Too stressed to self-regulate? Associations between stress, self-reported executive function, disinhibited eating, and BMI in women

OʼNeill, Jessica; Kamper‐DeMarco, Kimberly E.; Chen, Xuewei; Orom, Heather

doi:10.1016/j.eatbeh.2020.101417

Cited by 27 publications

(21 citation statements)

References 58 publications

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“…The general lack of predictability observed for fluid abilities in both types of models may be underscored by individual differences in the signal-to-noise ratio of the specific brain-behaviour relationships. Fluid abilities are more susceptible to factors including sleep, stress, and mood which directly influence executive functions and memory and less stable within an individual over time (63)(64)(65). Our inability to accurately predict most fluid abilities with our models provides support for the null hypothesis that fluid abilities/executive function are not strongly related to functional connectivity.…”

Section: Discussionmentioning

confidence: 66%

“…Many studies have linked functional connectivity to cognitive functioning (Casey, Galvan, & Hare, 2005;Casey, Giedd, & Thomas, 2000;Cole, Yarkoni, Repovs, Anticevic, & Braver, 2012;Moeller, Willmes, & Klein, 2015;Park & Friston, 2013;Seeley et al, 2007;Spreng, Stevens, Chamberlain, Gilmore, & Schacter, 2010;M. P. van den Heuvel, Stam, Kahn, & Hulshoff Pol, 2009) and many have predicted individual cognitive abilities from functional connectivity (Chen et al, 2020;Dhamala, Jamison, Jaywant, Dennis, & Kuceyeski, 2020;He et al, 2020;Li et al, 2019;Zimmermann, Griffiths, & McIntosh, 2018). Recent work in this area has shown global signal regression, or removal of trends in the fMRI signal, improves prediction accuracy (Li et al, 2019), machine and deep learning models perform comparably (He et al, 2020), and shared network features predict scores from distinct cognitive domains (Chen et al, 2020;Dhamala et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…P. van den Heuvel, Stam, Kahn, & Hulshoff Pol, 2009) and many have predicted individual cognitive abilities from functional connectivity (Chen et al, 2020;Dhamala, Jamison, Jaywant, Dennis, & Kuceyeski, 2020;He et al, 2020;Li et al, 2019;Zimmermann, Griffiths, & McIntosh, 2018). Recent work in this area has shown global signal regression, or removal of trends in the fMRI signal, improves prediction accuracy (Li et al, 2019), machine and deep learning models perform comparably (He et al, 2020), and shared network features predict scores from distinct cognitive domains (Chen et al, 2020;Dhamala et al, 2020). These studies aim to capture brain-behaviour relationships that exist between functional connectivity and cognitive abilities, but it remains unclear whether these relationships are consistent across the sexes.…”

Section: Introductionmentioning

confidence: 99%

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Shared functional connections within and between cortical networks predict cognitive abilities in adult males and females

Dhamala

Jamison

Jaywant

et al. 2021

Preprint

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A thorough understanding of sex-independent and sex-specific neurobiological features that underlie cognitive abilities in healthy individuals is essential for the study of neurological illnesses in which males and females differentially experience and exhibit cognitive impairment. Here, we evaluate sex-independent and sex-specific relationships between functional connectivity and individual cognitive abilities in 392 healthy young adults (196 males) from the Human Connectome Project. First, we establish that sex-independent models comparably predict crystallised abilities in males and females, but more accurately predict fluid abilities in males. Second, we demonstrate sex-specific models comparably predict crystallised abilities within and between sexes, and generally fail to predict fluid abilities in either sex. Third, we reveal that largely overlapping connections between visual, dorsal attention, ventral attention, and default mode networks are associated with better performance on crystallised and fluid cognitive tests in males and females, while connections within visual, somatomotor, and default mode networks are associated with poorer performance. Together, our findings suggest that shared neurobiological features of the functional connectome underlie crystallised and fluid abilities across the sexes.

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Section: Discussionmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Shared functional connections within and between cortical networks predict cognitive abilities in adult males and females

Dhamala

Jamison

Jaywant

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Hence, it is possible that the joint impact of environment on connectivity networks and crystallised abilities means it is easier to predict one from the other. Finally, crystallised abilities are more stable across the lifespan and generally less susceptible to a multitude of factors such as mood, stress, and sleep, all of which influence executive functions and memory (Nilsson et al, 2005; O'Neill, Kamper‐DeMarco, Chen, & Orom, 2020; Salthouse, 2010). This may also contribute to the higher predictability of crystallised abilities.…”

Section: Discussionmentioning

confidence: 99%

Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults

Dhamala

Jamison

Jaywant

et al. 2021

Human Brain Mapping

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White matter pathways between neurons facilitate neuronal coactivation patterns in the brain. Insight into how these structural and functional connections underlie complex cognitive functions provides an important foundation with which to delineate disease‐related changes in cognitive functioning. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how functional and structural brain connectivity relate to cognition. Specifically, we evaluate the extent to which functional and structural connectivity predict individual crystallised and fluid cognitive abilities in 415 unrelated healthy young adults (202 females) from the Human Connectome Project. We report three main findings. First, we demonstrate functional connectivity is more predictive of cognitive scores than structural connectivity, and, furthermore, integrating the two modalities does not increase explained variance. Second, we show the quality of cognitive prediction from connectome measures is influenced by the choice of grey matter parcellation, and, possibly, how that parcellation is derived. Third, we find that distinct functional and structural connections predict crystallised and fluid abilities. Taken together, our results suggest that functional and structural connectivity have unique relationships with crystallised and fluid cognition and, furthermore, studying both modalities provides a more comprehensive insight into the neural correlates of cognition.

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“…Linear regression models were used to determine whether stress, as a continuous variable, was associated with participant characteristics, additional psychological effects from COVID‐19 (e.g., anxiety, worry, concern), or weight‐management behaviors (Model 1). In Model 2, analyses were adjusted for both education level and BMI 13 . Statistical significance was set at p < 0.05.…”

Section: Methodsmentioning

confidence: 99%

Relationship between stress and weight management behaviors during the COVID‐19 pandemic among those enrolled in an internet program

Pellegrini

Webster

Hahn

et al. 2020

Obesity Science & Practice

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Background The COVID‐19 pandemic resulted in mandated stay‐at‐home orders, potentially resulting in changes in mental health (e.g., stress, anxiety) and challenges maintaining healthy dietary and physical activity behaviors. Objective This study examined how stress was associated with mental well‐being and weight loss behaviors during the COVID‐19 pandemic among adults enrolled in an internet‐based weight loss program. Methods Participants enrolled in a weight‐loss program residing in Rhode Island or Massachusetts, USA, completed a brief survey on their mental health and current weight‐loss behaviors during the COVID‐19 pandemic. Surveys were completed between 14 April 2020 and 21 April 2020, approximately one month after stay‐at‐home orders were mandated. Linear regression was used to examine associations between stress, mental health, and weight‐loss behaviors. Results A total of 99 participants completed the survey (79% female, 91% white, 52.2 ± 9.8 years, 34.0 ± 5.2 kg/m2, 77% reported moderate to extreme stress). Greater stress was associated with higher BMI (p = 0.04), higher education (p = 0.04), working more hours (p = 0.003), and having school‐age children at home (p = 0.002). Greater stress was also associated with higher levels of anxiety, worry, and concern regarding COVID‐19 (p's < 0.001) and having less time to spend on weight‐loss efforts (p < 0.001), after controlling for BMI and education. Conclusions Many individuals enrolled in a weight‐loss program experienced more stress during COVID‐19 compared to before the pandemic. This stress was related to more mental health challenges as well as more difficulties finding time for weight management efforts.

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Too stressed to self-regulate? Associations between stress, self-reported executive function, disinhibited eating, and BMI in women

Cited by 27 publications

References 58 publications

Shared functional connections within and between cortical networks predict cognitive abilities in adult males and females

Shared functional connections within and between cortical networks predict cognitive abilities in adult males and females

Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults

Relationship between stress and weight management behaviors during the COVID‐19 pandemic among those enrolled in an internet program

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