Neuroimaging Biomarker of Major Depressive Disorder

2016 ◽  
Vol 33 (S1) ◽  
pp. S492-S493
Author(s):  
N. Ichikawa ◽  
Y. Okamoto ◽  
G. Okada ◽  
G. Lisi ◽  
N. Yahata ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Functional Connectivity ◽  
Depressive Disorder ◽  
Learning Algorithm ◽  
Training Data ◽  
Fmri Data ◽  
Machine Learning Algorithm ◽  
Major Depressive ◽  
The Brain

IntroductionRecent studies have shown that it is important to understand the brain mechanism specifically by focusing on the common and unique functional connectivity in each disorder including depression.ObjectivesTo specify the biomarker of major depressive disorder (MDD), we applied the sparse machine learning algorithm to classify several types of affective disorders using the resting state fMRI data collected in multiple sites, and this study shows the results of depression as a part of those results.AimsThe aim of this study is to understand some specific pattern of functional connectivity in MDD, which would support diagnosis of depression and development of focused and personalized treatments in the future.MethodsThe neuroimaging data from patients with major depressive disorder (MDD, n = 100) and healthy control adults (HC: n = 100) from multiple sites were used for the training dataset. A completely separate dataset (n = 16) was kept aside for testing. After all preprocessing of fMRI data, based on one hundred and forty anatomical region of interests (ROIs), 9730 functional connectivities during resting states were prepared as the input of the sparse machine-learning algorithm.ResultsAs results, 20 functional connectivities were selected with the classification performance of Accuracy: 83.0% (Sensitivity: 81.0%, Specificity: 85.0%). The test data, which was completely separate from the training data, showed the performance accuracy of 83.3%.ConclusionsThe selected functional connectivities based on the sparse machine learning algorithm included the brain regions which have been associated with depression.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder

2019 ◽  
Vol 29 (12) ◽  
pp. 4958-4967 ◽  
Author(s):  
Juliana Corlier ◽  
Andrew Wilson ◽  
Aimee M Hunter ◽  
Nikita Vince-Cruz ◽  
David Krantz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Transcranial Magnetic Stimulation ◽  
Functional Connectivity ◽  
Clinical Outcome ◽  
Depressive Disorder ◽  
Clinical Improvement ◽  
Magnetic Stimulation ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Major Depressive

AbstractRepetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connectivity (FC). These changes may be related to the mechanism of action of rTMS and explain the variability in clinical outcome. We examined changes in electroencephalographic FC during the first rTMS treatment in 109 subjects treated with 10 Hz stimulation to left dorsolateral prefrontal cortex. All subjects subsequently received 30 treatments and clinical response was defined as ≥40% improvement in the inventory of depressive symptomatology-30 SR score at treatment 30. Connectivity change was assessed with coherence, envelope correlation, and a novel measure, alpha spectral correlation (αSC). Machine learning was used to develop predictive models of outcome for each connectivity measure, which were compared with prediction based upon early clinical improvement. Significant connectivity changes were associated with clinical outcome (P < 0.001). Machine learning models based on αSC yielded the most accurate prediction (area under the curve, AUC = 0.83), and performance improved when combined with early clinical improvement measures (AUC = 0.91). The initial rTMS treatment session produced robust changes in FC, which were significant predictors of clinical outcome of a full course of treatment for MDD.

scholarly journals Functional connectivity changes with rapid remission from moderate-to-severe major depressive disorder

2019 ◽  
Author(s):  
Xiaoqian Xiao ◽  
Brandon S. Bentzley ◽  
Eleanor J. Cole ◽  
Claudia Tischler ◽  
Katy H. Stimpson ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Emotion Regulation ◽  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Depressive Disorder ◽  
Major Depressive ◽  
Disease Heterogeneity ◽  
Limited Effectiveness ◽  
Brain Changes ◽  
The Brain

AbstractMajor depressive disorder (MDD) is prevalent and debilitating, and development of improved treatments is limited by insufficient understanding of the neurological changes associated with disease remission. In turn, efforts to elucidate these changes have been challenging due to disease heterogeneity as well as limited effectiveness, delayed onset, and significant off-target effects of treatments. We developed a form of repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex (lDLPFC) that in an open-label study was associated with remission from MDD in 90% of individuals in 1-5 days (Stanford Accelerated Intelligent Neuromodulation Therapy, SAINT). This provides a tool to begin exploring the functional connectivity (FC) changes associated with MDD remission. Resting-state fMRI scans were performed before and after SAINT in 18 participants with moderate-to-severe, treatment-resistant MDD. FC was determined between regions of interest defined a priori by well-described roles in emotion regulation. Following SAINT, FC was significantly decreased between subgenual cingulate cortex (sgACC) and 3 of 4 default mode network (DMN) nodes. Significant reductions in FC were also observed between the following: DLPFC-striatum, DLPFC-amygdala, DMN-amygdala, DMN-striatum, and amygdala-striatum. Greater clinical improvements were correlated with larger decreases in FC between DLPFC-amygdala and DLPFC-insula, as well as smaller decreases in FC between sgACC-DMN. Greater clinical improvements were correlated with lower baseline FC between DMN-DLPFC, DMN-striatum, and DMN-ventrolateral prefrontal cortex. The multiple, significant reductions in FC we observed following SAINT and remission from depression support the hypothesis that MDD is a state of hyper-connectivity within these networks, and rapid decoupling of network nodes may lead to rapid remission from depression.Significance statementMajor depressive disorder is common and debilitating. It has been difficult to study the brain changes associated with recovery from depression, because treatments take weeks-to-months to become effective, and symptoms fail to resolve in many people. We recently developed a type of magnetic brain stimulation called SAINT. SAINT leads to full remission from depression in 90% of people within 5 days. We used SAINT and functional magnetic resonance imaging to determine how the brain changes with rapid remission from depression. We found changes in areas of the brain associated with emotion regulation. This provides a significantly clearer picture of how the non-depressed brain differs from the depressed brain, which can be used to develop rapid and effective treatments for depression.

scholarly journals Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
P. M. Durai Raj Vincent ◽  
Nivedhitha Mahendran ◽  
Jamel Nebhen ◽  
N. Deepa ◽  
Kathiravan Srinivasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Common Mental Disorder ◽  
Classification Algorithms ◽  
It Professionals ◽  
Major Depressive ◽  
The Brain ◽  
Machine Learning Models ◽  
Better Than

Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals’ lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime. In simple terms, it is a mood disturbance that can persist for an individual for more than a few weeks to months. In MDD, in most cases, the individuals do not consult a professional, and even if being consulted, the results are not significant as the individuals find it challenging to identify whether they are depressed or not. Depression, most of the time, cooccurs with anxiety and leads to suicide in few cases, among the employees, who are about to handle the pressure at work and home and mostly unnoticing such problems. This is why this work aims to analyze the IT employees who are mostly working with targets. The artificial neural network, which is modeled loosely like the brain, has proved in recent days that it can perform better than most of the classification algorithms. This study has implemented the multilayered neural perceptron and experimented with the backpropagation technique over the data samples collected from IT professionals. This study aims to develop a model that can classify depressed individuals from those who are not depressed effectively with the data collected from them manually and through sensors. The results show that deep-MLP with backpropagation outperforms other machine learning-based models for effective classification.

scholarly journals Effects of escitalopram therapy on resting-state functional connectivity of subsystems of the default mode network in unmedicated patients with major depressive disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian Cui ◽  
Yun Wang ◽  
Rui Liu ◽  
Xiongying Chen ◽  
Zhifang Zhang ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Functional Connectivity ◽  
Depressive Disorder ◽  
Default Mode Network ◽  
Resting State ◽  
Fmri Data ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Major Depressive ◽  
Default Mode

AbstractAntidepressants are often the first-line medications prescribed for patients with major depressive disorder (MDD). Given the critical role of the default mode network (DMN) in the physiopathology of MDD, the current study aimed to investigate the effects of antidepressants on the resting-state functional connectivity (rsFC) within and between the DMN subsystems. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data from 36 unmedicated MDD patients at baseline and after escitalopram treatment for 12 weeks. The rs-fMRI data were also collected from 61 matched healthy controls at the time point with the same interval. Then, we decomposed the DMN into three subsystems based on a template from previous studies and computed the rsFC within and between the three subsystems. Finally, repeated measures analysis of covariance was conducted to identify the main effect of group and time and their interaction effect. We found that the significantly reduced within-subsystem rsFC in the DMN core subsystem in patients with MDD at baseline was increased after escitalopram treatment and became comparable with that in the healthy controls, whereas the reduced within-subsystem rsFC persisted in the DMN dorsal medial prefrontal cortex (dMPFC) and medial temporal subsystems in patients with MDD following escitalopram treatment. In addition, the reduced between-subsystem rsFC between the core and dMPFC subsystem showed a similar trend of change after treatment in patients with MDD. Moreover, our main results were confirmed using the DMN regions from another brain atlas. In the current study, we found different effects of escitalopram on the rsFC of the DMN subsystems. These findings deepened our understanding of the neuronal basis of antidepressants’ effect on brain function in patients with MDD. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377. Registration number: ChiCTR-OOC-17012566.

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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