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Functional connectivity networks have become a central focus in neuroscience as they reveal key higher dimensional features of normal and abnormal nervous system physiology. Functional networks reflect activity-based coupling between brain regions that may be constrained by relatively static anatomical connections, yet these networks appear to support tremendously dynamic behaviors. Within this growing field, the stability and temporal characteristics of functional connectivity brain networks have not been well characterized. We evaluated the temporal stability of spontaneous functional connectivity networks derived from multi-day scalp encephalogram (EEG) recordings in five healthy human subjects. Topological stability and graph characteristics of networks derived from averaged data epochs ranging from one second to multiple hours across different states of consciousness were compared. We show that although functional networks are highly variable on the order of seconds, stable network templates emerge after as little as ~100s of recording and persist across different states and frequency bands (albeit with slightly different characteristics in different states and frequencies). Within these network templates, the most common edges are markedly consistent, constituting a network “core”. Although average network topologies persist across time, measures of global network connectivity, density and clustering coefficient, are state- and frequency- specific, with sparsest but most highly clustered networks seen during sleep and in the gamma frequency band. These findings support the notion that a core functional organization underlies spontaneous cortical processing and may provide a reference template upon which unstable, transient, and rapidly adaptive long-range assemblies are overlaid in a frequency dependent manner.

All‐out exercise testing ( AOT ) has emerged as a method for quantifying critical speed ( CS ) and the curvature constant (D′). The AOT method was recently validated for shuttle running yet how that method compares with linear running is unknown. In the present study, we utilized a novel bi‐exponential model that derives CS and D′ with additional new parameters from the AOT method. Fourteen male athletes (age = 21.6 ± 2.2 years; height = 177 ± 70 cm; weight = 83.0 ± 11.8 kg) completed a graded exercise test ( GXT ) to derive maximum oxygen uptake ( ) and the average speed between gas exchange threshold and (sΔ50%), a linear AOT , and two shuttle AOT s. Measurement agreement was determined using intraclass correlation coefficient ( ICC ), typical error ( TE ), and coefficient of variation ( CV ). The y ‐asymptote ( ) of the speed‐time curve (3.52 ± 0.66 m·sec −1 ) did not differ from sΔ50% (3.49 ± 0.41 m·sec −1 ) or CS (3.77 ± 0.56 m·sec −1 ) ( P = 0.34). Strong agreement was observed for estimates of CS ( ICC = 0.92, TE = 0.18 m·sec −1 , and CV = 5.7%) and D′ ( ICC = 0.94, TE = 16.0 m, CV = 7.6%) with significant ( P < 0.01) correlations observed between and CS and between and ( r values of 0.74 and 0.84, respectively). The time constant of the decay in speed ( ) and the amplitude between maximal speed and ( ) emerged as unique metrics. The and metrics may glean new insights for prescribing and interpreting high‐intensity exercise using the AOT method.

Purpose Improvements in D′ (the fatigability constant for running) subsequent to training interventions remain elusive. High-intensity interval training (HIIT) within the severe intensity domain for short durations (< 2-min) have been theorized to improve D′. The purpose of the present study was to assess this in a group of moderately trained individuals. Methods Eighteen participants completed graded exercise testing (GXT), 40-m sprint testing and a 3-min all-out test (3MT) for running to determine key mechanistic and physiological parameters. Participants were randomly assigned into one of two groups based on intensity prescription (G 140% = 140% of critical speed [CS]), or time intervals (G 90-s = 90-s) to complete a twice-weekly training intervention for 6-weeks followed by re-assessment. Results No between-group differences were present either prior to or following the intervention. Substantial and meaningful improvements were detected during the post-intervention period for both groups for VO 2max 0.62 m/s; G 90-s : M = 0.46 m/s), D′ (G 140% : M = − 56.34 m; G 90-s : M = − 18.36 m), FI% (G 140% M = − 6.75%; G 90-s : M = − 4.38%) and maximal distance (G 140% : M = 49.67 m; G 90-s : M = 58.38 m).Conclusions The prescribed intensities and durations were insufficient to elicit improvements in D′. Improvements in D′ may be dependent on very short-duration intervals (i.e. < 60 to 90-s) at speeds exceeding 140% CS but below maximal sprint speed.

How parameters derived from oxygen uptake kinetics relate to critical speed is not fully understood, and how such parameters relate to more sport-specific performances, such as shuttle running, has not been investigated. Therefore, the primary aims of the present student were to examine the kinetics during all-out linear and shuttle running and compare physiological variables of all-out running to variables measured during a graded exercise test (GXT). Fifteen male soccer players performed a graded exercise test (GXT) and the kinetics from a series of three different 3-min all-out tests (3MT’s) were evaluated. achieved during the GXT did not differ from maximal achieved during the all-out tests (F = 1.85, p = 0.13) (overall ICC = 0.65; typical error = 2.48 ml∙kg-1∙min-1; coefficient of variation = 4.8%). A moderate, inverse correlation (r = -0.62, p = 0.02) was observed between τ (14.7 ± 1.92 s) and CS (3.96 ± 0.52 m∙s-1) despite the narrow SD for τ. No differences (p > 0.05) were observed for any of the kinetics between continuous and shuttle running bouts. The linear running 3MT (r3MT) represents a viable surrogate to the GXT and data beyond CS and D’ may be gleaned by using the bi-exponential speed-time model.

Purpose: To compare critical speed (CS) derived from all-out testing (AOT) for linear and shuttle running with metrics from a graded exercise test, the Yo-Yo Intermittent Recovery Test  Level 1 (YYIR1), and estimation of an 800-m-shuttle time trial. Methods: Twelve male rugby players completed a graded exercise test, the YYIR1, a linear AOT, shuttle AOTs of 25 and 50 m, and an 800-m-shuttle time trial consisting of 32 × 25-m shuttles. Results: Strong linear correlations were observed between maximum oxygen uptake () and CS (m·s−1) derived from the linear AOT (3.68 [0.62], r = .90, P < .01) and 50-m-shuttle AOT (3.19 [0.26], r = .83, P < .01). Conversely, showed lower correlations with speeds evoking CS from 25-m AOT (2.86 [0.18], r = .42, P = .18) and YYIR1 (4.36 [0.11], r = .55, P = .07). The 800-m time trial (213.58 [15.84] s) was best predicted using parameters from the 25-m AOT (r = .93, SEE = 6.60 s, P < .001). Conclusions: The AOT is a valuable method of assessing performance-specific fitness, with CS from linear and 50-m-shuttle AOTs being strong predictors of , rivaling metrics from the graded exercise test. The YYIR1 offered limited utility compared with the AOT method.

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