Familial risk for depression moderates neural circuitry in healthy preadolescents to predict adolescent depression symptoms in the Adolescent Brain Cognitive Development (ABCD) Study.

BACKGROUND: There is an imminent need to identify neural markers during preadolescence that are linked to developing depression during adolescence, especially among youth at elevated familial risk. However, longitudinal studies remain scarce and exhibit mixed findings. Here we aimed to elucidate functional connectivity (FC) patterns among preadolescents that interact with familial depression risk to predict depression two years later. METHODS: 9-10 year-olds in the Adolescent Brain Cognitive Development (ABCD) Study were classified as healthy (i.e., no lifetime psychiatric diagnoses) at high familial risk for depression (HR; n=559) or at low familial risk for psychopathology (LR; n=1203). Whole-brain seed-to-voxel resting-state FC patterns with the amygdala, putamen, nucleus accumbens, and caudate were calculated. Multi-level, mixed-effects regression analyses were conducted to test whether FC at ages 9-10 interacted with familial risk to predict depression symptoms at ages 11-12. RESULTS: HR youth demonstrated stronger associations between preadolescent FC and adolescent depression symptoms (ps<0.001) as compared to LR youth (ps>0.001), primarily among amygdala/striatal FC with visual and sensory/somatomotor networks. CONCLUSIONS: Preadolescent amygdala and striatal FC may be useful biomarkers of adolescent-onset depression, particularly for youth with family histories of depression. This research may point to neurobiologically-informed approaches to prevention and intervention for depression in adolescents.

Causally Probing the Role of the Hippocampus in Fear Discrimination: A Precision Functional Mapping-Guided, Transcranial Magnetic Stimulation Study in Participants With Posttraumatic Stress Symptoms.

Background: Fear overgeneralization is a promising pathogenic mechanism of clinical anxiety. A dominant model posits that hippocampal pattern separation failures drive overgeneralization. Hippocampal network-targeted transcranial magnetic stimulation (HNT-TMS) has been shown to strengthen hippocampal-dependent learning/memory processes. However, no study has examined whether HNT-TMS can alter fear learning/memory. Methods: Continuous theta burst stimulation was delivered to individualized left posterior parietal stimulation sites derived via seed-based connectivity, precision functional mapping, and electric field modeling methods. A vertex control site was also stimulated in a within-participant, randomized controlled design. Continuous theta burst stimulation was delivered prior to 2 visual discrimination tasks (1 fear based, 1 neutral). Multilevel models were used to model and test data. Participants were undergraduates with posttraumatic stress symptoms (final n = 25). Results: Main analyses did not indicate that HNT-TMS strengthened discrimination. However, multilevel interaction analyses revealed that HNT-TMS strengthened fear discrimination in participants with lower fear sensitization (indexed by responses to a control stimulus with no similarity to the conditioned fear cue) across multiple indices (anxiety ratings: ß = 0.10, 95% CI, 0.04 to 0.17, p = .001; risk ratings: ß = 0.07, 95% CI, 0.00 to 0.13, p = .037). Conclusions: Overgeneralization is an associative process that reflects deficient discrimination of the fear cue from similar cues. In contrast, sensitization reflects nonassociative responding unrelated to fear cue similarity. Our results suggest that HNT-TMS may selectively sharpen fear discrimination when associative response patterns, which putatively implicate the hippocampus, are more strongly engaged.

Fear overgeneralization is a promising pathogenic mechanism of clinical anxiety that is thought to be driven by deficient hippocampal discrimination. Using hippocampal network­targeted transcranial magnetic stimulation (HNT-TMS) in healthy participants with symptoms of posttraumatic stress, Webler et al. report that HNT-TMS did not strengthen discrimination overall, but it did strengthen fear discrimination in participants with lower fear sensitization. Sensitization reflects nonassociative fear responding unrelated to fear cue similarity and therefore is not expected to engage the hippocampal discrimination function. These results suggest that HNT-TMS may selectively sharpen fear discrimination when the hippocampal discrimination function is more strongly engaged.

Multi-level fMRI analysis applied to hemispheric specialization in the language network, functional areas, and their behavioral correlations in the ABCD sample.

Prior research suggests that the organization of the language network in the brain is left-dominant and becomes more lateralized with age and increasing language skill. The age at which specific components of the language network become adult-like varies depending on the abilities they subserve. So far, a large, developmental study has not included a language task paradigm, so we introduce a method to study resting-state laterality in the Adolescent Brain Cognitive Development (ABCD) study. Our approach mixes source timeseries between left and right homotopes of the (1) inferior frontal and (2) middle temporal gyri and (3) a region we term "Wernicke's area" near the supramarginal gyrus. Our large subset sample size of ABCD (n = 6153) allows improved reliability and validity compared to previous, smaller studies of brain-behavior associations. We show that behavioral metrics from the NIH Youth Toolbox and other resources are differentially related to tasks with a larger linguistic component over ones with less (e.g., executive function-dominant tasks). These baseline characteristics of hemispheric specialization in youth are critical for future work determining the correspondence of lateralization with language onset in earlier stages of development.

Neural Circuit Markers of Familial Risk for Depression Among Healthy Youths in the Adolescent Brain Cognitive Development Study.

BACKGROUND: Family history of depression is a robust predictor of early-onset depression, which may confer risk through alterations in neural circuits that have been implicated in reward and emotional processing. These alterations may be evident in youths who are at familial risk for depression but who do not currently have depression. However, the identification of robust and replicable findings has been hindered by few studies and small sample sizes. In the current study, we sought to identify functional connectivity (FC) patterns associated with familial risk for depression. METHODS: Participants included healthy (i.e., no lifetime psychiatric diagnoses) youths at high familial risk for depression (HR) (n = 754; at least one parent with a history of depression) and healthy youths at low familial risk for psychiatric problems (LR) (n = 1745; no parental history of psychopathology) who were 9 to 10 years of age and from the Adolescent Brain Cognitive Development (ABCD) Study sample. We conducted whole-brain seed-to-voxel analyses to examine group differences in resting-state FC with the amygdala, caudate, nucleus accumbens, and putamen. We hypothesized that HR youths would exhibit global amygdala hyperconnectivity and striatal hypoconnectivity patterns primarily driven by maternal risk. RESULTS: HR youths exhibited weaker caudate-angular gyrus FC than LR youths (α = 0.04, Cohen's d = 0.17). HR youths with a history of maternal depression specifically exhibited weaker caudate-angular gyrus FC (α = 0.03, Cohen's d = 0.19) as well as weaker caudate-dorsolateral prefrontal cortex FC (α = 0.04, Cohen's d = 0.21) than LR youths. CONCLUSIONS: Weaker striatal connectivity may be related to heightened familial risk for depression, primarily driven by maternal history. Identifying brain-based markers of depression risk in youths can inform approaches to improving early detection, diagnosis, and treatment.

Personalized functional brain network topography is associated with individual differences in youth cognition.

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain's functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex's sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

Resting-State Functional Connectivity Explained Psychotic-like Experiences in the General Population and Partially Generalized to Patients and Relatives.

Background: Psychotic-like experiences (PLEs) are considered the subclinical portion of the psychosis continuum. Research suggests that there are resting-state functional connectivity (rsFC) substrates of PLEs, yet it is unclear if the same substrates underlie more severe psychosis. Here, to our knowledge, we report the first study to build a cross-validated rsFC model of PLEs in a large community sample and directly test its ability to explain psychosis in an independent sample of patients with psychosis and their relatives. Methods: Resting-state FC of 855 healthy young adults from the WU-Minn Human Connectome Project (HCP) was used to predict PLEs with elastic net. An rsFC composite score based on the resulting model was correlated with psychotic traits and symptoms in 118 patients with psychosis, 71 nonpsychotic first-degree relatives, and 45 healthy control subjects from the psychosis HCP. Results: In the HCP, the cross-validated model explained 3.3% of variance in PLEs. Predictive connections spread primarily across the default, frontoparietal, cingulo-opercular, and dorsal attention networks. The model partially generalized to a younger, but not older, subsample in the psychosis HCP, explaining two measures of positive/disorganized psychotic traits (the Structured Interview for Schizotypy: ß = 0.25, pone-tailed = .027; the Schizotypy Personality Questionnaire positive factor: ß = 0.14, pone-tailed = .041). However, it did not differentiate patients from relatives and control subjects or explain psychotic symptoms in patients. Conclusions: Some rsFC substrates of PLEs are shared across the psychosis continuum. However, explanatory power was modest, and generalization was partial. It is equally important to understand shared versus distinct rsFC variances across the psychosis continuum.

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps.

Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.

The CBIT+TMS Trial: study protocol for a two-phase randomized controlled trial testing neuromodulation to augment behavior therapy for youth with chronic tics.

Background: Comprehensive Behavioral Intervention for Tics (CBIT) is a first-line treatment for tic disorders that aims to improve controllability over tics that an individual finds distressing or impairing. However, it is only effective for approximately half of patients. Supplementary motor area (SMA)-directed neurocircuitry plays a strong role in motor inhibition, and activity in this region is thought to contribute to tic expression. Targeted modulation of SMA using transcranial magnetic stimulation (TMS) may increase CBIT efficacy by improving patient ability to implement tic controllability behaviors. Methods: The CBIT+TMS trial is a two-phase, milestone driven early-stage randomized controlled trial. The trial will test whether augmenting CBIT with inhibitory, noninvasive stimulation of SMA with TMS modifies activity in SMA-mediated circuits and enhances tic controllability in youth ages 12-21 years with chronic tics. Phase 1 will directly compare two rTMS augmentation strategies (1Hz rTMS vs. cTBS) vs. sham in N = 60 participants. Quantifiable, a priori "Go/No Go Criteria" guide the decision to proceed to Phase 2 and selection of the optimal TMS regimen. Phase 2 will compare the optimal regimen vs. sham and test the link between neural target engagement and clinical outcomes in a new sample of N = 60 participants. Discussion: This clinical trial is one of few to date testing TMS augmentation of therapy in a pediatric sample. Results will provide insight into whether TMS is a potentially viable strategy for enhancing CBIT efficacy and reveal potential neural and behavioral mechanisms of change. Trial registration: ClinicalTrials.gov Identifier: NCT04578912.

Continuous Automated Analysis Workflow for MRS Studies.

Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review.The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps.The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory.Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages.

Subtypes in addiction and their neurobehavioral profiles across three functional domains.

Rates of return to use in addiction treatment remain high. We argue that the development of improved treatment options will require advanced understanding of individual heterogeneity in Substance Use Disorders (SUDs). We hypothesized that considerable individual differences exist in the three functional domains underlying addiction-approach-related behavior, executive function, and negative emotionality. We included N = 593 participants from the enhanced Nathan Kline Institute-Rockland Sample community sample (ages 18-59, 67% female) that included N = 420 Controls and N = 173 with past SUDs [54% female; N = 75 Alcohol Use Disorder (AUD) only, N = 30 Cannabis Use Disorder (CUD) only, and N = 68 Multiple SUDs]. To test our a priori hypothesis that distinct neuro-behavioral subtypes exist within individuals with past SUDs, we conducted a latent profile analysis with all available phenotypic data as input (74 subscales from 18 measures), and then characterized resting-state brain function for each discovered subtype. Three subtypes with distinct neurobehavioral profiles were recovered (p < 0.05, Cohen's D: 0.4-2.8): a "Reward type" with higher approach-related behavior (N = 69); a "Cognitive type" with lower executive function (N = 70); and a "Relief type" with high negative emotionality (N = 34). For those in the Reward type, substance use mapped onto resting-state connectivity in the Value/Reward, Ventral-Frontoparietal and Salience networks; for the Cognitive type in the Auditory, Parietal Association, Frontoparietal and Salience networks; and for the Relief type in the Parietal Association, Higher Visual and Salience networks (pFDR < 0.05). Subtypes were equally distributed amongst individuals with different primary SUDs (χ2 = 4.71, p = 0.32) and gender (χ2 = 3.44, p = 0.18). Results support functionally derived subtypes, demonstrating considerable individual heterogeneity in the multi-dimensional impairments in addiction. This confirms the need for mechanism-based subtyping to inform the development of personalized addiction medicine approaches.

Polyneuro risk scores capture widely distributed connectivity patterns of cognition.

Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework's ability to reliably capture brain-behavior relationships across 3 cognitive scores - general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans.

Objectives: Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations. Experimental Design: Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance. Principal Observations: Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better. Conclusions: BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.

Identifying body awareness-related brain network changes after Spring Forest Qigong™ practice or P.Volve low-intensity exercise in adults with chronic low back pain: a feasibility Phase I Randomized Clinical Trial.

Background: Chronic low back pain (cLBP) affects the quality of life of 52 million Americans and leads to an enormous personal and economic burden. A multidisciplinary approach to cLBP management is recommended. Since medication has limited efficacy and there are mounting concerns about opioid addiction, the American College of Physicians and American Pain Society recommend non-pharmacological interventions, such as mind and body approaches (e.g., Qigong, yoga, Tai Chi) before prescribing medications. Of those, Qigong practice might be most accessible given its gentle movements and because it can be performed standing, sitting, or lying down. The three available Qigong studies in adults with cLBP showed that Qigong reduced pain more than waitlist and equally well than exercise. Yet, the duration and/or frequency of Qigong practice were low (<12 weeks or less than 3x/week). The objectives of this study were to investigate the feasibility of practicing Spring Forest Qigong™ or performing P.Volve low intensity exercises 3x/week for 12 weeks, feasibility of recruitment, data collection, delivery of the intervention as intended, as well as identify estimates of efficacy on brain function and behavioral outcomes after Qigong practice or exercise. To our knowledge, this is the first study investigating the feasibility of the potential effect of Qigong on brain function in adults with cLBP. Methods: We conducted a feasibility Phase I Randomized Clinical Trial. Of the 36 adults with cLBP recruited between January 2020 and June 2021, 32 were enrolled and randomized to either 12 weeks of remote Spring Forest Qigong™ practice or remote P.Volve low-intensity exercises. Participants practiced at least 3x/week for 41min/session with online videos. Our main outcome measures were the Numeric Pain Rating Scale (highest, average, and lowest cLBP pain intensity levels in the prior week), assessed weekly and fMRI data (resting-state and task-based fMRI tasks: pain imagery, kinesthetic imagery of a Qigong movement, and robot-guided shape discrimination). We compared baseline resting-state connectivity and brain activation during fMRI tasks in adults with cLBP with data from a healthy control group (n=28) acquired in a prior study. Secondary outcomes included measures of function, disability, body awareness, kinesiophobia, balance, self-efficacy, core muscle strength, and ankle proprioceptive acuity with a custom-build device. Results: Feasibility of the study design and methods was demonstrated with 30 participants completing the study (94% retention) and reporting high satisfaction with the programs; 96% adherence to P.Volve low-intensity exercises, and 128% of the required practice intensity for Spring Forest Qigong™ practice. Both groups saw promising reductions in low back pain (effect sizes Cohen's d =1.01-2.22) and in most other outcomes ( d =0.90-2.33). Markers of ankle proprioception were not significantly elevated in the cLBP group after the interventions. Brain imaging analysis showed weaker parietal operculum and insula network connectivity in adults with cLBP (n=26), compared to data from a healthy control group (n=28). The pain imagery task elicited lower brain activation of insula, parietal operculum, angular gyrus and supramarginal gyrus at baseline in adults with cLBP than in healthy adults. Adults with cLBP had lower precentral gyrus activation than healthy adults for the Qigong movement and robot task at baseline. Pre-post brain function changes showed individual variability: Six (out of 13) participants in the Qigong group showed increased activation in the parietal operculum, angular gyrus, supramarginal gyrus, and precentral gyrus during the Qigong fMRI task. Interpretation: Our data indicate the feasibility and acceptability of using Spring Forest Qigong™ practice or P.Volve low-intensity exercises for cLBP relief showing promising results in terms of pain relief and associated symptoms. Our brain imaging results indicated brain function improvements after 12 weeks of Qigong practice in some participants, pointing to the need for further investigation in larger studies. Trial registration number: ClinicalTrials.gov: NCT04164225 .

Identifying Body Awareness-Related Brain Network Changes after Cognitive Multisensory Rehabilitation for Neuropathic Pain Relief in Adults with Spinal Cord Injury: Delayed Treatment arm Phase I Randomized Controlled Trial.

Background: Neuropathic pain after spinal cord injury (SCI) is notoriously hard to treat. Mechanisms of neuropathic pain are unclear, which makes finding effective treatments challenging. Prior studies have shown that adults with SCI have body awareness deficits. Recent imaging studies, including ours, point to the parietal operculum and insula as key areas for both pain perception and body awareness. Cognitive multisensory rehabilitation (CMR) is a physical therapy approach that helps improve body awareness for pain reduction and sensorimotor recovery. Based on our prior brain imaging work in CMR in stroke, we hypothesized that improving body awareness through restoring parietal operculum network connectivity leads to neuropathic pain relief and improved sensorimotor and daily life function in adults with SCI. Thus, the objectives of this study were to (1) determine baseline differences in resting-state and task-based functional magnetic resonance imaging (fMRI) brain function in adults with SCI compared to healthy controls and (2) identify changes in brain function and behavioral pain and pain-associated outcomes in adults with SCI after CMR. Methods: Healthy adults underwent a one-time MRI scan and completed questionnaires. We recruited community-dwelling adults with SCI-related neuropathic pain, with complete or incomplete SCI >3 months, and highest neuropathic pain intensity level of >3 on the Numeric Pain Rating Scale (NPRS). Participants with SCI were randomized into two groups, according to a delayed treatment arm phase I randomized controlled trial (RCT): Group A immediately received CMR intervention, 3x/week, 45 min/session, followed by a 6-week and 1-year follow-up. Group B started with a 6-week observation period, then 6 weeks of CMR, and a 1-year follow-up. Highest, average, and lowest neuropathic pain intensity levels were assessed weekly with the NPRS as primary outcome. Other primary outcomes (fMRI resting-state and functional tasks; sensory and motor function with the INSCI AIS exam), as well as secondary outcomes (mood, function, spasms, and other SCI secondary conditions), were assessed at baseline, after the first and second 6-week period. The INSCI AIS exam and questionnaires were repeated at the 1-year follow-up. Findings: Thirty-six healthy adults and 28 adults with SCI were recruited between September 2020 and August 2021, and of those, 31 healthy adults and 26 adults with SCI were enrolled in the study. All 26 participants with SCI completed the intervention and pre-post assessments. There were no study-related adverse events. Participants were 52±15 years of age, and 1-56 years post-SCI. During the observation period, group B did not show any reductions in neuropathic pain and did not have any changes in sensation or motor function (INSCI ASIA exam). However, both groups experienced a significant reduction in neuropathic pain after the 6-week CMR intervention. Their highest level of neuropathic pain of 7.81±1.33 on the NPRS at baseline was reduced to 2.88±2.92 after 6 weeks of CMR. Their change scores were 4.92±2.92 (large effect size Cohen's d =1.68) for highest neuropathic pain, 4.12±2.23 ( d =1.85) for average neuropathic pain, and 2.31±2.07 ( d =1.00) for lowest neuropathic pain. Nine participants out of 26 were pain-free after the intervention (34.62%). The results of the INSCI AIS testing also showed significant improvements in sensation, muscle strength, and function after 6 weeks of CMR. Their INSCI AIS exam increased by 8.81±5.37 points ( d =1.64) for touch sensation, 7.50±4.89 points ( d =1.53) for pin prick sensation, and 3.87±2.81 ( d =1.38) for lower limb muscle strength. Functional improvements after the intervention included improvements in balance for 17 out of 18 participants with balance problems at baseline; improved transfers for all of them and a returned ability to stand upright with minimal assistance in 12 out of 20 participants who were unable to stand at baseline. Those improvements were maintained at the 1-year follow-up. With regard to brain imaging, we confirmed that the resting-state parietal operculum and insula networks had weaker connections in adults with SCI-related neuropathic pain (n=20) compared to healthy adults (n=28). After CMR, stronger resting-state parietal operculum network connectivity was found in adults with SCI. Also, at baseline, as expected, right toe sensory stimulation elicited less brain activation in adults with SCI (n=22) compared to healthy adults (n=26). However, after CMR, there was increased brain activation in relevant sensorimotor and parietal areas related to pain and mental body representations (i.e., body awareness and visuospatial body maps) during the toe stimulation fMRI task. These brain function improvements aligned with the AIS results of improved touch sensation, including in the feet. Interpretation: Adults with chronic SCI had significant neuropathic pain relief and functional improvements, attributed to the recovery of sensation and movement after CMR. The results indicate the preliminary efficacy of CMR for restoring function in adults with chronic SCI. CMR is easily implementable in current physical therapy practice. These encouraging impressive results pave the way for larger randomized clinical trials aimed at testing the efficacy of CMR to alleviate neuropathic pain in adults with SCI. Clinical Trial registration: ClinicalTrials.gov Identifier: NCT04706208. Funding: AIRP2-IND-30: Academic Investment Research Program (AIRP) University of Minnesota School of Medicine. National Center for Advancing Translational Sciences of the National Institutes of Health Award Number UL1TR002494; the Biotechnology Research Center: P41EB015894, the National Institute of Neurological Disorders & Stroke Institutional Center Core Grants to Support Neuroscience Research: P30 NS076408; and theHigh-Performancee Connectome Upgrade for Human 3T MR Scanner: 1S10OD017974.

Identifying Body Awareness-Related Brain Network Changes After Cognitive Multisensory Rehabilitation for Neuropathic Pain Relief in Adults With Spinal Cord Injury: Protocol of a Phase I Randomized Controlled Trial.

Background: About 69% of the 299,000 Americans living with spinal cord injury (SCI) experience long-term debilitating neuropathic pain. New treatments are needed because current treatments do not provide enough pain relief. We have found that insular-opercular brain network alterations may contribute to neuropathic pain and that restoring this network could reduce neuropathic pain. Here, we outline a study protocol using a physical therapy approach, cognitive multisensory rehabilitation (CMR), which has been shown to restore OP1/OP4 connections in adults post stroke, to test our hypothesis that CMR can normalize pain perception through restoring OP1/OP4 connectivity in adults with SCI and relieve neuropathic pain. Objectives: To compare baseline brain function via resting-state and task-based functional magnetic resonance imaging in adults with SCI versus uninjured controls, and to identify changes in brain function and behavioral pain outcomes after CMR in adults with SCI. Methods: In this phase I randomized controlled trial, adults with SCI will be randomized into two groups: Group A will receive 6 weeks of CMR followed by 6 weeks of standard of care (no therapy) at home. Group B will start with 6 weeks of standard of care (no therapy) at home and then receive 6 weeks of CMR. Neuroimaging and behavioral measures are collected at baseline, after the first 6 weeks (A: post therapy, B: post waitlist), and after the second 6 weeks (A: post-therapy follow-up, B: post therapy), with follow-up of both groups up to 12 months. Conclusion: The successful outcome of our study will be a critical next step toward implementing CMR in clinical care to improve health in adults with SCI.

Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets.

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins.

Schizophrenia is characterized by abnormal brain structure such as global reductions in gray matter volume. Machine learning models trained to estimate the age of brains from structural neuroimaging data consistently show advanced brain-age to be associated with schizophrenia. Yet, it is unclear whether advanced brain-age is specific to schizophrenia compared to other psychotic disorders, and whether evidence that brain structure is "older" than chronological age actually reflects neurodevelopmental rather than atrophic processes. It is also unknown whether advanced brain-age is associated with genetic liability for psychosis carried by biological relatives of people with schizophrenia. We used the Brain-Age Regression Analysis and Computation Utility Software (BARACUS) prediction model and calculated the residualized brain-age gap of 332 adults (163 individuals with psychotic disorders: 105 schizophrenia, 17 schizoaffective disorder, 41 bipolar I disorder with psychotic features; 103 first-degree biological relatives; 66 controls). The model estimated advanced brain-ages for people with psychosis in comparison to controls and relatives, with no differences among psychotic disorders or between relatives and controls. Specifically, the model revealed an enlarged brain-age gap for schizophrenia and bipolar disorder with psychotic features. Advanced brain-age was associated with lower cognitive and general functioning in the full sample. Among relatives, cognitive performance and schizotypal symptoms were related to brain-age gap, suggesting that advanced brain-age is associated with the subtle expressions associated with psychosis. Exploratory longitudinal analyses suggested that brain aging was not accelerated in individuals with a psychotic disorder. In sum, we found that people with psychotic disorders, irrespective of specific diagnosis or illness severity, show indications of non-progressive, advanced brain-age. These findings support a transdiagnostic, neurodevelopmental formulation of structural brain abnormalities in psychotic psychopathology.

Reproducible brain-wide association studies require thousands of individuals.

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Learning Strategies for Contrast-agnostic Segmentation via SynthSeg for Infant MRI data.

Longitudinal studies of infants' brains are essential for research and clinical detection of neurodevelopmental disorders. However, for infant brain MRI scans, effective deep learning-based segmentation frameworks exist only within small age intervals due to the large image intensity and contrast changes that take place in the early postnatal stages of development. However, using different segmentation frameworks or models at different age intervals within the same longitudinal data set would cause segmentation inconsistencies and age-specific biases. Thus, an age-agnostic segmentation model for infants' brains is needed. In this paper, we present "Infant-SynthSeg", an extension of the contrast-agnostic SynthSeg segmentation framework applicable to MRI data of infants at ages within the first year of life. Our work mainly focuses on extending learning strategies related to synthetic data generation and augmentation, with the aim of creating a method that employs training data capturing features unique to infants' brains during this early-stage development. Comparison across different learning strategy settings, as well as a more-traditional contrast-aware deep learning model (nnU-net) are presented. Our experiments show that our trained Infant-SynthSeg models show consistently high segmentation performance on MRI scans of infant brains throughout the first year of life. Furthermore, as the model is trained on ground truth labels at different ages, even labels that are not present at certain ages (such as cerebellar white matter at 1 month) can be appropriately segmented via Infant-SynthSeg across the whole age range. Finally, while Infant-SynthSeg shows consistent segmentation performance across the first year of life, it is outperformed by age-specific deep learning models trained for a specific narrow age range.