EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition.

Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate "mediators". Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency-resolved functional connectivity measures as a mediator using the full EEG cross-spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive-definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a "compressed CST." The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix-valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well-studied 50+-year Barbados Nutrition Study dataset by comparing EEGs collected in school-age children (5-11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains.

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.

Spectral quantitative and semi-quantitative EEG provide complementary information on the life-long effects of early childhood malnutrition on cognitive decline.

Objective: This study compares the complementary information from semi-quantitative EEG (sqEEG) and spectral quantitative EEG (spectral-qEEG) to detect the life-long effects of early childhood malnutrition on the brain. Methods: Resting-state EEGs (N = 202) from the Barbados Nutrition Study (BNS) were used to examine the effects of protein-energy malnutrition (PEM) on childhood and middle adulthood outcomes. sqEEG analysis was performed on Grand Total EEG (GTE) protocol, and a single latent variable, the semi-quantitative Neurophysiological State (sqNPS) was extracted. A univariate linear mixed-effects (LME) model tested the dependence of sqNPS and nutritional group. sqEEG was compared with scores on the Montreal Cognitive Assessment (MoCA). Stable sparse classifiers (SSC) also measured the predictive power of sqEEG, spectral-qEEG, and a combination of both. Multivariate LME was applied to assess each EEG modality separately and combined under longitudinal settings. Results: The univariate LME showed highly significant differences between previously malnourished and control groups (p < 0.001); age (p = 0.01) was also significant, with no interaction between group and age detected. Childhood sqNPS (p = 0.02) and adulthood sqNPS (p = 0.003) predicted MoCA scores in adulthood. The SSC demonstrated that spectral-qEEG combined with sqEEG had the highest predictive power (mean AUC 0.92 ± 0.005). Finally, multivariate LME showed that the combined spectral-qEEG+sqEEG models had the highest log-likelihood (-479.7). Conclusion: This research has extended our prior work with spectral-qEEG and the long-term impact of early childhood malnutrition on the brain. Our findings showed that sqNPS was significantly linked to accelerated cognitive aging at 45-51 years of age. While sqNPS and spectral-qEEG produced comparable results, our study indicated that combining sqNPS and spectral-qEEG yielded better performance than either method alone, suggesting that a multimodal approach could be advantageous for future investigations. Significance: Based on our findings, a semi-quantitative approach utilizing GTE could be a valuable diagnostic tool for detecting the lasting impacts of childhood malnutrition. Notably, sqEEG has not been previously explored or reported as a biomarker for assessing the longitudinal effects of malnutrition. Furthermore, our observations suggest that sqEEG offers unique features and information not captured by spectral quantitative EEG analysis and could lead to its improvement.

Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG.

Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.

Accurate and Efficient Simulation of Very High-Dimensional Neural Mass Models with Distributed-Delay Connectome Tensors.

This paper introduces methods and a novel toolbox that efficiently integrates high-dimensional Neural Mass Models (NMMs) specified by two essential components. The first is the set of nonlinear Random Differential Equations (RDEs) of the dynamics of each neural mass. The second is the highly sparse three-dimensional Connectome Tensor (CT) that encodes the strength of the connections and the delays of information transfer along the axons of each connection. To date, simplistic assumptions prevail about delays in the CT, often assumed to be Dirac-delta functions. In reality, delays are distributed due to heterogeneous conduction velocities of the axons connecting neural masses. These distributed-delay CTs are challenging to model. Our approach implements these models by leveraging several innovations. Semi-analytical integration of RDEs is done with the Local Linearization (LL) scheme for each neural mass, ensuring dynamical fidelity to the original continuous-time nonlinear dynamic. This semi-analytic LL integration is highly computationally-efficient. In addition, a tensor representation of the CT facilitates parallel computation. It also seamlessly allows modeling distributed delays CT with any level of complexity or realism. This ease of implementation includes models with distributed-delay CTs. Consequently, our algorithm scales linearly with the number of neural masses and the number of equations they are represented with, contrasting with more traditional methods that scale quadratically at best. To illustrate the toolbox's usefulness, we simulate a single Zetterberg-Jansen and Rit (ZJR) cortical column, a single thalmo-cortical unit, and a toy example comprising 1000 interconnected ZJR columns. These simulations demonstrate the consequences of modifying the CT, especially by introducing distributed delays. The examples illustrate the complexity of explaining EEG oscillations, e.g., split alpha peaks, since they only appear for distinct neural masses. We provide an open-source Script for the toolbox.

Automatic quality control of the numerical accuracy of EEG lead fields.

Precise individualized EEG source localization is predicated on having accurate subject-specific Lead Fields (LFs) obtained from their Magnetic Resonance Images (MRI). LF calculation is a complex process involving several error-prone steps that start with obtaining a realistic head model from the MRI and finalizing with computationally expensive solvers such as the Boundary Element Method (BEM) or Finite Element Method (FEM). Current Big-Data applications require the calculation of batches of hundreds or thousands of LFs. LF. Quality Control is conventionally checked subjectively by experts, a procedure not feasible in practice for larger batches. To facilitate this step, we introduce the Lead Field Automatic-Quality Control Index (LF-AQI) that flags LF with potential errors. We base our LF-AQI on the assumption that LFs obtained from simpler head models, i.e., the homogeneous head model LF (HHM-LF) or spherical head model LF (SHM-LF), deviate only moderately from a "good" realistic test LF. Since these simpler LFs are easier to compute and check for errors, they may serve as "reference LF" to detect anomalous realistic test LF. We investigated this assumption by comparing correlation-based channel ρmin(ref,test)and source τmin(ref,test) similarity indices (SI) between "gold standards," i.e., very accurate FEM and BEM LFs, and the proposed references (HHM-LF and SHM-LF). Surprisingly we found that the most uncomplicated possible reference, HHM-LF had high SI values with the gold standards-leading us to explore further use of the channel ρmin(HHM-LF,test)and source τmin(HHM-LF,test)SI as a basis for our LF-AQI. Indeed, these SI successfully detected five simulated scenarios of LFs artifacts. This result encouraged us to evaluate the SI on a large dataset and thus define our LF-AQI. We thus computed the SI of 1251 LFs obtained from the Child Mind Institute (CMI) MRI dataset. When ρmin(HHM-LF,test)and source τmin(HHM-LF,test) were plotted for all test subjects on a 2D space, most were tightly clustered around the median of a high similarity centroid (HSC), except for a smaller proportion of outliers. We define the LF-AQI for a given LF as the log Euclidean distance between its SI and the HSC median. To automatically detect outliers, the threshold is at the 90th percentile of the CMI LF-AQIs (-0.9755). LF-AQI greater than this threshold flag individual LF to be checked. The robustness of this LF-AQI screening was checked by repeated out-of-sample validation. Strikingly, minor corrections in re-processing the flagged cases eliminated their status as outliers. Furthermore, the "doubtful" labels assigned by LF-AQI were validated by neuroscience students using a Likert scale questionnaire designed to manually check the LF's quality. Item Response Theory (IRT) analysis was applied to the questionnaire results to compute an optimized model and a latent variable θ for that model. A linear mixed model (LMM) between the θ and LF-AQI resulted in an effect with a Cohen's d value of 1.3 and a p-value <0.001, thus validating the correspondence of LF-AQI with the visual quality control. We provide an open-source pipeline to implement both LF calculation and its quality control to allow further evaluation of our index.

Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning.

Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10-20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox.

Alternative technologies for the production of popped sorghum: a comparative study.

Sorghum is the fifth most harvested crop worldwide, being the popped sorghum as one of the most common snacks in India and some Asian regions. Therefore, this study evaluated how the processing method influences the microstructure, volumetric and textural properties of popped sorghum microstructure, volumetric and textural properties. White sorghum "Paloma" variety (11% moisture) was assessed, which was popped using three processing methods: microwave, pan-frying, and hot salt-frying using three temperature levels. Volumetric and yield characteristics were evaluated for the popped kernels, as their microstructure and texture profile. The popped sorghum obtained through the hot salt-frying method had a microstructure composed of polygonal cells. Also, it showed the best volumetric characteristics (volume), good expansion index, and high process yield. Finally, the hot salt-frying method showed better textural features associated with the attributes of a satisfactory product for consumers.

The determinants of COVID-induced brain dysfunctions after SARS-CoV-2 infection in hospitalized patients.

The severity of the pandemic and its consequences on health and social care systems were quite diverse and devastating. COVID-19 was associated with an increased risk of neurological and neuropsychiatric disorders after SARS-CoV-2 infection. We did a cross-sectional study of 3 months post-COVID consequences of 178 Cuban subjects. Our study has a unique CUBAN COVID-19 cohort of hospitalized COVID-19 patients and healthy subjects. We constructed a latent variable for pre-health conditions (PHC) through Item Response Theory (IRT) and for post-COVID neuropsychiatric symptoms (Post-COVID-NPS) through Factor Analysis (FA). There seems to be a potential causal relationship between determinants of CIBD and post-COVID-NPS in hospitalized COVID-19 patients. The causal relationships accessed by Structural Equation Modeling (SEM) revealed that PHC (p < 0.001) and pre-COVID cognitive impairments (p < 0.001) affect the severity of COVID-19 patients. The severity of COVID-19 eventually results in enhanced post-COVID-NPS (p < 0.001), even after adjusting for confounders (age, sex, and pre-COVID-NPS). The highest loadings in PHC were for cardiovascular diseases, immunological disorders, high blood pressure, and diabetes. On the other hand, sex (p < 0.001) and pre-COVID-NPS including neuroticism (p < 0.001), psychosis (p = 0.005), cognition (p = 0.036), and addiction (p < 0.001) were significantly associated with post-COVID-NPS. The most common neuropsychiatric symptom with the highest loadings includes pain, fatigue syndrome, autonomic dysfunctionalities, cardiovascular disorders, and neurological symptoms. Compared to healthy people, COVID-19 patients with pre-health comorbidities or pre-neuropsychiatric conditions will have a high risk of getting severe COVID-19 and long-term post-COVID neuropsychiatric consequences. Our study provides substantial evidence to highlight the need for a complete neuropsychiatric follow-up on COVID-19 patients (with severe illness) and survivors (asymptomatic patients who recovered).

Early childhood malnutrition impairs adult resting brain function using near-infrared spectroscopy.

Introduction: Early childhood malnutrition affects 200+ million children under 5 years of age worldwide and is associated with persistent cognitive, behavioral and psychiatric impairments in adulthood. However, very few studies have investigated the long-term effects of childhood protein-energy malnutrition (PEM) on brain function using a functional hemodynamic brain imaging technique. Objective and methods: This study aims to investigate functional brain network alterations using near infrared spectroscopy (NIRS) in adults, aged 45-51 years, from the Barbados Nutrition Study (BNS) who suffered from a single episode of malnutrition restricted to their first year of life (n = 26) and controls (n = 29). A total of 55 individuals from the BNS cohort underwent NIRS recording at rest. Results and discussion: Using functional connectivity and permutation analysis, we found patterns of increased Pearson's correlation with a specific vulnerability of the frontal cortex in the PEM group (ps < 0.05). Using a graph theoretical approach, mixed ANCOVAs showed increased segregation (ps = 0.0303 and 0.0441) and decreased integration (p = 0.0498) in previously malnourished participants compared to healthy controls. These results can be interpreted as a compensatory mechanism to preserve cognitive functions, that could also be related to premature or pathological brain aging. To our knowledge, this study is the first NIRS neuroimaging study revealing brain function alterations in middle adulthood following early childhood malnutrition limited to the first year of life.

The Effect of Neuroepo on Cognition in Parkinson's Disease Patients Is Mediated by Electroencephalogram Source Activity.

We report on the quantitative electroencephalogram (qEEG) and cognitive effects of Neuroepo in Parkinson's disease (PD) from a double-blind safety trial (https://clinicaltrials.gov/, number NCT04110678). Neuroepo is a new erythropoietin (EPO) formulation with a low sialic acid content with satisfactory results in animal models and tolerance in healthy participants and PD patients. In this study, 26 PD patients were assigned randomly to Neuroepo (n = 15) or placebo (n = 11) groups to test the tolerance of the drug. Outcome variables were neuropsychological tests and resting-state source qEEG at baseline and 6 months after administering the drug. Probabilistic Canonical Correlation Analysis was used to extract latent variables for the cognitive and for qEEG variables that shared a common source of variance. We obtained canonical variates for Cognition and qEEG with a correlation of 0.97. Linear Mixed Model analysis showed significant positive dependence of the canonical variate cognition on the dose and the confounder educational level (p = 0.003 and p = 0.02, respectively). Additionally, in the mediation equation, we found a positive dependence of Cognition with qEEG for (p = < 0.0001) and with dose (p = 0.006). Despite the small sample, both tests were powered over 89%. A combined mediation model showed that 66% of the total effect of the cognitive improvement was mediated by qEEG (p = 0.0001), with the remaining direct effect between dose and Cognition (p = 0.002), due to other causes. These results suggest that Neuroepo has a positive influence on Cognition in PD patients and that a large portion of this effect is mediated by brain mechanisms reflected in qEEG.

Impact of Early Childhood Malnutrition on Adult Brain Function: An Evoked-Related Potentials Study.

More than 200 million children under the age of 5 years are affected by malnutrition worldwide according to the World Health Organization. The Barbados Nutrition Study (BNS) is a 55-year longitudinal study on a Barbadian cohort with histories of moderate to severe protein-energy malnutrition (PEM) limited to the first year of life and a healthy comparison group. Using quantitative electroencephalography (EEG), differences in brain function during childhood (lower alpha1 activity and higher theta, alpha2 and beta activity) have previously been highlighted between participants who suffered from early PEM and controls. In order to determine whether similar differences persisted into adulthood, our current study used recordings obtained during a Go-No-Go task in a subsample of the original BNS cohort [population size (N) = 53] at ages 45-51 years. We found that previously malnourished adults [sample size (n) = 24] had a higher rate of omission errors on the task relative to controls (n = 29). Evoked-Related Potentials (ERP) were significantly different in participants with histories of early PEM, who presented with lower N2 amplitudes. These findings are typically associated with impaired conflict monitoring and/or attention deficits and may therefore be linked to the attentional and executive function deficits that have been previously reported in this cohort in childhood and again in middle adulthood.

Causal effects of cingulate morphology on executive functions in healthy young adults.

In this study, we want to explore evidence for the causal relationship between the anatomical descriptors of the cingulate cortex (surface area, mean curvature-corrected thickness, and volume) and the performance of cognitive tasks such as Card Sort, Flanker, List Sort used as instruments to measure the executive functions of flexibility, inhibitory control, and working memory. We have performed this analysis in a cross-sectional sample of 899 healthy young subjects of the Human Connectome Project. To the best of our knowledge, this is the first study using causal inference to explain the relationship between cingulate morphology and the performance of executive tasks in healthy subjects. We have tested the causal model under a counterfactual framework using stabilized inverse probability of treatment weighting and marginal structural models. The results showed that the posterior cingulate surface area has a positive causal effect on inhibition (Flanker task) and cognitive flexibility (Card Sort). A unit increase (+1 mm2 ) in the posterior cingulate surface area will cause a 0.008% and 0.009% increase from the National Institute of Health (NIH) normative mean in Flankers (p-value <0.001), and Card Sort (p-value 0.005), respectively. Furthermore, a unit increase (+1 mm2 ) in the anterior cingulate surface area will cause a 0.004% (p-value <0.001) and 0.005% (p-value 0.001) increase from the NIH normative mean in Flankers and Card Sort. In contrast, the curvature-corrected-mean thickness only showed an association for anterior cingulate with List Sort (p = 0.034) but no causal effect.

Effects of non-invasive respiratory supports on inspiratory effort in moderate-severe COVID-19 patients. A randomized physiological study.

RATIONALE AND OBJECTIVE: Various forms of Non-invasive respiratory support (NRS) have been used during COVID-19, to treat Hypoxemic Acute Respiratory Failure (HARF), but it has been suggested that the occurrence of strenuous inspiratory efforts may cause Self Induced Lung Injury(P-SILI). The aim of this investigation was to record esophageal pressure, when starting NRS application, so as to better understand the potential risk of the patients in terms of P-SILI and ventilator induced lung injury (VILI). METHODS AND MEASUREMENTS: 21 patients with early de-novo respiratory failure due to COVID-19, underwent three 30 min trials applied in random order: high-flow nasal cannula (HFNC), continuous positive airway pressure (CPAP), and non-invasive ventilation (NIV). After each trial, standard oxygen therapy was reinstituted using a Venturi mask (VM). 15 patients accepted a nasogastric tube placement. Esophageal Pressure (ΔPes) and dynamic transpulmonary driving pressure (ΔPLDyn), together with the breathing pattern using a bioelectrical impedance monitor were recorded. Arterial blood gases were collected in all patients. MAIN RESULTS: No statistically significant differences in breathing pattern and PaCO2 were found. PaO2/FiO2 ratio improved significantly during NIV and CPAP vs VM. NIV was the only NRS to reduce significantly ΔPes vs. VM (-10,2 ±5 cmH20 vs -3,9 ±3,4). No differences were found in ΔPLDyn between NRS (10,2±5; 9,9±3,8; 7,6±4,3; 8,8±3,6 during VM, HFNC, CPAP and NIV respectively). Minute ventilation (Ve) was directly dependent on the patient's inspiratory effort, irrespective of the NRS applied. 14% of patients were intubated, none of them showing a reduction in ΔPes during NRS. CONCLUSIONS: In the early phase of HARF due to COVID-19, the inspiratory effort may not be markedly elevated and the application of NIV and CPAP ameliorates oxygenation vs VM. NIV was superior in reducing ΔPes, maintaining ΔPLDyn within a range of potential safety.

Harmonized-Multinational qEEG norms (HarMNqEEG).

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

Early protein energy malnutrition impacts life-long developmental trajectories of the sources of EEG rhythmic activity.

Protein Energy Malnutrition (PEM) has lifelong consequences on brain development and cognitive function. We studied the lifelong developmental trajectories of resting-state EEG source activity in 66 individuals with histories of Protein Energy Malnutrition (PEM) limited to the first year of life and in 83 matched classmate controls (CON) who are all participants of the 49 years longitudinal Barbados Nutrition Study (BNS). qEEGt source z-spectra measured deviation from normative values of EEG rhythmic activity sources at 5-11 years of age and 40 years later at 45-51 years of age. The PEM group showed qEEGt abnormalities in childhood, including a developmental delay in alpha rhythm maturation and an insufficient decrease in beta activity. These profiles may be correlated with accelerated cognitive decline.

Neural alterations in working memory of mild-moderate TBI: An fMRI study in Malaysia.

Working memory (WM) encompasses crucial cognitive processes or abilities to retain and manipulate temporary information for immediate execution of complex cognitive tasks in daily functioning such as reasoning and decision-making. The WM of individuals sustaining traumatic brain injury (TBI) was commonly compromised, especially in the domain of WM. The current study investigated the brain responses of WM in a group of participants with mild-moderate TBI compared to their healthy counterparts employing functional magnetic resonance imaging. All consented participants (healthy: n = 26 and TBI: n = 15) performed two variations of the n-back WM task with four load conditions (0-, 1-, 2-, and 3-back). The respective within-group effects showed a right hemisphere-dominance activation and slower reaction in performance for the TBI group. Random-effects analysis revealed activation difference between the two groups in the right occipital lobe in the guided n-back with cues, and in the bilateral occipital lobe, superior parietal region, and cingulate cortices in the n-back without cues. The left middle frontal gyrus was implicated in the load-dependent processing of WM in both groups. Further group analysis identified that the notable activation changes in the frontal gyri and anterior cingulate cortex are according to low and high loads. Though relatively smaller in scale, this study was eminent as it clarified the neural alterations in WM in the mild-moderate TBI group compared to healthy controls. It confirmed the robustness of the phenomenon in TBI with the reproducibility of the results in a heterogeneous non-Western sample.

Parámetros productivos y digestibilidad de pollos, utilizando cayeno (Hibiscus rosa-sinensis) y probiótico (Lactobacilius acidophilus) más pectina / Productive parameters and digestibility of chickens, using cayenne (Hibiscus rosa-sinensis) and probiotic (Lactobacilius acidophilus) plus pectin / Parâmetros produtivos e digestibilidade de frango, utilizando pimenta-caiena (Hibiscus rosa-sinensis) e probiótico (Lactobacilius acidophilus) mais pectina

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Abstract The growth in broiler chicken production has been a determining factor regarding Colombia's economy and a determining factor in promoting the countryside's economic development. Using probiotics containing lactic acid-producing bacteria in broiler chickens' diet contributes to intestinal flora integrity and stability, thereby hampering pathogen proliferation which helps prevent diseases and improve productivity. This research was aimed at evaluating broiler chicken nutrient digestibility and production parameters by including cayenne pepper flour (Hibiscus rosa-sinensis, L.) with and without a probiotic (Lactobacillus acidophilus) plus pectin in chicken feed to replace concentrated food. This project was carried out in Villavicencio in Colombia's Meta department; 90 broilers were fed on broiler starter concentrate for 15 days. T1 treatment consisted of concentrate commercial (CC) milled crumble, T2 CC plus 6% cayenne flour (CF) and T3 CC plus 12% CF; these treatments were compared with a probiotic (WP) and without it (WoP) plus pectin (PP). Cayenne pepper green stems and leaves were harvested 60 days after the last pruning for making CF; they were dehydrated for 72 hours at 60oC and then ground. The chickens' average weight was 675.1±50g; a randomised complete block design (RCBD) was used, along with Tukey's multiple comparison test for statistical analysis of the resulting data. Excreta were collected over a five-day period for estimating digestibility coefficients (DC). Broiler diets and excreta were analysed regarding dry matter (DM), protein, fibre, fat, non-nitrogenous extract (NNE) and neutral detergent fibre (NDF); % total digestible nutrients (TDN) and protein biological value (BV) were estimated. Production variables such as food consumption rate, weight gain, feed conversion and carcass yield were evaluated. T2 had the highest DM WoP DC (0.91) and DM WP DC (0.93) compared to those for T1 (0.71 DM WoP DC and DM WP DC 0.72) (p<0.05); the same happened for T2 protein (0.88 DM WoP DC and 0.92 DM WP DC) and fibre (0.82 DM WoP DC and 0.84 DM WP DC), such DC being greater (p<0.05) than those for T3 (protein 0.72 DM WoP DC and 0.81 DM WP DC and fibre 0.56 DM WoP DC and 0.57 DM WP DC). T2 had the highest TDN value: 92 (83%) compared to T3: 77 (56%). All treatments had DM similar consumption rates. Daily weight gain was higher (p<0.05) for T1 WoP (93.74g) and WP (89.78g) compared to T3 (84.08g T1 WoP and 81.95g WP); no differences were observed regarding T2. T2 WoP (2.0) and T3 WP (1.95) had the lowest food to weight conversion rates; the probiotic had an effect on this because T2 WP (1.74) was similar to T1 WoP (1.81) and WP (1.86). T1 WP (66.12%) and WoP (65.49%) and T2 WP (64.92%) had promising carcass yields compared to that for the other treatments. There were no differences between T2 and T1 regarding most variables evaluated here, meaning that commercial concentrate can be confidently replaced by 6% cayenne flour in broiler diets during their fattening phase.

Resumo O crescimento da produção avícola tem sido um fator determinante na economia, sendo um dos motores para promover o desenvolvimento econômico do campo colombiano. O uso de probióticos que contêm bactérias produtoras de ácido lático na ração de aves contribui para a integridade e estabilidade da flora intestinal, dificultando a proliferação de patógenos, o que ajuda a prevenir doenças e melhorar o desempenho produtivo. O objetivo desta pesquisa foi avaliar a digestibilidade dos nutrientes e parâmetros produtivos em frangos de corte incluindo a farinha de pimenta de Caiena (Hibiscus rosa sinensis) sem e com probiótico (Lactobacilius acidophilus) mais pectina em substituição ao concentrado. Este projeto foi realizado em Villavicencio, Meta, foram utilizados 90 frangos, durante 15 dias, eles foram alimentados com concentrado inicial. Os tratamentos foram: T1 concentrado comercial moído (CC), T2 CC e farinha de pimenta-caiena 6% (HC) e T3 CC e 12% HC, comparando-se esses tratamentos sem (SP) e com probiótico mais pectina (CP). Para a produção do HC, os caules verdes e as folhas de caiena foram colhidos 60 dias após o último corte, foram desidratados por 72 horas a 60 graus centígrados e moídos. O peso médio dos frangos foi de 675,1 ± 50g, sendo distribuídos em delineamento inteiramente casualizado e aplicado o teste de comparações múltiplas de Tukey. Para estimar os coeficientes de digestibilidade (COD), as excretas foram coletadas por cinco dias. As dietas e excretas foram analisadas: matéria seca (MS), proteína, fibra, gordura, extrato não nitrogenado (ENN) e fibra em detergente neutro (FDN). Foram estimados: nutrientes digestíveis totais (NDT), valor biológico (VB) da proteína. Variáveis ​​produtivas como: consumo de ração, ganho de peso, conversão alimentar e rendimento de carcaça também foram avaliadas. O maior DM COD (P <0,05) foi para T2 SP e CP: 0,91 e 0,93 VS T1 0,71 e 0,72, o mesmo aconteceu com o COD de proteína e fibra de T2 sendo maior (P <0,05) em relação ao T3: 0,88 e 0,92 VS 0,72 e 0,81; 0,82 e 084 VS 0,56 e 0,57, respectivamente. O maior valor de NDT foi para T2: 92,83% VS T3: 77,56%. O consumo de matéria seca foi semelhante para todos os tratamentos. O ganho de peso diário foi maior (P <0,05) em T1 CP e SP 93,74 e 89,78 g em comparação com T3 84,08 e 81,95 g, não foram observadas diferenças com T2. As conversões de alimentação para peso mais baixas foram para T2 SP (2,0) e T3 CP (1,95). Observando o efeito do probiótico em T2 CP (1,74). O desempenho de carcaça foi semelhante para todos os tratamentos, embora valores numericamente superiores tenham sido observados em T1 CP e SP; T2 CP: 66,12, 65,49 e 64,92%), em relação aos demais tratamentos. Na maioria das variáveis ​​avaliadas, T2 não apresentou diferença com T1, o que significa que nas dietas para aves em fase de engorda, o concentrado comercial pode ser substituído por 6% de farinha de pimenta de caiena.