پیش بینی افسردگی تک قطبی در کودکان با استفاده از اندازه گیری های مورفومتریک عصبی متعدد: روش طبقه بندی الگویی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|29788||2015||8 صفحه PDF||سفارش دهید||5735 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Psychiatric Research, Volume 62, March 2015, Pages 84–91
Background Diagnosis of pediatric neuropsychiatric disorders such as unipolar depression is largely based on clinical judgment – without objective biomarkers to guide diagnostic process and subsequent therapeutic interventions. Neuroimaging studies have previously reported average group-level neuroanatomical differences between patients with pediatric unipolar depression and healthy controls. In the present study, we investigated the utility of multiple neuromorphometric indices in distinguishing pediatric unipolar depression patients from healthy controls at an individual subject level. Methods We acquired structural T1-weighted scans from 25 pediatric unipolar depression patients and 26 demographically matched healthy controls. Multiple neuromorphometric indices such as cortical thickness, volume, and cortical folding patterns were obtained. A support vector machine pattern classification model was ‘trained’ to distinguish individual subjects with pediatric unipolar depression from healthy controls based on multiple neuromorphometric indices and model predictive validity (sensitivity and specificity) calculated. Results The model correctly identified 40 out of 51 subjects translating to 78.4% accuracy, 76.0% sensitivity and 80.8% specificity, chi-square p-value = 0.000049. Volumetric and cortical folding abnormalities in the right thalamus and right temporal pole respectively were most central in distinguishing individual patients with pediatric unipolar depression from healthy controls. Conclusions These findings provide evidence that a support vector machine pattern classification model using multiple neuromorphometric indices may qualify as diagnostic marker for pediatric unipolar depression. In addition, our results identified the most relevant neuromorphometric features in distinguishing PUD patients from healthy controls.
Major depressive disorder (MDD) or Unipolar Depression has a lifetime prevalence of 16.2% in the adult population and affecting approximately 2.5% of children and 8.3% of adolescents in the United States (Lewinsohn et al., 1994). Longitudinal studies have reported that a diagnosis of pediatric unipolar depression (PUD) is associated with an increased risk of recurrence during adulthood and that approximately 57.2% of adult MDD cases may have started during childhood (Carballo et al., 2011) (Harrington et al., 1990 and Rosso et al., 2005). In addition, PUD is associated with poor academic outcomes, impaired social functioning and elevated risks of substance abuse and other psychiatric comorbidities (Rao and Chen, 2009 and Shad et al., 2012). These facts underscore the need to elucidate the pathophysiological mechanism of PUD and identify objective biomarkers able to assist in PUD diagnosis and guide treatment management. In vivo neuroimaging studies have implicated multiple neuroanatomical structures in the pathophysiology of PUD. Notable findings include, reduced hippocampal ( Caetano et al., 2007, MacMaster and Kusumakar, 2004 and Rao et al., 2010), amygdala ( Rosso et al., 2005), striatum ( Matsuo et al., 2008), caudate ( Matsuo et al., 2008 and Shad et al., 2012) and increased left prefrontal cortex ( Nolan et al., 2002) volumes. In addition, white matter abnormalities have also been reported in the corpus callosum ( Caetano et al., 2008) and middle frontal gyrus ( Ma et al., 2007). However, despite these multiple studies, significant limitations still exist. First, a majority of these studies utilized pre-defined anatomical regions-of-interest whilst recent studies have shown that neuroanatomical alterations in neuropsychiatric disorders involves multiple circuits as opposed to single anatomical regions – which underlines potential benefits of using whole brain neuroimaging scan data ( Ecker et al., 2010 and Good et al., 2002). Second, previous studies have not investigated the predictive utility (high specificity and sensitivity) of in vivo neuroimaging scans in distinguishing PUD patients from healthy controls but largely reported average group-level differences. Notably, multiple studies in other neuropsychiatric disorders – including adult unipolar depression and pediatric bipolar disorder have shown great potential of in vivo neuroimaging scans together with pattern classification or machine learning algorithms in distinguishing individual patients with neuropsychiatric disorders from healthy controls ( Costafreda et al., 2009, Fu et al., 2008, Johnston et al., 2013, Mwangi et al., 2012, Mwangi et al., 2014a, Mwangi et al., 2014b, Nouretdinov et al., 2011, Orrù et al., 2012, Sun et al., 2009 and Zeng et al., 2012). Third, previous PUD studies have largely utilized single neuromorphometric measurements (e.g. volume alone) whilst combining multiple measurements (e.g. anatomical volume and cortical thickness) may offer a complimentary view of brain structure which may further improve prediction accuracy ( Ecker et al., 2010). In the present study, we set out to investigate the utility of multiple neuromorphometric measurements such as anatomical volume, cortical thickness, folding index, mean curvature, Gaussian curvature and intrinsic curvature index together with a machine learning algorithm in identifying individual subjects with PUD. These neuromorphometric measurements were extracted using Freesurfer software library (Fischl, 2012) and input into a support vector machine (SVM) (Vapnik, 1999) pattern classification model which was ‘trained’ to distinguish individual PUD patients from healthy controls. The model's ability to generalize from novel subjects' data was evaluated using a leave-one-out cross-validation (LOOCV) method which involved ‘training’ the model using all subjects but one – a process which was repeated until all subjects were left-out once. The ‘left-out’ subjects were used for estimating the model diagnostic accuracy, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and an area under receiver operating characteristic curve (AUROC). A review of machine learning applications in psychiatric neuroimaging is given elsewhere (Ecker et al., 2010, Mwangi et al., 2012, Mwangi et al., 2014a, Mwangi et al., 2014b and Orrù et al., 2012). In summary, the main objective of this study was to examine the predictive validity of multiple neuromorphometric measurements acquired from T1-weighted scans in distinguishing individual subjects with PUD from healthy controls.