Online Mindfulness-Based Cognitive Therapy in Patients with Depression

Post by Stephanie Williams

What's the science?

Many individuals with depression experience residual symptoms after receiving treatment. Some individuals may even experience a relapse after treatment. To combat relapse and to encourage full remission of symptoms, a mindfulness-based cognitive therapy program was designed to teach individuals to regulate their emotions by breaking out of harmful ruminating thought patterns. The program was designed with an online framework in order to bypass the usual barriers to in person-psychological interventions, including in-person travel times,  service costs, and long waiting list times, among others. This week in JAMA Psychiatry, Segal and colleagues assess whether the addition of an online mindfulness-based cognitive therapy program to regular treatment for depression can reduce residual symptoms, decrease relapse, and increase remission.

How did they do it?                             

To assess how well the online cognitive therapy program (“Mindful Mood Balance'') could help reduce depressive symptoms, the authors randomly assigned a large cohort (N=460) of participants with depression to one of two groups and assessed their progress over a 15 month period. The first 3 months of the study consisted of an active intervention phase, and the remaining 12 months were used as a follow-up phase. The treatment for the ‘usual depression care’ group included regular access to psychotropic medication and cognitive therapy sessions. The treatment for the mindfulness group was identical, except for the addition of the online mindfulness cognitive therapy program. To qualify for the study, participants must have experienced one depressive episode, have scored between 5 and 9 on PHQ-9, and have been older than 18. The mindfulness-based online cognitive therapy program was segmented into eight sessions. The core idea of the program was to teach participants how to break out of habitual, dysfunctional cognitive patterns (eg. depression-related rumination). To assess the effect of the program on participants’ moods, the authors administered a standard 9-item questionnaire, the PHQ-9, which is known to track depression severity. The authors assessed 3 primary outcomes of interest using the PHQ-9 results, including 1) the amount of reduction in residual symptom severity 2) the rate of remission (a score of 5 on the PHQ-9 was used as a threshold for remission) and 3) the rate of depressive relapse. The authors also administered a seven-item questionnaire related to generalized anxiety disorder, called GAD-7. They used this survey to assess the reduction in each participant’s anxiety symptoms. 

What did they find?

The authors found that the group that received the additional online mindfulness training showed a significantly greater reduction in symptoms across the entire study period compared to the usual depression care group. When the authors compared the reduction in residual symptoms for the two groups across the 12-month follow-up phase of the study, they found that patients who received the additional online training maintained their initial gains in symptom reduction. The authors also found residual symptoms of individuals who received usual depression care without the online program continued to decrease over the 12-month follow-up phase. When the authors assessed the rate of remission among subjects, they found that individuals in the group who received the additional online module achieved remission of their symptoms at a significantly higher rate (59.4%) compared to the group with standard treatment alone (47.0 %). The individuals in the online program treatment group continued to maintain their low rates of remission across the 12-month follow-up phase, while the standard treatment group showed increased rates of remission across the 12-month follow-up phase. When the authors assessed the relapse rate, they found a lower rate of relapse in the mindfulness program group (13.5%) compared to the group that received usual depression care (23%). Results from the generalized anxiety survey showed that the group receiving the additional online treatment showed a mean decrease in their anxiety scores.  

What's the impact?

The authors show that the addition of a low-cost, accessible online program can significantly attenuate depressive symptoms better than usual depressive care alone. These results will inform the treatment of future patients with depression, and provide encouraging evidence that better symptom reduction and remission can be achieved using additional treatment strategies. 

Segal et al. Outcomes of Online Mindfulness-Based Cognitive Therapy for Patients with Residual Depressive Symptoms. Jama Psychiatry. (2020). Access the original scientific publication here.

A Method for Detecting Plasticity in the Brain

Post by Leigh Christopher

What's the science?

The brain is plastic, meaning that the strength between synapses (connections between neurons) is altered after learning something new or creating a memory. Long term potentiation is the biological process of the strengthening of synaptic connections. This process is mediated by the insertion of AMPA receptors containing a GluA1 subunit into the synapse, which is followed by a replacement of these receptors with GluA1 lacking AMPA receptors. Therefore, the presence of GluA1 acts as a signal of recent learning-induced plasticity in the brain. Current methods used to detect synaptic plasticity are either slow or lack resolution. This week in PNAS, Dore and colleagues present a new method called SYNPLA (synaptic proximity ligation assay) for detecting the insertion of GluA1 containing AMPA receptors, in order to identify recent synaptic plasticity. 

How did they do it?

SYNPLA uses proximity ligation assay (PLA), a method that detects two proteins that are close together. This method relies on the use of antibodies to flag the proteins of interest. A second set of antibodies, each paired to oligonucleotides (short segments of DNA) are then used to detect the first set of antibodies. Lastly, a second complementary pair of oligonucleotides are added, and if they are close to one another, they will ligate and form a circle. This sequence can then by amplified (1000 times) to form a ball of DNA that is probed and observed with light microscopy as points where co-localized proteins exist (referred to here as PLA puncta). First, the authors expressed antibody detectable NRXN (a presynaptic protein), and antibody detectable NLGN (a postsynaptic protein) in neurons, and performed PLA in order to demonstrate that they were able to label synapse formation during development in cultured neurons. The authors then tested whether they could detect postsynaptic AMPA receptors containing GluA1 in cultured neurons and cultured hippocampal slices following chemically induced LTP (i.e. plasticity that is thought to occur during learning). Next, they went on to assess whether SYNPLA could detect learning-induced plasticity in rats in vivo. They injected either the auditory cortex or thalamus with a virus expressing antibody detectable NRXN (presynaptic protein) to detect the co-localization of this protein with postsynaptic AMPA containing GluA1. Rats underwent a defense conditioning paradigm where they heard an auditory tone, followed by a foot shock – this paradigm is known to induce fear learning and synaptic plasticity in the amygdala (fear center of the brain). They also performed SYNPLA on tissue sections of the amygdala as well as the lateral habenula which is known to process aversive stimuli.

What did they find?

SNYPLA was able to successfully detect and label synapse formation during development with a high specificity and signal-to-noise ratio. The authors were also able to detect the insertion of postsynaptic GluA1 containing AMPA receptors (a sign of potentiation) in neuron cultures and cultured hippocampal slices following chemically induced LTP, as demonstrated by a large increase in PLA puncta. Following the defense conditioning paradigm, the authors found that rats who underwent paired conditioning (paired tone and foot shock) showed increased levels of PLA puncta in the amygdala compared to control rats or rats who underwent unpaired conditioning, demonstrating that SNYPLA was able to detect synaptic plasticity in the amygdala in vivo following learning. They also observed increased PLA puncta in the lateral habenula (a region of the brain thought to be active during punishment or disappointment) for rats who underwent both the paired and unpaired conditioning paradigm compared to control rats, suggesting that plasticity occurs in this region whenever an aversive shock is administered (and not just for learning a fear response). 

What's the impact?

This is the first study to present a fast, high-resolution method for detecting learning-induced synaptic plasticity. Understanding which specific synapses have been modified by learning or memory is difficult. SYNPLA can quickly identify synaptic plasticity at specific synapses in defined pathways in the brain and can be used at the whole-brain level as a screening tool to detect recent learning and memory.

Dore et al. SYNPLA, a method to identify synapses displaying plasticity after learning. PNAS (2020). Access the original scientific publication here.

Dissecting Dynamic Inter-Network Relationships During Attention

Post by D. Chloe Chung

What's the science?

When we switch our minds from being restful to being attentive, different neural networks in our brain act in different ways. The dorsal attention network (DAN) and the salience network (SN) are known to be activated during attention-requiring tasks, while another network called the default mode network (DMN) behaves in the opposite direction - its activity is suppressed. Although previous studies using functional magnetic resonance imaging (fMRI) have reported this negative correlation between DMN and DAN/SN network activity, there is a lack of our knowledge on how this “anti-correlation” is relevant to human behaviors. This week in Nature Communications, Kucyi and colleagues present clear evidence that each network has a distinct response profile to attention-requiring tasks and dynamic relationships among these networks are closely related to the efficient switch between attention and rest.

How did they do it?

While fMRI offers important information on the activity of brain regions based on changes in the blood oxygen level, the authors chose to use intracranial electroencephalography (iEEG) that places electrodes directly on the surface of the brains of patients undergoing surgery. This way, the authors were able to record the electrical activity of the brain regions that constitute attention networks – DAN, SN, or DMN – with much higher temporal and anatomical resolution. The authors obtained iEEG data from more than 3500 sites across 31 human participants who were performing the attention-evaluating test, which shows images that gradually and continuously change every 800 milliseconds. In this test, participants were asked to specifically respond when images of city scenes presented to them changed to different city scenes, but not to respond when they changed to mountain scenes. When the participants successfully responded to image changes, their responses were categorized as “correct”, but otherwise, the responses were considered to be “incorrect”. During the iEEG recording, the authors measured the electrical activity within the high-frequency broadband that ranges from 70 to 170 Hz, as this activity range has been shown to well-represent the negative correlation between attention networks.

What did they find?

From the iEEG recording, the authors first detected an increase in high-frequency broadband activity from the brain regions that constitute the DAN/SN and a decrease in high-frequency broadband activity from the regions constituting the DMN. All of these changes occurred several hundred milliseconds after the image change during the attention-evaluating test and returned to baseline after 1 to 2 seconds. These changes in activity confirmed that the DAN/SN are activated while the DMN is deactivated when human participants paid attention to external stimuli. When they took a closer look at the precise timing of when the activity peaked in each network, the authors found that the DAN was the first to peak in its activity, followed by the SN, and then the DMN. This observation of a unique timing profile for each network suggests that there is a clear temporal lag in electrical activity across attention networks during attention-requiring tasks. By calculating the correlation between this temporal lag in the activity of attention networks and the accuracy of attention-task performance, the authors showed that the lagged anti-correlation between DAN and DMN was especially important for performance on the attention task. In addition to these findings, the authors found that when human subjects failed to correctly perform the attention-requiring tasks, activities of the DAN/ SN were noticeably elevated while the activity of DMN was not sufficiently suppressed. This interesting finding further supports the significance of anti-correlation among attention networks in accurate attentional performance.

What’s the impact?

This study is the first to show the significance of delays in the timing of activity among the DAN, SN and DMN networks in attention. Powered by a large group of human participants who had electrodes directly implanted within the brain areas that represent these different attention networks, this study provides findings that will be valuable in critically interpreting neuroimaging studies that investigate the brain states between rest and active tasks. What we learned from this study will also serve as a foundation for subsequent research on how these dynamic inter-network relationships can change as we age or develop neurological disorders.

Kucyi et al. Electrophysiological dynamics of antagonistic brain networks reflect attentional fluctuations. Nature Communications (2020). Access the original scientific publication here.