Predicting multiscan MRI outcomes in children with neurodevelopmental conditions following MRI simulator training

Changes in MRI head motion across development: typical development and ADHD

Head motion is a major confounding variable for magnetic resonance imaging (MRI) analysis, and is commonly seen in individuals with neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). This study investigated the trajectory of change in head motion in typically developing children and children with ADHD, and examined possible altered trajectories in head motion between children with remitted and persistent ADHD. 105 children with ADHD and 84 controls completed diffusion and resting-state functional MRI scans at up to three waves over ages 9-14 years. In-scanner head motion was calculated using framewise displacement, and longitudinal trajectories analyzed using generalized additive mixed modelling. Results revealed a significant age effect on framewise displacement where head motion decreased as age increased during both diffusion (p < .001) and resting-state functional MRI (p < .001). A significant effect of group was also observed; children with ADHD displayed greater framewise displacement than controls over the age range (diffusion MRI p = .036, functional MRI p = .004). Further analyses revealed continued elevation in head motion in children in remission from ADHD (diffusion MRI p = .020, functional MRI p = .011) compared to controls. Rates of change in head motion did not significantly differ between diagnostic groups. Findings indicate a critical link between in-scanner head motion and developmental age within children regardless of ADHD diagnosis, important to consider in studies of neurodevelopment. Findings also suggest change in head motion with age does not differ between individuals with remitted and persistent ADHD, adding further evidence that behavioral manifestations of ADHD may continue despite clinical remission.

Feasibility and success of a non-sedated brain MRI training protocol in 7-year-old children from rural and semi-rural Colombia

BACKGROUND

Brain magnetic resonance imaging (MRI) is a crucial tool for clinical evaluation of the brain and neuroscience research. Obtaining successful non-sedated MRI in children who live in resource-limited settings may be an additional challenge.

OBJECTIVE

To present a feasibility study of a novel, low-cost MRI training protocol used in a clinical research study in a rural/semi-rural region of Colombia and to examine neurodevelopmental factors associated with successful scans.

MATERIALS AND METHODS

Fifty-seven typically developing Colombian children underwent a training protocol and non-sedated brain MRI at age 7. Group training utilized a customized booklet, an MRI toy set, and a simple mock scanner. Children attended MRI visits in small groups of two to three. Resting-state functional and structural images were acquired on a 1.5-Tesla scanner with a protocol duration of 30-40 minutes. MRI success was defined as the completion of all sequences and no more than mild motion artifact. Associations between the Wechsler Preschool and Primary Scale of Intelligence (WPPSI), Movement Assessment Battery for Children (MABC), Behavioral Rating Inventory of Executive Function (BRIEF), Child Behavior Checklist (CBCL), and Adaptive Behavior Assessment System (ABAS) scores and MRI success were analyzed.

RESULTS

Mean (SD) age at first MRI attempt was 7.2 (0.2) years (median 7.2 years, interquartile range 7.1-7.3 years). Twenty-six (45.6%) participants were male. Fifty-one (89.5%) children were successful across two attempts; 44 (77.2%) were successful on their first attempt. Six (10.5%) were unsuccessful due to refusal or excessive motion. Age, sex, and scores across all neurodevelopmental assessments (MABC, TVIP, ABAS, BRIEF, CBCL, NIH Toolbox Flanker, NIH Toolbox Pattern Comparison, WPPSI) were not associated with likelihood of MRI success (P=0.18, 0.19, 0.38, 0.92, 0.84, 0.80, 1.00, 0.16, 0.75, 0.86, respectively).

CONCLUSION

This cohort of children from a rural/semi-rural region of Colombia demonstrated comparable MRI success rates to other published cohorts after completing a low-cost MRI familiarization training protocol suitable for low-resource settings. Achieving non-sedated MRI success in children in low-resource and international settings is important for the continuing diversification of pediatric research studies.

Age, Motion, Medical, and Psychiatric Associations With Incidental Findings in Brain MRI

Importance

Few investigations have evaluated rates of brain-based magnetic resonance imaging (MRI) incidental findings (IFs) in large lifespan samples, their stability over time, or their associations with health outcomes.

Objectives

To examine rates of brain-based IFs across the lifespan, their persistence, and their associations with phenotypic indicators of behavior, cognition, and health; to compare quantified motion with radiologist-reported motion and evaluate its associations with IF rates; and to explore IF consistency across multiple visits.

Design, Setting, and Participants

This cross-sectional study included participants from the Nathan Kline Institute-Rockland Sample (NKI-RS), a lifespan community-ascertained sample, and the Healthy Brain Network (HBN), a cross-sectional community self-referred pediatric sample focused on mental health and learning disorders. The NKI-RS enrolled participants (ages 6-85 years) between March 2012 and March 2020 and had longitudinal participants followed up for as long as 4 years. The HBN enrolled participants (ages 5-21 years) between August 2015 and October 2021. Clinical neuroradiology MRI reports were coded for radiologist-reported motion as well as presence, type, and clinical urgency (category 1, no abnormal findings; 2, no referral recommended; 3, consider referral; and 4, immediate referral) of IFs. MRI reports were coded from June to October 2021. Data were analyzed from November 2021 to February 2023.

Main Outcomes and Measures

Rates and type of IFs by demographic characteristics, health phenotyping, and motion artifacts; longitudinal stability of IFs; and Euler number in projecting radiologist-reported motion.

Results

A total of 1300 NKI-RS participants (781 [60.1%] female; mean [SD] age, 38.9 [21.8] years) and 2772 HBN participants (976 [35.2%] female; mean [SD] age, 10.0 [3.5] years) had health phenotyping and neuroradiology-reviewed MRI scans. IFs were common, with 284 of 2956 children (9.6%) and 608 of 1107 adults (54.9%) having IFs, but rarely of clinical concern (category 1: NKI-RS, 619 [47.6%]; HBN, 2561 [92.4%]; category 2: NKI-RS, 647 [49.8%]; HBN, 178 [6.4%]; category 3: NKI-RS, 79 [6.1%]; HBN, 30 [1.1%]; category 4: NKI-RS: 12 [0.9%]; HBN, 6 [0.2%]). Overall, 46 children (1.6%) and 79 adults (7.1%) required referral for their IFs. IF frequency increased with age. Elevated blood pressure and BMI were associated with increased T2 hyperintensities and age-related cortical atrophy. Radiologist-reported motion aligned with Euler-quantified motion, but neither were associated with IF rates.

Conclusions and Relevance

In this cross-sectional study, IFs were common, particularly with increasing age, although rarely clinically significant. While T2 hyperintensity and age-related cortical atrophy were associated with BMI and blood pressure, IFs were not associated with other behavioral, cognitive, and health phenotyping. Motion may not limit clinical IF detection.

Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage by design.

Factors associated with MRI success in children cooled for neonatal encephalopathy and controls

OBJECTIVE

To investigate if an association exists between motion artefacts on brain MRI and comprehension, co-ordination, or hyperactivity scores in children aged 6-8 years, cooled for neonatal encephalopathy (cases) and controls.

METHODS

Case children (n = 50) without cerebral palsy were matched with 43 controls for age, sex, and socioeconomic status. Children underwent T1-weighted (T1w), diffusion-weighted image (DWI) brain MRI and cognitive, behavioural, and motor skills assessment. Stepwise multivariable logistic regression assessed associations between unsuccessful MRI and comprehension (including Weschler Intelligence Scale for Children (WISC-IV) verbal comprehension, working memory, processing speed and full-scale IQ), co-ordination (including Movement Assessment Battery for Children (MABC-2) balance, manual dexterity, aiming and catching, and total scores) and hyperactivity (including Strengths and Difficulties Questionnaire (SDQ) hyperactivity and total difficulties scores).

RESULTS

Cases had lower odds of completing both T1w and DWIs (OR: 0.31, 95% CI 0.11-0.89). After adjusting for case-status and sex, lower MABC-2 balance score predicted unsuccessful T1w MRI (OR: 0.81, 95% CI 0.67-0.97, p = 0.022). Processing speed was negatively correlated with relative motion on DWI (r = -0.25, p = 0.026) and SDQ total difficulties score was lower for children with successful MRIs (p = 0.049).

CONCLUSIONS

Motion artefacts on brain MRI in early school-age children are related to the developmental profile.

IMPACT

Children who had moderate/severe neonatal encephalopathy are less likely to have successful MRI scans than matched controls. Motion artefact on MRI is associated with lower MABC-2 balance scores in both children who received therapeutic hypothermia for neonatal encephalopathy and matched controls, after controlling for case-status and sex. Exclusion of children with motion artefacts on brain MRI can introduce sampling bias, which impacts the utility of neuroimaging to understand the brain-behaviour relationship in children with functional impairments.

Effectiveness of mock scanners and preparation programs for successful magnetic resonance imaging: a systematic review and meta-analysis

This review aimed to summarise the effectiveness of preparation programs for magnetic resonance imaging (MRI) in children using mock scanners and the success rates by systematically reviewing the current literature. We initially identified 67 articles using the search terms "MRI," "mock" and "child" on online databases. All studies involving a preparation programme for MRI on children ages 18 years or younger, healthy children and those with medical diagnoses were included. The authors extracted data on study design, participant data, details of the MRI protocol and the total numbers of patients who underwent preparation programs and were scanned while awake, without sedation or general anesthesia. Twenty-three studies were included in this review. Preparation programs included in-home and hospital/research facility components; these consisted of a mock scanner, explanatory booklets, recorded MRI scan sounds and other educational materials. The success rate of MRI after the preparation programme reported in each study ranged from 40% to 100%. When all participants from studies that specifically assessed the efficacy of preparation programs were combined, participants who underwent a preparation programme (n = 196) were more likely to complete a successful MRI than those who did not undergo a preparation programme (n = 263) (odds ratio [OR] = 1.98). Our results suggest that preparation programs may help reduce the risk of children failing MRI scans.

Key considerations for child and adolescent MRI data collection

Cognitive neuroimaging researchers' ability to infer accurate statistical conclusions from neuroimaging depends greatly on the quality of the data analyzed. This need for quality control is never more evident than when conducting neuroimaging studies with children and adolescents. Developmental neuroimaging requires patience, flexibility, adaptability, extra time, and effort. It also provides us a unique, non-invasive way to understand the development of cognitive processes, individual differences, and the changing relations between brain and behavior over the lifespan. In this discussion, we focus on collecting magnetic resonance imaging (MRI) data, as it is one of the more complex protocols used with children and youth. Through our extensive experience collecting MRI datasets with children and families, as well as a review of current best practices, we will cover three main topics to help neuroimaging researchers collect high-quality datasets. First, we review key recruitment and retention techniques, and note the importance for consistency and inclusion across groups. Second, we discuss ways to reduce scan anxiety for families and ways to increase scan success by describing the pre-screening process, use of a scanner simulator, and the need to focus on participant and family comfort. Finally, we outline several important design considerations in developmental neuroimaging such as asking a developmentally appropriate question, minimizing data loss, and the applicability of public datasets. Altogether, we hope this article serves as a useful tool for those wishing to enter or learn more about developmental cognitive neuroscience.

Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.