Antonia N Kaczkurkin1, Aristeidis Sotiras2, Erica B Baller3, Ran Barzilay3, Monica E Calkins3, Ganesh B Chand4, Zaixu Cui3, Guray Erus4, Yong Fan4, Raquel E Gur5, Ruben C Gur6, Tyler M Moore3, David R Roalf3, Adon F G Rosen3, Kosha Ruparel3, Russell T Shinohara7, Erdem Varol8, Daniel H Wolf9, Christos Davatzikos4, Theodore D Satterthwaite9. 1. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychology, Vanderbilt University, Nashville, Tennessee. Electronic address: antoniak@pennmedicine.upenn.edu. 2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Washington University, St. Louis, Missouri. 3. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. 4. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania. 5. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. 6. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Philadelphia Veterans Administration Medical Center, Philadelphia, Pennsylvania. 7. Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania. 8. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Grossman Center for the Statistics of Mind, Center for Theoretical Neuroscience, Department of Statistics, Columbia University, New York, New York. 9. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
Abstract
BACKGROUND: Internalizing disorders such as anxiety and depression are common psychiatric disorders that frequently begin in youth and exhibit marked heterogeneity in treatment response and clinical course. Given that symptom-based classification approaches do not align with underlying neurobiology, an alternative approach is to identify neurobiologically informed subtypes based on brain imaging data. METHODS: We used a recently developed semisupervised machine learning method (HYDRA [heterogeneity through discriminative analysis]) to delineate patterns of neurobiological heterogeneity within youths with internalizing symptoms using structural data collected at 3T from a sample of 1141 youths. RESULTS: Using volume and cortical thickness, cross-validation methods indicated 2 highly stable subtypes of internalizing youths (adjusted Rand index = 0.66; permutation-based false discovery rate p < .001). Subtype 1, defined by smaller brain volumes and reduced cortical thickness, was marked by impaired cognitive performance and higher levels of psychopathology than both subtype 2 and typically developing youths. Using resting-state functional magnetic resonance imaging and diffusion images not considered during clustering, we found that subtype 1 also showed reduced amplitudes of low-frequency fluctuations in frontolimbic regions at rest and reduced fractional anisotropy in several white matter tracts. In contrast, subtype 2 showed intact cognitive performance and greater volume, cortical thickness, and amplitudes during rest compared with subtype 1 and typically developing youths, despite still showing clinically significant levels of psychopathology. CONCLUSIONS: We identified 2 subtypes of internalizing youths differentiated by abnormalities in brain structure, function, and white matter integrity, with one of the subtypes showing poorer functioning across multiple domains. Identification of biologically grounded internalizing subtypes may assist in targeting early interventions and assessing longitudinal prognosis.
BACKGROUND: Internalizing disorders such as anxiety and depression are common psychiatric disorders that frequently begin in youth and exhibit marked heterogeneity in treatment response and clinical course. Given that symptom-based classification approaches do not align with underlying neurobiology, an alternative approach is to identify neurobiologically informed subtypes based on brain imaging data. METHODS: We used a recently developed semisupervised machine learning method (HYDRA [heterogeneity through discriminative analysis]) to delineate patterns of neurobiological heterogeneity within youths with internalizing symptoms using structural data collected at 3T from a sample of 1141 youths. RESULTS: Using volume and cortical thickness, cross-validation methods indicated 2 highly stable subtypes of internalizing youths (adjusted Rand index = 0.66; permutation-based false discovery rate p < .001). Subtype 1, defined by smaller brain volumes and reduced cortical thickness, was marked by impaired cognitive performance and higher levels of psychopathology than both subtype 2 and typically developing youths. Using resting-state functional magnetic resonance imaging and diffusion images not considered during clustering, we found that subtype 1 also showed reduced amplitudes of low-frequency fluctuations in frontolimbic regions at rest and reduced fractional anisotropy in several white matter tracts. In contrast, subtype 2 showed intact cognitive performance and greater volume, cortical thickness, and amplitudes during rest compared with subtype 1 and typically developing youths, despite still showing clinically significant levels of psychopathology. CONCLUSIONS: We identified 2 subtypes of internalizing youths differentiated by abnormalities in brain structure, function, and white matter integrity, with one of the subtypes showing poorer functioning across multiple domains. Identification of biologically grounded internalizing subtypes may assist in targeting early interventions and assessing longitudinal prognosis.
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