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.

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.

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.

Discrediting microscopic pyuria and leucocyte esterase as diagnostic surrogates for infection in patients with lower urinary tract symptoms: results from a clinical and laboratory evaluation.

UNLABELLED: WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: Microscopic pyuria is widely used as a surrogate marker of infection, although there is little data supporting its use in patients who present with non-acute LUTS. The effects of urinary storage, preservation, and the use of laboratory methods to enhance leucocyte detection, are also unclear. This large, prospective study highlights the poor performance of dipstick urine analysis, and direct microscopy, as surrogate markers of UTI in patients with LUTS. A series of laboratory analyses also examine the effects of urine handling and processing on test integrity, which have important implications for clinical practice. OBJECTIVE: To evaluate the diagnostic performance of pyuria as a surrogate marker of urinary tract infection (UTI) in patients with chronic lower urinary tract symptoms (LUTS), and determine the impact of sample storage, cytocentrifugation, and staining techniques, on test performance. PATIENTS AND METHODS: Between 2008 and 2011, we recruited 1223 patients (120 men; 1103 women; mean age 54 years) with one or more LUTS from a specialist urological outpatient service. We conducted a prospective observational study to determine the performance of microscopic pyuria ≥10 wbc/µL as a surrogate marker of UTI in patients with LUTS. All patients provided clean-catch midstream urine (MSU) samples for analysis, and routine microbiological cultures were used as our reference standard. We also scrutinised the performance of dipstick leucocyte esterase ≥ 'trace' in the detection of microscopic pyuria. The influence of sample handling and processing on test performance was examined in a series of laboratory studies. The effects of storage on leucocyte decay were determined using repeated microscopic assessments of individual urine samples, to plot temporal changes in leucocyte numbers. This study used varied storage conditions (≈20 °C and 4 °C), and boric acid preservation. Paired microscopic assessments were used to determine the effects of centrifugation on leucocyte salvage in spun/unspun samples (relative centrifugal force range 39-157 g). Similar methods were used to assess microscopic leucocyte quantification in stained/unstained urine (Sternheimer-Malbin protocol). RESULTS: The positive predictive value (PPV) and negative predictive value (NPV) of pyuria as a surrogate marker of UTI were 0.40 (95% confidence interval [CI] 0.37-0.43) and 0.75 (95% CI 0.73-0.76), respectively. The dipstick was unable to identify significant microscopic pyuria (≥10 wbc/µL) in 60% of the samples: PPV 0.51 (95% CI 0.48-0.55); NPV 0.75 (95% CI 0.73-0.76). Microscopic pyuria performed poorly as a surrogate of UTI defined by bacterial culture. Whilst refrigeration and preservation did retard leucocyte loss (F = 11; DF = 2; P < 0.001), 40% of cells were still lost by 4 h. Centrifugation had an unpredictable influence on cell salvage (coefficient of variation 5750%) and the use of staining to improve leucocyte detection proved ineffective (Z = -0.356; P = 0.72). CONCLUSIONS: Pyuria performs badly as a surrogate of UTI in patients with LUTS. This is exacerbated by cell loss during storage, and neither centrifugation, nor staining, appears to confer any diagnostic advantage. Clinicians should be alerted to the significant limitations of these tests.

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.

Study of genetic variability, heritability, and genetic advance for yield-related traits in tomato (Solanum lycopersicon MILL.).

Tomato is one of the most significant vegetable crops, which provides several important dietary components. Pakistan has a significant low tomato yield compared to other countries because of low genetic diversity and the absence of improved cultivars. The present study aimed to investigate the genetic variability, heritability, and genetic advance for yield and yield-related traits in tomato. For this purpose, eight tomato parents and their 15 crosses or hybrids were evaluated to study the relevant traits. Significant variation was observed for all studied traits. Higher values of the genotypic coefficient of variability (GCV) and phenotypic coefficient of variability (PCV) were recorded for yield per plant (YP) (kg) (37.62% and 37.79%), as well as the number of fruits per cluster (NFRC) (31.52% and 31.71%), number of flowers per cluster (24.63 and 24.67), and single fruit weight (g) (23.49 and 23.53), which indicated that the selection for these traits would be fruitful. Higher heritability (h2) estimates were observed for the number of flowers per cluster (NFC) (0.99%), single fruit weight (SFW) (g) (0.99%), and yield per plant (YP) (kg) (0.99%). Single fruit weight (SFW) (g) exhibited higher values for all components of variability. High genetic advance as a % of the mean (GAM) coupled with higher heritability (h2) was noted for the yield per plant (YP) (kg) (52.58%) and the number of fruits per cluster (NFRC) (43.91). NFRC and SFW (g) had a highly significant correlation with YP (kg), while FSPC had a significant positive association with YP (kg), and these traits can be selected to enhance YP (kg). Among the 15 hybrids, Nagina × Continental, Pakit × Continental, and Roma × BSX-935 were selected as high-yielding hybrids for further evaluation and analysis. These findings revealed that the best performing hybrids could be used to enhance seed production and to develop high-yielding varieties. The parents could be further tested to develop hybrids suitable for changing climatic conditions. The selection of YP (kg), SFW (g), NFC, and NFRC would be ideal for selecting the best hybrids.

Stepwise Covariance-Free Common Principal Components (CF-CPC) With an Application to Neuroscience.

Finding the common principal component (CPC) for ultra-high dimensional data is a multivariate technique used to discover the latent structure of covariance matrices of shared variables measured in two or more k conditions. Common eigenvectors are assumed for the covariance matrix of all conditions, only the eigenvalues being specific to each condition. Stepwise CPC computes a limited number of these CPCs, as the name indicates, sequentially and is, therefore, less time-consuming. This method becomes unfeasible when the number of variables p is ultra-high since storing k covariance matrices requires O(k p 2) memory. Many dimensionality reduction algorithms have been improved to avoid explicit covariance calculation and storage (covariance-free). Here we propose a covariance-free stepwise CPC, which only requires O(k n) memory, where n is the total number of examples. Thus for n < < p, the new algorithm shows apparent advantages. It computes components quickly, with low consumption of machine resources. We validate our method CFCPC with the classical Iris data. We then show that CFCPC allows extracting the shared anatomical structure of EEG and MEG source spectra across a frequency range of 0.01-40 Hz.

An artificial intelligence that increases simulated brain-computer interface performance.

Objective.Brain-computer interfaces (BCIs) translate neural activity into control signals for assistive devices in order to help people with motor disabilities communicate effectively. In this work, we introduce a new BCI architecture that improves control of a BCI computer cursor to type on a virtual keyboard.Approach.Our BCI architecture incorporates an external artificial intelligence (AI) that beneficially augments the movement trajectories of the BCI. This AI-BCI leverages past user actions, at both long (100 s of seconds ago) and short (100 s of milliseconds ago) timescales, to modify the BCI's trajectories.Main results.We tested our AI-BCI in a closed-loop BCI simulator with nine human subjects performing a typing task. We demonstrate that our AI-BCI achieves: (1) categorically higher information communication rates, (2) quicker ballistic movements between targets, (3) improved precision control to 'dial in' on targets, and (4) more efficient movement trajectories. We further show that our AI-BCI increases performance across a wide control quality spectrum from poor to proficient control.Significance.This AI-BCI architecture, by increasing BCI performance across all key metrics evaluated, may increase the clinical viability of BCI systems.