645. Neural, Cognitive, and Clinical Effects of Prefrontal Cortex Stimulation in Depression Combined with Psychological Therapy: A Double-Blind Randomized Controlled Trial

Nord, Camilla L.; Halahakoon, D. Chamith; Limbachya, Tarun; Gray, Alan M.; Charpentier, Caroline J.; Lally, Níall; Aylward, Jessica; Pilling, Stephen; Roiser, Jonathan P.

doi:10.1016/j.biopsych.2017.02.1054

Cited by 1 publication

(1 citation statement)

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“…Disturbances within the fronto-parietal network are suggested to be strongly associated with the cognitive performance in patients with depression (Brzezicka, 2013 ). The abnormalities of FC in the fronto-parietal network have been reported in many MDD studies (Chen et al, 2017 ; Nord et al, 2017 ; Vasudev et al, 2017 ; Yu et al, 2017 ). There is one widely accepted hypothesis that the dorsal and lateral parts of the PFC are associated with more “cognitive” aspects of behavior, while the ventral and medial parts are mostly connected to “emotional” aspects of information processing (Brzezicka, 2013 ).…”

Section: Discussionmentioning

confidence: 78%

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity

Geng

Liu

et al. 2018

Front. Neurosci.

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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.

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Section: Discussionmentioning

confidence: 78%