To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Recent 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.
To 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.
The 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.
The 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.
As 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%.
The selected functional connectivities based on the sparse machine learning algorithm included the brain regions which have been associated with depression.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
Cognitive–behavioral therapy (CBT) is thought to be useful for chronic pain, with the pathology of the latter being closely associated with cognitive–emotional components. However, there are few resting-state functional magnetic resonance imaging (R-fMRI) studies. We used the independent component analysis method to examine neural changes after CBT and to assess whether brain regions predict treatment response.
We performed R-fMRI on a group of 29 chronic pain (somatoform pain disorder) patients and 30 age-matched healthy controls (T1). Patients were enrolled in a weekly 12-session group CBT (T2). We assessed selected regions of interest that exhibited differences in intrinsic connectivity network (ICN) connectivity strength between the patients and controls at T1, and compared T1 and T2. We also examined the correlations between treatment effects and rs-fMRI data.
Abnormal ICN connectivity of the orbitofrontal cortex (OFC) and inferior parietal lobule within the dorsal attention network (DAN) and of the paracentral lobule within the sensorimotor network in patients with chronic pain normalized after CBT. Higher ICN connectivity strength in the OFC indicated greater improvements in pain intensity. Furthermore, ICN connectivity strength in the dorsal posterior cingulate cortex (PCC) within the DAN at T1 was negatively correlated with CBT-related clinical improvements.
We conclude that the OFC is crucial for CBT-related improvement of pain intensity, and that the dorsal PCC activation at pretreatment also plays an important role in improvement of clinical symptoms via CBT.
It has been demonstrated that negatively distorted self-referential processing, in which individuals evaluate one's own self, is a pathogenic mechanism in subthreshold depression that has a considerable impact on the quality of life and carries an elevated risk of developing major depression. Behavioural activation (BA) is an effective intervention for depression, including subthreshold depression. However, brain mechanisms underlying BA are not fully understood. We sought to examine the effect of BA on neural activation during other perspective self-referential processing in subthreshold depression.
A total of 56 subjects underwent functional magnetic resonance imaging scans during a self-referential task with two viewpoints (self/other) and two emotional valences (positive/negative) on two occasions. Between scans, while the intervention group (n = 27) received BA therapy, the control group (n = 29) did not.
The intervention group showed improvement in depressive symptoms, increased activation in the dorsal medial prefrontal cortex (dmPFC), and increased reaction times during other perspective self-referential processing for positive words after the intervention. Also, there was a positive correlation between increased activation in the dmPFC and improvement of depressive symptoms. Additionally, there was a positive correlation between improvement of depressive symptoms and increased reaction times.
BA increased dmPFC activation during other perspective self-referential processing with improvement of depressive symptoms and increased reaction times which were associated with improvement of self-monitoring function. Our results suggest that BA improved depressive symptoms and objective monitoring function for subthreshold depression.
Email your librarian or administrator to recommend adding this to your organisation's collection.