health | Waseem Ashfaq

Comparing Cognitive and Affective Predictors of Craving

Fri, 24 Apr 2020 00:00:00 +0000

Health-risking behaviors (HRBs), e.g., excessive consumption of alcohol, tobacco, drugs and energy-dense food, contribute to long-term health problems, particularly among individuals who experienced early life adversity (EA). Though traditional executive control tasks are commonly assumed to be relevant for predicting real-world HRBs, recent work has called into question the ecological and predictive validity of these tasks. This study explores the predictive validity of cognitive and affective neural measures derived from a more passive cue reactivity task in a community sample of adults with self-control problems and a history of early adversity. We extracted trial-level estimates of whole-brain expression of canonical “inhibitory control” and “craving” patterns while participants viewed images of personally relevant health-risking substances during the cue reactivity task. Statistical modeling showed that greater trial-level expression of the “craving” and “inhibitory control” patterns predicted higher and lower desire ratings, respectively, for cue reactivity stimuli. However, only “craving” pattern expression predicted measures of real-world craving in daily life. Taken together, these results suggest that, among individuals with self-control problems, the real-world predictive validity of passive neural measures of affective processes may be superior to that of neural measures of executive control.

For more details, see the pre-print and poster .

Devaluing energy-dense foods for cancer control

Fri, 24 Apr 2020 00:00:00 +0000

This project is a large-scale randomized-control trial that investigates the efficacy and mechanisms of a healthy eating invention. Specifically, the RCT compares a cognitive reappraisal training, in which participants change the way they think about unhealthy food, to a behavioral response training, in which participants modify their physical motor responses to food stimuli to train neural inhibitory control circuits. Currently, I am working on developing a precision medicine analysis pipeline around a series of additional measures that have been added to this parent R01 as part of the National Center for Biotechnology Information’s ADOPT Project (Accumulating Data to Optimally Predict Obesity Treatment). The ADOPT project aims to solve the precision medicine problem for obesity treatment by identifying core measures assessing a range of behavioral, biological, environmental, and psychosocial factors that contribute to obesity. My role in this project is to develop the analytical infrastructure to create and validate composites of low-cost, easily administered individual difference measures that moderate response to the healthy eating interventions in terms of both magnitude and timing.

Predicting diabetes status from personality

Fri, 24 Apr 2020 00:00:00 +0000

Prior research suggests that diabetes is both a cause and consequence of various health behaviors and thus has complex links to personality. This study investigates the utility of personality in predicting diabetes status (Type 1, Type 2 or none) using a subset of items from the Synthetic Aperture Personality Assessment (SAPA) Project (N ≈ 645,000). We estimate the classification accuracy of various personality measures, including low- and high-dimensional data, using supervised machine learning techniques, e.g. random forests, support vector machines and neural networks. Results are compared across machine learning models and dimensionality of personality predictors. This project involves the automated generation of executable R scripts using non-standard evaluation within a tidyverse framework, allowing for parallel processing on high-performance computing clusters. This analysis informs the extent to which personality measurement could be applied in clinical contexts related to diabetes.

One Size Rarely Fits All

Sun, 08 Mar 2020 00:00:00 +0000

Think about the last time you had a splitting headache and needed some quick relief. Which bottle did you reach for? Tylenol or Advil? Why? Maybe you think of yourself as a “Tylenol person” or an “Advil person” because one of these has worked for you before. Or maybe one always upsets your stomach. The point is that not everyone experiences the same pain relief from one pill. Some people prefer Tylenol and others Advil, but nobody quite knows why.

The same is true of psychological treatments—they don’t work the same way for all people. People differ in how they respond to treatments for depression, anxiety, addiction, and so forth. The lingo in the field is that there are individual differences in treatment effectiveness. Why? Because, to state the obvious, no two people are exactly alike. And that fact poses a problem for the study of these treatments. Researchers test psychological treatments the same way they test drugs like Tylenol and Advil—using randomized controlled trials (RCTs). People are assigned at random to either one group, which receives a treatment, or another group, which does not. RCTs are an excellent way to assess the differences between the groups on average. The group difference indicates how well the treatment works. The problem is that researchers tend to treat people who differ from their group’s average as mere statistical fluctuations—“noise” in the signal.

The drawback of group-to-group comparisons is that they hide person-to-person differences that may be important. For example, even when one group does a lot better than another on average, there may still be many people in the first group who do not improve at all. Or maybe some people in the first group get worse even though their group got better overall. Instead of only thinking at the “group” level, we need to pay more attention to person-to-person differences. This will help us learn why treatments work only for some individuals and not others.

How do we learn for whom psychological treatments will work? There are three main things we need to know:

1) How does the treatment work?

At this point, there is strong evidence that Tylenol is an effective painkiller. But, even after decades of research, we still don’t fully understand how Tylenol relieves pain. Similarly, though we know that many psychological treatments are effective (at least for some people), we do not know how these treatments work. For example, researchers don’t know which parts of the brain are changed by most treatments. If we knew more specifics about how a treatment worked, we could improve it. We could focus our efforts on the parts of the treatment that make people better and remove the parts that don’t seem as important. And here’s the bonus: If we knew how treatments worked, that would help us learn for whom they work. Imagine we knew that a treatment reduced depression by decreasing self-criticism. In other words, a reduction in self-criticism is the one and only active ingredient in this treatment. If so, then people who criticize themselves a lot would be good candidates to benefit most from this treatment.

2) What are the unique causes of the problem for a given person?

To predict whether someone would benefit from a given treatment, we need to know more about the special factors underlying that person’s problem. In terms of pain, Tylenol is usually the better option for headache pain, and Advil is a good bet for inflammation. Similarly, a psychological treatment will work for a given person only if it addresses whatever caused the problem in the first place. Consider two people who both experience severe anxiety, but for different reasons. If genetic factors cause one person’s anxiety while past trauma is the main source of the other person’s anxiety, then these two individuals will likely need different types of treatment.

3) How does the treatment mesh with a person’s unique characteristics?

Like all drugs, Tylenol and Advil have side effects. And it turns out that they have different side effects depending on whether somebody is at risk for heart disease. So, people who have experienced a heart attack, for example, must carefully choose which pill to take when they have a headache. Similarly, whether or not a psychological treatment will work for somebody might depend on that person’s age, gender, or life history, to name just a few examples. The success of many treatments also depends on how severe a person’s problem is to begin with. Consider again the example of a treatment for depression that works on average for a group of people. Perhaps this treatment works best for people who are severely depressed because it focuses on a symptom that is more common in those people. A different treatment, which also works on average, might consistently decrease depression in mildly depressed people but not work at all for severely depressed people.

The bottom line

With answers to these three questions, researchers can begin to tailor psychological treatments to the unique needs of different people. We know that a given treatment will not work, at least not equally well, for every person. But being able to predict at the outset who will benefit and who won’t could save a lot of time and money. Rather than treating individual differences as “noise,” we can use them to glean potentially important information. If we can better understand why some people benefit from treatment while others don’t, we can make treatments more effective for more people. Eventually, this will allow more people to live healthier lives.

Note. I originally wrote this piece for Psychology Today. See the original post here.