Decoding the Neural Signature of Reward

Post by Leanna Kalinowski

The takeaway

Researchers have established a whole-brain machine-learning model that can predict the brain’s response to different levels of reward.

What's the science?

Our actions towards positive and negative outcomes are often dependent on the brain’s ability to process rewarding and punishing stimuli. Dysregulations in the brain’s reward processing system are therefore a hallmark sign of many neuropsychiatric disorders, including substance use disorders. Previous researchers have developed mathematical models that can predict how the brain responds to rewarding and punishing stimuli; however, these models often rely on the activity from single brain regions, making it difficult to generalize their findings. This week in NeuroImage, Speer and colleagues ran a series of reward tasks and developed a whole-brain machine learning model to predict the brain’s response to reward.

How did they do it?

To establish the machine learning model (referred to as the Brain Reward Signature or BRS), the researchers administered the Monetary-Incentive-Delay task to 40 participants. Each trial began with a cue phase, where participants are shown a cue that signals the monetary reward or punishment associated with the trial (potential reward of 5€, potential loss of 5€, or no monetary outcome). Following a brief delay, they were asked to press a button when a target square appeared for a limited amount of time. Depending on the trial cue and their accuracy in pressing the button, they were then informed as to whether they lost or gained money. Participants underwent 108 trials of this task, with brain activity being simultaneously measured using functional magnetic resonance imaging (fMRI).

To test the accuracy of their BRS model, the researchers then applied it to results from a publicly available dataset that used a slightly different version of the Monetary-Incentive-Delay task. In this task, everything but the cues were the same as before; instead of being shown one of three cues, the participants were shown one of five cues (potential reward of 5€, potential reward of 1€, potential loss of 5€, potential loss of 1€, or no monetary outcome).

To test whether their BRS model is specific to the neural signature of reward and not other emotional responses (i.e., disgust), the researchers then applied it to a newly developed Disgust-Delay Task. In this task, participants were asked to press a button when a target rectangle appeared and then were given feedback on whether they hit or missed the target. Then, if they hit the target, they were shown a neutral image; if not, they were shown a disgusting image. Participants completed 72 trials of this test, again with brain activity being measured using fMRI.

What did they find?

The researchers first found that their BRS model could predict the neural signature of monetary rewards versus losses in the Monetary-Incentive-Delay task with high accuracy. Brain regions critical to the BRS were often brain regions previously associated with monetary or reward-related tasks in previous studies (as identified by the NeuroSynth database). When testing the accuracy of their model on a dataset that used an expanded version of the task, the researchers found that it not only could predict monetary rewards versus losses but also could predict the magnitude of rewards and losses. When testing whether their model was specific to the neural signature of reward, the researchers found that their model could predict unsuccessful versus successful trials from the Disgust-Delay task (i.e., a non-monetary reward), but it could not predict neural differences in viewing a neutral versus disgusting image (i.e., a non-reward emotional response).

What's the impact?

This BRS model can successfully and accurately predict the magnitude of rewards and losses across different samples and tasks. Further, this model is specific to reward and not generalizable to other emotional responses (e.g., disgust). As this model is trained on full-brain responses, it is much more generalizable and reproducible than previous models that were trained on specific brain regions.

Access the original scientific publication here.

Inflammation in the Brain Drives Neurodegeneration in Tauopathy

Post by Elisa Guma

The takeaway

Neurodegeneration and tauopathy, but not amyloid deposition, are associated with increased immune markers in the brain of humans and mouse models. Importantly, reducing inflammation in mouse models is associated with a decrease in disease progression. 

What's the science?

Alzheimer’s disease is characterized by the deposition of amyloid-B plaques and intracellular tau neurofibrillary tangles in the brain, together with brain atrophy. Interestingly, regional patterns of brain atrophy mirror regional patterns of tau accumulation, but not amyloid deposition in the brain. While the pathology of Alzheimer’s disease remains to be fully elucidated, evidence suggests that the immune system may play an important role in disease pathology. This week in Nature, Chen and colleagues investigate the relationship between the immune system and neurodegeneration in two different mouse models of Alzheimer’s disease, one with amyloid-B deposition and the other with tauopathy, to better understand the contribution of the immune system to each of these hallmark features of the disease.

How did they do it?

The authors compared the immune system function in the brains of two transgenic mouse models, one with amyloid-B-deposition and the other with tauopathy, both created by crossing transgenic mice with human-APOE-knock-in mice. The authors performed single-cell RNA sequencing of immune cells from the meningeal and parenchymal lining surrounding the brains of male mice. They also performed immunohistochemical analyses of the parenchyma to characterize further the presence of T cells, microglia, and antibodies in both mouse models. To compare the findings in the mouse models to human Alzheimer’s disease, they performed the same immunohistochemistry experiments on brain samples of patients with Alzheimer’s disease at different levels of disease severity.

Next, the authors wanted to understand the specific role of several immune modulators in the immune response to tauopathy. They tested each of these by administering a neutralizing antibody to the mice. The first one they tested was IFN-gamma, a cytokine that can augment the immune response. The second one was T cells, and the third was the programmed cell death protein 1 (PDCD1), an immune checkpoint for T cells. They then evaluated the immune profile, accumulation of phosphorylated tau in the brain, and behavior. 

What did they find?

First, the authors found that only mice with tau pathology showed brain atrophy at 9.5 months of age, with regional patterns mirroring human disease. The authors found that 9.5-month-old tau mice had an increased presence of adaptive immune cells, including T cells, dendritic cells, and macrophages in their parenchyma and meninges compared to amyloid mice. Immunohistochemistry of the parenchyma confirmed that tau mice had elevated levels of T cells, enriched for INF-gamma transcripts, and microglia compared to amyloid mice. Importantly, similar elevations in T cell number were observed in the brain of humans with Alzheimer’s disease, particularly in regions with more tauopathy.

Next, the authors found that anti-IFN-gamma treatment resulted in attenuated brain atrophy in tau mice. Similarly, the T cell depletion treatment resulted in decreased brain atrophy, and a reduction in the overall number of microglia, suggesting that T cells in the brain of tau mice can indeed augment the number of microglia. Furthermore, T cell depletion improved performance on several memory tasks (short-term, hippocampal- and amygdala-dependent), and resulted in a decrease of phosphorylated tau (the conformation that allows it to accumulate into fibrils), resembling that of earlier disease stages. Blockade of PCDC1 also led to a decrease in tau-mediated neurodegeneration and p-tau staining. These data suggest that a reduction in immune mediators in the brain can attenuate some of the key features associated with disease progression in Alzheimer’s disease.

What's the impact?

This study suggests that tauopathy and neurodegeneration are linked to an immune system signature of activated microglia and T cells and that a reduction in the presence of these immune markers can delay disease progression. These mechanistic insights may aid in identifying therapeutic targets for preventing or slowing down neurodegeneration in Alzheimer’s disease. While these findings are compelling, the experiments were mostly performed in male mice - the need to replicate these findings in female mice is of paramount importance.

Increased Heart-Rate Leads to Increased Anxiety-Like Behavior in Mice

Post by Lani Cupo

The takeaway

The authors find evidence that emotional states emerge not only top-down, with the brain influencing the body, but also in a bottom-up fashion, with changes to the body (increased heart-rate) increasing anxiety-like behavior.

What's the science?

In acting, there are two techniques to embody a character and scene: a popular inside-out approach where the actor uses a variety of approaches to feel an emotion internally and then expresses that internal state, and an outside-in approach where actors mold the voice and body to capture the emotion and allow it to influence their internal feelings. However, to what degree the physiological state, such as heart and breathing rate, can contribute to the development of an emotional state (like anxiety) is still debated scientifically. This week in Nature, Hsueh and colleagues found that experimentally controlling the heart rate of mice increased anxiety-like behavior, identifying the brain structures involved in the effect.

How did they do it?

First, the authors used cutting-edge genetic engineering to develop a mouse whose heart-rate they could control with a laser mounted on a vest and directed towards the chest (towards the heart, but over the skin). By pulsing the light, the authors could stimulate a heart-rate up to 900 beats per minute, although they could not slow the heart-rate below baseline rates. Mimicking patterns of increased heart-rate observed during stressful contexts, the authors examined the behavior of these “paced” mice compared to controls in two different anxiety tests. They also included an operant test, which examined reward-seeking in a stressful context—during mild foot shocks of variable frequencies.

Next, the authors used an ex-vivo assessment (CLARITY and cell-staining for neural activation) to examine what brain regions might be involved mechanistically in the observed effect. To confirm the role of the identified brain regions in cardiac pacing, they recorded activity from neurons in this region in live mice while increasing the heart rate. Finally, the authors investigated whether inhibiting activity in this brain region inhibited the anxiety-like behaviors observed during increased heart-rate.

What did they find?

The authors observed increased anxiety-like behavior in paced mice compared to controls on both the open-field test and the elevated-plus maze. Importantly, there were no baseline differences between mobility or anxiety levels, suggesting the differences were due to the increased heart-rate rather than the experimental manipulations. In the operant test, there were also no baseline differences in reward-seeking behavior of the experimental mice, however when mild foot shocks were delivered with the reward in 10% of trials, the experimental mice had suppressed reward-seeking. This indicates an apprehensive behavior, where the risk of a foot shock decreases the mouse’s reward-seeking behavior.

Next, the authors identified the posterior insular cortex (pIC) as a region of interest - a brain region known to play a role in interoception. These results were further supported by the authors’ findings that neurons in the pIC were more active when the heart-rate was increased.

Finally, the authors found that inhibiting activation in the pIC reversed the effects of the increased heart-rate in reward-seeking. That is, mice with increased heart-rates but also inhibited pICs no longer differed from controls in their reward-seeking behavior, even with the risk of a foot shock. The authors also tested whether inhibition of another brain region (medial prefrontal cortex) or pIC inhibition without increased heart-rate decreased anxiety-like behavior, and found that they did not. This provides very strong evidence that the pIC is crucially involved in the connection between increased heart-rate and an anxious state.

What's the impact?

This study presents strong evidence that increased heart-rate can evoke anxious states, and that the pIC is integral in this relationship. The methods used in this research add new techniques to the neuroscientist’s tool box, and these findings can help to pave the way for effective interventions for those suffering from panic and anxiety disorders. 

Access the original scientific publication here