Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies

The German research consortium for the study of bipolar disorder (BipoLife): a quality assurance protocol for MR neuroimaging data

BACKGROUND

The German multicenter research consortium BipoLife aims to investigate the mechanisms underlying bipolar disorders. It focuses in particular on people at high risk of developing the disorder and young patients in the early stages of the disease. Functional and structural magnetic resonance imaging (MRI) data was collected in all participating centers. The collection of neuroimaging data in a longitudinal, multicenter study requires the implementation of a comprehensive quality assurance (QA) protocol. Here, we outline this protocol and illustrate its application within the BipoLife consortium.

METHODS

The QA protocol consisted of (1) a training of participating research staff, (2) regular phantom measurements to evaluate the MR scanner performance and its temporal stability across the course of the study, and (3) the assessment of the quality of human MRI data by evaluating a variety of image metrics (e.g., signal-to-noise ratio, ghosting level). In this article, we will provide an overview on these QA procedures and show exemplarily the influence of its application on the results of standard neuroimaging analysis pipelines.

DISCUSSION

The QA protocol helped to characterize the various MR scanners, to record their performance over the course of the study and to detect possible malfunctions at an early stage. It also assessed the quality of the human MRI data systematically to characterize its influence on various analyses. Furthermore, by setting up and publishing this protocol, we define standards that must be considered when analyzing data from the BipoLife consortium. It further promotes a systematic evaluation of data quality and a definition of subject inclusion criteria. In the long term, it will help to increase the chance of achieving clinically relevant results.

Identifying subgroups of urge suppression in Obsessive-Compulsive Disorder using machine learning

Obsessive-compulsive disorder (OCD) is phenomenologically heterogeneous. While predominant models suggest fear and harm prevention drive compulsions, many patients also experience uncomfortable sensory-based urges ("sensory phenomena") that may be associated with heightened interoceptive sensitivity. Using an urge-to-blink eyeblink suppression paradigm to model sensory-based urges, we previously found that OCD patients as a group had more eyeblink suppression failures and greater activation of sensorimotor-interoceptive regions than controls. However, conventional approaches assuming OCD homogeneity may obscure important within-group variability, impeding precision treatment development. This study investigated the heterogeneity of urge suppression failure in OCD and examined relationships with clinical characteristics and neural activation. Eighty-two patients with OCD and 38 controls underwent an fMRI task presenting 60-s blocks of eyeblink suppression alternating with free-blinking blocks. Latent profile analysis identified OCD subgroups based on number of erroneous blinks during suppression. Subgroups were compared on behavior, clinical characteristics, and brain activation during task. Three patient subgroups were identified. Despite similar overall OCD severity, the subgroup with the most erroneous eyeblinks had the highest sensory phenomena severity, interoceptive sensitivity, and subjective urge intensity. Compared to other subgroups, this subgroup exhibited more neural activity in somatosensory and interoceptive regions during the early phase (first 30 s) of blink suppression and reduced activity in the middle frontal gyrus during the late phase (second 30 s) as the suppression period elapsed. Heterogeneity of urge suppression in OCD was associated with clinical characteristics and brain function. Our results reveal potential treatment targets that could inform personalized medicine.

The effects of endogenous semantic variables during productive lexical retrieval: A Behavioral-Neural Dual Swinging Model (DSM)

Conceptual preparation is the very initial step in language production. Endogenous semantic variables, reflecting the inherent semantic properties of concepts, could influence the productive lexical retrieval by modulating both conceptual activation and lexical selection. Yet, empirical understandings on this process and underlying mechanisms remain limited. Here, inspired by previous theoretical models and preliminary findings, we proposed a Behavioral-Neural Dual Swinging Model (DSM), revealing the swinging process between conceptual facilitation and lexical interference and extending to neural resource allocation during these processes. To further test the model, we examined the joint effect of semantic richness and semantic density on productive word retrieval both behaviorally and neurally, using a picture naming paradigm. Results nicely support the DSM by showing that the productive retrieval is driven by the swinging between semantic richness-induced conceptual facilitation primarily managed in semantic-related regions and semantic density-induced lexical interference managed in control-related regions. Moreover, the conceptual facilitation accumulated from semantic richness plays a decisive role, mitigating interference from competitors as well as the neural demands in control-related regions.

Structural white matter abnormalities in Schizophrenia and associations with neurocognitive performance and symptom severity

Schizophrenia is associated with robust white matter (WM) abnormalities but influences of potentially confounding variables and relationships with cognitive performance and symptom severity remain to be fully determined. This study was designed to evaluate WM abnormalities based on diffusion tensor imaging (DTI) in individuals with schizophrenia, and their relationships with cognitive performance and symptom severity. Data from individuals with schizophrenia (SZ; n=138, mean age±SD=39.02±11.82; 105 males) and healthy controls (HC; n=143, mean age±SD=37.07±10.84; 102 males) were collected as part of the Function Biomedical Informatics Research Network Phase 3 study. Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were compared between individuals with schizophrenia and healthy controls, and their relationships with neurocognitive performance and symptomatology assessed. Individuals with SZ had significantly lower FA in forceps minor and the left inferior fronto-occipital fasciculus compared to HC. FA in several tracts were associated with speed of processing and attention/vigilance and the severity of the negative symptom alogia. This study suggests that regional WM abnormalities are fundamentally involved in the pathophysiology of schizophrenia and may contribute to cognitive performance deficits and symptom expression observed in schizophrenia.

Connectome predictive modeling of trait mindfulness

Introduction

Trait mindfulness refers to one's disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved.

Methods

To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites.

Results

In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model.

Conclusions

We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

Large Individual Differences in Functional Connectivity in the Context of Major Depression and Antidepressant Pharmacotherapy

Clinical studies of major depression (MD) generally focus on group effects, yet interindividual differences in brain function are increasingly recognized as important and may even impact effect sizes related to group effects. Here, we examine the magnitude of individual differences in relation to group differences that are commonly investigated (e.g., related to MD diagnosis and treatment response). Functional MRI data from 107 participants (63 female, 44 male) were collected at baseline, 2, and 8 weeks during which patients received pharmacotherapy (escitalopram, N = 68) and controls (N = 39) received no intervention. The unique contributions of different sources of variation were examined by calculating how much variance in functional connectivity was shared across all participants and sessions, within/across groups (patients vs controls, responders vs nonresponders, female vs male participants), recording sessions, and individuals. Individual differences and common connectivity across groups, sessions, and participants contributed most to the explained variance (>95% across analyses). Group differences related to MD diagnosis, treatment response, and biological sex made significant but small contributions (0.3-1.2%). High individual variation was present in cognitive control and attention areas, while low individual variation characterized primary sensorimotor regions. Group differences were much smaller than individual differences in the context of MD and its treatment. These results could be linked to the variable findings and difficulty translating research on MD to clinical practice. Future research should examine brain features with low and high individual variation in relation to psychiatric symptoms and treatment trajectories to explore the clinical relevance of the individual differences identified here.

Sensory over-responsivity and orbitofrontal cortex connectivity in obsessive-compulsive disorder

BACKGROUND

Sensory over-responsivity (SOR) in obsessive-compulsive disorder (OCD) is associated with illness severity and functional impairment. However, the neural substrates of SOR in OCD have not yet been directly probed.

METHODS

We examined resting-state global functional connectivity markers of SOR in 119 adults with OCD utilizing the CONN-fMRI Functional Connectivity Toolbox for SPM (v21a). We quantified SOR with the sensory sensitivity and sensory avoiding subscales of the Adult and Adolescent Sensory Profile (AASP). We also measured: OCD severity, with the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and Obsessive-Compulsive Inventory-Revised (OCI-R); sensory phenomena with the Sensory Phenomena Scale (SPS); general anxiety, with the Beck Anxiety Inventory (BAI); and depressive symptomatology, with Quick Inventory of Depressive Symptoms, Self-Report (QIDS-SR).

RESULTS

There was a significant positive relationship of SOR with global connectivity in anterior and medial OFC (Brodmanns area 11, k = 154, x = 14, y = 62, z = -18, whole-brain corrected at FWE p < 0.05).

LIMITATIONS

Future investigations should explore neural responses to sensory stimulation tasks in OCD and compare findings with those obtained in other conditions also characterized by high SOR, such as autism spectrum disorder.

CONCLUSIONS

This study implicates OFC functional connectivity as a neurobiological mechanism of SOR in OCD and suggests that the substrates of SOR in OCD may be dissociable from both that of other symptoms in OCD, and SOR in other disorders. With replication and extension, the finding may be leveraged to develop and refine treatments for OCD and investigate the pathophysiology of SOR in other conditions.

Effect of Phase Encoding Direction on Image Quality in Single-Shot EPI Diffusion-Weighted Imaging of the Breast

BACKGROUND

In breast diffusion-weighted imaging (DWI), distortion and physiologic artifacts affect clinical interpretation. Image quality can be optimized by addressing the effect of phase encoding (PE) direction on these artifacts.

PURPOSE

To compare distortion artifacts in breast DWI acquired with different PE directions and polarities, and to discuss their clinical implications.

STUDY TYPE

Prospective.

POPULATION

Eleven healthy volunteers (median age: 47 years old; range: 22-74 years old) and a breast phantom.

FIELD STRENGTH/SEQUENCE

Single-shot echo planar DWI and three-dimensional fast gradient echo sequences at 3 T.

ASSESSMENT

All DWI data were acquired with left-right, right-left, posterior-anterior, and anterior-posterior PE directions. In phantom data, displacement magnitude was evaluated by comparing the location of landmarks in anatomical and DWI images. Three breast radiologists (5, 17, and 23 years of experience) assessed the presence or absence of physiologic artifacts in volunteers' DWI datasets and indicated their PE-direction preference.

STATISTICAL TESTS

Analysis of variance with post-hoc tests were used to assess differences in displacement magnitude across DWI datasets and observers. A binomial test and a chi-squared test were used to evaluate if each in vivo DWI dataset had an equal probability (25%) of being preferred by radiologists. Inter-reader agreement was evaluated using Gwet's AC1 agreement coefficient. A P-value <0.05 was considered statistically significant.

RESULTS

In the phantom study, median displacement was the significantly largest in posterior-anterior data. While the displacement in the anterior-posterior and left-right data were equivalent (P = 0.545). In the in vivo data, there were no physiological artifacts observed in any dataset, regardless of PE direction. In the reader study, there was a significant preference for the posterior-anterior datasets which were selected 94% of the time. There was good agreement between readers (0.936).

DATA CONCLUSION

This study showed the impact of PE direction on distortion artifacts in breast DWI. In healthy volunteers, the posterior-to-anterior PE direction was preferred by readers.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Structural variability of the cerebral cortex in schizophrenia and its association with clinical symptoms

BACKGROUND

Substantial evidence indicates structural abnormalities in the cerebral cortex of patients with schizophrenia (SCZ), although their clinical implications remain unclear. Previous case-control studies have investigated group-level differences in structural abnormalities, although the study design cannot account for interindividual differences. Recent research has focused on the association between the heterogeneity of the cerebral cortex morphometric features and clinical heterogeneity.

METHODS

We used neuroimaging data from 420 healthy controls and 695 patients with SCZ from seven studies. Four cerebral cortex measures were obtained: surface area, gray matter volume, thickness, and local gyrification index. We calculated the coefficient of variation (CV) and person-based similarity index (PBSI) scores and performed group comparisons. Associations between the PBSI scores and cognitive functions were evaluated using Spearman's rho test and normative modeling.

RESULTS

Patients with SCZ had a greater CV of surface area and cortical thickness than those of healthy controls. All PBSI scores across cortical measures were lower in patients with SCZ than in HCs. In the patient group, the PBSI scores for gray matter volume and all cortical measures taken together positively correlated with the full-scale IQ scores. Patients with deviant PBSI scores for gray matter volume and all cortical measures taken together had lower full-scale IQ scores than those of other patients.

CONCLUSIONS

The cerebral cortex in patients with SCZ showed greater regional and global structural variability than that in healthy controls. Patients with deviant similarity of cortical structural profiles exhibited a lower general intelligence than those exhibited by the other patients.

Multilingual Language Diversity Protects Native Language Production under Different Control Demands

The use of multiple languages has been found to influence individuals' cognitive abilities. Although some studies have also investigated the effect of multilingualism on non-native language proficiency, fewer studies have focused on how multilingual experience affects native language production. This study investigated the effect of multilingualism on native language production, specifically examining control demands through a semantic Go/No-Go picture naming task. The multilingual experience was quantified using language entropy, which measures the uncertainty and diversity of language use. Control demands were achieved by manipulating the proportion of Go (i.e., naming) trials in different conditions. Results showed that as control demands increased, multilingual individuals exhibited poorer behavioral performance and greater brain activation throughout the brain. Moreover, more diverse language use was associated with higher accuracy in naming and more interconnected brain networks with greater involvement of domain-general neural resources and less domain-specific neural resources. Notably, the varied and balanced use of multiple languages enabled multilingual individuals to respond more efficiently to increased task demands during native language production.

Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the functional connectivity of the human brain. While rs-fMRI multi-site data can help to understand the inner working of the brain, the data acquisition and processing of this data has many challenges. One of the challenges is the variability of the data associated with different acquisitions sites, and different MRI machines vendors. Other factors such as population heterogeneity among different sites, with variables such as age and gender of the subjects, must also be considered. Given that most of the machine-learning models are developed using these rs-fMRI multi-site data sets, the intrinsic confounding effects can adversely affect the generalizability and reliability of these computational methods, as well as the imposition of upper limits on the classification scores. This work aims to identify the phenotypic and imaging variables producing the confounding effects, as well as to control these effects. Our goal is to maximize the classification scores obtained from the machine learning analysis of the Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI multi-site data. To achieve this goal, we propose novel methods of stratification to produce homogeneous sub-samples of the 17 ABIDE sites, as well as the generation of new features from the static functional connectivity values, using multiple linear regression models, ComBat harmonization models, and normalization methods. The main results obtained with our statistical models and methods are an accuracy of 76.4%, sensitivity of 82.9%, and specificity of 77.0%, which are 8.8%, 20.5%, and 7.5% above the baseline classification scores obtained from the machine learning analysis of the static functional connectivity computed from the ABIDE rs-fMRI multi-site data.

Auditory oddball hypoactivation in schizophrenia

Individuals with schizophrenia (SZ) show aberrant activations, assessed via functional magnetic resonance imaging (fMRI), during auditory oddball tasks. However, associations with cognitive performance and genetic contributions remain unknown. This study compares individuals with SZ to healthy volunteers (HVs) using two cross-sectional data sets from multi-center brain imaging studies. It examines brain activation to auditory oddball targets, and their associations with cognitive domain performance, schizophrenia polygenic risk scores (PRS), and genetic variation (loci). Both sample 1 (137 SZ vs. 147 HV) and sample 2 (91 SZ vs. 98 HV), showed hypoactivation in SZ in the left-frontal pole, and right frontal orbital, frontal pole, paracingulate, intracalcarine, precuneus, supramarginal and hippocampal cortices, and right thalamus. In SZ, precuneus activity was positively related to cognitive performance. Schizophrenia PRS showed a negative correlation with brain activity in the right-supramarginal cortex. GWA analyses revealed significant single-nucleotide polymorphisms associated with right-supramarginal gyrus activity. RPL36 also predicted right-supramarginal gyrus activity. In addition to replicating hypoactivation for oddball targets in SZ, this study identifies novel relationships between regional activity, cognitive performance, and genetic loci that warrant replication, emphasizing the need for continued data sharing and collaborative efforts.

Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.

Resting State Network Segregation Modulates Age-Related Differences in Language Production

Older adults typically exhibit decline in language production. However, how the brain supports or fails to support these processes is unclear. Moreover, there are competing hypotheses about the nature of age-related neural changes and whether age-related increases in neural activity reflect compensation or a decline in neural efficiency. In the current study, we investigated the neural bases of language production focusing on resting state functional connectivity. We hypothesized that language production performance, functional connectivity, and their relationship would differ as a function of age. Consistent with prior work, older age was associated with worse language production performance. Functional connectivity analyses showed that network segregation within the left hemisphere language network was maintained across adulthood. However, increased age was associated with lower whole brain network segregation. Moreover, network segregation was related to language production ability. In both network analyses, there were significant interactions with age-higher network segregation was associated with better language production abilities for younger and middle-aged adults, but not for older adults. Interestingly, there was a stronger relationship between language production and the whole brain network segregation than between production and the language network. These results highlight the utility of network segregation measures as an index of brain function, with higher network segregation associated with better language production ability. Moreover, these results are consistent with stability in the left hemisphere language network across adulthood and suggest that dedifferentiation among brain networks, outside of the language network, is a hallmark of aging and may contribute to age-related language production difficulties.

Task difficulty modulates age-related differences in functional connectivity during word production

Older adults typically report increased difficulty with language production, while its neural bases are less clear. The current study investigated the neural bases of age-related differences in language production at the word level and the modulating effect of task difficulty, focusing on task-based functional connectivity. Using an English phonological Go/No-Go picture naming task, task difficulty was manipulated by varying the proportion of naming trials (Go trials) and inhibition trials (No-Go trials) across runs. Behaviorally, compared to younger adults, older adults performed worse, and showed larger effects of task difficulty. Neurally, older adults had lower within language network connectivity compared to younger adults. Moreover, older adults' language network became less segregated as task difficulty increased. These results are consistent with the Compensation-Related Utilization of Neural Circuits Hypothesis, suggesting that the brain becomes less specified and efficient with increased task difficulty, and that these effects are stronger among older adults (i.e., more dedifferentiated).

Single Ventricle Reconstruction III: Brain Connectome and Neurodevelopmental Outcomes: Design, Recruitment, and Technical Challenges of a Multicenter, Observational Neuroimaging Study

Patients with hypoplastic left heart syndrome who have been palliated with the Fontan procedure are at risk for adverse neurodevelopmental outcomes, lower quality of life, and reduced employability. We describe the methods (including quality assurance and quality control protocols) and challenges of a multi-center observational ancillary study, SVRIII (Single Ventricle Reconstruction Trial) Brain Connectome. Our original goal was to obtain advanced neuroimaging (Diffusion Tensor Imaging and Resting-BOLD) in 140 SVR III participants and 100 healthy controls for brain connectome analyses. Linear regression and mediation statistical methods will be used to analyze associations of brain connectome measures with neurocognitive measures and clinical risk factors. Initial recruitment challenges occurred that were related to difficulties with: (1) coordinating brain MRI for participants already undergoing extensive testing in the parent study, and (2) recruiting healthy control subjects. The COVID-19 pandemic negatively affected enrollment late in the study. Enrollment challenges were addressed by: (1) adding additional study sites, (2) increasing the frequency of meetings with site coordinators, and (3) developing additional healthy control recruitment strategies, including using research registries and advertising the study to community-based groups. Technical challenges that emerged early in the study were related to the acquisition, harmonization, and transfer of neuroimages. These hurdles were successfully overcome with protocol modifications and frequent site visits that involved human and synthetic phantoms.

Single Ventricle Reconstruction III: Brain Connectome and Neurodevelopmental Outcomes: Design, Recruitment, and Technical Challenges of a Multicenter, Observational Neuroimaging Study

Patients with hypoplastic left heart syndrome who have been palliated with the Fontan procedure are at risk for adverse neurodevelopmental outcomes, lower quality of life, and reduced employability. We describe the methods (including quality assurance and quality control protocols) and challenges of a multi-center observational ancillary study, SVRIII (Single Ventricle Reconstruction Trial) Brain Connectome. Our original goal was to obtain advanced neuroimaging (Diffusion Tensor Imaging and Resting-BOLD) in 140 SVR III participants and 100 healthy controls for brain connectome analyses. Linear regression and mediation statistical methods will be used to analyze associations of brain connectome measures with neurocognitive measures and clinical risk factors. Initial recruitment challenges occurred related to difficulties with: 1) coordinating brain MRI for participants already undergoing extensive testing in the parent study, and 2) recruiting healthy control subjects. The COVID-19 pandemic negatively affected enrollment late in the study. Enrollment challenges were addressed by 1) adding additional study sites, 2) increasing the frequency of meetings with site coordinators and 3) developing additional healthy control recruitment strategies, including using research registries and advertising the study to community-based groups. Technical challenges that emerged early in the study were related to the acquisition, harmonization, and transfer of neuroimages. These hurdles were successfully overcome with protocol modifications and frequent site visits that involved human and synthetic phantoms.

Trial registration number

ClinicalTrials.gov Registration Number: NCT02692443.

Functional connectivity MRI quality control procedures in CONN

Quality control (QC) for functional connectivity magnetic resonance imaging (FC-MRI) is critical to ensure the validity of neuroimaging studies. Noise confounds are common in MRI data and, if not accounted for, may introduce biases in functional measures affecting the validity, replicability, and interpretation of FC-MRI study results. Although FC-MRI analysis rests on the assumption of adequate data processing, QC is underutilized and not systematically reported. Here, we describe a quality control pipeline for the visual and automated evaluation of MRI data implemented as part of the CONN toolbox. We analyzed publicly available resting state MRI data (N = 139 from 7 MRI sites) from the FMRI Open QC Project. Preprocessing steps included realignment, unwarp, normalization, segmentation, outlier identification, and smoothing. Data denoising was performed based on the combination of scrubbing, motion regression, and aCompCor - a principal component characterization of noise from minimally eroded masks of white matter and of cerebrospinal fluid tissues. Participant-level QC procedures included visual inspection of raw-level data and of representative images after each preprocessing step for each run, as well as the computation of automated descriptive QC measures such as average framewise displacement, average global signal change, prevalence of outlier scans, MNI to anatomical and functional overlap, anatomical to functional overlap, residual BOLD timeseries variability, effective degrees of freedom, and global correlation strength. Dataset-level QC procedures included the evaluation of inter-subject variability in the distributions of edge connectivity in a 1,000-node graph (FC distribution displays), and the estimation of residual associations across participants between functional connectivity strength and potential noise indicators such as participant's head motion and prevalence of outlier scans (QC-FC analyses). QC procedures are demonstrated on the reference dataset with an emphasis on visualization, and general recommendations for best practices are discussed in the context of functional connectivity and other fMRI analysis. We hope this work contributes toward the dissemination and standardization of QC testing performance reporting among peers and in scientific journals.

Functional magnetic resonance imaging of headache: Issues, best-practices, and new directions, a narrative review

OBJECTIVE

To ensure readers are informed consumers of functional magnetic resonance imaging (fMRI) research in headache, to outline ongoing challenges in this area of research, and to describe potential considerations when asked to collaborate on fMRI research in headache, as well as to suggest future directions for improvement in the field.

BACKGROUND

Functional MRI has played a key role in understanding headache pathophysiology, and mapping networks involved with headache-related brain activity have the potential to identify intervention targets. Some investigators have also begun to explore its use for diagnosis.

METHODS/RESULTS

The manuscript is a narrative review of the current best practices in fMRI in headache research, including guidelines on transparency and reproducibility. It also contains an outline of the fundamentals of MRI theory, task-related study design, resting-state functional connectivity, relevant statistics and power analysis, image preprocessing, and other considerations essential to the field.

CONCLUSION

Best practices to increase reproducibility include methods transparency, eliminating error, using a priori hypotheses and power calculations, using standardized instruments and diagnostic criteria, and developing large-scale, publicly available datasets.

The Role of fMRI in Drug Development: An Update

Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).

Increased White Matter Coherence Following Three and Six Months of Medical Cannabis Treatment

Background: Previous studies have demonstrated abnormal white matter (WM) microstructure in recreational cannabis consumers; however, the long-term impact of medical cannabis (MC) use on WM coherence is unknown. Accordingly, this study assessed the longitudinal impact of MC treatment on WM coherence. Given results from preclinical studies, we hypothesized that MC treatment would be associated with increased fractional anisotropy (FA) and reduced mean diffusivity (MD). Methods: As part of a larger, longitudinal investigation, patients interested in treating at least one medical condition with commercially available MC products of their choosing were assessed before initiating MC use (baseline n=37; female=25, male=12) and following three (n=31) and six (n=22) months of treatment. WM coherence was assessed via diffusion tensor imaging for bilateral regions of interest including the genu of the corpus callosum, anterior limb of the internal capsule, external capsule, and anterior corona radiata, as well as an occipital control region not expected to change over time. Results: In MC patients, FA values significantly increased bilaterally in several callosal regions relative to baseline following both 3 and 6 months of treatment; MD values significantly decreased in all callosal regions but only following 6 months of treatment. No significant changes in WM coherence were observed in the control region or in a pilot sample of treatment-as-usual patients (baseline n=14), suggesting that increased WM coherence observed in MC patients may be attributed to MC treatment as opposed to confounding factors. Interestingly, significant reductions in MD values correlated with higher cannabidiol (CBD) exposure but not Δ-9-tetrahydrocannabinol exposure. Conclusions: Overall, MC treatment was associated with increased WM coherence, which contrasts with prior research examining recreational cannabis consumers, likely related to inherent differences between recreational consumers and MC patients (e.g., product choice, age of onset). In addition, increased CBD exposure was associated with reduced MD following 6 months of treatment, extending evidence from preclinical research indicating that CBD may be neuroprotective against demyelination. However, additional research is needed to elucidate the clinical efficacy of MC treatment and the risks and benefits of long-term MC use.

Altered cerebellar-motor loop in benign adult familial myoclonic epilepsy type 1: The structural basis of cortical tremor

OBJECTIVE

Cortical tremor/myoclonus is the hallmark feature of benign adult familial myoclonic epilepsy (BAFME), the mechanism of which remains elusive. A hypothesis is that a defective control in the preexisting cerebellar-motor loop drives cortical tremor. Meanwhile, the basal ganglia system might also participate in BAFME. This study aimed to discover the structural basis of cortical tremor/myoclonus in BAFME.

METHODS

Nineteen patients with BAFME type 1 (BAFME1) and 30 matched healthy controls underwent T1-weighted and diffusion tensor imaging scans. FreeSurfer and spatially unbiased infratentorial template (SUIT) toolboxes were utilized to assess the motor cortex and the cerebellum. Probabilistic tractography was generated for two fibers to test the hypothesis: the dentato-thalamo-(M1) (primary motor cortex) and globus pallidus internus (GPi)-thalamic projections. Average fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) of each tract were extracted.

RESULTS

Cerebellar atrophy and dentate nucleus alteration were observed in the patients. In addition, patients with BAFME1 exhibited reduced AD and FA in the left and right dentato-thalamo-M1 nondecussating fibers, respectively false discovery rate (FDR) correction q < .05. Cerebellar projections showed negative correlations with somatosensory-evoked potential P25-N33 amplitude and were independent of disease duration and medication. BAFME1 patients also had increased FA and decreased MD in the left GPi-thalamic projection. Higher FA and lower RD in the right GPi-thalamic projection were also observed (FDR q < .05).

SIGNIFICANCE

The present findings support the hypothesis that the cerebello-thalamo-M1 loop might be the structural basis of cortical tremor in BAFME1. The basal ganglia system also participates in BAFME1 and probably serves a regulatory role.

Relationships between interoceptive sensibility and resting-state functional connectivity of the insula in obsessive-compulsive disorder

Patients with obsessive-compulsive disorder (OCD) exhibit abnormality in their subjective perception of internal sensation, a process known as interoceptive sensibility (IS), as well as altered functioning of the insula, a key neural structure for interoception. We investigated the multivariate structure of IS in 77 OCD patients and 53 controls and examined associations of IS with resting-state functional connectivity (FC) of the insula within the OCD group. For each group, principal component analysis was performed on 8 subscales of the Multidimensional Assessment of Interoceptive Awareness assessing putatively "adaptive" and "maladaptive" aspects of IS. Associations between IS components and insula FC in the OCD group were evaluated using seed regions placed in each of 3 subdivisions of the insula (posterior, anterior dorsal, and anterior ventral). Behaviorally, controls showed a 2-component solution broadly categorized into "adaptive" and "maladaptive" IS, while OCD patients exhibited a 3-component solution. The general tendency to notice or be aware of sensation loaded onto an "adaptive" IS component in controls but loaded onto both "adaptive" and "maladaptive" IS components in OCD. Within OCD, insula FC was differentially associated with distinct aspects of IS, identifying network connections that could serve as future targets for the modulation of IS in OCD.

Neural sensitivity to semantic neighbors is stable across the adult lifespan

As we age, language reflects patterns of both stability and change. On the one hand, vocabulary and semantic abilities are largely stable across the adult lifespan, yet lexical retrieval is often slower and less successful (i.e., slower picture naming times, increased tip of the tongue incidents). Although the behavioral bases of these effects have been well established, less is known about the brain regions that support these age-related differences. We used functional Magnetic Resonance Imaging (fMRI) to examine the neural basis of picture naming. Specifically, we were interested in whether older adults would be equally sensitive to semantic characteristics, specifically the number of semantic near neighbors. Near neighbors, defined here as items with a high degree of semantic feature overlap, were of interest as these are thought to elicit competition among potential candidates and increase naming difficulty. Consistent with prior reports, pictures with more semantic near neighbors were named more slowly and less accurately for all adults. Additionally, this interference for naming times was larger as age increased, starting around 30 years old. In contrast to the age-related behavioral slowing, the neural basis of these effects was stable across adulthood. Across all adults, a number of language-relevant regions including left posterior middle temporal gyrus and left inferior frontal gyrus, pars triangularis were sensitive to the number of near neighbors. Our results suggest that although middle-aged and older adults' picture naming is more slowed by increased semantic competition, the brain regions supporting semantic processes remain stable across the adult lifespan.

Multi-centre classification of functional neurological disorders based on resting-state functional connectivity

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Using machine learning on multi-centre data, FND patients were successfully classified with an accuracy of 72%.

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The angular- and supramarginal gyri, cingular- and insular cortex, and the hippocampus were the most discriminant regions.

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To provide diagnostic utility, future studies must include patients with similar symptoms but different diagnoses.

Background

Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a “rule-in” procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting.

Methods

This study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation).

Results

FND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%).

Conclusions

The results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms.

Combining MRI and cognitive evaluation to classify concussion in university athletes

Current methods of concussion assessment lack the objectivity and reliability to detect neurological injury. This multi-site study uses combinations of neuroimaging (diffusion tensor imaging and resting state functional MRI) and cognitive measures to train algorithms to detect the presence of concussion in university athletes. Athletes (29 concussed, 48 controls) completed symptom reports, brief cognitive evaluation, and MRI within 72 h of injury. Hierarchical linear regression compared groups on cognitive and neuroimaging measures while controlling for sex and data collection site. Logistic regression and support vector machine models were trained using cognitive and neuroimaging measures and evaluated for overall accuracy, sensitivity, and specificity. Concussed athletes reported greater symptoms than controls (∆R² = 0.32, p < .001), and performed worse on tests of concentration (∆R² = 0.07, p < .05) and delayed memory (∆R² = 0.17, p < .001). Concussed athletes showed lower functional connectivity within the frontoparietal and primary visual networks (p < .05), but did not differ on mean diffusivity and fractional anisotropy. Of the cognitive measures, classifiers trained using delayed memory yielded the best performance with overall accuracy of 71%, though sensitivity was poor at 46%. Of the neuroimaging measures, classifiers trained using mean diffusivity yielded similar accuracy. Combining cognitive measures with mean diffusivity increased overall accuracy to 74% and sensitivity to 64%, comparable to the sensitivity of symptom report. Trained algorithms incorporating both MRI and cognitive performance variables can reliably detect common neurobiological sequelae of acute concussion. The integration of multi-modal data can serve as an objective, reliable tool in the assessment and diagnosis of concussion.

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

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Sharing data from cooperative studies is essential to develop new biomarkers in MS.

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Differences in MRI acquisition, analysis, storage represent a substantial constraint.

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We review the state of the art and developments in the harmonization of MRI.

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We provide recommendations to harmonize large MRI datasets in the MS field.

There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.

Pediatric Cardiopulmonary Exercise Testing: Interoperability Through Domain Analysis Modeling and a National Survey

PURPOSE

The electronic health record, data science advances, and dynamic environmental and infectious threats to child health highlight the need for harmonized and interoperable approaches to pediatric cardiopulmonary exercise testing (CPET). Accordingly, we developed a terminology harmonization in exercise medicine and exercise science domain analysis model (THEMES DAM) to structure CPET data elements.

METHODS

THEMES DAM identified 114 data elements, including participant information, calibration, equipment, protocols, laboratory personnel, encouragement strategies, and analysis procedures. We used the THEMES DAM, vetted by the international data standards organization HL7, to construct a current-state survey of pediatric CPET centers in the United States. Forty-eight of 101 centers responded to a questionnaire covering seven major topic areas (38 items).

RESULTS

Centers predominantly performed between 100 and 500 tests annually. Cardiac disease represented 55% of referrals. Almost all centers calibrated gas concentrations and flow daily, but 42% never calibrated their treadmill or cycle ergometers. All centers measured V̇O2peakbut calculated differently. Centers used a variety of protocols (e.g., for treadmill: 61%, Bruce; 43%, modified Bruce; 59%, other); 44% calculated CPET slopes from submaximal portions of CPET (e.g., V̇O2-HR). All centers verbally encouraged participants, but only 40% used a standardized approach. The interpretation of CPET was done by physicians (60%), exercise physiologists (25%), exercise technicians (10%), nurses (1%), or others (4%). Ninety-one percent would agree to collaborate in multicenter research, 89% to establish dynamic reference values, and 83% to better interpret CPET.

CONCLUSIONS

The survey data and the implementation of THEMES DAM could accelerate interoperability across multiple centers. This would facilitate a nimble approach to create pediatric reference values responsive to the constantly changing health environment and stimulate novel approaches to CPET research and clinical application.

A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity

Recent studies showed that working with neuroimage data collected from different research facilities or locations may incur additional source dependency, affecting the overall statistical power. This problem can be mitigated with data harmonization approaches. Recently, the ComBat method has become commonly adopted for various neuroimage modalities. While open neuroimaging datasets are becoming more common, a substantial amount of data is still unable to be shared for various reasons. In addition, current approaches require moving all the data to a central location, which requires additional resources and creates redundant copies of the same datasets. To address these issues, we propose a decentralized harmonization approach that does not create redundant copies of the original datasets and performs remote operations on the datasets separately without sharing any individual subject data, ensuring a certain level of privacy and reducing regulatory hurdles. We proposed a novel approach called "Decentralized ComBat" which can harmonize datasets separately without combining the datasets. We tested our model by harmonizing functional network connectivity datasets from two traumatic brain injury studies in a decentralized way. Also, we used simulations to analyze the performance and scalability of our model when the number of data collection sites increases. We compare the output with centralized ComBat and show that the proposed approach produces similar results, increasing the sensitivity of the functional network connectivity analysis and validating our approach. Simulations show that our model can be easily scaled to many more datasets based on the requirement. In sum, we believe this provides a powerful tool, further complementing open data and allowing for integrating public and private datasets.

Development of a novel task-based functional magnetic resonance imaging phantom based on a bubble-compression approach

PURPOSE

Phantoms used in previous functional magnetic resonance imaging (fMRI) studies have drawbacks, such as a complicated circuit and equipment use, a single signal-change rate, and T₂ * values that do not correspond to those of living human brains. We aimed to develop a phantom for use in task-based fMRI studies (gradient-echo echo-planar imaging; GRE-EPI) with bioequivalent T₁ and T₂ * values, using an innovative method to control the rate of signal change.

METHODS

A gel phantom with T₁ and T₂ * values equivalent to that of the living brain gray matter was fixed in a 150 mm diameter container, with five holes, each of which could hold a 30-mL syringe. The gel phantom contained microscopic air bubbles; this made it possible to control the percent signal change by injector-induced water pressure changes. Using this phantom, we investigated the percent signal change, derived an equation that can approximately reproduce an arbitrary percent signal change, compared different gel phantom samples, investigated the change in relaxation time and bubble size during signal change, and assessed the change in values in each sample over time.

RESULTS

The relaxation time of the gel phantom was similar to the literature values for gray matter. The percent signal change achieved was approximately 0-13.51% and was dependent on the water pressure change. The derived equation was y = 0.000008x3 - 0.000771x2 + 0.034222x - 0.026054, with y being the percent signal change and x being the pressure in kPa; the reproducibility was high. No significant difference was detected among samples of gray matter gel phantoms (P > 0.05). The change in the rate of signal change with the change in water pressure was due to the change in T₂ * value with the change in bubble size. With pressure increasing from 0 to 151.7 kPa, the T₂ * value increased from 52 ms to 85 ms. The newly developed gel phantom was stable for 60 days, but its bubble size changed after 21 days.

CONCLUSION

We developed a novel phantom for use in fMRI, which could reproduce minute signal changes similar to the blood-oxygen-level-dependent effect and with bioequivalent T₁ and T₂ * values, and used an innovative method to control the percent signal change by compressing the air contained in the phantom for validation of fMRI using GRE-EPI. This phantom reproduced the percent signal change due to changes in T₂ * values, which is very similar to scanning a human body. This phantom is expected to be a powerful tool for advancing the study of task-based fMRI. This article is protected by copyright. All rights reserved.

NEMA NU 4-2008 performance evaluation and MR compatibility tests of an APD-based small animal PET-insert for simultaneous PET/MR imaging

An avalanche photodiode (APD)-based small animal positron emission tomography (PET)-insert was fully evaluated for its PET performance, as well as potential influences on magnetic resonance imaging (MRI) performance. This PET-insert has an extended axial field of view (FOV) compared with the previous design to increase system sensitivity, as well as an updated cooling and temperature regulation to enable stable and reproducible PET acquisitions. The PET performance was evaluated according to the National Electrical Manufacturers Association NU4-2008 protocol. The energy and timing resolution’s full width at half maximum were 16.1% and 4.7 ns, respectively. The reconstructed radial spatial resolution of the PET-insert was 1.8 mm full width at half maximum at the center FOV using filtered back projection for reconstruction and sensitivity was 3.68%. The peak noise equivalent count rates were 70 kcps for a rat-like and 350 kcps for a mouse-like phantom, respectively. Image quality phantom values and contrast recovery were comparable to state-of-the art PET-inserts and standalone systems. Regarding MR compatibility, changes in the mean signal-to-noise ratio for turbo spin echo and echo-planar imaging sequences were below 8.6%, for gradient echo sequences below 1%. Degradation of the mean homogeneity was below 2.3% for all tested sequences. The influence of the PET-insert on the B 0 maps was negligible and no influence on functional MRI sequences was detected. A mouse and rat imaging study demonstrated the feasibility of in vivo simultaneous PET/MRI.

Imbalance between default mode and sensorimotor connectivity is associated with perseverative thinking in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is highly heterogeneous. Although perseverative negative thinking (PT) is a feature of OCD, little is known about its neural mechanisms or relationship to clinical heterogeneity in the disorder. In a sample of 85 OCD patients, we investigated the relationships between self-reported PT, clinical symptom subtypes, and resting-state functional connectivity measures of local and global connectivity. Results indicated that PT scores were highly variable within the OCD sample, with greater PT relating to higher severity of the "unacceptable thoughts" symptom dimension. PT was positively related to local connectivity in subgenual anterior cingulate cortex (ACC), pregenual ACC, and the temporal poles-areas that are part of, or closely linked to, the default mode network (DMN)-and negatively related to local connectivity in sensorimotor cortex. While the majority of patients showed higher local connectivity strengths in sensorimotor compared to DMN regions, OCD patients with higher PT scores had less of an imbalance between sensorimotor and DMN connectivity than those with lower PT scores, with healthy controls exhibiting an intermediate pattern. Clinically, this imbalance was related to both the "unacceptable thoughts" and "symmetry/not-just-right-experiences" symptom dimensions, but in opposite directions. These effects remained significant after accounting for variance related to psychiatric comorbidity and medication use in the OCD sample, and no significant relationships were found between PT and global connectivity. These data indicate that PT is related to symptom and neural variability in OCD. Future work may wish to target this circuity when developing personalized interventions for patients with these symptoms.

ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability

The FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research products Findable, Accessible, Interoperable, and Reusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform in the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium combines the technological approach of decentralized analyses with the sociological approach of sharing data. In addition, ENIGMA + COINSTAC provides a platform to facilitate the use of machine-actionable data objects. We first present how ENIGMA and COINSTAC support the FAIR principles, and then showcase their integration with a decentralized meta-analysis of sex differences in negative symptom severity in schizophrenia, and finally present ongoing activities and plans to advance FAIR principles in ENIGMA + COINSTAC. ENIGMA and COINSTAC currently represent efforts toward improved Access, Interoperability, and Reusability. We highlight additional improvements needed in these areas, as well as future connections to other resources for expanded Findability.

A novel event-related fMRI supervoxels-based representation and its application to schizophrenia diagnosis

BACKGROUND AND OBJECTIVE

The schizophrenia diagnosis represents a difficult task because of the confusing descriptions of symptoms given by the patient, their similarity among several disorders, the lower familiarity with genetic predisposition, and the probably inadequate response to the treatment. Neuro-biological markers of schizophrenia, as a quantitative relationship between the psychiatrist's reports and the biology of the brain, could be used. Functional Magnetic Resonance Imaging (fMRI) obtain the subject's performance in cognitive tasks and may find significant differences between the patient's data and controls. The input data of classifiers may imply alterations in diagnosis; therefore, it is essential to ensure an adequate representation to describe the entire dataset classified.

METHODS

We propose a supervoxels-based representation calculated by two main steps: the short-range connectivity, supervoxels' generation using a Fuzzy Iterative Clustering algorithm, and the long-range connectivity, employing Detrended Cross-Correlation Analysis among supervoxels. The unrelated supervoxels, through a statistical test based on critical points calculated empirically, are removed. The remainder supervoxels are the input for feature selectors to extract the discriminative supervoxels. We implement support vector machine classifiers using the correlation coefficient of the significant supervoxels. The dataset of 1.5 Tesla was downloaded from the SchizConnect site, where the fMRI data, during an auditory oddball task, was acquired. We calculate the performance of the classifiers using a leave-one-out cross-validation and compute the area under the Receiver Operating Characteristic curve and a permutation test to ensure no bias in the classifiers.

RESULTS

According to the permutation test, with p-values less than the significance level of 0.05, the classifiers extract discriminative class structure from data where no bias is shown. Our supervoxels-based representation gets the maximum values of sensitivity, specificity, and accuracy of 92.9%, 100%, and 96.4%, respectively. The discriminative brain regions, to discern among patients and controls, are extracted; these regions also are mentioned by the related works.

CONCLUSIONS

The proposed representation, based on supervoxels, is a data-driven model that does not use predefined models of the signal nor pre-relocated brain regions of interest. The results are competitive against the related works, and the relevant supervoxels are related to the schizophrenia diagnosis.

Neuroimaging brain growth charts: A road to mental health

Mental disorders are common health concerns and contribute to a heavy global burden on our modern society. It is challenging to identify and treat them timely. Neuroimaging evidence suggests the incidence of various psychiatric and behavioral disorders is closely related to the atypical development of brain structure and function. The identification and understanding of atypical brain development provide chances for clinicians to detect mental disorders earlier, perhaps even prior to onset, and treat them more precisely. An invaluable and necessary method in identifying and monitoring atypical brain development are growth charts of typically developing individuals in the population. The brain growth charts can offer a series of standard references on typical neurodevelopment, representing an important resource for the scientific and medical communities. In the present paper, we review the relationship between mental disorders and atypical brain development from a perspective of normative brain development by surveying the recent progress in the development of brain growth charts, including four aspects on growth chart utility: 1) cohorts, 2) measures, 3) mechanisms, and 4) clinical translations. In doing so, we seek to clarify the challenges and opportunities in charting brain growth, and to promote the application of brain growth charts in clinical practice.

Satiety does not alter the ventral striatum's response to immediate reward in bulimia nervosa

Individuals with bulimia nervosa (BN) cycle between periods of binge-eating and compensatory behavior and periods of dietary restraint, suggesting extremes of under and overcontrol that may be metabolic-state related. This study examined the influence of hunger and satiety on impulsivity and neural responding during decision-making. Twenty-three women remitted from BN (RBN) and 20 healthy comparison women (CW) performed a delay discounting task after a 16-hr fast and following a standardized meal during functional neuroimaging. A dual-systems approach examined reward valuation (decision trials where the early reward option was available immediately) and cognitive control (all decision trials). Interactions of Group × Visit (Hungry, Fed) for immediate reward revealed that CW had greater activation when hungry versus fed in the ventral striatum and dorsal caudate, whereas RBN had greater response when fed versus hungry in the dorsal caudate. Compared to CW, RBN showed decreased response when hungry within the left dorsal caudate and ventral striatum and increased response when fed in bilateral dorsal caudate. No differences were found within cognitive control regions or with choice behavior. Reward sensitivity is normally increased when hungry and decreased when fed; our findings in CW provide further support of hunger-based reward sensitivity within the striatum. However, RBN showed no differences for hunger and satiety in the ventral striatum and greater activation in the dorsal caudate when fed compared to hungry. This suggests RBN may be less sensitive to reward when hungry but do not devalue reward when satiated, indicating altered metabolic modulation of self-regulatory control. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Structural brain correlates of cognitive function in schizophrenia: A meta-analysis

Schizophrenia is characterized by cognitive impairments and widespread structural brain abnormalities. Brain structure-cognition associations have been extensively studied in schizophrenia, typically involving individual cognitive domains or brain regions of interest. Findings in overlapping and diffuse brain regions may point to structural alterations in large-scale brain networks. We performed a systematic review and meta-analysis examining whether brain structure-cognition associations can be explained in terms of biologically meaningful brain networks. Of 7,261 screened articles, 88 were included in a series of meta-analyses assessing publication bias, heterogeneity, and study quality. Significant associations were found between overall brain structure and eight MATRICS-inspired cognitive domains. Brain structure mapped onto the seven Yeo functionally defined networks and extraneous structures (amygdala, hippocampus, and cerebellum) typically showed associations with conceptually related cognitive domains, with higher-level domains (e.g., executive function, social cognition) associated with more networks. These findings synthesize the extensive literature on brain structure and cognition in schizophrenia from a contemporary network neuroscience perspective and suggest that brain structure-cognition associations in schizophrenia may follow functional network architecture.

The German research consortium for the study of bipolar disorder (BipoLife): a magnetic resonance imaging study protocol

Background

Bipolar disorder is one of the most severe mental disorders. Its chronic course is associated with high rates of morbidity and mortality, a high risk of suicide and poor social and occupational outcomes. Despite the great advances over the last decades in understanding mental disorders, the mechanisms underlying bipolar disorder at the neural network level still remain elusive. This has severe consequences for clinical practice, for instance by inadequate diagnoses or delayed treatments. The German research consortium BipoLife aims to shed light on the mechanisms underlying bipolar disorders. It was established in 2015 and incorporates ten university hospitals across Germany. Its research projects focus in particular on individuals at high risk of bipolar disorder, young patients in the early stages of the disease and patients with an unstable highly relapsing course and/or with acute suicidal ideation.

Methods

Functional and structural magnetic resonance imaging (MRI) data was acquired across nine sites within three different studies. Obtaining neuroimaging data in a multicenter setting requires among others the harmonization of the acquisition protocol, the standardization of paradigms and the implementation of regular quality control procedures. The present article outlines the MRI imaging protocols, the acquisition parameters, the imaging paradigms, the neuroimaging quality assessment procedures and the number of recruited subjects.

Discussion

The careful implementation of a MRI study protocol as well as the adherence to well-defined quality assessment procedures is one key benchmark in the evaluation of the overall quality of large-scale multicenter imaging studies. This article contributes to the BipoLife project by outlining the rationale and the design of the MRI study protocol. It helps to set the necessary standards for follow-up analyses and provides the technical details for an in-depth understanding of follow-up publications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40345-021-00240-6.

The psychosis human connectome project: An overview

Investigations within the Human Connectome Project have expanded to include studies focusing on brain disorders. This paper describes one of the investigations focused on psychotic psychopathology: The psychosis Human Connectome Project (P-HCP). The data collected as part of this project were multimodal and derived from clinical assessments of psychopathology, cognitive assessments, instrument-based motor assessments, blood specimens, and magnetic resonance imaging (MRI) data. The dataset will be made publicly available through the NIMH Data Archive. In this report we provide specific information on how the sample of participants was obtained and characterized and describe the experimental tasks and procedures used to probe neural functions involved in psychotic disorders that may also mark genetic liability for psychotic psychopathology. Our goal in this paper is to outline the data acquisition process so that researchers intending to use these publicly available data can plan their analyses. MRI data described in this paper are limited to data acquired at 3 Tesla. A companion paper describes the study's 7 Tesla image acquisition protocol in detail, which is focused on visual perceptual functions in psychotic psychopathology.

The neural bases of multimodal sensory integration in older adults

Although hearing often declines with age, prior research has shown that older adults may benefit from multisensory input to a greater extent when compared to younger adults, a concept known as inverse effectiveness. While there is behavioral evidence in support of this phenomenon, less is known about its neural basis. The present fMRI study examined how older and younger adults processed multimodal auditory-visual (AV) phonemic stimuli which were either congruent or incongruent across modalities. Incongruent AV pairs were designed to elicit the McGurk effect. Behaviorally, reaction times were significantly faster during congruent trials compared to incongruent trials for both age groups, and overall older adults responded more slowly. The interaction was not significant suggesting that older adults processed the AV stimuli similarly to younger adults. Although there were minimal behavioral differences, age-related differences in functional activation were identified: Younger adults elicited greater activation than older adults in primary sensory regions including superior temporal gyrus, the calcarine fissure, and left post-central gyrus. In contrast, older adults elicited greater activation than younger adults in dorsal frontal regions including middle and superior frontal gyri, as well as dorsal parietal regions. These data suggest that while there is age-related stability in behavioral sensitivity to multimodal stimuli, the neural bases for this effect differed between older and younger adults. Our results demonstrated that older adults underrecruited primary sensory cortices and had increased recruitment of regions involved in executive function, attention, and monitoring processes, which may reflect an attempt to compensate.

Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study

Quality assurance (QA) is crucial in longitudinal and/or multi-site studies, which involve the collection of data from a group of subjects over time and/or at different locations. It is important to regularly monitor the performance of the scanners over time and at different locations to detect and control for intrinsic differences (e.g., due to manufacturers) and changes in scanner performance (e.g., due to gradual component aging, software and/or hardware upgrades, etc.). As part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Canadian Biomarker Integration Network in Depression (CAN-BIND), QA phantom scans were conducted approximately monthly for three to four years at 13 sites across Canada with 3T research MRI scanners. QA parameters were calculated for each scan using the functional Biomarker Imaging Research Network's (fBIRN) QA phantom and pipeline to capture between- and within-scanner variability. We also describe a QA protocol to measure the full-width-at-half-maximum (FWHM) of slice-wise point spread functions (PSF), used in conjunction with the fBIRN QA parameters. Variations in image resolution measured by the FWHM are a primary source of variance over time for many sites, as well as between sites and between manufacturers. We also identify an unexpected range of instabilities affecting individual slices in a number of scanners, which may amount to a substantial contribution of unexplained signal variance to their data. Finally, we identify a preliminary preprocessing approach to reduce this variance and/or alleviate the slice anomalies, and in a small human data set show that this change in preprocessing can have a significant impact on seed-based connectivity measurements for some individual subjects. We expect that other fMRI centres will find this approach to identifying and controlling scanner instabilities useful in similar studies.

Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294

A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.

Epigenetic prediction of 17β-estradiol and relationship to trauma-related outcomes in women

17β-estradiol (E2) levels in women correlate with multiple neuropsychiatric symptoms, including those that are stress-related. Furthermore, prior work from our group has demonstrated that E2 status influences DNA methylation (DNAm) across the genome. We developed and validated a DNAm-based predictor of E2 (one of four naturally occurring estrogens) using a training set of 183 females and a test set of 79 females from the same traumatized cohort. We showed that predicted E2 levels were highly correlated with measured E2 concentrations in our testing set (r ​= ​0.75, p ​= ​1.8e-15). We further demonstrated that predicted E2 concentrations, in combination with measured values, negatively correlated with current post-traumatic stress disorder (PTSD) (β ​= ​−0.38, p ​= ​0.01) and major depressive disorder (MDD) diagnoses (β ​= ​−0.45, p ​= ​0.02), as well as a continuous measure of PTSD symptom severity (β ​= ​−2.3, p ​= ​0.007) in females. Finally, we tested our predictor in an independent data set (n ​= ​85) also comprised of recently traumatized female subjects to determine if the predictor would generalize to a different population than the one on which it was developed. We found that the correlation between predicted and actual E2 concentrations in the external validation data set was also high (r ​= ​0.48, p ​= ​3.0e-6). While further validation is warranted, a DNAm predictor of E2 concentrations will advance our understanding of hormone-epigenetic interactions. Furthermore, such a DNAm predictor may serve as an epigenetic proxy for E2 concentrations and thus provide an important biomarker to better evaluate the contribution of E2 to current and potentially future psychiatric symptoms in samples for which E2 is not measured.

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Neuropsychiatric symptoms correlate with estradiol (E2) levels in females.

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We developed a DNA methylation-based E2 predictor using machine learning approach.

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Our predictor performed well in an external validation cohort of traumatized women.

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Predicted E2 concentrations correlated with stress-related phenotypes.

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Our model may serve as an epigenetic biomarker of E2 status in women.

Strategies to Understand the Weight-Reduced State: Genetics and Brain Imaging

Most individuals with obesity or overweight have difficulty maintaining weight loss. The weight-reduced state induces changes in many physiological processes that appear to drive weight regain. Here, we review the use of cell biology, genetics, and imaging techniques that are being used to begin understanding why weight regain is the normal response to dieting. As with obesity itself, weight regain has both genetic and environmental drivers. Genetic drivers for "thinness" and "obesity" largely overlap, but there is evidence for specific genetic loci that are different for each of these weight states. There is only limited information regarding the genetics of weight regain. Currently, most genetic loci related to weight point to the central nervous system as the organ responsible for determining the weight set point. Neuroimaging tools have proved useful in studying the contribution of the central nervous system to the weight-reduced state in humans. Neuroimaging technologies fall into three broad categories: functional, connectivity, and structural neuroimaging. Connectivity and structural imaging techniques offer unique opportunities for testing mechanistic hypotheses about changes in brain function or tissue structure in the weight-reduced state.

Changes in Intrinsic Brain Connectivity in Family-Focused Therapy Versus Standard Psychoeducation Among Youths at High Risk for Bipolar Disorder

OBJECTIVE

We compared intrinsic network connectivity in symptomatic youths at high risk (HR) for bipolar disorder (BD) and healthy comparison (HC) youths. In HR youths, we also investigated treatment-related changes in intrinsic connectivity after family-focused therapy for high-risk youths (FFT-HR) vs standardized family psychoeducation.

METHOD

HR youths (N = 34; age 9-17 years; mean 14 years, 56% girls and 44% boys) with depressive and/or hypomanic symptoms and at least 1 first- or second-degree relative with BD I or II were randomly assigned to 4 months of FFT-HR (12 sessions of psychoeducation, communication, and problem-solving skills training) or enhanced care (EC; 3 family and 3 individual psychoeducation sessions). Before and after 4 months of treatment, participants underwent resting state functional magnetic resonance imaging (rs-fMRI). A whole-brain independent component analysis compared rs-fMRI networks in HR youths and 30 age-matched HC youths at a pretreatment baseline. Then we identified pretreatment to posttreatment (4-month) changes in network connectivity in HR youths receiving FFT-HR (n = 16) or EC (n = 18) and correlated these changes with depression improvement.

RESULTS

At baseline, HR youths had greater connectivity between the ventrolateral prefrontal cortex (VLPFC) and the anterior default mode network (aDMN) than did HCs (p = .004). Over 4 months of treatment, FFT-HR-assigned HR youths had increased VLPFC-aDMN connectivity from pre- to posttreatment (p = .003), whereas HR youths in EC showed no significant change over time (p = .11) (treatment by time interaction, t₃₁ = 3.33, 95% CI = 0.27-1.14, p = .002]. Reduction in depression severity over 4 months inversely correlated with enhanced anterior DMN (r = -0.71) connectivity in the FFT-HR but not in the EC (r = -0.07) group (z = -2.17, p = .015).

CONCLUSION

Compared to standard psychoeducation, FFT-HR is associated with stronger connectivity between the VLPFC and aDMN, suggesting possible enhancements of self-awareness, illness awareness, and emotion regulation.

CLINICAL TRIAL REGISTRATION INFORMATION

Early Intervention for Youth at Risk for Bipolar Disorder; https://clinicaltrials.gov/; NCT01483391.

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

Tracking second language immersion across time: Evidence from a bi-directional longitudinal cross-linguistic fMRI study

Parallel cohorts of Hebrew speakers learning English in the U.S., and American-English speakers learning Hebrew in Israel were tracked over the course of two years of immersion in their L2. We utilised a functional MRI semantic judgement task with print and speech tokens, as well as a battery of linguistic and cognitive behavioural measures prior to and after immersion, to track changes in both L1 and L2 processing. fMRI activation for print tokens produced a similar network of activation in both English and Hebrew, irrespective of L1 or L2 status. Significant convergence of print and speech processing was also observed in both languages across a network of left-hemisphere regions joint for both L1 and L2. Despite significant increases in behavioural measures of L2 proficiency, only a few signs of longitudinal change in L2 brain activation were found. In contrast, L1 showed widespread differences in processing across time, suggesting that the neurobiological footprint of reading is dynamic and plastic even in adults, with L2 immersion impacting L1 processing. Print/speech convergence showed little longitudinal change, suggesting that it is a stable marker of the differences in L1 and L2 processing across L2 proficiency.

The intensity and connectivity of spontaneous brain activity in a language network relate to aging and language

Neuroimaging studies often either look at functional activation in response to an explicit task, or functional connectivity (i.e., interregional correlations) during resting-state. Few studies have looked at the intensity of brain activity or its relationship with age, behavior, and language. The current study investigated both intensity (i.e., the Amplitude of Low-Frequency Fluctuations, ALFF) and the functional connectivity of spontaneous brain activity during rest and their relationship with age and language. A life-span sample of individuals (N = 152) completed a battery of neuropsychological tests to assess basic cognitive functions and resting-state functional MRI data to assess spontaneous brain activity. Focusing on an extend language network, the mean ALFF and total degree were calculated for this network. We found that increased age was associated with more intense activity (i.e., higher ALFF) but lower within-network connectivity. Additionally, these increases in activity within the language network during resting-state were related to worse language ability, particularly in younger adults, supporting a dedifferentiation account of cognition. Our results support the utility of using resting-state data as an indicator of cognition and support the role of ALFF as a potential biomarker in characterizing the relationships between resting-state brain activity, age, and cognition.