Differential Abnormal Pattern of Anterior Cingulate Gyrus Activation in Unipolar and Bipolar Depression: an fMRI and Pattern Classification Approach

Abstract

Distinguishing bipolar disorder from major depressive disorder is a major challenge in psychiatric treatment. Consequently, there has been growing interest in identifying neuronal biomarkers of disorder-specific pathophysiological processes to differentiate affective disorders. Thirty-six depressed bipolar patients, 36 depressed unipolar patients, and 36 matched healthy controls (HCs) participated in an fMRI experiment. Emotional faces served as stimuli in a matching task. We investigated neural activation towards angry, fearful, and happy faces focusing on prototypical regions related to emotion processing, ie, the amygdala and the anterior cingulate gyrus (ACG). Furthermore, we employed a whole-brain and a multivariate pattern classification analysis. Unipolar patients showed abnormally reduced ACG activation toward happy and fearful faces compared with bipolar patients and HCs respectively. Furthermore, the whole-brain analysis revealed significantly increased activation in bipolar patients compared with unipolar patients in the fearful condition in the right frontal and parietal cortex. Moreover, the multivariate pattern classification analysis yielded significant classification rates of up to 72% based on ACG activation elicited by fearful faces. Our results question the rather 'amygdalocentric' neurobiological models of mood disorders. We observed patterns of abnormally reduced ventral and supragenual ACG activation, potentially indicating impaired bottom-up emotion processing and automatic emotion regulation specifically in unipolar but not in bipolar individuals.

Machine learning in major depression: From classification to treatment outcome prediction

“…Its advantages in MDD are not confined to diagnosis, but also allow for the prediction of future disease progression, of which the most salient advantage one is its applicability to individual‐level analysis. Table summarizes various aspects of 66 selected studies . Most of these works aim at developing computational approaches to discriminate MDD from controls or mood disorder subtypes and trying to develop tools to integrate imaging measurements into clinical practice.…”

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

“…Eight functional studies were found, 1 used arterial spin labelling (ASL) of anterior cingulate cortex reaching 52.8% ACC, 55 3 used resting‐state fMRI reaching between 61.7, 56 81% 57 and 82.22% ACC 58 and 4 used task‐based fMRI reaching between 59.72% and 83.5% ACC 59‐62 …”

“…Nine functional studies were identified. Among them 1 used ASL of anterior cingulate cortex reaching 80.6% ACC, 55 4 used resting‐state fMRI reaching between 80.5% and 93.1% ACC 57,65,68,69 and 4 used task‐based fMRI reaching between 67% and 90% 59,61,70,71 …”

“…Among those 16 studies, 10 also tried to classify BD vs HC. Notably, 3 of them reported much higher classification scores when classifying BD vs MDD in comparison with BD vs HC (Almeida et al 55 80.6% vs 52.8%, Bürger et al 59 72.22% vs 59.72%, Grotegerd et al, 61 90% vs 65%), 5 reported relatively comparable ACC (Jie et al, 58 88% vs 82.22% and 86% vs 81%, Li et al, 57 81% vs 86%, Mourão‐Miranda et al, 71 67% vs 64%, and Sacchet et al, 50 59.4% vs 55.9%) and 2 higher classification scores when classifying BD vs HC in comparison with BD vs MDD (Matsuo et al 44 88% vs 63% and Serpa et al 54 66% vs 54%). Regarding the interest of combining the features, Jie et al 65 also reported higher ACC when integrating multimodal features (ie, 92.07%) in comparison with single modality (90.89% for functional connectivity and 77.83% for grey matter volumes solely).…”

Abnormal Functional and Structural networks in Rectal Cancer with Depressive risk: A Graph Theory Analysis

“…A prior study suggested that regions sensitive to negative emotions were hyperactive in processing negative information [59], it was not surprising to find a significant positive correlation between increased nodal efficiency and HAMD in the amygdala. Moreover, the positive relationship between HAMD and decreased nodal efficiency revealed that abnormal activation of FN in RC patients might cause cognitive impairments and depressed mood [60]. Therefore, we speculated that alterations in brain network properties assist us to study the depressive risk in RC patients after chemotherapy and surgery.…”