Differential modulation of resting-state functional connectivity between amygdala and precuneus after acute physical exertion of varying intensity: indications for a role in affective regulation.

Introduction: Physical activity influences psychological well-being. This study aimed to determine the impact of exercise intensity on psychological well-being and alterations in emotion-related brain functional connectivity (FC). Methods: Twenty young, healthy, trained athletes performed a low- and high-intensity interval exercise (LIIE and HIIE) as well as a control condition in a within-subject crossover design. Before and after each condition, Positive And Negative Affect Scale (PANAS) was assessed as well as resting-state functional MRI (rs-fMRI). Voxel-wise FC was examined for bilateral amygdala seed region to whole-brain and emotion-related anatomical regions (e.g., insula, temporal pole, precuneus). Data analyses were performed using linear mixed-effect models with fixed factors condition and time. Results: The PANAS Positive Affect scale showed a significant increase after LIIE and HIIE and a significant reduction in Negative Affect after the control condition. In rs-fMRI, no significant condition-by-time interactions were observed between the amygdala and whole brain. Amygdala-precuneus FC analysis showed an interaction effect, suggesting reduced post-exercise anticorrelation after the control condition, but stable, or even slightly enhanced anticorrelation for the exercise conditions, especially HIIE. Discussion: In conclusion, both LIIE and HIIE had positive effects on mood and concomitant effects on amygdala-precuneus FC, particularly after HIIE. Although no significant correlations were found between amygdala-precuneus FC and PANAS, results should be discussed in the context of affective disorders in whom abnormal amygdala-precuneus FC has been observed.

Inverted U-shape-like functional connectivity alterations in cognitive resting-state networks depending on exercise intensity: An fMRI study.

Acute physical activity influences cognitive performance. However, the relationship between exercise intensity, neural network activity, and cognitive performance remains poorly understood. This study examined the effects of different exercise intensities on resting-state functional connectivity (rsFC) and cognitive performance. Twenty male athletes (27.3 ± 3.6 years) underwent cycling exercises of different intensities (high, low, rest/control) on different days in randomized order. Before and after, subjects performed resting-state functional magnetic resonance imaging and a behavioral Attention Network Test (ANT). Independent component analysis and Linear mixed effects models examined rsFC changes within ten resting-state networks. No significant changes were identified in ANT performance. Resting-state analyses revealed a significant interaction in the Left Frontoparietal Network, driven by a non-significant rsFC increase after low-intensity and a significant rsFC decrease after high-intensity exercise, suggestive of an inverted U-shape relationship between exercise intensity and rsFC. Similar but trend-level rsFC interactions were observed in the Dorsal Attention Network (DAN) and the Cerebellar Basal Ganglia Network. Explorative correlation analysis revealed a significant positive association between rsFC increases in the right superior parietal lobule (part of DAN) and better ANT orienting in the low-intensity condition. Results indicate exercise intensity-dependent subacute rsFC changes in cognition-related networks, but their cognitive-behavioral relevance needs further investigation.

Fractional amplitude of low-frequency fluctuations associated with µ-opioid and dopamine receptor distributions in the central nervous system after high-intensity exercise bouts.

Introduction: Dopaminergic, opiod and endocannabinoid neurotransmission are thought to play an important role in the neurobiology of acute exercise and, in particular, in mediating positive affective responses and reward processes. Recent evidence indicates that changes in fractional amplitude of low-frequency fluctuations (zfALFF) in resting-state functional MRI (rs-fMRI) may reflect changes in specific neurotransmitter systems as tested by means of spatial correlation analyses. Methods: Here, we investigated this relationship at different exercise intensities in twenty young healthy trained athletes performing low-intensity (LIIE), high-intensity (HIIE) interval exercises, and a control condition on three separate days. Positive And Negative Affect Schedule (PANAS) scores and rs-fMRI were acquired before and after each of the three experimental conditions. Respective zfALFF changes were analyzed using repeated measures ANOVAs. We examined the spatial correspondence of changes in zfALFF before and after training with the available neurotransmitter maps across all voxels and additionally, hypothesis-driven, for neurotransmitter maps implicated in the neurobiology of exercise (dopaminergic, opiodic and endocannabinoid) in specific brain networks associated with "reward" and "emotion." Results: Elevated PANAS Positive Affect was observed after LIIE and HIIE but not after the control condition. HIIE compared to the control condition resulted in differential zfALFF decreases in precuneus, temporo-occipital, midcingulate and frontal regions, thalamus, and cerebellum, whereas differential zfALFF increases were identified in hypothalamus, pituitary, and periaqueductal gray. The spatial alteration patterns in zfALFF during HIIE were positively associated with dopaminergic and µ-opioidergic receptor distributions within the 'reward' network. Discussion: These findings provide new insight into the neurobiology of exercise supporting the importance of reward-related neurotransmission at least during high-intensity physical activity.

Applications of near-infrared spectroscopy in "anaerobic" diagnostics - SmO2 kinetics reflect PCr dephosphorylation and correlate with maximal lactate accumulation and maximal pedalling rate.

We investigated the relationship of the time-dependent behaviour of muscle oxygen saturation SmO2(t), phosphagen energy supply WPCr(t) and blood lactate accumulation ΔBLC(t) during a 60-s all-out cycling sprint and tested SmO2(t) for correlations with the end of the fatigue-free state tFf, maximal pedalling rate PRmax and maximal blood lactate accumulation rate v̇Lamax. Nine male elite track cyclists performed four maximal sprints (3, 8, 12, 60 s) on a cycle ergometer. Crank force and cadence were monitored continuously to determine PRmax and tFf based on force-velocity profiles. SmO2 of the vastus lateralis muscle and respiratory gases were measured until the 30th minute after exercise. WPCr was calculated based on the fast component of the post-exercise oxygen uptake for each sprint. Before and for 30 minutes after each sprint, capillary blood samples were taken to determine the associated ΔBLC. Temporal changes of SmO2, WPCr and ΔBLC were analysed via non-linear regression analysis. v̇Lamax was calculated based on ΔBLC(t) as the highest blood lactate accumulation rate. All models showed excellent quality (R2 > 0.95). The time constant of SmO2(t) τSmO2 = 2.93 ± 0.65 s was correlated with the time constant of WPCr(t) τPCr = 3.23 ± 0.67 s (r = 0.790, p < 0.012), v̇Lamax = 0.95 ± 0.18 mmol · l-1 · s-1 (r = 0.768, p < 0.017) and PRmax = 299.51 ± 14.70 rpm (r = -0.670, p < 0.049). tFf was correlated with τSmO2 (r = 0.885, p < 0.001). Our results show a time-dependent reflection of SmO2 kinetics and phosphagen energy contribution during a 60-s maximal cycling sprint. A high v̇Lamax results in a reduction, a high PRmax in an increase of the desaturation rate. The half-life of SmO2 desaturation indicates the end of the fatigue-free state.

Comparison of different test protocols to determine maximal lactate steady state intensity in swimming.

OBJECTIVES: This study compared step test, lactate minimum (LM) test and reverse lactate threshold (RLT) test protocols with maximal lactate steady state (MLSS) in free-swimming. All test protocols used fixed duration increments and high work-rate resolution (≤ 0.03 m·s-1) to ensure high sensitivity. DESIGN: Validation study. METHODS: 23 swimmers or triathletes (12 male and 11 female) of different ages (19.0 ±â€¯5.9 yrs) and performance levels (400 m personal best 1.38 ±â€¯0.13 m·s-1, FINA points 490 ±â€¯118) completed an incremental step test (+0.03 m·s-1 every 3 min) to determine speed at 4 mmol·L-1 and at modified maximal distance method, a LM test, a RLT test and two to five 30 min tests (±0.015 m·s-1) to determine MLSS. Following a 200 m all-out and a 5 min rest, LM was determined during an incremental segment (+0.03 m·s-1 every 2 min) as the nadir of the speed-lactate curve. After a priming segment with four increments (+0.06 m·s-1), RLT was determined as the lactate apex during a reverse segment (-0.03 m·s-1) every 3 min. RESULTS: The mean differences (± limits of agreement) to speed at MLSS were +1.0 ±â€¯4.1% (speed at 4 mmol·L-1), +1.5 ±â€¯3.5% (modified maximum distance method), -0.2 ±â€¯4.7% (LM) and 2.0 ±â€¯3.1% (RLT). All threshold concepts showed good agreement with MLSS pace (intraclass correlation coefficient ≥ 0.886). CONCLUSIONS: Test protocols with a fixed step duration and fine increments allowed high accuracy in estimating MLSS pace. With similar criterion agreement to the LM and RLT tests, incremental step tests appear more practicable due to less prior knowledge required and derivation of individual training zones.

Reverse lactate threshold test accurately predicts maximal lactate steady state and 5 km performance in running.

This study evaluated the accuracy of the reverse lactate threshold (RLT) and the onset of blood lactate accumulation (OBLA; 4 mmol·L-1) to determine the running speed at the maximal lactate steady state (MLSS) and 5 km running performance in a field test approach. Study 1: 16 participants performed an RLT test, and 2 or more constant-speed tests, lasting 30 minutes each, to determine running speed at the MLSS. Study 2: 23 participants performed an RLT test and a 5000 m all-out run as an indicator of performance. The RLT test consisted of an initial lactate-priming segment, in which running speed was increased stepwise up to ~5% above the estimated MLSS, followed by a reverse segment in which speed was decreased by 0.1 m·s-1 every 180 s. RLT was determined using the highest lactate equivalent ([La-]/running speed) during the reverse segment. OBLA was determined during the priming segment and was set at a value of 4 mmol∙L1. The mean difference in MLSS was +0.06 ± 0.05 m·s-1 for RLT, and +0.13 ± 0.23 m·s-1 for OBLA. OBLA showed a good concordance with the MLSS (ICC = 0.83), whereas RLT revealed excellent concordance with the MLSS with an ICC = 0.98. RLT showed a very high correlation with 5000 m speed (r = 0.97). The RLT exhibited exceptional agreement to MLSS and 5000 m running performance. Due to this high accuracy, especially concerning the small intraindividual differences, the RLT test may be superior to common threshold concepts. Further research is needed to evaluate its sensitivity during the training process.

Higher Accuracy of the Lactate Minimum Test Compared to Established Threshold Concepts to Determine Maximal Lactate Steady State in Running.

This study evaluated the accuracy of the lactate minimum test, in comparison to a graded-exercise test and established threshold concepts (OBLA and mDmax) to determine running speed at maximal lactate steady state. Eighteen subjects performed a lactate minimum test, a graded-exercise test (2.4 m·s-1 start,+0.4 m·s-1 every 5 min) and 2 or more constant-speed tests of 30 min to determine running speed at maximal lactate steady state. The lactate minimum test consisted of an initial lactate priming segment, followed by a short recovery phase. Afterwards, the initial load of the subsequent incremental segment was individually determined and was increased by 0.1 m·s-1 every 120 s. Lactate minimum was determined by the lowest measured value (LMabs) and by a third-order polynomial (LMpol). The mean difference to maximal lactate steady state was+0.01±0.14 m·s-1 (LMabs), 0.04±0.15 m·s-1 (LMpol), -0.06±0.31 m·s1 (OBLA) and -0.08±0.21 m·s1 (mDmax). The intraclass correlation coefficient (ICC) between running velocity at maximal lactate steady state and LMabs was highest (ICC=0.964), followed by LMpol (ICC=0.956), mDmax (ICC=0.916) and OBLA (ICC=0.885). Due to the higher accuracy of the lactate minimum test to determine maximal lactate steady state compared to OBLA and mDmax, we suggest the lactate minimum test as a valid and meaningful concept to estimate running velocity at maximal lactate steady state in a single session for moderately up to well-trained athletes.

Accuracy of a Modified Lactate Minimum Test and Reverse Lactate Threshold Test to Determine Maximal Lactate Steady State.

Wahl, P, Manunzio, C, Vogt, F, Strütt, S, Volmary, P, Bloch, W, and Mester, J. Accuracy of a modified lactate minimum test and reverse lactate threshold test to determine maximal lactate steady state. J Strength Cond Res 31(12): 3489-3496, 2017-This study evaluated the accuracy of a modified lactate minimum test (mLMT), a modified reverse lactate threshold test (mRLT), compared with 2 established threshold concepts (onset of blood lactate accumulation [OBLA] and modified maximal deviation method [mDmax]) to determine power output at maximal lactate steady state (MLSS) in cycling. Nineteen subjects performed an mLMT, mRLT, graded exercise test (100 W start, +20 W every 3 minutes) and 3 or more constant-load tests of 30 minutes to determine power output at MLSS. The mLMT and mRLT both consisted of an initial lactate priming segment, followed by a short recovery phase. Afterward, the initial load of the subsequent incremental or reverse segment was calculated individually and was increased or decreased by 10 W every 90 seconds, respectively. The mean difference to MLSS was +2 ± 7 W (mLMT), +5 ± 10 W (mRLT), +9 ± 21 W (OBLA), and +6 ± 14 W (mDmax). The correlation between power output at MLSS and mLMT was highest (r = 0.99), followed by mRLT (r = 0.98), mDmax (r = 0.95), and OBLA (r = 0.90). Because of the higher accuracy of the mLMT and the mRLT to determine MLSS compared with OBLA and mDmax, we suggest both tests as valid and meaningful concepts to estimate power output at MLSS in one single test in moderately trained to well-trained athletes. Additionally, our modified tests provide anaerobic data and do not require detailed knowledge of the subjects' training status compared with previous LMT or RLT protocols.

Training Intensity Distribution and Changes in Performance and Physiology of a 2nd Place Finisher Team of the Race across America Over a 6 Month Preparation Period.

Aim: To monitor the training intensity distribution (TID) and the development of physiological and performance parameters. Methods: During their preparation period for the RAAM, 4 athletes (plus 1 additional backup racer) performed 3 testing sessions; one before, one after 3, and one after 6 months of training. VO2max, maximal rate of lactate accumulation (dLa/dtmax), critical power, power output at lactate minimum (MLSSP), peak and mean power output during a sprint test, heart rate recovery, isometric strength, jumping height, and body composition were determined. All training sessions were recorded with a power meter. The endurance TID was analyzed based on the time in zone approach, according to a classical 3-zone model, including all power data of training sessions, and a power specific 3-zone model, where time with power output below 50% of MLSSP was not considered. Results: The TID using the classical 3-zone model reflected a pyramidal TID (zone 1: 63 ± 16, zone 2: 28 ± 13 and zone 3: 9 ± 4%). The power specific 3-zone model resulted in a threshold-based TID (zone 1: 48 ± 13, zone 2: 39 ± 10, zone 3: 13 ± 4%). VO2max increased by 7.1 ± 5.3% (P = 0.06). dLa/dtmax decreased by 16.3 ± 8.1% (P = 0.03). Power output at lactate minimum and critical power increased by 10.3 ± 4.1 and 16.8 ± 6.2% (P = 0.01), respectively. No changes were found for strength parameters and jumps. Conclusion: The present study underlines that a threshold oriented TID results in only moderate increases in physiological parameters. The amount of training below 50% of MLSSp (~28% of total training time) is remarkably high. Researchers, trainers, and athletes should pay attention to the different ways of interpreting training power data, to gain realistic insights into the TID and the corresponding improvements in performance and physiological parameters.