Body Mass Index Moderates Brain Dynamics and Executive Function: A Structural Equation Modeling Approach

Original Research Manuscript

Body Mass Index Moderates Brain Dynamics and Executive Function: A Structural Equation Modeling Approach

Analysis Methods Connectivity

Abstract

Obesity is associated with negative physical and mental health outcomes. Being overweight/obese is also associated with executive functioning impairments and structural changes in the brain. However, the impact of body mass index (BMI) on the relationship between brain dynamics and executive function (EF) is unknown. The goal of the study was to assess the modulatory effects of BMI on brain dynamics and EF. A large sample of publicly available neuroimaging and neuropsychological assessment data collected from 253 adults (18–45 years; mean BMI 26.95 kg/m2 ± 5.90 SD) from the Nathan Kline Institute (NKI) were included (http://fcon_1000.projects.nitrc.org/indi/enhanced/). Participants underwent resting-state functional MRI and completed the Delis-Kaplan Executive Function System (D-KEFS) test battery (1). Time series were extracted from 400 brain nodes and used in a co-activation pattern (CAP) analysis. Dynamic CAP metrics including dwell time (DT), frequency of occurrence, and transitions were computed. Multiple measurement models were compared based on model fit with indicators from the D-KEFS assigned a priori (shifting, inhibition, and fluency). Multiple structural equation models were computed with interactions between BMI and the dynamic CAP metrics predicting the three latent factors of shifting, inhibition, and fluency while controlling for age, sex, and head motion. Models were assessed for the main effects of BMI and CAP metrics predicting the latent factors. A three-factor model (shifting, inhibition, and fluency) resulted in the best model fit. Significant interactions were present between BMI and CAP 2 (lateral frontoparietal (L-FPN), medial frontoparietal (M-FPN), and limbic nodes) and CAP 5 (dorsal frontoparietal (D-FPN), midcingulo-insular (M-CIN), somatosensory motor, and visual network nodes) DTs associated with shifting. A higher BMI was associated with a positive relationship between CAP DTs and shifting. Conversely, in average and low BMI participants, a negative relationship was seen between CAP DTs and shifting. Our findings indicate that BMI moderates the relationship between brain dynamics of networks important for cognitive control and shifting, an index of cognitive flexibility. Furthermore, higher BMI is linked with altered brain dynamic patterns associated with shifting.

Received

Correspondence lkupis@g.ucla.edu

DOI: 10.52294/8944e106-c54b-40d7-a620-925f7b074f99

Keywords: executive function, cognitive control, and decision-making, connectivity

INTRODUCTION

Overweight and obesity are prevalent in one-third of the global population (2) and 42.4% of adults in the United States (3). Obesity accounts for over 2.8 million deaths per year (4), and a body mass index (BMI) ≥30 is additionally a risk factor for greater complications as a result of the novel coronavirus (COVID-19) (5). Overweight (BMI 25 to <30) and obesity are typically considered physical health conditions associated with comorbid conditions such as type II diabetes and cardiovascular disease (6). In addition to these health concerns, obesity is increasingly linked with cognitive impairments and brain alterations (79). Cognitive impairments are found to worsen with increasing BMI (10,11) throughout the lifespan (11). Additionally, obesity during midlife is associated with greater risks of dementia (12) and brain atrophy in later life (13).

Accumulating evidence supports cognitive impairment in the form of executive function (EF) deficits in overweight/obese individuals (14,15). EFs are higher-order cognitive processes that enable goal-oriented behaviors (16,17) and are important for various aspects of daily functioning including maintaining a job (18), social functioning (19,20), and well-being (21). EFs can be divided into distinct but related components (22) including inhibition, cognitive flexibility, and updating (23,24). A recent meta-analysis revealed that individuals with obesity primarily show impairments on EF tasks that require inhibition, cognitive flexibility, working memory, decision-making, verbal fluency, and planning (15). Additionally, impairments in EF and overweight/obesity are associated with negative impacts on mental health such as anxiety and depression (25-28).

A common neuropsychological test used to assess EF is the Delis-Kaplan Executive Function System (D-KEFS) (1). The D-KEFS consists of nine tests of varying EF components; however, composite scores within the tests have been tested as construct-specific factors rather than stand-alone tests (29,30). The use of latent variables as dependent variables reduces the task impurity problem by tapping into the underlying construct rather than relying on one impure measure of a task. The latent variable is characterized by statistical extraction of the variance shared by multiple tasks that are thought to require the same executive control ability, resulting in a purer measure of the ability (31,32). The D-KEFS does not include direct tests within the latent factor of updating (i.e., continuously monitoring working memory and updating content), which is thought to be one of three EF constructs in well-known latent models of executive functioning (23). The three constructs instead include shifting, inhibition, and fluency (33). The three latent factors of D-KEFS are defined as follows: (1) shifting or the mental ability to switch or shift in response to changing stimuli (an index of cognitive flexibility) (34); (2) inhibition or the ability to control one’s behavior and thoughts to inhibit responses (16); and (3) fluency, thought to underlie executive control and updating (35), fluency in generating new designs (i.e., creativity) (36), and an index of verbal abilities.

Recent studies examining brain functional connectivity in overweight/obesity have identified alterations in brain networks rather than specific brain regions that may impact EF. Studies have reported network alterations among the midcingulo-insular/salience network (M-CIN), medial frontoparietal/default network (M-FPN), and lateral frontoparietal/central executive network (L-FPN) in overweight/obese individuals (37-45). The M-CIN plays a role in detecting salient information and coordinating transitions between the L-FPN and M-FPN; the L-FPN is involved in executive or control processes; the M-FPN is involved in self-referential thoughts and monitoring of the environment (46). The dynamic relationships among these three core neurocognitive networks are additionally thought to enable flexible cognition (46,47), important for EFs. Alterations among the M-CIN, L-FPN, and M-FPN in overweight/obesity provide further support for altered reward processing and EF, and cognitive and emotional processing of salient food cues (48). Alterations among these networks have also been previously associated with various neuropsychiatric disorders (49), suggesting these networks are important treatment targets for populations such as obese individuals.

Evidence of brain alterations among the three large-scale neurocognitive networks provides important insights into potential neural mechanisms underlying behavior; however, whole-brain functional connectivity studies have revealed alterations among other regions in overweight/obese individuals. Functional connectivity alterations have been observed between the aforementioned three large-scale networks and visual (39,45,50), limbic (44), sensorimotor (39,51), and dorsal frontoparietal networks (D-FPN; dorsal attention) (39). These findings suggest that it is important to examine whole-brain network relationships in overweight/obesity. Further, brain regions important for monitoring external and internal processes are altered in overweight/obesity (39-45) and suggest that BMI may alter the way network flexibility is associated with flexible behavior such that reduced network flexibility may be linked with poorer EF and adaptive behavior.

There are very few studies to date that have examined the relationship among EF, BMI, and the brain (52-54), and no study to date has examined the relationship among BMI, brain network dynamics, and EF. Brain network dynamics have previously been shown to predict EF performance irrespective of BMI (55). Recent work has also shown that brain network dynamics of the L-FPN, thought to underlie EFs, were correlated with BMI (56). Additionally, increased BMI (overweight/obesity) is associated with reduced cerebral blood flow (57). Neural activity in the brain is dependent on cerebral blood flow (58-60), and cerebral blood flow is correlated with functional connectivity strength (61). Further, brain dynamics represent time-varying brain states (62) that may also be modulated by cerebral blood flow (63). Combined with the previously noted influence of BMI on cerebral blood flow, it is plausible to infer that the relationship between brain dynamics and EF may be moderated by an individual’s BMI; however, this has not been previously tested.

Although there is evidence that dynamic brain function is associated with EF performance (55,64,65), brain dynamic patterns are not consistently associated with each EF (e.g., shifting but not inhibition or fluency/updating) (55,64), leading to the question of whether another variable (e.g., moderator) could be accounting for the differences. Further, altered functional connectivity among regions important for EF is accompanied by impaired EF in individuals with a higher BMI, but not in individuals within a healthy BMI (37). This suggests that the relationship between brain function and EF may vary depending on an individual’s BMI (e.g., optimal brain function is related to optimal EF in healthy-weight individuals, but poorer brain function is related to poorer EF in overweight/obese individuals). Together, this implies that BMI may be tested as a moderator of the relationship between brain dynamics and EF as previously done in other fields (66,67) to better understand how the relationship between two variables is affected by varying levels of BMI (68).

In this study, BMI was tested as a moderator primarily due to the following reasons: (1) previous evidence of brain dynamics supporting EF (55,64); (2) the unclear directionality among BMI, EF, and brain dynamics (69,70); (3) previous work examining brain structure and functional connectivity rather than brain dynamics; (4) access to cross-sectional data; (5) previous work using BMI as a moderator; and (6) the use of a population (young to middle-aged adults) where brain function is optimal (71-74) and less is known in this population regarding EF and brain function related to BMI (75,76). By adopting a moderator framework, the relationship between brain function and EF can be examined at different levels of BMI. Such insight may benefit researchers and clinicians when assessing young- to middle-aged adults at varying BMI levels and overweight/obese adults who may be at greater risk of altered time-varying brain function paired with poorer cognition.

Functional connectivity and structural neuroimaging methods have provided insight into brain organization differences in overweight/obese individuals; however, recent developments in neuroimaging posit dynamic methods, such as sliding window correlations (77,78) and co-activation patterns (CAPs) (77,79), may be applied to capture time-varying changes in the brain architecture (see (62)). Further, dynamic or time-varying methods may, in some cases, better capture relationships between brain function and cognition and behavior than static functional connectivity methods (80,81). Dynamic methods have also been shown to reveal relationships with BMI and behavior where static methods were unable to (56). CAPs, in particular, identify critical co-activating patterns that recur across time by averaging time points with similar spatial distributions of brain activity at either the whole-brain or region-of-interest level (82). Further, CAPs require the specification of fewer assumptions than sliding window methods as they do not rely on arbitrary definitions of window size. CAPs have also been utilized to study neuropsychiatric disorders such as autism (64,83,84) and dynamic network changes across the lifespan (Kupis et al. 2021). Despite the advantages to using dynamic MRI methods over static MRI methods, no study to date has examined dynamic brain network alterations during rest across BMI or its association with EF. Further, exploring relationships among brain networks using brain dynamics has shown to be beneficial for the study of EF due to the various networks underlying EF (55).

This study aims to explore BMI as a moderator of the relationship between whole-brain CAP dynamics and EF, indexed by latent factors of shifting, fluency, and inhibition, using structural equation modeling (SEM). Examination of the dynamic interactions among the M-CIN, L-FPN, and M-FPN has provided important information about the network interactions subserving cognition; however, large-scale network interactions with other brain regions, such as the visual network, also lend insight into flexible cognition (85). Therefore, whole-brain network co-activations were assessed in this study. We hypothesized that a higher BMI would be associated with an altered relationship between brain network dynamics among the M-CIN, M-FPN, and L-FPN and shifting, an index of cognitive flexibility (34).

METHODS

Participants

This study included a sample of 253 adults (18–45 years) from the publicly available Nathan Kline Institute—Rockland Sample (http://fcon_1000.projects.nitrc.org/indi/enhanced/). Inclusionary criteria were as follows: (1) available neuroimaging and behavioral data, (2) no current Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnosis, and (3) mean framewise displacement (FD) < 0.5 mm (Table 1). Institutional Review Board approval was obtained for this project, and written informed consent was obtained for all study participants.

MEASURES

Body Mass Index

BMI was calculated from weight in kilograms divided by height in meters squared (kg/m2) for all participants. Weight and height were measured during the study visit by study staff. Participants ranged in their BMI from underweight (<18.5 BMI), healthy weight (18.5 to <20 BMI), overweight (25 to <30 BMI), and obese (30 or higher BMI). For the purpose of this study, overweight/obesity are discussed interchangeably. See Figure S1 for a graphical distribution of BMI in this sample.

Table 1. Participant Demographics

 

N = 253
mean ± SD (minimum − maximum)

BMI (kg/m2)

26.95 ± 5.90 (16.26 − 49.96)

Age (years)

28.44 ± 7.55 (18.15 − 44.82)

Mean FD (mm)

0.23 ± 0.09 (0.08 − 0.49)

Sex

105 M/ 148 F

DF Switching

8.59 ± 2.94 (1.00 − 16.00)

TMT

9.97 ± 2.83 (1.00 − 15.00)

VF Switching

10.47 ± 3.56 (1.00 − 19.00)

CWIT Inhibition

10.26 ± 2.89 (1.00 − 16.00)

CWIT Inhibition/Switching

9.89 ± 3.04 (1.00 − 14.00)

Tower Total Achievement

9.99 ± 2.36 (2.00 − 19.00)

VF Letter Fluency

10.61 ± 3.44 (1.00 − 19.00)

VF Category Fluency

11.28 ± 3.64 (2.00 − 19.00)

DF Composite Score

10.42 ± 2.69 (4.00 − 18.00)

Note: BMI, body mass index; FD, framewise displacement; DF, Design Fluency; TMT, Trail Making Test; VF, Verbal Fluency; CWIT, Color-Word Interference Test.

Shifting

The D-KEFS was administered to all participants (1). The tasks with shifting (an index of cognitive flexibility) conditions within the D-KEFS include the Trail Making Test (TMT), the Design Fluency (DF) Test, and the Verbal Fluency (VF) Task. The TMT consists of five conditions, including the Number-Letter Switching condition (86). During the Number-Letter Switching condition, subjects switch back and forth between connecting numbers and letters (i.e., 1, A, 2, B, etc.) (87). The DF test consists of three conditions including a Switching condition. In the Switching condition, participants are asked to alternate between connecting empty and filled dots. Lastly, the VF test consists of three conditions, including the Category Switching condition. During the Category Switching condition, participants alternate between saying words from two different semantic categories.

Inhibition

The D-KEFS tasks with inhibition conditions included the Color-Word Interference Test (CWIT) and the Tower Test. The CWIT is a modified Stroop task and consists of four conditions including an inhibition and inhibition/switching condition. In the CWIT Inhibition condition, the participant is presented with color names that are written in incongruent ink color. The participant is required to name the ink color and ignore the written word. Therefore, participants have to inhibit saying the more automatic written word response. In the Inhibition/Switching condition, participants are presented with a page containing the words “red,” “green,” and “blue,” written in red, green, or blue ink. Some of the words are contained in a box, and the subject must switch between saying the color of the ink (word is not inside a box) or the color of the word (word inside a box). The Tower Test examines the participant’s ability to plan and carry out steps to attain the desired goal.

Fluency

The D-KEFS tasks with fluency conditions included the VF test and the DF test. The fluency measures in the VF test include the Letter Fluency and Category Fluency conditions. In both conditions, participants must generate as many words as possible within 60 seconds, beginning with either a specific letter or within a specific category. The DF test included trials where participants had to connect either empty or filled dots.

MRI Protocol

Three-dimensional magnetization-prepared rapid gradient-echo imaging (3D-MP-RAGE) structural scans and multiband (factor of 4) EPI-sequenced resting-state fMRI (rsfMRI) were acquired using a Siemens TrioTM 3.0 T MRI scanner. Scanning parameters were as follows: TR = 1400 ms, 2 × 2 × 2 mm, 64 interleaved slices, TE = 30 ms, flip angle = 65 degrees, field of view (FOV) = 224 mm, 404 volumes. Participants were instructed to keep their eyes open and fixate on a cross in the center of the screen during the 9.4-minute rsfMRI scan. For detailed MRI protocol information, see http://fcon_1000.projects.nitrc.org/indi/pro/nki.html.

Preprocessing and Postprocessing

Preprocessing steps were conducted using the Data Preprocessing Assistant for Resting-State fMRI Advanced edition (DPARSF-A; (88)), which uses FMRIB Software Library (FSL) and Statistical Parametric Mapping (SPM)-12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/), and were as follows: removal of the first five volumes to allow scanner signal to reach equilibrium, despiking, realignment, normalization directly to the 3 mm Montreal Neurological Institute (MNI) template, and smoothing (6 mm Full Width at Half Maximum (FWHM)).

Independent component analysis (ICA) was conducted using FSL’s MELODIC by means of automatic dimensionality estimation. The ICA-FIX classification algorithm was applied to the data (FMIRB’s ICA-FIX; (89)) using a subset of the participants to train FIX. ICA-FIX then classified ICA into noise and non-noise components for the rsfMRI data for individual subjects. The fMRI data also underwent nuisance covariance regression (linear detrend, Friston 24 motion parameters, global mean signal), despiking using AFNI’s 3dDespike algorithm, and bandpass filtering (0.01–0.10 Hz). Information about the data processed without global mean signal regression is included in Supplementary Materials.

Parcellation

A 400 node parcellation was used containing nodes within 17 networks ((90); https://github.com/Thomas­YeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer­2018_LocalGlobal). The parcellation incorporates local gradient and global similarity approaches from task-based and resting-state functional connectivity.

Co-activation Pattern Analysis

The time series were extracted from the 400 nodes for each subject and were converted to z-statistics and concatenated into one (nodes × timepoints) matrix (where the number of timepoints is 399 TR × 253 subjects). The matrix was then subjected to k-means clustering to determine the optimal number of clusters. The elbow criterion was applied to the cluster validity index (the ratio between within-cluster to between-cluster distance) for values of k = 2–20, and an optimal value of k = 5 was determined (Figure S2).

K-means clustering (squared Euclidean distance) was then applied to the matrix using the optimal k = 5 to produce five CAPs ("brain states"). CAP metrics were calculated and included: (a) dwell time (DT), calculated as the average number of continuous TRs that a participant stayed in a given brain state, (b) frequency of occurrence of brain states, calculated as an overall percentage that the brain state occurred throughout the duration of rsfMRI scan compared to other brain states, and (c) the number of transitions, calculated as the number of switches between brain states.

Statistical Analysis

The normative data were age-corrected for all D-KEFS variables. All data were screened for outliers, missingness in data, and tests of assumptions (see Supplementary Materials for more information about the assumptions). Additionally, each CAP was assessed prior to statistical modeling to determine if the brain regions co-activated in each CAP had theoretical support behind including the CAP in the models. Using a two-step procedure, a measurement model was evaluated first to ensure an acceptable fit for the data, and then a structural moderated model was examined. Confirmatory factor analysis (measurement model) and SEM were conducted in MPlus (91,92) using maximum likelihood to estimate model parameters and full information maximum likelihood approach to allow data to be included regardless of the pattern of missingness in the data. Code for all MPlus analyses is publicly available (https://github.com/lkupis/NKI_BMI). Covariates included mean FD, age, and sex. All models were assessed for the goodness of fit by examining the following: χ2, comparative fit index (CFI), standardized root-mean-square residual (SRMR), and root-mean-square error of approximation (RMSEA). χ2 > .05, CFI ≥ .95, SRMR values ≤ .08, and RMSEA values ≤ .06 indicated good model fit.

Confirmatory Factor Analysis

A three-factor model was tested based on prior findings of a three-factor model using the D-KEFS (33). The three factors were shifting, inhibition, and fluency. Additionally, all indicators used were scaled or age-adjusted scores (M = 10, SD = 3).

The indicators for shifting included the TMT Number-Letter Switching condition, the DF Switching condition, and the VF Switching condition scores. The shifting indicator in the TMT condition was the Number-Letter Switching-total score or time to completion. The shifting indicator in the DF Switching condition was the Switching Total Correct score or the number of unique designs drawn. The shifting indicator in the VF test was the total correct number of category switches made.

The indicators for inhibition included the CWIT Inhibition and Inhibition/Switching conditions and Tower total achievement score. The inhibition indicator for the CWIT Inhibition condition was the total number of correct responses. The inhibition indicator in the Tower Test was the Total Achievement score or the sum of points given in each trial. The CWIT shifting indicator included the total score for the number of correct switches made. Although the Inhibition/Switching condition could also potentially be used as an indicator for the shifting factor, previous work has found it to be involved in inhibition using the SEM framework (33).

The fluency indicators included the VF letter and category fluency scores, and the DF total composite score. The fluency indicators in the VF test included the Letter Fluency Total Correct score and the Category Fluency Total correct scores. The fluency indicator from the DF test was the total unique designs drawn across the two DF trials.

The three-factor model including shifting, inhibition, and fluency was evaluated first for statistical fit, and one- and two-factor models were evaluated thereafter because of previous theoretical evidence supporting both the unity and diversity of EFs (23). The one-factor model included all indicators under one factor or a “common EF.” Three two-factor models were tested with three combinations of the latent factors (i.e., shifting with inhibition; shifting with fluency; inhibition with fluency). The proposed model is presented in Figure 1.

Structural Model

The best-fitting model from the confirmatory factor analysis was tested within the framework of SEM. The latent variable(s) in the model were the dependent variables in the SEMs. The use of SEMs has been growing within the field of cognitive neuroscience (93) and brain dynamic analyses (94). First, BMI was tested as a moderator between each brain dynamic metric (DT, frequency of occurrence, and transitions) for each of the five CAPs and the latent variable (shifting, inhibition, or fluency) in an exploratory analysis. A moderator is a variable thought to affect the relationship between two other variables (68). A moderator was tested because there is previous evidence that brain dynamics support EF (55,64); however, the results were not consistent across all EFs suggesting the relationship between brain dynamics and EF may be dependent on a third variable for specific EFs. BMI was tested as the moderator due to previous work suggesting a link between brain dynamics and EF, and previous evidence that functional connectivity may give rise to poorer EF at certain levels of BMI, primarily in overweight/obese individuals (37,95). Additionally, the use of a moderator is beneficial when the relationships among variables are equivocal (70), as in BMI, brain dynamics, and EF (11,37). BMI and the brain dynamic metrics were mean centered to reduce multicollinearity (96).

Fig. 1. Confirmatory factor analysis. The proposed three-factor measurement model. VF, Verbal Fluency; TMT; Trail Making Test; DF, Design Fluency; CWIT, Color-Word Interference Test.