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.

Bioavailability and excretion profile of betacyanins - Variability and correlations between different excretion routes

The present study addresses the knowledge gap in betalain bioavailability, transformation and excretion. Analysis of renal and fecal excretion profiles in humans after consumption of beetroot revealed very low bioavailability (renal recovery of 0.13 %) and fast elimination of pigments (renal elimination rate constant of 0.16 h-1), while the majority of betalains underwent severe depletion during GI transit, evidenced by decarboxylation, deglucosidation and dehydrogenation. Betacyanin metabolite levels in human urine were positively associated with those in stools (p < 0.05), indicating significant impact of pigment metabolism in the gut on their bioavailability. In addition, the current study revealed large inter-individual and compositional variabilities of pigment after colonic fermentation compared with systemic metabolism, likely attributed to the increasing complexity of intestinal environment with diverse gut microbiota. To conclude, intestinal uptake and systemic metabolism of betacyanins are intimately associated with their intestinal biotransformation, with gut microbiota serving as a crucial factor.

Towards a qualitative theory of the interruption of eating behavior change

The poor maintenance of eating behavior change is one of the main obstacles to minimizing weight regain after weight loss during diets for non-surgical care of obese or overweight patients. We start with a known informal explanation of interruption in eating behavior change during severe restriction and formalize it as a causal network involving psychological variables, which we extend with energetic variables governed by principles of thermodynamics. The three core phenomena of dietary behavior change, i.e., non-initiation, initiation followed by discontinuation and initiation followed by non-discontinuation, are expressed in terms of the value of the key variable representing mood or psychological energy, the fluctuation of which is the result of three causal relationships. Based on our experimental knowledge of the time evolution profile of the three causal input variables, we then proceed to a qualitative analysis of the resulting theory, i.e., we consider an over-approximation of it which, after discretization, can be expressed in the form of a finite integer-based model. Using Answer Set Programming, we show that our formal model faithfully reproduces the three phenomena and, under a certain assumption, is minimal. We generalize this result by providing all the minimal models reproducing these phenomena when the possible causal relationships exerted on mood are extended to all the other variables (not just those assumed in the informal explanation), with arbitrary causality signs. Finally, by a direct analytical resolution of an under-approximation of our theory, obtained by assuming linear causalities, as a system of linear ODEs, we find exactly the same minimal models, proving that they are also equal to the actual minimal models of our theory since these are framed below and above by the models of the under-approximation and the over-approximation. We determine which parameters need to be person-specific and which can be considered invariant, i.e., we explain inter-individual variability. Our approach could pave the way for universally accepted theories in the field of behavior change and, more broadly, in other areas of psychology.

Dimensionality and optimal combination of autonomic fear-conditioning measures in humans

Fear conditioning, also termed threat conditioning, is a commonly used learning model with clinical relevance. Quantification of threat conditioning in humans often relies on conditioned autonomic responses such as skin conductance responses (SCR), pupil size responses (PSR), heart period responses (HPR), or respiration amplitude responses (RAR), which are usually analyzed separately. Here, we investigate whether inter-individual variability in differential conditioned responses, averaged across acquisition, exhibits a multi-dimensional structure, and the extent to which their linear combination could enhance the precision of inference on whether threat conditioning has occurred. In a mega-analytic approach, we re-analyze nine data sets including 256 individuals, acquired by the group of the last author, using standard routines in the framework of psychophysiological modeling (PsPM). Our analysis revealed systematic differences in effect size between measures across datasets, but no evidence for a multidimensional structure across various combinations of measures. We derive the statistically optimal weights for combining the four measures and subsets thereof, and we provide out-of-sample performance metrics for these weights, accompanied by bias-corrected confidence intervals. We show that to achieve the same statistical power, combining measures allows for a relevant reduction in sample size, which in a common scenario amounts to roughly 24%. To summarize, we demonstrate a one-dimensional structure of threat conditioning measures, systematic differences in effect size between measures, and provide weights for their optimal linear combination in terms of maximal retrodictive validity.

CSF protein ratios with enhanced potential to reflect Alzheimer's disease pathology and neurodegeneration

BACKGROUND

Amyloid and tau aggregates are considered to cause neurodegeneration and consequently cognitive decline in individuals with Alzheimer's disease (AD). Here, we explore the potential of cerebrospinal fluid (CSF) proteins to reflect AD pathology and cognitive decline, aiming to identify potential biomarkers for monitoring outcomes of disease-modifying therapies targeting these aggregates.

METHOD

We used a multiplex antibody-based suspension bead array to measure the levels of 49 proteins in CSF from the Swedish GEDOC memory clinic cohort at the Karolinska University Hospital. The cohort comprised 148 amyloid- and tau-negative individuals (A-T-) and 65 amyloid- and tau-positive individuals (A+T+). An independent sample set of 26 A-T- and 26 A+T+ individuals from the Amsterdam Dementia Cohort was used for validation. The measured proteins were clustered based on their correlation to CSF amyloid beta peptides, tau and NfL levels. Further, we used support vector machine modelling to identify protein pairs, matched based on their cluster origin, that reflect AD pathology and cognitive decline with improved performance compared to single proteins.

RESULTS

The protein-clustering revealed 11 proteins strongly correlated to t-tau and p-tau (tau-associated group), including mainly synaptic proteins previously found elevated in AD such as NRGN, GAP43 and SNCB. Another 16 proteins showed predominant correlation with Aβ42 (amyloid-associated group), including PTPRN2, NCAN and CHL1. Support vector machine modelling revealed that proteins from the two groups combined in pairs discriminated A-T- from A+T+ individuals with higher accuracy compared to single proteins, as well as compared to protein pairs composed of proteins originating from the same group. Moreover, combining the proteins from different groups in ratios (tau-associated protein/amyloid-associated protein) significantly increased their correlation to cognitive decline measured with cognitive scores. The results were validated in an independent cohort.

CONCLUSIONS

Combining brain-derived proteins in pairs largely enhanced their capacity to discriminate between AD pathology-affected and unaffected individuals and increased their correlation to cognitive decline, potentially due to adjustment of inter-individual variability. With these results, we highlight the potential of protein pairs to monitor neurodegeneration and thereby possibly the efficacy of AD disease-modifying therapies.

A strategy for deciphering the bioactive metabolites of Farfarae Flos by the inter-individual variability of the antitussive effect

Farfarae Flos is a commonly used traditional herb for the treatment of respiratory disorders. In this study, ultra-high-performance liquid chromatography coupled with time-of-flight mass spectrometry combined with the mass defect filter method was used for the qualitative analysis of Farfarae Flos metabolites in the lung tissues. Then a method for the simultaneous determination of 14 Farfarae Flos metabolites was developed and validated in terms of specificity, linearity, precision and accuracy, matrix effect and recovery. The method was applied to compare the lung tissue of Farfarae Flos treated mice, and 10 caffeoylquinic acid metabolites were higher in the mice with better antitussive effect. Further network pharmacology analysis and molecular docking results showed that these metabolites played an important role in the antitussive effect of Farfarae Flos. This study presented a novel strategy for deciphering the active compounds of herbal medicine by inter-individual variability of bioactivities.

How far are we from reliable metabolomics-based biomarkers? The often-overlooked importance of addressing inter-individual variability factors

Metabolomics has proven great potential to unravel the molecular basis of diseases. However, most attempts aimed at identifying reliable metabolomics-based biomarkers for diagnosis, prediction, and prognosis of diseases have repeatedly failed because of inconsistent results and unsatisfactory replication in independent cohorts. This review article explores the possible causes behind this reproducibility crisis, with special focus on the role that inter-individual variability factors play in modulating the susceptibility to disease development. Furthermore, we provide future perspectives on the applicability of metabolomics in biomedical research and its translatability into clinical practice.

Personalized redox biology: Designs and concepts

Personalized interventions are regarded as a next-generation approach in almost all fields of biomedicine, such as clinical medicine, exercise, nutrition and pharmacology. At the same time, an increasing body of evidence indicates that redox processes regulate, at least in part, multiple aspects of human physiology and pathology. As a result, the idea of applying personalized redox treatments to improve their efficacy has gained popularity among researchers in recent years. The aim of the present primer-style review was to highlight some crucial yet underappreciated methodological, statistical, and interpretative concepts within the redox biology literature, while also providing a physiology-oriented perspective on personalized redox biology. The topics addressed are: (i) the critical issue of investigating the potential existence of inter-individual variability; (ii) the importance of distinguishing a genuine and consistent response of a subject from a chance finding; (iii) the challenge of accurately quantifying the effect of a redox treatment when dealing with 'extreme' groups due to mathematical coupling and regression to the mean; and (iv) research designs and analyses that have been implemented in other fields, and can be reframed and exploited in a redox biology context.

Importance of cytochrome 3A4 and 2D6-mediated drug-drug interactions in oxycodone consumption among older adults hospitalized for hip fracture: a cross-sectional study

Hip fracture is a common injury and represents a major health problem with an increasing incidence. In older adults, opioids such as oxycodone are often preferred to other analgesics such as tramadol because of a lower risk of delirium. Different parameters, such as inhibition of cytochrome P450 (CYP450) 2D6 and/or 3A4, can potentially lead to pharmacokinetic variations of oxycodone representing a risk of adverse drugs effects or lack of drug response. There is a risk of drug-drug interactions involving CYP450 in older adults due to the high prevalence of polypharmacy. This study sought to identify patient characteristics that influence oxycodone administration. A single-center observational study included 355 patients with a hip fracture hospitalized in a geriatric postoperative unit. Composite endpoint based on form, duration, and timing to intake separated patients into three groups: "no oxycodone", "low oxycodone ", and "high oxycodone ". CYP450 interactions were studied based on a composite variable defining the most involved CYP450 pathways between CYP2D6 and CYP3A4. CYP450 interactions with CYP2D6 pathway involved were associated with the risk of "high oxycodone" [odds ratio adjusted on age and the type of hip fracture (OR*) 4.52, 95% confidence interval (CI) 1.39-16.83, p = 0.02)], as well as serum albumin levels (OR* 1.09, 95% CI 1.02-1.17, p = 0.01). Cognitive impairment was negatively associated with the risk of "high oxycodone" (OR* 0.38, 95% CI 0.18-0.77, p = 0.02). This study showed an association between CYP2D6 interactions and higher oxycodone consumption indirectly reflecting the existence of uncontrolled postoperative pain.

Unravelling phenolic metabotypes in the frame of the COMBAT study, a randomized, controlled trial with cranberry supplementation

Cranberry (poly)phenols may have potential health benefits. Circulating (poly)phenol metabolites can act as mediators of these effects, but they are subjected to an extensive inter-individual variability. This study aimed to quantify both plasma and urine (poly)phenol metabolites following a 12-week intake of a cranberry powder in healthy older adults, and to investigate inter-individual differences by considering the existence of urinary metabotypes related to dietary (poly)phenols. Up to 13 and 67 metabolites were quantified in plasma and urine respectively. Cranberry consumption led to changes in plasma metabolites, mainly hydroxycinnamates and hippuric acid. Individual variability in urinary metabolites was assessed using different data sets and a combination of statistical models. Three phenolic metabotypes were identified, colonic metabolism being the main driver for subject clustering. Metabotypes were characterized by quali-quantitative differences in the excretion of some metabolites such as phenyl-γ-valerolactones, hydroxycinnamic acids, and phenylpropanoic acids. Metabotypes were further confirmed when applying a model only focused on flavan-3-ol colonic metabolites. 5-(3',4'-dihydroxyphenyl)-γ-valerolactone derivatives were the most relevant metabolites for metabotyping. Metabotype allocation was well preserved after 12-week intervention. This metabotyping approach for cranberry metabolites represents an innovative step to handle the complexity of (poly)phenol metabolism in free-living conditions, deciphering the existence of metabotypes derived from the simultaneous consumption of different classes of (poly)phenols. These results will help contribute to studying the health effects of cranberries and other (poly)phenol-rich foods, mainly considering gut microbiota-driven individual differences.

P1NP and β-CTX-1 Responses to a Prolonged, Continuous Running Bout in Young Healthy Adult Males: A Systematic Review with Individual Participant Data Meta-analysis

BACKGROUND

Circulating biomarkers of bone formation and resorption are widely used in exercise metabolism research, but their responses to exercise are not clear. This study aimed to quantify group responses and inter-individual variability of P1NP and β-CTX-1 after prolonged, continuous running (60-120 min at 65-75% V̇O2max) in young healthy adult males using individual participant data (IPD) meta-analysis.

METHODS

The protocol was designed following PRISMA-IPD guidelines and was pre-registered on the Open Science Framework prior to implementation ( https://osf.io/y69nd ). Changes in P1NP and β-CTX-1 relative to baseline were measured during, immediately after, and in the hours and days following exercise. Typical hourly and daily variations were estimated from P1NP and β-CTX-1 changes relative to baseline in non-exercise (control) conditions. Group responses and inter-individual variability were quantified with estimates of the mean and standard deviation of the difference, and the proportion of participants exhibiting an increased response. Models were conducted within a Bayesian framework with random intercepts to account for systematic variation across studies.

RESULTS

P1NP levels increased during and immediately after running, when the proportion of response was close to 100% (75% CrI: 99 to 100%). P1NP levels returned to baseline levels within 1 h and over the next 4 days, showing comparable mean and standard deviation of the difference with typical hourly (0.1 ± 7.6 ng·mL-1) and daily (- 0.4 ± 5.7 ng·mL-1) variation values. β-CTX-1 levels decreased during and up to 4 h after running with distributions comparable to typical hourly variation (- 0.13 ± 0.11 ng·mL-1). There was no evidence of changes in β-CTX-1 levels during the 4 days after the running bout, when distributions were also similar between the running data and typical daily variation (- 0.03 ± 0.10 ng·mL-1).

CONCLUSION

Transient increases in P1NP were likely biological artefacts (e.g., connective tissue leakage) and not reflective of bone formation. Comparable small decreases in β-CTX-1 identified in both control and running data, suggested that these changes were due to the markers' circadian rhythm and not the running intervention. Hence, prolonged continuous treadmill running did not elicit bone responses, as determined by P1NP and β-CTX-1, in this population.

A systematic review of the neurobiological effects of theta-burst stimulation (TBS) as measured using functional magnetic resonance imaging (fMRI)

Theta burst stimulation (TBS) is associated with the modulation of a range of clinical, cognitive, and behavioural outcomes, but specific neurobiological effects remain somewhat unclear. This systematic literature review investigated resting-state and task-based functional magnetic resonance imaging (fMRI) outcomes post-TBS in healthy human adults. Fifty studies that applied either continuous-or intermittent-(c/i) TBS, and adopted a pretest-posttest or sham-controlled design, were included. For resting-state outcomes following stimulation applied to motor, temporal, parietal, occipital, or cerebellar regions, functional connectivity generally decreased in response to cTBS and increased in response to iTBS, though there were some exceptions to this pattern of response. These findings are mostly consistent with the assumed long-term depression (LTD)/long-term potentiation (LTP)-like plasticity effects of cTBS and iTBS, respectively. Task-related outcomes following TBS were more variable. TBS applied to the prefrontal cortex, irrespective of task or state, also produced more variable responses, with no consistent patterns emerging. Individual participant and methodological factors are likely to contribute to the variability in responses to TBS. Future studies assessing the effects of TBS via fMRI must account for factors known to affect the TBS outcomes, both at the level of individual participants and of research methodology.

Individual consistency in the learning abilities of honey bees: cognitive specialization within sensory and reinforcement modalities

The question of whether individuals perform consistently across a variety of cognitive tasks is relevant for studies of comparative cognition. The honey bee (Apis mellifera) is an appropriate model to study cognitive consistency as its learning can be studied in multiple elemental and non-elemental learning tasks. We took advantage of this possibility and studied if the ability of honey bees to learn a simple discrimination correlates with their ability to solve two tasks of higher complexity, reversal learning and negative patterning. We performed four experiments in which we varied the sensory modality of the stimuli (visual or olfactory) and the type (Pavlovian or operant) and complexity (elemental or non-elemental) of conditioning to examine if stable correlated performances could be observed across experiments. Across all experiments, an individual's proficiency to learn the simple discrimination task was positively and significantly correlated with performance in both reversal learning and negative patterning, while the performances in reversal learning and negative patterning were positively, yet not significantly correlated. These results suggest that correlated performances across learning paradigms represent a distinct cognitive characteristic of bees. Further research is necessary to examine if individual cognitive consistency can be found in other insect species as a common characteristic of insect brains.

Inter-individual variability in current direction for common tDCS montages

The direction of applied electric current relative to the cortical surface is a key determinant of transcranial direct current stimulation (tDCS) effects. Inter-individual differences in anatomy affect the consistency of current direction at a cortical target. However, the degree of this variability remains undetermined. Using current flow modelling (CFM), we quantified the inter-individual variability in tDCS current direction at a cortical target (left primary motor cortex, M1). Three montages targeting M1 using circular electrodes were compared: PA-tDCS directed current perpendicular to the central sulcus in a posterior-anterior direction relative to M1, ML-tDCS directed current parallel to the central sulcus in a medio-lateral direction, and conventional-tDCS applied electrodes over M1 and the contralateral forehead. In 50 healthy brain scans from the Human Connectome Project, we extracted current direction and intensity from the grey matter surface in the sulcal bank (M1BANK) and gyral crown (M1CROWN), and neighbouring primary somatosensory cortex (S1BANK and S1CROWN). Results confirmed substantial inter-individual variability in current direction (50%-150%) across all montages. Radial inward current produced by PA-tDCS was predominantly located in M1BANK, whereas for conventional-tDCS it was clustered in M1CROWN. The difference in radial inward current in functionally distinct subregions of M1 raises the testable hypothesis that PA-tDCS and conventional-tDCS modulate cortical excitability through different mechanisms. We show that electrode locations can be used to closely approximate current direction in M1 and precentral gyrus, providing a landmark-based method for tDCS application to address the hypothesis without the need for MRI. By contrast, ML-tDCS current was more tangentially orientated, which is associated with weaker somatic polarisation. Substantial inter-individual variability in current direction likely contributes to variable neuromodulation effects reported for these protocols, emphasising the need for individualised electrode montages, including the control of current direction.

Comparative transcriptome in large-scale human and cattle populations

BACKGROUND

Cross-species comparison of transcriptomes is important for elucidating evolutionary molecular mechanisms underpinning phenotypic variation between and within species, yet to date it has been essentially limited to model organisms with relatively small sample sizes.

RESULTS

Here, we systematically analyze and compare 10,830 and 4866 publicly available RNA-seq samples in humans and cattle, respectively, representing 20 common tissues. Focusing on 17,315 orthologous genes, we demonstrate that mean/median gene expression, inter-individual variation of expression, expression quantitative trait loci, and gene co-expression networks are generally conserved between humans and cattle. By examining large-scale genome-wide association studies for 46 human traits (average n = 327,973) and 45 cattle traits (average n = 24,635), we reveal that the heritability of complex traits in both species is significantly more enriched in transcriptionally conserved than diverged genes across tissues.

CONCLUSIONS

In summary, our study provides a comprehensive comparison of transcriptomes between humans and cattle, which might help decipher the genetic and evolutionary basis of complex traits in both species.

tDCS induced GABA change is associated with the simulated electric field in M1, an effect mediated by grey matter volume in the MRS voxel

Background and objective

Transcranial direct current stimulation (tDCS) has wide ranging applications in neuro-behavioural and physiological research, and in neurological rehabilitation. However, it is currently limited by substantial inter-subject variability in responses, which may be explained, at least in part, by anatomical differences that lead to variability in the electric field (E-field) induced in the cortex. Here, we tested whether the variability in the E-field in the stimulated cortex during anodal tDCS, estimated using computational simulations, explains the variability in tDCS induced changes in GABA, a neurophysiological marker of stimulation effect.

Methods

Data from five previously conducted MRS studies were combined. The anode was placed over the left primary motor cortex (M1, 3 studies, N = 24) or right temporal cortex (2 studies, N = 32), with the cathode over the contralateral supraorbital ridge. Single voxel spectroscopy was performed in a 2×2×2cm voxel under the anode in all cases. MRS data were acquired before and either during or after 1 mA tDCS using either a sLASER sequence (7T) or a MEGA-PRESS sequence (3T). sLASER MRS data were analysed using LCModel, and MEGA-PRESS using FID-A and Gannet. E-fields were simulated in a finite element model of the head, based on individual structural MR images, using SimNIBS. Separate linear mixed effects models were run for each E-field variable (mean and 95th percentile; magnitude, and components normal and tangential to grey matter surface, within the MRS voxel). The model included effects of time (pre or post tDCS), E-field, grey matter volume in the MRS voxel, and a 3-way interaction between time, E-field and grey matter volume. Additionally, we ran a permutation analysis using PALM to determine whether E-field anywhere in the brain, not just in the MRS voxel, correlated with GABA change.

Results

In M1, higher mean E-field magnitude was associated with greater anodal tDCS-induced decreases in GABA (t(24) = 3.24, p = 0.003). Further, the association between mean E-field magnitude and GABA change was moderated by the grey matter volume in the MRS voxel (t(24) = –3.55, p = 0.002). These relationships were consistent across all E-field variables except the mean of the normal component. No significant relationship was found between tDCS-induced GABA decrease and E-field in the temporal voxel. No significant clusters were found in the whole brain analysis.

Conclusions

Our data suggest that the electric field induced by tDCS within the brain is variable, and is significantly related to anodal tDCS-induced decrease in GABA, a key neurophysiological marker of stimulation. These findings strongly support individualised dosing of tDCS, at least in M1. Further studies examining E-fields in relation to other outcome measures, including behaviour, will help determine the optimal E-fields required for any desired effects.

Investigating the inter-individual variability of Astragali Radix against cisplatin-induced liver injury via 16S rRNA gene sequencing and LC/MS-based metabolomics

BACKGROUND

Cisplatin (CDDP), one of the widely used chemotherapeutic drugs, can induce a series of side effects, such as hepatotoxicity and gastrointestinal toxicity. Astragali Radix (AR) is widely used as the tonic herbal medicine in traditional Chinese medicine (TCM). However, there was no report about the hepatoprotective effect of AR against the cisplatin-induced hepatic damage.

PURPOSE

This study aimed to investigate the protective effect and potential mechanism of AR water extract against the cisplatin-induced liver injury.

METHODS

Cisplatin was utilized to induce the liver injury using ICR mice, and the protective effect of AR was evaluated by serum biochemistry indices and liver histopathology. Then UHPLC Q-TOF-MS/MS-based untargeted serum metabolomics approach combined with 16S rRNA-based microbiota analysis was used to explore the underlying biomarkers and mechanism about the liver-protective effect of AR.

RESULTS

AR could decrease the serum AST and ALT, ameliorate hepatic pathological damages caused by cisplatin. Serum metabolomics indicated AR could regulate the biosynthesis of unsaturated fatty acids, arachidonic acid metabolism, purine metabolism, and fatty acid biosynthesis. In addition, 16S rRNA gene sequencing analysis showed that AR could regulate cisplatin-induced gut microbiota disorder, especially the inflammation-related bacteria (p_Deferribacteres, g_Enterococcus, and g_Alistipes, etc.), and the short chain fatty acids (SCFAs)-producing bacteria (g_Alloprevotella, g_Intestinimoas, and g_Flavonifractor). Moreover, 7 mice (AR-7) showed better liver protective effect than the other 3 mice (AR-3), and their regulatory effect on the gut microbiota and serum metabolites were also different, indicating the presence of inter-individual variability for the liver protective effect of AR.

CONCLUSIONS

This study revealed the protective effect and the potential mechanisms of AR against cisplatin-induced liver injury, and found that inter-individual variability of the liver protective effect of AR was related to the host microbiome and metabolome. These findings provided new insight into the health effect of dietary AR as a functional food for cisplatin-based chemotherapy.

Genetic variants affecting chemical mediated skin immunotoxicity

The skin is an immune-competent organ and this function may be impaired by exposure to chemicals, which may ultimately result in immune-mediated dermal disorders. Interindividual variability to chemical-induced skin immune reactions is associated with intrinsic individual characteristics and their genomes. In the last 30-40 years, several genes influencing susceptibility to skin immune reactions were identified. The aim of this review is to provide information regarding common genetic variations affecting skin immunotoxicity. The polymorphisms selected for this review are related to xenobiotic-metabolizing enzymes (CYPA1 and CYPB1 genes), antioxidant defense (GSTM1, GSTT1, and GSTP1 genes), aryl hydrocarbon receptor signaling pathway (AHR and ARNT genes), skin barrier function transepidermal water loss (FLG, CASP14, and SPINK5 genes), inflammation (TNF, IL10, IL6, IL18, IL31, and TSLP genes), major histocompatibility complex (MHC) and neuroendocrine system peptides (CALCA, TRPV1, ACE genes). These genes present variants associated with skin immune responses and diseases, as well as variants associated with protecting skin immune homeostasis following chemical exposure. The molecular and association studies focusing on these genetic variants may elucidate their functional consequences and contribution in the susceptibility to skin immunotoxicity. Providing information on how genetic variations affect the skin immune system may reduce uncertainties in estimating chemical hazards/risks for human health in the future.

A double-blind randomized controlled trial combining cognitive training (CoRe) and neurostimulation (tDCS) in the early stages of cognitive impairment

BACKGROUND

The prevalence of neurodegenerative diseases is expected to increase over the next years, therefore, new methods able to prevent and delay cognitive decline are needed.

AIMS

To evaluate the effectiveness of a combined treatment protocol associating a computerized cognitive training (CoRe) with anodal transcranial direct current stimulation (tDCS).

METHODS

In this randomized controlled trial, 33 patients in the early stage of cognitive impairment were assigned to the experimental group (CoRE + real tDCS) or control group (CoRE + sham tDCS). In each group, the intervention lasted 3 consecutive weeks (4 sessions/week). A neuropsychological assessment was administered at baseline (T0), post-intervention (T1) and 6-months later (T2).

RESULTS

The CoRE + real tDCS group only improved in working memory and attention/processing speed at both T1 and T2. It reported a stable MMSE score at T2, while the CoRE + sham tDCS group worsened. Age, mood, and T0 MMSE score resulted to play a role in predicting treatment effects.

CONCLUSION

Combined multi-domain interventions may contribute to preventing or delaying disease progression.

TRIAL REGISTRATION

Trial registration number (ClinicalTrials.gov): NCT04118686.

Chronic mild stress paradigm as a rat model of depression: facts, artifacts, and future perspectives

RATIONALE

The chronic mild stress (CMS) paradigm was first described almost 40 years ago and has become a widely used model in the search for antidepressant drugs for major depression disorder (MDD). It has resulted in the publication of almost 1700 studies in rats alone. Under the original CMS procedure, the expression of an anhedonic response, a key symptom of depression, was seen as an essential feature of both the model and a depressive state. The prolonged exposure of rodents to unpredictable/uncontrollable mild stressors leads to a reduction in the intake of palatable liquids, behavioral despair, locomotor inhibition, anxiety-like changes, and vegetative (somatic) abnormalities. Many of the CMS studies do not report these patterns of behaviors, and they often fail to include consistent molecular, neuroanatomical, and physiological phenotypes of CMS-exposed animals.

OBJECTIVES

To critically review the CMS studies in rats so that conceptual and methodological flaws can be avoided in future studies.

RESULTS

Analysis of the literature supports the validity of the CMS model and its impact on the field. However, further improvements could be achieved by (i) the stratification of animals into 'resilient' and 'susceptible' cohorts within the CMS animals, (ii) the use of more refined protocols in the sucrose test to mitigate physiological and physical artifacts, and (iii) the systematic evaluation of the non-specific effects of CMS and implementation of appropriate adjustments within the behavioral tests.

CONCLUSIONS

We propose methodological revisions and the use of more advanced behavioral tests to refine the rat CMS paradigm, which offers a valuable tool for developing new antidepressant medications.

Evidence of cognitive specialization in an insect: proficiency is maintained across elemental and higher-order visual learning but not between sensory modalities in honey bees

Individuals differing in their cognitive abilities and foraging strategies may confer a valuable benefit to their social groups as variability may help responding flexibly in scenarios with different resource availability. Individual learning proficiency may either be absolute or vary with the complexity or the nature of the problem considered. Determining if learning abilities correlate between tasks of different complexity or between sensory modalities has a high interest for research on brain modularity and task-dependent specialisation of neural circuits. The honeybee Apis mellifera constitutes an attractive model to address this question due to its capacity to successfully learn a large range of tasks in various sensory domains. Here we studied whether the performance of individual bees in a simple visual discrimination task (a discrimination between two visual shapes) is stable over time and correlates with their capacity to solve either a higher-order visual task (a conceptual discrimination based on spatial relations between objects) or an elemental olfactory task (a discrimination between two odorants). We found that individual learning proficiency within a given task was maintained over time and that some individuals performed consistently better than others within the visual modality, thus showing consistent aptitude across visual tasks of different complexity. By contrast, performance in the elemental visual-learning task did not predict performance in the equivalent elemental olfactory task. Overall, our results suggest the existence of cognitive specialisation within the hive, which may contribute to ecological social success.

Inter-individual variability in habituation of anxiety-related responses within three mouse inbred strains

Inter-individual variability in behavioral and physiological response has become a well-established phenomenon in animal models of anxiety and other disorders. Such variability is even demonstrated within mouse inbred strains. A recent study showed that adaptive and non-adaptive anxiety phenotypes (measured as habituation and/or sensitization of anxiety responses) may differ within cohorts of 129 mice. This variability was expressed across both anxiety- and activity-related behavioral dimensions. These findings were based however on re-analysis of previously published data. The present study therefore aimed to empirically validate these findings in 129 mice. In addition, we assessed such inter-individuality in two other strains: BALB/c and C57BL/6. Males of three mouse inbred strains (BALB/c, C57BL/6 and 129S2) were behaviorally characterized through repeated exposure to a mild aversive stimulus (modified Hole Board, 4 consecutive trials). Behavioral observations were supplemented with assessment of circulating corticosterone levels. Clustering the individual response trajectories of behavioral and endocrine responses yielded two multidimensional response types of different adaptive value. Interestingly, these response types were displayed by individuals of all three strains. The response types differed significantly on anxiety and activity related behavioral dimensions but not on corticosterone concentrations. This study empirically confirms that adaptive capacities may differ within 129 cohorts. In addition, it extends this inter-individual variability in behavioral profiles to BALB/c and C57BL/6. Whether these two sub-types constitute differential anxiety phenotypes may differ per strain and requires further study.

Impact of inter-individual variability on the estimation of default mode network in temporal concatenation group ICA

Temporal concatenation group ICA (TC-GICA) is a widely used data-driven method to extract common functional brain networks among individuals. TC-GICA concatenates the time series of individual fMRI data and applies dimension reduction and ICA algorithms to decompose the data into group-level components. The default mode network (DMN) estimated using TC-GICA at relatively high model orders (i.e., large numbers of components) is split into multiple components. The split DMNs are topographically different from those estimated using other methods (e.g., seed-based correlation, clustering, graph theoretical analysis, and other ICA methods like gRAICAR and IVA-GL) and are inconsistent with the existing knowledge of DMN. We hypothesize that the "DMN-splitting'' phenomenon reflects the impact of inter-individual variability in data, which is propagated into the ICA decomposition via the data-concatenation step of TC-GICA. By systematically manipulating the amount of variability involved in the temporal concatenation in both simulated and several realistic datasets, we observed that as more variability was involved, the estimated DMN became less similar to the averaged functional connectivity (FC) pattern obtained using seed-based correlation analysis. The performance of the DMN estimation in TC-GICA also exhibited remarkable dependence on the model order settings. Further analyses revealed that the "DMN-splitting" in TC-GICA could be reproduced when involving large variability in the data-concatenation and performing ICA at high model orders. These results were replicated across multiple datasets and various software implementations. When applying ICA approaches that avoid temporal concatenation, such as gRAICAR and IVA-GL, to the same datasets, the estimated group-level DMN was more consistent with the seed-based FC pattern and was more robust to various model order settings. This study calls for caution when applying TC-GICA to datasets expected to have large inter-individual variability, such as pooling different experimental groups of subjects.

Measuring sleep regularity: Theoretical properties and practical usage of existing metrics

STUDY OBJECTIVES

Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual's average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics.

METHODS

Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: 'scrambling' the order of days; daily vs. weekly variation; naps; awakenings; 'all-nighters'; and length of study.

RESULTS

SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences.

CONCLUSIONS

Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

Quantitative assessment of inter-individual variability in fMRI-based human brain atlas

Background

Inter-individual variability is an inherent and ineradicable feature of group-level brain atlases that undermines their reliability for clinical and other applications. To date, there have been no reports quantifying inter-individual variability in brain atlases.

Methods

In the present study, we compared inter-individual variability in nine brain atlases by task-based functional magnetic resonance imaging (MRI) mapping of motor and temporal lobe language regions in both cerebral hemispheres. We analyzed complete motor and language task-based fMRI and T1 data for 893 young, healthy subjects in the Human Connectome Project database. Euclidean distances (EDs) between hotspots in specific brain regions were calculated from task-based fMRI and brain atlas data. General linear model parameters were used to investigate the influence of different brain atlases on signal extraction. Finally, the inter-individual variability of ED and extracted signals and interdependence of relevant indicators were statistically evaluated.

Results

We found that inter-individual variability of ED varied across the nine brain atlases (P<0.0001 for motor regions and P<0.0001 for language regions). There was no correlation between parcel number and inter-individual variability in left to right (LtoR; P=0.7959 for motor regions and P=0.2002 for language regions) and right to left (RtoL; P=0.7654 for motor regions and P=0.3544 for language regions) ED; however, LtoR (P≤0.0001) and RtoL (P≤0.0001) inter-individual variability differed according to brain region: the LtoR (P=0.0008) and RtoL (P=0.0004) inter-individual variability was greater for the right hand than for the left hand, the LtoR (P=0.0019) and RtoL (P=0.0179) inter-individual variability was greater for the right language than for the left language, but there was no such difference between the right foot and left foot (LtoR, P=0.2469 and RtoL, P=0.6140). Inter-individual variability in one motor region was positively correlated with mean values in the other three motor regions (left hand, P=0.0145; left foot, P=0.0103; right hand, P=0.1318; right foot, P=0.3785). Inter-individual variability in language region was positively correlated with mean values in the four motor regions (left language, P=0.0422; right language, P=0.0514). Signal extraction for LtoR (P<0.0001) and RtoL (P<0.0001) varied across the nine brain atlases, which also showed differences in inter-individual variability.

Conclusions

These results underscore the importance of quantitatively assessing the inter-individual variability of a brain atlas prior to use, and demonstrate that mapping motor regions by task-based fMRI is an effective method for quantitatively assessing the inter-individual variability in a brain atlas.