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Towards individualized cortical thickness assessment for clinical routine.

ABSTRACT: BACKGROUND:Cortical thickness measures the width of gray matter of the human cortex. It can be calculated from T1-weighted magnetic resonance images (MRI). In group studies, this measure has been shown to correlate with the diagnosis/prognosis of a number of neurologic and psychiatric conditions, but has not been widely adapted for clinical routine. One of the reasons for this might be that there is no reference system which allows to rate individual cortical thickness data with respect to a control population. METHODS:To address this problem, this study compared different methods to assess statistical significance of cortical thinning, i.e. atrophy. All compared methods were nonparametric and encompassed rating an individual subject's data set with respect to a control data population. Null distributions were calculated using data from the Human Connectome Project (HCP, n?=?1000), and an additional HCP data set (n?=?113) was used to calculate sensitivity and specificity to compare the different methods, whereas atrophy was simulated for sensitivity assessment. Validation measures were calculated for the entire cortex ("cumulative") and distinct brain regions ("regional") where possible. RESULTS:The approach yielding the highest combination of specificity and sensitivity implemented generating null distributions for anatomically distinct brain regions, based on the most extreme values observed in the population. With that method, while regional variations were observed, cumulative specificity of 98.9% and cumulative sensitivity at 80% was achieved for simulated atrophy of 23%. CONCLUSIONS:This study shows that validated rating of individual cortical thickness measures is possible, which can help clinicians in their daily routine to discover signs of atrophy before they become visually apparent on an unprocessed MRI. Furthermore, given different pathologies present with distinct atrophy patterns, the regional validation proposed here allows to detect distinct patterns of atrophy, which can further enhance differential diagnosis/prognosis.

SUBMITTER: Tahedl M

PROVIDER: S-EPMC7118882 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Towards individualized cortical thickness assessment for clinical routine.

Tahedl Marlene M

Journal of translational medicine 20200403 1

<h4>Background</h4>Cortical thickness measures the width of gray matter of the human cortex. It can be calculated from T1-weighted magnetic resonance images (MRI). In group studies, this measure has been shown to correlate with the diagnosis/prognosis of a number of neurologic and psychiatric conditions, but has not been widely adapted for clinical routine. One of the reasons for this might be that there is no reference system which allows to rate individual cortical thickness data with respect ...[more]

PMID: 32245485

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Project description:BackgroundTraditional clinical assessments often lack individualization, relying on standardized procedures that may not accommodate the diverse needs of patients, especially in early stages where personalized diagnosis could offer significant benefits. We aim to provide a machine-learning framework that addresses the individualized feature addition problem and enhances diagnostic accuracy for clinical assessments.MethodsIndividualized Clinical Assessment Recommendation System (iCARE) employs locally weighted logistic regression and Shapley Additive Explanations (SHAP) value analysis to tailor feature selection to individual patient characteristics. Evaluations were conducted on synthetic and real-world datasets, including early-stage diabetes risk prediction and heart failure clinical records from the UCI Machine Learning Repository. We compared the performance of iCARE with a Global approach using statistical analysis on accuracy and area under the ROC curve (AUC) to select the best additional features.FindingsThe iCARE framework enhances predictive accuracy and AUC metrics when additional features exhibit distinct predictive capabilities, as evidenced by synthetic datasets 1-3 and the early diabetes dataset. Specifically, in synthetic dataset 1, iCARE achieved an accuracy of 0·999 and an AUC of 1·000, outperforming the Global approach with an accuracy of 0·689 and an AUC of 0·639. In the early diabetes dataset, iCARE shows improvements of 1·5-3·5% in accuracy and AUC across different numbers of initial features. Conversely, in synthetic datasets 4-5 and the heart failure dataset, where features lack discernible predictive distinctions, iCARE shows no significant advantage over global approaches on accuracy and AUC metrics.InterpretationiCARE provides personalized feature recommendations that enhance diagnostic accuracy in scenarios where individualized approaches are critical, improving the precision and effectiveness of medical diagnoses.FundingThis work was supported by startup funding from the Department of Psychology at the University of Kansas provided to A.A., and the R01MH125740 award from NIH partially supported J.M.G.'s work.

Project description:ObjectiveCross-sectional, longitudinal, and genetic associations exist between irritability and depression. Prior studies have examined developmental trajectories of irritability, clinical outcomes, and associations with child and familial depression. However, studies have not integrated neurobiological measures. The present study examined developmental trajectories of irritability, clinical outcomes, and cortical structure among preschoolers oversampled for depressive symptoms.MethodBeginning at 3 to 5 years old, a sample of 271 children enriched for early depressive symptoms were assessed longitudinally by clinical interview. Latent class mixture models identified trajectories of irritability severity. Risk factors, clinical outcomes, and cortical thickness were compared across trajectory classes. Cortical thickness measures were extracted from 3 waves of magnetic resonance imaging at 7 to 12 years of age.ResultsThree trajectory classes were identified among these youth: 53.50% of children exhibited elevated irritability during preschool that decreased longitudinally, 30.26% exhibited consistently low irritability, and 16.24% exhibited consistently elevated irritability. Compared with other classes, the elevated irritability class exhibited higher rates of maternal depression, early life adversity, later psychiatric diagnoses, and functional impairment. Further, elevated baseline irritability predicted later depression beyond adversity and personal and maternal depression history. The elevated irritability class exhibited a thicker cortex in the left superior frontal and temporal gyri and the right inferior parietal lobule.ConclusionIrritability manifested with specific developmental trajectories in this sample enriched for early depression. Persistently elevated irritability predicted poor psychiatric outcomes, higher risk for later depression, and decreased overall function later in development. Greater frontal, temporal, and parietal cortical thickness also was found, providing neural correlates of this risk trajectory.

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Towards individualized cortical thickness assessment for clinical routine.

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Towards individualized cortical thickness assessment for clinical routine.

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