Brain Regulation of Emotional Conflict Predicts Antidepressant Treatment Response for Depression

Post by Elisa Guma

What's the science?

Major depression is a chronic and disabling mood disorder, for which the primary treatment is traditionally antidepressant medications. These are effective for a subset of individuals, however, for many they do not afford much improvement over placebo treatment. Given this heterogeneity, it is thought that an individuals’ neurobiological characteristics might predict their response to treatment. This week in Nature Human Behaviour, Fonzo and colleagues investigated whether an individual’s response to or regulation of emotional conflict and associated neural response could predict treatment outcome to an antidepressant, sertraline, compared to placebo in a double-blind trial.

How did they do it?

The authors used functional magnetic resonance imaging (MRI) data on an emotional conflict task from a large double-blind trial, Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC). In the trial, participants were randomized into an 8-week course of sertraline (an antidepressant) or placebo, and depressive symptoms were measured each week. The authors chose to focus their study on emotional conflict, as its regulation is critical to wellbeing, it engages cognitive and emotion related brain circuitry, is often dysregulated in depression, and is thought to be of relevance to the efficacy of antidepressant treatment. The task performed while undergoing functional MRI involved the presentation of an emotional face with either a fearful or happy expression, with an overlaid emotional word (i.e. fear or happy). Participants were instructed to identify the facial emotion while trying to ignore the emotional word. Stimuli were either presented as congruent (i.e. face and word match), or incongruent (emotional conflict). First, the brain regions that were more active during this task were identified, as well as the relationship between their activity and task performance. Next, the authors built a prediction model (using relevance vector machines — a type of machine learning model) to assess whether the neural response associated with emotional conflict regulation could predict treatment outcome. 

What did they find?

In keeping with other large clinical trials, the sertraline treated group had a slightly larger improvement in symptoms than the placebo group. During the task, the incongruent trials (emotional conflict trials) resulted in a slower reaction time. The neural response to this conflict was identified as a conflict response network and included activation of the dorsomedial and dorsolateral prefrontal cortices, ventrolateral prefrontal cortex and anterior insula, and deactivation of the ventromedial prefrontal cortex and anterior medial prefrontal cortex as well as the posterior cingulate, precuneus, hippocampus, and parahippocampal gyri. The authors found that better emotional conflict regulation (i.e. mitigation of the conflict effect on conflict trials that were preceded by another conflict trial) was positively associated with greater dampening of the conflict response network activation.

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In addition, the authors found that individuals who were more successful at dampening their emotional conflict response network during conflict regulation (i.e. adapting to interference when the face and word do not match) also had more symptom improvement due to sertraline rather than placebo. Interestingly, the prediction model the authors built was able to reliably predict how treatment changed depressive symptoms when the model was trained (built) using data from the group receiving sertraline, but it did not work when trained on the placebo group. This suggests that the model developed on sertraline outcome reflects a sertraline-specific signal that explains a meaningful effect of treatment, separate from the placebo response. The model was not related to or affected by depression diagnosis or clinical severity.

What's the impact?

The authors were able to identify a neural trait sensitive to medication response within a broader clinical diagnosis of depression. This trait was related to the brain’s ability to adapt its response to emotional conflict and was a better predictor of treatment outcome than either the clinical measures or the behavioural response alone. Future studies could incorporate a broader range of brain imaging modalities and behavioural assessments, and assess generalizability to other antidepressant medications. These findings highlight the heterogeneity in treatment response within a clinically defined population and suggest that individual neurobiological characteristics could predict treatment response. 

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Fonzo et al. Brain regulation of emotional conflict predicts antidepressant treatment response for depression. Nature Human Behaviour (2019). Access the original scientific publication here.

Tracking Neurons During Development to Understand How Neural Circuits Form

Post by Sarah Hill

What's the science?

Neurons never act alone, but instead, organize into coordinated cellular ensembles or 'circuits' to direct behaviors. Linking how newborn neurons are arranged into coordinated networks during development has previously been limited by available technologies, leaving an incomplete picture of how neural circuits are formed. However, recent advances in imaging and computational methods have offered insight into this process. This week in Cell, Wan and colleagues present a new imaging framework for tracking neurons from cell birth to emergence of synchronized global activity, shedding new light on how neural circuits assemble during development.                

How did they do it?

The authors developed an imaging method based on light-sheet fluorescence microscopy to simultaneously track the identities, lineages, migration, and activation of newborn neurons, and demonstrated the approach in the zebrafish spinal cord. First, they imaged a whole zebrafish embryo using cell-type-specific markers to identify neuron types, trace cell lineages, and monitor neuronal movements. They then performed functional imaging of the embryos to record neuronal activation during circuit formation. Additional experiments were carried out to establish how ensembles of neurons become coordinated in their activation along with multiple segments of the spinal cord, as well as how synchronized activity on the left and right sides of the spinal cord is established. Using the imaging data, they pieced together how early spinal cord neurons assemble into a fully functional circuit.        

What did they find?

Through this new imaging method, the authors successfully reconstructed an assembly of the spinal cord circuit at the single-cell level. Using the zebrafish as a model, they identified three key stages in spinal cord circuit development. First, nascent motor neurons (cells that execute motor movements) pair up with other neurons in the same spinal segment to form local ensembles of synchronized activity. In stage II, the local ensembles merge based on size into a globally synchronized ensemble that spans multiple segments. Local spinal microcircuits continue to merge until only two neural ensembles remain, on the left and right sides of the spinal cord. In stage III, alternating left-right activation is synchronized by commissural interneurons (cells that project to the opposite side of the spinal cord) recruited into the global ensembles relatively late in the process.   

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What's the impact?

This is the first study to trace the development of a neural circuit at the single-cell level, from neuronal birth to emergence of a functional circuit. Importantly, the imaging framework proposed in this study can be readily translated to neural circuits beyond the spinal cord and all computational methods are open-source. 

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Wan et al. Single-Cell Reconstruction of Emerging Population Activity in an Entire Developing Circuit. Cell (2019). Access the original scientific publication here.

Dopamine Stimulation Modulates the Balance Between Ignoring and Updating Information

Post by Flora Moujaes

What's the science?

Deficits in working memory are a common feature of numerous psychiatric disorders, however, are not effectively treated by available therapies. Working memory deficits have often been attributed to altered dopaminergic signalling, but dopamine’s specific role has not been clearly defined. For example, dopamine has been shown to be integral to the ability to ignore distracting stimuli, but less research has been done on how this may relate to flexibility and the ability to update working memory representations. Furthermore, how individual differences affect dopamine’s modulation of working memory is unclear. Research has indicated that the ability to gate the contents of working memory is linked to both the balance between D1 and D2 dopamine receptors and tonic dopamine levels (i.e. the ‘sustained’ background dopamine release). Although it is difficult to measure tonic dopamine levels in the human brain, it may be possible to use baseline working memory performance as a proxy. This week in Journal of Psychopharmacology, Fallon and colleagues from Masud Husain’s lab at the University of Oxford explore how dopamine D2 receptor stimulation modulates both working memory and cognitive control, and how this relates to individual differences in baseline working memory performance.

How did they do it?

Researchers manipulated dopamine in 26 healthy older adults. Older adults were chosen as, like Parkinson’s patients, they show a depletion of dopaminergic functioning. However, without the progressive neuronal pathology seen in Parkinson’s they provide a clearer window into the effect dopamine has on cognitive functioning. Participants completed two sessions following either the administration of a single dose of cabergoline (1 mg), a relatively selective D2 dopamine agonist, or placebo. The order in which they received the dopamine agonist or placebo was counterbalanced to avoid order effects. Each session consisted of three tasks. The first was the Ignore/Update working memory Task, designed to assess the ability to ignore or update information in working memory. In this task, participants were shown two differently coloured arrows and required to encode their orientations. They then saw a second pair of arrows that they either had to disregard in the ignore condition, or use to replace the previous orientation information held in working memory in the update condition. The second task was the Baseline working memory Task, which provided a baseline measure of working memory ability as participants merely had to remember the orientation of an arrow after a delay. The third task was the Response Conflict (Simon) Task, which was used to obtain a relatively working memory-free measure of cognitive control. In this task, participants had to indicate which way an arrow was pointing, and the arrow’s location on the screen was either congruent or incongruent to its direction.

What did they find?

Baseline working memory ability modulates the direction of dopamine’s effect on ignoring vs. updating: Researchers found that dopamine D2 receptor stimulation did not influence overall working memory recall, but did modulate the balance between ignoring and updating in divergent ways according to baseline working memory performance. High-working memory individuals were relatively better at ignoring compared to updating after drug administration, whereas the opposite occurred in low-working memory individuals. This indicates that increased dopamine can enhance the robustness of mental representations in high-working memory individuals, but makes representations less stable and flexible in low-working memory individuals. Dopamine has common, but antagonistic, effects on ignoring and overcoming response conflict: The ability to overcome response conflict was not affected by drug administration, but the researchers did find a negative relationship between the effect the drug had on response conflict performance and ignoring. This indicates that both response conflict, a working memory free measure of cognitive control, and ignoring, a sub-process of working memory, are coupled to dopaminergic stimulation levels.
What's the impact?

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What’s the impact?

Overall, this study provides a clearer window into the effect dopamine has on cognitive functioning, which may have wider implications for disorders such as Parkinson’s. Furthermore, these findings highlight the importance both of accounting for individual differences and decomposing working memory into its subcomponents when assessing the effects of dopaminergic drugs. This study also suggests that dopamine-altering cognitive enhancers may be of minimal benefit, as augmenting D2 stimulation acts as a double-edged sword: improving one cognitive function at the expense of another. Future studies would benefit by examining the effects of both dopamine agonists and antagonists in the same sample, in order to more closely examine how the balance between D1 and D2 dopamine receptors relates to working memory.

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Fallon et al. Dopamine D2 receptor stimulation modulates the balance between ignoring and updating according to baseline working memory ability. Journal of Psychopharmacology (2019). Access the original scientific publication here.