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

ABSTRACT: 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.

SUBMITTER: Drobinin V

PROVIDER: S-EPMC7303399 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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

Drobinin Vladislav V Van Gestel Holly H Helmick Carl A CA Schmidt Matthias H MH Bowen Chris V CV Uher Rudolf R

Brain and behavior 20200418 6

<h4>Introduction</h4>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.<h4>Methods</h4>We obtained repeated MRI scans, an average of 2.7 ± 1.4 weeks apart, from 50 you ...[more]

PMID: 32304355

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Project description:BackgroundMost mental disorders begin in adolescence; however, there are gaps in our understanding of youth mental health. Clinical and policy gaps arise from our current inability to predict, from amongst all youth who experience mild behavioural disturbances, who will go on to develop a mental illness, what that illness will be, and what can be done to change its course and prevent its worsening to a serious mental illness (SMI). There are also gaps in our understanding of how known risk factors set off neurobiological changes that may play a role in determining who will develop a SMI. Project goals are (i) to identify youth at different stages of risk of SMI so that intervention can begin as soon as possible and (ii) to understand the triggers of these mental illnesses.MethodThis 2-site longitudinal study will recruit 240 youth, ages 12-25, who are at different stages of risk for developing a SMI. The sample includes (a) healthy individuals, (b) symptom-free individuals who have a first-degree relative with a SMI, (c) youth who are experiencing distress and may have mild symptoms of anxiety or depression, and (d) youth who are already demonstrating attenuated symptoms of SMI such as bipolar disorder or psychosis. We will assess, every 6 months for one year, a wide range of clinical and psychosocial factors to determine which factors can be used to predict key outcomes. We will also assess neuroimaging and peripheral markers. We will develop and validate a prediction algorithm that includes demographic, clinical and psychosocial predictors. We will also determine if adding biological markers to our algorithm improves prediction.DiscussionOutcomes from this study include an improved clinical staging model for SMI and prediction algorithms that can be used by health care providers as decision-support tools in their practices. Secondly, we may have a greater understanding of clinical, social and cognitive factors associated with the clinical stages of development of a SMI, as well as new insights from neuroimaging and later neurochemical biomarker studies regarding predisposition to SMI development and progression through the clinical stages of illness.

Project description:BackgroundIn major depressive disorder (MDD), self-blame-related fMRI measures have shown the potential to be used as prognostic markers for recurrence risk. Like most potential fMRI markers, however, their reliability is unclear. Here, we probed the internal reliability of self-blame-related fMRI measures, as well as the impact of different modelling approaches on reliability metrics and validity.MethodsInternal consistency (i.e. split-half reliability) was calculated for blood oxygen level-dependent (BOLD) responses and psychophysiological interactions (PPI) related to self-blame-related biases in medication-free remitted MDD participants (n = 81) and healthy controls (n = 41). Trial-length was modelled using three durations (0, 2 and 5 s), which was convolved with the haemodynamic response function (HRF) with and without time and dispersion derivatives. Intraclass correlation coefficients (ICCs) were calculated for simple contrasts examining activation to self-blaming emotions and other-blaming emotions and the more complex contrast of the subtraction-based difference between self- and other-blaming emotions within the following a priori ROIs: right superior anterior temporal lobe seed region, anterior subgenual cingulate cortex, posterior subgenual cortex and right striatum / pallidum.ResultsAcross ROIs, we obtained fair reliability (ICC ≥ 0.40) for simple, but poor reliability (ICC < 0.40) for more complex fMRI measures related to self-blame. Despite this low internal consistency of complex measures at the individual level, we observed robust activation at the group-level, reproducing previously published results.ConclusionsWhile simple BOLD contrasts had fair reliability, previously employed PPI models had poor reliability and simple connectivity measures lacked predictive validity. This calls for the development of functional connectivity measures that strike a better balance between reliability and validity for future clinical applications, which require robust measures at the individual rather than group-level.

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

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

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