Reliability of multimodal MRI brain measures in youth at risk for mental illness

Exploring the associations between the presence, characteristics, and biopsychosocial covariates of pain and lifetime depression in adolescents: A cross-sectional ABCD study analysis

INTRODUCTION

Depression and pain co-occur, even during adolescence. However, there is limited knowledge on the association between pain and lifetime depression, and which biopsychosocial measures are associated with this co-occurrence.

METHODS

Cross-sectional analysis of the Adolescent Brain and Cognitive Development (ABCD) two-year follow-up. We explored associations between the presence and characteristics of past month pain (intensity, duration, activity limitations, and number of pain sites) and lifetime depression using logistic regression. We explored associations of brain structure, physical, behavioural, emotional, social, and cognitive measures with lifetime depression and past month pain compared to having had one or neither condition using multinomial logistic regression.

RESULTS

A total of 5211 adolescents (mean age = 12.0 years) who had: (1) no lifetime mental ill-health and no pain (n = 3327); (2) pain only (n = 1407); (3) lifetime depressive disorder but no pain (n = 272); and (4) lifetime depressive disorder and pain (n = 205) were included. Pain presence was associated with lifetime depression (OR[95%CI]: 1.76 [1.45, 2.13], p < 0.001). Pain-related activity limitations (1.13 [1.06, 1.21], p < 0.001) and the number of pain sites (1.06 [1.02, 1.09], p < 0.001) were associated with lifetime depression. Various behavioural, emotional, social, and cognitive, but not brain structure or physical measures, were associated with lifetime depression and past month pain.

LIMITATIONS

Longitudinal analyses should validate prognostic markers on predicting co-occurring depression and pain.

CONCLUSIONS

Results support an association between the presence and characteristics of pain and lifetime depression during adolescence and could indicate the need for more integrated recognition and clinical care of youth experiencing both depression and pain.

Causal Relationship between Aging and Anorexia Nervosa: A White-Matter-Microstructure-Mediated Mendelian Randomization Analysis

This study employed a two-step Mendelian randomization analysis to explore the causal relationship between telomere length, as a marker of aging, and anorexia nervosa and to evaluate the mediating role of changes in the white matter microstructure across different brain regions. We selected genetic variants associated with 675 diffusion magnetic resonance imaging phenotypes representing changes in brain white matter. F-statistics confirmed the validity of the instruments, ensuring robust causal inference. Sensitivity analyses, including heterogeneity tests, horizontal pleiotropy tests, and leave-one-out tests, validated the results. The results show that telomere length is significantly negatively correlated with anorexia nervosa in a unidirectional manner (p = 0.017). Additionally, changes in specific white matter structures, such as the internal capsule, corona radiata, posterior thalamic radiation, left cingulate gyrus, left longitudinal fasciculus, and left forceps minor (p < 0.05), were identified as mediators. These findings enhance our understanding of the neural mechanisms, underlying the exacerbation of anorexia nervosa with aging; emphasize the role of brain functional networks in disease progression; and provide potential biological targets for future therapeutic interventions.

Intracortical Myelin in Youths at Risk for Depression

Background

Major depressive disorder (MDD) is a leading cause of disability. To understand why depression develops, it is important to distinguish between early neural markers of vulnerability that precede the onset of MDD and features that develop during depression. Recent neuroimaging findings suggest that reduced global and regional intracortical myelination (ICM), especially in the lateral prefrontal cortex, may be associated with depression, but it is unknown whether it is a precursor or a consequence of MDD. The study of offspring of affected parents offers the opportunity to distinguish between precursors and consequences by examining individuals who carry high risk at a time when they have not experienced depression.

Methods

We acquired 129 T1-weighted and T2-weighted scans from 56 (25 female) unaffected offspring of parents with depression and 114 scans from 63 (34 female) unaffected offspring of parents without a history of depression (ages 9 to 16 years). To assess scan quality, we calculated test-retest reliability. We used the scan ratios to calculate myelin maps for 68 cortical regions. We analyzed data using mixed-effects modeling.

Results

ICM did not differ between high and low familial risk youths in global (B = 0.06, SE = 0.03, p = .06) or regional (B = 0.05, SE = 0.03, p = .08) analyses. Our pediatric sample had high ICM reliability (intraclass correlation coefficient = 0.79; 95% CI, 0.55-0.88).

Conclusions

Based on our results, reduced ICM does not appear to be a precursor of MDD. Future studies should examine ICM in familial high-risk youths across a broad developmental period.

PhiPipe: A multi-modal MRI data processing pipeline with test-retest reliability and predicative validity assessments

Magnetic resonance imaging (MRI) has been one of the primary instruments to measure the properties of the human brain non-invasively in vivo. MRI data generally needs to go through a series of processing steps (i.e., a pipeline) before statistical analysis. Currently, the processing pipelines for multi-modal MRI data are still rare, in contrast to single-modal pipelines. Furthermore, the reliability and validity of the output of the pipelines are critical for the MRI studies. However, the reliability and validity measures are not available or adequate for almost all pipelines. Here, we present PhiPipe, a multi-modal MRI processing pipeline. PhiPipe could process T1-weighted, resting-state BOLD, and diffusion-weighted MRI data and generate commonly used brain features in neuroimaging. We evaluated the test-retest reliability of PhiPipe's brain features by computing intra-class correlations (ICC) in four public datasets with repeated scans. We further evaluated the predictive validity by computing the correlation of brain features with chronological age in three public adult lifespan datasets. The multivariate reliability and predictive validity of the PhiPipe results were also evaluated. The results of PhiPipe were consistent with previous studies, showing comparable or better reliability and validity when compared with two popular single-modality pipelines, namely DPARSF and PANDA. The publicly available PhiPipe provides a simple-to-use solution to multi-modal MRI data processing. The accompanied reliability and validity assessments could help researchers make informed choices in experimental design and statistical analysis. Furthermore, this study provides a framework for evaluating the reliability and validity of image processing pipelines.

The relationship between brain structure and general psychopathology in preadolescents

BACKGROUND

An emerging body of literature has indicated that broad, transdiagnostic dimensions of psychopathology are associated with alterations in brain structure across the life span. The current study aimed to investigate the relationship between brain structure and broad dimensions of psychopathology in the critical preadolescent period when psychopathology is emerging.

METHODS

This study included baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study^® (n = 11,875; age range = 9-10 years; male = 52.2%). General psychopathology, externalizing, internalizing, and thought disorder dimensions were based on a higher-order model of psychopathology and estimated using Bayesian plausible values. Outcome variables included global and regional cortical volume, thickness, and surface area.

RESULTS

Higher levels of psychopathology across all dimensions were associated with lower volume and surface area globally, as well as widespread and pervasive alterations across the majority of cortical and subcortical regions studied, after adjusting for sex, race/ethnicity, parental education, income, and maternal psychopathology. The relationships between general psychopathology and brain structure were attenuated when adjusting for cognitive functioning. There were no statistically significant relationships between psychopathology and cortical thickness in this sample of preadolescents.

CONCLUSIONS

The current study identified lower cortical volume and surface area as transdiagnostic biomarkers for general psychopathology in preadolescence. Future research may focus on whether the widespread and pervasive relationships between general psychopathology and brain structure reflect cognitive dysfunction that is a feature across a range of mental illnesses.

The developmental brain age is associated with adversity, depression, and functional outcomes among adolescents

BACKGROUND

Most psychiatric disorders emerge in the second decade of life. In the present study we examined whether environmental adversity, developmental antecedents, major depressive disorder (MDD), and functional impairment correlate with deviation from normative brain development in adolescence.

METHODS

We trained a brain age prediction model using 189 structural MRI brain features in 1299 typically developing adolescents (age range 9-19 years old, M = 13.5, SD = 3.04), validated the model in a holdout set of 322 adolescents (M = 13.5, SD = 3.07), and used it to predict age in an independent risk-enriched cohort of 150 adolescents (M = 13.6, SD = 2.82). We tested associations between the brain-age-gap and adversity, early antecedents, depression, and functional impairment.

RESULTS

We accurately predicted chronological age in typically developing adolescents (mean absolute error (MAE) = 1.53 years). The model generalized to the validation set (MAE = 1.55 years, 1.98 bias adjusted) and to the independent at-risk sample (MAE = 1.49 years, 1.86 bias adjusted). The brain age estimate was reliable in repeated scans (intra class correlation = 0.94). Experience of environmental advertises (β = 0.18, 95% CI [0.04, 0.31], p = 0.02), diagnosis of MDD (β = 0.61, 95% CI [0.23, 0.99], p = 0.01) and functional impairment (β = 0.16, 95% CI [0.05, 0.27], p = 0.01) were associated with a positive brain-age-gap.

CONCLUSIONS

Risk factors, diagnosis, and impact of mental illness are associated with an older appearing brain during development.

Threat or thrill? the neural mechanisms underlying the development of anxiety and risk taking in adolescence

Anxiety is common in adolescence and has been linked to a plethora of negative outcomes across development. While previous studies of anxiety have focused on threat sensitivity, less work has considered the concurrent development of threat- and reward-related neural circuitry and how these circuits interact and compete during puberty to influence typical adolescent behaviors such as increased risk taking and exploration. The current review integrates relevant findings from clinical and developmental neuroimaging studies to paint a multidimensional picture of adolescent-onset anxiety against the backdrop of typical adolescent development. Ultimately, this paper argues that longitudinal neuroimaging studies tracking approach and avoidance motivations across development are needed to fully understand the mechanisms underlying the development of anxiety in adolescence and to identify and provide effective interventions for at-risk youth.

Reliability of multimodal MRI brain measures in youth at risk for mental illness

INTRODUCTION

A new generation of large-scale studies is using neuroimaging to investigate adolescent brain development across health and disease. However, imaging artifacts such as head motion remain a challenge and may be exacerbated in pediatric clinical samples. In this study, we assessed the scan-rescan reliability of multimodal MRI in a sample of youth enriched for risk of mental illness.

METHODS

We obtained repeated MRI scans, an average of 2.7 ± 1.4 weeks apart, from 50 youth (mean age 14.7 years, SD = 4.4). Half of the sample (52%) had a diagnosis of an anxiety disorder; 22% had attention-deficit/hyperactivity disorder (ADHD). We quantified reliability with the test-retest intraclass correlation coefficient (ICC).

RESULTS

Gray matter measurements were highly reliable with mean ICCs as follows: cortical volume (ICC = 0.90), cortical surface area (ICC = 0.89), cortical thickness (ICC = 0.82), and local gyrification index (ICC = 0.85). White matter volume reliability was excellent (ICC = 0.98). Diffusion tensor imaging (DTI) components were also highly reliable. Fractional anisotropy was most consistently measured (ICC = 0.88), followed by radial diffusivity (ICC = 0.84), mean diffusivity (ICC = 0.81), and axial diffusivity (ICC = 0.78). We also observed regional variability in reconstruction, with some brain structures less reliably reconstructed than others.

CONCLUSIONS

Overall, we showed that developmental MRI measures are highly reliable, even in youth at risk for mental illness and those already affected by anxiety and neurodevelopmental disorders. Yet, caution is warranted if patterns of results cluster within regions of lower reliability.