Association study of morpho-phenological traits in quinoa (Chenopodium quinoa Willd.) using SSR markers.

In this study, the genetic and molecular diversity of 60 quinoa accessions was assessed using agronomically important traits related to grain yield as well as microsatellite (SSR) markers, and informative markers linked to the studied traits were identified using association study. The results showed that most of the studied traits had a relatively high diversity, but grain saponin and protein content showed the highest diversity. High diversity was also observed in all SSR markers, but KAAT023, KAAT027, KAAT036, and KCAA014 showed the highest values for most of the diversity indices and can be introduced as the informative markers to assess genetic diversity in quinoa. Population structure analysis showed that the studied population probably includes two subclusters, so that out of 60 quinoa accessions, 29 (48%) and 23 (38%) accessions were assigned to the first and second subclusters, respectively, and eight (13%) accessions were considered as the mixed genotypes. The study of the population structure using Structure software showed two possible subgroups (K = 2) in the studied population and the results of the bar plot confirmed it. Association study using the general linear model (GLM) and mixed linear model (MLM) identified the number of 35 and 32 significant marker-trait associations (MTAs) for the first year (2019) and 37 and 35 significant MTAs for the second year (2020), respectively. Among the significant MTAs identified for different traits, the highest number of significant MTAs were obtained for grain yield and 1000-grain weight with six and five MTAs, respectively.

Using a centered general linear model for detection of interactions among biomarkers.

The dummy variable based general linear model (gLM) is commonly used to model categorical factors and their interactions. However, the main factors and their interactions in a general linear model are often correlated even when the factors are independently distributed. Alternatively, the classical two-way factorial analysis of variance (ANOVA) model can avoid the correlation between the main factors and their interactions when the main factors are independent. But the ANOVA model is hardly applicable to a regular linear regression model especially in the presence of other covariates due to constraints on its model parameters. In this study, a centered general linear model (cgLM) is proposed for modeling interactions between categorical factors based on their centered dummy variables. We show that the cgLM can avoid the correlation between the main factors and their interactions as the ANOVA model when the main factors are independent. Meanwhile, similar to gLM, it can be used in regular regression and fitted conveniently using the standard least square approach by choosing appropriate baselines to avoid constraints on its model parameters. The potential advantage of cgLM over gLM for detection of interactions in model building procedures is also illustrated and compared via a simulation study. Finally, the cgLM is applied to a postmortem brain gene expression data set.

COVID-19 infections, recoveries, and mortality: an ANOVA model of locations and administrative areas in Saudi Arabia.

Background: Saudi Arabia has 13 administrative areas, all of which have been seriously affected by the COVID-19 epidemic regardless of their features. Being the largest and a prominent Arab country, epidemic intensity and dynamics have importance, especially in the era of Vision 2030 where infrastructure development and growth to enhance quality of life has of prime focus. Aims: This analysis aims to trace the differentials in COVID-19 infections, recoveries, and deaths across the country depending upon various demographic and developmental dimensions and interactions. Data and methods: This analysis used Saudi Arabia Ministry of Health data from March 15th, 2020 to August 31st, 2022, by classifying administrative areas and locations to build a generalized linear model (3 × 3): three types of administrative areas (major, middle-sized, and others) and localities (major, medium-sized, and others). Apart from two-way ANOVA, an one-way ANOVA also carried out in addition to calculating mean values of infections, recoveries, and deaths. Results: A total of 205 localities were affected with varying severity, which are based on local demographics. Both the administrative areas and localities had a significant number of cases of infections, recoveries, and mortality, which are influenced by relationships and interactions, leading to differential mean values and proportional distributions across various types of administrative areas and localities. Conclusion: There is dynamism that major administrative areas have lesser threats from the epidemics whereas medium-sized ones have serious threats. Moreover, an interaction of administrative areas and localities explains the dynamics of epidemic spread under varying levels of infrastructure preparedness. Thus, this study presents lessons learned to inform policies, programs, and development plans, especially for regional, urban, and infrastructure areas, considering grassroots level issues and diversity.

Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine.

High confidence and reproducibility are still challenges in bottom-up mass spectrometric N-glycopeptide identification. The collision energy used in the MS/MS measurements and the database search engine used to identify the species are perhaps the two most decisive factors. We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering the highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied and studied the behavior of Byonic, pGlyco, and GlycoQuest scores. We found that search engines show a range of behavior between peptide-centric and glycan-centric, which manifests itself already in the dependence of optimal collision energy on m/z. Using classical statistical and machine learning methods, we revealed that peptide hydrophobicity, glycan and peptide masses, and the number of mobile protons also have significant and search-engine-dependent influence, as opposed to a series of other parameters we probed. We envisioned an MS/MS workflow making a smart collision energy choice based on online available features such as the hydrophobicity (described by retention time) and glycan mass (potentially available from a scout MS/MS). Our assessment suggests that this workflow can lead to a significant gain (up to 100%) in the identification confidence, particularly for low-scoring hits close to the filtering limit, which has the potential to enhance reproducibility of N-glycopeptide analyses. Data are available via MassIVE (MSV000093110).

Spatial variations in the associations of mental distress with sleep insufficiency in the United States: a county-level spatial analysis.

In this research, we conducted hierarchical multiple regression and complex sample general linear model (CSGLM) to expand knowledge on factors contributing to mental distress, particularly from a geographic perspective. Based on the Getis-Ord G* hot-spot analysis, geographic distribution of both FMD and insufficient sleep showed several contiguous hotspots in southeast regions. Moreover, in the hierarchical regression, even after accounting for potential covariates and multicollinearity, a significant association between FMD and insufficient sleep was found, explaining that mental distress increases with increasing insufficient sleep (R2 = 0.835). In the CSGLM, a R2 value of 0.782 indicated that the CSGLM procedure provided concrete evidence that FMD was significantly related to sleep insufficiency even after taking complex sample designs and weighting adjustments in the BRFSS into account. This geographic association between FMD and insufficient sleep based on cross-county study has not been reported before in the literature. These findings suggest a need for further investigation on geographic disparity on mental distress and insufficient sleep and have novel implications in our understanding of the etiology of mental distress.

Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme.

BACKGROUND: Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance. METHOD: 11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies. RESULT: The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value = 0.011) and 0.6970 (p-value = 0.017), respectively. For 81.82 % of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18 % when applying the correction strategies. CONCLUSION: Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.

Corrigendum: Alterations via inter-regional connective relationships in Alzheimer's disease.

[This corrects the article DOI: 10.3389/fnhum.2023.1276994.].

Prefrontal cortical hemodynamics and functional network organization during Tai Chi standing meditation: an fNIRS study.

Introduction: Tai Chi standing meditation (Zhan Zhuang, also called pile standing) is characterized by meditation, deep breathing, and mental focus based on theories of traditional Chinese medicine. The purpose of the present study was to explore prefrontal cortical hemodynamics and the functional network organization associated with Tai Chi standing meditation by using functional near-infrared spectroscopy (fNIRS). Methods: Twenty-four channel fNIRS signals were recorded from 24 male Tai Chi Quan practitioners (54.71 ± 8.04 years) while standing at rest and standing during Tai Chi meditation. The general linear model and the SPM method were used to analyze the fNIRS signals. Pearson correlation was calculated to determine the functional connectivity between the prefrontal cortical sub-regions. The small world properties of the FC networks were then further analyzed based on graph theory. Results: During Tai Chi standing meditation, significantly higher concentrations of oxygenated hemoglobin were observed in bilateral dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), frontal eye field (FEF), and pre-motor cortex (PMC) compared with the values measured during standing rest (p < 0.05). Simultaneously, significant decreases in deoxygenated hemoglobin concentration were observed in left VLPFC, right PMC and DLPFC during Tai Chi standing meditation than during standing rest (p < 0.05). Functional connectivity between the left and right PFC was also significantly stronger during the Tai Chi standing meditation (p < 0.05). The functional brain networks exhibited small-world architecture, and more network hubs located in DLPFC and VLPFC were identified during Tai Chi standing meditation than during standing rest. Discussion: These findings suggest that Tai Chi standing meditation introduces significant changes in the cortical blood flow and the brain functional network organization.

Improved cerebrovascular reactivity mapping using coherence weighted general linear model in the frequency domain.

Cerebrovascular reactivity (CVR) is a prognostic indicator of cerebrovascular health. Estimating CVR from endogenous end-tidal carbon dioxide (CO2) fluctuation and MRI signal recorded under resting state can be difficult due to the poor signal-to-noise ratio (SNR) of signals. Thus, we aimed to improve the method of estimating CVR from end-tidal CO2 and MRI signals. We proposed a coherence weighted general linear model (CW-GLM) to estimate CVR from the Fourier coefficients weighted by the signal coherence in frequency domain, which confers two advantages. First, it requires no signal alignment in time domain, which simplifies experimental methods. Second, it limits the GLM analysis within the frequency band where CO2 and MRI signals are highly correlated, which automatically suppresses noise and nuisance signals. We compared the performance of our method with time-domain GLM (TD-GLM) and frequency-domain GLM (FD-GLM) in both synthetic and in-vivo data; wherein we calculated CVR from signals recorded under both resting state and sinusoidal stimulus. In synthetic data, CW-GLM has a remarkable performance on CVR estimation from narrow band signals with a mean-absolute error of 0.7 % (gray matter) and 1.2 % (white matter), which was lower than all the other methods. Meanwhile, CW-GLM maintains a comparable performance on CVR estimation from resting signals, with a mean-absolute error of 4.1 % (gray matter) and 8 % (white matter). The superior performance was maintained across the 36 in-vivo measurements, with CW-GLM exhibiting limits of agreement of -16.7 % - 9.5 % between CVR calculated from the resting and sinusoidal CO2 paradigms which was 12 % - 209 % better than current time-domain methods. Evaluating of the cross-coherence spectrum revealed highest signal coherence within the frequency band from 0.01 Hz to 0.05 Hz, which overlaps with previously recommended frequency band (0.02 Hz to 0.04 Hz) for CVR analysis. Our data demonstrates that CW-GLM can work as a self-adaptive band-pass filter to improve CVR robustness, while also avoiding the need for signal temporal alignment.

Alterations via inter-regional connective relationships in Alzheimer's disease.

Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.

Intonation Units in Spontaneous Speech Evoke a Neural Response.

Spontaneous speech is produced in chunks called intonation units (IUs). IUs are defined by a set of prosodic cues and presumably occur in all human languages. Recent work has shown that across different grammatical and sociocultural conditions IUs form rhythms of ∼1 unit per second. Linguistic theory suggests that IUs pace the flow of information in the discourse. As a result, IUs provide a promising and hitherto unexplored theoretical framework for studying the neural mechanisms of communication. In this article, we identify a neural response unique to the boundary defined by the IU. We measured the EEG of human participants (of either sex), who listened to different speakers recounting an emotional life event. We analyzed the speech stimuli linguistically and modeled the EEG response at word offset using a GLM approach. We find that the EEG response to IU-final words differs from the response to IU-nonfinal words even when equating acoustic boundary strength. Finally, we relate our findings to the body of research on rhythmic brain mechanisms in speech processing. We study the unique contribution of IUs and acoustic boundary strength in predicting delta-band EEG. This analysis suggests that IU-related neural activity, which is tightly linked to the classic Closure Positive Shift (CPS), could be a time-locked component that captures the previously characterized delta-band neural speech tracking.SIGNIFICANCE STATEMENT Linguistic communication is central to human experience, and its neural underpinnings are a topic of much research in recent years. Neuroscientific research has benefited from studying human behavior in naturalistic settings, an endeavor that requires explicit models of complex behavior. Usage-based linguistic theory suggests that spoken language is prosodically structured in intonation units. We reveal that the neural system is attuned to intonation units by explicitly modeling their impact on the EEG response beyond mere acoustics. To our understanding, this is the first time this is demonstrated in spontaneous speech under naturalistic conditions and under a theoretical framework that connects the prosodic chunking of speech, on the one hand, with the flow of information during communication, on the other.

Altered Heart Rate Variability During Rest in Schizophrenia: A State Marker.

BACKGROUND: Autonomic nervous system (ANS) imbalance has been reported in a number of psychiatric disorders such as depression, schizophrenia, panic disorder, etc. Autonomic dysfunction in schizophrenia has been associated with the symptoms and manifestation of psychosis. Heart rate variability (HRV) as a tool has been widely used to assess ANS activity and the effect of disease on the sympathovagal balance. Therefore, in the present study, HRV derived from electrocardiogram (ECG) lead II at rest was investigated in order to understand the changes in frequency domain measures in patients with schizophrenia and their first-degree relatives compared to healthy controls. METHODS: Twenty-five patients with schizophrenia, 24 first-degree relatives of patients, and 24 healthy controls (Diagnostic and Statistical Manual of Mental Disorders (DSM)-5; 18-45 years) were included in the study. HRV of the subjects was measured after five minutes of rest. ECG lead II was recorded for five minutes and HRV was analysed in the frequency domain: low frequency (LF), high frequency (HF), total power, and LF/HF ratio. HRV parameters and heart rate were statistically analysed for group comparisons using general linear model multivariate analysis. RESULTS: Patients had significantly higher minimum heart rate and lower HF (normalized units (nu)) compared to their first-degree relatives. A trend was observed in HF (nu) with the lowest in patients followed by healthy controls and first-degree relatives and LF/HF ratio was the highest in patients followed by healthy controls and first-degree relatives, although not statistically significant. No significant difference was found between first-degree relatives and healthy controls. CONCLUSION: The alteration of HRV in schizophrenia could be attributed to reduction in vagal tone and sympathetic dominance, which in turn could serve as state markers of schizophrenia.

Genome-wide association studies in rice germplasm reveal significant genomic regions for root and yield-related traits under aerobic and irrigated conditions.

The development of nutrient-use efficient rice lines is a priority amidst the changing climate and depleting resources viz., water, land, and labor for achieving sustainability in rice cultivation. Along with the traditional transplanted irrigated system of cultivation, the dry direct-seeded aerobic system is gaining ground nationwide. The root-related traits play a crucial role in nutrient acquisition, adaptation and need to be concentrated along with the yield-attributing traits. We phenotyped an association panel of 118 rice lines for seedling vigour index (SVI) traits at 14 and 21 days after sowing (DAS), root-related traits at panicle initiation (PI) stage in polythene bags under controlled aerobic condition, yield and yield-related traits under the irrigated condition at ICAR-IIRR, Hyderabad, Telangana; irrigated and aerobic conditions at ARS, Dhadesugur, Raichur, Karnataka. The panel was genotyped using simple sequence repeats (SSR) markers and genome-wide association studies were conducted for identifying marker-trait associations (MTAs). Significant correlations were recorded for root length, root dry weight with SVI, root volume at the PI stage, number of productive tillers per plant, spikelet fertility, the total number of grains per panicle with grain yield per plant under irrigated conditions, and the total number of grains per panicle with grain yield per plant under aerobic condition. The panel was divided into three sub-groups (K = 3) and correlated with the principal component analysis. The maximum number of MTAs were found on chromosomes 2, 3, and 12 with considerable phenotypic variability. Consistent MTAs were recorded for SVI traits at 14 and 21 DAS (RM25310, RM80, RM22961, RM1385), yield traits under irrigated conditions (RM2584, RM5179, RM410, RM20698, RM14753) across years at ICAR-IIRR, grain yield per plant (RM22961, RM1146) under the aerobic condition, grain yield per plant at irrigated ICAR-IIRR and SVI (RM5501), root traits at PI stage (RM2584, RM80, RM410, RM1146, RM18472). Functionally relevant genes near the MTAs through in-silico expression analysis in root and panicle tissues viz., HBF2 bZIP transcription factor, WD40 repeat-like domain, OsPILS6a auxin efflux carrier, WRKY108, OsSCP42, OsMADS80, nodulin-like domain-containing protein, amino acid transporter using various rice expression databases were identified. The identified MTAs and rice lines having high SVI traits (Langphou, TI-128, Mouli, TI-124, JBB-631-1), high yield under aerobic (Phouren, NPK-43, JBB-684, Ratnamudi, TI-112), irrigated conditions (KR-209, KR-262, Phouren, Keibi-Phou, TI-17), robust root traits like root length (MoirangPhou-Angouba, Wangoo-Phou, JBB-661, Dissi, NPK-45), root volume (Ratnachudi, KJ-221, Mow, Heimang-Phou, PUP-229) can be further employed in breeding programs for the targeted environments aimed at improving seedling vigour, yield-related traits under irrigated condition, aerobic condition as adaptability to water-saving technology.

Arousal modulates the amygdala-insula reciprocal connectivity during naturalistic emotional movie watching.

Emotional arousal is a complex state recruiting distributed cortical and subcortical structures, in which the amygdala and insula play an important role. Although previous neuroimaging studies have showed that the amygdala and insula manifest reciprocal connectivity, the effective connectivities and modulatory patterns on the amygdala-insula interactions underpinning arousal are still largely unknown. One of the reasons may be attributed to static and discrete laboratory brain imaging paradigms used in most existing studies. In this study, by integrating naturalistic-paradigm (i.e., movie watching) functional magnetic resonance imaging (fMRI) with a computational affective model that predicts dynamic arousal for the movie stimuli, we investigated the effective amygdala-insula interactions and the modulatory effect of the input arousal on the effective connections. Specifically, the predicted dynamic arousal of the movie served as regressors in general linear model (GLM) analysis and brain activations were identified accordingly. The regions of interest (i.e., the bilateral amygdala and insula) were localized according to the GLM activation map. The effective connectivity and modulatory effect were then inferred by using dynamic causal modeling (DCM). Our experimental results demonstrated that amygdala was the site of driving arousal input and arousal had a modulatory effect on the reciprocal connections between amygdala and insula. Our study provides novel evidence to the underlying neural mechanisms of arousal in a dynamical naturalistic setting.

The Magnitude of Contralateral Suppression of Otoacoustic Emissions Is Ear- and Age-Dependent.

The maturation of the uncrossed medial olivocochlear (UMOC) efferent remains poorly documented to date. The UMOC efferent system allows listeners to not only detect but also to process, recognize, and discriminate auditory stimuli. Its fibers can be explored non-invasively by recording the effect of contralateral acoustic stimulation (CAS), resulting in a decrease in the amplitude of transient evoked otoacoustic emissions (TEOAE). The objective of the present cross-sectional study was to investigate how the effectiveness of this system varies with age in healthy subjects aged 8 years to adulthood. For this purpose, 120 right-handed native French-speaking subjects (57 females and 63 males) were divided into five age groups of 24 subjects each: 8y-10y, 10y-11y6m, 11y6m-13y, 13y-17y, and ≥18y. TEOAE amplitudes with and without CAS were recorded. The equivalent attenuation (EA) was calculated, corresponding to the change in TEOAE amplitude equivalent to the effect generated by CAS. General linear models were performed to control for the effect of ear, sex, and age on EA. No sex effect was found. A stronger EA was consistently found regardless of age group in the right ear compared to the left. In contrast to the right ear, for which, on average, EA remained constant across age groups, an increasingly weaker TEOAE suppression effect with age was found in the left ear, reinforcing the asymmetrical functioning of the UMOC efferent system in favor of the right ear in adulthood. Further studies are needed to investigate the lateralization of the UMOC efferent system and its changes over time in cases of atypical or reversed cortical asymmetries, especially in subjects with specific learning disorders.

Clustering and pedestrian crashes prediction modelling: Amman case.

Pedestrian casualties are a severe domestic as well as international problem. This study analyses the spatial distribution of pedestrian casualties to define contributory factors and delineate the means for their prediction. Three years of crash data were collected along with other factors and analysed using kernel density estimation (KDE), spatial autocorrelation (Moran's I), cluster K-Means, spatial regression, and general linear regressions (GLM). Kernel density estimate defines a cluster of pedestrian deaths within 1250 meters. According to Moran's I, 17/22 attributes about casualties, road networks, demographics, and land use have positive values, indicating similar importance clustering. The spatial pattern of pedestrian casualties is random and insignificant and does not change with time. Casualties are negatively related to the surrounding attributes, indicating a tendency towards dispersion. A K-Means analysis of multiple variables revealed that when variables included in the clustering were higher, the variance explanation percentage was lower. In the multi-variable GLM assuming Poisson distribution, the road network length alone or with the house permits combined were the best predictors of casualties. Classic regressions were not significantly enhanced by spatial dimension, and none of the autoregressive coefficients were significant. The predictions from the Poisson-based GLM model are similar to the classic regressions.

Wild canids and the ecological traps facing the climate change and deforestation in the Amazon Forest.

Ecological traps occur when species choose to settle in lower-quality habitats, even if this reduces their survival or productivity. This happens in situations of drastic environmental changes, resulting from anthropogenic pressures. In long term, this could mean the extinction of the species. We investigated the dynamics of occurrence and distribution of three canid species (Atelocynus microtis, Cerdocyon thous, and Spheotos venaticus) considering human threats to their habitats in the Amazon Rainforest. We analyzed the environmental thresholds for the occurrence of these species and related to the future projections of climatic niches for each one. All three species will be negatively affected by climate change in the future, with losses of up to 91% of the suitable area of occurrence in the Brazilian Amazon. A. microtis appear to be more forest-dependent and must rely on the goodwill of decision-makers to be maintained in the future. For C. thous and S. venaticus, climatic variables and those associated with anthropogenic disturbances that modulate their niches today may not act the same way in the future. Even though C. thous is least dependent on the Amazon Forest; this species may be affected in the future due to the ecological traps. S. venaticus, can also undergo the same process, but perhaps more drastically due to the lower ecological plasticity of this species compared to C. thous. Our results suggest that the ecological traps may put these two species at risk in the future. Using the canid species as a model, we had the opportunity to investigate these ecological effects that can affect a large part of the Amazonian fauna in the current scenario. Considering the high degree of environmental degradation and deforestation in the Amazon Rainforest, the theory of ecological traps must be discussed at the same level as the habitat loss, considering the strategies for preserving the Amazon biodiversity.

Synchrony-Division Neural Multiplexing: An Encoding Model.

Cortical neurons receive mixed information from the collective spiking activities of primary sensory neurons in response to a sensory stimulus. A recent study demonstrated an abrupt increase or decrease in stimulus intensity and the stimulus intensity itself can be respectively represented by the synchronous and asynchronous spikes of S1 neurons in rats. This evidence capitalized on the ability of an ensemble of homogeneous neurons to multiplex, a coding strategy that was referred to as synchrony-division multiplexing (SDM). Although neural multiplexing can be conceived by distinct functions of individual neurons in a heterogeneous neural ensemble, the extent to which nearly identical neurons in a homogeneous neural ensemble encode multiple features of a mixed stimulus remains unknown. Here, we present a computational framework to provide a system-level understanding on how an ensemble of homogeneous neurons enable SDM. First, we simulate SDM with an ensemble of homogeneous conductance-based model neurons receiving a mixed stimulus comprising slow and fast features. Using feature-estimation techniques, we show that both features of the stimulus can be inferred from the generated spikes. Second, we utilize linear nonlinear (LNL) cascade models and calculate temporal filters and static nonlinearities of differentially synchronized spikes. We demonstrate that these filters and nonlinearities are distinct for synchronous and asynchronous spikes. Finally, we develop an augmented LNL cascade model as an encoding model for the SDM by combining individual LNLs calculated for each type of spike. The augmented LNL model reveals that a homogeneous neural ensemble model can perform two different functions, namely, temporal- and rate-coding, simultaneously.

Decision-making and frontoparietal resting-state functional connectivity among impulsive-compulsive diagnoses. Insights from a Bayesian approach.

The Iowa Gambling Task (IGT) is one of the most widely used paradigms for assessing decision-making. An impairment in this process may be linked to several psychopathological disorders, such as obsessive-compulsive disorder (OCD), substance abuse disorder (SUD) or attention-deficit/hyperactivity disorder (ADHD), which could make it a good candidate for being consider a transdiagnostic domain. Resting-state functional connectivity (rsFC) has been proposed as a promising biomarker of decision-making. In this study, we aimed to identify idiosyncratic decision-making profiles among healthy people and impulsive-compulsive spectrum patients during the IGT, and to investigate the role of frontoparietal network (FPN) rsFC as a possible biomarker of different decision-making patterns. Using functional near-infrared spectroscopy (fNIRS), rsFC of 114 adults (34 controls; 25 OCD; 41 SUD; 14 ADHD) was obtained. Then, they completed the IGT. Hybrid clustering methods based on individual deck choices yielded three decision-makers subgroups. Cluster 1 (n = 27) showed a long-term advantageous strategy. Cluster 2 (n = 25) presented a maladaptive decision-making strategy. Cluster 3 (n = 62) did not develop a preference for any deck during the task. Interestingly, the proportion of participants in each cluster was not different between diagnostic groups. A Bayesian general linear model showed no credible differences in the IGT performance between diagnostic groups nor credible evidence to support the role of FPN rsFC as a biomarker of decision-making under the IGT context. This study highlights the importance of exploring in depth the behavioral and neurophysiological variables that may drive decision-making in clinical and healthy populations.

Single-leg stance on a challenging surface can enhance cortical activation in the right hemisphere - A case study.

Maintaining body balance, whether static or dynamic, is critical in performing everyday activities and developing and optimizing basic motor skills. This study investigates how a professional alpine skier's brain activates on the contralateral side during a single-leg stance. Continuous-wave functional near-infrared spectroscopy (fNIRS) signals were recorded with sixteen sources and detectors over the motor cortex to investigate brain hemodynamics. Three different tasks were performed: barefooted walk (BFW), right-leg stance (RLS), and left-leg stance (LLS). The signal processing pipeline includes channel rejection, the conversation of raw intensities into hemoglobin concentration changes using modified Beer-Lambert law, baseline zero-adjustments, z-normalization, and temporal filtration. The hemodynamic brain signal was estimated using a general linear model with a 2-gamma function. Measured activations (t-values) with p-value <0.05 were only considered as statistically significant active channels. Compared to all other conditions, BFW has the lowest brain activation. LLS is associated with more contralateral brain activation than RLS. During LLS, higher brain activation was observed across all brain regions. The right hemisphere has comparatively more activated regions-of-interest. Higher ΔHbO demands in the dorsolateral prefrontal, pre-motor, supplementary motor cortex, and primary motor cortex were observed in the right hemisphere relative to the left which explains higher energy demands for balancing during LLS. Broca's temporal lobe was also activated during both LLS and RLS. Comparing the results with BFW- which is considered the most realistic walking condition-, it is concluded that higher demands of ΔHbO predict higher motor control demands for balancing. The participant struggled with balance during the LLS, showing higher ΔHbO in both hemispheres compared to two other conditions, which indicates the higher requirement for motor control to maintain balance. A post-physiotherapy exercise program is expected to improve balance during LLS, leading to fewer changes to ΔHbO.