Abstract
Magnetic resonance imaging studies have reported significant functional and structural differences between depressed patients and controls. Little attention has been given, however, to the abnormalities in anatomical connectivity in depressed patients. In the present study, we aim to investigate the alterations in connectivity of whole-brain anatomical networks in those suffering from major depression by using machine learning approaches. Brain anatomical networks were extracted from diffusion magnetic resonance images obtained from both 22 first-episode, treatment-naive adults with major depressive disorder and 26 matched healthy controls. Using machine learning approaches, we differentiated depressed patients from healthy controls based on their whole-brain anatomical connectivity patterns and identified the most discriminating features that represent between-group differences. Classification results showed that 91.7% (patients = 86.4%, controls = 96.2%; permutation test, p<0.0001) of subjects were correctly classified via leave-one-out cross-validation. Moreover, the strengths of all the most discriminating connections were increased in depressed patients relative to the controls, and these connections were primarily located within the cortical-limbic network, especially the frontal-limbic network. These results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease.
Highlights
Major depressive disorder (MDD), which has been linked to a 15% suicide rate among those suffering from the disorder, serious social problems and tremendous economic loss, both directly and indirectly, has been ranked by the World Health Organization as the number one reason why people file for disability benefits [1]
Functional magnetic resonance imaging (MRI) studies have reported abnormalities in several specific brain areas in patients suffering from depression, including the amygdala [3], hippocampus [4], caudate, ventral striatum [5], orbitofrontal cortex (OFC) [6], prefrontal cortex [7], subgenual cingulate and thalamus [8]
Further evidence from functional connectivity studies of depressed patients reveal altered network connectivity in the limbic-cortical-striatal-pallidal-thalamic circuit (LCSPT) [1], the prefrontal-limbic network [16], the default-mode network (DMN) [8,17], the cerebellar network [18], the cognitive control network and the affective network [19]; some researchers speculate that dysfunction in these circuits or networks can produce pathological emotional symptoms [1]
Summary
Major depressive disorder (MDD), which has been linked to a 15% suicide rate among those suffering from the disorder, serious social problems and tremendous economic loss, both directly and indirectly, has been ranked by the World Health Organization as the number one reason why people file for disability benefits [1]. Tremendous efforts have been made to understand the neuropsychology and etiology of depression, little is known about its pathogenesis These days, magnetic resonance imaging (MRI) provides a powerful tool for exploring the neuropathology of this complex mental disorder [2]. Functional MRI (fMRI) studies have reported abnormalities in several specific brain areas in patients suffering from depression, including the amygdala [3], hippocampus [4], caudate, ventral striatum [5], orbitofrontal cortex (OFC) [6], prefrontal cortex [7], subgenual cingulate and thalamus [8]. The anatomical basis of these disorder-related connectivity abnormalities both within and across different functional networks remains unclear
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