Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines

Quality assessment and control of unprocessed anatomical, functional, and diffusion MRI of the human brain using MRIQC

Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by MRIQC. The protocol describes how to execute the software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the "rating widget" ─ a utility that enables rapid annotation and minimizes bookkeeping errors. Integrating proper quality control checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged.

The magic, memory, and curiosity fMRI dataset of people viewing magic tricks

Videos of magic tricks offer lots of opportunities to study the human mind. They violate the expectations of the viewer, causing prediction errors, misdirect attention, and elicit epistemic emotions. Herein we describe and share the Magic, Memory, and Curiosity (MMC) Dataset where 50 participants watched 36 magic tricks filmed and edited specifically for functional magnetic imaging (fMRI) experiments. The MMC Dataset includes a contextual incentive manipulation, curiosity ratings for the magic tricks, and incidental memory performance tested a week later. We additionally measured individual differences in working memory and constructs relevant to motivated learning. fMRI data were acquired before, during, and after learning. We show that both behavioural and fMRI data are of high quality, as indicated by basic validation analysis, i.e., variance decomposition as well as intersubject correlation and seed-based functional connectivity, respectively. The richness and complexity of the MMC Dataset will allow researchers to explore dynamic cognitive and motivational processes from various angles during task and rest.

Understanding Depression in Autism: The Role of Subjective Perception and Anterior Cingulate Cortex Volume

Background

The prevalence of depression is elevated in individuals with autism spectrum disorder (ASD) compared to the general population, yet the reasons for this disparity remain unclear. While social deficits central to ASD may contribute to depression, it is uncertain whether social interaction behavior themselves or individuals' introspection about their social behaviors are more impactful. Although the anterior cingulate cortex (ACC) and amygdala are frequently implicated in ASD, depression, and social functioning, it is unknown if these regions explain differences between ASD adults with and without co-occurring depression.

Methods

The present study contrasted observed vs. subjective perception of autism symptoms and social performances assessed with both standardized measures and a lab task, in 65 sex-balanced (52.24% male) autistic young adults. We also quantified ACC and amygdala volume with 7-Tesla structural neuroimaging to examine correlations with depression and social functioning.

Results

We found that ASD individuals with depression exhibited differences in subjective evaluations including heightened self-awareness of ASD symptoms, lower subjective satisfaction with social relations, and less perceived affiliation during the social interaction task, yet no differences in corresponding observed measures, compared to those without depression. Larger ACC volume was related to depression, greater self-awareness of ASD symptoms, and worse subjective satisfaction with social interactions. In contrast, amygdala volume, despite its association with clinician-rated ASD symptoms, was not related to depression.

Limitations

Due to the cross-sectional nature of our study, we cannot determine the directionality of the observed relationships. Additionally, we included only individuals with an IQ over 60 to ensure participants could complete the social task, which excluded many on the autism spectrum. We also utilized self-reported depression indices instead of clinically diagnosed depression, which may limit the comprehensiveness of the findings.

Conclusions

Our approach highlights the unique role of subjective perception of autism symptoms and social interactions, beyond the observable manifestation of social interaction in ASD, in contributing to depression, with the ACC playing a crucial role. These findings imply possible heterogeneity of ASD concerning co-occurring depression. Using neuroimaging, we were able to demarcate depressive phenotypes co-occurring alongside autistic phenotypes.

Probing the neurocardiac circuit in trauma and posttraumatic stress

The neurocardiac circuit is integral to physiological regulation of threat and trauma-related responses. However, few direct investigations of brain-behavior associations with replicable physiological markers of PTSD have been conducted. The current study probed the neurocardiac circuit by examining associations among its core regions in the brain (e.g., insula, hypothalamus) and the periphery (heart rate [HR], high frequency heart rate variability [HF-HRV], and blood pressure [BP]). We sought to characterize these associations and to determine whether there were differences by PTSD status. Participants were N = 315 (64.1 % female) trauma-exposed adults enrolled from emergency departments as part of the prospective AURORA study. Participants completed a deep phenotyping session (e.g., fear conditioning, magnetic resonance imaging) two weeks after emergency department admission. Voxelwise analyses revealed several significant interactions between PTSD severity 8-weeks posttrauma and psychophysiological recordings on hypothalamic connectivity to the prefrontal cortex (PFC), insula, superior temporal sulcus, and temporoparietaloccipital junction. Among those with PTSD, diastolic BP was directly correlated with right insula-hypothalamic connectivity, whereas the reverse was found for those without PTSD. PTSD status moderated the association between systolic BP, HR, and HF-HRV and hypothalamic connectivity in the same direction. While preliminary, our findings may suggest that individuals with higher PTSD severity exhibit compensatory neural mechanisms to down-regulate autonomic imbalance. Additional study is warranted to determine how underlying mechanisms (e.g., inflammation) may disrupt the neurocardiac circuit and increase cardiometabolic disease risk in PTSD.

Functional connectivity associations with menstrual pain characteristics in adolescents: an investigation of the triple network model

ABSTRACT

Menstrual pain is associated with deficits in central pain processing, yet neuroimaging studies to date have all been limited by focusing on group comparisons of adult women with vs without menstrual pain. This study aimed to investigate the role of the triple network model (TNM) of brain networks in adolescent girls with varied menstrual pain severity ratings. One hundred participants (ages 13-19 years) completed a 6-min resting state functional magnetic resonance imaging (fMRI) scan and rated menstrual pain severity, menstrual pain interference, and cumulative menstrual pain exposure. Imaging analyses included age and gynecological age (years since menarche) as covariates. Menstrual pain severity was positively associated with functional connectivity between the cingulo-opercular salience network (cSN) and the sensory processing regions, limbic regions, and insula, and was also positively associated with connectivity between the left central executive network (CEN) and posterior regions. Menstrual pain interference was positively associated with connectivity between the cSN and widespread brain areas. In addition, menstrual pain interference was positively associated with connectivity within the left CEN, whereas connectivity both within the right CEN and between the right CEN and cortical areas outside the network (including the insula) were negatively associated with menstrual pain interference. Cumulative menstrual pain exposure shared a strong negative association with connectivity between the default mode network and other widespread regions associated with large-scale brain networks. These findings support a key role for the involvement of TNM brain networks in menstrual pain characteristics and suggest that alterations in pain processing exist in adolescents with varying levels of menstrual pain.

Specific Patterns of Endogenous Functional Connectivity Are Associated With Harm Avoidance in Obsessive-Compulsive Disorder

BACKGROUND

Individuals with obsessive-compulsive disorder (OCD) show persistent avoidance behaviors, often in the absence of actual threat. Quality-of-life costs and heterogeneity support the need for novel brain-behavior intervention targets. Informed by mechanistic and anatomical studies of persistent avoidance in rodents and nonhuman primates, our goal was to test whether connections within a hypothesized persistent avoidance-related network predicted OCD-related harm avoidance (HA), a trait measure of persistent avoidance. We hypothesized that 1) HA, not an OCD diagnosis, would be associated with altered endogenous connectivity in at least one connection in the network; 2) HA-specific findings would be robust to comorbid symptoms; and 3) reliable findings would replicate in a holdout testing subsample.

METHODS

Using resting-state functional connectivity magnetic resonance imaging, cross-validated elastic net for feature selection, and Poisson generalized linear models, we tested which connections significantly predicted HA in our training subsample (n = 73; 71.8% female; healthy control group n = 36, OCD group n = 37); robustness to comorbidities; and replicability in a testing subsample (n = 30; 56.7% female; healthy control group n = 15, OCD group n = 15).

RESULTS

Stronger inverse connectivity between the right dorsal anterior cingulate cortex and right basolateral amygdala and stronger positive connectivity between the right ventral anterior insula and left ventral striatum were associated with greater HA across groups. Network connections did not discriminate OCD diagnostic status or predict HA-correlated traits, suggesting sensitivity to trait HA. The dorsal anterior cingulate cortex-basolateral amygdala relationship was robust to controlling for comorbidities and medication in individuals with OCD and was also predictive of HA in our testing subsample.

CONCLUSIONS

Stronger inverse dorsal anterior cingulate cortex-basolateral amygdala connectivity was robustly and reliably associated with HA across groups and in OCD. Results support the relevance of a cross-species persistent avoidance-related network to OCD, with implications for precision-based approaches and treatment.

PubChem synonym filtering process using crowdsourcing

PubChem ( https://pubchem.ncbi.nlm.nih.gov ) is a public chemical information resource containing more than 100 million unique chemical structures. One of the most requested tasks in PubChem and other chemical databases is to search chemicals by name (also commonly called a "chemical synonym"). PubChem performs this task by looking up chemical synonym-structure associations provided by individual depositors to PubChem. In addition, these synonyms are used for many purposes, including creating links between chemicals and PubMed articles (using Medical Subject Headings (MeSH) terms). However, these depositor-provided name-structure associations are subject to substantial discrepancies within and between depositors, making it difficult to unambiguously map a chemical name to a specific chemical structure. The present paper describes PubChem's crowdsourcing-based synonym filtering strategy, which resolves inter- and intra-depositor discrepancies in synonym-structure associations as well as in the chemical-MeSH associations. The PubChem synonym filtering process was developed based on the analysis of four crowd-voting strategies, which differ in the consistency threshold value employed (60% vs 70%) and how to resolve intra-depositor discrepancies (a single vote vs. multiple votes per depositor) prior to inter-depositor crowd-voting. The agreement of voting was determined at six levels of chemical equivalency, which considers varying isotopic composition, stereochemistry, and connectivity of chemical structures and their primary components. While all four strategies showed comparable results, Strategy I (one vote per depositor with a 60% consistency threshold) resulted in the most synonyms assigned to a single chemical structure as well as the most synonym-structure associations disambiguated at the six chemical equivalency contexts. Based on the results of this study, Strategy I was implemented in PubChem's filtering process that cleans up synonym-structure associations as well as chemical-MeSH associations. This consistency-based filtering process is designed to look for a consensus in name-structure associations but cannot attest to their correctness. As a result, it can fail to recognize correct name-structure associations (or incorrect ones), for example, when a synonym is provided by only one depositor or when many contributors are incorrect. However, this filtering process is an important starting point for quality control in name-structure associations in large chemical databases like PubChem.

Temporal Interactions between Neural Proxies forMemory Recall, Negative Affect, and EmotionRegulation in Major Depression

Dysfunction in emotion regulation (ER) and autobiographical memory are components of major depressive disorder (MDD). However, little is known about how they mechanistically interact with mood disturbances in real time. Using machine learning-based neural signatures, we can quantify negative affect (NA), ER, and memory continuously to evaluate how these processes dynamically interact in MDD. Unmedicated individuals with MDD (N=45) and healthy volunteers (HV; N=38) completed a negative autobiographical memory functional magnetic resonance imaging task wherein they recalled, distanced from (an ER strategy), and immersed into memories. We used a negative affect signature (PINES) and an emotion regulation signature (ERS) to quantify moment-to-moment NA and ER. We then examined whether memory engagement, indexed by hippocampal activity, predicted subsequent change in PINES and ERS over time. During memory recall and immersion, greater hippocampal activity predicted increased PINES across groups. During distancing, greater hippocampal activity in HVs predicted increased ERS but not PINES. In MDD, greater hippocampal activity predicted increased PINES but not ERS. Findings suggest abnormalities in the real-time relationship between memory, NA, and ER in MDD. During distancing, as predicted, HVs showed an attenuation of the linkage between memory engagement and NA, and they had subsequent increases in ER following memory reactivation. In contrast, MDD was characterized by continued linkage between memory engagement and NA, without subsequent increases in ER. Deficits in engagement of ER and ineffective modulation of NA following negative memory recall may contribute to the mood disturbances in MDD and are potential targets for clinical intervention.

The past, present, and future of the brain imaging data structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Neural segregation and integration of sensory, decision, and action processes during object categorization

Neural and computational evidence suggests that perceptual decisions depend on an evidence accumulation process. The gradual reveal fMRI method, which prolongs a decision to match the slow temporal resolution of fMRI measurements, has classified dorsal visual stream regions as "Action" (alternatively, "Moment of Recognition" or "Commitment") and ventral visual stream regions as "Accumulator." Previous gradual reveal fMRI studies, however, only tested actions that were in response to decisions and, thus, related to evidence accumulation. To fully dissociate the contribution of sensory, decision, and motor components to Action and Accumulator regions in the dorsal and ventral visual streams, we extended the gradual reveal paradigm to also include responses made to cues where no decision was necessary. We found that the lateral occipital cortex in the ventral visual stream showed a highly selective Accumulator profile, whereas regions in the fusiform gyrus were influenced by action generation. Dorsal visual stream regions showed strikingly similar profiles as classical motor regions and also as regions of the salience network. These results suggest that the dorsal and ventral visual streams may appear highly segregated because they include a small number of regions that are highly selective for Accumulator or Action. However, the streams may be more integrated than previously thought and this integration may be accomplished by regions with graded responses that are less selective (i.e., more distributed).

Hippocampal mechanisms resolve competition in memory and perception

Behaving adaptively requires selection of relevant memories and sensations and suppression of competing ones. We hypothesized that these mechanisms are linked, such that hippocampal computations that resolve competition in memory also shape the precision of sensory representations to guide selective attention. We leveraged f MRI-based pattern similarity, receptive field modeling, and eye tracking to test this hypothesis in humans performing a memory-dependent visual search task. In the hippocampus, differentiation of competing memories predicted the precision of memory-guided eye movements. In visual cortex, preparatory coding of remembered target locations predicted search successes, whereas preparatory coding of competing locations predicted search failures due to interference. These effects were linked: stronger hippocampal memory differentiation was associated with lower competitor activation in visual cortex, yielding more precise preparatory representations. These results demonstrate a role for memory differentiation in shaping the precision of sensory representations, highlighting links between mechanisms that overcome competition in memory and perception.

Abnormal multi-layered dynamic cortico-subcortical functional connectivity in major depressive disorder and generalized anxiety disorder

Comorbidity has been frequently observed between generalized anxiety disorder (GAD) and major depressive disorder (MDD), however, common and distinguishable alterations in the topological organization of functional brain networks remain poorly understood. We sought to determine a robust and sensitive functional connectivity marker for diagnostic classification and symptom severity prediction. Multi-layered dynamic functional connectivity including whole brain, network-node and node-node layers via graph theory and gradient analyses were applied to functional MRI resting-state data obtained from 31 unmedicated GAD and 34 unmedicated MDD patients as well as 33 age and education matched healthy controls (HC). GAD and MDD symptoms were assessed using Penn State Worry Questionnaire and Beck Depression Inventory II, respectively. Three network measures including global properties (i.e., global efficiency, characteristic path length), regional nodal property (i.e., degree) and connectivity gradients were computed. Results showed that both patient groups exhibited abnormal dynamic cortico-subcortical topological organization compared to healthy controls, with MDD > GAD > HC in degree of randomization. Furthermore, our multi-layered dynamic functional connectivity network model reached 77% diagnostic accuracy between GAD and MDD and was highly predictive of symptom severity, respectively. Gradients of functional connectivity for superior frontal cortex-subcortical regions, middle temporal gyrus-subcortical regions and amygdala-cortical regions contributed more in this model compared to other gradients. We found shared and distinct cortico-subcortical connectivity features in dynamic functional brain networks between GAD and MDD, which together can promote the understanding of common and disorder-specific topological organization dysregulations and facilitate early neuroimaging-based diagnosis.

A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.

DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets

In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets.

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.

Multimodal neuroimaging data from a 5-week heart rate variability biofeedback randomized clinical trial

We present data from the Heart Rate Variability and Emotion Regulation (HRV-ER) randomized clinical trial testing effects of HRV biofeedback. Younger (N = 121) and older (N = 72) participants completed baseline magnetic resonance imaging (MRI) including T1-weighted, resting and emotion regulation task functional MRI (fMRI), pulsed continuous arterial spin labeling (PCASL), and proton magnetic resonance spectroscopy (1H MRS). During fMRI scans, physiological measures (blood pressure, pulse, respiration, and end-tidal CO2) were continuously acquired. Participants were randomized to either increase heart rate oscillations or decrease heart rate oscillations during daily sessions. After 5 weeks of HRV biofeedback, they repeated the baseline measurements in addition to new measures (ultimatum game fMRI, training mimicking during blood oxygen level dependent (BOLD) and PCASL fMRI). Participants also wore a wristband sensor to estimate sleep time. Psychological assessment comprised three cognitive tests and ten questionnaires related to emotional well-being. A subset (N = 104) provided plasma samples pre- and post-intervention that were assayed for amyloid and tau. Data is publicly available via the OpenNeuro data sharing platform.

Arterial hypertension and β-amyloid accumulation have spatially overlapping effects on posterior white matter hyperintensity volume: a cross-sectional study

BACKGROUND

White matter hyperintensities (WMH) in subjects across the Alzheimer's disease (AD) spectrum with minimal vascular pathology suggests that amyloid pathology-not just arterial hypertension-impacts WMH, which in turn adversely influences cognition. Here we seek to determine the effect of both hypertension and Aβ positivity on WMH, and their impact on cognition.

METHODS

We analysed data from subjects with a low vascular profile and normal cognition (NC), subjective cognitive decline (SCD), and amnestic mild cognitive impairment (MCI) enrolled in the ongoing observational multicentre DZNE Longitudinal Cognitive Impairment and Dementia Study (n = 375, median age 70.0 [IQR 66.0, 74.4] years; 178 female; NC/SCD/MCI 127/162/86). All subjects underwent a rich neuropsychological assessment. We focused on baseline memory and executive function-derived from multiple neuropsychological tests using confirmatory factor analysis-, baseline preclinical Alzheimer's cognitive composite 5 (PACC5) scores, and changes in PACC5 scores over the course of three years (ΔPACC5).

RESULTS

Subjects with hypertension or Aβ positivity presented the largest WMH volumes (pFDR < 0.05), with spatial overlap in the frontal (hypertension: 0.42 ± 0.17; Aβ: 0.46 ± 0.18), occipital (hypertension: 0.50 ± 0.16; Aβ: 0.50 ± 0.16), parietal lobes (hypertension: 0.57 ± 0.18; Aβ: 0.56 ± 0.20), corona radiata (hypertension: 0.45 ± 0.17; Aβ: 0.40 ± 0.13), optic radiation (hypertension: 0.39 ± 0.18; Aβ: 0.74 ± 0.19), and splenium of the corpus callosum (hypertension: 0.36 ± 0.12; Aβ: 0.28 ± 0.12). Elevated global and regional WMH volumes coincided with worse cognitive performance at baseline and over 3 years (pFDR < 0.05). Aβ positivity was negatively associated with cognitive performance (direct effect-memory: - 0.33 ± 0.08, pFDR < 0.001; executive: - 0.21 ± 0.08, pFDR < 0.001; PACC5: - 0.29 ± 0.09, pFDR = 0.006; ΔPACC5: - 0.34 ± 0.04, pFDR < 0.05). Splenial WMH mediated the relationship between hypertension and cognitive performance (indirect-only effect-memory: - 0.05 ± 0.02, pFDR = 0.029; executive: - 0.04 ± 0.02, pFDR = 0.067; PACC5: - 0.05 ± 0.02, pFDR = 0.030; ΔPACC5: - 0.09 ± 0.03, pFDR = 0.043) and WMH in the optic radiation partially mediated that between Aβ positivity and memory (indirect effect-memory: - 0.05 ± 0.02, pFDR = 0.029).

CONCLUSIONS

Posterior white matter is susceptible to hypertension and Aβ accumulation. Posterior WMH mediate the association between these pathologies and cognitive dysfunction, making them a promising target to tackle the downstream damage related to the potentially interacting and potentiating effects of the two pathologies.

TRIAL REGISTRATION

German Clinical Trials Register (DRKS00007966, 04/05/2015).

Regional superficial amygdala resting-state functional connectivity in adults infers childhood maltreatment severity

Background

Childhood maltreatment (CM) is a potential risk factor for some neuropsychiatric disorders in adulthood (e.g. depression and anxiety) and alters trajectories of brain development. Accumulating evidence suggests that functional connectivity of the limbic system, especially the amygdala, is highly associated with childhood maltreatment, although not all studies have found this. These inconsistent results may be due to differential alterations of amygdala resting-state functional connectivity (rsFC) following childhood maltreatment.

Objective

Our aim was to investigate the relationship between the rsFC of amygdala subregions and CM severity, as well as to develop a stable rsFC-based model for inferring the severity of CM.

Methods

In this study, we employed the Childhood Trauma Questionnaire (CTQ) to assess CM severity in each individual. We explored the relationship between the rsFC of amygdala subregions (i.e. centromedial -CMA, basolateral -BLA, superficial-SFA amygdala) and CM experience in a discovery dataset of n = 110 healthy Chinese participants by linear multiple regression analysis. Subsequent dimensional and categorical approach were performed to elucidate the relationship between rsFCs and CM severity and CM subtypes, respectively. A support vector regression model was then conducted to validate the associations between rsFCs and total CTQ scores. Moreover, we also verified the model into another independent replication dataset (n = 38).

Results

Our findings suggested that childhood maltreatment was negatively associated with rsFC between the right superficial amygdala and perigenual anterior cingulate cortex (pgACC)/postcentral gyrus (PCG) but not the other two amygdala subregions. Moreover, SFA-pgACC coupling was more associated with physical neglect whereas the SFA-PCG was more related to emotional neglect. In addition, supervised machine learning confirmed that using these two rsFCs as predictors could stably estimate continuous maltreatment severity in both discovery and replication datasets.

Conclusion

The current study supports that the rsFCs of superficial amygdala are related to childhood maltreatment and which may be a potential biomarker for the effects of childhood maltreatment-related psychiatric disorders (i.e. depression and anxiety).

K2S Challenge: From Undersampled K-Space to Automatic Segmentation

Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.

Construction of a searchable database for gene expression changes in spinal cord injury experiments

Spinal cord injury (SCI) is a debilitating disease resulting in an estimated 18,000 new cases in the United States on an annual basis. Significant behavioral research on animal models has led to a large amount of data, some of which has been catalogued in the Open Data Commons for Spinal Cord Injury (ODC-SCI). More recently, high throughput sequencing experiments have been utilized to understand molecular mechanisms associated with SCI, with nearly 6,000 samples from over 90 studies available in the Sequence Read Archive. However, to date, no resource is available for efficiently mining high throughput sequencing data from SCI experiments. Therefore, we have developed a protocol for processing RNA-Seq samples from high-throughput sequencing experiments related to SCI resulting in both raw and normalized data that can be efficiently mined for comparisons across studies as well as homologous discovery across species. We have processed 1,196 publicly available RNA-seq samples from 50 bulk RNA-Seq studies across nine different species, resulting in an SQLite database that can be used by the SCI research community for further discovery. We provide both the database as well as a web-based front-end that can be used to query the database for genes of interest, differential gene expression, genes with high variance, and gene set enrichments.

EstroGene database reveals diverse temporal, context-dependent and directional estrogen receptor regulomes in breast cancer

As one of the most successful cancer therapeutic targets, estrogen receptor-α (ER/ESR1) has been extensively studied in decade-long. Sequencing technological advances have enabled genome-wide analysis of ER action. However, reproducibility is limited by different experimental design. Here, we established the EstroGene database through centralizing 246 experiments from 136 transcriptomic, cistromic and epigenetic datasets focusing on estradiol-treated ER activation across 19 breast cancer cell lines. We generated a user-friendly browser ( https://estrogene.org/ ) for data visualization and gene inquiry under user-defined experimental conditions and statistical thresholds. Notably, documentation-based meta-analysis revealed a considerable lack of experimental details. Comparison of independent RNA-seq or ER ChIP-seq data with the same design showed large variability and only strong effects could be consistently detected. We defined temporal estrogen response metasignatures and showed the association with specific transcriptional factors, chromatin accessibility and ER heterogeneity. Unexpectedly, harmonizing 146 transcriptomic analyses uncovered a subset of E2-bidirectionally regulated genes, which linked to immune surveillance in the clinical setting. Furthermore, we defined context dependent E2 response programs in MCF7 and T47D cell lines, the two most frequently used models in the field. Collectively, the EstroGene database provides an informative resource to the cancer research community and reveals a diverse mode of ER signaling.

Open and reproducible neuroimaging: From study inception to publication

Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

The Executive Functioning Paradox in Substance Use Disorders

Deficits in neurocognitive functioning are trait-like vulnerabilities that have been widely studied in persons with substance use disorders (SUD), but their role in the craving-use association and relapse vulnerability remains poorly understood. The main objectives of this study were to examine whether executive capacities moderate the magnitude of the craving-substance use relationship, and if this influence is correlated with the functional connectivity of cerebral networks, combining rsfMRI examinations and ecological momentary assessment (EMA). Eighty-six patients beginning outpatient treatment for alcohol, tobacco or cannabis addiction and 40 healthy controls completed neuropsychological tests followed by EMA to collect real-time data on craving. Fifty-four patients and 30 healthy controls also completed a resting-state fMRI before the EMA. Among the patients with SUD, better verbal fluency and resistance to interference capacities were associated with a greater propensity to use substances when the individual was experiencing craving. Preliminary rsfMRI results identified specific networks that interacted with executive performance capacities to influence the magnitude of the craving-use association. Individuals with better executive functioning may be more prone to relapse after craving episodes. Specifically, better resistance to interference and cognitive flexibility skills may reduce attention to distracting stimuli, leading to a greater awareness of craving and susceptibility to use substances.

An artificial-intelligence-based age-specific template construction framework for brain structural analysis using magnetic resonance images

It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age-specific brain variations and tendencies across different ages. In this article, we present an AI-based age-specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1-weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5-year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study.

How Machine Learning is Powering Neuroimaging to Improve Brain Health

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Estimating Intrinsic Manifold Dimensionality to Classify Task-Related Information in Human and Non-Human Primate Data

Feature selection, or dimensionality reduction, has become a standard step in reducing large-scale neural datasets into usable signals for brain-machine interface and neurofeedback decoders. Current techniques in fMRI data reduce the number of voxels (features) by performing statistics on individual voxels or using traditional techniques that utilize linear combinations of features (e.g., principal component analysis (PCA)). However, these methods often do not account for the cross-correlations found across voxels and do not sufficiently reduce the feature space to support efficient real-time feedback. To overcome these limitations, we propose using factor analysis on fMRI data. This technique has become increasingly popular for extracting a minimal number of latent features to explain high-dimensional data in non-human primates (NHPs). Here, we demonstrate these methods in both NHP and human data. In NHP subjects (n=2), we reduced the number of features to an average of 26.86% and 14.86% of the total feature space to build our multinomial classifier. In one NHP subject, the average accuracy of classifying eight target locations over 64 sessions was 62.43% (+/-6.19%) compared to a PCA-based classifier with 60.26% (+/-6.02%). In healthy fMRI subjects, we reduced the feature space to an average of 0.33% of the initial space. Group average (n=5) accuracy of FA-based category classification was 74.33% (+/- 4.91%) compared to a PCA-based classifier with 68.42% (+/-4.79%). FA-based classifiers can maintain the performance fidelity observed with PCA-based decoders. Importantly, FA-based methods allow researchers to address specific hypotheses about how underlying neural activity relates to behavior.

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low-frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

A Chinese multi-modal neuroimaging data release for increasing diversity of human brain mapping

The big-data use is becoming a standard practice in the neuroimaging field through data-sharing initiatives. It is important for the community to realize that such open science effort must protect personal, especially facial information when raw neuroimaging data are shared. An ideal tool for the face anonymization should not disturb subsequent brain tissue extraction and further morphological measurements. Using the high-resolution head images from magnetic resonance imaging (MRI) of 215 healthy Chinese, we discovered and validated a template effect on the face anonymization. Improved facial anonymization was achieved when the Chinese head templates but not the Western templates were applied to obscure the faces of Chinese brain images. This finding has critical implications for international brain imaging data-sharing. To facilitate the further investigation of potential culture-related impacts on and increase diversity of data-sharing for the human brain mapping, we released the 215 Chinese multi-modal MRI data into a database for imaging Chinese young brains, namely'I See your Brains (ISYB)', to the public via the Science Data Bank ( https://doi.org/10.11922/sciencedb.00740 ).

A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images

No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images. In this work, a survey covering recently introduced NR-IQA methods for the assessment of MR images is presented. First, typical distortions are reviewed and then popular NR methods are characterized, taking into account the way in which they describe MR images and create quality models for prediction. The survey also includes protocols used to evaluate the methods and popular benchmark databases. Finally, emerging challenges are outlined along with an indication of the trends towards creating accurate image prediction models.

Structural language impairment in Autism Spectrum Disorder versus Loss of Autism Diagnosis: Behavioral and neural characteristics

This study probed for structural language impairment using behavioral and functional neuroimaging methods in individuals with Autism Spectrum Disorder (ASD) and those diagnosed with ASD in childhood who no longer meet criteria for ASD, referred to as Loss of Autism Diagnosis (LAD¹). Participants were drawn from Fein et al. (2013): ASD (n = 35), LAD (n = 31), and Neurotypical (NT; n = 34). Criteria for structural language impairment were: Scores ≤ 82 on Clinical Evaluation of Language Fundamentals-4 (CELF) Core Language, an omnibus measure of language; and scores ≤ 7 on CELF Recalling Sentences, a clinical marker of structural language impairment. Task-based fMRI examined lateralization of significantly activated language-related brain regions in groups with structural language impairment (LI²) versus normal-range language (LN³), collapsed across ASD, LAD¹, and NT status. Results showed no ASD versus LAD group differences in the proportion of participants with structural language impairment according to either metric (Recalling Sentences or Core Language). Functional MRI results indicated greater left hemisphere lateralization within significantly activated regions in the LI² group. Structural language abilities were not meaningfully associated with either social abilities or lifetime ADHD symptoms in LI² subgroups, further suggesting the presence of structural language impairment. Findings indicate the presence of persistent structural language difficulty even in the absence of ASD symptoms in some individuals within the LAD¹ group and unique patterns of language-related neural specialization for language function in LI² relative to LN³.

Caltech Conte Center, a multimodal data resource for exploring social cognition and decision-making

This data release of 117 healthy community-dwelling adults provides multimodal high-quality neuroimaging and behavioral data for the investigation of brain-behavior relationships. We provide structural MRI, resting-state functional MRI, movie functional MRI, together with questionnaire-based and task-based psychological variables; many of the participants have multiple datasets from retesting over the course of several years. Our dataset is distinguished by utilizing open-source data formats and processing tools (BIDS, FreeSurfer, fMRIPrep, MRIQC), providing data that is thoroughly quality checked, preprocessed to various extents and available in multiple anatomical spaces. A customizable denoising pipeline is provided as open-source code that includes tools for the generation of functional connectivity matrices and initialization of individual difference analyses. Behavioral data include a comprehensive set of psychological assessments on gold-standard instruments encompassing cognitive function, mood and personality, together with exploratory factor analyses. The dataset provides an in-depth, multimodal resource for investigating associations between individual differences, brain structure and function, with a focus on the domains of social cognition and decision-making.

Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group

The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.

Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning

BACKGROUND

Magnetic resonance imaging (MRI)-based morphometry and relaxometry are proven methods for the structural assessment of the human brain in several neurological disorders. These procedures are generally based on T1-weighted (T1w) and/or T2-weighted (T2w) MRI scans, and rigid and affine registrations to a standard template(s) are essential steps in such studies. Therefore, a fully automatic quality control (QC) of these registrations is necessary in big data scenarios to ensure that they are suitable for subsequent processing.

METHOD

A supervised machine learning (ML) framework is proposed by computing similarity metrics such as normalized cross-correlation, normalized mutual information, and correlation ratio locally. We have used these as candidate features for cross-validation and testing of different ML classifiers. For 5-fold repeated stratified grid search cross-validation, 400 correctly aligned, 2000 randomly generated misaligned images were used from the human connectome project young adult (HCP-YA) dataset. To test the cross-validated models, the datasets from autism brain imaging data exchange (ABIDE I) and information eXtraction from images (IXI) were used.

RESULTS

The ensemble classifiers, random forest, and AdaBoost yielded best performance with F1-scores, balanced accuracies, and Matthews correlation coefficients in the range of 0.95-1.00 during cross-validation. The predictive accuracies reached 0.99 on the Test set #1 (ABIDE I), 0.99 without and 0.96 with noise on Test set #2 (IXI, stratified w.r.t scanner vendor and field strength).

CONCLUSIONS

The cross-validated and tested ML models could be used for QC of both T1w and T2w rigid and affine registrations in large-scale MRI studies.

Management and Quality Control of Large Neuroimaging Datasets: Developments From the Barcelonaβeta Brain Research Center

Recent decades have witnessed an increasing number of large to very large imaging studies, prominently in the field of neurodegenerative diseases. The datasets collected during these studies form essential resources for the research aiming at new biomarkers. Collecting, hosting, managing, processing, or reviewing those datasets is typically achieved through a local neuroinformatics infrastructure. In particular for organizations with their own imaging equipment, setting up such a system is still a hard task, and relying on cloud-based solutions, albeit promising, is not always possible. This paper proposes a practical model guided by core principles including user involvement, lightweight footprint, modularity, reusability, and facilitated data sharing. This model is based on the experience from an 8-year-old research center managing cohort research programs on Alzheimer’s disease. Such a model gave rise to an ecosystem of tools aiming at improved quality control through seamless automatic processes combined with a variety of code libraries, command line tools, graphical user interfaces, and instant messaging applets. The present ecosystem was shaped around XNAT and is composed of independently reusable modules that are freely available on GitLab/GitHub. This paradigm is scalable to the general community of researchers working with large neuroimaging datasets.

Variations in structural MRI quality significantly impact commonly used measures of brain anatomy

Subject motion can introduce noise into neuroimaging data and result in biased estimations of brain structure. In-scanner motion can compromise data quality in a number of ways and varies widely across developmental and clinical populations. However, quantification of structural image quality is often limited to proxy or indirect measures gathered from functional scans; this may be missing true differences related to these potential artifacts. In this study, we take advantage of novel informatic tools, the CAT12 toolbox, to more directly measure image quality from T1-weighted images to understand if these measures of image quality: (1) relate to rigorous quality-control checks visually completed by human raters; (2) are associated with sociodemographic variables of interest; (3) influence regional estimates of cortical surface area, cortical thickness, and subcortical volumes from the commonly used Freesurfer tool suite. We leverage public-access data that includes a community-based sample of children and adolescents, spanning a large age-range (N = 388; ages 5-21). Interestingly, even after visually inspecting our data, we find image quality significantly impacts derived cortical surface area, cortical thickness, and subcortical volumes from multiple regions across the brain (~ 23.4% of all areas investigated). We believe these results are important for research groups completing structural MRI studies using Freesurfer or other morphometric tools. As such, future studies should consider using measures of image quality to minimize the influence of this potential confound in group comparisons or studies focused on individual differences.

Representation of social content in dorsomedial prefrontal cortex underlies individual differences in agreeableness trait

Personality traits reflect key aspects of individual variability in different psychological domains. Understanding the mechanisms that give rise to these differences requires an exhaustive investigation of the behaviors associated with such traits, and their underlying neural sources. Here we investigated the mechanisms underlying agreeableness, one of the five major dimensions of personality, which has been linked mainly to socio-cognitive functions. In particular, we examined whether individual differences in the neural representations of social information are related to differences in agreeableness of individuals. To this end, we adopted a multivariate representational similarity approach that captured within single individuals the activation pattern similarity of social and non-social content, and tested its relation to the agreeableness trait in a hypothesis-driven manner. The main result confirmed our prediction: processing social and non-social content led to similar patterns of activation in individuals with low agreeableness, while in more agreeable individuals these patterns were more dissimilar. Critically, this association between agreeableness and encoding similarity of social and random content was significant only in the dorsomedial prefrontal cortex, a brain region consistently involved during attributions of mental states. The present finding reveals the link between neural mechanisms underlying social information processing and agreeableness, a personality trait highly related to socio-cognitive abilities, thereby providing a step forward in characterizing its neural determinants. Furthermore, it emphasizes the advantage of multivariate pattern analysis approaches in capturing and understanding the neural sources of individual variations.

Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data

Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such “brain age prediction” vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47–73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole‐brain region‐based or voxel‐based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross‐validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel‐level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research.

Technical Note: MRQy - An open-source tool for quality control of MR imaging data

PURPOSE

There is an increasing availability of large imaging cohorts [such as through The Cancer Imaging Archive (TCIA)] for computational model development and imaging research. To ensure development of generalizable computerized models, there is a need to quickly determine relative quality differences in these cohorts, especially when considering MRI datasets which can exhibit wide variations in image appearance. The purpose of this study is to present a quantitative quality control tool, MRQy, to help interrogate MR imaging datasets for: (a) site- or scanner-specific variations in image resolution or image contrast, and (b) imaging artifacts such as noise or inhomogeneity; which need correction prior to model development.

METHODS

Unlike existing imaging quality control tools, MRQy has been generalized to work with images from any body region to efficiently extract a series of quality measures (e.g., noise ratios, variation metrics) and MR image metadata (e.g., voxel resolution and image dimensions). MRQy also offers a specialized HTML5-based front-end designed for real-time filtering and trend visualization of quality measures.

RESULTS

MRQy was used to evaluate (a) n = 133 brain MRIs from TCIA (7 sites) and (b) n = 104 rectal MRIs (3 local sites). MRQy measures revealed significant site-specific variations in both cohorts, indicating potential batch effects. Before processing, MRQy measures could be used to identify each of the seven sites within the TCIA cohort with 87.5%, 86.4%, 90%, 93%, 90%, 60%, and 92.9% accuracy and the three sites within the rectal cohort with 91%, 82.8%, and 88.9% accuracy using unsupervised clustering. After processing, none of the sites could be distinctively clustered via MRQy measures in either cohort; suggesting that batch effects had been largely accounted for. Marked differences in specific MRQy measures were also able to identify outlier MRI datasets that needed to be corrected for common acquisition artifacts.

CONCLUSIONS

MRQy is designed to be a standalone, unsupervised tool that can be efficiently run on a standard desktop computer. It has been made freely accessible and open-source at http://github.com/ccipd/MRQy for community use and feedback.

Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners

•We present Neuroharmony, a harmonization tool for images from unseen scanners. •We developed Neuroharmony using a total of 15,026 sMRI images. •The tool was able to reduce scanner-related bias from unseen scans. •Neuroharmony represents a significant step towards imaging-based clinical tools. •Neuroharmony is available at https://github.com/garciadias/Neuroharmony .

A data resource from concurrent intracranial stimulation and functional MRI of the human brain

Mapping the causal effects of one brain region on another is a challenging problem in neuroscience that we approached through invasive direct manipulation of brain function together with concurrent whole-brain measurement of the effects produced. Here we establish a unique resource and present data from 26 human patients who underwent electrical stimulation during functional magnetic resonance imaging (es-fMRI). The patients had medically refractory epilepsy requiring surgically implanted intracranial electrodes in cortical and subcortical locations. One or multiple contacts on these electrodes were stimulated while simultaneously recording BOLD-fMRI activity in a block design. Multiple runs exist for patients with different stimulation sites. We describe the resource, data collection process, preprocessing using the fMRIPrep analysis pipeline and management of artifacts, and provide end-user analyses to visualize distal brain activation produced by site-specific electrical stimulation. The data are organized according to the brain imaging data structure (BIDS) specification, and are available for analysis or future dataset contributions on openneuro.org including both raw and preprocessed data.