Dataset Information

Unsupervised classification of major depression using functional connectivity MRI.

ABSTRACT: The current diagnosis of psychiatric disorders including major depressive disorder based largely on self-reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting-state functional magnetic resonance imaging scans in the absence of clinical information. Twenty-four medication-naive patients with major depression and 29 demographically similar healthy individuals underwent resting-state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting-state functional connectivity patterns and showed that a maximum margin clustering-based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group-level clustering consistency of 92.5% and an individual-level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering-based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders.

SUBMITTER: Zeng LL

PROVIDER: S-EPMC6869344 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Unsupervised classification of major depression using functional connectivity MRI.

Zeng Ling-Li LL Shen Hui H Liu Li L Hu Dewen D

Human brain mapping 20130424 4

The current diagnosis of psychiatric disorders including major depressive disorder based largely on self-reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting-state functional magnetic resonance imaging scans in the absence of clinical information. Twenty-four medication-naive patients with major depression ...[more]

PMID: 23616377

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Project description:Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.

Project description:Hypothalamus communication with the rest of the brain and peripheral target tissues is critically important for many physiological and psychological functions. These functions include maintaining neuroendocrine circadian rhythms and managing affective processes. The hypothalamus maintains both direct neural connections within the brain and it also controls a variety of neuroendocrine processes that can influence target tissues throughout the body. Dysregulation of the hypothalamic pituitary adrenal axis and hyperactivity of the subgenual cortex are both frequently observed in depression. However, many details of how the hypothalamus, the hypothalamic pituitary adrenal (HPA) axis, and the subgenual cingulate interact with each other are unknown. We hypothesized that resting-state functional connectivity between the hypothalamus and the subgenual cortex would be associated with altered circadian rhythm in patients with depression and depressive symptoms. We also hypothesized that this would be most apparent in patients that have major depression with psychotic symptoms, who typically have the most robust HPA-axis dysregulation. Resting-state functional magnetic resonance imaging (fMRI) scans were collected to observe low-frequency resting-state functional connectivity patterns of the hypothalamus in 39 healthy participants, 39 patients with major depression, and 22 patients with major depression with psychotic symptoms. Hourly overnight measures of cortisol secretion and multiple measures of psychiatric symptom severity were also collected on all. Strong hypothalamic functional connectivity with the subgenual cortex was observed in healthy participants. This connectivity was significantly reduced in patients with psychotic major depression. Increased cortisol secretion during the circadian nadir and reduced connectivity were both associated with symptom severity. Reduced connectivity and high cortisol secretion during the circadian nadir are both useful for explaining a significant amount of variance in symptom severity that occurs between healthy participants and depressed patients. However, only cortisol secretion was useful for explaining the severity of symptoms within the depressed groups. This study suggests that the communication between the hypothalamus and the subgenual cortex is disrupted in patients with major depression with psychotic features. It also suggests that these disruptions are associated with increased symptom severity and may be a cause or a consequence of cortisol dysregulation.

Dataset Information

Unsupervised classification of major depression using functional connectivity MRI.

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Unsupervised classification of major depression using functional connectivity MRI.

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