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.

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.

The Heritability of Human Connectomes: a Causal Modeling Analysis.

The heritability of human connectomes is crucial for understanding the influence of genetic and environmental factors on variability in connectomes, and their implications for behavior and disease. However, current methods for studying heritability assume an associational rather than a causal effect, or rely on strong distributional assumptions that may not be appropriate for complex, high-dimensional connectomes. To address these limitations, we propose two solutions: first, we formalize heritability as a problem in causal inference, and identify measured covariates to control for unmeasured confounding, allowing us to make causal claims. Second, we leverage statistical models that capture the underlying structure and dependence within connectomes, enabling us to define different notions of connectome heritability by removing common structures such as scaling of edge weights between connectomes. We then develop a non-parametric test to detect whether causal heritability exists after taking principled steps to adjust for these commonalities, and apply it to diffusion connectomes estimated from the Human Connectome Project. Our findings reveal that heritability can still be detected even after adjusting for potential confounding like neuroanatomy, age, and sex. However, once we address for rescaling between connectomes, our causal tests are no longer significant. These results suggest that previous conclusions on connectome heritability may be driven by rescaling factors. Together, our manuscript highlights the importance for future works to continue to develop data-driven heritability models which faithfully reflect potential confounders and network structure.

Standardizing human brain parcellations.

Using brain atlases to localize regions of interest is a requirement for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization. Standardization can facilitate consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Our group has worked to consolidate an extensive selection of popular human brain atlases into a single, curated, open-source library, where they are stored following a standardized protocol with accompanying metadata, which can serve as the basis for future atlases. The repository containing the atlases, the specification, as well as relevant transformation functions is available in the neuroparc OSF registered repository or https://github.com/neurodata/neuroparc .

Predementia Brain Changes in Progranulin Mutation: A Systematic Review of Neuroimaging Evidence.

BACKGROUND: Mutations in the progranulin (GRN) gene are a major cause of familial frontotemporal dementia. They result in a loss of progranulin levels and in GRN-related brain degenerative changes that unfold over years if not decades. The aim of our review was to summarize the evidence on emerging functional and structural brain abnormalities in carriers of GRN mutations. SUMMARY: We performed a systematic search for studies that used at least one modality (structural MRI, fMRI, fluorodeoxyglucose positron emission tomography, diffusion tensor imaging) to compare mutation carriers to non-carrier controls. Our search produced 13 studies published between 2008 and 2017, the majority cross-sectional, with carrier sample sizes ranging from 5 to 65. Key Messages: The aggregate findings suggest that (1) measurable brain changes are detectable in at least some mutation carriers 20-25 years prior to disease onset; (2) functional/metabolic changes progress more consistently over time than structural changes; (3) the topographic pattern is anterior to posterior, not always asymmetric, and maps onto known functional networks.

A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

BACKGROUND: Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment. METHOD: An elastic net and random forest were trained to predict depression symptoms and related disability after an 8-week course of an Internet intervention, Deprexis, involving adults (N = 283) from across the USA. Candidate predictors included psychopathology, demographics, treatment expectancies, treatment usage, and environmental context obtained from population databases. Model performance was evaluated using predictive R2$\lpar R_{{\rm pred}}^2\rpar\comma $ the expected variance explained in a new sample, estimated by 10 repetitions of 10-fold cross-validation. RESULTS: An ensemble model was created by averaging the predictions of the elastic net and random forest. Model performance was compared with a benchmark linear autoregressive model that predicted each outcome using only its baseline. The ensemble predicted more variance in post-treatment depression (8.0% gain, 95% CI 0.8-15; total $R_{{\rm pred}}^2 \; $= 0.25), disability (5.0% gain, 95% CI -0.3 to 10; total $R_{{\rm pred}}^2 \; $= 0.25), and well-being (11.6% gain, 95% CI 4.9-19; total $R_{{\rm pred}}^2 \; $= 0.29) than the benchmark model. Important predictors included comorbid psychopathology, particularly total psychopathology and dysthymia, low symptom-related disability, treatment credibility, lower access to therapists, and time spent using certain Deprexis modules. CONCLUSION: A number of variables predict symptom improvement following an Internet intervention, but each of these variables makes relatively small contributions. Machine learning ensembles may be a promising statistical approach for identifying the cumulative contribution of many weak predictors to psychosocial depression treatment response.

Attentional bias modification treatment for depression: Study protocol for a randomized controlled trial.

Theoretical models and empirical research point to negatively biased attention as a maintaining factor in depression. Although preliminary studies suggest experimentally modifying attentional biases (i.e., attentional bias modification; ABM) reduces depression symptoms and depression risk, relatively few rigorous studies with clinical samples have been completed. This clinical trial examines the impact of ABM on a sample of adults (N = 123) with elevated depression severity who also exhibit at least modest levels of negatively biased attention prior to treatment. Participants will be randomly assigned to either active ABM, placebo ABM, or an assessment-only control condition. Individuals assigned to ABM will complete 5 trainings per week (2 in-clinic, 3 brief trainings at-home) during a four-week period. Throughout this four-week period, participants will complete weekly assessments of symptom severity and putative treatment mediators measured across different levels of analysis (e.g., eye tracking, behavioral measures, and functional Magnetic Resonance Imaging). This article details the rationale and design of the clinical trial, including methodological issues that required more extensive consideration. Our findings may not only point to an easily-accessible, efficacious treatment for depression but may also provide a meaningful test of whether a theoretically important construct, negatively biased attention, maintains depression.

Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization.

Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of PTSD risk after trauma exposure is a critical precursor to large-scale deployment of early interventions that may reduce individual suffering and societal costs. Toward this aim, we applied ensemble machine learning to predict PTSD screening status three months after severe injury using cost-effective and minimally invasive data. Participants (N = 271) were recruited at a Level 1 Trauma Center where they provided variables routinely collected at the hospital, including pulse, injury severity, and demographics, as well as psychological variables, including self-reported current depression, psychiatric history, and social support. Participant zip codes were used to extract contextual variables including population total and density, average annual income, and health insurance coverage rates from publicly available U.S. Census data. Machine learning yielded good prediction of PTSD screening status 3 months post-hospitalization, AUC = 0.85 95% CI [0.83, 0.86], and significantly outperformed all benchmark comparison models in a cross-validation procedure designed to yield an unbiased estimate of performance. These results demonstrate that good prediction can be attained from variables that individually have relatively weak predictive value, pointing to the promise of ensemble machine learning approaches that do not rely on strong isolated risk factors.

Highways of the emotional intellect: white matter microstructural correlates of an ability-based measure of emotional intelligence.

Individuals differ in their ability to understand emotional information and apply that understanding to make decisions and solve problems effectively - a construct known as Emotional Intelligence (EI). While considerable evidence supports the importance of EI in social and occupational functioning, the neural underpinnings of this capacity are relatively unexplored. We used Tract-Based Spatial Statistics (TBSS) to determine the white matter correlates of EI as measured by the ability-based Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Participants included 32 healthy adults (16 men; 16 women), aged 18-45 years. White matter integrity in key tracts was positively correlated with the Strategic Area branches of the MSCEIT (Understanding Emotions and Managing Emotions), but not the Experiential branches (Perceiving and Facilitating Emotions). Specifically, the Understanding Emotions branch was associated with greater fractional anisotropy (FA) within somatosensory and sensory-motor fiber bundles, particularly those of the left superior longitudinal fasciculus and corticospinal tract. Managing Emotions was associated with greater FA within frontal-affective association tracts including the anterior forceps and right uncinate fasciculus, along with frontal-parietal cingulum and interhemispheric corpus callosum tracts. These findings suggest that specific components of EI are directly related to the structural microarchitecture of major axonal pathways.

Exposure to Blue Light Increases Subsequent Functional Activation of the Prefrontal Cortex During Performance of a Working Memory Task.

STUDY OBJECTIVES: Prolonged exposure to blue wavelength light has been shown to have an alerting effect, and enhances performance on cognitive tasks. A small number of studies have also shown that relatively short exposure to blue light leads to changes in functional brain responses during the period of exposure. The extent to which blue light continues to affect brain functioning during a cognitively challenging task after cessation of longer periods of exposure (i.e., roughly 30 minutes or longer), however, has not been fully investigated. METHODS: A total of 35 healthy participants (18 female) were exposed to either blue (469 nm) (n = 17) or amber (578 nm) (n = 18) wavelength light for 30 minutes in a darkened room, followed immediately by functional magnetic resonance imaging (fMRI) while undergoing a working memory task (N-back task). RESULTS: Participants in the blue light condition were faster in their responses on the N-back task and showed increased activation in the dorsolateral (DLPFC) and ventrolateral (VLPFC) prefrontal cortex compared to those in the amber control light condition. Furthermore, greater activation within the VLPFC was correlated with faster N-back response times. CONCLUSIONS: This is the first study to suggest that a relatively brief, single exposure to blue light has a subsequent beneficial effect on working memory performance, even after cessation of exposure, and leads to temporarily persisting functional brain changes within prefrontal brain regions associated with executive functions. These findings may have broader implication for using blue-enriched light in a variety of work settings where alertness and quick decision-making are important.

Gray matter volume and executive functioning correlate with time since injury following mild traumatic brain injury.

Most people who sustain a mild traumatic brain injury (mTBI) will recover to baseline functioning within a period of several days to weeks. A substantial minority of patients, however, will show persistent symptoms and mild cognitive complaints for much longer. To more clearly delineate how the duration of time since injury (TSI) is associated with neuroplastic cortical volume changes and cognitive recovery, we employed voxel-based morphometry (VBM) and select neuropsychological measures in a cross-sectional sample of 26 patients with mTBI assessed at either two-weeks, one-month, three-months, six-months, or one-year post injury, and a sample of 12 healthy controls. Longer duration of TSI was associated with larger gray matter volume (GMV) within the ventromedial prefrontal cortex (vmPFC) and right fusiform gyrus, and better neurocognitive performance on measures of visuospatial design fluency and emotional functioning. In particular, volume within the vmPFC was positively correlated with design fluency and negatively correlated with symptoms of anxiety, whereas GMV of the fusiform gyrus was associated with greater design fluency and sustained visual psychomotor vigilance performance. Moreover, the larger GMV seen among the more chronic individuals was significantly greater than healthy controls, suggesting possible enlargement of these regions with time since injury. These findings are interpreted in light of burgeoning evidence suggesting that cortical regions often exhibit structural changes following experience or practice, and suggest that with greater time since an mTBI, the brain displays compensatory remodeling of cortical regions involved in emotional regulation, which may reduce distractibility during attention demanding visuo-motor tasks.

Daytime sleepiness is associated with altered resting thalamocortical connectivity.

Thalamocortical connectivity is believed to underlie basic alertness, motor, sensory information processing, and attention processes. This connectivity appears to be disrupted by total sleep deprivation, but it is not known whether it is affected by normal variations in general daytime sleepiness in nonsleep deprived persons. Healthy adult participants completed the Epworth Sleepiness Scale and underwent resting-state functional MRI. Functional connectivity between the thalamus and other regions of the cortex was examined and correlated with Epworth Sleepiness Scale scores. Greater sleepiness was associated with inverse (i.e. lower or more negative) connectivity between the bilateral thalamus and cortical regions involved in somatosensory and motor functions, potentially reflecting the disengagement of sensory and motor processing from the stream of consciousness.