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Introduction: Sensory-processing sensitivity (SPS) refers to interindividual differences in sensitivity to positive and negative environmental stimuli and reflects the concept of differential susceptibility. The Highly Sensitive Person Scale (HSPS) has been utilized to capture the multifaceted experiences of highly sensitive individuals. The scale's total score (i.e., the sum of the subfactors) is an indicator of high sensitivity. However, it cannot differentiate between the contributions of the specific subfactors. Consequently, interpreting the total score cannot help resolve the current theoretical debate about how individuals integrate the positive and negative aspects of sensitivity, whereas a multidimensional profile should be able to offer a more comprehensive understanding. Intriguingly, in variable-centered research, the subfactors' differential associations with external constructs in negative or positive trait spaces have suggested heterogeneity (i.e., interindividual differences) among highly sensitive individuals. Thus, person-centered approaches should be better suited to address this heterogeneity. Methods: To explore heterogeneity within the highly sensitive population, we conducted a three-step Latent Profile Analysis in two independent German-speaking samples (N = 1,102; N = 526). Subsequently, we employed the Five-Factor Model of personality to provide a detailed description of the latent sensitivity groups. Results: Beyond the frequently identified quantitative three-class differentiation of sensitivity groups, we obtained a four-class model that included two qualitatively different high-sensitivity groups, each displaying distinct HSPS subfactor and personality patterns that corresponded to prototypical personality profiles. Within these high sensitivity groups, (i) the Confident Sensitivity Group exhibited average Neuroticism, significantly above-average Openness, and slightly above-average Extraversion. By contrast, (ii) the Vulnerable Sensitivity Group displayed the typical personality pattern of significantly above-average Neuroticism, below-average Extraversion, and slightly above-average Openness. Personality analyses revealed that features such as passiveness, internalizing tendencies, giftedness, and aesthetics, often commonly ascribed to all highly sensitive individuals, are features that differ across distinct sensitivity groups. Discussion: To avoid over- or underestimating sensitivity effects, future research should consider these interindividual differences in highly sensitive individuals. For instance, studies could focus on the different associations of sensitivity groups with abilities, health aspects, emotion regulation and intervention outcomes, taking into account the different environmental factors that shape the type of sensitivity.

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The aim of the current study was to investigate interindividual differences in muscle thickness of the rectus femoris (MTRF) following 12 wk of resistance training (RT) or high-intensity interval training (HIIT) to explore the genetic architecture underlying skeletal muscle hypertrophy and to construct predictive models. We conducted musculoskeletal ultrasound assessments of the MTRF response in 440 physically inactive adults after the 12-wk exercise period. A genome-wide association study was used to identify variants associated with the MTRF response, separately for RT and HIIT. Using the polygenic predictor score (PPS), we estimated the genetic contribution to exercise-induced hypertrophy. Predictive models for the MTRF response were constructed using random forest (RF), support vector mac (SVM), and generalized linear model (GLM) in 10 cross-validated approaches. MTRF increased significantly after both RT (8.8%, P < 0.05) and HIIT (5.3%, P < 0.05), but with considerable interindividual differences (RT: -13.5 to 38.4%, HIIT: -14.2 to 30.7%). Eleven lead single-nucleotide polymorphisms in RT and eight lead single-nucleotide polymorphisms in HIIT were identified at a significance level of P < 1 × 10-5. The PPS was associated with the MTRF response, explaining 47.2% of the variation in response to RT and 38.3% of the variation in response to HIIT. Notably, the GLM and SVM predictive models exhibited superior performance compared with RF models (P < 0.05), and the GLM demonstrated optimal performance with an area under curve of 0.809 (95% confidence interval: 0.669-0.949). Factors such as PPS, baseline MTRF, and exercise protocol exerted influence on the MTRF response to exercise, with PPS being the primary contributor. The GLM and SVM predictive model, incorporating both genetic and phenotypic factors, emerged as promising tools for predicting exercise-induced skeletal muscle hypertrophy.NEW & NOTEWORTHY The interindividual variability induced muscle hypertrophy by resistance training (RT) or high-intensity interval training (HIIT) and the associated genetic architecture remain uncertain. We identified genetic variants that underlie RT- or HIIT-induced muscle hypertrophy and established them as pivotal factors influencing the response regardless of the training type. The genetic-phenotype predictive model developed has the potential to identify nonresponders or individuals with low responsiveness before engaging in exercise training.

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Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

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Dopaminergic dysfunction in the basal ganglia, particularly in the posterior putamen, is often viewed as the primary pathological mechanism behind motor slowing (i.e. bradykinesia) in Parkinson's disease. However, striatal dopamine loss fails to account for interindividual differences in motor phenotype and rate of decline, implying that the expression of motor symptoms depends on additional mechanisms, some of which may be compensatory in nature. Building on observations of increased motor-related activity in the parieto-premotor cortex of Parkinson patients, we tested the hypothesis that interindividual differences in clinical severity are determined by compensatory cortical mechanisms and not just by basal ganglia dysfunction. Using functional MRI, we measured variability in motor- and selection-related brain activity during a visuomotor task in 353 patients with Parkinson's disease (≤5 years disease duration) and 60 healthy controls. In this task, we manipulated action selection demand by varying the number of possible actions that individuals could choose from. Clinical variability was characterized in two ways. First, patients were categorized into three previously validated, discrete clinical subtypes that are hypothesized to reflect distinct routes of α-synuclein propagation: diffuse-malignant (n = 42), intermediate (n = 128) or mild motor-predominant (n = 150). Second, we used the scores of bradykinesia severity and cognitive performance across the entire sample as continuous measures. Patients showed motor slowing (longer response times) and reduced motor-related activity in the basal ganglia compared with controls. However, basal ganglia activity did not differ between clinical subtypes and was not associated with clinical scores. This indicates a limited role for striatal dysfunction in shaping interindividual differences in clinical severity. Consistent with our hypothesis, we observed enhanced action selection-related activity in the parieto-premotor cortex of patients with a mild-motor predominant subtype, both compared to patients with a diffuse-malignant subtype and controls. Furthermore, increased parieto-premotor activity was related to lower bradykinesia severity and better cognitive performance, which points to a compensatory role. We conclude that parieto-premotor compensation, rather than basal ganglia dysfunction, shapes interindividual variability in symptom severity in Parkinson's disease. Future interventions may focus on maintaining and enhancing compensatory cortical mechanisms, rather than only attempting to normalize basal ganglia dysfunction.

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Background: Genetic variation may significantly impact an individual's susceptibility to diseases, particularly when combined with specific nutrients. Additionally, genetic variations can lead to interindividual differences in metabolic responses. Objective: The present study explores the association between gene variants and observed interindividual differences in metabolic responses. Methods: The study included 30 healthy males (aged 20-34) who underwent a fasting period and subsequently consumed a standardized meal. Blood samples were collected both before and after the meal to assess metabolic changes. BMI served as an indirect measure for assessing physiological responses associated with body composition. Appetite changes were assessed using an online Visual Analog 100-point Scale. Buccal swabs were collected to analyze genetic variants in single nucleotide polymorphisms (SNPs). Results: The data underwent multiple regression analysis, revealing significant associations with 3 SNPs and their metabolic status: the insulin-receptor substrate 1 (IRS1) gene variant rs2943641, genotypes CT and CC, with elevated fasting insulin levels (R2 = 0.639, P = < 0.0001); the mitochondrial uncoupling protein 1 (UCP1) gene variant rs1800592, genotypes GG and GA, with increased BMI (R2 = 0.261, P = 0.007); and the peroxisome proliferator-activated receptor γ2 (PPARγ2) gene variant rs1801282, genotypes GG and GC, with increased BMI (R2 = 0.200, P = 0.024). Conclusions: Therefore, our study established significant associations between these 3 SNPs and differences in fasting insulin levels and BMI within our cohort.

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In recent years, the field of neuroimaging has undergone a paradigm shift, moving away from the traditional brain mapping approach towards the development of integrated, multivariate brain models that can predict categories of mental events. However, large interindividual differences in both brain anatomy and functional localization after standard anatomical alignment remain a major limitation in performing this type of analysis, as it leads to feature misalignment across subjects in subsequent predictive models. This article addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common latent template map. Our proposed Bayesian functional group-wise registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. We achieve the probabilistic registration with inverse-consistency by utilizing the generalized Bayes framework with a loss function for the symmetric group-wise registration. It models the latent template with a Gaussian process, which helps capture spatial features in the template, producing a more precise estimation. We evaluate the method in simulation studies and apply it to data from an fMRI study of thermal pain, with the goal of using functional brain activity to predict physical pain. We find that the proposed approach allows for improved prediction of reported pain scores over conventional approaches. Received on 2 January 2017. Editorial decision on 8 June 2021.

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Underlying classes capture differences between patient symptom trajectories during psychological therapy. This has not been explored for one-to-one internet-delivered therapy or functional impairment trajectories. Patients experiencing depression or anxiety received cognitive-behavioural therapy with a therapist using an online chat platform (N = 52,029). Trajectory classes of depression symptoms (PHQ9), anxiety symptoms (GAD7) and functional impairment (WSAS) were investigated using growth mixture modelling. Multinomial regressions tested associations between baseline variables and trajectory class. A four-class trajectory model was selected for each outcome, and these were highly similar. Each outcome showed three classes with initially moderate-severe symptoms or impairment: one demonstrated no change, one gradual improvement and one fast improvement. A fourth class had mild baseline scores and minimal improvement. In the moderate-severe classes, patients in the two with improvement were more likely to be employed and not to have obsessive-compulsive disorder. Fast improvement was likelier than gradual improvement or no change for patients with older age, no disability (e.g., physical, learning), or lower comorbid symptom or impairment scores. Associations with functional impairment classes were more similar to associations with depression classes than anxiety classes. Results were largely consistent with findings from face-to-face therapy. This study is an important step towards personalising therapy in terms of suitability and continuation.

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Much of the neural machinery of the early visual cortex, from the extraction of local orientations to contextual modulations through lateral interactions, is thought to have developed to provide a sparse encoding of contour in natural scenes, allowing the brain to process efficiently most of the visual scenes we are exposed to. Certain visual stimuli, however, cause visual stress, a set of adverse effects ranging from simple discomfort to migraine attacks, and epileptic seizures in the extreme, all phenomena linked with an excessive metabolic demand. The theory of efficient coding suggests a link between excessive metabolic demand and images that deviate from natural statistics. Yet, the mechanisms linking energy demand and image spatial content in discomfort remain elusive. Here, we used theories of visual coding that link image spatial structure and brain activation to characterize the response to images observers reported as uncomfortable in a biologically based neurodynamic model of the early visual cortex that included excitatory and inhibitory layers to implement contextual influences. We found three clear markers of aversive images: a larger overall activation in the model, a less sparse response, and a more unbalanced distribution of activity across spatial orientations. When the ratio of excitation over inhibition was increased in the model, a phenomenon hypothesised to underlie interindividual differences in susceptibility to visual discomfort, the three markers of discomfort progressively shifted toward values typical of the response to uncomfortable stimuli. Overall, these findings propose a unifying mechanistic explanation for why there are differences between images and between observers, suggesting how visual input and idiosyncratic hyperexcitability give rise to abnormal brain responses that result in visual stress.

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The comparison of odor functional maps in rodents demonstrates a high degree of inter-individual variability in glomerular activity patterns. There are substantial methodological difficulties in the interindividual assessment of local permutations in the glomerular patterns, since the position of anatomical extracranial landmarks, as well as the size, shape and angular orientation of olfactory bulbs can vary significantly. A new method for defining anatomical coordinates of active glomeruli in the rat olfactory bulb has been developed. The method compares the interindividual odor functional maps and calculates probabilistic maps of glomerular activity with adjustment. This adjustment involves rotation, scaling and shift of the functional map relative to its expected position in probabilistic map, computed according to the anatomical coordinates. The calculation of the probabilistic map of the odorant-specific response compensates for potential anatoamical errors due to individual variability in olfactory bulb dimensions and angular orientation. We show its efficiency on real data from a large animal sample recorded by two-photon calcium imaging in dorsal surface of the rat olfactory bulb. The proposed method with probabilistic map calculation enables the spatial consistency of the effects of individual odorants in different rats to be assessed and allow stereotypical positions of odor-specific clusters in the glomerular layer of the olfactory bulb to be identified.

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Relying on existing literature to identify suitable techniques for characterizing individual differences presents practical and methodological challenges. These challenges include the frequent absence of detailed descriptions of raw data, which hinders the assessment of analysis appropriateness, as well as the exclusion of data points deemed outliers, or the reliance on comparing only extreme groups by categorizing continuous variables into upper and lower quartiles. Despite the availability of algorithmic modeling in standard statistical software, investigations into individual differences predominantly focus on factor analysis and parametric tests. To address these limitations, this application-oriented study proposes a comprehensive approach that leverages behavioral responses through the use of signal detection theory and clustering techniques. Unlike conventional methods, signal detection theory considers both sensitivity and bias, offering insights into the intricate interplay between perceptual ability and decision-making processes. On the other hand, clustering techniques enable the identification and classification of distinct patterns within the dataset, allowing for the detection of singular behaviors that form the foundation of individual differences. In a broader framework, these combined approaches prove particularly advantageous when analyzing large and heterogeneous datasets provided by data archive platforms. By applying these techniques more widely, our understanding of the cognitive and behavioral processes underlying learning can be expedited and enhanced.

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In many daily situations, two or more individuals need to coordinate their actions to achieve a common goal and perform successfully. Past research on joint action has predominantly focused on the question of how such interactions are accomplished. Here we focus on the impact of inter-individual, trait-like differences to predict joint action performance. More specifically, we examined whether performance in a joint action task is moderated by the (in)congruence of individuals' motive dispositions. To this end, 27 dyads performed a joint action task in which they had to navigate a ball through a maze with each partner using a joystick and each being responsible for either moving the ball along the x-axis or the y-axis. As dependent measures, we analyzed dyads' performance (times and errors). As trait-like predictors, we assessed implicit and explicit motives by means of the Picture Story Exercise and the Unified Motive Scale, respectively. Linear regression modeling revealed that congruent explicit affiliation motives predict faster best times and that higher congruent implicit achievement motives are associated with reduced errors. Exploratory Response Surface Analyses yielded identical results for the affiliation motive. These findings provide initial evidence to suggest that interindividual differences and in motives as well as their fit are related to joint action performance. Future directions of this new paradigm and novel ways to analyze dyadic motive fits and their relation to joint action performance are discussed.

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How do people infer the Bayesian posterior probability from stated base rate, hit rate, and false alarm rate? This question is not only of theoretical relevance but also of practical relevance in medical and legal settings. We test two competing theoretical views: single-process theories versus toolbox theories. Single-process theories assume that a single process explains people's inferences and have indeed been observed to fit people's inferences well. Examples are Bayes's rule, the representativeness heuristic, and a weighing-and-adding model. Their assumed process homogeneity implies unimodal response distributions. Toolbox theories, in contrast, assume process heterogeneity, implying multimodal response distributions. After analyzing response distributions in studies with laypeople and professionals, we find little support for the single-process theories tested. Using simulations, we find that a single process, the weighing-and-adding model, nevertheless can best fit the aggregate data and, surprisingly, also achieve the best out-of-sample prediction even though it fails to predict any single respondent's inferences. To identify the potential toolbox of rules, we test how well candidate rules predict a set of over 10,000 inferences (culled from the literature) from 4,188 participants and 106 different Bayesian tasks. A toolbox of five non-Bayesian rules plus Bayes's rule captures 64% of inferences. Finally, we validate the Five-Plus toolbox in three experiments that measure response times, self-reports, and strategy use. The most important conclusion from these analyses is that the fitting of single-process theories to aggregate data risks misidentifying the cognitive process. Antidotes to that risk are careful analyses of process and rule heterogeneity across people.

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Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

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Understanding the individual factors that modulate flavor perception is a central issue for the development of personalized diets strategies to fight obesity. This study aimed to investigate differences in flavor perception between adults with normal weight and those with obesity, as well as some potential biological factors related to these differences. To do that, liking and flavor perception intensity were measured against retronasal olfactory (pineapple, butter, tropical and chocolate) and taste attributes (sweetness, umami and bitter) in 77 individuals grouped as normalweight or obese, according to their body mass index (BMI). Unstimulated saliva was collected from all participants and characterized in terms of salivary flow, total protein content, total antioxidant capacity, total esterase activity and bacterial composition through 16S rDNA amplicon sequencing. The results showed that participants displayed differences in flavor perception according to their BMI group. Thus, the group with obesity showed significant lower liking and intensity scores for low calorie related food aroma (pineapple and tropical), lower taste intensity scores for sweet and umami, and a higher acceptability for umami than the group with normal weight. Significant differences between BMI groups were observed for salivary biochemical variables and specific bacterial taxa, some of which were significantly correlated to flavor intensity. This work suggests for the first time the existence of an oral-brain axis that might contribute to the development or perpetuation of obesity, which opens new and interesting avenues of research.

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In everyday life, action and decision-making often run in parallel. Action-based models argue that action and decision-making strongly interact and, more specifically, that action can bias decision-making. This embodied decision bias is thought to originate from changes in motor costs and/or cognitive crosstalk. Recent research confirmed embodied decision biases for different tasks including walking and manual movements. Yet, whether such biases generalize within individuals across different tasks remains to be determined. To test this, we used two different decision-making tasks that have independently been shown to reliably produce embodied decision biases. In a within-participant design, participants performed two tasks in a counterbalanced fashion: (i) a walking paradigm for which it is known that motor costs systematically influence reward decisions, and (ii) a manual movement task in which motor costs and cognitive crosstalk have been shown to impact reward decisions. In both tasks, we successfully replicated the predicted embodied decision biases. However, there was no evidence that the strength of the biases correlated between tasks. Hence, our findings do not confirm that embodied decision biases transfer between tasks. Future research is needed to examine whether this lack of transfer may be due to different causes underlying the impact of motor costs on decisions and the impact of cognitive crosstalk or task-specific differences.

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Listening can be conceptualized as a process of active inference, in which the brain forms internal models to integrate auditory information in a complex interaction of bottom-up and top-down processes. We propose that individuals vary in their "prediction tendency" and that this variation contributes to experiential differences in everyday listening situations and shapes the cortical processing of acoustic input such as speech. Here, we presented tone sequences of varying entropy level, to independently quantify auditory prediction tendency (as the tendency to anticipate low-level acoustic features) for each individual. This measure was then used to predict cortical speech tracking in a multi speaker listening task, where participants listened to audiobooks narrated by a target speaker in isolation or interfered by 1 or 2 distractors. Furthermore, semantic violations were introduced into the story, to also examine effects of word surprisal during speech processing. Our results show that cortical speech tracking is related to prediction tendency. In addition, we find interactions between prediction tendency and background noise as well as word surprisal in disparate brain regions. Our findings suggest that individual prediction tendencies are generalizable across different listening situations and may serve as a valuable element to explain interindividual differences in natural listening situations.

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NEW FINDINGS: What is the central question of this study? Does prescribing exercise intensity using physiological thresholds create a more homogeneous exercise stimulus than using traditional intensity anchors? What is the main finding and its importance? Prescribing exercise using physiological thresholds, notably critical power, reduced the variability in exercise tolerance and acute metabolic responses. At higher intensities, approaching or exceeding the transition from heavy to severe intensity exercise, the imprecision of using fixed % V ̇ O 2 max ${\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}$  as an intensity anchor becomes amplified. ABSTRACT: The objective of this study was to determine whether the variability in exercise tolerance and physiological responses is lower when exercise is prescribed relative to physiological thresholds (THR) compared to traditional intensity anchors (TRAD). Ten individuals completed a series of maximal exercise tests and a series of moderate (MOD), heavy (HVY) and severe intensity (HIIT) exercise bouts prescribed using THR intensity anchors (critical power and gas exchange threshold) and TRAD intensity anchors (maximum oxygen uptake; V ̇ O 2 max ${\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}$ ). There were no differences in exercise tolerance or acute response variability between MODTHR and MODTRAD . All individuals completed HVYTHR but only 30% completed HVYTRAD . Compared to HVYTHR , where work rates were all below critical power, work rates in HVYTRAD exceeded critical power in 70% of individuals. There was, however, no difference in acute response variability between HVYTHR and HVYTRAD . All individuals completed HIITTHR but only 20% completed HIITTRAD . The variability in peak (F = 0.274) and average (F = 0.318) blood lactate responses was lower in HIITTHR compared to HIITTRAD . The variability in W' depletion (the finite work capacity above critical power) after the final interval bout was lower in HIITTHR compared to HIITTRAD (F = 0.305). Using physiological thresholds to prescribe exercise intensity reduced the heterogeneity in exercise tolerance and physiological responses to exercise spanning the boundary between the heavy and severe intensity domains. To increase the precision of exercise intensity prescription, it is recommended that, where possible, physiological thresholds are used in place of V ̇ O 2 max ${\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{max}}}}$ .

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PURPOSE: To examine the longitudinal relationships between shoulder internal and external rotation (IR and ER) strength, maturity status, and swim performance (aim 1). To determine whether maturity status mediated (partially/fully) the relationship between shoulder IR/ER strength and performance in age-group swimmers (aim 2). METHODS: Using a repeated-measures design, anthropometrics, maturity status, shoulder IR/ER strength, and 200-m front-crawl velocity were assessed over 3 competition seasons in N = 82 Australian male competitive swimmers (10-15 y). For aim 1, linear mixed models examined longitudinal relationships between assessed variables. For aim 2, causal mediation analyses examined proportional (in)direct contributions of maturity status between shoulder IR strength and swim performance. RESULTS: For aim 1, linear mixed models identified a significant relationship between shoulder IR strength and swim performance over time (F1,341.25 = 16.66, P < .001, marginal R2 = .13, conditional R2 = .91). However, maturity status was influential (ΔAkaike information criterion = -75.8, χ2 = 19.98, P < .001), suggesting removal of the shoulder IR strength-swim velocity relationship (F1,214.1 = 0.02, P = .88). For aim 2, mediation analyses identified maturity status as fully mediating the shoulder IR strength-swim velocity relationship (92.30%, P < .001). CONCLUSIONS: Shoulder IR and ER strength did not account for variance in longitudinal age-group swim performance independent of maturity status. Interindividual differences in maturity status fully explained the relationship between shoulder IR/ER strength and swim performance. For practitioners, findings promote the need to account for maturation status and question the rationale for upper-limb strength assessment during maturational years.

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Introduction: Value hierarchies, as motivational goals anchored in the self-schema, may be correlated with spontaneous activity in the resting brain, especially those involving self-relevance. This study aims to investigate the neural correlates of value hierarchies from the perspective of typology. Methods: A total of 610 Chinese college students (30.31% women), aged 18 to 23, completed the personal values questionnaire and underwent resting-state functional magnetic resonance imaging. Results: The latent profile analysis revealed three personal value profiles: traditional social orientation, modernized orientation, and undifferentiated orientation. Neuroimaging results revealed that individuals with modernized orientation prioritized openness to change value, and this personal-focus is related to the higher low-frequency amplitude of the posterior insula; individuals with traditional social orientation prioritized self-transcendence and conservation values, and this social-focus is related to the stronger functional connectivity of the middle insula with the inferior temporal gyrus, temporal gyrus, posterior occipital cortex, and basal ganglia, as well as weaker functional connections within the right middle insula. Discussion: Taken together, these findings potentially indicate the intra-generational differentiation of contemporary Chinese emerging adults' value hierarchies. At the neural level, these are correlated with brain activities involved in processing self- and other-relevance.