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

The role of sex in the association between cannabis use disorder and resting-state functional connectivity

While Cannabis use disorder (CUD) is twice as prevalent in males, females transition more quickly from heavy use to CUD and experience more severe withdrawal. These clinically relevant sex differences contrast the lack of knowledge about the underlying brain mechanisms. This study investigated the relationship between CUD and resting-state functional brain connectivity (RSFC), assessing potential sex differences herein. RSFC of the Salience Network (SN), Basal Ganglia Network (BGN), Executive Control Network (ECN), and Default Mode Network (DMN) was compared between 152 individuals (76 males) with CUD and 114 matched controls (47 males). Within the CUD group, relationships between RSFC and heaviness of cannabis use, age of onset, and CUD symptom severity, along with their associations with sex, were investigated. CUD and control groups showed similar RSFC across all networks, regardless of sex. In the CUD group, heavier cannabis use correlated with higher RSFC across all networks and earlier age of onset was related to higher RSFC in the anterior SN, BGN, left ECN, and dorsal DMN. These associations were similar for males and females. CUD severity was related to higher RSFC in the anterior SN, which was moderated by sex, with a positive association seen only in males. In conclusion, CUD may not necessarily be associated with altered RSFC. Individual use characteristics such age of onset and severity of use may determine the potential impact of cannabis use on RSFC in a largely similar way in males and females.

Multivariate patterns among multimodal neuroimaging and clinical, cognitive, and daily functioning characteristics in bipolar disorder

Individuals with bipolar disorder (BD) show heterogeneity in clinical, cognitive, and daily functioning characteristics, which challenges accurate diagnostics and optimal treatment. A key goal is to identify brain-based biomarkers that inform patient stratification and serve as treatment targets. The objective of the present study was to apply a data-driven, multivariate approach to quantify the relationship between multimodal imaging features and behavioral phenotypes in BD. We pooled structural, task and resting-state functional magnetic resonance imaging (MRI), and clinical, cognitive, and functioning data from 167 fully or partly remitted patients with BD from three studies conducted at the same site. We performed canonical correlation analysis (CCA) to investigate multivariate relations among the 56 imaging and 23 behavioral features in patients. Data from 46 matched healthy controls were included for covariate-adjusted standardization of patients' scores and for group comparisons. The imaging and behavioral data sets showed a strong canonical correlation (r = 0.84, p = .004). Among the behavioral variables, cognitive test scores across psychomotor speed, verbal memory, and verbal fluency were associated with the multimodal imaging variate comprising task activation within the dorsolateral prefrontal cortex and supramarginal gyrus, also when other clinical and daily functioning variables were considered. Task activation within the dorsal prefrontal and parietal cognitive control areas constitutes a potential pro-cognitive treatment target.

Multi-Metric Approach for the Comparison of Denoising Techniques for Resting-State fMRI

Despite the increasing use of resting-state functional magnetic resonance imaging (rs-fMRI) data for studying the spontaneous functional interactions within the brain, the achievement of robust results is often hampered by insufficient data quality and by poor knowledge of the most effective denoising methods. The present study aims to define an appropriate denoising strategy for rs-fMRI data by proposing a robust framework for the quantitative and comprehensive comparison of the performance of multiple pipelines made available by the newly proposed HALFpipe software. This will ultimately contribute to standardizing rs-fMRI preprocessing and denoising steps. Fifty-three participants took part in the study by undergoing a rs-fMRI session. Synthetic rs-fMRI data from one subject were also generated. Nine different denoising pipelines were applied in parallel to the minimally preprocessed fMRI data. The comparison was conducted by computing previously proposed and novel metrics that quantify the degree of artifact removal, signal enhancement, and resting-state network identifiability. A summary performance index, accounting for both noise removal and information preservation, was proposed. The results confirm the performance heterogeneity of different denoising pipelines across the different quality metrics. In both real and synthetic data, the summary performance index favored the denoising strategy including the regression of mean signals from white matter and cerebrospinal fluid brain areas and global signal. This pipeline resulted in the best compromise between artifact removal and preservation of the information on resting-state networks. Our study provided useful methodological tools and key information on the effectiveness of multiple denoising strategies for rs-fMRI data. Besides providing a robust comparison approach that could be adapted to other fMRI studies, a suitable denoising pipeline for rs-fMRI data was identified, which could be used to improve the reproducibility of rs-fMRI findings.

Make it worth it: Effort-reward modulations on reinforcement-learning and prediction-error signaling across adolescence

Adolescence is characterized by significant shifts in effort allocation. A well-known neuro-economic framework suggests that rewards help overcome potential effort costs. However, few studies have examined the neurobiological mechanisms by which rewards and associated effort costs drive adolescent learning. This study utilized functional magnetic resonance imaging in a sample of adolescents (N = 146, 13-25 years) and employed a reinforcement-learning paradigm that manipulated effort and reward levels, by varying task demands and varying potential rewards. The analysis of trial-by-trial learning signals (reward prediction errors) and behavioral learning performance demonstrated that greater reward levels enhanced adolescent learning, especially when faced with greater effort demands. Moreover, this effect was more pronounced in those experiencing greater effort demands: younger adolescents and adolescents who place less value on effort for demanding tasks. Neuroimaging results revealed that the dorsal anterior cingulate cortex (dACC) was a key region in signaling the interaction between reward and effort demands. That is, greater reward strengthened prediction error coding in the dACC, particularly under conditions of greater task demands, with these effects being more pronounced in younger adolescents and adolescents who place less value on effort for demanding tasks. These findings support a role for dACC in the engagement of cognitive control, especially in situations where more cognitive control would be beneficial despite its associated effort costs, such as in high-demanding learning situations. This comprehensive approach aims to inform strategies for supporting effort allocation in learning during this crucial developmental period.

Exploring the link between inflammation and brain function after metabolic-bariatric surgery: A year-long fMRI study

BACKGROUND

Metabolic-bariatric surgery (MBS) transcends weight loss and offers wide-ranging health benefits, including positive effects on brain function. However, the mechanisms behind these effects remain unclear, particularly in the context of significant postoperative changes in the inflammatory profile characteristic of MBS. Understanding how inflammation influences postoperative brain function can enhance our decision-making on patient eligibility for MBS and create new opportunities to improve the outcomes of this popular treatment.

OBJECTIVE

To identify brain regions where spontaneous neural activity and functional connectivity are linked with the evolving inflammatory profile following MBS.

METHODS

We investigated the relationship between the perioperative ratio of interleukin (IL)-6 to IL-10 and both the amplitude of low-frequency fluctuation (ALFF) and functional connectivity across 375 brain regions. We examined 36 patients at three time points: 1 week before, and 3 and 12 months after laparoscopic sleeve gastrectomy.

RESULTS

Initially, the IL-6/IL-10 ratio increased during the early postoperative period but then decreased to levels lower than the preoperative values 1 year after surgery. We observed that ALFF in four subcortical structures decreased with a rising IL-6/IL-10 ratio and increased with a declining ratio. Conversely, 16 cortical regions displayed the opposite trend. Additionally, functional connectivity between the left insula and bilateral medial prefrontal cortex increased with a rising IL-6/IL-10 ratio and decreased with a declining ratio.

CONCLUSIONS

Our study is the first to identify brain regions significantly linked to inflammation after MBS. Importantly, many of the discovered areas were previously shown to be involved in the pathogenesis of obesity or are targets of contemporary medical treatments. Consequently, our findings offer valuable insights for future obesity research, especially in the context of potential therapeutic opportunities.

Anhedonia relates to reduced striatal reward anticipation in depression but not in schizophrenia or bipolar disorder: A transdiagnostic study

Anhedonia, i.e., the loss of pleasure or lack of reactivity to reward, is a core symptom of major psychiatric conditions. Altered reward processing in the striatum has been observed across mood and psychotic disorders, but whether anhedonia transdiagnostically contributes to these deficits remains unclear. We investigated associations between self-reported anhedonia and neural activation during reward anticipation and consumption across patients with schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MD), and healthy controls (HC). Using the Monetary Incentive Delay paradigm, we acquired functional magnetic resonance imaging data sets in 227 participants (18-65 years), including patients with SZ (n = 44), BD (n = 47), MD (n = 56), and HC (n = 80). To capture anhedonia, three items of the Symptom Checklist-90-R were entered into exploratory factor analysis, which resulted in a single anhedonia factor. Associations between anhedonia and neural activation were assessed within a striatal region-of-interest and exploratorily across the whole brain (pFWE < .05). Self-reported anhedonia was high in MD, low in HC, and intermediate in SZ and BD. During reward anticipation, anhedonia correlated with reduced striatal activation; however, the correlation depended on diagnostic group. Specifically, the effect was driven by a negative relationship between anhedonia and dorsal striatal (putamen) activity within the MD group; for reward consumption, no correlations were found. Our results indicate that anticipatory anhedonia in MD may relate to reduced behavioral motivation via disrupted encoding of motor plans in the dorsal striatum. Future transdiagnostic research should stratify participants by anhedonia levels to achieve more homogeneous samples in terms of underlying neurobiology.

Differential Functional Connectivity of Frontolimbic Circuit During Symptom Provocation in Distinct Symptom Profiles of Obsessive-Compulsive Disorder: Connectivité fonctionnelle différentielle du circuit frontolimbique durant la provocation de symptômes dans des profils symptomatiques distincts du trouble obsessionnel-compulsif

BackgroundEmotional processing deficits and frontolimbic dysfunction have been observed in patients with obsessive-compulsive disorder (OCD), with inconsistent evidence possibly due to symptom heterogeneity. We compared the functional activation and connectivity patterns of the frontolimbic structures during symptom provocation between patients with distinct symptom profiles of OCD.MethodsThirty-seven symptomatic OCD subjects were recruited and categorized based on predominant symptom profiles to contamination/washing symptom group (OCD-C, n = 19) and taboo thoughts group (OCD-T, n = 18), along with 17 healthy controls (HCs). All subjects were evaluated with comprehensive clinical assessments and functional magnetic resonance imaging while appraising personalized disorder-specific stimuli with contrasting neutral stimuli as part of an individualized symptom provocation task. Region of interest analyses and task-dependent seed-to-voxel connectivity of the frontolimbic circuit were compared between the groups, with correction employed for multiple comparisons.ResultsOCD-C subjects had decreased task-dependent mean activation of the left amygdala (adjusted mean difference = 13.48, p= 0.03) and right hippocampus (adjusted mean difference = 13.48, p = 0.04) compared to HC. Task-modulated functional connectivity analyses revealed that OCD-C had decreased connectivity of the right hippocampus with bilateral supplementary motor cortex and anterior cingulate gyrus (T = -5.11, p = 0.04); right insula with left cerebellum (T = -5.47, p = 0.02); and left insula with inferior temporal gyrus (T = -6.27, p = 0.03) than HC. OCD-T subjects had greater connectivity of right insula with left cerebellum (T = 6.64, p < 0.001) than OCD-C and increased connectivity of medial frontal cortex with right lateral occipital cortex (T = 5.08, p < 0.001) than HC.ConclusionsContamination-related symptoms were associated with decreased activation and connectivity of amygdala and hippocampus during symptom provocation, while the taboo thoughts were associated with increased connectivity of the insular cortex and medial frontal cortex. These findings suggest that distinct neurobiological markers may underlie the clinical heterogeneity of OCD.

Magnetic resonance imaging in traumatic brain injury: a survey of clinical practitioners' experiences and views on current practice and obstacles

INTRODUCTION

Magnetic resonance imaging (MRI) has revolutionized our capacity to examine brain alterations in traumatic brain injury (TBI). However, little is known about the level of implementation of MRI techniques in clinical practice in TBI and associated obstacles.

METHODS

A diverse set of health professionals completed 19 multiple choice and free text survey questions.

RESULTS

Of the 81 respondents, 73.4% reported that they acquire/order MRI scans in TBI patients, and 66% indicated they would prefer MRI be more often used with this cohort. The greatest impediment for MRI usage was scanner availability (57.1%). Less than half of respondents (42.1%) indicated that they perform advanced MRI analysis. Factors such as dedicated experts within the team (44.4%) and user-friendly MRI analysis tools (40.7%), were listed as potentially helpful to implement advanced MRI analyses in clinical practice.

CONCLUSION

Results suggest a wide variability in the purpose, timing, and composition of the scanning protocol of clinical MRI after TBI. Three recommendations are described to broaden implementation of MRI in clinical practice in TBI: 1) development of a standardized multimodal MRI protocol; 2) future directions for the use of advanced MRI analyses; 3) use of low-field MRI to overcome technical/practical issues with high-field MRI.

Design and methods of the research unit 5187 PREACT (towards precision psychotherapy for non-respondent patients: from signatures to predictions to clinical utility) - a study protocol for a multicentre observational study in outpatient clinics

INTRODUCTION

Cognitive-behavioural therapy (CBT) works-but not equally well for all patients. Less than 50% of patients with internalising disorders achieve clinically meaningful improvement, with negative consequences for patients and healthcare systems. The research unit (RU) 5187 seeks to improve this situation by an in-depth investigation of the phenomenon of treatment non-response (TNR) to CBT. We aim to identify bio-behavioural signatures associated with TNR, develop predictive models applicable to individual patients and enhance the utility of predictive analytics by collecting a naturalistic cohort with high ecological validity for the outpatient sector.

METHODS AND ANALYSIS

The RU is composed of nine subprojects (SPs), spanning from clinical, machine learning and neuroimaging science and service projects to particular research questions on psychological, electrophysiological/autonomic, digital and neural signatures of TNR. The clinical study SP 1 comprises a four-centre, prospective-longitudinal observational trial where we recruit a cohort of 585 patients with a wide range of internalising disorders (specific phobia, social anxiety disorder, panic disorder, agoraphobia, generalised anxiety disorder, obsessive-compulsive disorder, post-traumatic stress disorder, and unipolar depressive disorders) using minimal exclusion criteria. Our experimental focus lies on emotion (dys)-regulation as a putative key mechanism of CBT and TNR. We use state-of-the-art machine learning methods to achieve single-patient predictions, incorporating pretrained convolutional neural networks for high-dimensional neuroimaging data and multiple kernel learning to integrate information from various modalities. The RU aims to advance precision psychotherapy by identifying emotion regulation-based biobehavioural markers of TNR, setting up a multilevel assessment for optimal predictors and using an ecologically valid sample to apply findings in diverse clinical settings, thereby addressing the needs of vulnerable patients.

ETHICS AND DISSEMINATION

The study has received ethical approval from the Institutional Ethics Committee of the Department of Psychology at Humboldt-Universität zu Berlin (approval no. 2021-01) and the Ethics Committee of Charité-Universitätsmedizin Berlin (approval no. EA1/186/22).Results will be disseminated through peer-reviewed journals and presentations at national and international conferences. Deidentified data and analysis scripts will be made available to researchers within the RU via a secure server, in line with ethical guidelines and participant consent. In compliance with European and German data protection regulations, patient data will not be publicly available through open science frameworks but may be shared with external researchers on reasonable request and under appropriate data protection agreements.

TRIAL REGISTRATION NUMBER

DRKS00030915.

MRI-based surrogates of brain clearance in narcolepsy type 1

Brain clearance involves the drainage of waste molecules from the brain, a process that is suggested to be amplified during sleep. Recently proposed MRI-based methods attempt to approximate human brain clearance with surrogate measures. The current study aimed to explore whether two brain clearance surrogates are altered in narcolepsy. We processed diffusion-weighted and functional resting-state images to extract two surrogates: Diffusion Tensor Imaging Along the Perivascular Space (DTI-ALPS index), and dBOLD-CSF coupling. Both measures were analysed in 12 drug-free, awake people with narcolepsy type 1 and 11 age- and sex-matched controls, as well as in relation to clinical features. We also assessed the correlation between the DTI-ALPS index and dBOLD-CSF coupling. The DTI-ALPS index and dBOLD-CSF coupling amplitude did not show significant differences between narcolepsy and controls, nor significant relations with the severity of excessive daytime sleepiness. We found a significant correlation between dBOLD-CSF coupling and sleep efficiency, as well as a significant correlation between the DTI-ALPS index and dBOLD-CSF coupling. The hypothesis of altered brain clearance in narcolepsy type 1 is not supported by evidence from the current study. The two surrogates correlated with each other, suggesting that both offer different perspectives from the same underlying physiology. Yet, the suitability of the surrogates as brain clearance markers remains debatable. Whereas DTI is not exclusively sensitive to perivascular fluid, dBOLD-CSF coupling is reflecting large-scale CSF motion. Future work should explore other surrogate markers, preferably during sleep, to better understand the possible role of altered brain clearance in narcolepsy type 1 symptomatology.

Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer's Disease Research Center postmortem repository

Histopathology provides critical insights into the neurological processes inducing neurodegenerative diseases and their impact on the brain, but brain banks combining histology and neuroimaging data are difficult to create. As part of an ongoing global effort to establish new brain banks providing both high-quality neuroimaging scans and detailed histopathology examinations, the South Texas Alzheimer's Disease Re- search Center postmortem repository was recently created with the specific purpose of studying comorbid dementias. As the repository is reaching a milestone of two hundred brain donations and a hundred curated MRI sessions are ready for processing, robust statistical analyses can now be conducted. In this work, we report the very first morphometry analysis conducted with this new data set. We describe the processing pipelines that were specifically developed to exploit the available MRI sequences, and we explain how we addressed several postmortem neuroimaging challenges, such as the separation of brain tissues from fixative fluids, the need for updated brain atlases, and the tissue contrast changes induced by brain fixation. In general, our results establish that a combination of structural MRI sequences can provide enough informa- tion for state-of-the-art Deep Learning algorithms to almost perfectly separate brain tissues from a formalin buffered solution. Regional brain volumes are challenging to measure in postmortem scans, but robust estimates sensitive to sex differences and age trends, reflecting clinical diagnosis, neuropathology findings, and the shrinkage induced by tissue fixation can be obtained. We hope that the new processing methods developed in this work, such as the lightweight Deep Networks we used to identify the formalin signal in multimodal MRI scans and the MRI synthesis tools we used to fix our anisotropic resolution brain scans, will inspire other research teams working with postmortem MRI scans.

Development and validation of a machine learning model to predict cognitive behavioral therapy outcome in obsessive-compulsive disorder using clinical and neuroimaging data

Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. In this study, we aimed to develop predictive models using clinical and neuroimaging data from the multicenter Enhancing Neuro-Imaging and Genetics through Meta-Analysis (ENIGMA)-OCD consortium. Baseline clinical and resting-state functional magnetic imaging (rs-fMRI) data from 159 adult patients aged 18-60 years (88 female) with OCD who received CBT at four treatment/neuroimaging sites were included. Fractional amplitude of low frequency fluctuations, regional homogeneity and atlas-based functional connectivity were computed. Clinical CBT response and remission were predicted using support vector machine and random forest classifiers on clinical data only, rs-fMRI data only, and the combination of both clinical and rs-fMRI data. The use of only clinical data yielded an area under the ROC curve (AUC) of 0.69 for predicting remission (p=0.001). Lower baseline symptom severity, younger age, an absence of cleaning obsessions, unmedicated status, and higher education had the highest model impact in predicting remission. The best predictive performance using only rs-fMRI was obtained with regional homogeneity for remission (AUC=0.59). Predicting response with rs-fMRI generally did not exceed chance level. Machine learning models based on clinical data may thus hold promise in predicting remission after CBT for OCD, but the predictive power of multicenter rs-fMRI data is limited.

Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium

OBJECTIVE

Obsessive-compulsive disorder (OCD) is associated with altered brain function related to processing of negative emotions. To investigate neural correlates of negative valence in OCD, we pooled fMRI data of 633 individuals with OCD and 453 healthy controls from 16 studies using different negatively-valenced tasks across the ENIGMA-OCD Working-Group.

METHODS

Participant data were processed uniformly using HALFpipe, to extract voxelwise participant-level statistical images of one common first-level contrast: negative vs. neutral stimuli. In pre-registered analyses, parameter estimates were entered into Bayesian multilevel models to examine whole-brain and regional effects of OCD and its clinically relevant features - symptom severity, age of onset, and medication status.

RESULTS

We provided a proof-of-concept that participant-level data can be combined across several task paradigms and observed one common task activation pattern across individuals with OCD and controls that encompasses fronto-limbic and visual areas implicated in negative valence. Compared to controls, individuals with OCD showed very strong evidence of weaker activation of the bilateral occipital cortex (P+<0.001) and adjacent visual processing regions during negative valence processing that was related to greater OCD severity, late-onset of disease and an unmedicated status. Individuals with OCD also showed stronger activation in the orbitofrontal, subgenual anterior cingulate and ventromedial prefrontal cortex (all P+<0.1) that was related to greater OCD severity and late onset.

CONCLUSION

In the first mega-analysis of this kind, we replicate previous findings of stronger ventral prefrontal activation in OCD during negative valence processing and highlight the lateral occipital cortex as an important region implicated in altered negative valence processing.

Family and parenting factors are associated with emotion regulation neural function in early adolescent girls with elevated internalizing symptoms

A prominent tripartite model proposes that parent role modeling of emotion regulation, emotion socialization behaviors, and the emotional climate of the family are important for young people's emotional development. However, limited research has examined the neural mechanisms at play. Here, we examined the associations between family and parenting factors, the neural correlates of emotional reactivity and regulation, and internalizing symptoms in early adolescent girls. Sixty-four female adolescents aged 10-12 years with elevated internalizing symptoms completed emotional reactivity, implicit (affect labeling) and explicit (cognitive reappraisal) emotion regulation tasks during functional magnetic resonance imaging. Positive family emotional climate was associated with greater activation in the anterior cingulate and middle temporal cortices during emotional reactivity. Maternal emotion regulation difficulties were associated with increased frontal pole and supramarginal gyrus activation during affect labeling, whereas supportive maternal emotion socialization and positive family emotional climate were associated with activation in prefrontal regions, including inferior frontal and superior frontal gyri, respectively, during cognitive reappraisal. No mediating effects of brain function were observed in the associations between family/parenting factors and adolescent symptoms. These findings highlight the role of family and parenting behaviors in adolescent emotion regulation neurobiology, and contribute to prominent models of adolescent emotional development.

Cannabis cue-reactivity in cannabis use disorder: Diverging evidence in two distinct cannabis cultures

BACKGROUND

Cannabis policies and attitudes play a role in the development and presentation of cannabis use disorder (CUD), but it is unclear how these factors are related to biomarkers of addiction. The current study examined cross-cultural differences in cannabis attitudes, cannabis cue-reactivity in the brain and its associations with cannabis use measures and cannabis attitudes.

DESIGN

Cross-sectional fMRI study.

SETTING

The Netherlands (NL) and Texas (TX), USA.

PARTICIPANTS

104 cannabis users with CUD (44% female; NL-CUD = 54, TX-CUD = 50) and 83 non-using controls (52% female; NL-CON = 50, TX-CON = 33).

MEASUREMENTS

Self-reported positive (perceived benefits) and negative (perceived harms) cannabis attitudes and tactile cannabis cue-reactivity assessed using a 3T MRI scanner.

FINDINGS

While the CUD group overall was more positive and less negative about cannabis and reported higher craving, the TX-CUD group reported significantly more positive and less negative attitudes and less craving than the NL-CUD group. Cannabis cue-reactivity was observed in the CUD group in clusters including the precuneus, lateral occipital cortex, frontal medial cortex, nucleus accumbens, and thalamus. In the TX-CUD group, a positive association was observed between symptom severity and cue-induced craving and cannabis cue-reactivity in precuneus and occipital cortex clusters, while a negative association was observed in the NL-CUD group. In these clusters, individuals with more positive attitudes exhibited a positive association between craving and cue-reactivity and those with less positive attitudes exhibited a negative association. No associations with quantity of use were observed.

CONCLUSIONS

Cue-induced craving might be deferentially associated with cannabis cue-reactivity across distinct cannabis use environments.

Functional connectivity of the pediatric brain

PURPOSE

This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders.

METHODS

The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents.

RESULTS

Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders.

CONCLUSION

Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies.

Processing, evaluating, and understanding FMRI data with afni_proc.py

FMRI data are noisy, complicated to acquire, and typically go through many steps of processing before they are used in a study or clinical practice. Being able to visualize and understand the data from the start through the completion of processing, while being confident that each intermediate step was successful, is challenging. AFNI's afni_proc.py is a tool to create and run a processing pipeline for FMRI data. With its flexible features, afni_proc.py allows users to both control and evaluate their processing at a detailed level. It has been designed to keep users informed about all processing steps: it does not just process the data, but also first outputs a fully commented processing script that the users can read, query, interpret, and refer back to. Having this full provenance is important for being able to understand each step of processing; it also promotes transparency and reproducibility by keeping the record of individual-level processing and modeling specifics in a single, shareable place. Additionally, afni_proc.py creates pipelines that contain several automatic self-checks for potential problems during runtime. The output directory contains a dictionary of relevant quantities that can be programmatically queried for potential issues and a systematic, interactive quality control (QC) HTML. All of these features help users evaluate and understand their data and processing in detail. We describe these and other aspects of afni_proc.py here using a set of task-based and resting-state FMRI example commands.

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.

Within-person biological mechanisms of mood variability in childhood and adolescence

Mood variability, the day-to-day fluctuation in mood, differs between individuals and develops during adolescence. Because adolescents show higher mood variability and average mood than children and adults, puberty might be a potential biological mechanism underlying this increase. The goal of this preregistered developmental study was to examine the neural and hormonal underpinnings of adolescent-specific within-person changes in mood variability, with a specific focus on testosterone, cortisol, pubertal status, and resting-state functional brain connectivity. Data from two longitudinal cohorts were used: the L-CID twin study (aged 7-13, N at the first timepoint = 258) and the accelerated Leiden Self-Concept study (SC; aged 11-21, N at the first timepoint = 138). In both studies resting-state functional magnetic resonance imaging (rs-fMRI) data was collected, as well as daily mood. Additionally, in the SC study self-reported puberty testosterone and cortisol were collected. Random intercept cross-lagged panel models (RI-CLPM) were used to study the within-person relations between these biological measures and mood variability and average mood. Mood variability and average mood peaked in adolescence and testosterone levels and self-reported puberty also showed an increase. Connectivity between prefrontal cortex (dlPFC and vmPFC) and subcortical regions (caudate, amygdala) decreased across development. Moreover, higher testosterone predicted average negative mood at the next time point, but not vice versa. Further, stronger vmPFC-amygdala functional connectivity predicted decreases in mood variability. Here, we show that brain connectivity during development is an important within-person biological mechanism of the development of mood in adolescents. PRACTITIONER POINTS: Mood variability peaks in adolescence. Within-person changes in testosterone predict within-person changes in mood. Within-person changes in vmPFC-amygdala connectivity predict within-person changes in mood variability.

A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth, and interactive QC with afni_proc.py and more

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically underdiscussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single-subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, afni_proc.py. These items include a modular HTML document that covers full single-subject processing from the raw data through statistical modeling, several review scripts in the results directory of processed data, and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria, or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each "QC block," as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.

Hippocampus, amygdala, and insula activation in response to romantic relationship dissolution stimuli: A case-case-control fMRI study on emerging adult students

BACKGROUND

Romantic relationship dissolutions (RRDs) are associated with posttraumatic stress symptoms (PTSS). Functional magnetic resonance imaging in RRD studies indicate overlapping neural activation similar to posttraumatic stress disorder. These studies combine real and hypothetical rejection, and lack contextual information and control and/or comparison groups exposed to non-RRD or DSM-5 defined traumatic events.

AIM

We investigated blood oxygen level dependent (BOLD) activation in the hippocampus, amygdala, and insula of participants with RRDs compared with other traumatic or non-trauma stressors.

METHODS

Emerging adults (mean age = 21.54 years; female = 74.7 %) who experienced an RRD (n = 36), DSM-5 defined trauma (physical and/or sexual assault: n = 15), or a non-RRD or DSM-5 stressor (n = 28) completed PTSS, depression, childhood trauma, lifetime trauma exposure, and attachment measures. We used a general and customised version of the International Affective Picture System to investigate responses to index-trauma-related stimuli. We used mixed linear models to assess between-group differences, and ANOVAs and Spearman's correlations to analyse factors associated with BOLD activation.

RESULTS

BOLD activity increased between index-trauma stimuli as compared to neutral stimuli in the hippocampus and amygdala, with no significant difference between the DSM-5 Trauma and RRD groups. Childhood adversity, sexual orientation, and attachment style were associated with BOLD activation changes. Breakup characteristics (e.g., initiator status) were associated with increased BOLD activation in the hippocampus and amygdala, in the RRD group.

CONCLUSION

RRDs should be considered as potentially traumatic events. Breakup characteristics are risk factors for experiencing RRDs as traumatic.

LIMITATION

Future studies should consider more diverse representation across sex, ethnicity, and sexual orientation.

Data-driven Discovery of the Central Autonomic Network: Dynamic Integration of HRV and Multivariate fMRI Connectivity

INTRODUCTION

Although the interaction between the brain and the heart, through the autonomic nervous system, is an established phenomenon, multimodal studies that have explored their bidirectional interplay are still limited.

AIM

In this context, the objective of the present study was to investigate the coupling between sympathetic and vagal dynamics and brain functional connectivity during resting state, thanks to simultaneously acquired electrocardiogram and functional magnetic resonance imaging (fMRI) data.

METHODS

Twenty healthy controls (67.42 ± 10.81 years, 60% females) were included in the study. Unimodal fMRI and heart rate variability (HRV) results were integrated in a joint analysis framework. Trivariate dynamic functional connectivity (dFC) features were correlated with time-varying HRV parameters to identify brain regions involved in autonomic modulation.

RESULTS

In a data-driven approach, the present analysis allowed to extract triplets of brain regions whose dFC was coupled with both sympathetic and vagal activity dynamics. The identified brain regions often belonged to the central autonomic network, which is a network of brain structures that are involved in the regulation of autonomic processes at high central level.

CONCLUSION

The present multimodal HRV and fMRI dFC analysis provided new findings on the physiological brain-heart interactions, paving the way to explore the same mechanisms in disorders of the brain-heart axis.

A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth and interactive QC with afni_proc.py and more

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically under-discussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, afni_proc.py. These items include: a modular HTML document that covers full single subject processing from the raw data through statistical modeling; several review scripts in the results directory of processed data; and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each "QC block", as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.

Topological differences of striato-thalamo-cortical circuit in functional brain network between premature ejaculation patients with and without depression

INTRODUCTION

Premature ejaculation (PE), a common male sexual dysfunction, often accompanies by abnormal psychological factors, such as depression. Recent neuroimaging studies have revealed structural and functional brain abnormalities in PE patients. However, there is limited neurological evidence supporting the comorbidity of PE and depression. This study aimed to explore the topological changes of the functional brain networks of PE patients with depression.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 60 PE patients (30 with depression and 30 without depression) and 29 healthy controls (HCs). Functional brain networks were constructed for all participants based on rs-fMRI data. The nodal parameters including nodal centrality and efficiency were calculated by the method of graph theory analysis and then compared between groups. In addition, the results were corrected for multiple comparisons by family-wise error (FWE) (p < .05).

RESULTS

PE patients with depression had increased degree centrality and global efficiency in the right pallidum, as well as increased degree centrality in the right thalamus when compared with HCs. PE patients without depression showed increased degree centrality in the right pallidum and thalamus, as well as increased global efficiency in the right precuneus, pallidum, and thalamus when compared with HCs. PE patients with depression demonstrated decreased degree centrality in the right pallidum and thalamus, as well as decreased global efficiency in the right precuneus, pallidum, and thalamus when compared to those without depression. All the brain regions above survived the FWE correction.

CONCLUSION

The results suggested that increased and decreased functional connectivity, as well as the capability of global integration of information in the brain, might be related to the occurrence of PE and the comorbidity depression in PE patients, respectively. These findings provided new insights into the understanding of the pathological mechanisms underlying PE and those with depression.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

Neural and cortisol responses to acute psychosocial stress in work-related burnout: The Regensburg Burnout Project

BACKGROUND

While several attempts have been made to elucidate the pathophysiology of burnout, neural stress responses have not yet been investigated. Therefore, the aim of this study was to examine salivary cortisol and - for the first time - neural responses to acute psychosocial stress within a strictly specified sample consisting of individuals suffering from burnout (BO group) and a healthy comparison group (HC group).

METHODS

After a multi-stage recruitment procedure based on burnout symptomatology and pathogenesis, 55 individuals suffering from burnout (25 women) and 61 individuals serving as HC group (31 women) out of an initial sample of 1022 volunteers were exposed to acute psychosocial stress during functional magnetic resonance imaging (fMRI) applying ScanSTRESS.

RESULTS

No differences were found between the BO and the HC group with respect to cortisol and mean neural stress responses. However, an exploratory comparison of neural stress responses of the first and second run of ScanSTRESS (exposure-time effect) revealed group-specific response patterns in one cluster peaking in the left dorsal anterior cingulate cortex (dACC). While the neural activation of the HC group was higher in the first compared to the second run of ScanSTRESS (i.e., decreasing activation), this pattern was reversed in the BO group (i.e., increasing activation).

CONCLUSIONS

Our analysis mainly did not provide evidence for altered acute cortisol and mean neural stress responses in burnout. However, the BO group was characterized by a limited capacity to show decreasing activation over stress exposure-time and exhibited instead increasing activation. Importantly, this group difference manifested in the left dACC which is both involved in neural stress processing and affected in individuals suffering from burnout. Given the present results, it seems promising to further examining temporal dynamics of neural stress responses in (sub-) clinical conditions such as burnout.

Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.

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.

Intrusive Traumatic Re-Experiencing Domain: Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Background

Intrusive traumatic re-experiencing domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective.

Methods

Data were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (n= 584) and included itemized PTSD symptom scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and trauma-exposed (TE)-only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. A random forest classification model was built on a training set using cross-validation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated.

Results

rsFC signatures differentiated TE-only participants from PTSD and ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group.

Conclusions

Neural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology.

A cross-cultural fMRI investigation of cannabis approach bias in individuals with cannabis use disorder

Introduction

As cannabis policies and attitudes become more permissive, it is crucial to examine how the legal and social environment influence neurocognitive mechanisms underlying cannabis use disorder (CUD). The current study aimed to assess whether cannabis approach bias, one of the mechanisms proposed to underlie CUD, differed between environments with distinct recreational cannabis policies (Amsterdam, The Netherlands (NL) and Dallas, Texas, United States of America (TX)) and whether individual differences in cannabis attitudes affect those differences.

Methods

Individuals with CUD (NL-CUD: 64; TX-CUD: 48) and closely matched non-using controls (NL-CON: 50; TX-CON: 36) completed a cannabis approach avoidance task (CAAT) in a 3T MRI. The cannabis culture questionnaire was used to measure cannabis attitudes from three perspectives: personal, family/friends, and state/country attitudes.

Results

Individuals with CUD demonstrated a significant behavioral cannabis-specific approach bias. Individuals with CUD exhibited higher cannabis approach bias-related activity in clusters including the paracingulate gyrus, anterior cingulate cortex, and frontal medial cortex compared to controls, which was no longer significant after controlling for gender. Site-related differences emerged in the association between cannabis use quantity and cannabis approach bias activity in the putamen, amygdala, hippocampus, and insula, with a positive association in the TX-CUD group and a negative association in the NL-CUD group. This was not explained by site differences in cannabis attitudes.

Conclusions

Pinpointing the underlying mechanisms of site-related differences-including, but not limited to, differences in method of administration, cannabis potency, or patterns of substance co-use-is a key challenge for future research.

Association of executive function with suicidality based on resting-state functional connectivity in young adults with subthreshold depression

Subthreshold depression (StD) is associated an increased risk of developing major depressive disorder (MDD) and suicidality. Suicidality could be linked to distress intolerance and use of context-dependent strategies. We identified neural correlates of executive functioning among the hubs in the resting-state functional connectome (rs-FCN) and examined associations with recent suicidality in StD and MDD. In total, 79 young adults [27 StD, 30 MDD, and 23 healthy controls (HC)] were scanned using magnetic resonance imaging. Neurocognitive measures of the mean latency to correct five moves in the One Touch Stockings of Cambridge (OTSMLC5), spatial working memory between errors (SWMBE), rapid visual information processing A' (RVPA'), and the stop signal reaction time in the stop signal test (SSTSSRT) were obtained. Global graph metrics were calculated to measure the network integration, segregation, and their balance in the rs-FCN. Regional graph metrics reflecting the number of neighbors (degree centrality; DC), participation in the shortcuts (betweenness centrality; BC), and accessibility to intersections (eigenvector centrality; EC) in the rs-FCN defined group-level hubs for StD, HC, and MDD, separately. Global network metrics were comparable among the groups (all P > 0.05). Among the group-level hubs, regional graph metrics of left dorsal anterior insula (dAI), right dorsomedial prefrontal cortex (dmPFC), right rostral temporal thalamus, right precuneus, and left postcentral/middle temporal/anterior subgenual cingulate cortices were different among the groups. Further, significant associations with neurocognitive measures were found in the right dmPFC with SWMBE, and left dAI with SSTSSRT and RVPA'. Shorter OTSMLC5 was related to the lower centralities of right thalamus and suffer of recent 1-year suicidal ideation (all Ps < 0.05 in ≥ 2 centralities out of DC, BC, and EC). Collectively, salience and thalamic networks underlie spatial strategy and planning, response inhibition, and suicidality in StD and MDD. Anti-suicidal therapies targeting executive function and modulation of salience-thalamic network in StD and MDD are required.

Fronto-striato-thalamic circuit connectivity and neuromelanin in schizophrenia: an fMRI and neuromelanin-MRI study

Changes in dopamine and fronto-striato-thalamic (FST) circuit functional connectivity are prominent in schizophrenia. Dopamine is thought to underlie connectivity changes, but experimental evidence for this hypothesis is lacking. Previous studies examined the association in some of the connections using positron emission tomography (PET) and functional MRI (fMRI); however, PET has disadvantages in scanning patients, such as invasiveness. Excessive dopamine induces neuromelanin (NM) accumulation, and NM-MRI is suggested as a noninvasive proxy measure of dopamine function. We aimed to investigate the association between NM and FST circuit connectivity at the network level in patients with schizophrenia. We analysed substantia nigra NM-MRI and resting-state fMRI data from 29 schizophrenia patients and 63 age- and sex-matched healthy controls (HCs). We identified the FST subnetwork with abnormal connectivity found in schizophrenia patients compared to that of HCs and investigated the relationship between constituting connectivity and NM-MRI signal. We found a higher NM signal (t = -2.12, p = 0.037) and a hypoconnected FST subnetwork (FWER-corrected p = 0.014) in schizophrenia patients than in HCs. In the hypoconnected subnetwork of schizophrenia patients, lower left supplementary motor area-left caudate connectivity was associated with a higher NM signal (β = -0.38, p = 0.042). We demonstrated the association between NM and FST circuit connectivity. Considering that the NM-MRI signal reflects dopamine function, our results suggest that dopamine underlies changes in FST circuit connectivity, which supports the dopamine hypothesis. In addition, this study reveals implications for the future use of NM-MRI in investigations of the dopamine system.

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark is implemented in a fully reproducible framework, where the provided research objects enable readers to reproduce or modify core computations, as well as the figures of the article using the Jupyter Book project and the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep software package. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods. Our reproducible benchmark infrastructure will facilitate such continuous evaluation in the future, and may also be applied broadly to different tools or even research fields.

Working memory-related brain activity in cannabis use disorder: The role of cross-cultural differences in cannabis attitudes

Cannabis legislation and attitudes towards use are changing. Given that evidence from cultural neuroscience research suggests that culture influences the neurobiological mechanisms underlying behaviour, it is of great importance to understand how cannabis legislation and attitudes might affect the brain processes underlying cannabis use disorder. Brain activity of 100 dependent cannabis users and 84 controls was recorded during an N-back working memory (WM) task in participants from the Netherlands (NL; users = 60, controls = 52) and Texas, USA (TX; users = 40, controls = 32). Participants completed a cannabis culture questionnaire as a measure of perceived benefits (positive) and perceived harms (negative) of cannabis from their personal, friends-family's and country-state's perspectives. Amount of cannabis use (grams/week), DSM-5 CUD symptoms and cannabis use-related problems were assessed. Cannabis users self-reported more positive and less negative (personal and friends-family) cannabis attitudes than controls, with this effect being significantly larger in the TX cannabis users. No site difference in country-state attitudes was observed. TX cannabis users, compared with NL cannabis users, and those cannabis users perceiving more positive country-state attitudes showed a more positive association between grams/week and WM-related activity in the superior parietal lobe. NL cannabis users, compared with TX cannabis users, and those cannabis users with less positive personal attitudes showed a more positive association between grams/week and WM-load-related activity in the temporal pole. Both site and cultural attitudes moderated the association of quantity of cannabis use with WM- and WM-load-related activity. Importantly, differences in legislation did not align with perceived cannabis attitudes and appear to be differentially associated with cannabis use-related brain activity.

Environmental effects on brain functional networks in a juvenile twin population

The brain's intrinsic organization into large-scale functional networks, the resting state networks (RSN), shows complex inter-individual variability, consolidated during development. Nevertheless, the role of gene and environment on developmental brain functional connectivity (FC) remains largely unknown. Twin design represents an optimal platform to shed light on these effects acting on RSN characteristics. In this study, we applied statistical twin methods to resting-state functional magnetic resonance imaging (rs-fMRI) scans from 50 young twin pairs (aged 10-30 years) to preliminarily explore developmental determinants of brain FC. Multi-scale FC features were extracted and tested for applicability of classical ACE and ADE twin designs. Epistatic genetic effects were also assessed. In our sample, genetic and environmental effects on the brain functional connections largely varied between brain regions and FC features, showing good consistency at multiple spatial scales. Although we found selective contributions of common environment on temporo-occipital connections and of genetics on frontotemporal connections, the unique environment showed a predominant effect on FC link- and node-level features. Despite the lack of accurate genetic modeling, our preliminary results showed complex relationships between genes, environment, and functional brain connections during development. A predominant role of the unique environment on multi-scale RSN characteristics was suggested, which needs replications on independent samples. Future investigations should especially focus on nonadditive genetic effects, which remain largely unexplored.

Demonstrating quality control procedures for fMRI in DPABI

Quality control (QC) is an important stage for functional magnetic resonance imaging (fMRI) studies. The methods for fMRI QC vary in different fMRI preprocessing pipelines. The inflating sample size and number of scanning sites for fMRI studies further add to the difficulty and working load of the QC procedure. Therefore, as a constituent part of the Demonstrating Quality Control Procedures in fMRI research topic in Frontiers, we preprocessed a well-organized open-available dataset using DPABI pipelines to illustrate the QC procedure in DPABI. Six categories of DPABI-derived reports were used to eliminate images without adequate quality. After the QC procedure, twelve participants (8.6%) were categorized as excluded and eight participants (5.8%) were categorized as uncertain. More automatic QC tools were needed in the big-data era while visually inspecting images was still indispensable now.

Harmonizing Ethno-Regionally Diverse Datasets to Advance the Global Epidemiology of Dementia

Understanding dementia and cognitive impairment is a global effort needing data from multiple sources across diverse ethno-regional groups. Methodological heterogeneity means that these data often require harmonization to make them comparable before analysis. We discuss the benefits and challenges of harmonization, both retrospective and prospective, broadly and with a focus on data types that require particular sorts of approaches, including neuropsychological test scores and neuroimaging data. Throughout our discussion, we illustrate general principles and give examples of specific approaches in the context of contemporary research in dementia and cognitive impairment from around the world.

Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.