scholarly journals Abnormal Default-Mode Network Homogeneity in Melancholic and Nonmelancholic Major Depressive Disorder at Rest

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Meiqi Yan ◽  
Xilong Cui ◽  
Feng Liu ◽  
Huabing Li ◽  
Renzhi Huang ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Healthy Controls ◽  
Major Depressive ◽  
Middle Temporal ◽  
Inferior Temporal Gyrus ◽  
Medial Frontal Gyrus ◽  
The Right

Background. Melancholic depression has been assumed as a severe type of major depressive disorder (MDD). We aimed to explore if there were some distinctive alterations in melancholic MDD and whether the alterations could be used to discriminate the melancholic MDD and nonmelancholic MDD. Methods. Thirty-one outpatients with melancholic MDD, thirty-three outpatients with nonmelancholic MDD, and thirty-two age- and gender-matched healthy controls were recruited. All participants were scanned by resting-state functional magnetic resonance imaging (fMRI). Imaging data were analyzed with the network homogeneity (NH) and support vector machine (SVM) methods. Results. Both patient groups exhibited increased NH in the right PCC/precuneus and right angular gyrus and decreased NH in the right middle temporal gyrus compared with healthy controls. Compared with nonmelancholic patients and healthy controls, melancholic patients exhibited significantly increased NH in the bilateral superior medial frontal gyrus and decreased NH in the left inferior temporal gyrus. But merely for melancholic patients, the NH of the right middle temporal gyrus was negatively correlated with TEPS total and contextual anticipatory scores. SVM analysis showed that a combination of NH values in the left superior medial frontal gyrus and left inferior temporal gyrus could distinguish melancholic patients from nonmelancholic patients with accuracy, sensitivity, and specificity of 79.66% (47/59), 70.97% (22/31), and 89.29%(25/28), respectively. Conclusion. Our findings showed distinctive network homogeneity alterations in melancholic MDD which may be potential imaging markers to distinguish melancholic MDD and nonmelancholic MDD.

scholarly journals Altered Fractional Amplitude of Low-Frequency Fluctuation in Major Depressive Disorder and Bipolar Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Yan Qiu ◽  
Min Yang ◽  
Sujuan Li ◽  
Ziwei Teng ◽  
Kun Jin ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Pearson Correlation ◽  
Low Frequency ◽  
Frequency Fluctuation ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Major Depressive ◽  
The Right

Background: Discriminating between major depressive disorder (MDD) and bipolar disorder (BD) remains challenging and cognitive deficits in MDD and BD are generally recognized. In this study, the fractional amplitude of low-frequency fluctuation (fALFF) approach was performed to explore neural activity and cognition in first-episode, drug-naïve BD and MDD patients, as well as the relationship between altered fALFF values and clinical or psychometric variables.Methods: A total of 21 BD patients, 25 MDD patients, and 41 healthy controls (HCs) completed clinical assessments and resting-state functional magnetic resonance imaging (rs-fMRI) scans in this study. The rs-fMRI data were analyzed by fALFF method and Pearson correlation analyses were performed between altered fALFF values and clinical variables or cognition. Support vector machine (SVM) was adopted to identify the three groups from each other with abnormal fALFF values in the brain regions obtained by group comparisons.Results: (1) The fALFF values were significantly different in the frontal lobe, temporal lobe, and left precuneus among three groups. In comparison to HCs, BD showed increased fALFF values in the right inferior temporal gyrus (ITG) and decreased fALFF values in the right middle temporal gyrus, while MDD showed decreased fALFF values in the right cerebellar lobule IV/V. In comparison to MDD, BD showed decreased fALFF values in bilateral posterior cingulate gyrus and the right cerebellar lobule VIII/IX. (2) In the BD group, a negative correlation was found between increased fALFF values in the right ITG and years of education, and a positive correlation was found between decreased fALFF values in the right cerebellar lobule VIII/IX and visuospatial abilities. (3) The fALFF values in the right cerebellar lobule VIII/IX may have the ability to discriminate BD patients from MDD patients, with sensitivity, specificity, and accuracy all over 0.70.Conclusions: Abnormal brain activities were observed in BD and MDD and were related with cognition in BD patients. The abnormality in the cerebellum can be potentially used to identify BD from MDD patients.

scholarly journals Disrupted Regional Homogeneity in Melancholic and Non-melancholic Major Depressive Disorder at Rest

2021 ◽  
Vol 12 ◽  
Author(s):  
Meiqi Yan ◽  
Yuqiong He ◽  
Xilong Cui ◽  
Feng Liu ◽  
Huabing Li ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Regional Homogeneity ◽  
Fusiform Gyrus ◽  
Support Vector ◽  
Healthy Controls ◽  
Melancholic Depression ◽  
Major Depressive ◽  
Middle Occipital Gyrus ◽  
The Right

Background: Melancholic depression has been viewed as one severe subtype of major depressive disorder (MDD). However, it is unclear whether melancholic depression has distinct changes in brain imaging. We aimed to explore specific or distinctive alterations in melancholic MDD and whether the alterations could be used to separate melancholic MDD from non-melancholic MDD or healthy controls.Materials and Methods: Thirty-one outpatients with melancholic MDD and thirty-three outpatients with non-melancholic MDD and thirty-two age- and gender-matched healthy controls were recruited. All participants were scanned by resting-state functional magnetic resonance imaging (fMRI). Imaging data were analyzed with the regional homogeneity (ReHo) and support vector machine (SVM) methods.Results: Melancholic MDD patients exhibited lower ReHo in the right superior occipital gyrus/middle occipital gyrus than non-melancholic MDD patients and healthy controls. Merely for non-melancholic MDD patients, decreased ReHo in the right middle frontal gyrus was negatively correlated with the total HRSD-17 scores. SVM analysis results showed that a combination of abnormal ReHo in the right fusiform gyrus/cerebellum Crus I and the right superior occipital gyrus/middle occipital gyrus exhibited the highest accuracy of 83.05% (49/59), with a sensitivity of 90.32% (28/31), and a specificity of 75.00% (21/28) for discriminating patients with melancholic MDD from patients with non-melancholic MDD. And a combination of abnormal ReHo in the right fusiform gyrus/cerebellum VI and left postcentral gyrus/precentral gyrus exhibited the highest accuracy of 98.41% (62/63), with a sensitivity of 96.77% (30/31), and a specificity of 100.00%(32/32) for separating patients with melancholic MDD from healthy controls.Conclusion: Our findings showed the distinctive ReHo pattern in patients with melancholic MDD and found brain area that may be associated with the pathophysiology of non-melancholic MDD. Potential imaging markers for discriminating melancholic MDD from non-melancholic MDD or healthy controls were reported.

scholarly journals Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

2016 ◽  
Author(s):  
David M Schnyer ◽  
Peter C. Clasen ◽  
Christopher Gonzalez ◽  
Christopher G Beevers
Keyword(s):  
Major Depressive Disorder ◽  
White Matter ◽  
Depressive Disorder ◽  
Right Hemisphere ◽  
Diffusion Tensor ◽  
Support Vector ◽  
Healthy Controls ◽  
Major Depressive ◽  
Predictive Information ◽  
Brain White Matter

AbstractUsing MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n = 25) and healthy controls (n = 25), SVM learning accurately (70%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.

Emotional processing and executive functions in major depressive disorder: dorsal prefrontal activity correlates with performance in the intra–extra dimensional set shift

2010 ◽  
Vol 22 (6) ◽  
pp. 269-279 ◽  
Author(s):  
Alexander Heinzel ◽  
Georg Northoff ◽  
Heinz Boeker ◽  
Peter Boesiger ◽  
Simone Grimm
Keyword(s):  
Major Depressive Disorder ◽  
Prefrontal Cortex ◽  
Depressive Disorder ◽  
Executive Functions ◽  
Healthy Subjects ◽  
Emotional Processing ◽  
Cognitive Impairments ◽  
Healthy Controls ◽  
Major Depressive ◽  
The Right

Heinzel A, Northoff G, Boeker H, Boesiger P, Grimm S. Emotional processing and executive functions in major depressive disorder: dorsal prefrontal activity correlates with performance in the intra–extra dimensional set shift.Objective:Major depressive disorder (MDD) is characterised by predominately negatively valenced emotional symptoms that are often accompanied by cognitive impairments. We posited that cognitive impairments in MDD are related to altered emotional processing in prefrontal cortex.Methods:We compared 20 medication-free patients with MDD and 29 matched healthy controls. Both groups performed an emotional task during functional magnetic resonance imaging (fMRI). Furthermore, they completed the intra–extra dimensional set shift (IED) test probing for cognitive impairments. Then we correlated the results of the IED with the changes in fMRI BOLD signal in MDD patients and healthy subjects.Results:The subcategory of the IED applying extradimensional shift (EDS) showed a divergent performance of the MDD group committing significantly more errors than the control group. Correlating the EDS errors with fMRI signal changes, the healthy subjects showed a positive correlation with the right ventrolateral prefrontal cortex and the right orbitofrontal cortex. MDD subjects, in contrast, showed a positive correlation in right dorsolateral prefrontal cortex (DLPFC) and a negative correlation in the left dorsomedial prefrontal cortex (DMPFC).Conclusion:We hypothesise that the differential correlation in healthy controls and MDD patients may reflect the use of different strategies in their performance. The impaired executive functions, as reflected by altered processing in right DLPFC and left DMPFC, may implicitly influence emotional processing in patients suffering from MDD.

scholarly journals Emotion regulation in emerging adults with major depressive disorder and frequent cannabis use

2020 ◽  
Author(s):  
Emily S. Nichols ◽  
Jacob Penner ◽  
Kristen A. Ford ◽  
Michael Wammes ◽  
Richard W.J. Neufeld ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Emotion Regulation ◽  
Depressive Symptoms ◽  
Depressive Disorder ◽  
Emerging Adults ◽  
Superior Temporal Gyrus ◽  
Cannabis Use ◽  
Middle Temporal Gyrus ◽  
Major Depressive ◽  
Middle Temporal

AbstractIn people with mental health issues, approximately 20% have co-occurring substance use, often involving cannabis. Although emotion regulation can be affected both by major depressive disorder (MDD) and by cannabis use, the relationship among all three factors is unknown. In this study, we used fMRI to evaluate the effect that cannabis use and MDD have on brain activation during an emotion regulation task. Differences were assessed in 74 emerging adults aged 16-23 with and without MDD who either used or did not use cannabis. Severity of depressive symptoms, emotion regulation style, and age of cannabis use onset were also measured. Both MDD and cannabis use interacted with the emotion regulation task in the left temporal lobe, however the location of the interaction differed for each factor. Specifically, MDD showed an interaction with emotion regulation in the middle temporal gyrus, whereas cannabis use showed an interaction in the superior temporal gyrus. Emotion regulation style predicted activity in the right superior frontal gyrus, however, this did not interact with MDD or cannabis use. Severity of depressive symptoms interacted with the emotion regulation task in the left middle temporal gyrus. The results highlight the influence of cannabis use and MDD on emotion regulation processing, suggesting that both may have a broader impact on the brain than previously thought.

scholarly journals Abnormal Fractional Amplitude of Low-Frequency Fluctuation as a Potential Imaging Biomarker for First-Episode Major Depressive Disorder: A Resting-State fMRI Study and Support Vector Machine Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Yujun Gao ◽  
Xi Wang ◽  
Zhenying Xiong ◽  
Hongwei Ren ◽  
Ruoshi Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Major Depressive Disorder ◽  
Resting State ◽  
Low Frequency ◽  
Diagnostic Value ◽  
First Episode ◽  
Support Vector ◽  
Healthy Controls ◽  
Major Depressive ◽  
The Right

Objective: Major depressive disorder (MDD) is a psychiatric disorder with serious negative health outcomes; however, there is no reliable method of diagnosis. This study explored the clinical diagnostic value of the fractional amplitude of low-frequency fluctuation (fALFF) based on the support vector machine (SVM) method for the diagnosis of MDD.Methods: A total of 198 first-episode MDD patients and 234 healthy controls were involved in this study, and all participants underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Imaging data were analyzed with the fALFF and SVM methods.Results: Compared with the healthy controls, the first-episode MDD patients showed higher fALFF in the left mid cingulum, right precuneus, and left superior frontal gyrus (SFG). The increased fALFF in these three brain regions was positively correlated with the executive control reaction time (ECRT), and the increased fALFF in the left mid cingulum and left SFG was positively correlated with the 17-item Hamilton Rating Scale for Depression (HRSD-17) scores. The SVM results showed that increased fALFF in the left mid cingulum, right precuneus, and left SFG exhibited high diagnostic accuracy of 72.92% (315/432), 71.76% (310/432), and 73.84% (319/432), respectively. The highest diagnostic accuracy of 76.39% (330/432) was demonstrated for the combination of increased fALFF in the right precuneus and left SFG, along with a sensitivity of 84.34% (167/198), and a specificity of 70.51% (165/234).Conclusion: Increased fALFF in the left mid cingulum, right precuneus, and left SFG may serve as a neuroimaging marker for first-episode MDD. The use of the increased fALFF in the right precuneus and left SFG in combination showed the best diagnostic value.

scholarly journals Detection of Major Depressive Disorder Using Vocal Acoustic Analysis and Machine Learning

2020 ◽  
Author(s):  
Caroline Wanderley Espinola ◽  
Juliana Carneiro Gomes ◽  
Jessiane Mônica Silva Pereira ◽  
Wellington Pinheiro dos Santos
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Acoustic Analysis ◽  
Psychiatric Patients ◽  
Support Vector ◽  
Healthy Controls ◽  
Major Depressive ◽  
Machine Learning Classification ◽  
Medical Evaluation

AbstractPurposeDiagnosis and treatment in psychiatry are still highly dependent on reports from patients and on clinician judgement. This fact makes them prone to memory and subjectivity biases. As for other medical fields, where objective biomarkers are available, there has been an increasing interest in the development of such tools in psychiatry. To this end, vocal acoustic parameters have been recently studied as possible objective biomarkers, instead of otherwise invasive and costly methods. Patients suffering from different mental disorders, such as major depressive disorder (MDD), may present with alterations of speech. These can be described as uninteresting, monotonous and spiritless speech, low voice.MethodsThirty-three individuals (11 males) over 18 years old were selected, 22 of which being previously diagnosed with MDD, and 11 healthy controls. Their speech was recorded in naturalistic settings, during a routine medical evaluation for psychiatric patients, and in different environments for healthy controls. Voices from third parties were removed. The recordings were submitted to to a vocal feature extraction algorithm, and to different machine learning classification techniques.ResultsThe results showed that support vector machines (SVM) models provided the greatest classification performances for different kernels, with PUK kernel providing accuracy of 89.14% for the detection of MDD.ConclusionThe use of machine learning classifiers with vocal acoustics features has shown to be very promising for the detection of major depressive disorder, but further tests with a larger sample will be necessary to validate our findings.

scholarly journals Phosphodiesterase 8A to discriminate in blood samples depressed patients and suicide attempters from healthy controls based on A-to-I RNA editing modifications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicolas Salvetat ◽  
Fabrice Chimienti ◽  
Christopher Cayzac ◽  
Benjamin Dubuc ◽  
Francisco Checa-Robles ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Rna Editing ◽  
Whole Blood ◽  
Healthy Controls ◽  
Major Depressive ◽  
Suicide Attempters ◽  
Blood Samples ◽  
Depressed Patients ◽  
The Brain

AbstractMental health issues, including major depressive disorder, which can lead to suicidal behavior, are considered by the World Health Organization as a major threat to global health. Alterations in neurotransmitter signaling, e.g., serotonin and glutamate, or inflammatory response have been linked to both MDD and suicide. Phosphodiesterase 8A (PDE8A) gene expression is significantly decreased in the temporal cortex of major depressive disorder (MDD) patients. PDE8A specifically hydrolyzes adenosine 3′,5′-cyclic monophosphate (cAMP), which is a key second messenger involved in inflammation, cognition, and chronic antidepressant treatment. Moreover, alterations of RNA editing in PDE8A mRNA has been described in the brain of depressed suicide decedents. Here, we investigated PDE8A A-to-I RNA editing-related modifications in whole blood of depressed patients and suicide attempters compared to age-matched and sex-matched healthy controls. We report significant alterations of RNA editing of PDE8A in the blood of depressed patients and suicide attempters with major depression, for which the suicide attempt took place during the last month before sample collection. The reported RNA editing modifications in whole blood were similar to the changes observed in the brain of suicide decedents. Furthermore, analysis and combinations of different edited isoforms allowed us to discriminate between suicide attempters and control groups. Altogether, our results identify PDE8A as an immune response-related marker whose RNA editing modifications translate from brain to blood, suggesting that monitoring RNA editing in PDE8A in blood samples could help to evaluate depressive state and suicide risk.

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