêmes voies de la substance blanche, ce qui affecte directement les résultats, la quantification et l'interprétation de la tractographie. Dans cette étude, nous visons à évaluer et quantifier la variabilité qui découle de différents protocoles de segmentation des faisceaux. Grâce à un appel ouvert aux utilisateurs de la tractographie par fibres, y compris les anatomistes, les cliniciens et les développeurs d'algorithmes, 42 équipes indépendantes ont reçu des ensembles traités de lignes de rationalisation du cerveau entier humain et ont été invitées à segmenter 14 fascicules de substance blanche sur six sujets. Au total, nous avons reçu 57 protocoles de segmentation de faisceau différents, ce qui a permis des analyses détaillées basées sur le volume et sur la rationalisation des accords et des désaccords entre les protocoles pour chaque voie de fibre. Les résultats montrent que même lorsqu'on leur donne exactement les mêmes ensembles de lignes de flux sous-jacentes, la variabilité entre les protocoles pour la segmentation des faisceaux est plus grande que toutes les autres sources de variabilité dans le processus de dissection virtuelle, y compris la variabilité au sein des protocoles et la variabilité entre les sujets. Afin de favoriser l'utilisation de la dissection de faisceaux de tractographie dans les contextes cliniques de routine, et en tant qu'outil analytique fondamental, les efforts futurs doivent viser à résoudre et à réduire cette hétérogénéité. Bien qu'une validation externe soit nécessaire pour vérifier l'exactitude anatomique des dissections de faisceaux, la réduction de l'hétérogénéité est une étape vers une recherche reproductible et peut être obtenue par l'utilisation d'une nomenclature et de définitions standard des faisceaux de matière blanche et de contraintes et de décisions bien choisies dans le processus de dissection. Resumen La segmentación del haz de materia blanca mediante tractografía de fibra por resonancia magnética de difusión se ha convertido en el método de elección para identificar las vías de fibra de materia blanca in vivo en cerebros humanos. Sin embargo, al igual que otros análisis de datos complejos, existe una variabilidad considerable en los protocolos y técnicas de segmentación. Esto puede dar lugar a diferentes reconstrucciones de las mismas vías de materia blanca previstas, lo que afecta directamente a los resultados de la tractografía, la cuantificación y la interpretación. En este estudio, nuestro objetivo es evaluar y cuantificar la variabilidad que surge de diferentes protocolos para la segmentación de paquetes. A través de una convocatoria abierta a los usuarios de la tractografía de fibra, incluidos anatomistas, médicos y desarrolladores de algoritmos, 42 equipos independientes recibieron conjuntos procesados de líneas aerodinámicas de todo el cerebro humano y se les pidió que segmentaran 14 fascículos de materia blanca en seis sujetos. En total, recibimos 57 protocolos de segmentación de paquetes diferentes, lo que permitió análisis detallados basados en el volumen y en la simplificación de la concordancia y el desacuerdo entre los protocolos para cada vía de fibra. Los resultados muestran que incluso cuando se les dan exactamente los mismos conjuntos de líneas de flujo subyacentes, la variabilidad entre protocolos para la segmentación de paquetes es mayor que todas las demás fuentes de variabilidad en el proceso de disección virtual, incluida la variabilidad dentro de los protocolos y la variabilidad entre sujetos. Para fomentar el uso de la disección del haz de tractografía en entornos clínicos de rutina, y como una herramienta analítica fundamental, los esfuerzos futuros deben apuntar a resolver y reducir esta heterogeneidad. Aunque se necesita una validación externa para verificar la precisión anatómica de las disecciones de haces, la reducción de la heterogeneidad es un paso hacia la investigación reproducible y se puede lograr mediante el uso de nomenclatura estándar y definiciones de haces de materia blanca y restricciones y decisiones bien elegidas en el proceso de disección. Abstract White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process. أصبح تجزئة حزمة المادة البيضاء باستخدام التصوير المقطعي بألياف التصوير بالرنين المغناطيسي المنتشر الطريقة المفضلة لتحديد مسارات ألياف المادة البيضاء في الجسم الحي في أدمغة الإنسان. ومع ذلك، مثل التحليلات الأخرى للبيانات المعقدة، هناك تباين كبير في بروتوكولات وتقنيات التجزئة. يمكن أن يؤدي ذلك إلى عمليات إعادة بناء مختلفة لنفس مسارات المادة البيضاء المقصودة، مما يؤثر بشكل مباشر على نتائج التصوير الشعاعي والقياس الكمي والتفسير. في هذه الدراسة، نهدف إلى تقييم وقياس التباين الذي ينشأ عن بروتوكولات مختلفة لتجزئة الحزمة. من خلال دعوة مفتوحة لمستخدمي تصوير المسالك الليفية، بما في ذلك علماء التشريح والأطباء ومطوري الخوارزميات، تم إعطاء 42 فريقًا مستقلًا مجموعات معالجة من خطوط الدماغ الكاملة البشرية وطُلب منهم تقسيم 14 كراسة مادة بيضاء على ستة مواضيع. في المجموع، تلقينا 57 بروتوكولًا مختلفًا لتجزئة الحزم، مما مكن من إجراء تحليلات مفصلة قائمة على الحجم وقائمة على التبسيط للاتفاق والاختلاف بين البروتوكولات لكل مسار من مسارات الألياف. تظهر النتائج أنه حتى عند إعطاء نفس مجموعات التبسيط الأساسية بالضبط، فإن التباين عبر بروتوكولات تجزئة الحزمة أكبر من جميع مصادر التباين الأخرى في عملية التشريح الافتراضية، بما في ذلك التباين داخل البروتوكولات والتباين عبر الموضوعات. من أجل تعزيز استخدام تشريح حزمة تخطيط المسالك في البيئات السريرية الروتينية، وكأداة تحليلية أساسية، يجب أن تهدف المساعي المستقبلية إلى حل هذا التباين وتقليله. على الرغم من الحاجة إلى التحقق الخارجي للتحقق من الدقة التشريحية لتشريح الحزم، فإن الحد من عدم التجانس هو خطوة نحو البحث القابل للتكرار ويمكن تحقيقه من خلال استخدام التسميات القياسية وتعريفات حزم المادة البيضاء والقيود والقرارات المختارة جيدًا في عملية التشريح.

This resource defines the Brain Imaging Data Structure (BIDS) specification, including the core specification as well as many modality-specific extensions. To get started, check out the introduction. For an overview of the BIDS ecosystem, visit the BIDS homepage. The entire specification can also be browsed in an HTML version. See Appendix I for a list of the BIDS contributors who jointly created this specification.

Magnetic resonance imaging (MRI) is a non-destructive technique that is capable of localizing pathologies and assessing other anatomical features (e.g., tissue volume, microstructure, and white matter connectivity) in postmortem, ex vivo human brains. However, when brains are removed from the skull and cerebrospinal fluid (i.e., their normal in vivo magnetic environment), air bubbles and air–tissue interfaces typically cause magnetic susceptibility artifacts that severely degrade the quality of ex vivo MRI data. In this report, we describe a relatively simple and cost-effective experimental setup for acquiring artifact-free ex vivo brain images using a clinical MRI system with standard hardware. In particular, we outline the necessary steps, from collecting an ex vivo human brain to the MRI scanner setup, and have also described changing the formalin (as might be necessary in longitudinal postmortem studies). Finally, we share some representative ex vivo MRI images that have been acquired using the proposed setup in order to demonstrate the efficacy of this approach. We hope that this protocol will provide both clinicians and researchers with a straight-forward and cost-effective solution for acquiring ex vivo MRI data from whole postmortem human brains.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Contraction-level invariant surface electromyography pattern recognition introduces the decrease of training time and decreases the limitation of clinical prostheses. This study intended to examine whether a signal pre-processing method named frequency division technique (FDT) for online myoelectric pattern recognition classification is robust against contraction-level variation, and whether this pre-processing method has an advantage over traditional time-domain pattern recognition techniques even in the absence of muscle contraction-level variation. Eight healthy and naïve subjects performed wrist contractions during two degrees of freedom goal-oriented tasks, divided in three groups of type I, type II, and type III. The performance of these tasks, when the two different methods were used, was quantified by completion rate, completion time, throughput, efficiency, and overshoot. The traditional and the FDT method were compared in four runs, using combinations of normal or high muscle contraction level, and the traditional method or FDT. The results indicated that FDT had an advantage over traditional methods in the tested real-time myoelectric control tasks. FDT had a much better median completion rate of tasks (95%) compared to the traditional method (77.5%) among non-perfect runs, and the variability in FDT was strikingly smaller than the traditional method (p < 0.001). Moreover, the FDT method outperformed the traditional method in case of contraction-level variation between the training and online control phases (p = 0. 005 for throughput in type I tasks with normal contraction level, p = 0.006 for throughput in type II tasks, and p = 0.001 for efficiency with normal contraction level of all task types). This study shows that FDT provides advantages in online myoelectric control as it introduces robustness over contraction-level variations.

The hippocampal formation is an uniquely infolded anatomical structure in the medial temporal lobe and it is involved in a broad range of cognitive and emotional processes. It consists of anatomically and functionally different subfields, including the subiculum (SUB), cornu ammonis areas (CA), and the dentate gyrus (DG). However, despite ample research on learning and plasticity of the hippocampal formation, heritability of its structural and functional organization is not fully known. To answer this question, we extracted microstructurally sensitive neuroimaging (i.e., T1w/T2w ratios) and resting-state functional connectivity information along hippocampal subfield surfaces from a sample of healthy twins and unrelated individuals of the Human Connectome Project Dataset. Our findings robustly demonstrate that functional connectivity and local microstructure of hippocampal subfields are highly heritable. Second, we found marked covariation and genetic correlation between the microstructure of the hippocampal subfields and the isocortex, indicating shared genetic factors influencing the microstructure of the hippocampus and isocortex. In both structural and functional measures, we observed a dissociation of cortical projections across subfields. In sum, our study shows that the functional and structural organization of the hippocampal formation is heritable and has a genetic relation to divergent macroscale functional networks within the isocortex.