Brain Volume Predicts Skewed Locomotor Output and Lower Temporal Regularity

Brain Volume Predicts Skewed Locomotor Output and Lower Temporal Regularity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Brain Volume Predicts Skewed Locomotor Output and Lower Temporal Regularity Olivier Demers, Arian Yavari, Sanaz Ghaffari, Raana Nouri, Masoud Majidi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7197268/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Daily locomotor activity patterns vary widely between individuals, reflecting underlying kinematic strategies, energetic trade-offs, and circadian regulation. Here we integrate wearable accelerometry and brain MRI to examine how locomotor kinematics (movement intensity and variability) relate to brain size through an allometric biomechanics lens, and how these links depend on time-of-day (temporal behavior scaling) and align with the pace-of-life continuum. In a cohort of adults (n = 52, 27 male), we found that individuals with larger brains exhibited slower, more intermittent daily movement profiles – characterized by lower overall activity, higher skewness and kurtosis of activity distributions, and reduced complexity (entropy) – suggestive of more controlled, energy-conserving behavior. In contrast, smaller-brained individuals had more continuous, regular, and symmetric activity patterns. Sex differences in circadian locomotor dynamics were also evident: females showed faster morning ramp-up and more symmetric activity distributions, whereas males exhibited greater daytime skewness and leptokurtosis, consistent with divergent circadian pacing. Regional correlations were sharpened when normalizing by overall brain size, localizing brain-behavior correlatoins to temporal (salience) and frontal cortices. Our findings reveal an allometric scaling of human activity rhythms: larger brain volume is associated with a “slower-paced” daily locomotor regimen, analogous to the slow-fast continuum observed across species. This diurnal structural coupling underscores how brain anatomy may influence not just how much we move, but how and when we move, supporting the idea that humans manifest individualized pace-of-life strategies rooted in neurobiology. Biological sciences/Neuroscience Biological sciences/Physiology Figures Figure 1 Figure 2 Figure 3 Introduction Human movement follows structured, individual rhythms shaped by neuromotor control, energy demands, and circadian timing, yet how these vary with brain anatomy remains poorly understood. Decades of comparative biology have established that animal behavior and physiology obey allometric scaling laws, with body and brain size tightly constraining metabolic rates and activity tempos [ 1 , 2 ]. In general, larger-bodied species exhibit slower, more intermittent behavior patterns compared to smaller animals, a principle linked to differences in mechanical efficiency, heat dissipation, and nervous system processing latency. Such scaling relationships are not limited to interspecies comparisons: within-species variation in neuroanatomical structure may also impose constraints on locomotor pacing and temporal complexity. Yet whether human behavioral rhythms manifest this kind of neurostructural allometry remains unclear. Brain size in particular occupies a central position in theories of behavioral scaling. While much attention has been directed at encephalization as a cross-species correlate of intelligence and social complexity, its implications for basic motor control, arousal regulation, and time-use patterns in everyday life have received comparatively little focus. Evidence from primate studies suggests that increased brain volume is accompanied by life-history shifts—such as longer developmental periods and slower reproductive rates—that may reflect a broader strategy of energetic conservation and stability [ 3 ]. At a neurophysiological level, larger brains exhibit thermodynamic constraints that necessitate lower firing rates, sparser coding, and longer conduction delays [ 4 ]. These biophysical limitations may reduce the capacity for rapid, continuous motor output, favoring instead a regime of punctuated, controlled movement bursts. In humans, such a hypothesis predicts that individuals with larger brains should show reduced mean movement levels, greater intermittency in behavioral expression, and less moment-to-moment unpredictability—hallmarks of an energy-efficient locomotor strategy. Surprisingly, this possibility has not been explicitly tested in naturalistic settings, where movement is unconstrained by laboratory tasks or experimental conditions. The theoretical underpinnings of this hypothesis also align with the pace-of-life syndrome (POLS), a conceptual framework in behavioral ecology positing that organisms differ in the speed and consistency of their behavioral and physiological functions along a slow–fast continuum [ 5 , 6 ]. “Fast” individuals tend to be more active, bold, and variable in behavior, with higher metabolic rates and shorter lifespans, while “slow” individuals adopt more conservative strategies characterized by reduced activity, longer developmental periods, and increased behavioral regularity. Although originally developed to explain interspecies diversity, POLS theory has increasingly been applied within species, including humans, to account for trait covariation across domains such as personality, stress physiology, and circadian preferences [ 7 ]. Notably, sex-specific expressions of pace-of-life strategies have also been observed, with males and females often differing in activity timing, variability, and response to environmental challenges [ 8 ]. However, relatively little is known about the neuroanatomical correlates of these strategies in humans, especially in the domain of spontaneous, daily locomotor behavior. Recent theoretical and empirical work has begun to outline how brain structure may shape not just cognitive function, but also the tempo and regularity of basic motor outputs across daily cycles. Thermodynamic models suggest that as brain volume increases, the capacity for high-frequency neuronal firing and dense network activation decreases, due to constraints on energy dissipation and signal conduction [ 4 ]. This prediction is supported by comparative analyses showing that species with larger brains exhibit slower processing speeds, lower arousal responsiveness, and reduced behavioral reactivity to external stimuli [ 9 , 10 ]. Importantly, these patterns are paralleled by differences in neural oscillation frequencies and timing dynamics, which scale predictably with brain size and impose limits on the rapidity of sensorimotor cycles [ 10 ]. In humans, recent morphometric studies have shown that brain regions do not scale uniformly with total intracranial volume; rather, association cortices such as the prefrontal and parietal lobes often exhibit hyperallometric growth, potentially amplifying individual differences in behavioral control and complexity [ 11 ]. These insights provide a compelling rationale to examine whether such regional and global volumetric differences are mirrored in the structure of daily locomotor output—specifically in the variability, skewness, and temporal complexity of movement behavior. Building on this framework, a small but growing body of research has begun to characterize how human locomotor behavior exhibits scale-invariant properties across time, reflecting intrinsic constraints on motor control and behavioral organization. Analyses of wearable accelerometry data across large cohorts have revealed that daily activity patterns follow fractal-like dynamics, with features such as burstiness, heavy-tailed distributions, and long-range temporal correlations [ 12 ]. These findings suggest that spontaneous human movement is not only structured, but adheres to conserved statistical signatures that transcend individual differences in lifestyle or context. Importantly, these scale-free properties appear to reflect internal system constraints—potentially neural in origin—rather than purely external schedules or environmental demands. When considered alongside the neurostructural scaling literature, this implies that variability in brain volume and cortical architecture may partly determine how tightly or loosely locomotor output is organized over time. Larger brains, operating under slower and more energetically costly signaling regimes, may favor more intermittent, punctuated activity profiles with reduced entropy and increased skewness—consistent with an energy-conserving behavioral mode. In humans, this relationship likely extends beyond global brain volume to include regional specializations shaped by development and experience. Together, these lines of evidence converge on a core hypothesis: that human movement rhythms are not merely outcomes of choice or environment, but reflect embodied constraints rooted in the architecture of the brain itself. By linking structural neuroanatomy to wearable-based markers of temporal complexity and energetic strategy, the present study seeks to illuminate this overlooked dimension of behavioral neuroscience, offering a novel lens on how the brain sculpts the tempo of our daily lives. Materials and Methods Participants Fifty-two healthy young adults (26 male, 26 female; age range: 18–38 years) were recruited from the Bishop’s University and Université de Sherbrooke communities. All participants provided written informed consent in accordance with ethics approval from the Centre intégré universitaire de santé et de services sociaux de l’Estrie – Centre hospitalier universitaire de Sherbrooke (CIUSSS de l’Estrie – CHUS) . All methods were performed in accordance with the relevant guidelines and regulations. Participants were screened to exclude any neurological or psychiatric disorders, sleep disorders, or use of medications affecting the central nervous system. Smartwatch Data Acquisition Each participant wore a Samsung Galaxy Watch Active2 continuously over a 30-day monitoring period. This device features: 8-channel green LED photoplethysmography (PPG) sensor array 3-axis accelerometer, gyroscope, and gravity sensor 360 x 360 Super AMOLED display (1.4") Tizen OS (programmable environment with direct sensor access) Battery capacity: 340 mAh (approximately 36 hours per charge) A custom data acquisition app was developed using the official Tizen SDK and deployed on the Samsung Galaxy Watch devices to enable energy-efficient, periodic sampling. Specifically, the built-in accelerometer and photoplethysmography (PPG) sensors were activated for 1 minute every 10 minutes at a sampling rate of 10 Hz, yielding approximately 4,320 minutes of data per subject. This intermittent recording protocol enabled high-resolution locomotor capture while minimizing battery consumption. Participants were instructed to wear the device at all times, including overnight. Charging was typically done once daily during low-activity windows and took under 30 minutes. Adherence was verified visually during postprocessing, no subjects were excluded. MRI Data Acquisition MRI data were collected using a 3T Philips Ingenia scanner at the CIUSSS de l’Estrie – CHUS imaging facility. Anatomical Scan A high-resolution T1-weighted structural image was acquired using a 3D gradient echo sequence with the following parameters: TR = 7.9 ms, TE = 3.5 ms Flip angle = 8° Field of view = 240 × 240 × 150 mm Matrix = 240 × 240 × 150 Voxel size = 1 mm isotropic All anatomical images were visually inspected for motion artifacts, no subjects were excluded. Wearable Data Preprocessing and Feature Extraction For all analyses, we focused on the tri-axial accelerometry (ACC) channels (axes x, y, z), discarding the gyroscope and gravity channels as they were highly correlated to ACC. Raw tri-axial accelerometry data were collected at 10 Hz from the Samsung Galaxy Watch Active2 and processed to derive high-resolution 24-hour behavioral profiles for each subject. For each 1-minute burst, we computed the sum(abs(diff)) of the X, Y and Z-axis acceleration, a proxy for gross motor activity (GMA): GMA values were binned by recording hour (0–23) across the 30-day monitoring period (Fig. 3 ) or by minute (Fig. 2 ) for the 1440-minute (intra-day) analysis. Within each hourly bin, values were aggregated across days to compute mean activity per hour, forming subject-specific locomotor curves. From the 1440-minute daily activity curves, we computed the following metrics for each subject: Mean and Standard Deviation : Summary statistics over the full 24-hour curve. Skewness and Kurtosis : Higher-order shape descriptors, capturing asymmetry and peakedness. Entropy : Computed using Shannon entropy over a 20-bin normalized histogram of each subject’s daily curve: Let \(\:x=\left\{{x}_{1},{x}_{2},\dots\:{x}_{n}\right\}\) be the set of acceleration magnitudes within a given time window. We computed the normalized histogram \(\:{p}_{i}\) over 20 bins (excluding zero-probability bins) and then computed Shannon entropy: Peak Time : Normalized index (0–1) corresponding to the minute of maximum activity (np.argmax()). Morning Rise Slope : The slope of the best-fit line (via linear regression) between 5:00 AM and 9:00 AM (minutes 300–540), reflecting rate of morning activation. All metrics were aggregated into a feature matrix (52 subjects × 9 features). Hourly and Inter-Day Metrics To assess diurnal structure and inter-day stability, raw accelerometry values (acc_x) were additionally summarized into a day × hour matrix for each subject (median: ~25 valid days per subject, 24 hours per day). For each hour of the day, we computed the following metrics across days: Mean and Standard Deviation : Capturing the central tendency and variability in hourly activation. Skewness : Quantified asymmetry in the hourly activity distributions. Entropy : Computed from 10-bin histograms of hourly values across days, see formula above. These metrics yielded four 24-hour curves per subject, each summarizing inter-day regularity in hour-specific activation. Averaging these curves across time yielded four additional global features (denoted DH_mean, DH_std, DH_skewness , and DH _ entropy ) appended to the original feature matrix (final size: 52 × 13). Sex Differences To examine sex differences, subjects were grouped by binary sex. Group-mean 1440-minute activity curves and 24-hour metric curves were plotted with shaded standard error margins. Between-group differences for each feature were tested using independent-sample Welch’s t -tests, and visualized using histograms and kernel density estimates. For each hourly metric, we also performed 24 separate t -tests across hours and annotated significant differences ( p < 0.05) in time series plots. Brain Morphometry Structural brain volumes were obtained for each subject using FreeSurfer-derived regional segmentation. Whole-brain T1-weighted MRI volumes were parcellated and left/right hemisphere values were averaged to yield 34 bilateral cortical ROI volumes. All ROI values were expressed in native voxel space and subsequently normalized by each subject’s total brain volume (BrainSeg) where specified. Correlation Analyses We tested for associations between brain structure and activity metrics using Pearson correlation. Each of the 13 wearable-derived features was correlated with total brain volume. Second, we assessed correlations between each feature and the 34 normalized ROI volumes, producing a 9 × 34 (Fig. 2 ) or 4 × 34 × 24 (Fig. 3 ) matrix of correlation coefficients and corresponding p -values. False discovery rate (FDR) correction (Benjamini–Hochberg method) was applied across the full correlation matrix (per analysis) to control for multiple comparisons. Results with q < 0.15 or uncorrected p < 0.05 were annotated in all figures. Visualization Time series, histograms, and density curves were visualized using matplotlib and seaborn. Heatmaps of correlation matrices were plotted with significance markers (black * = p < 0.05, uncorrected, gold star = q < 0.15). Cortical maps of statistically significant correlations were rendered on the fsaverage5 inflated surface using Mayavi, with ROI-level significance counts mapped onto the left hemisphere for visualization. Results Figure 1: Contrasting Locomotor Rhythms and Brain Volumes in Two Behaviorally Dissimilar Individuals Figure panels A and B illustrate the differences in accelerometry-derived activity patterns between two maximally dissimilar subjects (Subjects 32 and 37). Panel A shows the 1440-minute accX time series, where Subject 32 exhibits a high and sustained level of daytime activity, while Subject 37 remains nearly inactive throughout the day. Panel B displays the distribution of accX values, highlighting marked differences in statistical properties: Subject 32 shows higher mean, lower skewness, and lower kurtosis, consistent with a broad, symmetric activity profile, whereas Subject 37 displays a highly skewed, leptokurtic distribution reflecting sparse, punctuated movement. These contrasts exemplify the range of behavioral phenotypes captured by the accelerometry data. Panels C and D illustrate the day–hour structure and hourly statistical characteristics of accX activity for Subjects 32 and 37. Panel C shows day × hour heatmaps of accX values, where Subject 32 displays consistent daytime activity across many days, while Subject 37 exhibits sparse, irregular movement with isolated high-activity episodes, often confined to specific hours. Panel D summarizes hourly metrics averaged across valid days: Subject 32 exhibits markedly higher mean and standard deviation, with a clear diurnal profile peaking mid-day, and higher entropy reflecting more complex patterns. In contrast, Subject 37 shows minimal variation, extreme skewness, and low entropy—indicative of a highly inactive and punctuated movement pattern. These results complement the time-series view in Panels A–B and highlight distinct temporal organization of behavior. Panel E shows the correlation matrix across 13 extracted activity metrics. Strong positive correlations cluster among mean, standard deviation, and magnitude-based features (e.g., 1440_mean, total_activity, DH_mean), while entropy and skewness are negatively correlated with most intensity-based metrics, suggesting that more active individuals exhibit more regular and symmetric activity patterns. Specifically, entropy and skewness are inversely correlated because right-skewed distributions reflect sparse, burst-like movement—where most values are near zero with occasional high peaks—leading to lower temporal unpredictability and thus lower entropy. Panel F presents structural MRI slices for the two subjects shown in Panels A–D. Subject 37 has a larger brain volume (1,992,634 voxels) than Subject 32 (1,531,949 voxels) and exhibits more sporadic and less consistent activity patterns. This observation aligns with allometric scaling principles, where larger-brained individuals often display slower or less frequent behavioral rhythms. Figure 1 - Accelerometry-derived behavioral contrasts between two maximally dissimilar subjects (Subjects 32 and 37). (A) 1440-minute accX time series highlighting consistent daytime activity in Subject 32 versus sparse movement in Subject 37. (B) accX distributions reveal pronounced differences in statistical shape, with Subject 37 exhibiting high skewness and kurtosis. (C) Day × hour heatmaps show regular diurnal structure in Subject 32 versus irregular, punctuated activity in Subject 37. (D) Hourly metrics averaged across days confirm higher amplitude, variability, and entropy in Subject 32. (E) Correlation matrix of all 13 extracted metrics reveals a strong positive cluster among intensity-based features and inverse relationships between entropy and skewness. (F) Structural MRI slices illustrate that Subject 37 has a larger brain volume, consistent with slower, more intermittent movement patterns observed in larger-brained individuals. Panel A presents a comprehensive group-level comparison of accelerometry-derived daily activity profiles and derived statistical features between males (n = 27) and females (n = 25). The top plot depicts the dense 1440-minute (1-day) mean activity curves after pooling ~ 30 days of data per subject and averaging across three accelerometer channels. A clear diurnal pattern is observed, with a sharp rise in activity around 7:00 AM, a plateau from mid-morning through early evening, and a steep decline after 8:00 PM. Males generally exhibit slightly lower activity levels than females during daytime hours, and a more gradual early-morning rise, though the overall difference is not statistically significant (t = − 1.51, p = 0.139). Similarly, temporal variability across the day (std) shows a modest sex difference (t = − 1.19, p = 0.239), with females showing slightly more variability. Skewness is significantly greater in males than in females (t = 2.44, p = 0.0185), suggesting more right-tailed activity distributions among men. Females exhibit significantly greater morning slope values (t = − 2.41, p = 0.0201), indicating a steeper acceleration in activity following wake onset. Kurtosis is also significantly higher in males (t = 2.25, p = 0.0307), reflecting more peaked activity distributions. Entropy did not differ significantly between groups (t = − 1.12, p = 0.268). Other features such as peak timing (t = 0.66, p = 0.513), total activity (t = − 1.51, p = 0.139), and mean vector magnitude (t = − 1.48, p = 0.146) also did not differ significantly by sex. Together, these findings indicate that while overall activity levels are comparable across sexes, the temporal structure and shape of daily activity patterns—particularly skewness, kurtosis, and morning slope—exhibit robust sex-linked differences, suggesting divergent circadian dynamics and behavioral regulation between males and females. Panel B presents scatter plots illustrating the relationships between total brain volume (BrainSeg) and nine accelerometry-derived activity features. Each plot includes sex-specific data points (blue = male, pink = female) with regression lines and correlation statistics for all subjects and males/females separately. Mean activity shows a modest negative association with brain volume (r = − 0.27, p = 0.053), driven more by males (r = − 0.21, p = 0.29) than females (r = − 0.16, p = 0.443), though not statistically significant. Standard deviation is weakly and non-significantly correlated with brain volume across all groups (All: r = − 0.19, p = 0.187). Skewness shows a significant positive correlation overall (r = 0.32, p = 0.019), primarily due to the female subgroup (r = 0.35, p = 0.088), suggesting that larger-brained females tend to have more right-skewed activity distributions. Kurtosis is significantly positively correlated with brain volume overall (r = 0.31, p = 0.025), an effect again driven entirely by females (r = 0.46, p = 0.020). Entropy trends negatively with brain volume (r = − 0.20, p = 0.164), with a significant effect in females (r = − 0.49, p = 0.012) but not in males (r ≈ 0.00, p = 0.985). Peak time shows no meaningful associations in any group (All: r = 0.01, p = 0.95). Morning slope is weakly negatively associated with brain volume overall (r = − 0.22, p = 0.114), with non-significant trends in both sexes. Mean vector magnitude shows a near-significant negative trend (All: r = − 0.27, p = 0.054), mirroring total activity. Total activity similarly trends negative (r = − 0.27, p = 0.053), though again not reaching significance in either subgroup. Summary: Shape-based features—skewness, kurtosis, and entropy—show the strongest and most consistent associations with brain volume, driven primarily by female sub-group. Amplitude- and timing-based features (e.g., mean, total activity, peak time) show weaker and largely non-significant relationships. Panel C presents two heatmaps depicting Pearson correlation coefficients (r-values) between DK atlas cortical ROI volumes and accelerometry-derived features, either using raw volumes (left) or volumes normalized to total brain size (right). In the raw matrix, strong and widespread positive correlations emerge between shape-related ACC features—particularly kurtosis, skewness, and entropy—and medial and lateral prefrontal regions (e.g., rostralmiddlefrontal, superiorfrontal), temporal areas (e.g., fusiform, inferior temporal), and supramarginal cortex. Many of these associations survive FDR thresholding (q < 0.15, gold asterisks), suggesting robust region-specific coupling. When adjusting for global brain size, a more selective pattern emerges: entropy remains negatively associated with regions like precuneus and entorhinal cortex, while kurtosis shows positive correlations with lateral temporal and frontal poles. Additionally, total activity and vector magnitude measures become more salient, now showing positive associations with lateral occipital, temporal pole, and superior parietal cortices. These shifts indicate that normalization sharpens the specificity of cortical associations, revealing that both amplitude and shape characteristics of daily activity patterns are linked to distinct regional structural variability, above and beyond overall brain volume. Panel D visualizes the number of significant associations (p < 0.05) between ACC-derived features and cortical ROI volumes (normalized by brain size) across the left hemisphere. The strongest concentration of associations is localized to salience and DMN-linked regions (superior temporal, supramarginal, insula, precuneus, superior frontal, rostral middle frontal, and caudal anterior cingulate). Figure 2 - Sex differences and brain-behavior relationships in accelerometry-derived activity. (A) Daily activity curves and feature distributions reveal that males exhibit greater skewness, kurtosis, and slower morning rise than females, despite similar overall activity levels. (B) Brain volume correlates most strongly with shape-based activity features—skewness, kurtosis, and entropy—especially in females. (C) Cortical ROI volumes are significantly associated with multiple activity features, particularly in frontal, temporal, and parietal regions, with normalization revealing distinct regional patterns. (D) Surface map highlights spatial concentration of significant associations in salience and DMN-linked cortical areas. Panel A displays hourly patterns of x-axis accelerometry data across days, comparing three example participants (two males and one female, top row) and group-level sex differences in hourly metrics (bottom row). Individual day × hour matrices reveal substantial inter-day variability, with clearer daytime activation and higher amplitude in some subjects (e.g., s52, s3) compared to others (s6). Aggregated plots below show that females exhibit significantly higher inter-day mean and standard deviation of hourly activity during the afternoon and evening (12:00–21:00), while males display higher early-morning activity (12:00–3:00). Inter-day skewness is consistently greater in males during daytime, indicating more right-tailed activity distributions, whereas females show elevated entropy from mid-morning to evening. These temporal dynamics underscore sex-specific differences in inter-day activity profiles. Panel B reveals a clear diurnal pattern in the correlation between hourly ACC metrics and brain volume, with mean, standard deviation, and especially entropy showing strong negative correlations during daytime hours and shifting toward positive or null correlations at night. In contrast, skewness exhibits the opposite trend—positively correlated with brain volume during the afternoon and evening, but trending negative during the early morning hours. Among these, entropy shows the most robust associations, particularly between 12:00–18:00 (FDR q < 0.15), indicating that larger brain volume predicts lower inter-day entropy and higher activity skew, reflecting a shift toward punctuated, energy-conserving rhythms. Panel C shows the relationship between brain volume and four ACC-derived inter-day activity metrics averaged over daytime (12–6pm) and nighttime (12–6am) hours. During the daytime, the strongest effect is a significant negative correlation between entropy and brain volume (r = − 0.31, p = 0.0243), especially in females (r = − 0.36). Mean activity also trends negatively with brain volume (r = − 0.27), while skewness shows a weaker positive trend (r = 0.19), consistent with the daytime pattern in Panel B. Standard deviation shows minimal associations. At night, patterns generally reverse. Entropy shows no significant correlation, while skewness and standard deviation are negatively associated with brain volume, particularly in females (skewness: r = − 0.36; std: r = − 0.46), indicating that larger-brained women tend to have more symmetric and stable nocturnal activity. Mean activity shows no relationship overall but trends negatively in females. Altogether, these results reinforce a time-dependent inversion: negative correlations during the day (especially entropy) shift toward null or positive patterns at night. Panel D presents four heatmaps showing hourly correlations between raw cortical ROI volumes and ACC-derived metrics—mean, standard deviation, skewness, and entropy—revealing distinct spatiotemporal coupling patterns. The mean and standard deviation maps show widespread negative correlations during daytime hours, particularly in DMN-linked regions (precuneus, superior frontal, superior parietal, rostral anterior cingulate), suggesting that larger regional volumes are associated with reduced and more stable activity in those periods. In contrast, skewness shows broad positive correlations in the same regions, associating increased asymmetry in movement patterns with greater regional volume. The entropy map reveals the most consistent and extensive effects, with strong negative correlations spanning 8:00–20:00, especially in precuneus, superior frontal, and rostral anterior cingulate—many surviving FDR correction (q < 0.15) highlighting a robust inverse relationship between structural size and within-hour activity variability during waking hours. Panel E displays the same set of hourly correlations as Panel D but uses ROI volumes normalized by total brain size (BrainSeg), revealing more focal and specific structure-function relationships. The mean and standard deviation maps show attenuated and spatially restricted negative correlations, primarily localized to salience-linked regions in the temporal fold, with fewer significant effects overall. Similar patterns were observed for skewness and entropy. Similar to the intra-day measures, normalizing regional volume by total volume highlights a core subset of regions in the salience/DMN networks where local structural differences predict temporal complexity and asymmetry of activity, emphasizing targeted rather than global effects of brain anatomy on behavior. Panel F visualizes the number of significant hourly correlations (p < 0.05) between normalized ROI volumes and ACC features projected onto the cortical surface. Consistent with Panel E, entropy and mean activity show the densest clusters of associations in lateral frontal (e.g., rostralmiddlefrontal), inferior parietal, and temporal cortices. Entropy in particular shows widespread involvement of supramarginal, superiortemporal, and precuneus regions, reinforcing its role as the most structurally sensitive feature. Discussion Our findings reveal a striking association between brain volume and the temporal structure of daily locomotor behavior, consistent with theoretical models of neurostructural scaling. Larger-brained individuals exhibited less frequent, more punctuated movement profiles—characterized by lower overall activity, higher skewness and kurtosis, and reduced entropy—especially during daytime hours. These results provide empirical support for the idea that scale-free dynamics observed in the brain [ 13 ] may extend to overt behavior, reinforcing the notion of fractal coupling between neural structure, neural dynamics, and real-world activity. At the structural level, our observations align with recent work showing that brain morphometry follows region-specific scaling gradients across the adult lifespan [ 14 ]. We found that regional normalization sharpened the relationship between cortical volume and movement dynamics, especially in association cortices (e.g., frontal, temporal, parietal). Importantly, volumetric associations were strongest in control and salience-related hubs, including the superior frontal gyrus, insula, precuneus, and supramarginal gyrus, suggesting that anatomical variation in attention switching, interoception, and default mode regulation may influence both the pacing and structure of daily behavior. We also observed consistent effects in the superior temporal gyrus, a key motor hub for speech and vocal articulation, potentially linking structural variability in communicative motor control to broader movement patterns. Notably, these structure–behavior effects appeared to be largely driven by the female sub-population. Females exhibited stronger and more widespread correlations between brain volume and locomotor features, particularly skewness and entropy. This suggests that within-group variation in brain anatomy may have a more behaviorally salient impact in women, potentially due to tighter coupling between neural control systems and peripheral activity. In addition, the overall effects observed in the pooled sample are likely amplified by the well-established sex difference in absolute brain volume, with males generally exhibiting larger brains. This anatomical disparity introduces a broader variance structure that may exaggerate associations at the group level, even when individual correlations are stronger among females. The combination of sex-based differences in both mean volume and intra-group sensitivity underscores the complex, multilevel nature of brain–behavior scaling. These patterns may also reflect hormonal modulation of neurovascular or functional systems, as highlighted in recent multimodal studies of menstrual cycle effects on brain physiology. For example, fluctuations in estrogen and progesterone have been shown to exert opposing and spatially distinct effects on cerebral blood flow—progesterone reducing anterior perfusion, while estrogen modestly increases posterior flow [ 15 ]. These physiological shifts may interact with the underlying neural architecture to produce sex-specific behavioral patterns, reinforcing the need to model both hormonal state and structural scale when characterizing brain–behavior coupling. The behavioral profiles associated with larger brain volumes were not simply less active, but more structured and conservative. These traits echo prior work linking higher sense of life purpose to more cohesive and less fragmented activity rhythms [ 16 ], suggesting that both psychological and biological traits can converge on similar behavioral phenotypes—slower, more energy-efficient, and temporally predictable movement. Moreover, previous large-scale studies have shown that even light-intensity activity levels are positively associated with gray and white matter volumes [ 17 ], reinforcing the bidirectional relationship between brain integrity and movement patterns. This structure–behavior link is further supported by work demonstrating that free-living physical activity is correlated with both structural volumes and functional connectivity patterns [ 18 ]. Our results, showing strong coupling between activity complexity (especially entropy and skewness) and cortical anatomy, suggest that individuals with larger and more developed control regions may engage in more internally regulated and less reactive movement behavior. This dovetails with emerging models of top-down control over circadian behavior: recent work shows that individuals with stronger frontoparietal connectivity—and greater lag between autonomic readiness and behavioral activation—tend to move in anticipation of physiological rhythms, suggesting a cortical override of bottom-up autonomic signals [ 19 ]. Our observed associations between frontoparietal volume and punctuated locomotor output may reflect this same anticipatory control mechanism operating at a structural level. The patterns of reduced entropy in individuals with larger brains align with models of neural control that emphasize dynamical systems properties of the brain during real-world locomotion (Monroe et al., 2023) [ 20 ]. These models highlight how sample entropy and other temporal complexity metrics reveal subtle regulatory processes in sensorimotor control—features we find mirrored in gross movement dynamics via accelerometry. Notably, our entropy–volume findings are consistent with studies linking EEG and body kinematics through entropy and recurrence-based coupling metrics [ 21 ], suggesting a unified statistical language for capturing complexity across neural and motor systems. Such fine-grained structure–function relationships are made possible by the use of high-resolution morphometric data. As shown in recent ultra-high-resolution MRI work, cortical architecture can be resolved at a scale that captures meaningful individual variability [ 22 ]. Our study leverages this precision to isolate volumetric predictors of behavioral rhythms, revealing spatially specific associations in salience and control-related areas—especially when adjusting for total brain size. This refinement reflects the growing consensus that structural MRI, when paired with wearable sensors, offers a powerful lens on embodied cognition. Finally, the broader evolutionary relevance of our findings is underscored by evidence that scale-invariant patterns of motor behavior emerge early in development. Infants display dynamic scaling in motor output that mirrors the fractal-like intermittency observed in adults [ 23 ]. This developmental continuity suggests that temporal structure in behavior is not a learned artifact of adult lifestyle but is likely rooted in intrinsic neural constraints present across the lifespan. In sum, our study supports the hypothesis that human behavioral rhythms—particularly the shape and complexity of daily movement—are scaled by brain anatomy in predictable ways. These findings expand the purview of neurostructural scaling theories, linking them to embodied activity patterns in naturalistic settings. They also support a broader framework in which energy conservation, neural processing efficiency, and behavioral regularity co-evolve as part of individualized pace-of-life strategies. Future work should explore how these structural–behavioral couplings shift in clinical populations, developmental transitions, or in response to lifestyle interventions aimed at modifying brain or behavior. Declarations Author Contributions S.G. and MB recruited participants and led data acquisition, including MRI and smartwatch recordings. O.D. developed the smartwatch firmware in C for raw data capture and extraction. A.Y., R.N., and S.R. performed statistical analyses and signal processing. R.B. conceived and supervised the study, provided funding, and oversaw all aspects of study design and interpretation. All authors contributed to manuscript preparation and approved the final version. Data Availability All raw and preprocessed data generated and analyzed during this study are available from the corresponding author ( [email protected] ) upon reasonable request. Code Availability All Python and Bash scripts used for data processing, analysis, and visualization are available from the corresponding author upon reasonable request. Funding: this work was funded by the National Science and Engineering Research Council of Canada (NSERC) Discovery Grant program RGPIN1507. Declaration of competing interests: the authors declare no conflict of interest. Ethics declarations and approval for human experiments: All participants provided written informed consent in accordance with ethics approval from the Centre intégré universitaire de santé et de services sociaux de l’Estrie – Centre hospitalier universitaire de Sherbrooke (CIUSSS de l’Estrie – CHUS) . Acknowledgements: this work was funded by the National Science and Engineering Research Council of Canada (NSERC) Discovery Grant program RGPIN1507. References Armstrong, E. (1983). Relative brain size and metabolism in mammals. Science, 220 , 1302–1304. DOI: 10.1126/science.6407108 Martin, R. D. (1981). Relative brain size and basal metabolic rate in terrestrial vertebrates. Nature, 293 , 57–60. DOI: 10.1038/293057a0 Barrickman, N. L., Bastian, M. L., Isler, K., & van Schaik, C. P. (2008). Life history costs and benefits of encephalization: a comparative test using data from long-term studies of primates in the wild. Journal of Human Evolution, 54 (5), 568–590. DOI: 10.1016/j.jhevol.2007.08.012 Karbowski, J. (2009). Thermodynamic constraints on neural dimensions, firing rates, brain temperature and size. Journal of Computational Neuroscience, 27 (3), 415–436. DOI: 10.1007/s10827-009-0153-7 Ricklefs, R. E., & Wikelski, M. (2002). The physiology/life-history nexus. Trends in Ecology & Evolution, 17 (10), 462–468. DOI: 10.1016/S0169-5347(02)02578-8 Réale, D., Garant, D., Humphries, M. M., Bergeron, P., Careau, V., & Montiglio, P.-O. (2010). Personality and the emergence of the pace-of-life syndrome concept at the population level. Philosophical Transactions of the Royal Society B: Biological Sciences, 365 (1560), 4051–4063. DOI: 10.1098/rstb.2010.0208 Dammhahn, M., Dingemanse, N. J., Niemelä, P. T., & Réale, D. (2018). Pace-of-life syndromes: a framework for the adaptive integration of behavior, physiology and life history. Behavioral Ecology and Sociobiology, 72 , 62. DOI: 10.1007/s00265-018-2473-y Immonen, E., Hämäläinen, A., Schuett, W., & Tarka, M. (2018). Evolution of sex-specific pace-of-life syndromes: genetic architecture and physiological mechanisms. Behavioral Ecology and Sociobiology, 72 (3), 60. DOI: 10.1007/s00265-018-2462-1 Herculano-Houzel, S. (2012). Encephalization quotient and neuron scaling patterns: connecting brain size and numbers of neurons. Frontiers in Human Neuroscience, 6 . DOI: 10.3389/fnhum.2012.xxxxxx Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304 (5679), 1926–1929. DOI: 10.1126/science.1099745 Reardon, P. K., et al. (2018). Normative brain size variation and brain shape diversity in humans. Science, 360 (6394), 1222–1227. DOI: 10.1126/science.aar2578 Chialvo, D. R., Gonzalez Torrado, A. M., Gudowska-Nowak, E., Ochab, J. K., Montoya, P., Nowak, M. A., & Tagliazucchi, E. (2015). How we move is universal: scaling in the average shape of human activity. Papers in Physics, 7 , 070017. DOI: 10.4279/PIP.070017 Grosu, G. F., Hopp, A. V., Moca, V. V., Bârzan, H., Ciuparu, A., Ercsey-Ravasz, M., Winkel, M., Linde, H., & Mureșan, R. C. (2023). The fractal brain: scale-invariance in structure and dynamics. Cerebral Cortex, 33 (8), 4574–4605. DOI: 10.1093/cercor/bhac363 Riccardi, N., Teghipco, A., Newman-Norlund, S., Newman-Norlund, R., Rangus, I., Rorden, C., Fridriksson, J., & Bonilha, L. (2025). Distinct brain age gradients across the adult lifespan reflect diverse neurobiological hierarchies. Communications Biology, 8 (1), 802. DOI: 10.1038/s42003-025-08228-z Côté, S., Butler, R., Michaud, V., Lavallée, E., Croteau, É., Mendrek, A., Lepage, J.-F., & Whittingstall, K. (2021). The regional effect of serum hormone levels on cerebral blood flow in healthy nonpregnant women. Human Brain Mapping, 42 (17), 5677–5688. DOI: 10.1002/hbm.25646 Sutin, A. R., Stephan, Y., Kekäläinen, T., Luchetti, M., & Terracciano, A. (2025). Purpose in life and accelerometer-measured physical activity among older adults. Psychology & Health, 40 (1), 126–140. DOI: 10.1080/08870446.2023.2200414 Spartano, N. L., Davis-Plourde, K. L., Himali, J. J., Andersson, C., Pase, M. P., Maillard, P., DeCarli, C., Murabito, J. M., Beiser, A. S., Vasan, R. S., & Seshadri, S. (2019). Association of accelerometer-measured light-intensity physical activity with brain volume: the Framingham Heart Study. JAMA Network Open, 2 (4), e192745. DOI: 10.1001/jamanetworkopen.2019.2745 Domingos, J. P., Picó-Pérez, M., Magalhães, R., Moreira, J., Sousa, N., Pêgo, J. M., & Santos, N. C. (2021). Free-living physical activity measured with a wearable device is associated with larger hippocampus volume and greater functional connectivity in healthy older adults: an observational, cross-sectional study in Northern Portugal. Frontiers in Aging Neuroscience, 13 , 729060. DOI: 10.3389/fnagi.2021.729060 Demers, O., Ghaffari, S., Li, C., & Butler, R. (2025). Stronger cognitive control enables early behavioral rise independent of physiological activation. Research Square (preprint). DOI: 10.21203/rs.3.rs-7057835/v1 Monroe, D. C., Berry, N. T., Fino, P. C., & Rhea, C. K. (2023). A dynamical systems approach to characterizing brain–body interactions during movement: challenges, interpretations, and recommendations. Sensors, 23 (14), 6296. DOI: 10.3390/s23146296 Song, S., & Nordin, A. D. (2021). Mobile electroencephalography for studying neural control of human locomotion. Frontiers in Human Neuroscience, 15 , 749017. DOI: 10.3389/fnhum.2021.749017 Cabalo, D. G., et al. (2025). Multimodal precision MRI of the individual human brain at ultra-high fields. Scientific Data, 12 (1), 526. DOI: 10.1038/s41597-025-04863-7 Chua, Y. W., Jiménez-Sánchez, L., Ledsham, V., O’Carroll, S., Cox, R. F. A., Andonovic, I., Tachtatzis, C., Boardman, J. P., Fletcher-Watson, S., Rowe, P., & Delafield-Butt, J. (2025). A multi-level analysis of motor and behavioural dynamics in 9-month-old preterm and term-born infants during changing emotional and interactive contexts. Scientific Reports, 15 (1), 952. DOI: 10.1038/s41598-024-83194-w Montgomery, S. H., Mundy, N. I., & Barton, R. A. (2016). Brain evolution and development: adaptation, allometry and constraint. Proceedings of the Royal Society B: Biological Sciences, 283 (1838), 20160433. DOI: 10.1098/rspb.2016.0433 Scarpazza, C., & De Simone, M. S. (2016). Voxel-based morphometry: current perspectives. Neuroscience and Neuroeconomics, 5 , 19–35. DOI: 10.2147/NAN.S84220 Sol, D., Prego, A., Olivé, L., Genovart, M., Oro, D., & Hernández-Matías, A. (2025). Adaptations to marine environments and the evolution of slow-paced life histories in endotherms. Nature Communications, 16 , 4265. DOI: 10.1038/s41467-025-59273-5 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 24 Nov, 2025 Editor assigned by journal 18 Nov, 2025 Editor invited by journal 30 Jul, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 28 Jul, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7197268","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":550849452,"identity":"c580c590-9931-4e2c-b2fb-0a17f97d78ef","order_by":0,"name":"Olivier Demers","email":"","orcid":"","institution":"Université de Sherbrooke","correspondingAuthor":false,"prefix":"","firstName":"Olivier","middleName":"","lastName":"Demers","suffix":""},{"id":550849453,"identity":"bed6f503-12c3-4bf9-8237-41c00d71556c","order_by":1,"name":"Arian Yavari","email":"","orcid":"","institution":"Bishop's University","correspondingAuthor":false,"prefix":"","firstName":"Arian","middleName":"","lastName":"Yavari","suffix":""},{"id":550849454,"identity":"d73198ea-3d4f-4297-a077-f2bbc1729cf8","order_by":2,"name":"Sanaz Ghaffari","email":"","orcid":"","institution":"Bishop's University","correspondingAuthor":false,"prefix":"","firstName":"Sanaz","middleName":"","lastName":"Ghaffari","suffix":""},{"id":550849455,"identity":"daac2506-a7d4-479f-a3c3-9e092a3957f1","order_by":3,"name":"Raana Nouri","email":"","orcid":"","institution":"Bishop's University","correspondingAuthor":false,"prefix":"","firstName":"Raana","middleName":"","lastName":"Nouri","suffix":""},{"id":550849456,"identity":"4e0dab02-b499-4ab0-ae96-714929914e9a","order_by":4,"name":"Masoud Majidi","email":"","orcid":"","institution":"Bishop's University","correspondingAuthor":false,"prefix":"","firstName":"Masoud","middleName":"","lastName":"Majidi","suffix":""},{"id":550849457,"identity":"0a50dccc-5463-43b8-8d79-3072e9d00023","order_by":5,"name":"Maryam Bayatzadeh","email":"","orcid":"","institution":"Bishop's University","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Bayatzadeh","suffix":""},{"id":550849458,"identity":"1cf621b2-e532-48b4-af7c-0abcc715e679","order_by":6,"name":"Salar Rezayani","email":"","orcid":"","institution":"Bishop's University","correspondingAuthor":false,"prefix":"","firstName":"Salar","middleName":"","lastName":"Rezayani","suffix":""},{"id":550849459,"identity":"1691e1f7-e0de-4eae-b406-ffd4d0dd8248","order_by":7,"name":"Russell Butler","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACCSB+wMAgxyDB2MBMrBbGhgQGBmPStSQ2AK0jTov57B7zBwkV99I33G5u/lxQcceegf3wA7xaZO6cMWxIOFOcu+HOwTbpGWeeJTbwpBngd5dEjmFDYltC7oYbiW3MvG2HE4BOJUbLv4R0gxuJzZ+BWuwZJNg/EKGlISEBqKVBGqiFsUGCh4AtMscKZyQcSzCcCfILD9AvbTw5Bfi1SDdv+PChJkGe73b74888wBDjZz++Aa8WdHCAgY0k9WAto2AUjIJRMArQAQCBbUpK/IuUOQAAAABJRU5ErkJggg==","orcid":"","institution":"Bishop's University","correspondingAuthor":true,"prefix":"","firstName":"Russell","middleName":"","lastName":"Butler","suffix":""}],"badges":[],"createdAt":"2025-07-23 14:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7197268/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7197268/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97122824,"identity":"468bc968-b80f-4180-b64e-9898b8bfd75c","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3440430,"visible":true,"origin":"","legend":"","description":"","filename":"accpaperfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/be831873c85c445da75963b5.docx"},{"id":97122820,"identity":"66d992b8-5962-4c00-9835-2f2738e3c0a2","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"json","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9551,"visible":true,"origin":"","legend":"","description":"","filename":"d0f342af73b6406aa23e4944fd510fa3.json","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/ef81b84d4c4892911b683d4a.json"},{"id":97141969,"identity":"731796a8-9a2c-4cf2-9409-03bbb22afcf7","added_by":"auto","created_at":"2025-12-01 10:07:14","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95615,"visible":true,"origin":"","legend":"","description":"","filename":"d0f342af73b6406aa23e4944fd510fa31enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/b3a55703d0af98a7dde98acc.xml"},{"id":97122823,"identity":"0c887ca8-b712-4a21-8866-b053bd28b438","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1179456,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/86d6c1e668d421082a110043.pdf"},{"id":97122832,"identity":"6e47e27e-da33-4404-8f3a-20b43f487a27","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1578662,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/053cde682c624338521e06fc.pdf"},{"id":97141781,"identity":"fdbf4912-b78b-4431-af36-440f818b787a","added_by":"auto","created_at":"2025-12-01 10:07:00","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1596308,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/0fcaa8bf06e818aaa21553ef.pdf"},{"id":97141244,"identity":"9dcdba71-b2a2-453c-8a08-0304ee429de8","added_by":"auto","created_at":"2025-12-01 10:06:28","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":641381,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/9073f6b01d567768865e155d.png"},{"id":97141283,"identity":"03245e71-7d5c-4ac6-ac6c-43a0c536efda","added_by":"auto","created_at":"2025-12-01 10:06:32","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":884349,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/46ae4a271c5d9b724b974e26.png"},{"id":97122831,"identity":"87cdde28-359a-44c8-99ae-e458f8b6f2fd","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1868829,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/e6b7a831c53bd5a80a3f9970.png"},{"id":97122825,"identity":"7f017db6-4c54-4a8e-bb04-2d375919ea92","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124828,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/6425ba4a19e7ba5c044b611d.png"},{"id":97122828,"identity":"ac435ca0-a85d-4698-b175-04eb215972fc","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":221347,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/f70273c17a8170a9917a5733.png"},{"id":97142717,"identity":"87ac91ab-ca35-4261-a1cf-66c6ed0ac95c","added_by":"auto","created_at":"2025-12-01 10:07:55","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":324633,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/fe3c81de213a7253f3ad796e.png"},{"id":97122826,"identity":"1d0f457d-ef57-4cc0-8911-076e15ee80db","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92972,"visible":true,"origin":"","legend":"","description":"","filename":"d0f342af73b6406aa23e4944fd510fa31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/82754f030356247e8f5fb19a.xml"},{"id":97122829,"identity":"af86334b-12d5-431b-9289-993d4290527c","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106783,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/a718ad924d47f18e2932a241.html"},{"id":97122818,"identity":"50405b42-874f-471b-b2a1-89606f692181","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":243322,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAccelerometry-derived behavioral contrasts between two maximally dissimilar subjects (Subjects 32 and 37).\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e (A) 1440-minute accX time series highlighting consistent daytime activity in Subject 32 versus sparse movement in Subject 37. (B) accX distributions reveal pronounced differences in statistical shape, with Subject 37 exhibiting high skewness and kurtosis. (C) Day × hour heatmaps show regular diurnal structure in Subject 32 versus irregular, punctuated activity in Subject 37. (D) Hourly metrics averaged across days confirm higher amplitude, variability, and entropy in Subject 32. (E) Correlation matrix of all 13 extracted metrics reveals a strong positive cluster among intensity-based features and inverse relationships between entropy and skewness. (F) Structural MRI slices illustrate that Subject 37 has a larger brain volume, consistent with slower, more intermittent movement patterns observed in larger-brained individuals.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/09a118d37858b4ec33904614.png"},{"id":97122817,"identity":"12b5bcbf-c154-44a7-b5f4-ce2da0b5f57f","added_by":"auto","created_at":"2025-12-01 08:02:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":335655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSex differences and brain-behavior relationships in accelerometry-derived activity.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e (A) Daily activity curves and feature distributions reveal that males exhibit greater skewness, kurtosis, and slower morning rise than females, despite similar overall activity levels. (B) Brain volume correlates most strongly with shape-based activity features—skewness, kurtosis, and entropy—especially in females. (C) Cortical ROI volumes are significantly associated with multiple activity features, particularly in frontal, temporal, and parietal regions, with normalization revealing distinct regional patterns. (D) Surface map highlights spatial concentration of significant associations in salience and DMN-linked cortical areas.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/28f177ab164f145c8d2d50dd.png"},{"id":97141085,"identity":"fe72bb60-10a7-4b21-a7a5-d3b3d814e78b","added_by":"auto","created_at":"2025-12-01 10:06:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":448610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTime-of-day–resolved associations between brain structure and inter-day locomotor dynamics.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e (A) Day × hour matrices and hourly metric curves highlight subject-level and sex-specific differences in activity patterns, with females showing higher daytime variability and entropy. (B) Correlation plots reveal strong negative associations between brain volume and entropy during daytime, reversing at night; skewness shows the opposite trend. (C) Averaged day/night metrics confirm time-dependent shifts in structure-function relationships, especially among females. (D) Hourly correlations between raw cortical ROI volumes and ACC features show widespread diurnal effects, particularly for entropy and skewness in DMN-linked regions. (E) Normalizing ROI volumes reveals more focal effects, concentrated in salience and DMN hubs. (F) Surface projection of significant correlations emphasizes the spatial density of entropy-related associations in lateral frontal, parietal, and temporal cortices.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/d5673c3585eb5e4e0a43f57c.png"},{"id":97145143,"identity":"f658901e-d41e-4d7c-bc2f-b29cda2cfefc","added_by":"auto","created_at":"2025-12-01 10:13:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1849774,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7197268/v1/0cf483be-5ad8-4593-b818-4a58a40e350b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain Volume Predicts Skewed Locomotor Output and Lower Temporal Regularity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman movement follows structured, individual rhythms shaped by neuromotor control, energy demands, and circadian timing, yet how these vary with brain anatomy remains poorly understood. Decades of comparative biology have established that animal behavior and physiology obey allometric scaling laws, with body and brain size tightly constraining metabolic rates and activity tempos [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In general, larger-bodied species exhibit slower, more intermittent behavior patterns compared to smaller animals, a principle linked to differences in mechanical efficiency, heat dissipation, and nervous system processing latency. Such scaling relationships are not limited to interspecies comparisons: within-species variation in neuroanatomical structure may also impose constraints on locomotor pacing and temporal complexity. Yet whether human behavioral rhythms manifest this kind of neurostructural allometry remains unclear.\u003c/p\u003e\u003cp\u003eBrain size in particular occupies a central position in theories of behavioral scaling. While much attention has been directed at encephalization as a cross-species correlate of intelligence and social complexity, its implications for basic motor control, arousal regulation, and time-use patterns in everyday life have received comparatively little focus. Evidence from primate studies suggests that increased brain volume is accompanied by life-history shifts\u0026mdash;such as longer developmental periods and slower reproductive rates\u0026mdash;that may reflect a broader strategy of energetic conservation and stability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. At a neurophysiological level, larger brains exhibit thermodynamic constraints that necessitate lower firing rates, sparser coding, and longer conduction delays [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These biophysical limitations may reduce the capacity for rapid, continuous motor output, favoring instead a regime of punctuated, controlled movement bursts. In humans, such a hypothesis predicts that individuals with larger brains should show reduced mean movement levels, greater intermittency in behavioral expression, and less moment-to-moment unpredictability\u0026mdash;hallmarks of an energy-efficient locomotor strategy. Surprisingly, this possibility has not been explicitly tested in naturalistic settings, where movement is unconstrained by laboratory tasks or experimental conditions.\u003c/p\u003e\u003cp\u003eThe theoretical underpinnings of this hypothesis also align with the pace-of-life syndrome (POLS), a conceptual framework in behavioral ecology positing that organisms differ in the speed and consistency of their behavioral and physiological functions along a slow\u0026ndash;fast continuum [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u0026ldquo;Fast\u0026rdquo; individuals tend to be more active, bold, and variable in behavior, with higher metabolic rates and shorter lifespans, while \u0026ldquo;slow\u0026rdquo; individuals adopt more conservative strategies characterized by reduced activity, longer developmental periods, and increased behavioral regularity. Although originally developed to explain interspecies diversity, POLS theory has increasingly been applied within species, including humans, to account for trait covariation across domains such as personality, stress physiology, and circadian preferences [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Notably, sex-specific expressions of pace-of-life strategies have also been observed, with males and females often differing in activity timing, variability, and response to environmental challenges [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, relatively little is known about the neuroanatomical correlates of these strategies in humans, especially in the domain of spontaneous, daily locomotor behavior.\u003c/p\u003e\u003cp\u003eRecent theoretical and empirical work has begun to outline how brain structure may shape not just cognitive function, but also the tempo and regularity of basic motor outputs across daily cycles. Thermodynamic models suggest that as brain volume increases, the capacity for high-frequency neuronal firing and dense network activation decreases, due to constraints on energy dissipation and signal conduction [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This prediction is supported by comparative analyses showing that species with larger brains exhibit slower processing speeds, lower arousal responsiveness, and reduced behavioral reactivity to external stimuli [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Importantly, these patterns are paralleled by differences in neural oscillation frequencies and timing dynamics, which scale predictably with brain size and impose limits on the rapidity of sensorimotor cycles [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In humans, recent morphometric studies have shown that brain regions do not scale uniformly with total intracranial volume; rather, association cortices such as the prefrontal and parietal lobes often exhibit hyperallometric growth, potentially amplifying individual differences in behavioral control and complexity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These insights provide a compelling rationale to examine whether such regional and global volumetric differences are mirrored in the structure of daily locomotor output\u0026mdash;specifically in the variability, skewness, and temporal complexity of movement behavior.\u003c/p\u003e\u003cp\u003eBuilding on this framework, a small but growing body of research has begun to characterize how human locomotor behavior exhibits scale-invariant properties across time, reflecting intrinsic constraints on motor control and behavioral organization. Analyses of wearable accelerometry data across large cohorts have revealed that daily activity patterns follow fractal-like dynamics, with features such as burstiness, heavy-tailed distributions, and long-range temporal correlations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings suggest that spontaneous human movement is not only structured, but adheres to conserved statistical signatures that transcend individual differences in lifestyle or context. Importantly, these scale-free properties appear to reflect internal system constraints\u0026mdash;potentially neural in origin\u0026mdash;rather than purely external schedules or environmental demands. When considered alongside the neurostructural scaling literature, this implies that variability in brain volume and cortical architecture may partly determine how tightly or loosely locomotor output is organized over time. Larger brains, operating under slower and more energetically costly signaling regimes, may favor more intermittent, punctuated activity profiles with reduced entropy and increased skewness\u0026mdash;consistent with an energy-conserving behavioral mode.\u003c/p\u003e\u003cp\u003eIn humans, this relationship likely extends beyond global brain volume to include regional specializations shaped by development and experience. Together, these lines of evidence converge on a core hypothesis: that human movement rhythms are not merely outcomes of choice or environment, but reflect embodied constraints rooted in the architecture of the brain itself. By linking structural neuroanatomy to wearable-based markers of temporal complexity and energetic strategy, the present study seeks to illuminate this overlooked dimension of behavioral neuroscience, offering a novel lens on how the brain sculpts the tempo of our daily lives.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifty-two healthy young adults (26 male, 26 female; age range: 18\u0026ndash;38 years) were recruited from the Bishop\u0026rsquo;s University and Universit\u0026eacute; de Sherbrooke communities. All participants provided written informed consent in accordance with ethics approval from the \u003cem\u003eCentre int\u0026eacute;gr\u0026eacute; universitaire de sant\u0026eacute; et de services sociaux de l\u0026rsquo;Estrie \u0026ndash; Centre hospitalier universitaire de Sherbrooke (CIUSSS de l\u0026rsquo;Estrie \u0026ndash; CHUS)\u003c/em\u003e. \u003cstrong\u003e\u0026nbsp;All methods were performed in accordance with the relevant guidelines and regulations.\u003c/strong\u003e Participants were screened to exclude any neurological or psychiatric disorders, sleep disorders, or use of medications affecting the central nervous system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmartwatch Data Acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach participant wore a \u003cstrong\u003eSamsung Galaxy Watch Active2\u003c/strong\u003e continuously over a 30-day monitoring period. This device features:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e8-channel green LED photoplethysmography (PPG) sensor array\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e3-axis accelerometer, gyroscope, and gravity sensor\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e360 x 360 Super AMOLED display (1.4\u0026quot;)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTizen OS (programmable environment with direct sensor access)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eBattery capacity: 340 mAh (approximately 36 hours per charge)\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eA custom data acquisition app was developed using the official Tizen SDK and deployed on the Samsung Galaxy Watch devices to enable energy-efficient, periodic sampling. Specifically, the built-in accelerometer and photoplethysmography (PPG) sensors were activated for 1 minute every 10 minutes at a sampling rate of 10 Hz, yielding approximately 4,320 minutes of data per subject. This intermittent recording protocol enabled high-resolution locomotor capture while minimizing battery consumption.\u003c/p\u003e\n\u003cp\u003eParticipants were instructed to wear the device at all times, including overnight. Charging was typically done once daily during low-activity windows and took under 30 minutes. Adherence was verified visually during postprocessing, no subjects were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI Data Acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMRI data were collected using a \u003cstrong\u003e3T Philips Ingenia scanner\u003c/strong\u003e at the CIUSSS de l\u0026rsquo;Estrie \u0026ndash; CHUS imaging facility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnatomical Scan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA high-resolution T1-weighted structural image was acquired using a 3D gradient echo sequence with the following parameters:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eTR\u0026thinsp;=\u0026thinsp;7.9 ms, TE\u0026thinsp;=\u0026thinsp;3.5 ms\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eFlip angle\u0026thinsp;=\u0026thinsp;8\u0026deg;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eField of view\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 \u0026times; 150 mm\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMatrix\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 \u0026times; 150\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eVoxel size\u0026thinsp;=\u0026thinsp;\u003cstrong\u003e1 mm isotropic\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll anatomical images were visually inspected for motion artifacts, no subjects were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWearable Data Preprocessing and Feature Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all analyses, we focused on the tri-axial accelerometry (ACC) channels (axes x, y, z), discarding the gyroscope and gravity channels as they were highly correlated to ACC. Raw tri-axial accelerometry data were collected at 10 Hz from the Samsung Galaxy Watch Active2 and processed to derive high-resolution 24-hour behavioral profiles for each subject. For each 1-minute burst, we computed the sum(abs(diff)) of the X, Y and Z-axis acceleration, a proxy for gross motor activity (GMA):\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eGMA values were binned by recording hour (0\u0026ndash;23) across the 30-day monitoring period (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) or by minute (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) for the 1440-minute (intra-day) analysis. Within each hourly bin, values were aggregated across days to compute mean activity per hour, forming subject-specific locomotor curves.\u003c/p\u003e\n\u003cp\u003eFrom the 1440-minute daily activity curves, we computed the following metrics for each subject:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMean and Standard Deviation\u003c/strong\u003e: Summary statistics over the full 24-hour curve.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eSkewness and Kurtosis\u003c/strong\u003e: Higher-order shape descriptors, capturing asymmetry and peakedness.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eEntropy\u003c/strong\u003e: Computed using Shannon entropy over a 20-bin normalized histogram of each subject\u0026rsquo;s daily curve:\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eLet \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x=\\left\\{{x}_{1},{x}_{2},\\dots\\:{x}_{n}\\right\\}\\)\u003c/span\u003e\u003c/span\u003e be the set of acceleration magnitudes within a given time window. We computed the normalized histogram \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{i}\\)\u003c/span\u003e\u003c/span\u003e over 20 bins (excluding zero-probability bins) and then computed Shannon entropy:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cstrong\u003ePeak Time\u003c/strong\u003e: Normalized index (0\u0026ndash;1) corresponding to the minute of maximum activity (np.argmax()).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cstrong\u003eMorning Rise Slope\u003c/strong\u003e: The slope of the best-fit line (via linear regression) between 5:00 AM and 9:00 AM (minutes 300\u0026ndash;540), reflecting rate of morning activation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eAll metrics were aggregated into a feature matrix (52 subjects \u0026times; 9 features).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHourly and Inter-Day Metrics\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTo assess diurnal structure and inter-day stability, raw accelerometry values (acc_x) were additionally summarized into a day \u0026times; hour matrix for each subject (median: ~25 valid days per subject, 24 hours per day). For each hour of the day, we computed the following metrics across days:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cstrong\u003eMean and Standard Deviation\u003c/strong\u003e: Capturing the central tendency and variability in hourly activation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cstrong\u003eSkewness\u003c/strong\u003e: Quantified asymmetry in the hourly activity distributions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cstrong\u003eEntropy\u003c/strong\u003e: Computed from 10-bin histograms of hourly values across days, see formula above.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eThese metrics yielded four 24-hour curves per subject, each summarizing inter-day regularity in hour-specific activation. Averaging these curves across time yielded four additional global features (denoted \u003cem\u003eDH_mean, DH_std, DH_skewness\u003c/em\u003e, and \u003cem\u003eDH\u003c/em\u003e_\u003cem\u003eentropy\u003c/em\u003e) appended to the original feature matrix (final size: 52 \u0026times; 13).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSex Differences\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTo examine sex differences, subjects were grouped by binary sex. Group-mean 1440-minute activity curves and 24-hour metric curves were plotted with shaded standard error margins. Between-group differences for each feature were tested using independent-sample Welch\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests, and visualized using histograms and kernel density estimates. For each hourly metric, we also performed 24 separate \u003cem\u003et\u003c/em\u003e-tests across hours and annotated significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in time series plots.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eBrain Morphometry\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eStructural brain volumes were obtained for each subject using FreeSurfer-derived regional segmentation. Whole-brain T1-weighted MRI volumes were parcellated and left/right hemisphere values were averaged to yield 34 bilateral cortical ROI volumes. All ROI values were expressed in native voxel space and subsequently normalized by each subject\u0026rsquo;s total brain volume (BrainSeg) where specified.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCorrelation Analyses\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eWe tested for associations between brain structure and activity metrics using Pearson correlation. Each of the 13 wearable-derived features was correlated with total brain volume. Second, we assessed correlations between each feature and the 34 normalized ROI volumes, producing a 9 \u0026times; 34 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) or 4 \u0026times; 34 \u0026times; 24 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) matrix of correlation coefficients and corresponding \u003cem\u003ep\u003c/em\u003e-values.\u003c/p\u003e\u003cp\u003eFalse discovery rate (FDR) correction (Benjamini\u0026ndash;Hochberg method) was applied across the full correlation matrix (per analysis) to control for multiple comparisons. Results with \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.15 or uncorrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were annotated in all figures.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eVisualization\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTime series, histograms, and density curves were visualized using matplotlib and seaborn. Heatmaps of correlation matrices were plotted with significance markers (black * = p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, uncorrected, gold star\u0026thinsp;=\u0026thinsp;q\u0026thinsp;\u0026lt;\u0026thinsp;0.15). Cortical maps of statistically significant correlations were rendered on the fsaverage5 inflated surface using Mayavi, with ROI-level significance counts mapped onto the left hemisphere for visualization.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eFigure 1: Contrasting Locomotor Rhythms and Brain Volumes in Two Behaviorally Dissimilar Individuals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure panels A and B illustrate the differences in accelerometry-derived activity patterns between two maximally dissimilar subjects (Subjects 32 and 37). Panel A shows the 1440-minute accX time series, where Subject 32 exhibits a high and sustained level of daytime activity, while Subject 37 remains nearly inactive throughout the day. Panel B displays the distribution of accX values, highlighting marked differences in statistical properties: Subject 32 shows higher mean, lower skewness, and lower kurtosis, consistent with a broad, symmetric activity profile, whereas Subject 37 displays a highly skewed, leptokurtic distribution reflecting sparse, punctuated movement. These contrasts exemplify the range of behavioral phenotypes captured by the accelerometry data.\u003c/p\u003e\u003cp\u003ePanels C and D illustrate the day\u0026ndash;hour structure and hourly statistical characteristics of accX activity for Subjects 32 and 37. Panel C shows day \u0026times; hour heatmaps of accX values, where Subject 32 displays consistent daytime activity across many days, while Subject 37 exhibits sparse, irregular movement with isolated high-activity episodes, often confined to specific hours. Panel D summarizes hourly metrics averaged across valid days: Subject 32 exhibits markedly higher mean and standard deviation, with a clear diurnal profile peaking mid-day, and higher entropy reflecting more complex patterns. In contrast, Subject 37 shows minimal variation, extreme skewness, and low entropy\u0026mdash;indicative of a highly inactive and punctuated movement pattern. These results complement the time-series view in Panels A\u0026ndash;B and highlight distinct temporal organization of behavior.\u003c/p\u003e\u003cp\u003ePanel E shows the correlation matrix across 13 extracted activity metrics. Strong positive correlations cluster among mean, standard deviation, and magnitude-based features (e.g., 1440_mean, total_activity, DH_mean), while entropy and skewness are negatively correlated with most intensity-based metrics, suggesting that more active individuals exhibit more regular and symmetric activity patterns. Specifically, entropy and skewness are inversely correlated because right-skewed distributions reflect sparse, burst-like movement\u0026mdash;where most values are near zero with occasional high peaks\u0026mdash;leading to lower temporal unpredictability and thus lower entropy.\u003c/p\u003e\u003cp\u003ePanel F presents structural MRI slices for the two subjects shown in Panels A\u0026ndash;D. Subject 37 has a larger brain volume (1,992,634 voxels) than Subject 32 (1,531,949 voxels) and exhibits more sporadic and less consistent activity patterns. This observation aligns with allometric scaling principles, where larger-brained individuals often display slower or less frequent behavioral rhythms.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 1 - Accelerometry-derived behavioral contrasts between two maximally dissimilar subjects (Subjects 32 and 37).\u003c/b\u003e \u003cem\u003e(A) 1440-minute accX time series highlighting consistent daytime activity in Subject 32 versus sparse movement in Subject 37. (B) accX distributions reveal pronounced differences in statistical shape, with Subject 37 exhibiting high skewness and kurtosis. (C) Day \u0026times; hour heatmaps show regular diurnal structure in Subject 32 versus irregular, punctuated activity in Subject 37. (D) Hourly metrics averaged across days confirm higher amplitude, variability, and entropy in Subject 32. (E) Correlation matrix of all 13 extracted metrics reveals a strong positive cluster among intensity-based features and inverse relationships between entropy and skewness. (F) Structural MRI slices illustrate that Subject 37 has a larger brain volume, consistent with slower, more intermittent movement patterns observed in larger-brained individuals.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePanel A presents a comprehensive group-level comparison of accelerometry-derived daily activity profiles and derived statistical features between males (n\u0026thinsp;=\u0026thinsp;27) and females (n\u0026thinsp;=\u0026thinsp;25). The top plot depicts the dense 1440-minute (1-day) mean activity curves after pooling\u0026thinsp;~\u0026thinsp;30 days of data per subject and averaging across three accelerometer channels. A clear diurnal pattern is observed, with a sharp rise in activity around 7:00 AM, a plateau from mid-morning through early evening, and a steep decline after 8:00 PM. Males generally exhibit slightly lower activity levels than females during daytime hours, and a more gradual early-morning rise, though the overall difference is not statistically significant (t = \u0026minus;\u0026thinsp;1.51, p\u0026thinsp;=\u0026thinsp;0.139). Similarly, temporal variability across the day (std) shows a modest sex difference (t = \u0026minus;\u0026thinsp;1.19, p\u0026thinsp;=\u0026thinsp;0.239), with females showing slightly more variability. Skewness is significantly greater in males than in females (t\u0026thinsp;=\u0026thinsp;2.44, p\u0026thinsp;=\u0026thinsp;0.0185), suggesting more right-tailed activity distributions among men. Females exhibit significantly greater morning slope values (t = \u0026minus;\u0026thinsp;2.41, p\u0026thinsp;=\u0026thinsp;0.0201), indicating a steeper acceleration in activity following wake onset. Kurtosis is also significantly higher in males (t\u0026thinsp;=\u0026thinsp;2.25, p\u0026thinsp;=\u0026thinsp;0.0307), reflecting more peaked activity distributions. Entropy did not differ significantly between groups (t = \u0026minus;\u0026thinsp;1.12, p\u0026thinsp;=\u0026thinsp;0.268). Other features such as peak timing (t\u0026thinsp;=\u0026thinsp;0.66, p\u0026thinsp;=\u0026thinsp;0.513), total activity (t = \u0026minus;\u0026thinsp;1.51, p\u0026thinsp;=\u0026thinsp;0.139), and mean vector magnitude (t = \u0026minus;\u0026thinsp;1.48, p\u0026thinsp;=\u0026thinsp;0.146) also did not differ significantly by sex. Together, these findings indicate that while overall activity levels are comparable across sexes, the temporal structure and shape of daily activity patterns\u0026mdash;particularly skewness, kurtosis, and morning slope\u0026mdash;exhibit robust sex-linked differences, suggesting divergent circadian dynamics and behavioral regulation between males and females.\u003c/p\u003e\u003cp\u003ePanel B presents scatter plots illustrating the relationships between total brain volume (BrainSeg) and nine accelerometry-derived activity features. Each plot includes sex-specific data points (blue\u0026thinsp;=\u0026thinsp;male, pink\u0026thinsp;=\u0026thinsp;female) with regression lines and correlation statistics for all subjects and males/females separately.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMean activity shows a modest negative association with brain volume (r = \u0026minus;\u0026thinsp;0.27, p\u0026thinsp;=\u0026thinsp;0.053), driven more by males (r = \u0026minus;\u0026thinsp;0.21, p\u0026thinsp;=\u0026thinsp;0.29) than females (r = \u0026minus;\u0026thinsp;0.16, p\u0026thinsp;=\u0026thinsp;0.443), though not statistically significant.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStandard deviation is weakly and non-significantly correlated with brain volume across all groups (All: r = \u0026minus;\u0026thinsp;0.19, p\u0026thinsp;=\u0026thinsp;0.187).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSkewness shows a significant positive correlation overall (r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;0.019), primarily due to the female subgroup (r\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;=\u0026thinsp;0.088), suggesting that larger-brained females tend to have more right-skewed activity distributions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eKurtosis is significantly positively correlated with brain volume overall (r\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;0.025), an effect again driven entirely by females (r\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;=\u0026thinsp;0.020).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEntropy trends negatively with brain volume (r = \u0026minus;\u0026thinsp;0.20, p\u0026thinsp;=\u0026thinsp;0.164), with a significant effect in females (r = \u0026minus;\u0026thinsp;0.49, p\u0026thinsp;=\u0026thinsp;0.012) but not in males (r\u0026thinsp;\u0026asymp;\u0026thinsp;0.00, p\u0026thinsp;=\u0026thinsp;0.985).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePeak time shows no meaningful associations in any group (All: r\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;=\u0026thinsp;0.95).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMorning slope is weakly negatively associated with brain volume overall (r = \u0026minus;\u0026thinsp;0.22, p\u0026thinsp;=\u0026thinsp;0.114), with non-significant trends in both sexes.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMean vector magnitude shows a near-significant negative trend (All: r = \u0026minus;\u0026thinsp;0.27, p\u0026thinsp;=\u0026thinsp;0.054), mirroring total activity.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTotal activity similarly trends negative (r = \u0026minus;\u0026thinsp;0.27, p\u0026thinsp;=\u0026thinsp;0.053), though again not reaching significance in either subgroup.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSummary: Shape-based features\u0026mdash;skewness, kurtosis, and entropy\u0026mdash;show the strongest and most consistent associations with brain volume, driven primarily by female sub-group. Amplitude- and timing-based features (e.g., mean, total activity, peak time) show weaker and largely non-significant relationships.\u003c/p\u003e\u003cp\u003ePanel C presents two heatmaps depicting Pearson correlation coefficients (r-values) between DK atlas cortical ROI volumes and accelerometry-derived features, either using raw volumes (left) or volumes normalized to total brain size (right). In the raw matrix, strong and widespread positive correlations emerge between shape-related ACC features\u0026mdash;particularly kurtosis, skewness, and entropy\u0026mdash;and medial and lateral prefrontal regions (e.g., rostralmiddlefrontal, superiorfrontal), temporal areas (e.g., fusiform, inferior temporal), and supramarginal cortex. Many of these associations survive FDR thresholding (q\u0026thinsp;\u0026lt;\u0026thinsp;0.15, gold asterisks), suggesting robust region-specific coupling. When adjusting for global brain size, a more selective pattern emerges: entropy remains negatively associated with regions like precuneus and entorhinal cortex, while kurtosis shows positive correlations with lateral temporal and frontal poles. Additionally, total activity and vector magnitude measures become more salient, now showing positive associations with lateral occipital, temporal pole, and superior parietal cortices. These shifts indicate that normalization sharpens the specificity of cortical associations, revealing that both amplitude and shape characteristics of daily activity patterns are linked to distinct regional structural variability, above and beyond overall brain volume.\u003c/p\u003e\u003cp\u003ePanel D visualizes the number of significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between ACC-derived features and cortical ROI volumes (normalized by brain size) across the left hemisphere. The strongest concentration of associations is localized to salience and DMN-linked regions (superior temporal, supramarginal, insula, precuneus, superior frontal, rostral middle frontal, and caudal anterior cingulate).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003e- Sex differences and brain-behavior relationships in accelerometry-derived activity.\u003c/b\u003e \u003cem\u003e(A) Daily activity curves and feature distributions reveal that males exhibit greater skewness, kurtosis, and slower morning rise than females, despite similar overall activity levels. (B) Brain volume correlates most strongly with shape-based activity features\u0026mdash;skewness, kurtosis, and entropy\u0026mdash;especially in females. (C) Cortical ROI volumes are significantly associated with multiple activity features, particularly in frontal, temporal, and parietal regions, with normalization revealing distinct regional patterns. (D) Surface map highlights spatial concentration of significant associations in salience and DMN-linked cortical areas.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePanel A displays hourly patterns of x-axis accelerometry data across days, comparing three example participants (two males and one female, top row) and group-level sex differences in hourly metrics (bottom row). Individual day \u0026times; hour matrices reveal substantial inter-day variability, with clearer daytime activation and higher amplitude in some subjects (e.g., s52, s3) compared to others (s6). Aggregated plots below show that females exhibit significantly higher inter-day mean and standard deviation of hourly activity during the afternoon and evening (12:00\u0026ndash;21:00), while males display higher early-morning activity (12:00\u0026ndash;3:00). Inter-day skewness is consistently greater in males during daytime, indicating more right-tailed activity distributions, whereas females show elevated entropy from mid-morning to evening. These temporal dynamics underscore sex-specific differences in inter-day activity profiles.\u003c/p\u003e\u003cp\u003ePanel B reveals a clear diurnal pattern in the correlation between hourly ACC metrics and brain volume, with mean, standard deviation, and especially entropy showing strong negative correlations during daytime hours and shifting toward positive or null correlations at night. In contrast, skewness exhibits the opposite trend\u0026mdash;positively correlated with brain volume during the afternoon and evening, but trending negative during the early morning hours. Among these, entropy shows the most robust associations, particularly between 12:00\u0026ndash;18:00 (FDR q\u0026thinsp;\u0026lt;\u0026thinsp;0.15), indicating that larger brain volume predicts lower inter-day entropy and higher activity skew, reflecting a shift toward punctuated, energy-conserving rhythms.\u003c/p\u003e\u003cp\u003ePanel C shows the relationship between brain volume and four ACC-derived inter-day activity metrics averaged over daytime (12\u0026ndash;6pm) and nighttime (12\u0026ndash;6am) hours. During the daytime, the strongest effect is a significant negative correlation between entropy and brain volume (r = \u0026minus;\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;0.0243), especially in females (r = \u0026minus;\u0026thinsp;0.36). Mean activity also trends negatively with brain volume (r = \u0026minus;\u0026thinsp;0.27), while skewness shows a weaker positive trend (r\u0026thinsp;=\u0026thinsp;0.19), consistent with the daytime pattern in Panel B. Standard deviation shows minimal associations.\u003c/p\u003e\u003cp\u003eAt night, patterns generally reverse. Entropy shows no significant correlation, while skewness and standard deviation are negatively associated with brain volume, particularly in females (skewness: r = \u0026minus;\u0026thinsp;0.36; std: r = \u0026minus;\u0026thinsp;0.46), indicating that larger-brained women tend to have more symmetric and stable nocturnal activity. Mean activity shows no relationship overall but trends negatively in females. Altogether, these results reinforce a time-dependent inversion: negative correlations during the day (especially entropy) shift toward null or positive patterns at night.\u003c/p\u003e\u003cp\u003ePanel D presents four heatmaps showing hourly correlations between raw cortical ROI volumes and ACC-derived metrics\u0026mdash;mean, standard deviation, skewness, and entropy\u0026mdash;revealing distinct spatiotemporal coupling patterns. The mean and standard deviation maps show widespread negative correlations during daytime hours, particularly in DMN-linked regions (precuneus, superior frontal, superior parietal, rostral anterior cingulate), suggesting that larger regional volumes are associated with reduced and more stable activity in those periods. In contrast, skewness shows broad positive correlations in the same regions, associating increased asymmetry in movement patterns with greater regional volume. The entropy map reveals the most consistent and extensive effects, with strong negative correlations spanning 8:00\u0026ndash;20:00, especially in precuneus, superior frontal, and rostral anterior cingulate\u0026mdash;many surviving FDR correction (q\u0026thinsp;\u0026lt;\u0026thinsp;0.15) highlighting a robust inverse relationship between structural size and within-hour activity variability during waking hours.\u003c/p\u003e\u003cp\u003ePanel E displays the same set of hourly correlations as Panel D but uses ROI volumes normalized by total brain size (BrainSeg), revealing more focal and specific structure-function relationships. The mean and standard deviation maps show attenuated and spatially restricted negative correlations, primarily localized to salience-linked regions in the temporal fold, with fewer significant effects overall. Similar patterns were observed for skewness and entropy. Similar to the intra-day measures, normalizing regional volume by total volume highlights a core subset of regions in the salience/DMN networks where local structural differences predict temporal complexity and asymmetry of activity, emphasizing targeted rather than global effects of brain anatomy on behavior. Panel F visualizes the number of significant hourly correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between normalized ROI volumes and ACC features projected onto the cortical surface. Consistent with Panel E, entropy and mean activity show the densest clusters of associations in lateral frontal (e.g., rostralmiddlefrontal), inferior parietal, and temporal cortices. Entropy in particular shows widespread involvement of supramarginal, superiortemporal, and precuneus regions, reinforcing its role as the most structurally sensitive feature.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings reveal a striking association between brain volume and the temporal structure of daily locomotor behavior, consistent with theoretical models of neurostructural scaling. Larger-brained individuals exhibited less frequent, more punctuated movement profiles\u0026mdash;characterized by lower overall activity, higher skewness and kurtosis, and reduced entropy\u0026mdash;especially during daytime hours. These results provide empirical support for the idea that scale-free dynamics observed in the brain [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] may extend to overt behavior, reinforcing the notion of fractal coupling between neural structure, neural dynamics, and real-world activity.\u003c/p\u003e\u003cp\u003eAt the structural level, our observations align with recent work showing that brain morphometry follows region-specific scaling gradients across the adult lifespan [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We found that regional normalization sharpened the relationship between cortical volume and movement dynamics, especially in association cortices (e.g., frontal, temporal, parietal). Importantly, volumetric associations were strongest in control and salience-related hubs, including the superior frontal gyrus, insula, precuneus, and supramarginal gyrus, suggesting that anatomical variation in attention switching, interoception, and default mode regulation may influence both the pacing and structure of daily behavior. We also observed consistent effects in the superior temporal gyrus, a key motor hub for speech and vocal articulation, potentially linking structural variability in communicative motor control to broader movement patterns.\u003c/p\u003e\u003cp\u003eNotably, these structure\u0026ndash;behavior effects appeared to be largely driven by the female sub-population. Females exhibited stronger and more widespread correlations between brain volume and locomotor features, particularly skewness and entropy. This suggests that within-group variation in brain anatomy may have a more behaviorally salient impact in women, potentially due to tighter coupling between neural control systems and peripheral activity. In addition, the overall effects observed in the pooled sample are likely amplified by the well-established sex difference in absolute brain volume, with males generally exhibiting larger brains. This anatomical disparity introduces a broader variance structure that may exaggerate associations at the group level, even when individual correlations are stronger among females. The combination of sex-based differences in both mean volume and intra-group sensitivity underscores the complex, multilevel nature of brain\u0026ndash;behavior scaling. These patterns may also reflect hormonal modulation of neurovascular or functional systems, as highlighted in recent multimodal studies of menstrual cycle effects on brain physiology. For example, fluctuations in estrogen and progesterone have been shown to exert opposing and spatially distinct effects on cerebral blood flow\u0026mdash;progesterone reducing anterior perfusion, while estrogen modestly increases posterior flow [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These physiological shifts may interact with the underlying neural architecture to produce sex-specific behavioral patterns, reinforcing the need to model both hormonal state and structural scale when characterizing brain\u0026ndash;behavior coupling.\u003c/p\u003e\u003cp\u003eThe behavioral profiles associated with larger brain volumes were not simply less active, but more structured and conservative. These traits echo prior work linking higher sense of life purpose to more cohesive and less fragmented activity rhythms [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], suggesting that both psychological and biological traits can converge on similar behavioral phenotypes\u0026mdash;slower, more energy-efficient, and temporally predictable movement. Moreover, previous large-scale studies have shown that even light-intensity activity levels are positively associated with gray and white matter volumes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], reinforcing the bidirectional relationship between brain integrity and movement patterns.\u003c/p\u003e\u003cp\u003eThis structure\u0026ndash;behavior link is further supported by work demonstrating that free-living physical activity is correlated with both structural volumes and functional connectivity patterns [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our results, showing strong coupling between activity complexity (especially entropy and skewness) and cortical anatomy, suggest that individuals with larger and more developed control regions may engage in more internally regulated and less reactive movement behavior. This dovetails with emerging models of top-down control over circadian behavior: recent work shows that individuals with stronger frontoparietal connectivity\u0026mdash;and greater lag between autonomic readiness and behavioral activation\u0026mdash;tend to move in anticipation of physiological rhythms, suggesting a cortical override of bottom-up autonomic signals [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our observed associations between frontoparietal volume and punctuated locomotor output may reflect this same anticipatory control mechanism operating at a structural level.\u003c/p\u003e\u003cp\u003eThe patterns of reduced entropy in individuals with larger brains align with models of neural control that emphasize dynamical systems properties of the brain during real-world locomotion (Monroe et al., 2023) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These models highlight how sample entropy and other temporal complexity metrics reveal subtle regulatory processes in sensorimotor control\u0026mdash;features we find mirrored in gross movement dynamics via accelerometry. Notably, our entropy\u0026ndash;volume findings are consistent with studies linking EEG and body kinematics through entropy and recurrence-based coupling metrics [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], suggesting a unified statistical language for capturing complexity across neural and motor systems.\u003c/p\u003e\u003cp\u003eSuch fine-grained structure\u0026ndash;function relationships are made possible by the use of high-resolution morphometric data. As shown in recent ultra-high-resolution MRI work, cortical architecture can be resolved at a scale that captures meaningful individual variability [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our study leverages this precision to isolate volumetric predictors of behavioral rhythms, revealing spatially specific associations in salience and control-related areas\u0026mdash;especially when adjusting for total brain size. This refinement reflects the growing consensus that structural MRI, when paired with wearable sensors, offers a powerful lens on embodied cognition.\u003c/p\u003e\u003cp\u003eFinally, the broader evolutionary relevance of our findings is underscored by evidence that scale-invariant patterns of motor behavior emerge early in development. Infants display dynamic scaling in motor output that mirrors the fractal-like intermittency observed in adults [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This developmental continuity suggests that temporal structure in behavior is not a learned artifact of adult lifestyle but is likely rooted in intrinsic neural constraints present across the lifespan.\u003c/p\u003e\u003cp\u003eIn sum, our study supports the hypothesis that human behavioral rhythms\u0026mdash;particularly the shape and complexity of daily movement\u0026mdash;are scaled by brain anatomy in predictable ways. These findings expand the purview of neurostructural scaling theories, linking them to embodied activity patterns in naturalistic settings. They also support a broader framework in which energy conservation, neural processing efficiency, and behavioral regularity co-evolve as part of individualized pace-of-life strategies. Future work should explore how these structural\u0026ndash;behavioral couplings shift in clinical populations, developmental transitions, or in response to lifestyle interventions aimed at modifying brain or behavior.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.G. and MB recruited participants and led data acquisition, including MRI and smartwatch recordings. O.D. developed the smartwatch firmware in C for raw data capture and extraction. A.Y., R.N., and S.R. performed statistical analyses and signal processing. R.B. conceived and supervised the study, provided funding, and oversaw all aspects of study design and interpretation. All authors contributed to manuscript preparation and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All raw and preprocessed data generated and analyzed during this study are available from the corresponding author ([email protected]) upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All Python and Bash scripts used for data processing, analysis, and visualization are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e this work was funded by the National Science and Engineering Research Council of Canada (NSERC) Discovery Grant program RGPIN1507.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests:\u003c/strong\u003e the authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations and approval for human experiments:\u0026nbsp;\u003c/strong\u003eAll participants provided written informed consent in accordance with ethics approval from the \u003cem\u003eCentre intégré universitaire de santé et de services sociaux de l’Estrie – Centre hospitalier universitaire de Sherbrooke (CIUSSS de l’Estrie – CHUS)\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003ethis work was funded by the National Science and Engineering Research Council of Canada (NSERC) Discovery Grant program RGPIN1507.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArmstrong, E. (1983). Relative brain size and metabolism in mammals. \u003cem\u003eScience, 220\u003c/em\u003e, 1302\u0026ndash;1304. DOI: 10.1126/science.6407108\u003c/li\u003e\n\u003cli\u003eMartin, R. D. (1981). Relative brain size and basal metabolic rate in terrestrial vertebrates. \u003cem\u003eNature, 293\u003c/em\u003e, 57\u0026ndash;60. DOI: 10.1038/293057a0\u003c/li\u003e\n\u003cli\u003eBarrickman, N. L., Bastian, M. L., Isler, K., \u0026amp; van Schaik, C. P. (2008). Life history costs and benefits of encephalization: a comparative test using data from long-term studies of primates in the wild. \u003cem\u003eJournal of Human Evolution, 54\u003c/em\u003e(5), 568\u0026ndash;590. DOI: 10.1016/j.jhevol.2007.08.012\u003c/li\u003e\n\u003cli\u003eKarbowski, J. (2009). Thermodynamic constraints on neural dimensions, firing rates, brain temperature and size. \u003cem\u003eJournal of Computational Neuroscience, 27\u003c/em\u003e(3), 415\u0026ndash;436. DOI: 10.1007/s10827-009-0153-7\u003c/li\u003e\n\u003cli\u003eRicklefs, R. E., \u0026amp; Wikelski, M. (2002). The physiology/life-history nexus. \u003cem\u003eTrends in Ecology \u0026amp; Evolution, 17\u003c/em\u003e(10), 462\u0026ndash;468. DOI: 10.1016/S0169-5347(02)02578-8\u003c/li\u003e\n\u003cli\u003eR\u0026eacute;ale, D., Garant, D., Humphries, M. M., Bergeron, P., Careau, V., \u0026amp; Montiglio, P.-O. (2010). Personality and the emergence of the pace-of-life syndrome concept at the population level. \u003cem\u003ePhilosophical Transactions of the Royal Society B: Biological Sciences, 365\u003c/em\u003e(1560), 4051\u0026ndash;4063. DOI: 10.1098/rstb.2010.0208\u003c/li\u003e\n\u003cli\u003eDammhahn, M., Dingemanse, N. J., Niemel\u0026auml;, P. T., \u0026amp; R\u0026eacute;ale, D. (2018). Pace-of-life syndromes: a framework for the adaptive integration of behavior, physiology and life history. \u003cem\u003eBehavioral Ecology and Sociobiology, 72\u003c/em\u003e, 62. DOI: 10.1007/s00265-018-2473-y\u003c/li\u003e\n\u003cli\u003eImmonen, E., H\u0026auml;m\u0026auml;l\u0026auml;inen, A., Schuett, W., \u0026amp; Tarka, M. (2018). Evolution of sex-specific pace-of-life syndromes: genetic architecture and physiological mechanisms. \u003cem\u003eBehavioral Ecology and Sociobiology, 72\u003c/em\u003e(3), 60. DOI: 10.1007/s00265-018-2462-1\u003c/li\u003e\n\u003cli\u003eHerculano-Houzel, S. (2012). Encephalization quotient and neuron scaling patterns: connecting brain size and numbers of neurons. \u003cem\u003eFrontiers in Human Neuroscience, 6\u003c/em\u003e. DOI: 10.3389/fnhum.2012.xxxxxx\u003c/li\u003e\n\u003cli\u003eBuzs\u0026aacute;ki, G., \u0026amp; Draguhn, A. (2004). Neuronal oscillations in cortical networks. \u003cem\u003eScience, 304\u003c/em\u003e(5679), 1926\u0026ndash;1929. DOI: 10.1126/science.1099745\u003c/li\u003e\n\u003cli\u003eReardon, P. K., et al. (2018). Normative brain size variation and brain shape diversity in humans. \u003cem\u003eScience, 360\u003c/em\u003e(6394), 1222\u0026ndash;1227. DOI: 10.1126/science.aar2578\u003c/li\u003e\n\u003cli\u003eChialvo, D. R., Gonzalez Torrado, A. M., Gudowska-Nowak, E., Ochab, J. K., Montoya, P., Nowak, M. A., \u0026amp; Tagliazucchi, E. (2015). How we move is universal: scaling in the average shape of human activity. \u003cem\u003ePapers in Physics, 7\u003c/em\u003e, 070017. DOI: 10.4279/PIP.070017\u003c/li\u003e\n\u003cli\u003eGrosu, G. F., Hopp, A. V., Moca, V. V., B\u0026acirc;rzan, H., Ciuparu, A., Ercsey-Ravasz, M., Winkel, M., Linde, H., \u0026amp; Mureșan, R. C. (2023). The fractal brain: scale-invariance in structure and dynamics. \u003cem\u003eCerebral Cortex, 33\u003c/em\u003e(8), 4574\u0026ndash;4605. DOI: 10.1093/cercor/bhac363\u003c/li\u003e\n\u003cli\u003eRiccardi, N., Teghipco, A., Newman-Norlund, S., Newman-Norlund, R., Rangus, I., Rorden, C., Fridriksson, J., \u0026amp; Bonilha, L. (2025). Distinct brain age gradients across the adult lifespan reflect diverse neurobiological hierarchies. \u003cem\u003eCommunications Biology, 8\u003c/em\u003e(1), 802. DOI: 10.1038/s42003-025-08228-z\u003c/li\u003e\n\u003cli\u003eC\u0026ocirc;t\u0026eacute;, S., Butler, R., Michaud, V., Lavall\u0026eacute;e, E., Croteau, \u0026Eacute;., Mendrek, A., Lepage, J.-F., \u0026amp; Whittingstall, K. (2021). The regional effect of serum hormone levels on cerebral blood flow in healthy nonpregnant women. \u003cem\u003eHuman Brain Mapping, 42\u003c/em\u003e(17), 5677\u0026ndash;5688. DOI: 10.1002/hbm.25646\u003c/li\u003e\n\u003cli\u003eSutin, A. R., Stephan, Y., Kek\u0026auml;l\u0026auml;inen, T., Luchetti, M., \u0026amp; Terracciano, A. (2025). Purpose in life and accelerometer-measured physical activity among older adults. \u003cem\u003ePsychology \u0026amp; Health, 40\u003c/em\u003e(1), 126\u0026ndash;140. DOI: 10.1080/08870446.2023.2200414\u003c/li\u003e\n\u003cli\u003eSpartano, N. L., Davis-Plourde, K. L., Himali, J. J., Andersson, C., Pase, M. P., Maillard, P., DeCarli, C., Murabito, J. M., Beiser, A. S., Vasan, R. S., \u0026amp; Seshadri, S. (2019). Association of accelerometer-measured light-intensity physical activity with brain volume: the Framingham Heart Study. \u003cem\u003eJAMA Network Open, 2\u003c/em\u003e(4), e192745. DOI: 10.1001/jamanetworkopen.2019.2745\u003c/li\u003e\n\u003cli\u003eDomingos, J. P., Pic\u0026oacute;-P\u0026eacute;rez, M., Magalh\u0026atilde;es, R., Moreira, J., Sousa, N., P\u0026ecirc;go, J. M., \u0026amp; Santos, N. C. (2021). Free-living physical activity measured with a wearable device is associated with larger hippocampus volume and greater functional connectivity in healthy older adults: an observational, cross-sectional study in Northern Portugal. \u003cem\u003eFrontiers in Aging Neuroscience, 13\u003c/em\u003e, 729060. DOI: 10.3389/fnagi.2021.729060\u003c/li\u003e\n\u003cli\u003eDemers, O., Ghaffari, S., Li, C., \u0026amp; Butler, R. (2025). Stronger cognitive control enables early behavioral rise independent of physiological activation. \u003cem\u003eResearch Square\u003c/em\u003e (preprint). DOI: 10.21203/rs.3.rs-7057835/v1\u003c/li\u003e\n\u003cli\u003eMonroe, D. C., Berry, N. T., Fino, P. C., \u0026amp; Rhea, C. K. (2023). A dynamical systems approach to characterizing brain\u0026ndash;body interactions during movement: challenges, interpretations, and recommendations. \u003cem\u003eSensors, 23\u003c/em\u003e(14), 6296. DOI: 10.3390/s23146296\u003c/li\u003e\n\u003cli\u003eSong, S., \u0026amp; Nordin, A. D. (2021). Mobile electroencephalography for studying neural control of human locomotion. \u003cem\u003eFrontiers in Human Neuroscience, 15\u003c/em\u003e, 749017. DOI: 10.3389/fnhum.2021.749017\u003c/li\u003e\n\u003cli\u003eCabalo, D. G., et al. (2025). Multimodal precision MRI of the individual human brain at ultra-high fields. \u003cem\u003eScientific Data, 12\u003c/em\u003e(1), 526. DOI: 10.1038/s41597-025-04863-7\u003c/li\u003e\n\u003cli\u003eChua, Y. W., Jim\u0026eacute;nez-S\u0026aacute;nchez, L., Ledsham, V., O\u0026rsquo;Carroll, S., Cox, R. F. A., Andonovic, I., Tachtatzis, C., Boardman, J. P., Fletcher-Watson, S., Rowe, P., \u0026amp; Delafield-Butt, J. (2025). A multi-level analysis of motor and behavioural dynamics in 9-month-old preterm and term-born infants during changing emotional and interactive contexts. \u003cem\u003eScientific Reports, 15\u003c/em\u003e(1), 952. DOI: 10.1038/s41598-024-83194-w\u003c/li\u003e\n\u003cli\u003eMontgomery, S. H., Mundy, N. I., \u0026amp; Barton, R. A. (2016). Brain evolution and development: adaptation, allometry and constraint. \u003cem\u003eProceedings of the Royal Society B: Biological Sciences, 283\u003c/em\u003e(1838), 20160433. DOI: 10.1098/rspb.2016.0433\u003c/li\u003e\n\u003cli\u003eScarpazza, C., \u0026amp; De Simone, M. S. (2016). Voxel-based morphometry: current perspectives. \u003cem\u003eNeuroscience and Neuroeconomics, 5\u003c/em\u003e, 19\u0026ndash;35. DOI: 10.2147/NAN.S84220\u003c/li\u003e\n\u003cli\u003eSol, D., Prego, A., Oliv\u0026eacute;, L., Genovart, M., Oro, D., \u0026amp; Hern\u0026aacute;ndez-Mat\u0026iacute;as, A. (2025). Adaptations to marine environments and the evolution of slow-paced life histories in endotherms. \u003cem\u003eNature Communications, 16\u003c/em\u003e, 4265. DOI: 10.1038/s41467-025-59273-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7197268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7197268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDaily locomotor activity patterns vary widely between individuals, reflecting underlying kinematic strategies, energetic trade-offs, and circadian regulation. Here we integrate wearable accelerometry and brain MRI to examine how locomotor kinematics (movement intensity and variability) relate to brain size through an allometric biomechanics lens, and how these links depend on time-of-day (temporal behavior scaling) and align with the pace-of-life continuum. In a cohort of adults (n\u0026thinsp;=\u0026thinsp;52, 27 male), we found that individuals with larger brains exhibited slower, more intermittent daily movement profiles \u0026ndash; characterized by lower overall activity, higher skewness and kurtosis of activity distributions, and reduced complexity (entropy) \u0026ndash; suggestive of more controlled, energy-conserving behavior. In contrast, smaller-brained individuals had more continuous, regular, and symmetric activity patterns. Sex differences in circadian locomotor dynamics were also evident: females showed faster morning ramp-up and more symmetric activity distributions, whereas males exhibited greater daytime skewness and leptokurtosis, consistent with divergent circadian pacing. Regional correlations were sharpened when normalizing by overall brain size, localizing brain-behavior correlatoins to temporal (salience) and frontal cortices. Our findings reveal an allometric scaling of human activity rhythms: larger brain volume is associated with a \u0026ldquo;slower-paced\u0026rdquo; daily locomotor regimen, analogous to the slow-fast continuum observed across species. This diurnal structural coupling underscores how brain anatomy may influence not just how much we move, but how and when we move, supporting the idea that humans manifest individualized pace-of-life strategies rooted in neurobiology.\u003c/p\u003e","manuscriptTitle":"Brain Volume Predicts Skewed Locomotor Output and Lower Temporal Regularity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:02:47","doi":"10.21203/rs.3.rs-7197268/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-11-24T11:28:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T07:52:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-30T10:42:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T19:28:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-28T19:25:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5009c6c5-d6f7-428b-876a-516fe5c7dabd","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58613440,"name":"Biological sciences/Neuroscience"},{"id":58613441,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2025-12-01T08:02:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:02:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7197268","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7197268","identity":"rs-7197268","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}