Development of self-derivation through memory integration and relations with world knowledge.

Accumulating world knowledge is a major task of development and education. The productive process of self-derivation through memory integration seemingly is a valid model of the process. To test the model, we examined relations between generation and retention of new factual knowledge via self-derivation through integration and world knowledge as measured by standardised assessments. We also tested whether the productive process of self-derivation predicted world knowledge even when a measure of learning through direct instruction also was considered. Participants were 162 children ages 8-12 years (53% female; 15% Black, 6% Asian, 1% Arab, 66% White, 5% mixed race, 7% unreported; 1% Latinx). Age accounted for a maximum of 4% of variance in self-derivation and retention. In contrast, substantial individual variability related to general knowledge and content knowledge in several domains, explaining 20-40% variance. In each domain for which self-derivation performance was a unique predictor, it explained a nominally greater share of the variance than the measure of learning through direct instruction. The findings imply that individual variability in self-derivation has functional consequences for accumulation of semantic knowledge across the elementary-school years.

Demographic factors associated with within-individual variability of lung function for adults with cystic fibrosis: A UK registry study.

BACKGROUND: Lung function is a key outcome used in the evaluation of disease progression in cystic fibrosis. The variability of individual lung function measurements over time (within-individual variability) has been shown to predict subsequent lung function changes. Nevertheless, the association between within-individual lung function variability and demographic and genetic covariates has not been quantified. METHODS: We performed a longitudinal analysis of data from a cohort of 7099 adults with cystic fibrosis (between 18 and 49 years old) from the UK cystic fibrosis registry, containing annual review data between 1996 and 2020. A mixed-effects location-scale model is used to quantify mean FEV1 (forced expiratory volume in 1 s) trajectories and FEV1 within-individual variability as a function of sex, age at annual review, diagnosis after first year of life, homozygous F508 genotype and birth cohort. RESULTS: Mean FEV1 decreased with age and lung function variability showed a near-quadratic trend by age. Males showed higher FEV1 mean and variability than females across the whole age range. Earlier diagnosis and homozygous F508 genotype were also associated with higher FEV1 mean and variability. Individuals who died during follow-up showed on average higher lung function variability than those who survived. CONCLUSIONS: Key variables known to be linked with mean lung function in cystic fibrosis are also associated with an individual's lung function variability. This work opens new avenues to understand the role played by lung function variability in disease progression and its utility in predicting key outcomes such as mortality.

Multiple Stressors Induce Amygdalohippocampal Volume Reduction in Adult Male Rats as Detected by Longitudinal Structural Magnetic Resonance Imaging.

Background: Traumatic events can cause long-lasting and uncontrollable fear and anxiety. Posttraumatic stress disorder is an intractable mental disorder, and neurobiological mechanisms using animal models are expected to help development of posttraumatic stress disorder treatment. In this study, we combined multiple stress (MS) and longitudinal in vivo magnetic resonance imaging to reveal the effects of long-lasting anxiety-like behaviors on adult male rat brains. Methods: Twelve male Wistar rats (8 weeks old) were exposed to the MS of 1-mA footshocks and forced swimming, while 12 control rats were placed in a plastic cage. Contextual fear conditioning with 0.1-mA footshocks in a context different from the MS was conducted 15 days after the MS for both groups. Three retention tests were administered after 24 hours and 9 and 16 days. Two magnetic resonance imaging scans were conducted, one on the day before MS induction and one the day after the third retention test, with a 32-day interval. Results: The MS group showed greater freezing responses than the control group in all retention tests. Whole-brain voxel-based morphometry analyses revealed reduced gray matter volume in the anterior amygdalohippocampal area in MS group rats compared with control rats. These volume changes were negatively associated with freezing time in the third retention test in the MS group. Conclusions: These results suggest that individual variability in the amygdalohippocampal area may be related to long-lasting fear responses after severe stress.

Traumatic events can cause long-lasting and uncontrollable fear and anxiety. In this study, we combined multiple stress (MS) and longitudinal in vivo magnetic resonance imaging to reveal the effects of long-lasting anxiety-like behaviors on adult male rat brains. The MS group showed greater freezing responses than the control group in all retention tests. Brain morphometry analyses revealed reduced gray matter volume in the anterior amygdalohippocampal area in MS group rats compared with control rats. These results suggest that individual variability in the amygdalohippocampal area may be related to long-lasting fear responses after severe stress.

Structure-Based Analysis of Cefaclor Pharmacokinetic Diversity According to Human Peptide Transporter-1 Genetic Polymorphism.

Cefaclor is a substrate of human-peptide-transporter-1 (PEPT1), and the impact of inter-individual pharmacokinetic variation due to genetic polymorphisms of solute-carrier-family-15-member-1 (SLC15A1) has been a topic of great debate. The main objective of this study was to analyze and interpret cefaclor pharmacokinetic variations according to genetic polymorphisms in SLC15A1 exons 5 and 16. The previous cefaclor bioequivalence results were integrated with additional SLC15A1 exons 5 and 16 genotyping results. An analysis of the structure-based functional impact of SLC15A1 exons 5 and 16 genetic polymorphisms was recently performed using a PEPT1 molecular modeling approach. In cefaclor pharmacokinetic analysis results according to SLC15A1 exons 5 and 16 genetic polymorphisms, no significant differences were identified between genotype groups. Furthermore, in the population pharmacokinetic modeling, genetic polymorphisms in SLC15A1 exons 5 and 16 were not established as effective covariates. PEPT1 molecular modeling results also confirmed that SLC15A1 exons 5 and 16 genetic polymorphisms did not have a significant effect on substrate interaction with cefaclor and did not have a major effect in terms of structural stability. This was determined by comprehensively considering the insignificant change in energy values related to cefaclor docking due to point mutations in SLC15A1 exons 5 and 16, the structural change in conformations confirmed to be less than 0.05 Å, and the relative stabilization of molecular dynamic simulation energy values. As a result, molecular structure-based analysis recently suggested that SLC15A1 exons 5 and 16 genetic polymorphisms of PEPT1 were limited to being the main focus in interpreting the pharmacokinetic diversity of cefaclor.

Exploring the thermally-controlled fentanyl transdermal therapy to provide constant drug delivery by physics-based digital twins.

Transdermal drug delivery is suitable for low-molecular-weight drugs with specific lipophilicity, like fentanyl, which is widely used for cancer-induced pain management. However, fentanyl's transdermal therapy displays high intra-individual variability. Factors like skin characteristics at application sites and ambient temperature contribute to this variation. In this study, we developed a physics-based digital twin of the human body to cope with this variability and propose better adapted setups. This twin includes an in-silico skin model for drug penetration, a pharmacokinetic model, and a pharmacodynamic model. Based on the results of our simulations, applying the patch on the flank (side abdominal area) showed a 15.3 % higher maximum fentanyl concentration in the plasma than on the chest. Additionally, the time to reach this maximum concentration when delivered through the flank was 19.8 h, which was 10.3 h earlier than via the upper arm. Finally, this variation led to an 18 % lower minimum pain intensity for delivery via the flank than the chest. Moreover, the impact of seasonal changes on ambient temperature and skin temperature by considering the activity level was investigated. Based on our result, the fentanyl uptake flux by capillaries increased by up to 11.8 % from an inactive state in winter to an active state in summer. We also evaluated the effect of controlling fentanyl delivery by adjusting the temperature of the patch to alleviate the pain to reach a mild pain intensity (rated three on the VAS scale). By implementing this strategy, the average pain intensity decreased by 1.1 points, and the standard deviation for fentanyl concentration in plasma and average pain intensity reduced by 37.5 % and 33.3 %, respectively. Therefore, our digital twin demonstrated the efficacy of controlled drug release through temperature regulation, ensuring the therapy toward the intended target outcome and reducing therapy outcome variability. This holds promise as a potentially useful tool for physicians.

Cardiovascular and cardiorespiratory effects of high-intensity interval training in body fat responders and non-responders.

This study aimed to investigate cardiovascular and cardiorespiratory adaptations to exercise intervention among participants who showed higher (responders-RSBFP) and lower (non-responders-NRSBFP) levels of body fat percentage (BFP) responsiveness. Adolescents (42.5% males) participated in a ten-week school-based high-intensity interval training (HIIT), followed by a comparison of BFP, blood pressure (BP), and cardiorespiratory fitness (CRF). RSBFP age of 16.15 ± 0.36 years, body height 170.82 ± 8.16 cm, weight 61.23 ± 12.80 kg, and BMI 20.86 ± 3.29 kg/m2. Meanwhile, NRSBFP age of 16.04 ± 0.36 years, body height 168.17 ± 8.64 cm, weight 57.94 ± 8.62 kg, and BMI 20.47 ± 2.24 kg/m2. HIIT intervention impacted BFP, with a higher decrease in the RSBFP than the NRSBFP (ΔBFPRs = - 2.30 ± 3.51(10.34%) vs. ΔBFPNRs = 1.51 ± 1.54(6.96%) p < 0.001). The primary comparison showed a statistically significant interaction effect in relation to CRF (F(1,71) = 14.12; p < 0.001). Detailed comparisons showed large and significant CRF changes in RSBFP (7.52%; d = 0.86; p < 0.001) but not in NRSBFP (2.01%; d = 0.11; p = 0.576). In addition, RSBFP and NRSBFP benefited equally in SBP (5.49%, d = 0.75; p < 0.001; 4.95%, d = 0.74; p < 0.001, respectively). These findings highlight that exercise benefits on body fat may be mainly related to gains in CRF. Due to substantial intra-individual variability in adaptation, there is a need for personalized intervention tailored for those with different reaction thresholds in body mass components.

A Novel Approach to Measuring an Old Construct: Aligning the Conceptualisation and Operationalisation of Cognitive Flexibility.

A successful adjustment to dynamic changes in one's environment requires contingent adaptive behaviour. Such behaviour is underpinned by cognitive flexibility, which conceptually is part of fluid intelligence. We argue, however, that conventional approaches to measuring fluid intelligence are insufficient in capturing cognitive flexibility. We address the discrepancy between conceptualisation and operationalisation by introducing two newly developed tasks that aim at capturing within-person processes of dealing with novelty. In an exploratory proof-of-concept study, the two flexibility tasks were administered to 307 university students, together with a battery of conventional measures of fluid intelligence. Participants also provided information about their Grade Point Averages obtained in high school and in their first year at university. We tested (1) whether an experimental manipulation of a requirement for cognitive inhibition resulted in systematic differences in difficulty, (2) whether these complexity differences reflect psychometrically differentiable effects, and (3) whether these newly developed flexibility tasks show incremental value in predicting success in the transition from high school to university over conventional operationalisations of fluid intelligence. Our findings support the notion that cognitive flexibility, when conceptualised and operationalised as individual differences in within-person processes of dealing with novelty, more appropriately reflects the dynamics of individuals' behaviour when attempting to cope with changing demands.

Staying self-regulated in the classroom: The role of children's executive functions and situational factors.

BACKGROUND: Self-regulation is crucial for children's learning and development. Several studies have explored children's inter-individual differences in self-regulation, but little is known about sources of intra-individual variation. AIMS: This study addressed the variability of children's self-regulation across typical classroom situations and how this might be associated with children's executive functions (EFs). SAMPLE: The study included 148 children (54.7% girls; Mage = 56.73 months). METHODS: Self-regulation was assessed with an observational measure in teacher-led and child-led activities within naturalistic classroom settings. Children's EFs were assessed with direct assessments at the start and end of the school year. RESULTS: Linear mixed-effect models showed that children demonstrated higher levels of self-regulation in child-led in comparison with teacher-led activities. Children with higher levels of EFs at the start of the school year showed less variation across teacher-led and child-led activities in comparison with children with lower levels of EFs. Regarding other aspects of the classroom context, neither the group size in which the activity took place nor which school subject it was focused on were associated with children's self-regulation. However, in teacher-led activities the type of interaction involved in the activity and the type of task influenced children's self-regulation. CONCLUSION: These results suggest that children who start school with higher levels of EFs are more able to adapt to different situations, highlighting the importance of fostering these skills in early childhood. In turn, children with lower levels of EFs may need additional support from teachers to remain self-regulated across different contexts.

Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies.

The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images. Validated in three independent longitudinal healthy cohorts (40, 23, and 60 healthy participants, respectively) with two common brain atlases, we found that the vTSN could robustly resolve inter-subject variability with high test-retest reliability. In contrast to the regional-based texture similarity networks (rTSNs) that calculate radiomic features based on region-of-interest information, vTSNs had higher inter- and intra-subject variability ratios and test-retest reliability in connectivity strength and network topological properties. Moreover, the Spearman correlation indicated a stronger association of the gene expression similarity network (GESN) with vTSNs than with rTSNs (vTSN: r = 0.600, rTSN: r = 0.433, z = 39.784, P < 0.001). Hierarchical clustering identified 3 vTSN subnets with differential association patterns with 13 coexpression modules, 16 neurotransmitters, 7 electrophysiology, 4 metabolism, and 2 large-scale structural and 4 functional organization maps. Moreover, these subnets had unique biological hierarchical organization from the subcortex-limbic system to the ventral neocortex and then to the dorsal neocortex. Based on 424 unrelated, qualified healthy subjects from the Human Connectome Project, we found that vTSNs could sensitively represent sex differences, especially for connections in the subcortex-limbic system and between the subcortex-limbic system and the ventral neocortex. Moreover, a multivariate variance component model revealed that vTSNs could explain a significant proportion of inter-subject behavioral variance in cognition (80.0 %) and motor functions (63.4 %). Finally, using 494 healthy adults (aged 19-80 years old) from the Southwest University Adult Lifespan Dataset, the Spearman correlation identified a significant association between aging and vTSN strength, especially within the subcortex-limbic system and between the subcortex-limbic system and the dorsal neocortex. In summary, our proposed vTSN is robust in uncovering individual variability and neurobiological brain processes, which can serve as biologically plausible measures for linking biological processes and human behavior.

Dual model transfer learning to compensate for individual variability in brain-computer interface.

BACKGROUND AND OBJECTIVE: Recent advancements in brain-computer interface (BCI) technology have seen a significant shift towards incorporating complex decoding models such as deep neural networks (DNNs) to enhance performance. These models are particularly crucial for sophisticated tasks such as regression for decoding arbitrary movements. However, these BCI models trained and tested on individual data often face challenges with limited performance and generalizability across different subjects. This limitation is primarily due to a tremendous number of parameters of DNN models. Training complex models demands extensive datasets. Nevertheless, group data from many subjects may not produce sufficient decoding performance because of inherent variability in neural signals both across individuals and over time METHODS: To address these challenges, this study proposed a transfer learning approach that could effectively adapt to subject-specific variability in cortical regions. Our method involved training two separate movement decoding models: one on individual data and another on pooled group data. We then created a salience map for each cortical region from the individual model, which helped us identify the input's contribution variance across subjects. Based on the contribution variance, we combined individual and group models using a modified knowledge distillation framework. This approach allowed the group model to be universally applicable by assigning greater weights to input data, while the individual model was fine-tuned to focus on areas with significant individual variance RESULTS: Our combined model effectively encapsulated individual variability. We validated this approach with nine subjects performing arm-reaching tasks, with our method outperforming (mean correlation coefficient, r = 0.75) both individual (r = 0.70) and group models (r = 0.40) in decoding performance. In particular, there were notable improvements in cases where individual models showed low performances (e.g., r = 0.50 in the individual decoder to r = 0.61 in the proposed decoder) CONCLUSIONS: These results not only demonstrate the potential of our method for robust BCI, but also underscore its ability to generalize individual data for broader applicability.

Association between intra-individual variability and prefrontal cortex activity measured by functional Near Infrared Spectroscopy (fNIRS) in children with ADHD.

This study uses fNIRS to determine whether there is a difference in the relationship between intra-individual variability and frontal lobe activity between ADHD patients and typically developing children. A total of 28 subjects (14 in ADHD patient group and 14 in control group) participated in this study. The subjects were tested for K-SADS and intelligence, and then the frontal lobe activity of the subjects was measured by continuous performance test, using functional near-infrared spectroscopy (NIRSIT). Processing speed index was significantly lower in the ADHD patient group than in the control group (p = .04). The CPT test results showed a positive correlation in the activity of the right dorsolateral prefrontal region in the patient group, but not at a statistically significant level. In the control group, activity showed a significant level of negative correlation with commission and hit reaction time standard deviation (p = .023; p = .063 respectively). In contrary to ADHD patient group, activation of the right dorsolateral prefrontal area was significantly correlated with reduction of intra-individual variability. This result showing that the relationship between activation of the right dorsolateral prefrontal area of the ADHD patient group and intra-individual variability shows a different pattern from typically developing children.

Variability of haptoglobin beta-chain proteoforms.

Existing knowledge on changes of the haptoglobin (Hp) molecule suggests that it may exist in multiple proteoforms, which obviously exhibit different functions. Using two-dimensional electrophoresis (2DE) in combination with mass spectrometry and immunodetection, we have analyzed blood plasma samples from both healthy donors and patients with primary grade IV glioblastoma (GBM), and obtained a detailed composite 2DE distribution map of ß-chain proteoforms, as well as the full-length form of Hp (zonulin). Although the total level of plasma Hp exceeded normal values in cancer patients (especially patients with GBM), the presence of particuar proteoforms, detected by their position on the 2DE map, was very individual. Variability was found in both zonulin and the Hp ß-chain. The presence of an alkaline form of zonulin in plasma can be considered a conditional, but insufficient, GBM biomarker. In other words, we found that at the level of minor proteoforms of Hp, even in normal conditions, there was a high individual variability. On the one hand, this raises questions about the reasons for such variability, if it is present not only in Hp, but also in other proteins. On the other hand, this may explain the discrepancy between the number of experimentally detected proteoforms and the theoretically possible ones not only in Hp, but also in other proteins.

Prefrontal intra-individual ERP variability and its asymmetry: exploring its biomarker potential in mild cognitive impairment.

BACKGROUND: The worldwide trend of demographic aging highlights the progress made in healthcare, albeit with health challenges like Alzheimer's Disease (AD), prevalent in individuals aged 65 and above. Its early detection at the mild cognitive impairment (MCI) stage is crucial. Event-related potentials (ERPs) obtained by averaging EEG segments responded to repeated events are vital for cognitive impairment research. Consequently, examining intra-trial ERP variability is vital for comprehending fluctuations within psychophysiological processes of interest. This study aimed to investigate cognitive deficiencies and instability in MCI using ERP variability and its asymmetry from a prefrontal two-channel EEG device. METHODS: In this study, ERP variability for both target and non-target responses was examined using the response variance curve (RVC) in a sample comprising 481 participants with MCI and 1,043 age-matched healthy individuals. The participants engaged in auditory selective attention tasks. Cognitive decline was assessed using the Seoul Neuropsychological Screening Battery (SNSB) and the Mini-Mental State Examination (MMSE). The research employed various statistical methods, including independent t-tests, and univariate and multiple logistic regression analyses. These analyses were conducted to investigate group differences and explore the relationships between neuropsychological test results, ERP variability and its asymmetry measures, and the prevalence of MCI. RESULTS: Our results showed that patients with MCI exhibited unstable cognitive processing, characterized by increased ERP variability compared to cognitively normal (CN) adults. Multiple logistic regression analyses confirmed the association between ERP variability in the target and non-target responses with MCI prevalence, independent of demographic and neuropsychological factors. DISCUSSION: The unstable cognitive processing in the MCI group compared to the CN individuals implies abnormal neurological changes and reduced and (or) unstable attentional maintenance during cognitive processing. Consequently, utilizing ERP variability measures from a portable EEG device could serve as a valuable addition to the conventional ERP measures of latency and amplitude. This approach holds significant promise for identifying mild cognitive deficits and neural alterations in individuals with MCI.

Using Targeted Metabolomics to Unravel Phenolic Metabolites of Plant Origin in Animal Milk.

Milk holds a high nutritional value and is associated with diverse health benefits. The understanding of its composition of (poly)phenolic metabolites is limited, which necessitates a comprehensive evaluation of the subject. This study aimed at analyzing the (poly)phenolic profile of commercial milk samples from cows and goats and investigating their sterilization treatments, fat content, and lactose content. Fingerprinting of phenolic metabolites was achieved by using ultra-high-performance liquid chromatography coupled with triple-quadrupole mass spectrometry (UHPLC-QqQ-MS/MS). Two hundred and three potential microbial and phase II metabolites of the main dietary (poly)phenols were targeted. Twenty-five metabolites were identified, revealing a diverse array of phenolic metabolites in milk, including isoflavones and their microbial catabolites equol and O-desmethylangolensin, phenyl-γ-valerolactones (flavan-3-ol microbial catabolites), enterolignans, urolithins (ellagitannin microbial catabolites), benzene diols, and hippuric acid derivates. Goat's milk contained higher concentrations of these metabolites than cow's milk, while the sterilization process and milk composition (fat and lactose content) had minimal impact on the metabolite profiles. Thus, the consumption of goat's milk might serve as a potential means to supplement bioactive phenolic metabolites, especially in individuals with limited production capacity. However, further research is needed to elucidate the potential health effects of milk-derived phenolics.

Characterizing Factors Associated with Excess Body Weight: A Descriptive Study Using Principal Component Analysis in a Population with Overweight and Obesity.

Obesity is a worldwide epidemic, making it crucial to understand how it can be effectively prevented/treated. Considering that obesity is a multifactorial condition, this article carried out a baseline cross-sectional study of the variables involved in the disorder. Eighty-four subjects with overweight/obesity were recruited. Dietary baseline information was obtained by analysing three 24 h recalls. Resting metabolic rate was measured using indirect calorimetry, physical activity was measured through accelerometry, cardiometabolic parameters were determined in blood samples and body composition via anthropometry and bioimpedance. A univariant and multivariate exploratory approach was carried out using principal component analysis (PCA). Large inter-individual variability was observed in dietetic, biochemical, and physical activity measurements (coefficient of variation ≥ 30%), but body composition was more uniform. Volunteers had an unbalanced diet and low levels of physical activity. PCA reduced the 26 analysed variables to 4 factors, accounting for 65.4% of the total data variance. The main factor was the "dietetic factor", responsible for 24.0% of the total variance and mainly related to energy intake, lipids, and saturated fatty acids. The second was the "cardiometabolic factor" (explaining 16.8% of the variability), the third was the "adiposity factor" (15.2%), and the last was the "serum cholesterol factor" (9.4%).

Individual-specific postural discomfort prediction using decision tree models.

The objective of the current study was to explore the utilization of the decision tree (DT) algorithm to model posture-discomfort relationships at the individual level. The DT algorithm has the advantage that it makes no assumptions about the distribution of data, is robust in representing non-linear data with noise, and produces white-box models that are interpretable. Individual-level modelling is essential for examining individual-specific postural discomfort perception processes and understanding the inter-individual variability. It also has practical applications, including the development of individual-specific digital human models and more precise and informative population accommodation analysis. Individual-specific DT models were generated using postural discomfort rating data for various seated upper body postures to predict discomfort based on postural and task variables. The individual-specific DT models accurately predicted postural discomfort and revealed large inter-individual variability in the modelling results. DT modelling is expected to greatly facilitate investigating the human discomfort perception process.

Behavior can be decoded across the cortex when individual differences are considered.

Group-level analyses have typically associated behavioral signatures with a constrained set of brain areas. Here we show that two behavioral metrics - reaction time (RT) and confidence - can be decoded across the cortex when each individual is considered separately. Subjects (N=50) completed a perceptual decision-making task with confidence. We built models decoding trial-level RT and confidence separately for each subject using the activation patterns in one brain area at a time after splitting the entire cortex into 200 regions of interest (ROIs). At the group level, we replicated previous results by showing that both RT and confidence could be decoded from a small number of ROIs (12.0% and 3.5%, respectively). Critically, at the level of the individual, both RT and confidence could be decoded from most brain regions even after Bonferroni correction (90.0% and 72.5%, respectively). Surprisingly, we observed that many brain regions exhibited opposite brain-behavior relationships across individuals, such that, for example, higher activations predicted fast RTs in some subjects but slow RTs in others. These results were further replicated in a second dataset. Lastly, we developed a simple test to determine the robustness of decoding performance, which showed that several hundred trials per subject are required for robust decoding. These results show that behavioral signatures can be decoded from a much broader range of cortical areas than previously recognized and suggest the need to study the brain-behavior relationship at both the group and the individual level.

One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response.

Introduction: To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response. Methods: Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients. Results: Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization. Discussion: Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability. Clinical trial registration: https://clinicaltrials.gov, identifier NCT00519428.

Urine Albumin-Creatinine Ratio Variability in People With Type 2 Diabetes: Clinical and Research Implications.

RATIONALE & OBJECTIVE: Evidence has demonstrated that albuminuria is a key diagnostic and prognostic marker of diabetic chronic kidney disease, but the impact of its day-to-day variability has not been adequately considered. This study quantified within-individual variability of albuminuria in people with type 2 diabetes to inform clinical albuminuria monitoring. STUDY DESIGN: Descriptive cross-sectional analysis. SETTING & PARTICIPANTS: People with type 2 diabetes (n=826, 67.1 [IQR, 60.3-72.4] years, 64.9% male) participating in the Progression of Diabetic Complications (PREDICT) cohort study. EXPOSURE: Four spot urine collections for measurement of urinary albumin-creatinine ratio (UACR) within 4 weeks. OUTCOME: Variability of UACR. ANALYTICAL APPROACH: We characterized within-individual variability (coefficient of variation [CV], 95% limits of random variation, intraclass correlation coefficient), developed a calculator displaying probabilities that any observed difference between a pair of UACR values truly exceeded a 30% difference, and estimated the ranges of diagnostic uncertainty to inform a need for additional UACR collections to exclude or confirm albuminuria. Multiple linear regression examined factors influencing UACR variability. RESULTS: We observed high within-individual variability (CV 48.8%; 95% limits of random variation showed a repeated UACR to be as high/low as 3.78/0.26 times the first). If a single-collection UACR increased from 2 to 5mg/mmol, the probability that UACR actually increased by at least 30% was only 50%, rising to 97% when 2 collections were obtained at each time point. The ranges of diagnostic uncertainty were 2.0-4.0mg/mmol after an initial UACR test, narrowing to 2.4-3.2 and 2.7-2.9mg/mmol for the mean of 2 and 3 collections, respectively. Some factors correlated with higher (female sex; moderately increased albuminuria) or lower (reduced estimated glomerular filtration rate and sodium-glucose cotransporter 2 inhibitor/angiotensin-converting enzyme inhibitor/angiotensin receptor blocker treatment) within-individual UACR variability. LIMITATIONS: Reliance on the mean of 4 UACR collections as the reference standard for albuminuria. CONCLUSIONS: UACR demonstrates a high degree of within-individual variability among individuals with type 2 diabetes. Multiple urine collections for UACR may improve capacity to monitor changes over time in clinical and research settings but may not be necessary for the diagnosis of albuminuria. PLAIN-LANGUAGE SUMMARY: Albuminuria (albumin in urine) is a diagnostic and prognostic marker of diabetic chronic kidney disease. However, albuminuria can vary within an individual from day to day. We compared 4 random spot urinary albumin-creatinine ratio (UACR) samples from 826 participants. We found that a second UACR collection may be as small as a fourth or as large as almost 4 times the first sample's UACR level. This high degree of variability presents a challenge to our ability to interpret changes in albuminuria. Multiple collections have been suggested as a solution. We have constructed tools that may aid clinicians in deciding how many urine collections are required to monitor and diagnose albuminuria. Multiple urine collections may be required for individual monitoring but not necessarily for diagnosis.

Analysis of modulations of mental fatigue on intra-individual variability from single-trial event related potentials.

BACKGROUND: Intra-individual variability (IIV), a measure of variance within an individual's performance, has been demonstrated as metrics of brain responses for neural functionality. However, how mental fatigue modulates IIV remains unclear. Consequently, the development of robust mental fatigue detection methods at the single-trial level is challenging. NEW METHODS: Based on a long-duration flanker task EEG dataset, the modulations of mental fatigue on IIV were explored in terms of response time (RT) and trial-to-trial latency variations of event-related potentials (ERPs). Specifically, latency variations were quantified using residue iteration decomposition (RIDE) to reconstruct latency-corrected ERPs. We compared reconstructed ERPs with raw ERPs by means of temporal principal component analysis (PCA). Furthermore, a single-trial classification pipeline was developed to detect the changes of mental fatigue levels. RESULTS: We found an increased IIV in the RT metric in the fatigue state compared to the alert state. The same sequence of ERPs (N1, P2, N2, P3a, P3b, and slow wave, or SW) was separated from both raw and reconstructed ERPs using PCA, whereas differences between raw and reconstructed ERPs in explained variances for separated ERPs were found owing to IIV. Particularly, a stronger N2 was detected in the fatigue than alert state after RIDE. The single-trial fatigue detection pipeline yielded an acceptable accuracy of 73.3%. COMPARISON WITH EXISTING METHODS: The IIV has been linked to aging and brain disorders, and as an extension, our finding demonstrates IIV as an efficient indicator of mental fatigue. CONCLUSIONS: This study reveals significant modulations of mental fatigue on IIV at the behavioral and neural levels and establishes a robust mental fatigue detection pipeline.