Physical Health Status Strongly Influences Working Memory

Batya Swift Yasgur, MA, LSW

December 21, 2017

Higher physical endurance, fluid intelligence, and better cognitive function are associated with higher levels of cohesiveness in brain networks involved in working memory (WM), new research shows.

On the other hand, suboptimal cardiovascular and metabolic control, as well as poor lifestyle behaviors, such as nicotine and cannabis use, have a detrimental effect on these networks.

"We have identified modifiable risk factors in what we consider to be healthy living, and in that sense, what we are saying is that the integrity of brain networks is intimately linked to physical health," lead investigator Sophia Frangou, MD, PhD, professor of psychiatry , Icahn School of Medicine at Mount Sinai, New York City, told Medscape Medical News.

"Simply put, we might say that what's good for the heart is good for the brain, since the variables we identified have cardiovascular consequences," she added.

The study was published online December 5 in Molecular Psychiatry.

Realistic Analysis

WM operations "engage multiple brain regions" and "critically depend on the coordinated activity" of a dorsal cortical network, the authors write.

Studying the WM is "central to cognitive neuroscience because WM supports higher-order cognitive abilities" as well as "lower-order mental operations that require cognitive control."

Despite its importance, the topic has not been adequately addressed in previous research, which has focused only on a limited number of imaging and behavioral variables, the authors note.

"Previous studies have looked only at each variable, one at a time, such as the effects of BMI [body mass index] on a particular brain measure, ignoring the fact that each individual possesses many characteristics and brain variables," Dr Frangou said.

"Rather than looking at two variables in isolation, we tried to create a more realistic context incorporating both imaging and behavioral measures that are often correlated," she added.

The team also "wanted to get an idea of how all of these factors, considered together, can have input into working memory, which would allow us to create a ranking in terms of how each trait and characteristic, in the presence of all the others, can impact the integrity of the working network in activity and connectivity at rest and during a task."

To glean this information, the investigators analyzed fMRI data regarding brain activity and connectivity in 823 healthy individuals (mean age, 29 years; 462 women) participating in the Human Connectome Project (HCP).

The neuroimaging was conducted while participants were performing the HCP version of the 2-back task, a test of WM.

Using advanced statistical techniques, the investigators then assessed the relationship between the WM brain map and 116 variables that were considered as a single dataset in the global analysis and as five subsets (ie, modules) that corresponded to psychometric measures of sensorimotor processing, affective cognition, nonaffective cognition, mental health and personality, and physical health and lifestyle choices.

The images generated from each individual dataset were subjected to a random-effects group-level 1-sample t-test that identified suprathreshold clusters (P < .05), with family-wise error correction at the voxel level. The clusters were located bilaterally in the dorsolateral prefrontal cortex (dlPFC), the the parietal cortex (PAR), the dorsal anterior cingulate cortex (dACC), the middle temporal gyrus, and the visual cortex (VC).

Further analysis of each dataset correlated the WM-network volumes of interest (VOIs), so that each pair of VOIs was correlated to create a resting-state and task-related functional connectivity matrix for every participant.

Sparse canonical correlation analyses, a statistical technique, was applied to the datasets.

"The correlation analysis looks at two datasets and asks whether highly connected patterns can be identified between variables," said Dr Frangou.

"Sparsity is necessary to eliminate redundant or irrelevant features from large datasets," she added.

Role of Physical Endurance "Unexpected"

The researchers identified bilateral clusters located in the dlPFC, the dACC, the PAR, the VC, and the middle temporal gyrus that corresponded to the nodes of the 2-back WM network. The resulting 24 variables composed the WM activation module.

Analysis of the functional connectivity of the WM network showed 66 task-related and 66 resting-state functional connectivity variables that composed the task-and resting-state functional connectivity module.

Global sparse canonical correlation analysis quantified the relationship between the two sets of measurements (n = 200 neuroimaging variables and 116 behavioral variables) and found that the two datasets were significantly associated (r = 0.50, P = .00002).

The behavioral-health variables that had the highest correlations with the imaging variate included psychometric measures (fluid intelligence, memory, reading/ language, visuospatial orientation, sustained attention, mental flexibility, and emotional recognition), behavioral traits relating to aggression, physical characteristics relating to physical endurance, BMI, hemoglobin A1c levels, and lifestyle choices (alcohol use and smoking).

The researchers found that the WM task activation variate was significantly associated with affective and nonaffective cognition, mental health, personality, physical health, and lifestyle.

Task- and resting-state functional connectivity variates were primarily associated with the physical health and lifestyle module. Positive correlations included better physical endurance and higher hematocrit levels and sleep quality. On the other hand, elevated BMI, high blood pressure, and poor glucose control had a negative effect.

The association between physical health measures was not confined to the WM network; it was also observed in connection to whole-brain functional connectivity.

Several types of additional statistical analyses were conducted, and the results were "virtually unchanged."

"This was a much more complex analysis that is more realistic in understanding how brain and body interact in context of all human characteristics," Dr Frangou commented.

She acknowledged that "what was relatively unexpected as a determinant of good working memory was good physical endurance."

On the other hand, factors that had a deleterious impact "were the usual suspects, such as smoking, cannabis, and higher glycosylated hemoglobin."

Because the participants were generally healthy, "this suggests that one does not have to be at an extreme of ill health for these lifestyle factors to have a negative impact on working memory," she pointed out.

Integrated Approach

Commenting on the study for Medscape Medical News, Tor Wager, PhD, professor of psychology, neuroscience, and cognitive science, University of Colorado, Boulder, who was not involved in the study, said the study utilized a "really interesting technique that was a smart way of establishing a relationship between a set of brain variables and a set of behavioral and personality variables,"

He noted that the correlation of WM with cardiovascular health and lifestyle "fits with other work that has shown that even moderate exercise is a good predictor of cognitive function, especially during aging."

Social, cognitive, and physical engagement are "really important," he emphasized.

Moreover, "finding things that are meaningful has its own value in terms of quality of life and joy in life and is also likely to have health benefits."

He suggested that clinicians "should encourage people to increase physical activity on a case-by-case basis."

Dr Frangou added that the findings are particularly relevant to treating psychiatric patients.

"The factors we identified as negatively affecting working memory, such as drug abuse, smoking, and high BMI, are more prevalent among psychiatric patients."

She encouraged "an integrated physical and mental health approach with this population."

Dr Frangou received support from the National Institutes of Health and the European Unit FP7 program. Other authors received support from several sources, which are listed in the original article.

Mol Psychiatry. Published online December 5, 2017. Full text

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