Quality Control of Structural MRI Images Applied Using FreeSurfer—A Hands-On Workflow to Rate Motion Artifacts

In structural magnetic resonance imaging motion artifacts are common, especially when not scanning healthy young adults. It has been shown that motion affects the analysis with automated image-processing techniques (e.g. FreeSurfer). This can bias results. Several developmental and adult studies have found reduced volume and thickness of gray matter due to motion artifacts. Thus, quality control is necessary in order to ensure an acceptable level of quality and to define exclusion criteria of images (i.e. determine participants with most severe artifacts). However, information about the quality control workflow and image exclusion procedure is largely lacking in the current literature and the existing rating systems differ. Here we propose a stringent workflow of quality control steps during and after acquisition of T1-weighted images, which enables researchers dealing with populations that are typically affected by motion artifacts to enhance data quality and maximize sample sizes. As an underlying aim we established a thorough quality control rating system for T1-weighted images and applied it to the analysis of developmental clinical data using the automated processing pipeline FreeSurfer. This hands-on workflow and quality control rating system will aid researchers in minimizing motion artifacts in the final data set, and therefore enhance the quality of structural magnetic resonance imaging studies

Adolescent to young adult longitudinal development of subcortical volumes in two European sites with four waves

Adolescent subcortical structural brain development might underlie psychopathological symptoms, which often emerge in adolescence. At the same time, sex differences exist in psychopathology, which might be mirrored in underlying sex differences in structural development. However, previous studies showed inconsistencies in subcortical trajectories and potential sex differences. Therefore, we aimed to investigate the subcortical structural trajectories and their sex differences across adolescence using for the first time a single cohort design, the same quality control procedure, software, and a general additive mixed modeling approach. We investigated two large European sites from ages 14 to 24 with 503 participants and 1408 total scans from France and Germany as part of the IMAGEN project including four waves of data acquisition. We found significantly larger volumes in males versus females in both sites and across all seven subcortical regions. Sex differences in age-related trajectories were observed across all regions in both sites. Our findings provide further evidence of sex differences in longitudinal adolescent brain development of subcortical regions and thus might eventually support the relationship of underlying brain development and different adolescent psychopathology in boys and girls.</p

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

Best practices in clinical developmental structural neuroimaging

Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms of neurodevelopmental disorders, including attention deficit hyperactivity disorder (ADHD), conduct disorder (CD), and oppositional defiant disorder (ODD). Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct clinical developmental sMRI studies face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in ADHD, CD, and ODD. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss the impact of choices on statistical analysis and interpretation of results. We call for greater completeness and transparency in methods reports to advance understanding of brain structural alterations in neurodevelopmental disorders

Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders

Abstract Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders

Adolescent to young adult longitudinal development across 8 years for matching emotional stimuli during functional magnetic resonance imaging

We investigated development from adolescence to young adulthood of neural bottom-up and top-down processes using a functional magnetic resonance imaging task on emotional attention. We followed 249 participants from age 14–22 in up to four waves resulting in 687 total scans of a matching task in which participants decided whether two pictures were the same including distracting emotional or neutral scenes. We applied generalized additive mixed models and a reliability approach for longitudinal analysis. Reaction times and error rates decreased longitudinally. For top-down processing, we found a longitudinal increase for the bilateral inferior frontal gyrus (IFG) for negative stimuli and in the left IFG also for positive and neutral stimuli. For bottom-up activation in the bilateral amygdala, we found a relative stability for negative and neutral stimuli. For positive stimuli, there was an increase starting in the twenties. Results show ongoing behavioral and top-down prefrontal development relatively independent from emotional valence. Amygdala bottom-up activation remained stable except for positive stimuli. Current findings add to the sparse literature on longitudinal top-down and bottom-up development into young adulthood and emphasize the role of reliability. These findings might help to characterize healthy in contrast to dysfunctional development of emotional attention

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

App-Based Mindfulness Meditation Training and an Audiobook Intervention Reduce Symptom Severity but Do Not Modify Backward Inhibition in Adolescent Obsessive-Compulsive Disorder: Evidence from an EEG Study

(1) Background: 1–2% of children and adolescents are affected by Obsessive-Compulsive Disorder (OCD). The rigid, repetitive features of OCD and an assumed disability to inhibit recent mental representations are assumed to have led to a paradoxical advantage in that the Backward Inhibition (BI) effect was recently found to be lower in adolescents with OCD as compared to healthy controls. It was hypothesized that app-based mindfulness meditation training could reduce the disability to inhibit recent mental representations and thus increase the BI-effect by adapting cognitive flexibility and inhibition abilities according to healthy controls. (2) Methods: 58 adolescents (10–19 years) with OCD were included in the final sample of this interviewer-blind, randomized controlled study. Participants were allocated to an intervention group (app-based mindfulness meditation training) or an (active) control group (app-based audiobook) for eight weeks. Symptom (CY-BOCS), behavioral (reaction times and mean accuracy), and neurophysiological changes (in EEG) of the BI-effect were analyzed in a pre-post design. (3) Results: The intervention and the control group showed an intervention effect (Reliable Change Index: 67%) with a significant symptom reduction. Contrary to the hypothesis, the BI-effect did not differ between pre vs. post app-based mindfulness meditation training. In addition, as expected the audiobook application showed no effects. Thus, we observed no intervention-specific differences with respect to behavioral (reaction times and mean accuracy) or with respect to neurophysiological (perceptual [P1], attentional [N1], conflict monitoring [N2] or updating and response selection [P3]) processes. However, in an exploratory approach, we revealed that the BI-effect decreased in participants who did not benefit from using an app, regardless of group. (4) Conclusions: Both listening to an app-based mindfulness meditation training and to an audiobook reduce symptom severity in adolescent OCD as measured by the CY-BOCS; however, they have no specific effect on BI. The extent of the baseline BI-effect might be considered as an intra-individual component to predict the benefit of both mindfulness meditation training and listening to an audiobook

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.ISSN:1359-4184ISSN:1476-557