Cardiovascular Fitness and Sleep, but not Physical Activity, are Associated with Improved Brain Functional Connectivity in Older Adults.

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David Wing, Bart Roelands, Julie Loebach Wetherell, Jeanne F. Nichols, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4361076/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Oct, 2024 Read the published version in Sports Medicine-Open → Version 1 posted 5 You are reading this latest preprint version Abstract Background Aging results in changes in resting state functional connectivity within key networks associated with cognition. Cardiovascular function, physical activity, sleep, and body composition may influence these age-related changes in the brain. Better understanding these associations may help clarify mechanisms related to brain aging and guide interventional strategies to reduce these changes. In a large (n = 398) sample of healthy community dwelling older adults we conducted cross sectional analyses of the relationship between several modifiable behaviors and resting state functional connectivity within the brain in key regions associated with cognition and emotional regulation. Additionally, maximal aerobic capacity with a graded exercise test, physical activity and sleep with accelerometers, and body composition with dual energy x-ray absorptiometry were assessed. Associations were explored both through correlation and best vs. worst group comparisons. Results Greater cardiovascular fitness, but not larger volume of daily physical activity, was associated with greater connectivity within the Default Monde and Salience Networks, both of which are key networks associated with aging. Better sleep, in terms of increased total sleep time, higher sleep efficiency and fewer nighttime awakenings was also associated with greater connectivity within multiple networks including the Default Mode, Executive Control, and Salience Networks. Higher body fat percentage was associated with increased connectivity in the Dorsal Attentional Network. Conclusion These findings confirm and expand on previous work indicating that, in older adults, higher levels of cardiovascular fitness and better sleep in terms of greater efficiency and less total awakenings, but not greater volume of physical activity or lower volume of body fat are associated with increased functional connectivity within key brain regions. Also, assessing sleep quality and quantity may be a useful tool for identifying potentially clinically meaningful declines in brain function. Functional Connectivity Brain Health Maximal Cardiovascular Fitness Successful Aging Physical Activity Body Composition Sleep Quality Sleep Quantity Figures Figure 1 KEY POINTS Cardiovascular fitness is associated with younger/healthier brains in terms of functional connectivity of key regions associated with cognitive capacity and emotional regulation. Greater functional connectivity is observed across multiple regions with increased total sleep quantity and quality. Volume of physical activity is not associated with greater connectivity in any of the regions associated with cognition, although there is an association with increased connectivity in the Motor Control Network. BACKGROUND Preservation of cognitive performance and emotional wellness are critical for successful aging ​(1,2)​. While lifestyle behaviors are considered important to slowing age-related changes and preserving brain function, relatively little is known about the physiological mechanisms underlying brain aging. Given the worldwide increases in longevity ​(3)​ there is a need to consider methods to slow declines associated with aging and better preserve function across the lifespan. Cardiovascular fitness is based on the amount of oxygen that the body can utilize during maximal effort (V02 max ). Although somewhat modifiable through training, there is a substantial genetic component to fitness when considered in absolute terms. In contrast, physical activity is the amount of activity an individual accumulates throughout the day, often characterized in various intensity levels with moderate to vigorous intensity widely recognized as being the most beneficial to improvements in overall health ​(4)​. There is a substantial body of research indicating that both overall cardiovascular fitness, and regular physical activity have protective and restorative effects on age-related cognitive decline and the development of neurodegenerative diseases. ​(5–11)​. However, other literature indicates that both fitness and physical activity have minimal effects on the structurally derived biological age of the brain ​(12,13)​ or have found no differences in cognitive outcomes after substantial changes in fitness ​(14)​. Furthermore, studies relating physical activity to structural integrity have shown variable impact with generally small effect size when relationships are detected at all ​(15–17)​. Similarly, there are studies indicating links between fitness and increased functional connectivity (FC) ​(18–20)​ which is a measure of the ability of the brain to communicate both within- and between-networks associated with higher level function including the ability to pay attention, regulate emotion, recall memories, and complete complicated muti-stage tasks. Despite these positive findings, there are other studies which had discordant findings with no changes in the FC in brain networks predicted to be associated with changes in cardiovascular fitness ​(21)​ or even declines in observed FC associated with increased physical activity ​(21,22)​. Being overweight or obese, often indirectly estimated via the body mass index (BMI), is also commonly associated with (poor) brain health independent of its impact of measures of fitness (when fitness is measured in relative terms as milliliters of oxygen per kilogram of body weight per minute or (ml of O2/kg/min)). Specifically, higher BMI has been associated with lower volume of grey matter across several brain regions ​(23)​ and obesity, particularly central obesity, is associated with increased risk of developing Alzheimer’s Disease (AD) ​(24,25)​. Similarly, high BMI has been associated with reduced FC in key resting state brain networks associated with cognitive function ​(26,27)​. Recent systematic reviews of cross-sectional studies have concluded that central obesity measured via waist circumference is correlated with structural declines ​(28)​ and impaired cognition ​(29)​. It is worth noting that many of these studies have relied on relatively crude measures of obesity, such as BMI and waist circumference, to draw these conclusions. However, recently published studies using more sophisticated Dual X-Ray Absorptiometry (DXA) based measures of body fatness have had similar findings, with visceral adiposity being associated with older/less structurally sound brains ​(12,13)​. Finally, both sleep quality and quantity exhibit equivocal associations with elements of brain health including structure, connectivity, and risk of developing neurocognitive disease. Specifically, lower values for both sleep quantity and efficiency (a marker of sleep quality) were associated with reduced connectivity in the Default Mode Network (DMN) in children ​(30)​, while only efficiency had the same association in adolescents ​(31)​. Similarly, associations between (poor) sleep and (reduced) between- and within-network connectivity is observed in working age, but not older adults ​(32)​. Sleep related disease may also be related to neurodegeneration. Indeed, obstructive sleep apnea (OSA) is associated both with increases in blood markers associated with the development of AD ​(33)​ and with reduced FC in cognitively normal adults (34,35) but​ not those with mild cognitive impairment (35). Specific resting state networks are implicated in cognitive aging. Although meaningful differences in FC between younger and older adults have been found across a broad range of cortical networks, the bulk of the research suggests that differences are most pronounced in the DMN ​(19,20,36,37)​, the Executive Control Network (ECN) ​(19)​ and the Salience Network (SAL) ​(19,38)​ with a lesser, but still significant difference detected in the Dorsal Attentional Network (DAN) ​(19)​. In contrast, networks associated with sensory systems, including visual processing (VIS), and motor control (MOT) show minimal associations with older age ​(19,37,39)​, and seem to be more sensitive to movement and sensory engagement. To better understand differences in network connectivity we have examined cross-sectional differences in FC in the DMN, ECN, SAL, DAN, MOT and VIS in response to physiological and behavioral traits. Specifically, these analyses replicate/expand on the findings of ​Voss et al., (19)​ exploring the role of fitness and physical activity and FC using a somewhat larger, and similarly well characterized population of older adults. Further, we extend those findings by also examining associations between body composition and sleep respectively with the FC of these key regions. We hypothesized that superior cardiorespiratory fitness (as measured by estimated maximal aerobic capacity) will be associated with improved relative FC in the DMN, ECN, and SAL, and that higher levels of physical activity will be associated with superior/greater resting MOT activation. Additionally, we hypothesized that higher levels of adiposity, particularly higher levels of visceral adipose tissue (VAT), will be associated with less connectivity in the DMN, ECN and SAL. Finally, we hypothesized that more sleep, both in terms of overall minutes of sleep and sleep efficiency, would be associated with increased connectivity in those regions. METHODS Participants These analyses were conducted on data gathered during the baseline measurement of a longitudinal intervention set in two urban areas and approved by the Institutional Review Board at both institutions. All participants provided informed consent to participate. The larger group of 607 older adults has been extensively described elsewhere ​(14,40)​. Key details meaningful to the current analyses include that participants were between ages 65 and 84 and had self-reported cognitive complaints but were free from assessed cognitive impairment (defined as < 11 on Short Blessed Test ​(41)​) or diagnosed neurodegenerative disease. Additionally, participants were excluded if they were currently using glucocorticoid or diabetes medication, were too physically active (defined as > 60 min/week of moderate to vigorous exercise any week within the last 6 months) or reported alcohol or substance abuse within the previous six months. Physical Measures (GXT, DXA, Accelerometery) Graded Exercise Testing (GXT) The majority of participants completed a GXT using a treadmill (Quinton QStress, Cardiac Science, Chelmsford, Mass) with a substantially smaller number (< 10%) who were unable to walk without holding on to the treadmill handrails using an electronically braked cycle ergometer (LODE Excalibur, Netherlands) to volitional maximal exertion. The testing protocol has been described previously ​(12,40)​. In brief, under the supervision of a physician, participants warmed-up for three to five minutes and then intensity was increased at a level equal to approximately 0.6 METS per two-minute stage with active motivation from study staff (n = 2 minimum offering ongoing and enthusiastic verbal encouragement). Exercise grew progressively harder until the physician ended the test based upon potentially dangerous changes in the ECG reading or an extreme hypertensive response (SBP > 220 or DBP > 110), or the participant indicated that they were unwilling/unable to continue. Individuals were excluded if they did not reach a minimum of 85% of their age predicted heart rate maximum (220-age) or testing was stopped by the physician prior to volitional fatigue. “Fitness” was calculated as maximal capacity calculated in metabolic equivalents of task (METs) using the American College of Sports Medicine’s algorithm designed for walking. Based on the presumed linear relationship in oxygen consumption while coming to a metabolic steady state in response to a new workload, partial stages were scored in 30 second increments using the formula METs last_completed_stage + 0.25* METs difference_between_stages * number of 30 second increments completed in the new stage. Dual X-Ray Absorptiometry (DXA) to estimate body composition A GE Lunar Prodigy at one location and an iDXA (both GE/Lunar, Madison, WI) at the other were used to estimate segmental body composition and provide absolute values (measured in grams or kilograms) for fat and lean tissue and bone mass for the arms and legs and trunk as well as secondary height-dependent regions titled android (centered on the abdomen) and gynoid (centered on the upper thighs). Visceral fat values were also derived from the android region. This method of estimating visceral fat has had good agreement compared to 3-D imaging techniques in both men and women ​(42,43)​. Participants were positioned in line with best practice recommendations ​(44)​ and external artifacts were removed whenever possible. For participants who were larger than the available scan field, a “hemi-scan” was acquired by scanning only the right side of the body and replicating those values for the “missing” limb. Accelerometry: Measurement of physical activity : Participants were asked to maintain their normal behaviors during a 10 day period during which they were equipped with an Actigraph GT9X + Link (ActiGraph Inc, Pensacola, FL) deployed in line with best practice recommendations (45, 46) ​on the participant’s non-dominant wrist continuously except when engaging in water-based activities like swimming or bathing. This tri-axial accelerometer has been shown to be both valid and reliable across the age span ​(45,49,50)​. After 10 days of deployment, devices were recovered and data were downloaded and screened for completeness and potential device malfunction in line with established practice ​(45,47,51)​. Data processing included applying a screening algorithm to detect non-wear ​(52,53)​ and aggregating raw data into “counts per minute” in the x, y and z axes independently using Actilife (Actigraph’s proprietary software). Vector magnitude was calculated using the square root of the sum of the squares of the three axes to incorporate intensity, frequency, and duration of movement. This metric has been recommended for use in assessing physical activity during a 24-hour wear period ​(54)​ particularly when the device has been deployed at the wrist. Accelerometry: Measurement of sleep : The same device was used to calculate sleep time in total minutes, wake after sleep onset (WASO) both in terms of number of awakenings and number of minute awake, and sleep efficiency using an algorithm that has been validated for use in adults ​(55)​. Participants were specifically asked to maintain their normal sleep rhythms during the wear period. The window of observation was derived from sleep journals in which participants indicated the time that they had begun trying to sleep, and the time that they first woke up in the morning (i.e., there was no effort to record incidental, or undesired, nighttime awakening). If a sleep journal was not maintained, previously validated methods were utilized to estimate the time of sleep onset and wake time ​(56)​. Imaging Measures Imaging Acquisition : Three magnetic resonance imaging (MRI) scanners at two sites were used to acquire resting state functional MRI (rs-fMRI) data (UCSD: GE MR750 3T scanner (GE, Milwaukee, WI) with an 8-channel head coil; WUSTL: Siemens 3T Trio and 3T Prisma-FIT (Erlangen, Germany) with a 20-channel head coil. T1-weighted (T1w) and T2-weighted (T2w) structural scans were performed for purposes of image registration and radiological screening of the participants (UCSD: T1 MPRAGE, TE = 3.036 ms, TI = 1,000 ms, 1.0 mm3 voxels; T2 CUBE, TR = 3,300 ms, TE = 73.37 ms, 1.0 mm3 voxels; WUSTL: T1 MPRAGE, TR = 2,400 ms, TE = 3.16 ms, TI = 1,000 ms, 1mm3 voxels; T2 SPACE, TR = 3,200 ms, TE = 458 ms, 1mm3 voxels). Four rs-fMRI scans (140 frames per run) were acquired per subject using a multi-echo sequence (UCSD: TR = 2,740 ms, TE = 14.8, 28.4, 42, 55.6 ms; 4.0 mm 3 voxels; WUSTL: TR = 2,960 ms, TE = 15, 31.3, 47.6, 63.9 ms; 4.0 mm 3 voxels) with a total run time of 6.4 min (UCSD) or 6.9 min (WUSTL) per run or 25.6 and 27.6 minutes respectively. Gradient echo field maps were acquired for later use in correction of susceptibility-related image distortion. During fMRI, participants were shown a neutral video without audio (e.g., nature scenes) synchronized to the start of each run and were asked to stay awake without engaging in any sort of meditation. Image Preprocessing fMRI data processing largely followed previously described methods ​(57)​ which makes use of several FSL modules ​(58)​. Briefly, rigid body motion correction both within- and across-runs was computed on data summed over all echos. applied. Slice timing correction was applied to each echo. Bias field inhomogeneities were corrected using the FAST module in FSL ​(59)​. Atlas transformation was computed by composition of transforms (individual frame → frame average →T2w→T1w→ atlas representative target). T1w→ atlas registration was computed with FSL FNIRT. Three atlas representative targets, all representing the 711-2B version of Talairach space, had been previously prepared for each of the scanners to accommodate scanner-specific differences in T1w contrast. Final resampling of the fMRI data in 3mm3 voxel 711-2B space was accomplished in one step incorporating distortion correction (FSL PERELUDE &FUGUE), previously computed bias field correction, and the composition of all spatial transforms. The multi-echo data were then modeled according to standard theory ​(60)​ fitting the four echoes to a monoexponential model S_t = S_0t⋅exp(-R_2t^⋆⋅TE_k:, where indexes frame, indexes echo, and is reconstructed intensity extrapolated to echo time 0. Frame-to-frame variation in was suppressed by averaging over the whole run and the fMRI data were modeled at a TE of 30ms according to S_t= (S_0 ) ̅⋅exp(-R_2t^⋆⋅30ms). The modeled data in each run then were intensity normalized (one multiplicative scalar applied to all voxels and frames) to achieve an intensity mode value of 1000. Denoising was effected on the fMRI data virtually concatenated across the 4 runs. Frame censoring was computed based on DVARS ​(61), with the criterion adjusted to compensate for baseline variability using a previously described method based on fitting the distribution DVARS values to a gamma function ​(62)​. Subsequent steps ignored all censored frames. The data were denoised using a CompCor-like scheme with regressors derived from motion correction [temporally filtered to suppress respiration-related factitious head motion ​(63), white matter, ventricles, extra-axial cerebral spinal fluid (CSF), and the whole brain global signal (64). Image derived regressors were based on tissue class segmentations computed by FreeSurfer 6.0.0 ​(65)​. Additional denoising included bandpass temporal filtering retaining frequencies in the range 0.01–0.1 Hz and spatial filtering (Gaussian blur of 6 mm in each cardinal direction). Finally, the (scanner-specific) response evoked by the movie was averaged over all participants and subtracted from each individual’s data. ROI/Network Creation : Relevant voxel locations were initially identified using the Big Brain 300 parcelation ​(66)​ excluding subcortical and cerebellar RIO’s. In an effort to confirm/replicate earlier findings we projected the regions described by ​Voss et al. (19)​ onto the Seitzman ROI’s. Thus, three networks were defined: the salience (SAL), motor control (MOT), and visual (VIS) networks. Two additional networks described by Voss et al, the default mode network (DMN) and dorsal attention network (DAN) were substantially different when compared to Seitzman et al. ‘s parcellation. Accordingly, we utilized both with labels DMN and DAN for Voss defined regions and BSDMN and BSDAN for Seitzman. Finally, because Seitzman et al. did not identify an executive control network (ECN) we utilized the Voss visual representations and included voxels identified as frontoparietal and DMN within the Sietzman designations. A visual representation of Seitzman-Voss mapping for SAL is shown in Fig. 1 below, and mapping for all five examined networks are shown in supplementary Figs. 1 through 5. The average correlation value, defined as the average correlation across the ROI x ROI pairs within each network was calculated and assessed as the within network connectivity value for subsequent analyses. Figure 1 TITLE: Seitzman and Voss Coordinates and ROI’s–Salience Network LEGEND: Seitzman ROI’s as designated by different colors on the top left (A) and Voss ROI’s are designated on the bottom right (B). The overlay areas of the two are shown on the top right (A’) (Note: Visual representation of overlay between other networks of interest are available in supplementary materials) Statistics Statistical Analysis SPSS version 28 was used to conduct all statistical analyses. Participants were excluded from any analysis for which they had missing values. Descriptive statistics (proportions, mean ± standard deviation) were used to characterize the population, and t-tests were used to compare differences by study site and sex. Correlations between physiological/behavioral variables and FC within the regions of interest were evaluated while controlling for sex, location, and years of education. Additionally, unstandardized residual values were created using multiple linear regression that controlled for covariates. Specifically, we controlled for age, sex, and location. Additionally, we included physical activity as a covariate for analyses involving fitness, and fitness as a covariate for analyses involving physical activity and body composition. The unstandardized residuals then represent the individual differences in the variable of interest after variance from the covariates has been accounted for. To further evaluate the potential differences in FC at key networks as a function of fitness (or activity, or fatness, or sleep) we divided participants into the top and bottom 25% of the cohort based upon the unstandardized residual values. We then compared these groups with independent samples t-tests following the hypothesis that the “best” 25% would have greater FC than the “worst” 25% using Bonferroni adjustments to correct for multiple comparisons. RESULTS A total of 398 participants (195 San Diego, 203 WUSTL) were included in the overall analyses. Of these 398, forty-five participants who did not continue to maximal effort (determined as not reaching 85% of age predicted heart rate max or having the study physician end the test prior to volitional fatigue) were excluded from the analysis of fitness (n = 353 for fitness measures). Five participants did not have enough night-time accelerometer wear and three insufficient daytime wear for inclusion (4 night or days respectively) leaving 393 participants included in analyses regarding sleep and 395 in daily physical activity. The sample self-identified largely as white (n = 314, 79%) with a smaller percentage identifying as black (n = 37, 9%), white with Hispanic ethnicity (n = 24, 6%) and Asian (13, 3%) with the remaining 10 individuals refusing to answer or indicating that they identified with multiple racial/ethnic categories. The sample was predominantly female (78%), and females in the sample population were younger than males. As expected, based on population level statistics, women had lower maximal cardiovascular fitness, higher overall body fat percentage and lower lean body mass. However, men had greater VAT and less overall physical activity as measured in VM CPM. Finally, although FC across the majority of the networks was not significantly different by gender, women had greater connectivity in the DMN. Variables with statistically significant difference, along with confidence intervals are included in supplementary materials table 1 . There was a significant difference between the locations for participants' maximal cardiovascular fitness and connectivity with St. Louis having a population with higher fitness and less connectivity. Means, standard deviations and p values for difference by location for all demographic, physiological, and connectivity values are shown in Table 1 . Mean differences and confidence intervals of the difference for variables with significant differences for both location and sex are shown in supplementary materials table 1 . Table 1 Sample Characteristics by Intervention Location Total Group UCSD WUSTL p= % Female 78 79 73 0.61 Age (yrs) 71.3 (4.7) 71.6 (4.7) 71.1 (4.8) 0.246 Education (yrs) 16.2 (2.2) 16.2 (2) 16.2 (2.3) 0.791 Maximal Cardiovascular Fitness (METS) 7.1 (1.8) 6.6 (1.5) 7.6 (1.9) < .001 Body Fat (%) 40.2 (7.4) 40.3 (7.5) 40.2 (7.4) 0.902 Lean Tissue (g) 42786.2 (8486) 42556.5 (8870) 43006.8 (8115.9) 0.597 Visceral Adipose Tissue (g) 1257.3 (866.9) 1276.3 (871.6) 1239.2 (864.1) 0.671 Sleep Efficiency (%) 84.5 (6.3) 84.2 (6.2) 84.8 (6.4) 0.317 Total Sleep Time (min) 387.9 (51.8) 385.4 (52.1) 390.3 (51.5) 0.348 Nightly Awake Time (min) 71.3 (30.9) 72.8 (31.4) 69.8 (30.4) 0.341 Nightly Awakenings (n) 18.4 (6) 18.7 (6.2) 18 (5.9) 0.218 Total Movement (VM CPM) 1956.8 (502.9) 2005 (512) 1910.7 (490.9) 0.062 DMN Connectivity 0.225 (0.066) 0.251 (0.063) 0.199 (0.058) < .001 ECN Connectivity 0.084 (0.032) 0.101 (0.031) 0.067 (0.024) < .001 DAN Connectivity 0.106 (0.033) 0.123 (0.03) 0.09 (0.027) < .001 SAL Connectivity 0.339 (0.103) 0.384 (0.092) 0.295 (0.095) < .001 MOT Connectivity 0.272 (0.112) 0.319 (0.109) 0.228 (0.095) < .001 VIS Connectivity 0.204 (0.068) 0.235 (0.07) 0.174 (0.051) < .001 BSDMN Connectivity 0.131 (0.045) 0.151 (0.043) 0.112 (0.039) < .001 BSDAN Connectivity 0.176 (0.062) 0.19 (0.061) 0.162 (0.061) < .001 Yrs = Years; METS = Metabolic Equivalent of Task; VM = Vector Magnitude; CPM = Counts per minute; g = grams; min = minutes; DMN = Default Mode Network; ECN = Executive Control Network; DAN = Dorsal Attentional Network; SAL = Salience Network; MOT = Motor Control Network; VIS = Visual Network; BS = Ben Sietzman defined network. After controlling for age, sex, and location all resting state functional networks had significant positive correlation (p = < 0.001 to 0.005) except for between VIS and BSDAN (p = 0.763). Significant correlations between age and the SAL, VIS and BSDMN networks was observed (p = < 0.001, 0.026 and 0.016 respectively). Additionally, cardiovascular fitness was correlated with DMN (p = 0.008 r = 0.142), SAL (p = 0.005, r = 0.152) and BSDMN (p = 0.008 r = 0.143) but not with ECN, DAN, MOT, VIS, or BSDAN (p range = 0.248 to 0.982). When exploring associations between FC and body composition, the only significant associations were between percent body fat and the VIS (p = 0.03, r = 0.117), DMN (p = 0.05; r = 0.105), and DAN (p = 0.047; r = 0.107) networks. Total physical activity was associated with greater MOT connectivity (p = 0.06; r = 0.124). Finally, greater sleep efficiency was associated with greater connectivity in the SAL (p = 0.007; r = 0.137) and BSDMN (p = 0.016; r = 0.122) networks. Total sleep time was inversely associated with ECN, MOT, and VIS connectivity (p = 0.016, 0.025 and 0.027 and r=-0.121, -0.114 and − 0.112 respectively). Total amount of time awake during (attempted) sleep periods was negatively associated with multiple networks including SAL (p = < 0.001, r= -0.18), MOT (p = 0.007; r= -0.136), BSDMN (p = 0.038; r=-0.105) and BSDAN (p = 0.043; r=-0.102). The number of times awakened during a sleep period (regardless of total length of time awake) was negatively associated with ECN (p = 0.001; r=-0.162) and MOT (p = 0.013; r=-0.126). Results of the t-tests comparing the best versus worst quartiles within key metrics of interest are shown in Table 2 . The most fit participants had significantly more connectivity in the DMN, SAL and BSDMN compared with the least fit (p = 0.029, 0.007 and 0.011 respectively). Additionally, the most fat (by percentage) quartile had higher connectivity in the DAN (p = 0.041) with no differences observed for lean tissue or VAT (p > 0.05). Individuals with the most overall physical activity had higher MOT connectivity (p = 0.015). The quartile with the best sleep efficiency showed greater connectivity in the DMN (p = 0.045), SAL (0.013), BSDMN (p = 0.003) and BSDAN (p = 0.038) while those with the greatest overall time asleep showed lower connectivity only in the DMN (p = 0.049) and BSDMN (p = 0.032). The quartile with the lowest amount of time awake during sleep had greater connectivity in the SAL (p = < 0.001), MOT (p = 0.009), BSDMN (p = 0.013) and BSDAN (p = 0.008) networks while those with the fewest number of times awakening showed increased connectivity in the ECN (p = 0.016), SAL (p = 0.014), MOT (0.03) and BSDAN (p = 0.039). Table 2 T-tests and Confidence Intervals comparing first (top 25%) vs fourth (bottom 25%) quartile. Note: desirability of first vs. fourth quartile varies according to metric Fitness Body Fat VAT Physical Activity Sleep Quality Sleep Time Wake Time Wake number (METS) (%) (kg) (CPM) (%) (min) (min) (n) p values = with significant values denoted in bold DMN 0.029 0.057 0.255 0.306 0.045 0.049 0.075 0.256 ECN 0.919 0.379 0.443 0.275 0.316 0.248 0.058 0.016 DAN 0.225 0.041 0.138 0.079 0.106 0.253 0.266 0.808 SAL 0.007 0.300 0.201 0.472 0.013 0.807 < .001 0.014 BSDMN 0.011 0.887 0.734 0.232 0.003 0.032 0.013 0.228 BSDAN 0.871 0.658 0.652 0.166 0.038 0.28 0.008 0.039 Mot 0.766 0.408 0.316 0.007 0.085 0.223 0.009 0.030 Vis 0.865 0.070 0.281 0.165 0.652 0.522 0.847 0.228 95% Confidence intervals = with significant values denoted in bold DMN -0.0409 to -0.0022 -0.0372 to 0.0006 -0.0302 to 0.0081 0.0096 to -0.0239 -0.0376 to -0.0005 -0.0368 to -0.0001 -0.0016 to 0.0344 -0.0075 to 0.0281 ECN -0.0089 to 0.0099 -0.0145 to 0.0055 -0.0141 to 0.0062 0.0045 to -0.0061 -0.0134 to 0.0043 -0.0037 to 0.0141 -0.0003 to 0.018 0.0021 to 0.0204 DAN -0.0164 to 0.0039 -0.0187 to -0.0004 -0.0166 to 0.0023 0.0051 to -0.0174 -0.0172 to 0.0017 -0.0142 to 0.0038 -0.0042 to 0.015 -0.0108 to 0.0084 SAL -0.0718 to -0.0114 -0.0142 to 0.0457 -0.011 to 0.0519 0.0165 to -0.0337 -0.0669 to -0.0081 -0.0256 to 0.0328 0.0233 to 0.0791 0.0075 to 0.0656 BSDMN -0.0296 to -0.0039 -0.0134 to 0.0116 -0.0106 to 0.0151 0.0066 to -0.0178 -0.0316 to -0.0064 -0.0267 to -0.0012 0.0033 to 0.0277 -0.0047 to 0.0196 BSDAN -0.0205 to 0.0174 -0.0154 to 0.0243 -0.024 to 0.015 0.0098 to -0.0289 -0.0359 to -0.001 -0.028 to 0.0081 0.006 to 0.04 0.001 to 0.0375 Mot -0.039 to 0.0288 -0.0489 to 0.0199 -0.0165 to 0.0506 0.0158 to -0.0702 -0.0583 to 0.0038 -0.0116 to 0.0496 0.0107 to 0.0736 0.0034 to 0.0658 Vis -0.0228 to 0.0191 -0.034 to 0.0013 -0.0321 to 0.0094 0.0104 to -0.0104 -0.015 to 0.024 -0.0119 to 0.0233 -0.022 to 0.0181 -0.0074 to 0.0309 Abbreviations: METS = Metabolic Equivalent of Task; VM = Vector Magnitude; CPM = Counts per minute; kg = kilograms; min = minutes; DMN = Default Mode Network; ECN = Executive Control Network; DAN = Dorsal Attentional Network; SAL = Salience Network; MOT = Motor Control Network; VIS = Visual Network; BS = Ben Sietzman defined network. It is worth noting that the choices made for the spatial location of component ROIs within key networks have some impact on the analyses findings and resulting conclusions. In general, both correlative and comparative significance (or non-significance) was observed in both the Seitzman and Voss defined DMN and DAN concurrently. However, that was not always the case (see Table 2 for differences in significance by definitional region). We focused on the Voss defined regions to better position our conclusions with existing literature regarding fitness, fatness, activity, and sleep. DISCUSSION Although previous studies ​(19)​ have found associations between (younger) age and (increased) FC in the DMN, ECN, and SAL, we did not expect to have similar findings due to the relatively homogeneous age of our older adult population. In line with our expectations, we did not find age related associations in the ECN, however, we did find associations suggesting reduced connectivity among older individuals in the SAL network, with less robust findings in the DMN and VIS. Although the strength of the associations are quite modest, this adds further evidence to suggest that aging is associated with less connectivity in these key regions associated with internal reflection and emotional regulation. Perhaps more interestingly, this study also provides cross-sectional analyses indicating that some, but not all, modifiable behaviors are associated with changes in the functional connectivity within key brain networks associated with cognition. More specifically, better fitness and sleep, but not greater volume of physical activity was associated with increased within-network connectivity. Further, higher body fat (by percentage) was also associated with increased connectivity in the DAN. These results help us to better understand the impact of lifestyle factors on (brain) aging and provide additional insight into possible mechanisms underlying age related differences in the brain. This, in turn, may help to provide targets for interventions and measurement tools to calculate age-related changes in the brain without the need for expensive and difficult to receive imaging. It is worth noting at the outset that there is likely some amount of error associated with the fact that we measured “volitional” vs. “true” maximal capacity. Specifically, because we purposely focused on a (n at least recently) sedentary population, it is possible that individuals quit the GXT assessment prior to their actual maximal capacity due to excessive perceived exertion or localized muscle fatigue. However, we believe that the level of encouragement offered helped to ensure a high level of effort ​(67) ​and excluding individuals who did not reach an adequate minimal threshold of effort (85% of APHRM) gives a reasonably accurate ranking of participant fitness, particularly when considering top vs. bottom quartiles. We found (positive) relationships between aerobic fitness and the FC of the DMN and SAL regions, replicating previous results in this area ​(18,19,68)​. While “brain age" is multifactorial, these results suggest that participants with increased fitness have “younger” brains based on studies showing that younger individuals have greater connectivity in the DMN, ECN and SAL. ​(19)​. Although it is impossible to ascertain causality from these cross-sectional analyses, the meaningfulness of these associations is further strengthened when comparing FC in the most fit vs. least fit quartiles where not only did we find statistical significance but also a mean difference in connectivity greater than 10% of the baseline mean of the total population. Given the known associations between cognition and the ability to regulate emotions in the implicated regions (DMN and SAL respectively) maintaining aerobic capacity may be important to successful aging of the brain in addition to its impact on cardiovascular and metabolic health. In contrast, we did not find that daily volume of physical activity had significant association with cognitively important networks. While utilizing wrist worn devices limited our ability to differentiate activity intensity, Bassett et al ​(54)​ found that this metric was sufficient to identify meaningful differences in health across the NHANES population and recommended it as a metric to accurately measure physical activity using a more desirable wear location that would also allow researchers to assess sleep behaviors. Further, these results confirm the findings of Peven et al., ​(2019)​ who found minimal differences in the association between overall PA and FC in any of the brain regions associated with higher cognition. Further, although different metrics of physical activity time/intensity were utilized, combined with the fitness findings discussed earlier, these findings further validate Voss et al’s​ (2016) claims​ that differences in FC attributable to cardiovascular fitness are independent of regular physical activity. The observed associations between all day physical activity and increased FC in the MOT network seem reasonable on an intuitive level. Interestingly, a scoping review of the literature did not yield the identification of studies having done meaningful analyses in this area, although there is literature linking increased connectivity with reduced function in stroke victims compared to healthy controls ​(69)​ and in sedentary youth compared to endurance runners ​(70)​ This, along with findings suggesting that older age is not associated with differences of any kind in the MOT network ​Voss et al. (19) suggest that increased connectivity at rest may not always be a good thing, depending on the brain region/network being examined. The findings in the present sample indicate minimal impact of body composition on FC. Indeed, although there are some modest correlations between various networks and body fat percentage, neither lean tissue nor VAT showed any associations. Interestingly, and in opposition to our a priori hypothesis, the directionality both in terms of correlation and least vs. most (percent) fat group comparison, is positive indicating that more fat is associated with greater connectivity, particularly in the cognitively important DAN. Although only modestly (negatively) associated with increasing age ​(19)​ the DAN is associated with the ability to eliminate distractions/focus and to establish and pursue goals, and significantly lower DAN FC has been observed both in (BMI-based) obese children ​(71)​ and adults ​(72)​. It may be that the population measured in the current study, consisting of entirely older adults with less than a third having a BMI that would qualify them as obese, exhibit different characteristics than populations reported in previous studies. Further, the data presented here allow for a deeper look at the overall physical phenotype by utilizing DXA derived percentage of body fat instead of BMI, a comparatively crude metric of body composition, indicating that differences in connectivity associated with size alone (i.e., BMI) may be different than those associated with overall fatness (i.e., body fat percentage). This highlights the importance of considering more precise metrics when evaluating physical phenotypes and their associations with biological and physiological processes. In agreement with our a priori hypothesis, sleep quantity was positively associated with FC across multiple networks. Specifically, our findings expand to a new population (healthy older adults) previous findings in working age adults that indicate increased total sleep is associated with increased DMN connectivity both acutely (​Killgore et al., 2012)​ and over an extended period ​(74)​. Additionally, greater sleep efficiency is associated with increased FC in both the DMN and the SAL, while increased awakenings (both in terms of number and total time awake) are associated with decreased FC in the SAL and MOT These findings agree with similar findings in children ​(30)​ and further with Kong et al.'s ​(2018)​ findings that systematically interrupted sleep reduced connectivity over multiple regions, with worsening effect over time. Interestingly, in this study, more awakenings, but not total amount of time awake, are also associated with declines in the ECN. Although directionality cannot be determined, taken together this collection of associations suggest that good sleep is either meaningfully predictive of FC across multiple regions, or if causality is in the opposite direction, that changes in sleep efficiency and number of awakenings might be a useful tool for identifying potentially clinically meaningful declines more quickly than currently existing methods (which rely largely on self-report of cognitive decline). Although existing literature suggests more minutes of sleep are associated with greater FC in working age adults ​(32)​, we expected that the total amount of sleep in minutes per night would be associated with greater connectivity. However, we had opposite findings, with total sleep time inversely associated with FC in the DMN and ECN, and with the population quartile with the highest sleep minutes having less FC in the DMN than those with the least. This contrasts with much of the current literature that indicate that both too little and too much sleep is associated with older age ​(76,77)​, reduced performance on standardized cognitive tests ​(78,79)​ and higher incidence of Alzheimer’s Disease ​(80)​. While inferring causality from these cross-sectional data must be avoided, and longitudinal studies of sleep and FC patterns would be very helpful, these data may indicate that individuals with lower connectivity, particularly in areas associated with higher level cognition and emotional regulation require more sleep, particularly if that sleep is interrupted with multiple wake periods. Alternatively, it may be that metrics other than total sleep time, like slow wave or REM sleep, are responsible for the observed differences. Strengths of this study center on our use of high-quality measurements in a large population of older adults. Indeed, gold standard assessment including maximal exercise tests (aerobic capacity), DXA (body composition), and accelerometry (physical activity and sleep) are rarely found together in studies of this size. Using these high-quality tools contributes to the likelihood that there are fewer sources of error than might come from estimation algorithms, proxy measures, or self-report. However, limitations also exist. As noted above, the cross-sectional nature of the study makes drawing meaningful conclusions regarding the directionality of observed relationships fraught. Indeed, particularly given the highly interconnected nature of many aspects of human aging (and health), there is a possibility of a shared etiology or mechanism that accounts for all observed associations. Additionally, because of the inclusion/exclusion criteria this exclusively older adult population who had a relatively narrow range of physical activity levels (i.e., all were self-reported sedentary in the past year) and were free from many of the diseases and conditions that may have substantial effect on brain health in the larger population, our findings may not be evident in other groups, particularly younger and/or more active individuals. A limitation exclusive to the sleep-based data is that we only captured night-time sleep behavior and did not gather data on daytime napping. This may have contributed to an underestimation of total sleep time which could lead to mischaracterizing the relationship(s) between both sleep quantity and quality with FC. CONCLUSION In this population of community dwelling older adults greater cardiovascular fitness, but not greater volume of physical activity, was associated with increased functional connectivity in regions which are suggestive of a younger/healthier brain. Also, (better) sleep in terms of efficiency and number of wakeful periods per night and lower amounts of overall sleep time were also associated with increased connectivity in key regions associated with brain health. Finally, total body fat percentage was also surprisingly associated with higher connectivity in the DAN. These findings, in combination with similar findings by other research groups, suggest that interventions to preserve functional connectivity with increasing age should be focused on maintaining cardiovascular fitness and ensuring high quality sleep rather than simply increasing the total volume of physical activity or controlling body composition. In particular, future research exploring longitudinal changes associated with interventions designed to improve fitness or sleep would be valuable to better understand causality of these associations and establish the degree to which short(er) term changes in behavior can effect meaningful changes in functional connectivity. Abbreviations AD = Alzheimer’s Disease BMI = Body Mass Index BSDAN = Seitzman defined Dorsal Attentional Network BSDMN = Seitzman Defined Default Mode Network CSF = Cerebral Spinal Fluid DAN = Dorsal Attentional Network DMN =Default Mode Network DXA = Dual X-Ray Absorptiometry ECN = Executive Control Network FC = Functional Connectivity GXT =Graded Exercise Testing MET = Metabolic equivalents of task ml of O2/kg/min =milliliters of oxygen per kilogram of body weight per minute MOT = Motor Control Network MRI =Magnetic Resonance Imaging OSA = Obstructive Sleep Apnea ROI = Region of Interest rs-fMRI =resting state fMRI SAL =Salience Network V02 max = The amount of oxygen that the body can utilize during maximal effort VAT = Visceral Adipose Tissue VIS = Visual Processing Network VM CPM =Vector Magnitude Counts per minute WASO =Wake After Sleep Onset Declarations Ethical Approval and consent to participate: Both institutions received approval from the Institutional Review Board (IRB) and written informed consent was obtained from all participants. Consent for Publication: Not applicable Data Availability : Both the baseline data used here, and the longitudinal data from these participants, are held by the primary investigator at Washington University in St. Louis and can be acquired with a formal data request that includes a data sharing agreement across institutions/investigators and a formal proposed project outline. Competing Interests: All authors (David Wing, Bart Roelands, Julie Loebach Wetherell, Jeanne F Nichols, Romain Meeusen, Job G. Godino, Joshua S. Shimony, Abraham Z. Snyder, Tomoyuki Nishino, Ginger E Nicol, Guy Nagels, Lisa T. Eyler, and Eric J. Lenze) declare that they have no competing interests. Acknowledgments : We would like to thank the many members of the MEDEX measurement and intervention delivery teams. In particular, we would like to recognize Mr. Michael Higgins, Ms. Mia Green, Ms. Mary Ulrich, Mr. Andrew Scott, Mr. Zachary Bellicini, Ms. Michelle Voegtle, and Dr. David Sinacore for their key contributions to data collection and intervention delivery. Funding : We would like to thank and recognize our funder, the National Institute of Health (# ) Author’s Contributions: David Wing: Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing-Original, Visualization; Bart Roelands: Writing-Review, Supervision; Julie Loebach Wetherell: Conceptualization, Resources, Writing-Review, Funding; Jeanne F Nichols: Methodology, Investigation, Data Curation, Writing-Review, Supervision; Romain Meeusen: Writing-Review, Supervision; Job G. Godino: Writing-Review, Supervision; Joshua S. Shimony: Methodology, Resources, Writing-Review; Abraham Z. 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Gratton C, Dworetsky A, Coalson RS, Adeyemo B, Laumann TO, Wig GS, et al. Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity. Neuroimage. 2020;217. Raut R V., Mitra A, Snyder AZ, Raichle ME. On time delay estimation and sampling error in resting-state fMRI. Neuroimage. 2019;194. Fischl B. FreeSurfer. Vol. 62, NeuroImage. 2012. Seitzman BA, Gratton C, Marek S, Raut R V., Dosenbach NUF, Schlaggar BL, et al. A set of functionally defined brain regions with improved representation of the subcortex and cerebellum. Neuroimage. 2020;206. Midgley AW, Earle K, McNaughton LR, Siegler JC, Clough P, Earle F. Exercise tolerance during VO2max testing is a multifactorial psychobiological phenomenon. Research in Sports Medicine. 2017;25(4). Voss MW, Erickson KI, Prakash RS, Chaddock L, Malkowski E, Alves H, et al. 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Killgore WDS, Schwab ZJ, Weiner MR. Self-reported nocturnal sleep duration is associated with next day resting state functional connectivity. Neuroreport. 2012;23(13). Khalsa S, Mayhew SD, Przezdzik I, Wilson R, Hale J, Goldstone A, et al. Variability in cumulative habitual sleep duration predicts waking functional connectivity. Sleep. 2016;39(1). Kong D, Liu R, Song L, Zheng J, Zhang J, Chen W. Altered long- and short-range functional connectivity density in healthy subjects after sleep deprivations. Front Neurol. 2018;9(JUL). Kocevska D, Cremers LGM, Lysen TS, Luik AI, Ikram MA, Vernooij MW, et al. Sleep complaints and cerebral white matter: A prospective bidirectional study. J Psychiatr Res [Internet]. 2019 May 1 [cited 2022 Feb 15];112:77–82. Available from: https://pubmed.ncbi.nlm.nih.gov/30861469/ Kocevska D, Lysen TS, Dotinga A, Koopman-Verhoeff ME, Luijk MPCM, Antypa N, et al. Sleep characteristics across the lifespan in 1.1 million people from the Netherlands, United Kingdom and United States: a systematic review and meta-analysis. Nat Hum Behav. 2021 Jan 1;5(1):113–22. Faubel R, LÓpez-GarcÍa E, Guallar-CastillÓn P, Graciani A, Banegas JR, RodrÍguez-Artalejo F. Usual sleep duration and cognitive function in older adults in Spain. J Sleep Res [Internet]. 2009 Dec [cited 2022 Oct 2];18(4):427–35. Available from: https://pubmed.ncbi.nlm.nih.gov/19691473/ Mohlenhoff BS, Insel PS, Mackin RS, Neylan TC, Flenniken D, Nosheny R, et al. Total Sleep Time Interacts with Age to Predict Cognitive Performance Among Adults. J Clin Sleep Med [Internet]. 2018 Sep 15 [cited 2022 Oct 2];14(9):1587–94. Available from: https://pubmed.ncbi.nlm.nih.gov/30176964/ Lucey BP, Wisch J, Boerwinkle AH, Landsness EC, Toedebusch CD, McLeland JS, et al. Sleep and longitudinal cognitive performance in preclinical and early symptomatic Alzheimer’s disease. Brain [Internet]. 2021 Sep 1 [cited 2022 Sep 24];144(9):2852–62. Available from: https://pubmed.ncbi.nlm.nih.gov/34668959/ Supplementary Files SUPPLEMENTARY1table5figuresSubmissionCopy.docx Cite Share Download PDF Status: Published Journal Publication published 19 Oct, 2024 Read the published version in Sports Medicine-Open → Version 1 posted Reviewers agreed at journal 08 May, 2024 Reviewers invited by journal 07 May, 2024 Editor invited by journal 06 May, 2024 Editor assigned by journal 03 May, 2024 First submitted to journal 03 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4361076","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299505313,"identity":"23bc5ba5-6ea9-4a63-8c29-3cb750a9a965","order_by":0,"name":"David Wing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDCCA4wNDAwVBxgY2BnYoCIMDBKEtZwBKmNmJloLEDO2kaKF70Zy28Ov8+7I8TfzH3vA2GZnz3eA+eBtHjxaJG8kthvLbntmLHGYmd2AsS05ceYBtmRrfFoMbie2SUtuO5zYcJiZTQLowgSDAzxm0oS1zDlcPx+qxd7gAP83glokPzYcTjCAamHccICHDa8WyfsP26QZjh023HiY2Uwi4RzQL4fZjC3n4NHCd+b4M8kfNYfl5Y43PpP4UAYMsePND2+8waMFBJjhzkgAcwkoBwHGH0QoGgWjYBSMghEMACQ5UIjDnf2QAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1883-9448","institution":"School of Bone Densitometry, University of California San Diego","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"","lastName":"Wing","suffix":""},{"id":299505314,"identity":"07521195-5826-48b0-9e65-66a5d62419ca","order_by":1,"name":"Bart Roelands","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Bart","middleName":"","lastName":"Roelands","suffix":""},{"id":299505315,"identity":"f79c26eb-896d-4a18-ad73-716802e68880","order_by":2,"name":"Julie Loebach Wetherell","email":"","orcid":"","institution":"University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Julie","middleName":"Loebach","lastName":"Wetherell","suffix":""},{"id":299505316,"identity":"ed948f84-318e-48ce-9894-930433561b54","order_by":3,"name":"Jeanne F. Nichols","email":"","orcid":"","institution":"University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Jeanne","middleName":"F.","lastName":"Nichols","suffix":""},{"id":299505317,"identity":"23cde322-94fe-45ff-934b-8b35a15d79ee","order_by":4,"name":"Romain Meeusen","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Romain","middleName":"","lastName":"Meeusen","suffix":""},{"id":299505318,"identity":"7b784b52-c9a4-47d8-95c1-87c584178c80","order_by":5,"name":"Job G. Godino","email":"","orcid":"","institution":"University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Job","middleName":"G.","lastName":"Godino","suffix":""},{"id":299505319,"identity":"ce3e0f30-4bd3-486f-87f2-69457ce6298a","order_by":6,"name":"Joshua Shimony","email":"","orcid":"","institution":"Washington University In St Louis: Washington University in St Louis","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Shimony","suffix":""},{"id":299505320,"identity":"e5a026bd-f04b-410d-bff8-a4a9d65a2aa2","order_by":7,"name":"Abraham Z Snyder","email":"","orcid":"","institution":"Washington University In St Louis: Washington University in St Louis","correspondingAuthor":false,"prefix":"","firstName":"Abraham","middleName":"Z","lastName":"Snyder","suffix":""},{"id":299505321,"identity":"e3609817-98cf-48ab-bb33-9ff3f2723db2","order_by":8,"name":"Tomoyuki Nishino","email":"","orcid":"","institution":"Washington University In St Louis: Washington University in St Louis","correspondingAuthor":false,"prefix":"","firstName":"Tomoyuki","middleName":"","lastName":"Nishino","suffix":""},{"id":299505322,"identity":"bc9fd2fd-ff21-4cb4-bf8b-1ead4a1fffca","order_by":9,"name":"Ginger Nichol","email":"","orcid":"","institution":"Washington University In St Louis: Washington University in St Louis","correspondingAuthor":false,"prefix":"","firstName":"Ginger","middleName":"","lastName":"Nichol","suffix":""},{"id":299505323,"identity":"c59344a2-9197-40b9-a778-29ba722224bd","order_by":10,"name":"Guy Nagels","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Guy","middleName":"","lastName":"Nagels","suffix":""},{"id":299505324,"identity":"cce9d811-65ad-4205-99d2-fa5ee2cfe500","order_by":11,"name":"Lisa T. Eyler","email":"","orcid":"","institution":"University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"T.","lastName":"Eyler","suffix":""},{"id":299505325,"identity":"aa5ba665-a048-4f07-ac18-6cf965556e5c","order_by":12,"name":"Eric J Lenze","email":"","orcid":"","institution":"Washington University In St Louis: Washington University in St Louis","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"J","lastName":"Lenze","suffix":""}],"badges":[],"createdAt":"2024-05-02 21:50:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4361076/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4361076/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40798-024-00778-6","type":"published","date":"2024-10-19T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56548216,"identity":"7cefb7c1-6a71-409a-bae7-efc23af59ee1","added_by":"auto","created_at":"2024-05-15 15:40:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":626512,"visible":true,"origin":"","legend":"\u003cp\u003eSeitzman and Voss Coordinates and ROI’s--Salience Network\u003c/p\u003e\n\u003cp\u003eSeitzman ROI’s as designated by different colors on the top left (A) and Voss ROI’s are designated on the bottom right (B). The overlay areas of the two are shown on the top right (A’)\u003c/p\u003e\n\u003cp\u003e(Note: Visual representation of overlay between other networks of interest are available in supplementary materials)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4361076/v1/864f8f4db2fc81831d2c6d6d.jpeg"},{"id":67149726,"identity":"fd97bc09-b64b-4518-bb65-086f3ac51ba3","added_by":"auto","created_at":"2024-10-21 16:13:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1528130,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4361076/v1/81f0df0f-1e18-4037-9357-59a4f2d2f0ce.pdf"},{"id":56548191,"identity":"3dda0dcc-00b5-49a7-8904-f0e8f3345c57","added_by":"auto","created_at":"2024-05-15 15:40:32","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2484207,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARY1table5figuresSubmissionCopy.docx","url":"https://assets-eu.researchsquare.com/files/rs-4361076/v1/87a98c554c2c5d204ec0f5c0.docx"}],"financialInterests":"","formattedTitle":"Cardiovascular Fitness and Sleep, but not Physical Activity, are Associated with Improved Brain Functional Connectivity in Older Adults.","fulltext":[{"header":"KEY POINTS","content":"\u003col\u003e\n \u003cli\u003eCardiovascular fitness is associated with younger/healthier brains in terms of functional connectivity of key regions associated with cognitive capacity and emotional regulation.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGreater functional connectivity is observed across multiple regions with increased total sleep quantity and quality.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVolume of physical activity is not associated with greater connectivity in any of the regions associated with cognition, although there is an association with increased connectivity in the Motor Control Network.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"BACKGROUND","content":"\u003cp\u003ePreservation of cognitive performance and emotional wellness are critical for successful aging ​(1,2)​. While lifestyle behaviors are considered important to slowing age-related changes and preserving brain function, relatively little is known about the physiological mechanisms underlying brain aging. Given the worldwide increases in longevity ​(3)​ there is a need to consider methods to slow declines associated with aging and better preserve function across the lifespan.\u003c/p\u003e \u003cp\u003eCardiovascular fitness is based on the amount of oxygen that the body can utilize during maximal effort (V02\u003csub\u003emax\u003c/sub\u003e). Although somewhat modifiable through training, there is a substantial genetic component to fitness when considered in absolute terms. In contrast, physical activity is the amount of activity an individual accumulates throughout the day, often characterized in various intensity levels with moderate to vigorous intensity widely recognized as being the most beneficial to improvements in overall health ​(4)​. There is a substantial body of research indicating that both overall cardiovascular fitness, and regular physical activity have protective and restorative effects on age-related cognitive decline and the development of neurodegenerative diseases. ​(5\u0026ndash;11)​. However, other literature indicates that both fitness and physical activity have minimal effects on the structurally derived biological age of the brain ​(12,13)​ or have found no differences in cognitive outcomes after substantial changes in fitness ​(14)​. Furthermore, studies relating physical activity to structural integrity have shown variable impact with generally small effect size when relationships are detected at all ​(15\u0026ndash;17)​. Similarly, there are studies indicating links between fitness and increased functional connectivity (FC) ​(18\u0026ndash;20)​ which is a measure of the ability of the brain to communicate both within- and between-networks associated with higher level function including the ability to pay attention, regulate emotion, recall memories, and complete complicated muti-stage tasks. Despite these positive findings, there are other studies which had discordant findings with no changes in the FC in brain networks predicted to be associated with changes in cardiovascular fitness ​(21)​ or even declines in observed FC associated with increased physical activity ​(21,22)​.\u003c/p\u003e \u003cp\u003eBeing overweight or obese, often indirectly estimated via the body mass index (BMI), is also commonly associated with (poor) brain health independent of its impact of measures of fitness (when fitness is measured in relative terms as milliliters of oxygen per kilogram of body weight per minute or (ml of O2/kg/min)). Specifically, higher BMI has been associated with lower volume of grey matter across several brain regions ​(23)​ and obesity, particularly central obesity, is associated with increased risk of developing Alzheimer\u0026rsquo;s Disease (AD) ​(24,25)​. Similarly, high BMI has been associated with reduced FC in key resting state brain networks associated with cognitive function ​(26,27)​. Recent systematic reviews of cross-sectional studies have concluded that central obesity measured via waist circumference is correlated with structural declines ​(28)​ and impaired cognition ​(29)​. It is worth noting that many of these studies have relied on relatively crude measures of obesity, such as BMI and waist circumference, to draw these conclusions. However, recently published studies using more sophisticated Dual X-Ray Absorptiometry (DXA) based measures of body fatness have had similar findings, with visceral adiposity being associated with older/less structurally sound brains ​(12,13)​.\u003c/p\u003e \u003cp\u003eFinally, both sleep quality and quantity exhibit equivocal associations with elements of brain health including structure, connectivity, and risk of developing neurocognitive disease. Specifically, lower values for both sleep quantity and efficiency (a marker of sleep quality) were associated with reduced connectivity in the Default Mode Network (DMN) in children ​(30)​, while only efficiency had the same association in adolescents ​(31)​. Similarly, associations between (poor) sleep and (reduced) between- and within-network connectivity is observed in working age, but not older adults ​(32)​. Sleep related disease may also be related to neurodegeneration. Indeed, obstructive sleep apnea (OSA) is associated both with increases in blood markers associated with the development of AD ​(33)​ and with reduced FC in cognitively normal adults (34,35) but​ not those with mild cognitive impairment (35).\u003c/p\u003e \u003cp\u003eSpecific resting state networks are implicated in cognitive aging. Although meaningful differences in FC between younger and older adults have been found across a broad range of cortical networks, the bulk of the research suggests that differences are most pronounced in the DMN ​(19,20,36,37)​, the Executive Control Network (ECN) ​(19)​ and the Salience Network (SAL) ​(19,38)​ with a lesser, but still significant difference detected in the Dorsal Attentional Network (DAN) ​(19)​. In contrast, networks associated with sensory systems, including visual processing (VIS), and motor control (MOT) show minimal associations with older age ​(19,37,39)​, and seem to be more sensitive to movement and sensory engagement.\u003c/p\u003e \u003cp\u003eTo better understand differences in network connectivity we have examined cross-sectional differences in FC in the DMN, ECN, SAL, DAN, MOT and VIS in response to physiological and behavioral traits. Specifically, these analyses replicate/expand on the findings of ​Voss et al., (19)​ exploring the role of fitness and physical activity and FC using a somewhat larger, and similarly well characterized population of older adults. Further, we extend those findings by also examining associations between body composition and sleep respectively with the FC of these key regions. We hypothesized that superior cardiorespiratory fitness (as measured by estimated maximal aerobic capacity) will be associated with improved relative FC in the DMN, ECN, and SAL, and that higher levels of physical activity will be associated with superior/greater resting MOT activation. Additionally, we hypothesized that higher levels of adiposity, particularly higher levels of visceral adipose tissue (VAT), will be associated with less connectivity in the DMN, ECN and SAL. Finally, we hypothesized that more sleep, both in terms of overall minutes of sleep and sleep efficiency, would be associated with increased connectivity in those regions.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cstrong\u003eParticipants\u003c/strong\u003e \u003cp\u003e These analyses were conducted on data gathered during the baseline measurement of a longitudinal intervention set in two urban areas and approved by the Institutional Review Board at both institutions. All participants provided informed consent to participate. The larger group of 607 older adults has been extensively described elsewhere ​(14,40)​. Key details meaningful to the current analyses include that participants were between ages 65 and 84 and had self-reported cognitive complaints but were free from assessed cognitive impairment (defined as \u0026lt;\u0026thinsp;11 on Short Blessed Test ​(41)​) or diagnosed neurodegenerative disease. Additionally, participants were excluded if they were currently using glucocorticoid or diabetes medication, were too physically active (defined as \u0026gt;\u0026thinsp;60 min/week of moderate to vigorous exercise any week within the last 6 months) or reported alcohol or substance abuse within the previous six months.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhysical Measures (GXT, DXA, Accelerometery)\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eGraded Exercise Testing (GXT)\u003c/strong\u003e \u003cp\u003eThe majority of participants completed a GXT using a treadmill (Quinton QStress, Cardiac Science, Chelmsford, Mass) with a substantially smaller number (\u0026lt;\u0026thinsp;10%) who were unable to walk without holding on to the treadmill handrails using an electronically braked cycle ergometer (LODE Excalibur, Netherlands) to volitional maximal exertion. The testing protocol has been described previously ​(12,40)​. In brief, under the supervision of a physician, participants warmed-up for three to five minutes and then intensity was increased at a level equal to approximately 0.6 METS per two-minute stage with active motivation from study staff (n\u0026thinsp;=\u0026thinsp;2 minimum offering ongoing and enthusiastic verbal encouragement). Exercise grew progressively harder until the physician ended the test based upon potentially dangerous changes in the ECG reading or an extreme hypertensive response (SBP\u0026thinsp;\u0026gt;\u0026thinsp;220 or DBP\u0026thinsp;\u0026gt;\u0026thinsp;110), or the participant indicated that they were unwilling/unable to continue. Individuals were excluded if they did not reach a minimum of 85% of their age predicted heart rate maximum (220-age) or testing was stopped by the physician prior to volitional fatigue. \u0026ldquo;Fitness\u0026rdquo; was calculated as maximal capacity calculated in metabolic equivalents of task (METs) using the American College of Sports Medicine\u0026rsquo;s algorithm designed for walking. Based on the presumed linear relationship in oxygen consumption while coming to a metabolic steady state in response to a new workload, partial stages were scored in 30 second increments using the formula METs \u003csub\u003elast_completed_stage\u003c/sub\u003e + 0.25* METs \u003csub\u003edifference_between_stages\u003c/sub\u003e* number of 30 second increments completed in the new stage.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDual X-Ray Absorptiometry (DXA) to estimate body composition\u003c/strong\u003e \u003cp\u003eA GE Lunar Prodigy at one location and an iDXA (both GE/Lunar, Madison, WI) at the other were used to estimate segmental body composition and provide absolute values (measured in grams or kilograms) for fat and lean tissue and bone mass for the arms and legs and trunk as well as secondary height-dependent regions titled android (centered on the abdomen) and gynoid (centered on the upper thighs). Visceral fat values were also derived from the android region. This method of estimating visceral fat has had good agreement compared to 3-D imaging techniques in both men and women ​(42,43)​.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eParticipants were positioned in line with best practice recommendations ​(44)​ and external artifacts were removed whenever possible. For participants who were larger than the available scan field, a \u0026ldquo;hemi-scan\u0026rdquo; was acquired by scanning only the right side of the body and replicating those values for the \u0026ldquo;missing\u0026rdquo; limb.\u003c/p\u003e \u003cp\u003e\u003cem\u003eAccelerometry: Measurement of physical activity\u003c/em\u003e: Participants were asked to maintain their normal behaviors during a 10 day period during which they were equipped with an Actigraph GT9X\u0026thinsp;+\u0026thinsp;Link (ActiGraph Inc, Pensacola, FL) deployed in line with best practice recommendations (45, 46) ​on the participant\u0026rsquo;s non-dominant wrist continuously except when engaging in water-based activities like swimming or bathing. This tri-axial accelerometer has been shown to be both valid and reliable across the age span ​(45,49,50)​. After 10 days of deployment, devices were recovered and data were downloaded and screened for completeness and potential device malfunction in line with established practice ​(45,47,51)​. Data processing included applying a screening algorithm to detect non-wear ​(52,53)​ and aggregating raw data into \u0026ldquo;counts per minute\u0026rdquo; in the x, y and z axes independently using Actilife (Actigraph\u0026rsquo;s proprietary software). Vector magnitude was calculated using the square root of the sum of the squares of the three axes to incorporate intensity, frequency, and duration of movement. This metric has been recommended for use in assessing physical activity during a 24-hour wear period ​(54)​ particularly when the device has been deployed at the wrist.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAccelerometry: Measurement of sleep\u003c/em\u003e: The same device was used to calculate sleep time in total minutes, wake after sleep onset (WASO) both in terms of number of awakenings and number of minute awake, and sleep efficiency using an algorithm that has been validated for use in adults ​(55)​. Participants were specifically asked to maintain their normal sleep rhythms during the wear period. The window of observation was derived from sleep journals in which participants indicated the time that they had begun trying to sleep, and the time that they first woke up in the morning (i.e., there was no effort to record incidental, or undesired, nighttime awakening). If a sleep journal was not maintained, previously validated methods were utilized to estimate the time of sleep onset and wake time ​(56)​.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eImaging Measures\u003c/h2\u003e \u003cp\u003e \u003cem\u003eImaging Acquisition\u003c/em\u003e: Three magnetic resonance imaging (MRI) scanners at two sites were used to acquire resting state functional MRI (rs-fMRI) data (UCSD: GE MR750 3T scanner (GE, Milwaukee, WI) with an 8-channel head coil; WUSTL: Siemens 3T Trio and 3T Prisma-FIT (Erlangen, Germany) with a 20-channel head coil. T1-weighted (T1w) and T2-weighted (T2w) structural scans were performed for purposes of image registration and radiological screening of the participants (UCSD: T1 MPRAGE, TE\u0026thinsp;=\u0026thinsp;3.036 ms, TI\u0026thinsp;=\u0026thinsp;1,000 ms, 1.0 mm3 voxels; T2 CUBE, TR\u0026thinsp;=\u0026thinsp;3,300 ms, TE\u0026thinsp;=\u0026thinsp;73.37 ms, 1.0 mm3 voxels; WUSTL: T1 MPRAGE, TR\u0026thinsp;=\u0026thinsp;2,400 ms, TE\u0026thinsp;=\u0026thinsp;3.16 ms, TI\u0026thinsp;=\u0026thinsp;1,000 ms, 1mm3 voxels; T2 SPACE, TR\u0026thinsp;=\u0026thinsp;3,200 ms, TE\u0026thinsp;=\u0026thinsp;458 ms, 1mm3 voxels). Four rs-fMRI scans (140 frames per run) were acquired per subject using a multi-echo sequence (UCSD: TR\u0026thinsp;=\u0026thinsp;2,740 ms, TE\u0026thinsp;=\u0026thinsp;14.8, 28.4, 42, 55.6 ms; 4.0 mm\u003csup\u003e3\u003c/sup\u003e voxels; WUSTL: TR\u0026thinsp;=\u0026thinsp;2,960 ms, TE\u0026thinsp;=\u0026thinsp;15, 31.3, 47.6, 63.9 ms; 4.0 mm\u003csup\u003e3\u003c/sup\u003e voxels) with a total run time of 6.4 min (UCSD) or 6.9 min (WUSTL) per run or 25.6 and 27.6 minutes respectively. Gradient echo field maps were acquired for later use in correction of susceptibility-related image distortion. During fMRI, participants were shown a neutral video without audio (e.g., nature scenes) synchronized to the start of each run and were asked to stay awake without engaging in any sort of meditation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImage Preprocessing\u003c/strong\u003e \u003cp\u003efMRI data processing largely followed previously described methods ​(57)​ which makes use of several FSL modules ​(58)​. Briefly, rigid body motion correction both within- and across-runs was computed on data summed over all echos. applied. Slice timing correction was applied to each echo. Bias field inhomogeneities were corrected using the FAST module in FSL ​(59)​. Atlas transformation was computed by composition of transforms (individual frame \u0026rarr; frame average \u0026rarr;T2w\u0026rarr;T1w\u0026rarr; atlas representative target). T1w\u0026rarr; atlas registration was computed with FSL FNIRT. Three atlas representative targets, all representing the 711-2B version of Talairach space, had been previously prepared for each of the scanners to accommodate scanner-specific differences in T1w contrast. Final resampling of the fMRI data in 3mm3 voxel 711-2B space was accomplished in one step incorporating distortion correction (FSL PERELUDE \u0026amp;FUGUE), previously computed bias field correction, and the composition of all spatial transforms.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe multi-echo data were then modeled according to standard theory ​(60)​ fitting the four echoes to a monoexponential model S_t\u0026thinsp;=\u0026thinsp;S_0t\u0026sdot;exp(-R_2t^⋆\u0026sdot;TE_k:, where indexes frame, indexes echo, and is reconstructed intensity extrapolated to echo time 0. Frame-to-frame variation in was suppressed by averaging over the whole run and the fMRI data were modeled at a TE of 30ms according to S_t= (S_0 ) ̅\u0026sdot;exp(-R_2t^⋆\u0026sdot;30ms). The modeled data in each run then were intensity normalized (one multiplicative scalar applied to all voxels and frames) to achieve an intensity mode value of 1000.\u003c/p\u003e \u003cp\u003eDenoising was effected on the fMRI data virtually concatenated across the 4 runs. Frame censoring was computed based on DVARS ​(61), with the criterion adjusted to compensate for baseline variability using a previously described method based on fitting the distribution DVARS values to a gamma function ​(62)​. Subsequent steps ignored all censored frames. The data were denoised using a CompCor-like scheme with regressors derived from motion correction [temporally filtered to suppress respiration-related factitious head motion ​(63), white matter, ventricles, extra-axial cerebral spinal fluid (CSF), and the whole brain global signal (64). Image derived regressors were based on tissue class segmentations computed by FreeSurfer 6.0.0 ​(65)​. Additional denoising included bandpass temporal filtering retaining frequencies in the range 0.01\u0026ndash;0.1 Hz and spatial filtering (Gaussian blur of 6 mm in each cardinal direction). Finally, the (scanner-specific) response evoked by the movie was averaged over all participants and subtracted from each individual\u0026rsquo;s data.\u003c/p\u003e \u003cp\u003e \u003cem\u003eROI/Network Creation\u003c/em\u003e: Relevant voxel locations were initially identified using the Big Brain 300 parcelation ​(66)​ excluding subcortical and cerebellar RIO\u0026rsquo;s. In an effort to confirm/replicate earlier findings we projected the regions described by ​Voss et al. (19)​ onto the Seitzman ROI\u0026rsquo;s. Thus, three networks were defined: the salience (SAL), motor control (MOT), and visual (VIS) networks. Two additional networks described by Voss et al, the default mode network (DMN) and dorsal attention network (DAN) were substantially different when compared to Seitzman et al. \u0026lsquo;s parcellation. Accordingly, we utilized both with labels DMN and DAN for Voss defined regions and BSDMN and BSDAN for Seitzman. Finally, because Seitzman et al. did not identify an executive control network (ECN) we utilized the Voss visual representations and included voxels identified as frontoparietal and DMN within the Sietzman designations. A visual representation of Seitzman-Voss mapping for SAL is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below, and mapping for all five examined networks are shown in supplementary Figs.\u0026nbsp;1 through 5. The average correlation value, defined as the average correlation across the ROI x ROI pairs within each network was calculated and assessed as the within network connectivity value for subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTITLE:\u003c/h2\u003e \u003cp\u003eSeitzman and Voss Coordinates and ROI\u0026rsquo;s\u0026ndash;Salience Network\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLEGEND:\u003c/h2\u003e \u003cp\u003eSeitzman ROI\u0026rsquo;s as designated by different colors on the top left (A) and Voss ROI\u0026rsquo;s are designated on the bottom right (B). The overlay areas of the two are shown on the top right (A\u0026rsquo;)\u003c/p\u003e \u003cp\u003e(Note: Visual representation of overlay between other networks of interest are available in supplementary materials)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eStatistical Analysis\u003c/strong\u003e \u003cp\u003eSPSS version 28 was used to conduct all statistical analyses. Participants were excluded from any analysis for which they had missing values. Descriptive statistics (proportions, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) were used to characterize the population, and t-tests were used to compare differences by study site and sex. Correlations between physiological/behavioral variables and FC within the regions of interest were evaluated while controlling for sex, location, and years of education. Additionally, unstandardized residual values were created using multiple linear regression that controlled for covariates. Specifically, we controlled for age, sex, and location. Additionally, we included physical activity as a covariate for analyses involving fitness, and fitness as a covariate for analyses involving physical activity and body composition. The unstandardized residuals then represent the individual differences in the variable of interest after variance from the covariates has been accounted for. To further evaluate the potential differences in FC at key networks as a function of fitness (or activity, or fatness, or sleep) we divided participants into the top and bottom 25% of the cohort based upon the unstandardized residual values. We then compared these groups with independent samples t-tests following the hypothesis that the \u0026ldquo;best\u0026rdquo; 25% would have greater FC than the \u0026ldquo;worst\u0026rdquo; 25% using Bonferroni adjustments to correct for multiple comparisons.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 398 participants (195 San Diego, 203 WUSTL) were included in the overall analyses. Of these 398, forty-five participants who did not continue to maximal effort (determined as not reaching 85% of age predicted heart rate max or having the study physician end the test prior to volitional fatigue) were excluded from the analysis of fitness (n\u0026thinsp;=\u0026thinsp;353 for fitness measures). Five participants did not have enough night-time accelerometer wear and three insufficient daytime wear for inclusion (4 night or days respectively) leaving 393 participants included in analyses regarding sleep and 395 in daily physical activity.\u003c/p\u003e \u003cp\u003eThe sample self-identified largely as white (n\u0026thinsp;=\u0026thinsp;314, 79%) with a smaller percentage identifying as black (n\u0026thinsp;=\u0026thinsp;37, 9%), white with Hispanic ethnicity (n\u0026thinsp;=\u0026thinsp;24, 6%) and Asian (13, 3%) with the remaining 10 individuals refusing to answer or indicating that they identified with multiple racial/ethnic categories. The sample was predominantly female (78%), and females in the sample population were younger than males. As expected, based on population level statistics, women had lower maximal cardiovascular fitness, higher overall body fat percentage and lower lean body mass. However, men had greater VAT and less overall physical activity as measured in VM CPM. Finally, although FC across the majority of the networks was not significantly different by gender, women had greater connectivity in the DMN. Variables with statistically significant difference, along with confidence intervals are included in supplementary materials table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThere was a significant difference between the locations for participants' maximal cardiovascular fitness and connectivity with St. Louis having a population with higher fitness and less connectivity. Means, standard deviations and p values for difference by location for all demographic, physiological, and connectivity values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Mean differences and confidence intervals of the difference for variables with significant differences for both location and sex are shown in supplementary materials table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Characteristics by Intervention Location\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUCSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWUSTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep=\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.3 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.6 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.1 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.2 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.2 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.2 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximal Cardiovascular Fitness (METS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.1 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Fat (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.2 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.3 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.2 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLean Tissue (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42786.2 (8486)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42556.5 (8870)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43006.8 (8115.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Adipose Tissue (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1257.3 (866.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1276.3 (871.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1239.2 (864.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep Efficiency (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.5 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.2 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.8 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Sleep Time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e387.9 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e385.4 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e390.3 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNightly Awake Time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.3 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.8 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.8 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNightly Awakenings (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.4 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.7 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Movement (VM CPM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1956.8 (502.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2005 (512)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1910.7 (490.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMN Connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.225 (0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.251 (0.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.199 (0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECN Connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.084 (0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.101 (0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067 (0.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAN Connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.106 (0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.123 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09 (0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAL Connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.339 (0.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.384 (0.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.295 (0.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOT Connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.272 (0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.319 (0.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.228 (0.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIS Connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.204 (0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.235 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.174 (0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSDMN Connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.131 (0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.151 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.112 (0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSDAN Connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.176 (0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19 (0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.162 (0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eYrs\u0026thinsp;=\u0026thinsp;Years; METS\u0026thinsp;=\u0026thinsp;Metabolic Equivalent of Task; VM\u0026thinsp;=\u0026thinsp;Vector Magnitude; CPM\u0026thinsp;=\u0026thinsp;Counts per minute; g\u0026thinsp;=\u0026thinsp;grams; min\u0026thinsp;=\u0026thinsp;minutes; DMN\u0026thinsp;=\u0026thinsp;Default Mode Network; ECN\u0026thinsp;=\u0026thinsp;Executive Control Network; DAN\u0026thinsp;=\u0026thinsp;Dorsal Attentional Network; SAL\u0026thinsp;=\u0026thinsp;Salience Network; MOT\u0026thinsp;=\u0026thinsp;Motor Control Network; VIS\u0026thinsp;=\u0026thinsp;Visual Network; BS\u0026thinsp;=\u0026thinsp;Ben Sietzman defined network.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003c/h2\u003e \u003cp\u003eAfter controlling for age, sex, and location all resting state functional networks had significant positive correlation (p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001 to 0.005) except for between VIS and BSDAN (p\u0026thinsp;=\u0026thinsp;0.763). Significant correlations between age and the SAL, VIS and BSDMN networks was observed (p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 0.026 and 0.016 respectively). Additionally, cardiovascular fitness was correlated with DMN (p\u0026thinsp;=\u0026thinsp;0.008 r\u0026thinsp;=\u0026thinsp;0.142), SAL (p\u0026thinsp;=\u0026thinsp;0.005, r\u0026thinsp;=\u0026thinsp;0.152) and BSDMN (p\u0026thinsp;=\u0026thinsp;0.008 r\u0026thinsp;=\u0026thinsp;0.143) but not with ECN, DAN, MOT, VIS, or BSDAN (p range\u0026thinsp;=\u0026thinsp;0.248 to 0.982).\u003c/p\u003e \u003cp\u003eWhen exploring associations between FC and body composition, the only significant associations were between percent body fat and the VIS (p\u0026thinsp;=\u0026thinsp;0.03, r\u0026thinsp;=\u0026thinsp;0.117), DMN (p\u0026thinsp;=\u0026thinsp;0.05; r\u0026thinsp;=\u0026thinsp;0.105), and DAN (p\u0026thinsp;=\u0026thinsp;0.047; r\u0026thinsp;=\u0026thinsp;0.107) networks. Total physical activity was associated with greater MOT connectivity (p\u0026thinsp;=\u0026thinsp;0.06; r\u0026thinsp;=\u0026thinsp;0.124).\u003c/p\u003e \u003cp\u003eFinally, greater sleep efficiency was associated with greater connectivity in the SAL (p\u0026thinsp;=\u0026thinsp;0.007; r\u0026thinsp;=\u0026thinsp;0.137) and BSDMN (p\u0026thinsp;=\u0026thinsp;0.016; r\u0026thinsp;=\u0026thinsp;0.122) networks. Total sleep time was inversely associated with ECN, MOT, and VIS connectivity (p\u0026thinsp;=\u0026thinsp;0.016, 0.025 and 0.027 and r=-0.121, -0.114 and \u0026minus;\u0026thinsp;0.112 respectively). Total amount of time awake during (attempted) sleep periods was negatively associated with multiple networks including SAL (p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001, r= -0.18), MOT (p\u0026thinsp;=\u0026thinsp;0.007; r= -0.136), BSDMN (p\u0026thinsp;=\u0026thinsp;0.038; r=-0.105) and BSDAN (p\u0026thinsp;=\u0026thinsp;0.043; r=-0.102). The number of times awakened during a sleep period (regardless of total length of time awake) was negatively associated with ECN (p\u0026thinsp;=\u0026thinsp;0.001; r=-0.162) and MOT (p\u0026thinsp;=\u0026thinsp;0.013; r=-0.126).\u003c/p\u003e \u003cp\u003eResults of the t-tests comparing the best versus worst quartiles within key metrics of interest are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The most fit participants had significantly more connectivity in the DMN, SAL and BSDMN compared with the least fit (p\u0026thinsp;=\u0026thinsp;0.029, 0.007 and 0.011 respectively). Additionally, the most fat (by percentage) quartile had higher connectivity in the DAN (p\u0026thinsp;=\u0026thinsp;0.041) with no differences observed for lean tissue or VAT (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Individuals with the most overall physical activity had higher MOT connectivity (p\u0026thinsp;=\u0026thinsp;0.015). The quartile with the best sleep efficiency showed greater connectivity in the DMN (p\u0026thinsp;=\u0026thinsp;0.045), SAL (0.013), BSDMN (p\u0026thinsp;=\u0026thinsp;0.003) and BSDAN (p\u0026thinsp;=\u0026thinsp;0.038) while those with the greatest overall time asleep showed lower connectivity only in the DMN (p\u0026thinsp;=\u0026thinsp;0.049) and BSDMN (p\u0026thinsp;=\u0026thinsp;0.032). The quartile with the lowest amount of time awake during sleep had greater connectivity in the SAL (p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MOT (p\u0026thinsp;=\u0026thinsp;0.009), BSDMN (p\u0026thinsp;=\u0026thinsp;0.013) and BSDAN (p\u0026thinsp;=\u0026thinsp;0.008) networks while those with the fewest number of times awakening showed increased connectivity in the ECN (p\u0026thinsp;=\u0026thinsp;0.016), SAL (p\u0026thinsp;=\u0026thinsp;0.014), MOT (0.03) and BSDAN (p\u0026thinsp;=\u0026thinsp;0.039).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eT-tests and Confidence Intervals comparing first (top 25%) vs fourth (bottom 25%) quartile. Note: desirability of first vs. fourth quartile varies according to metric\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFitness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBody Fat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSleep Quality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSleep Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWake Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWake number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(METS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(CPM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep values\u0026thinsp;=\u0026thinsp;with significant values denoted in bold\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSDMN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSDAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e95% Confidence intervals\u0026thinsp;=\u0026thinsp;with significant values denoted in bold\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.0409 to -0.0022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0372 to 0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0302 to 0.0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0096 to -0.0239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.0376 to -0.0005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.0368 to -0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0016 to 0.0344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0075 to 0.0281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0089 to 0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0145 to 0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0141 to 0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0045 to -0.0061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0134 to 0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0037 to 0.0141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0003 to 0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.0021 to 0.0204\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0164 to 0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.0187 to -0.0004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0166 to 0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0051 to -0.0174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0172 to 0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0142 to 0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0042 to 0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0108 to 0.0084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.0718 to -0.0114\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0142 to 0.0457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.011 to 0.0519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0165 to -0.0337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.0669 to -0.0081\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0256 to 0.0328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0233 to 0.0791\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.0075 to 0.0656\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSDMN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.0296 to -0.0039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0134 to 0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0106 to 0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0066 to -0.0178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.0316 to -0.0064\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.0267 to -0.0012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0033 to 0.0277\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0047 to 0.0196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSDAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0205 to 0.0174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0154 to 0.0243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.024 to 0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0098 to -0.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.0359 to -0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.028 to 0.0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.006 to 0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.001 to 0.0375\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.039 to 0.0288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0489 to 0.0199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0165 to 0.0506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0158 to -0.0702\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0583 to 0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0116 to 0.0496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0107 to 0.0736\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.0034 to 0.0658\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0228 to 0.0191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.034 to 0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0321 to 0.0094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0104 to -0.0104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.015 to 0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0119 to 0.0233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.022 to 0.0181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0074 to 0.0309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations:\u003c/p\u003e \u003cp\u003eMETS\u0026thinsp;=\u0026thinsp;Metabolic Equivalent of Task; VM\u0026thinsp;=\u0026thinsp;Vector Magnitude; CPM\u0026thinsp;=\u0026thinsp;Counts per minute; kg\u0026thinsp;=\u0026thinsp;kilograms; min\u0026thinsp;=\u0026thinsp;minutes; DMN\u0026thinsp;=\u0026thinsp;Default Mode Network; ECN\u0026thinsp;=\u0026thinsp;Executive Control Network; DAN\u0026thinsp;=\u0026thinsp;Dorsal Attentional Network; SAL\u0026thinsp;=\u0026thinsp;Salience Network; MOT\u0026thinsp;=\u0026thinsp;Motor Control Network; VIS\u0026thinsp;=\u0026thinsp;Visual Network; BS\u0026thinsp;=\u0026thinsp;Ben Sietzman defined network.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIt is worth noting that the choices made for the spatial location of component ROIs within key networks have some impact on the analyses findings and resulting conclusions. In general, both correlative and comparative significance (or non-significance) was observed in both the Seitzman and Voss defined DMN and DAN concurrently. However, that was not always the case (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for differences in significance by definitional region). We focused on the Voss defined regions to better position our conclusions with existing literature regarding fitness, fatness, activity, and sleep.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAlthough previous studies ​(19)​ have found associations between (younger) age and (increased) FC in the DMN, ECN, and SAL, we did not expect to have similar findings due to the relatively homogeneous age of our older adult population. In line with our expectations, we did not find age related associations in the ECN, however, we did find associations suggesting reduced connectivity among older individuals in the SAL network, with less robust findings in the DMN and VIS. Although the strength of the associations are quite modest, this adds further evidence to suggest that aging is associated with less connectivity in these key regions associated with internal reflection and emotional regulation.\u003c/p\u003e \u003cp\u003ePerhaps more interestingly, this study also provides cross-sectional analyses indicating that some, but not all, modifiable behaviors are associated with changes in the functional connectivity within key brain networks associated with cognition. More specifically, better fitness and sleep, but not greater volume of physical activity was associated with increased within-network connectivity. Further, higher body fat (by percentage) was also associated with increased connectivity in the DAN. These results help us to better understand the impact of lifestyle factors on (brain) aging and provide additional insight into possible mechanisms underlying age related differences in the brain. This, in turn, may help to provide targets for interventions and measurement tools to calculate age-related changes in the brain without the need for expensive and difficult to receive imaging.\u003c/p\u003e \u003cp\u003eIt is worth noting at the outset that there is likely some amount of error associated with the fact that we measured \u0026ldquo;volitional\u0026rdquo; vs. \u0026ldquo;true\u0026rdquo; maximal capacity. Specifically, because we purposely focused on a (n at least recently) sedentary population, it is possible that individuals quit the GXT assessment prior to their actual maximal capacity due to excessive perceived exertion or localized muscle fatigue. However, we believe that the level of encouragement offered helped to ensure a high level of effort ​(67) ​and excluding individuals who did not reach an adequate minimal threshold of effort (85% of APHRM) gives a reasonably accurate ranking of participant fitness, particularly when considering top vs. bottom quartiles.\u003c/p\u003e \u003cp\u003eWe found (positive) relationships between aerobic fitness and the FC of the DMN and SAL regions, replicating previous results in this area ​(18,19,68)​. While \u0026ldquo;brain age\" is multifactorial, these results suggest that participants with increased fitness have \u0026ldquo;younger\u0026rdquo; brains based on studies showing that younger individuals have greater connectivity in the DMN, ECN and SAL. ​(19)​. Although it is impossible to ascertain causality from these cross-sectional analyses, the meaningfulness of these associations is further strengthened when comparing FC in the most fit vs. least fit quartiles where not only did we find statistical significance but also a mean difference in connectivity greater than 10% of the baseline mean of the total population. Given the known associations between cognition and the ability to regulate emotions in the implicated regions (DMN and SAL respectively) maintaining aerobic capacity may be important to successful aging of the brain in addition to its impact on cardiovascular and metabolic health.\u003c/p\u003e \u003cp\u003eIn contrast, we did not find that daily volume of physical activity had significant association with cognitively important networks. While utilizing wrist worn devices limited our ability to differentiate activity intensity, Bassett et al ​(54)​ found that this metric was sufficient to identify meaningful differences in health across the NHANES population and recommended it as a metric to accurately measure physical activity using a more desirable wear location that would also allow researchers to assess sleep behaviors. Further, these results confirm the findings of Peven et al., ​(2019)​ who found minimal differences in the association between overall PA and FC in any of the brain regions associated with higher cognition. Further, although different metrics of physical activity time/intensity were utilized, combined with the fitness findings discussed earlier, these findings further validate Voss et al\u0026rsquo;s​ (2016) claims​ that differences in FC attributable to cardiovascular fitness are independent of regular physical activity.\u003c/p\u003e \u003cp\u003eThe observed associations between all day physical activity and increased FC in the MOT network seem reasonable on an intuitive level. Interestingly, a scoping review of the literature did not yield the identification of studies having done meaningful analyses in this area, although there is literature linking increased connectivity with reduced function in stroke victims compared to healthy controls ​(69)​ and in sedentary youth compared to endurance runners ​(70)​ This, along with findings suggesting that older age is not associated with differences of any kind in the MOT network ​Voss et al. (19) suggest that increased connectivity at rest may not always be a good thing, depending on the brain region/network being examined.\u003c/p\u003e \u003cp\u003eThe findings in the present sample indicate minimal impact of body composition on FC. Indeed, although there are some modest correlations between various networks and body fat percentage, neither lean tissue nor VAT showed any associations. Interestingly, and in opposition to our a priori hypothesis, the directionality both in terms of correlation and least vs. most (percent) fat group comparison, is positive indicating that more fat is associated with greater connectivity, particularly in the cognitively important DAN. Although only modestly (negatively) associated with increasing age ​(19)​ the DAN is associated with the ability to eliminate distractions/focus and to establish and pursue goals, and significantly lower DAN FC has been observed both in (BMI-based) obese children ​(71)​ and adults ​(72)​. It may be that the population measured in the current study, consisting of entirely older adults with less than a third having a BMI that would qualify them as obese, exhibit different characteristics than populations reported in previous studies. Further, the data presented here allow for a deeper look at the overall physical phenotype by utilizing DXA derived percentage of body fat instead of BMI, a comparatively crude metric of body composition, indicating that differences in connectivity associated with size alone (i.e., BMI) may be different than those associated with overall fatness (i.e., body fat percentage). This highlights the importance of considering more precise metrics when evaluating physical phenotypes and their associations with biological and physiological processes.\u003c/p\u003e \u003cp\u003eIn agreement with our a priori hypothesis, sleep quantity was positively associated with FC across multiple networks. Specifically, our findings expand to a new population (healthy older adults) previous findings in working age adults that indicate increased total sleep is associated with increased DMN connectivity both acutely (​Killgore et al., 2012)​ and over an extended period ​(74)​. Additionally, greater sleep efficiency is associated with increased FC in both the DMN and the SAL, while increased awakenings (both in terms of number and total time awake) are associated with decreased FC in the SAL and MOT These findings agree with similar findings in children ​(30)​ and further with Kong et al.'s ​(2018)​ findings that systematically interrupted sleep reduced connectivity over multiple regions, with worsening effect over time. Interestingly, in this study, more awakenings, but not total amount of time awake, are also associated with declines in the ECN. Although directionality cannot be determined, taken together this collection of associations suggest that good sleep is either meaningfully predictive of FC across multiple regions, or if causality is in the opposite direction, that changes in sleep efficiency and number of awakenings might be a useful tool for identifying potentially clinically meaningful declines more quickly than currently existing methods (which rely largely on self-report of cognitive decline).\u003c/p\u003e \u003cp\u003eAlthough existing literature suggests more minutes of sleep are associated with greater FC in working age adults ​(32)​, we expected that the total amount of sleep in minutes per night would be associated with greater connectivity. However, we had opposite findings, with total sleep time inversely associated with FC in the DMN and ECN, and with the population quartile with the highest sleep minutes having less FC in the DMN than those with the least. This contrasts with much of the current literature that indicate that both too little and too much sleep is associated with older age ​(76,77)​, reduced performance on standardized cognitive tests ​(78,79)​ and higher incidence of Alzheimer\u0026rsquo;s Disease ​(80)​. While inferring causality from these cross-sectional data must be avoided, and longitudinal studies of sleep and FC patterns would be very helpful, these data may indicate that individuals with lower connectivity, particularly in areas associated with higher level cognition and emotional regulation require more sleep, particularly if that sleep is interrupted with multiple wake periods. Alternatively, it may be that metrics other than total sleep time, like slow wave or REM sleep, are responsible for the observed differences.\u003c/p\u003e \u003cp\u003eStrengths of this study center on our use of high-quality measurements in a large population of older adults. Indeed, gold standard assessment including maximal exercise tests (aerobic capacity), DXA (body composition), and accelerometry (physical activity and sleep) are rarely found together in studies of this size. Using these high-quality tools contributes to the likelihood that there are fewer sources of error than might come from estimation algorithms, proxy measures, or self-report. However, limitations also exist. As noted above, the cross-sectional nature of the study makes drawing meaningful conclusions regarding the directionality of observed relationships fraught. Indeed, particularly given the highly interconnected nature of many aspects of human aging (and health), there is a possibility of a shared etiology or mechanism that accounts for all observed associations. Additionally, because of the inclusion/exclusion criteria this exclusively older adult population who had a relatively narrow range of physical activity levels (i.e., all were self-reported sedentary in the past year) and were free from many of the diseases and conditions that may have substantial effect on brain health in the larger population, our findings may not be evident in other groups, particularly younger and/or more active individuals. A limitation exclusive to the sleep-based data is that we only captured night-time sleep behavior and did not gather data on daytime napping. This may have contributed to an underestimation of total sleep time which could lead to mischaracterizing the relationship(s) between both sleep quantity and quality with FC.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this population of community dwelling older adults greater cardiovascular fitness, but not greater volume of physical activity, was associated with increased functional connectivity in regions which are suggestive of a younger/healthier brain. Also, (better) sleep in terms of efficiency and number of wakeful periods per night and lower amounts of overall sleep time were also associated with increased connectivity in key regions associated with brain health. Finally, total body fat percentage was also surprisingly associated with higher connectivity in the DAN. These findings, in combination with similar findings by other research groups, suggest that interventions to preserve functional connectivity with increasing age should be focused on maintaining cardiovascular fitness and ensuring high quality sleep rather than simply increasing the total volume of physical activity or controlling body composition. In particular, future research exploring longitudinal changes associated with interventions designed to improve fitness or sleep would be valuable to better understand causality of these associations and establish the degree to which short(er) term changes in behavior can effect meaningful changes in functional connectivity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eAD = Alzheimer\u0026rsquo;s Disease \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eBMI = Body Mass Index \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eBSDAN = Seitzman defined Dorsal Attentional Network\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eBSDMN = Seitzman Defined Default Mode Network\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eCSF = Cerebral Spinal Fluid \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eDAN = Dorsal Attentional Network \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eDMN =Default Mode Network \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eDXA = Dual X-Ray Absorptiometry \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eECN = Executive Control Network \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eFC = Functional Connectivity \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eGXT =Graded Exercise Testing \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eMET = Metabolic equivalents of task \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eml of O2/kg/min =milliliters of oxygen per kilogram of body weight per minute\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eMOT = Motor Control Network\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eMRI =Magnetic Resonance Imaging \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eOSA = Obstructive Sleep Apnea \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eROI = Region of Interest\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003ers-fMRI =resting state fMRI \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eSAL =Salience Network \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eV02\u003csub\u003emax\u003c/sub\u003e = The amount of oxygen that the body can utilize during maximal effort \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eVAT = Visceral Adipose Tissue\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eVIS = Visual Processing Network \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eVM CPM =Vector Magnitude Counts per minute\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\n \u003cp\u003eWASO =Wake After Sleep Onset\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and consent to participate: \u0026nbsp;\u003c/strong\u003eBoth institutions received approval from the Institutional Review Board (IRB) and written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e: Both the baseline data used here, and the longitudinal data from these participants, are held by the primary investigator at Washington University in St. Louis and can be acquired with a formal data request that includes a data sharing agreement across institutions/investigators and a formal proposed project outline. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eAll authors (David Wing, Bart Roelands, Julie Loebach Wetherell, Jeanne F Nichols, Romain Meeusen, Job G. Godino, Joshua S. Shimony, Abraham Z. Snyder, Tomoyuki Nishino, Ginger E Nicol, Guy Nagels, Lisa T. Eyler, and Eric J. Lenze) declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: \u0026nbsp;We would like to thank the many members of the MEDEX measurement and intervention delivery teams. In particular, we would like to recognize Mr. Michael Higgins, Ms. Mia Green, Ms. Mary Ulrich, Mr. Andrew Scott, Mr. Zachary Bellicini, Ms. Michelle Voegtle, and Dr. David Sinacore for their key contributions to data collection and intervention delivery.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: \u0026nbsp;We would like to thank and recognize our funder, the National Institute of Health (#\u0026nbsp; \u0026nbsp; \u0026nbsp;)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contributions:\u003c/strong\u003e \u003cstrong\u003eDavid Wing:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing-Original, Visualization; \u003cstrong\u003eBart Roelands:\u003c/strong\u003e Writing-Review, Supervision; \u003cstrong\u003eJulie Loebach Wetherell:\u0026nbsp;\u003c/strong\u003e Conceptualization, Resources, Writing-Review, Funding; \u0026nbsp;\u003cstrong\u003eJeanne F Nichols:\u0026nbsp;\u003c/strong\u003eMethodology, Investigation, Data Curation, Writing-Review, Supervision;\u003cstrong\u003e\u0026nbsp;Romain Meeusen:\u0026nbsp;\u003c/strong\u003eWriting-Review, Supervision;\u003cstrong\u003e\u0026nbsp;Job G. Godino:\u0026nbsp;\u003c/strong\u003eWriting-Review, Supervision;\u003cstrong\u003e\u0026nbsp;Joshua S. Shimony:\u0026nbsp;\u003c/strong\u003e Methodology, Resources, Writing-Review; \u003cstrong\u003eAbraham Z. Snyder:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Software, Formal Analysis, Writing-Review; \u003cstrong\u003eTomoyuki Nishino:\u0026nbsp;\u003c/strong\u003eMethodology, Software, Formal Analysis, Data Curation, Visualization, Writing-Review;\u003cstrong\u003e\u0026nbsp;Ginger E Nicol:\u0026nbsp;\u003c/strong\u003eMethodology, Writing-Review; \u003cstrong\u003eGuy Nagels:\u0026nbsp;\u003c/strong\u003eWriting-Review, Supervision;\u003cstrong\u003e\u0026nbsp;Lisa T. Eyler:\u0026nbsp;\u003c/strong\u003eMethodology, Writing-Review, Supervision; \u003cstrong\u003eEric J. Lenze:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Resources, Writing-Review, Supervision, Funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e: \u003cstrong\u003eBart Roelands\u003c/strong\u003e is a Collen-Francqui research professor. \u003cstrong\u003eBart Roelands and Romain Meeusen\u003c/strong\u003e are members of the Strategic Research Program Exercise and the Brain in Health \u0026amp; Disease: The Added Value of Human-Centered Robotics (SRP17 and SRP77).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBrewster GS, Peterson L, Roker R, Ellis ML, Edwards JD. Depressive Symptoms, Cognition, and Everyday Function Among Community-Residing Older Adults. J Aging Health [Internet]. 2017 Apr 1 [cited 2023 Sep 5];29(3):367\u0026ndash;88. 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Available from: https://pubmed.ncbi.nlm.nih.gov/34668959/ \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Functional Connectivity, Brain Health, Maximal Cardiovascular Fitness, Successful Aging, Physical Activity, Body Composition, Sleep Quality, Sleep Quantity","lastPublishedDoi":"10.21203/rs.3.rs-4361076/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4361076/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAging results in changes in resting state functional connectivity within key networks associated with cognition. Cardiovascular function, physical activity, sleep, and body composition may influence these age-related changes in the brain. Better understanding these associations may help clarify mechanisms related to brain aging and guide interventional strategies to reduce these changes. In a large (n\u0026thinsp;=\u0026thinsp;398) sample of healthy community dwelling older adults we conducted cross sectional analyses of the relationship between several modifiable behaviors and resting state functional connectivity within the brain in key regions associated with cognition and emotional regulation. Additionally, maximal aerobic capacity with a graded exercise test, physical activity and sleep with accelerometers, and body composition with dual energy x-ray absorptiometry were assessed. Associations were explored both through correlation and best vs. worst group comparisons.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGreater cardiovascular fitness, but not larger volume of daily physical activity, was associated with greater connectivity within the Default Monde and Salience Networks, both of which are key networks associated with aging. Better sleep, in terms of increased total sleep time, higher sleep efficiency and fewer nighttime awakenings was also associated with greater connectivity within multiple networks including the Default Mode, Executive Control, and Salience Networks. Higher body fat percentage was associated with increased connectivity in the Dorsal Attentional Network.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings confirm and expand on previous work indicating that, in older adults, higher levels of cardiovascular fitness and better sleep in terms of greater efficiency and less total awakenings, but not greater volume of physical activity or lower volume of body fat are associated with increased functional connectivity within key brain regions. Also, assessing sleep quality and quantity may be a useful tool for identifying potentially clinically meaningful declines in brain function.\u003c/p\u003e","manuscriptTitle":"Cardiovascular Fitness and Sleep, but not Physical Activity, are Associated with Improved Brain Functional Connectivity in Older Adults.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 15:40:25","doi":"10.21203/rs.3.rs-4361076/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-05-08T12:16:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-07T04:30:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Sports Medicine-Open","date":"2024-05-06T21:42:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-03T13:38:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sports Medicine-Open","date":"2024-05-03T09:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b348b68e-b7eb-4130-947f-9f7fad5e9f7e","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T16:10:48+00:00","versionOfRecord":{"articleIdentity":"rs-4361076","link":"https://doi.org/10.1186/s40798-024-00778-6","journal":{"identity":"sports-medicine-open","isVorOnly":false,"title":"Sports Medicine-Open"},"publishedOn":"2024-10-19 15:57:41","publishedOnDateReadable":"October 19th, 2024"},"versionCreatedAt":"2024-05-15 15:40:25","video":"","vorDoi":"10.1186/s40798-024-00778-6","vorDoiUrl":"https://doi.org/10.1186/s40798-024-00778-6","workflowStages":[]},"version":"v1","identity":"rs-4361076","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4361076","identity":"rs-4361076","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}