Predicting Alzheimer’s disease: Vascular Risk and β-Amyloid Interact to Increase Tau Accumulation

Post by Deborah Joye

What's the science?

Alzheimer’s disease is hallmarked by the accumulation of both beta-amyloid (Aβ) protein aggregates between nerve cells and tau protein ‘tangles’ that build up inside of cells. Build-up of Aβ and tau ultimately results in cell death. By the time a diagnosis occurs, build-up of tau and Aβ has usually progressed past the state where intervention is possible. Recently, there has been an increasing effort to identify preclinical risk factors that may aid in preventative measures against Alzheimer’s disease. Studies have shown that cerebrovascular disease (affecting blood flow to the brain) and Alzheimer’s disease pathology often co-occur in older adults, however it’s not clear how cerebrovascular disease might contribute to the progression of Alzheimer’s pathology. This week in Annals of Neurology, Rabin and colleagues use positron emission tomography (PET) imaging to demonstrate that increases in vascular disease may interact with Aβ build-up to increase accumulation of tau tangles.

How did they do it?

The authors measured vascular risk in 152 clinically normal older adults (mean age = 73.5 ± 6.1 years) using the well-validated Framingham Heart Study cardiovascular disease risk algorithm (which measures risk using relevant factors such as age, weight, and blood pressure). The authors then performed PET imaging on the same individuals using specific tracers that bind to Aβ (11C Pittsburgh compound B) and tau (18F-flortaucipir). Since tau can build up in individuals that do not develop Alzheimer’s disease, tau accumulation was measured in two places: the entorhinal cortex, a common site of early tau deposition in aging populations, and the inferior temporal cortex, an early site of tau accumulation associated with Alzheimer’s disease. The authors then used linear regression models (statistical model that investigates the relationship between two variables) to examine vascular risk and Aβ as interactive predictors of tau deposition, controlling for individual variables such as age and sex.

What did they find?

The authors’ analysis revealed an interaction between vascular risk and Aβ build-up that was associated with tau accumulation in the inferior temporal cortex, a region where tau accumulation occurs early on the the progression of  Alzheimer’s disease. Specifically, when vascular risk was higher and Aβ build-up was higher, there was an increase in tau buildup. Interestingly, vascular risk and Aβ build-up were not associated with increased tau accumulation in the entorhinal cortex, suggesting that vascular risk may interact with specifically in the pre-clinical stages of Alzheimer’s disease.

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What's the impact?

This study is the first to associate vascular risk and development of neural pathology predictive of Alzheimer’s disease. The identification of risk factors associated with preclinical Alzheimer’s disease is a crucial component of furthering Alzheimer’s research and treatment. Though it remains unclear whether vascular risk or Aβ accumulation occurs first, this study provides insight into potential behavioral interventions which may attenuate the progression of Aβ-related tau accumulation often associated with Alzheimer’s disease.

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Rabin et al., Vascular Risk and β-Amyloid Are Synergistically Associated with Cortical Tau, Annals of Neurology (2018), Access the original scientific publication here.

The Emotional and Rational Sides of Persuasion: How do They Interact?

Post by Anastasia Sares

What's the science?

Persuasive messages surround us every day, and they often make appeals to our emotions in order to change our behavior. However, we are not always influenced by emotional persuasion and often need to regulate our emotions to control our response. The brain regions involved in emotional reactions, emotional regulation, and decision making are well known. However, whether these brain regions can predict our response to a persuasive message and how these regions work together, remains unclear. This week in the Journal of Neuroscience, Doré and colleagues tested how different brain areas work together in response to persuasive messages in order to influence our decision-making, in the context of an anti-smoking campaign.

How did they do it?

The data used in this study came from two sources: 1) An MRI (magnetic resonance imaging) experiment in which smokers were shown anti-smoking ads with a number of different graphic images. The authors measured the neural response to each image (based on the blood-oxygen-level-dependent, or BOLD signal), and also asked each participant to rate how much each image made them want to quit. 2) An email campaign, with the same ads and images as the MRI study, sent out to likely smokers. Each email had only one of the images, but the rest of the ad was the same for everyone. The authors measured the click-through rate for each image - in other words, how many times a person who opened the email would proceed to click on a link leading to a website with material to help them quit.

The authors decided to build and test a model of the persuasion process using information based on past research. The amygdala reacts to negative, emotional images like the ones they used, while the ventro-medial prefrontal cortex (vmPFC) integrates different kinds of information before making a decision, and is involved in self-control. A previous meta-analysis (linked here), has also shown a diffuse pattern of brain areas involved in emotion regulation, which they hypothesized would be involved in regulating emotional responses. Given this information, they measured whether successful persuasion of a participant (i.e. participant saying the ad made them want to quit smoking, or click-throughs) could be predicted by brain activity within these regions and whether responses were modulated by activity in the previously defined emotion-regulation pattern. They added regions to the analysis one by one to evaluate their contributions (using techniques including Bayesian statistics and multilevel modeling).

What did they find?

The study yielded three main findings. First, they found that anti-smoking images that produced the most activity in the amygdala were also more likely to make participants say that they want to quit smoking. Not only that, but the images with the most amygdala activity also had higher click-through rates in the email ad campaign. This highlighted the effect of raw emotion on decision-making. Second, the relationship between amygdala activity and decision making was mediated by activity in the vmPFC. In other words, the amygdala seemed to be communicating with the vmPFC, which would then determine the response. This highlighted the vmPFC’s role as an integrator. Lastly, expression of the brain pattern involved in emotional regulation (previously defined in a meta-analysis) dampened the emotion-driven response to an ad, suggesting that this network plays a key role in regulating emotional responses to persuasive stimuli.

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What's the impact?

Doré and colleagues offered evidence for a clear, mechanistic description the persuasion process, “connecting the dots” that other researchers have drawn to obtain a better picture. We still have a lot more to learn about what factors go into emotion regulation, but this is a good start. This study also demonstrates that brain activity, observed in a scanner, can predict real-world behaviors, like clicking on an ad.

Doré et al. Neural mechanisms of emotion regulation moderate the predictive value of affective and value-related brain responses to persuasive messages. Journal of Neuroscience (2019). Access the original scientific publication here.

Using Brain Connectivity to Predict Treatment Response to Deep Brain Stimulation in OCD

Post by Thomas Brown

What's the science?

Deep brain stimulation (DBS) is a surgical procedure in which an electrode is placed into the brain of a patient, administering electrical pulses that regulate abnormal neuronal activity. DBS is most famous for its use in ameliorating tremors and dyskinesia in Parkinson’s Disease. Recent research has shown that DBS can also be effective in improving symptoms of psychiatric disorders including depression. Recent studies have also investigated the effectiveness of DBS in treating symptoms of obsessive compulsive disorder (OCD) in patients who don’t respond to treatment, however these studies have provided variable results. It is still unclear which patients respond well to DBS for OCD and what factors can predict good clinical outcomes.  This week in Biological Psychiatry, Baldermann and colleagues assessed whether brain connectivity profiles can predict treatment outcomes after DBS in patients with OCD.  

How did they do it?

The authors recruited 22 patients with treatment-resistant obsessive compulsive disorder, who were undergoing DBS of the internal capsule/nucleus accumbens (an area of the brain which has been previously implicated in psychiatric conditions such as OCD). The authors acquired diffusion magnetic resonance imaging (dMRI) scans to measure the integrity of the brain’s white matter (connections) in a subgroup of 10 patients, in addition to an anatomical MRI scan (which looked at brain anatomy). Diffusion magnetic resonance imaging was used to visualise white matter tract connections within the brain. DTI is useful in understanding how a particular brain region is connected with the rest of the brain (i.e. a connectivity profile). The authors assessed connections between the internal capsule/nucleus accumbens and the rest of the brain (i.e. it’s connectivity profile). Brain imaging data from the Human Connectome Project was also used as a control group. Statistical tests were carried out in order to correlate connectivity patterns with a variety of factors, including clinical outcome one year post-surgery. The authors used connectivity maps specific to both patients and to healthy controls to test the relationships between connectivity and clinical factors in two separate subgroups of patients and then cross-validated using leave-one-out cross validation (in order to assess reproducibility). Finally, they combined the two subgroups for the main analysis.

What did they find?

In patients who had one year of DBS treatment and lower OCD severity, the authors found increased connectivity between the internal capsule/nucleus accumbens and the medial and lateral prefrontal cortex (bilaterally). They also found that increased connectivity between the stimulation site and the middle frontal gyrus was correlated with clinical improvement of OCD symptoms. This indicates that changes in connectivity can be used to predict whether DBS treatment had a positive clinical outcome.

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What's the impact?

This study demonstrates a correlation between structural brain connectivity and severity of OCD. Secondly, it provides evidence that this connectivity can be used to predict clinical outcomes as well as identifies several brain targets to inform future OCD studies. These findings suggest that in combination with structural brain imaging, DBS could be a useful tool in treating patients with OCD. Finally, it demonstrates that electrical brain stimulation can change brain connectivity, providing further evidence for that DBS may exert its effects by modifying brain connectivity.


Baldermann et al., Connectivity profile predictive of effective deep brain stimulation in obsessive compulsive disorder. Biological Psychiatry (2018). Access the original scientific publication here.