Gray matter abnormalities in Major Depressive Disorder: A meta-analysis of voxel based morphometry studies

Bora, Emre; Fornito, Alex; Pantelis, Christos; Yücel, Murat

doi:10.1016/j.jad.2011.03.049

Cited by 665 publications

(579 citation statements)

References 75 publications

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“…These specific differences were not found in UD, indicating that the alterations of the mesolimbic system in BD involve the VTA-NAcc pathway as well as larger parts of the reward circuitry. These results correspond well with findings from meta analyses investigating structural alterations in BD, repeatedly reporting altered insula and basal ganglia structure in BD compared with HC (Bora et al, 2012). Similarly, functional MRI studies highlighted the role of the insula for reward processing in BD (Phillips et al, 2008a).…”

Section: Reward Processing In Ud and Bdsupporting

confidence: 88%

Reward Processing in Unipolar and Bipolar Depression: A Functional MRI Study

Redlich

Dohm

Grotegerd

et al. 2015

Neuropsychopharmacol

143

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Differentiating bipolar disorders (BD) from unipolar depression (UD) remains a major clinical challenge. The identification of neurobiological markers may help to differentiate these disorders, particularly during depressive episodes. This cross-sectional study, including 33 patients with UD, 33 patients with BD, and 34 healthy controls, is one of the first to directly compare UD and BD with respect to reward processing. A card-guessing paradigm was employed and brain activity associated with reward processing was investigated by means of fMRI. A 3 (group) × 2 (condition: reward4control, loss4control) ANOVA was conducted using the nucleus accumbens (NAcc) as ROI. Furthermore, a whole-brain approach was applied. A functional connectivity analysis was performed to characterize diagnosis-related alterations in the functional coupling between the NAcc and other brain areas. The ANOVA revealed higher activity for healthy controls (HCs) than for BD and UD in the NAcc during reward processing. Moreover, UD showed a higher functional connectivity between the NAcc and the VTA than HC. The patients groups could be differentiated in that BD showed a decreased activation, in the reward condition, of the NAcc, caudate nucleus, thalamus, putamen, insula, and prefrontal areas compared with UD. These results may help to refine the understanding of neural correlates of reward processing in both disorders, and to understand the neural underpinnings of anhedonia, a core symptom of depressive episodes.

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Section: Reward Processing In Ud and Bdsupporting

confidence: 88%

Reward Processing in Unipolar and Bipolar Depression: A Functional MRI Study

Redlich

Dohm

Grotegerd

et al. 2015

Neuropsychopharmacol

143

View full text Add to dashboard Cite

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“…A study in adolescents with different anxiety disorders including GAD indicates no differences between GAD and healthy subjects as well (Gerra et al., 2000). The result was contrary to hypotheses for MRI data as brain anatomical differences in GAD and MD in areas such as the amygdala or parts of the basal ganglia have been repeatedly reported (Bora, Fornito, et al., 2012; Bora, Harrison, et al. 2012; Hilbert et al., 2014; Kempton et al., 2011).…”

Section: Discussionmentioning

confidence: 99%

Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study

et al. 2017

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BackgroundGeneralized anxiety disorder (GAD) is difficult to recognize and hard to separate from major depression (MD) in clinical settings. Biomarkers might support diagnostic decisions. This study used machine learning on multimodal biobehavioral data from a sample of GAD, MD and healthy subjects to differentiate subjects with a disorder from healthy subjects (case‐classification) and to differentiate GAD from MD (disorder‐classification).MethodsSubjects with GAD (n = 19), MD without GAD (n = 14), and healthy comparison subjects (n = 24) were included. The sample was matched regarding age, sex, handedness and education and free of psychopharmacological medication. Binary support vector machines were used within a nested leave‐one‐out cross‐validation framework. Clinical questionnaires, cortisol release, gray matter (GM), and white matter (WM) volumes were used as input data separately and in combination.ResultsQuestionnaire data were well‐suited for case‐classification but not disorder‐classification (accuracies: 96.40%, p < .001; 56.58%, p > .22). The opposite pattern was found for imaging data (case‐classification GM/WM: 58.71%, p = .09/43.18%, p > .66; disorder‐classification GM/WM: 68.05%, p = .034/58.27%, p > .15) and for cortisol data (38.02%, p = .84; 74.60%, p = .009). All data combined achieved 90.10% accuracy (p < .001) for case‐classification and 67.46% accuracy (p = .0268) for disorder‐classification.ConclusionsIn line with previous evidence, classification of GAD was difficult using clinical questionnaire data alone. Particularly cortisol and GM volume data were able to provide incremental value for the classification of GAD. Findings suggest that neurobiological biomarkers are a useful target for further research to delineate their potential contribution to diagnostic processes.

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“…Due to its simplicity of use, VBM has motivated many neuroscientists to characterize the specific abnormalities of graymatter volume throughout the brain in MDD [15,19,20]. Recent meta-analysis of VBM in MDD has shown strong evidence of gray-matter volume reduction in the anterior cingulate cortex (ACC) [21][22][23]. The ACC is involved in multiple cognitive and affective functions, such as decision-making [24], empathy [25], conflict-monitoring [26], working memory, attention, and informationprocessing [27].…”

Section: Mr Imaging Of Brain Morphology and Structurementioning

confidence: 99%

Molecular, Functional, and Structural Imaging of Major Depressive Disorder

Zhang

Zhu

et al. 2016

Neurosci. Bull.

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Major depressive disorder (MDD) is a significant cause of morbidity and mortality worldwide, correlating with genetic susceptibility and environmental risk factors. Molecular, functional, and structural imaging approaches have been increasingly used to detect neurobiological changes, analyze neurochemical correlates, and parse pathophysiological mechanisms underlying MDD. We reviewed recent neuroimaging publications on MDD in terms of molecular, functional, and structural alterations as detected mainly by magnetic resonance imaging (MRI) and positron emission tomography. Altered structure and function of brain regions involved in the cognitive control of affective state have been demonstrated. An abnormal default mode network, as revealed by resting-state functional MRI, is likely associated with aberrant metabolic and serotonergic function revealed by radionuclide imaging. Further multi-modal investigations are essential to clarify the characteristics of the cortical network and serotonergic system associated with behavioral and genetic variations in MDD.

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Gray matter abnormalities in Major Depressive Disorder: A meta-analysis of voxel based morphometry studies

Cited by 665 publications

References 75 publications

Reward Processing in Unipolar and Bipolar Depression: A Functional MRI Study

Reward Processing in Unipolar and Bipolar Depression: A Functional MRI Study

Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study

Molecular, Functional, and Structural Imaging of Major Depressive Disorder

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