brainlife.io: a decentralized and open-source cloud platform to support neuroscience research.

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

Variability in the analysis of a single neuroimaging dataset by many teams.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Integrating direct electrical brain stimulation with the human connectome.

Neurological and neurodevelopmental conditions are a major public health concern for which new therapies are urgently needed. The development of effective therapies relies on the precise mapping of the neural substrates causally involved in behaviour generation. Direct electrical stimulation (DES) performed during cognitive and neurological monitoring in awake surgery is currently considered the gold standard for the causal mapping of brain functions. However, DES is limited by the focal nature of the stimulation sites, hampering a real holistic exploration of human brain functions at the network level. We used 4137 DES points derived from 612 glioma patients in combination with human connectome data-resting-state functional MRI, n = 1000 and diffusion weighted imaging, n = 284-to provide a multimodal description of the causal macroscale functional networks subtending 12 distinct behavioural domains. To probe the validity of our procedure, we (i) compared the network topographies of healthy and clinical populations; (ii) tested the predictive capacity of DES-derived networks; (iii) quantified the coupling between structural and functional connectivity; and (iv) built a multivariate model able to quantify single subject deviations from a normative population. Lastly, we probed the translational potential of DES-derived functional networks by testing their specificity and sensitivity in identifying critical neuromodulation targets and neural substrates associated with postoperative language deficits. The combination of DES and human connectome data resulted in an average 29.4-fold increase in whole brain coverage compared to DES alone. DES-derived functional networks are predictive of future stimulation points (97.8% accuracy) and strongly supported by the anatomical connectivity of subcortical stimulations. We did not observe any significant topographical differences between the patients and the healthy population at both group and single subject level. Showcasing concrete clinical applications, we found that DES-derived functional networks overlap with effective neuromodulation targets across several functional domains, show a high degree of specificity when tested with the intracranial stimulation points of a different stimulation technique and can be used effectively to characterize postoperative behavioural deficits. The integration of DES with the human connectome fundamentally advances the quality of the functional mapping provided by DES or functional imaging alone. DES-derived functional networks can reliably predict future stimulation points, have a strong correspondence with the underlying white matter and can be used for patient specific functional mapping. Possible applications range from psychiatry and neurology to neuropsychology, neurosurgery and neurorehabilitation.

Connectivity by the Frontal Aslant Tract (FAT) Explains Local Functional Specialization of the Superior and Inferior Frontal Gyri in Humans When Choosing Predictive over Reactive Strategies: A Tractography-Guided TMS Study.

Predictive and reactive behaviors represent two mutually exclusive strategies in a sensorimotor task. Predictive behavior consists in internally estimating timing and features of a target stimulus and relies on a cortical medial frontal system [superior frontal gyrus (SFG)]. Reactive behavior consists in waiting for actual perception of the target stimulus and relies on the lateral frontal cortex [inferior frontal gyrus (IFG)]. We investigated whether SFG-IFG connections by the frontal aslant tract (FAT) can mediate predictive/reactive interactions. In 19 healthy human volunteers, we applied online transcranial magnetic stimulation (TMS) to six spots along the medial and lateral terminations of the FAT, during the set period of a delayed reaction task. Such scenario can be solved using either predictive or reactive strategies. TMS increased the propensity toward reactive behavior if applied to a specific portion of the IFG and increased predictive behavior when applied to a specific SFG spot. The two active spots in the SFG and IFG were directly connected by a sub-bundle of FAT fibers as indicated by diffusion-weighted imaging (DWI) tractography. Since FAT connectivity identifies two distant cortical nodes with opposite functions, we propose that the FAT mediates mutually inhibitory interactions between SFG and IFG to implement a "winner takes all" decisional process. We hypothesize such role of the FAT to be domain-general, whenever competition occurs between internal predictive and external reactive behaviors. Finally, we also show that anatomic connectivity is a powerful factor to explain and predict the spatial distribution of brain stimulation effects.SIGNIFICANCE STATEMENT We interact with sensory cues adopting two main mutually-exclusive strategies: (1) trying to anticipate the occurrence of the cue or (2) waiting for the GO-signal to be manifest and react to it. Here, we showed, by using noninvasive brain stimulation [transcranial magnetic stimulation (TMS)], that two specific cortical regions in the superior frontal gyrus (SFG) and the inferior frontal gyrus (IFG) have opposite roles in facilitating a predictive or a reactive strategy. Importantly these two very distant regions but with highly interconnected functions are specifically connected by a small white matter bundle, which mediates the direct competition and exclusiveness between predictive and reactive strategies. More generally, implementing anatomic connectivity in TMS studies strongly reduces spatial noise.

DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients.

T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis.

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation.

Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.

Effects of supra-total resection in neurocognitive and oncological outcome of high-grade gliomas comparing asleep and awake surgery.

PURPOSE: Awake surgery is an established technique for resection of low-grade gliomas, while its possible benefit for resection of high-grade gliomas (HGGs) needs further confirmations. This retrospective study aims to compare overall survival, extent of resection (EOR) and cognitive outcome in two groups of HGGs patients submitted to asleep or awake surgery. METHODS: Thirty-three patients submitted to Gross Total Resection of contrast-enhancing area of HGGs were divided in two homogeneous groups: awake (AWg; N = 16) and asleep surgery (ASg; N = 17). All patients underwent to an extensive neuropsychological assessment before surgery (time_1), 1-week (time_2) and 4-months (time_3) after surgery. We performed analyses to assess differences in cognitive performances between groups, cognitive outcomes in each group and EOR. A comparison of overall survival (OS) between the two groups was conducted. RESULTS: Statistical analyses showed no differences between groups at time_2 and time_3 in each cognitive domain, excluding selective attention that resulted higher in the AWg before surgery. Regarding cognitive outcomes, we found a reversible worsening of memory and constructional praxis, and a significant recovery at time_3, similar for both groups. Assessment of time_3 in respect to time_1 never showed differences (all ps > .074). Moreover we found a significant lower level of tumor infiltration after surgery for AWg (p < .05), with an influence on OS (p < .05). Indeed, patients of AWg showed a significant longer OS in comparison to those in the ASg (p < .01). This result was confirmed even considering only wildtype Glioblastoma (p < .05). CONCLUSION: These results indicate that awake surgery, and in general a supra-total resection of enhancing area, can improve OS in HGGs patients, preserving neuro-cognitive profile and quality of life.

Reputational priors magnify striatal responses to violations of trust.

Humans learn to trust each other by evaluating the outcomes of repeated interpersonal interactions. However, available prior information on the reputation of traders may alter the way outcomes affect learning. Our functional magnetic resonance imaging study is the first to allow the direct comparison of interaction-based and prior-based learning. Twenty participants played repeated trust games with anonymous counterparts. We manipulated two experimental conditions: whether or not reputational priors were provided, and whether counterparts were generally trustworthy or untrustworthy. When no prior information is available our results are consistent with previous studies in showing that striatal activation patterns correlate with behaviorally estimated reinforcement learning measures. However, our study additionally shows that this correlation is disrupted when reputational priors on counterparts are provided. Indeed participants continue to rely on priors even when experience sheds doubt on their accuracy. Notably, violations of trust from a cooperative counterpart elicited stronger caudate deactivations when priors were available than when they were not. However, tolerance to such violations appeared to be mediated by prior-enhanced connectivity between the caudate nucleus and ventrolateral prefrontal cortex, which anticorrelated with retaliation rates. Moreover, on top of affecting learning mechanisms, priors also clearly oriented initial decisions to trust, reflected in medial prefrontal cortex activity.

A revision of the dorsal origin of the frontal aslant tract (FAT) in the superior frontal gyrus: a DWI-tractographic study.

The frontal aslant tract (FAT) is a white matter tract connecting the superior frontal gyrus (SFG) to the inferior frontal gyrus (IFG). Its dorsal origin is identified in humans in the medial wall of the SFG, in the supplementary motor complex (SM-complex). However, empirical observation shows that many FAT fibres appear to originate from the dorsal, rather than medial, portion of the SFG. We quantitatively investigated the actual origin of FAT fibres in the SFG, specifically discriminating between terminations in the medial wall and in the convexity of the SFG. We analysed data from 105 subjects obtained from the Human Connectome Project (HCP) database. We parcelled the cortex of the IFG, dorsal SFG and medial SFG in several regions of interest (ROIs) ordered in a caudal-rostral direction, which served as seed locations for the generation of streamlines. Diffusion imaging data (DWI) was processed using a multi-shell multi-tissue CSD-based algorithm. Results showed that the number of streamlines originating from the dorsal wall of the SFG significantly exceeds those from the medial wall of the SFG. Connectivity patterns between ROIs indicated that FAT sub-bundles are segregated in parallel circuits ordered in a caudal-rostral direction. Such high degree of coherence in the streamline trajectory allows to establish pairs of homologous cortical parcels in the SFG and IFG. We conclude that the frontal origin of the FAT is found in both dorsal and medial surfaces of the superior frontal gyrus.

Spontaneous unilateral spatial neglect recovery after brain tumour resection: A multimodal diffusion and rs-fMRI case report.

Patients with unilateral spatial neglect (USN) are unable to explore or to report stimuli presented in the left personal and extra-personal space. USN is usually caused by lesion of the right parietal lobe: nowadays, it is also clear the key role of structural connections (the second and the third branch of the right Superior Longitudinal Fasciculus, respectively, SLF II and III) and functional networks (Dorsal and Ventral Attention Network, respectively, DAN and VAN) in USN. In this multimodal case report, we have merged those structural and functional information derived from a patient with a right parietal lobe tumour and USN before surgery. Functional, structural and neuropsychological data were also collected 6 months after surgery, when the USN was spontaneously recovered. Diffusion metrics and Functional Connectivity (FC) of the right SLF and DAN, before and after surgery, were compared with the same data of a patient with a tumour in a similar location, but without USN, and with a control sample. Results indicate an impairment in the right SLF III and a reduction of FC of the right DAN in patients with USN before surgery compared to controls; after surgery, when USN was recovered, patient's diffusion metrics and FC showed no differences compared to the controls. This single case and its multimodal approach reinforce the crucial role of the right SLF III and DAN in the development and recovery of egocentric and allocentric extra-personal USN, highlighting the need to preserve these structural and functional areas during brain surgery.

Accurate localization and coactivation profiles of the frontal eye field and inferior frontal junction: an ALE and MACM fMRI meta-analysis.

The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccade and antisaccade paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, task-switching and Stroop paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we leveraged the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal, and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides the meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.

Supervised tractogram filtering using Geometric Deep Learning.

A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute.

The arcuate fasciculus: Combining structure and function into surgical considerations.

BACKGROUND: Two Centuries from today, Karl Friedrich Burdach attributed the nomenclature "arcuate fasciculus" to a white matter (WM) pathway connecting the frontal to the temporal cortices by arching around the Sylvian fissure. Although this label remained essentially unvaried, the concepts related to it and the characterization of the structural properties of this bundle evolved along with the methodological progress of the past years. Concurrently, the functional relevance of the arcuate fasciculus (AF) classically restricted to the linguistic domain has extended to further cognitive abilities. These features make it a relevant structure to consider in a large variety of neurosurgical procedures. OBJECTIVE: Herein, we build on our previous review uncovering the connectivity provided by the Superior Longitudinal System, including the AF, and provide a handy representation of the structural organization of the AF by considering the frequency of defined reports in the literature. By adopting the same approach, we implement an account of which functions are mediated by this WM bundle. We highlight how this information can be transferred to the neurosurgical field by presenting four surgical cases of glioma resection requiring the evaluation of the relationship between the AF and the nearby structures, and the safest approaches to adopt. CONCLUSIONS: Our cumulative overview reports the most common wiring patterns and functional implications to be expected when approaching the study of the AF, while still considering seldom descriptions as an account of interindividual variability. Given its extension and the variety of cortical territories it reaches, the AF is a pivotal structure for different cognitive functions, and thorough understanding of its structural wiring and the functions it mediates is necessary for preserving the patient's cognitive abilities during glioma resection.

brainlife.io: A decentralized and open source cloud platform to support neuroscience research.

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

An integrated TMS-EEG and MRI approach to explore the interregional connectivity of the default mode network.

Explorations of the relation between brain anatomy and functional connections in the brain are crucial for shedding more light on network connectivity that sustains brain communication. In this study, by means of an integrative approach, we examined both the structural and functional connections of the default mode network (DMN) in a group of sixteen healthy subjects. For each subject, the DMN was extracted from the structural and functional resonance imaging data; the areas that were part of the DMN were defined as the regions of interest. Then, the target network was structurally explored by diffusion-weighted imaging, tested by neurophysiological means, and retested by means of concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG). A series of correlational analyses were performed to explore the relationship between the amplitude of early-latency TMS-evoked potentials and the indexes of structural connectivity (weighted number of fibres and fractional anisotropy). Stimulation of the left or right parietal nodes of the DMN-induced activation in the contralateral parietal and frontocentral electrodes within 60 ms; this activation correlated with fractional anisotropy measures of the corpus callosum. These results showed that distant secondary activations after target stimulation can be predicted based on the target's anatomical connections. Interestingly, structural features of the corpus callosum predicted the activation of the directly connected nodes, i.e., parietal-parietal nodes, and of the broader DMN network, i.e., parietal-frontal nodes, as identified with functional magnetic resonance imaging. Our results suggested that the proposed integrated approach would allow us to describe the contributory causal relationship between structural connectivity and functional connectivity of the DMN.

Segregated circuits for phonemic and semantic fluency: A novel patient-tailored disconnection study.

Phonemic and semantic fluency are neuropsychological tests widely used to assess patients' language and executive abilities and are highly sensitive tests in detecting language deficits in glioma patients. However, the networks that are involved in these tasks could be distinct and suggesting either a frontal (phonemic) or temporal (semantic) involvement. 42 right-handed patients (26 male, mean age = 52.5 years, SD=±13.3) were included in this retrospective study. Patients underwent awake (54.8%) or asleep (45.2%) surgery for low-grade (16.7%) or high-grade-glioma (83.3%) in the frontal (64.3%) or temporal lobe (35.7%) of the left (50%) or right (50%) hemisphere. Pre-operative tractography was reconstructed for each patient, with segmentation of the inferior fronto-occipital fasciculus (IFOF), arcuate fasciculus (AF), uncinate fasciculus (UF), inferior longitudinal fasciculus (ILF), third branch of the superior longitudinal fasciculus (SLF-III), frontal aslant tract (FAT), and cortico-spinal tract (CST). Post-operative percentage of damage and disconnection of each tract, based on the patients' surgical cavities, were correlated with verbal fluencies scores at one week and one month after surgery. Analyses of differences between fluency scores at these timepoints (before surgery, one week and one month after surgery) were performed; lesion-symptom mapping was used to identify the correlation between cortical areas and post-operative scores. Immediately after surgery, a transient impairment of verbal fluency was observed, that improved within a month. Left hemisphere lesions were related to a worse verbal fluency performance, being a damage to the left superior frontal or temporal gyri associated with phonemic or semantic fluency deficit, respectively. At a subcortical level, disconnection analyses revealed that fluency scores were associated to the involvement of the left FAT and the left frontal part of the IFOF for phonemic fluency, and the association was still present one month after surgery. For semantic fluency, the correlation between post-surgery performance emerged for the left AF, UF, ILF and the temporal part of the IFOF, but disappeared at the follow-up. This approach based on the patients' pre-operative tractography, allowed to trace for the first time a dissociation between white matter pathways integrity and verbal fluency after surgery for glioma resection. Our results confirm the involvement of a frontal anterior pathway for phonemic fluency and a ventral temporal pathway for semantic fluency. Finally, our longitudinal results suggest that the frontal executive pathway requires a longer interval to recover compared to the semantic one.

The vertical superior longitudinal fascicle and the vertical occipital fascicle.

Association fibers of the human brain have long been considered to exclusively follow an anterior-posterior direction. Using magnetic resonance imaging techniques that allow in-vivo fiber dissection, vertically oriented association fibers have been rediscovered or newly described. Aside from the frontal aslant tract (FAT) in the frontal lobe, the vertical occipital fascicle (VOF) and the vertical portion of the superior longitudinal fascicle system (vSLF) have been studied in recent years. The aim of this review was to give an overview on the current knowledge regarding these two fiber tracts. A review of the available literature in the Medline database was conducted to gather all available publications dealing with either the VOF or the vSLF. One thousand two hundred seventy-three articles were obtained from the literature search of which a total of 71 articles met the final inclusion criteria of this review. We describe the history of the discovery of the respective fiber tract, its anatomical course and its boundaries integrating blunt fiber dissection studies and functional MRI/tractography studies. We discuss the functional properties of the respective fiber tract and its relevance in neurosurgery. The VOF is a fiber tract that has been discovered in the late XIX century and long been forgotten before being rediscovered in the 1970's. It lies lateral to the fibers of the sagittal stratum and mainly connects the superior and inferior occipital lobe. It plays a major role in reading and visual word and language comprehension and is said to be the main link between dorsal and ventral visual streams. The vSLF has many synonyms and is part of the superior longitudinal fascicle system. Recent studies were able to provide more insight into this set of fiber tracts showing distinct connections running from the superior and inferior parietal lobule to the posterior part of the temporal lobe. Its functional role is still not completely cleared. It is said to play a role in visual and auditory semantic language comprehension. It lies directly lateral to the arcuate fascicle. The VOF and the vSLF are vertically oriented fiber tracts connecting the temporo-parieto-occipital region and play a major role in the communication of dorsal and ventral visual streams (VOF), reading (VOF, vSLF) and visual and auditory semantic language comprehension (vSLF). They can consistently be identified using ex vivo blunt dissection techniques and in-vivo fiber tractography. Because of their localization and orientation these two fiber tracts can be combined to a fiber bundle system called posterior transverse system (PTS).

Planning Brain Tumor Resection Using a Probabilistic Atlas of Cortical and Subcortical Structures Critical for Functional Processing: A Proof of Concept.

BACKGROUND: Functional preoperative planning for resection of intrinsic brain tumors in eloquent areas is still a challenge. Predicting subcortical functional framework is especially difficult. Direct electrical stimulation (DES) is the recommended technique for resection of these lesions. A reliable probabilistic atlas of the critical cortical epicenters and subcortical framework based on DES data was recently published. OBJECTIVE: To propose a pipeline for the automated alignment of the corticosubcortical maps of this atlas with T1-weighted MRI. METHODS: To test the alignment, we selected 10 patients who underwent resection of brain lesions by using DES. We aligned different cortical and subcortical functional maps to preoperative volumetric T1 MRIs (with/without gadolinium). For each patient we quantified the quality of the alignment, and we calculated the match between the location of the functional sites found at DES and the functional maps of the atlas. RESULTS: We found an accurate brain extraction and alignment of the functional maps with both the T1 MRIs of each patient. The matching analysis between functional maps and functional responses collected during surgeries was 88% at cortical and, importantly, 100% at subcortical level, providing a further proof of the correct alignment. CONCLUSION: We demonstrated quantitatively and qualitatively the reliability of this tool that may be used for presurgical planning, providing further functional information at the cortical level and a unique probabilistic prevision of distribution of the critical subcortical structures. Finally, this tool offers the chance for multimodal planning through integrating this functional information with other neuroradiological and neurophysiological techniques.

The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services.

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.