Predictive learning, prediction errors, and attention: evidence from event-related potentials and eye tracking

Prediction error (‘‘surprise’’) affects the rate of learning: We learn more rapidly about cues for which we initially make incorrect predictions than cues for which our initial predictions are correct. The current studies employ electrophysiological measures to reveal early attentional differentiation of events that differ in their previous involvement in errors of predictive judgment. Error-related events attract more attention, as evidenced by features of event-related scalp potentials previously implicated in selective visual attention (selection negativity, augmented anterior N1). The earliest differences detected occurred around 120 msec after stimulus onset, and distributed source localization (LORETA) indicated that the inferior temporal regions were one source of the earliest differences. In addition, stimuli associated with the production of prediction errors show higher dwell times in an eyetracking procedure. Our data support the view that early attentional processes play a role in human associative learning

Validity of Infrared 3-dimensional Scanning for Estimation of Body Composition: A 4-Compartment Model Comparison

Multiple infrared 3-dimensional (3D) scanning technologies exist, including time of flight (ToF) scanners and structured light scanners with static (SL-S) and dynamic (SL-D) configurations. ToF scanners measure depth by using the round-trip time of reflected photons, whereas SL scanners measure deformations in light patterns and allow for creation of a depth image using geometric triangulation. Recently, 3D scanning technologies have been proposed as novel methods of body composition assessment. PURPOSE: The purpose of this analysis was to examine the validity of four different commercially-available 3D scanners for estimation of body fat percentage (BF%) as compared to a 4-compartment (4C) model criterion. METHODS: After an overnight fast, 101 adults (63 F, 38 M; age: 29.3 ± 13.5 y; BMI: 24.3 ± 3.9 kg/m2; BF%: 24.6 ± 8.3%) completed assessments via dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), bioimpedance spectroscopy (BIS), a standard body mass scale, and four infrared 3D scanners. Two scanners (3DSSL-D1; 3DSSL-D2) utilized structured light scanning with a dynamic configuration, one utilized structured light scanning with a static configuration (3DSSL-S), and one utilized time-of-flight technology (3DSToF). Using the equation of Wang et al. (2002), a criterion 4C estimate of BF% was obtained using DXA for bone mineral, ADP for body volume, scale for body mass, and BIS for total body water. BF% estimates were compared using one-way ANOVA with Bonferroni adjustment for multiple comparisons, and additional evaluations were conducted using the correlation coefficient (r), constant error (CE), standard error of the estimate (SEE), total error (TE), and 95% limits of agreement (LOA). RESULTS: Estimates of BF% did not significantly differ between 4C and any of the 3D scanners. However, metrics of group, individual, and prediction errors varied between scanners: 3DSSL-D1: p=1.0; CE: 0.4%; r: 0.91; SEE: 2.5%; TE: 3.6%; LOA: ±7.0%; 3DSSL-D2: p= 1.0; CE: 0.8%; r: 0.86; SEE: 4.2%; TE: 4.7%; LOA: ±9.2%; 3DSSL-S: p= 1.0; CE: 1.0%; r: 0.81; SEE: 4.0%; TE: 5.0%; LOA: ±9.7%; 3DSToF: p=0.08; CE: -2.9%; r: 0.86, SEE: 2.5%; TE: 5.2%; LOA: ±8.6%. CONCLUSION: All three structured light scanners exhibited low magnitudes of group error (CE ≤ 1%) and may be valid assessment methods when analyzing the body composition of groups. 3DSSL-D1 exhibited the lowest group-level error (i.e. CE), prediction errors (i.e. SEE; TE), and individual error (i.e. LOA) of all scanners. Therefore, this device was deemed the most valid 3D scanner for body composition assessment. 3DSSL-D2, 3DSSL-S, and 3DSToF exhibited comparable TE, although group-level error was lower in 3DSSL-D2 and 3DSSL-S, while the SEE and individual-level error was lower for 3DSToF. However, individual-level errors were relatively high with all scanners (LOA ≥ 7%), which calls into question the utility of these methods for assessing the body composition of individuals. Nonetheless, additional research is needed regarding the ability of 3DS to successfully detect changes in body composition over time

Comparison of Regional Body Composition Estimates Obtained from Dual-energy X-ray Absorptiometry and Single-frequency Bioelectrical Impedance Analysis

The anatomical distribution of fat mass (FM) and lean mass (LM) is significant for health and athletic performance. Dual-energy x-ray absorptiometry (DXA) is often used for regional body composition analysis but is not portable, often inaccessible, and costly, while single-frequency bioelectrical impedance analysis (SFBIA) is a more affordable and accessible alternative. PURPOSE: The purpose of this analysis was to compare regional body composition estimates obtained via DXA and SFBIA. METHODS: After an overnight food and fluid fast, 102 adults (64 F, 38 M; age: 29.2 ± 13.4 y; BMI: 24.3 ± 3.9 kg/m2; BF%: 24.6 ± 8.3%) underwent assessments via DXA and SBFIA, each of which provided estimates of FM and LM for the whole body, torso, legs, and arms. DXA scans were performed using custom-made foam blocks to enhance accuracy of regional body composition estimates. SFBIA was performed using an 8-lead device with a 12-channel multiplexer. Both DXA and SFBIA were performed in the supine position. DXA was designated as the criterion method, and body composition estimates were compared using paired-samples t-tests using a Bonferroni-corrected significance level of p ≤ 0.00625. Additional evaluations were conducted using the correlation coefficient (r), constant error (CE), standard error of the estimate (SEE), and total error (TE). RESULTS: Correlations between DXA and SFBIA were high, and the magnitude of errors was generally small: LMTOTAL (r: 0.97; CE: 1.4 kg; SEE: 2.7 kg; TE: 2.9 kg), LMLEGS (r: 0.85; CE: -0.3 kg; SEE: 2.0 kg; TE: 2.1 kg), LMTORSO (r: 0.92; CE: 1.0 kg; SEE: 2.2 kg; TE: 2.5 kg), LMARMS (r: 0.96; CE: 0.6 kg; SEE: 0.6 kg; TE: 0.8 kg), FMTOTAL (r: 0.95; CE: -2.3 kg; SEE: 2.6 kg; TE: 3.5 kg), FMLEGS (r: 0.83; CE: -1.0 kg; SEE: 1.2 kg; TE: 2.0 kg), FMTORSO (r: 0.90; CE: -1.3 kg; SEE: 2.2 kg; TE: 2.6 kg), and FMARMS (r: 0.89; CE: -0.1 kg; SEE: 0.5 kg; TE: 0.5 kg). Despite the relatively small magnitude of differences in FM and LM estimates between DXA and SFBIA, results of paired-samples t-tests indicated that all differences were statistically significant (p \u3c 0.0001), with the exception of LMLEGS (p=0.13) and FMARMS (p=0.11). CONCLUSION: Despite the fact that body composition estimates for most regions exhibited statistically significant differences between DXA and SFBIA, the strong correlations (r: 0.83 to 0.97) and relatively low magnitude of error (CE: -2.3 to 1.4 kg; TE: 0.8 to 3.5 kg) indicate that SFBIA may be an acceptable alternative to DXA when regional body composition is being evaluated and DXA is unavailable. However, additional research is needed to determine the ability of SFBIA to accurately track changes in regional body composition over time. Due to its low cost, portability, and ease of use, the presently examined SFBIA device may represent a useful tool for the evaluation of regional body composition when more advanced methods are unavailable

Functionally aberrant electrophysiological cortical connectivities in first episode medication-naive schizophrenics from three psychiatry centers

Functional dissociation between brain processes is widely hypothesized to account for aberrations of thought and emotions in schizophrenic patients. The typically small groups of analyzed schizophrenic patients yielded different neurophysiological findings, probably because small patient groups are likely to comprise different schizophrenia subtypes. We analyzed multichannel eyes- closed resting EEG from three small groups of acutely ill, first episode productive schizophrenic patients before start of medication (from three centers: Bern N = 9; Osaka N = 9; Berlin N = 12) and their controls. Low resolution brain electromagnetic tomography (LORETA) was used to compute intracortical source model-based lagged functional connectivity not biased by volume conduction effects between 19 cortical regions of interest (ROIs). The connectivities were compared between controls and patients of each group. Conjunction analysis determined six aberrant cortical functional connectivities that were the same in the three patient groups. Four of these six concerned the facilitating EEG alpha-1 frequency activity; they were decreased in the patients. Another two of these six connectivities concerned the inhibiting EEG delta frequency activity; they were increased in the patients. The principal orientation of the six aberrant cortical functional connectivities was sagittal; five of them involved both hemispheres. In sum, activity in the posterior brain areas of preprocessing functions and the anterior brain areas of evaluation and behavior control functions were compromised by either decreased coupled activation or increased coupled inhibition, common across schizophrenia subtypes in the three patient groups. These results of the analyzed three independent groups of schizophrenics support the concept of functional dissociation

Development of a digital tool to overcome the challenges of rural food SMEs

It has been recognised that throughout the UK, rural economies have a significant potential for growth but despite the potential for growth, many rural businesses face barriers that prohibit their expansion. In this study, we focus on one particular group of rural small- to medium-sized enterprises (SMEs): food and drink producers. Through user engagement activities, we identify the issues and needs associated with distributing products to the market, in order to understand the main issues which prevent rural food and drink SMEs from expansion, and to establish the requirements for a digital solution to this challenge

Validity of Four-Compartment Model Body Fat Using Single- or Multi-frequency Bioelectrical Impedance Analysis to Estimate Body Water

Most common body composition assessment techniques make assumptions about the body, including the density and hydration of fat-free mass (FFM). An advantage of the four-compartment (4C) model is the ability to take these FFM characteristics into account when assessing body composition, thus reducing potential error. The total body water (TBW) estimate utilized in 4C models is particularly important due to the large contribution of water to an adult human’s total body mass (~40 - 70%) and FFM (~68 - 81%); however, the impact of utilizing different estimates of TBW within 4C model has not been fully explored. PURPOSE: The purpose of this investigation was to examine the validity of body fat percentage (BF%) estimates produced by 4C models utilizing single- or multi-frequency bioelectrical impedance analysis (BIA) TBW estimates as compared to a criterion 4C with TBW from bioimpedance spectroscopy (BIS). METHODS: After an overnight food and fluid fast, a sample of 101 adults (63 F, 38 M; age: 29.3 ± 13.5 y; BMI: 24.3 ± 4.0 kg/m2; BF%: 24.5 ± 8.3%) completed assessments via dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), BIS, single-frequency BIA (SFBIA), multi-frequency BIA (MFBIA) and a body mass scale. A criterion 4C model (4CBIS) estimate of BF% was obtained using DXA for bone mineral, ADP for body volume, scale for body mass, and BIS for TBW. BIS was used as the reference TBW method due to its more direct estimation of TBW via mathematical procedures (i.e. Cole modeling and mixture theories) as compared to the prediction equations used by BIA. Alternate 4C estimates of BF% were produced using TBW values from MFBIA (4CMFBIA) and SFBIA (4CSFBIA). BF% estimates were compared using one-way ANOVA, and additional evaluations were conducted using the coefficient of determination (R2), constant error (CE), total error (TE), and 95% limits of agreement (LOA). RESULTS: BF% did not differ between 4CBIS (24.5 ± 8.3%), 4CMFBIA (24.4 ± 8.9%), and 4CSFBIA (25.7 ± 8.3%; p=0.52). 4CMFBIA exhibited negligible CE (-0.1 ± 2.3%), R2 of 0.97, TE of 2.3%, and LOA of 4.4%. 4CSFBIA exhibited a small CE (1.2 ± 1.2%), R2 of 0.98, TE of 1.6%, and LOA of 2.3%. CONCLUSION: At the group level, BF% estimates did not differ between any 4C model, indicating that both SFBIA and MFBIA can serve as viable alternatives to BIS for TBW estimation. Although the magnitude of group error (i.e. CE) was slightly smaller in 4CMFBIA, the individual error (i.e. LOA) and total error were smaller in 4CSFBIA,indicating that SFBIA TBW estimates may be more appropriate when tracking body composition changes within individuals using a 4C model. While the MFBIA and SFBIA technologies employed in the present study exhibited good validity, these results may not be attributable to all BIA analyzers. The quality of assessment device, affordability, portability and ease of use should be considered when utilizing an impedance-based technology for TBW estimation in a 4C model

Tracking Resistance Training-Induced Changes in Body Composition via 3-Dimensional Optical Scanning

Tracking changes in body composition is potentially useful for monitoring health status, disease risk, and results of lifestyle interventions. In active individuals, evaluating body composition changes over time may provide useful information regarding the effectiveness of nutrition and exercise programs. PURPOSE: The purpose of this study was to compare changes in body composition estimates obtained from a 4-compartment (4C) model and a 3-dimensional optical (3DO) scanner in resistance-trained males. METHODS: Twenty resistance-trained males underwent assessments via 4C and 3DO before and after 6 weeks of supervised resistance training plus overfeeding with a high-calorie protein/carbohydrate supplement. To generate the 4C model, tests were performed using dual-energy x-ray absorptiometry, air displacement plethysmography, and bioimpedance spectroscopy. Changes in fat mass (ΔFM) and fat-free mass (ΔFFM) detected by 3DO were compared with the reference 4C model using paired-samples t-tests, Bland-Altman analysis, equivalence testing, and evaluation of validity metrics. RESULTS: Both ΔFM (mean ± SD: 4C: 0.6 ± 1.1 kg; 3DO: 1.9 ± 1.9 kg) and ΔFFM (4C: 3.2 ± 1.7 kg; 3DO: 1.9 ± 1.4 kg) differed between methods (p \u3c 0.002). The correlation (r) for ΔFM was 0.49 (95% confidence interval [CI]: 0.06 to 0.77) and was 0.42 (95% CI: -0.03 to 0.73) for ΔFFM. The total error for ΔFM and ΔFFM estimates was 2.1 kg. ΔFFM demonstrated equivalence between methods based on a ± 2 kg (~62% of 4C change) equivalence interval, whereas ΔFM failed to exhibit equivalence even with a 100% equivalence interval. Proportional bias was observed for ΔFM but not ΔFFM. CONCLUSION: Our data indicate that changes in FM and FFM detected by a 3D scanner did not exhibit strong agreement with changes detected by a 4C model. However, within the context of our study, agreement in FFM changes was superior to agreement in FM changes based on the results of equivalence testing and lack of proportional bias in FFM changes. Therefore, depending on the level of accuracy needed, the error in FFM changes observed for the 3D scanner may be potentially acceptable for some applications. Future research should investigate the utility of 3D scanners for monitoring changes in body composition and anthropometric variables in healthy and clinical populations, as well as investigate novel body phenotypes that may be associated with disease risk or health status

Influence of Subject Presentation on Body Composition Estimates from Dual-Energy X-Ray Absorptiometry, Air Displacement Plethysmography, and Bioelectrical Impedance Analysis

Body composition assessment devices are commonly employed to track changes associated with exercise or nutritional interventions. However, many individuals undergo body composition assessments with little to no pre-testing standardization of dietary intake or physical activity, potentially introducing error into their results. PURPOSE: To examine the validity of unstandardized body composition assessments relative to standardized assessments using three common body composition assessment devices. METHODS: Twenty-three resistance-trained males (Mean ± SD; 21.6 ± 2.6 years; 71.3 ± 6.8 kg; 177.4 ± 5.9 cm; 17.4 ± 4.1% DXA-derived percent body fat [%BF]) underwent paired body composition assessments via dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), and single-frequency bioelectrical impedance analysis (BIA). Each participant’s initial standardized body composition assessments were performed in the morning following an overnight food and fluid fast and 12 hours of exercise and caffeine abstention, and all unstandardized assessments were performed later during the same day following ad libitum daily activities. Unstandardized estimates of %BF and fat-free mass (FFM) for each device were compared with device-specific standardized values using paired-samples t-tests, line of identity analysis, evaluation of validity metrics, Bland-Altman analysis, and equivalence testing. RESULTS: The total error between standardized and unstandardized %BF estimates was 0.66% for DXA [95% confidence interval {CI}: 0.56-0.76%], 1.60% for ADP [95% CI: 1.50-1.70%], and 1.85% for BIA [95% CI: 1.75-1.95%]. The total error for FFM estimates was 0.75kg for DXA [95% CI: 0.65-0.85kg], 1.15kg for ADP [95% CI: 1.06-1.25kg], and 1.68 kg for BIA [95%CI: 1.58-1.78]. %BF estimates did not differ between paired measurements for DXA (p = 0.17) or ADP (p = 0.10) but differed between BIA (p \u3c 0.001) assessments. Similarly, FFM estimates did not differ between paired measurements for DXA (p = 0.40) or ADP (p = 0.78) but differed between BIA assessments (p \u3c 0.001). All paired assessments for each outcome produced regression line slopes which differed from the line of identity (p \u3c 0.001). Only BIA %BF estimates exhibited an intercept that differed from the line of identity (p \u3c 0.001). No proportional bias was detected for any outcome. Equivalence was demonstrated between %BF estimates for DXA but not ADP or BIA, based on a ±1%BF equivalence interval. Equivalence was demonstrated for all FFM estimates except BIA, based on a ±1kg equivalence interval. CONCLUSION: Our findings suggest that DXA body composition estimates are more robust when conducted in an unstandardized state relative to ADP or BIA. These results can inform the choice of body composition assessment methodology when pre-testing standardization is not possible

Relationship Between Muscular Performance Changes and Increases in Body Mass During Overfeeding Plus Resistance Training

Two critical components of muscular performance are muscular strength (e.g., the maximal load that can be lifted for a given exercise) and muscular endurance (e.g., the maximal number of repetitions that can be performed at a given load). When seeking improvements in muscular performance, it is common to employ nutritional strategies that create an energy surplus and a resultant gain in body mass. Varying rates of body mass gain are often prescribed to optimize training adaptations, including improvements in muscular performance; however, the relationship between rate of body mass gain and muscular performance improvements, if any, is not entirely clear. PURPOSE: The purpose of this analysis was to elucidate if there is a relationship between the rate of body mass gain and changes in muscular performance resulting from a resistance training program. METHODS: Nineteen resistance-trained males (age: 21.7 ± 2.6; body mass [BM]: 74.1 ± 11.5 kg; body fat percentage: 13.7 ± 5.2%; bench press maximal strength: 1.3 ± 0.2 x BM; leg press maximal strength: 3.4 ± 0.9 x BM) completed a supervised resistance training program plus overfeeding. Muscular performance testing took place at baseline and after the 6-week intervention. For the bench press and leg press exercises, strength was assessed via 1-repetition maximum (1RM), and endurance was assessed via repetitions to failure using 70% of the baseline 1RM. Simple linear regression analysis was used to determine if the relative rate of BM gain was related to relative improvements in maximal muscular strength and endurance. Standardized regression coefficients (β) and associated 95% confidence intervals (CI) were generated. RESULTS: The rate of BM gain was related to improvements in bench press 1RM (p=0.05; β=0.46 [0.02, 0.89], mean [95% CI]) and endurance (p=0.007, β =0.61 [0.23, 1.00]), but not leg press 1RM (p=0.16, β =0.33 [-0.11, 0.78]) or endurance (p=0.76, β = 0.08 [-0.42, 0.58]). A 1.0% increase in the relative rate of BM gain corresponded to relative increases of 1.2% (CI of 0.1 to 2.4%) in bench press 1RM and 6.7% (CI of 2.5 to 10.9%) in bench press repetitions to failure. CONCLUSION: The relative rate of body mass gain was positively related to performance improvements in the bench press exercise, but not the leg press exercise. One speculative explanation for this relationship is that the increase in upper body muscularity that results from body mass gain during resistance training could have decreased the range of motion on the bench press exercise, thereby facilitating easier execution of the movement for both strength and endurance tests

Comparison of Indirect Calorimetry and Common Prediction Equations for Evaluating Changes in Resting Metabolic Rate Induced by Resistance Training and a Hypercaloric Diet

An individual’s resting metabolic rate (RMR) is commonly the largest contributor to total daily energy expenditure. Prediction equations are most often employed by practitioners to estimate RMR, due to their superior practicality in many settings relative to laboratory methods like indirect calorimetry (IC). The ability to quantify RMR change over time may be more valuable than cross-sectional estimates as practitioners can then utilize these changes to prescribe adjustments to one’s nutritional intake. PURPOSE: The purpose of this study was to assess the validity of several commonly used prediction equations to track RMR changes during a hypercaloric nutrition intervention and supervised exercise training program. METHODS: Twenty generally healthy males (mean ± standard deviation; age: 21.9 ± 2.6 years; height: 178.1 ± 6.9 cm; body mass: 72.2 ± 7.3 kg; fat-free mass index: 18.9 ± 1.5 kg/m2 ; bench press strength: 1.3 ± 0.2 kg/kg BM; leg press strength: 3.4 ± 0.9 kg/kg BM) completed a supervised resistance training program in conjunction with a hypercaloric diet. The protocol lasted 6 weeks, and participants completed RMR assessments via IC pre-and post-intervention to obtain reference values. Existing RMR prediction equations based on body mass or fat-free mass were also evaluated. Equivalence testing was used to evaluate whether each prediction equation demonstrated equivalence with IC based on a ± 50 kcal/d equivalence region, and the confidence limits for the two-one-sided t-tests were calculated. Null hypothesis significance testing was performed, and Bland-Altman analyses were utilized alongside linear regression to assess the degree of proportional bias. RESULTS: IC RMR values increased by 165 ± 97 kcal/d. All prediction equations underestimated RMR changes, relative to IC, with magnitudes ranging from 75 to 132 kcal/d, while also displaying unacceptable levels of negative proportional bias. Additionally, all prediction equations significantly differed from measured IC values, and no equation demonstrated equivalence with IC. CONCLUSION: These findings suggest the examined prediction equations are not acceptable for tracking RMR changes in resistance-trained males, within the context of the present study. The consistent underestimation of RMR changes indicates that the input variables, and their weights within the prediction equations, were insufficient to adequately explain the observed changes in RMR