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Yoshimura, M.

Publications and source records attributed to Yoshimura, M..

At least 19 recordsLinked to original sources

Host-like Temperature Unlocks Glucose Uptake and Metabolism in Pathogenic Leptospira

Pathogenic Leptospira transitions between environmental reservoirs and mammalian hosts, expose the bacterium to distinct temperature conditions. Although Leptospira is generally considered to rely its growth primarily on long-chain fatty acids and to have limited capacity to use glucose, whether host-like temperature alters glucose-associated metabolic capacity remains unclear. Here, we used transcriptome-integrated genome-scale metabolic modeling to examine temperature-dependent metabolic states in pathogenic Leptospira. Models contextualized with transcriptomic data from cultures at 37{degrees}C predicted increased flux through glucose transport, glucose phosphorylation, and downstream glucose-associated reactions than it did at 29-30{degrees}C. Similar temperature-dependent predictions were obtained for another pathogenic Leptospira species under in vitro conditions. Consistent with these findings, analysis of leptospires cultivated intraperitoneally in dialysis membrane chambers also predicted enhanced glucose uptake and metabolism under host-temperature conditions. In vitro validation experiments showed increased bacterial uptake or accumulation of a fluorescent glucose analogue, elevated expression of candidate glucose transporter genes, and glucose-dependent enhancement of bacterial growth at 37{degrees}C. Treatment with a glucose degradation pathway inhibitor, 2-deoxy-D-glucose further supported a contribution of glucose-associated processes to proliferation at 37{degrees}C. Together, these findings indicate that pathogenic Leptospira displays condition-dependent glucose uptake and glucose-associated metabolic activity that become apparent at host-like temperature, opposed to glucose-incompetent in an environmental-like temperature. This prediction-driven framework refines the conventional view of Leptospira carbon metabolism and provides a basis for future studies of glucose-associated metabolic capacity during mammalian infection. ImportanceEnvironmental reservoirs play a central role in the transmission of many bacterial pathogens, yet the metabolic mechanisms that enable adaptation to both environmental and host-associated niches remain largely unknown. Using a prediction-driven framework that combines contextualized genome-scale metabolic modeling with multi-omics analyses and experimental validation, we discovered that pathogenic Leptospira activates glucose uptake and metabolism only under host-like conditions. This finding resolves a long-standing misconception arising from studies performed under conventional laboratory conditions and fundamentally revises our understanding of Leptospira physiology. More importantly, our study establishes an integrative systems biology strategy for uncovering condition-dependent metabolic traits that would be difficult to identify experimentally alone, with broad applicability to diverse environmentally transmitted pathogens.

microbiology↗

Soil bacterium Massilia secretes metabolites that promote Leptospira growth

Understanding pathogen metabolism is critical for identifying key functions for drug targeting, establishing effective in vitro experimental systems, etc., particularly for metabolically unique organisms such as Leptospira. Pathogenic Leptospira are thought to infect humans from environmental sources; however, direct isolation from environmental samples remains technically challenging and is not yet well established. Here, we report that a ubiquitous environmental bacterium, Massilia sp., produces metabolites that promote the growth of Leptospira interrogans, encountered through an incidental contamination event, and identified in this study. Gas chromatography-tandem mass spectrometry (GC-MS/MS) analysis showed demonstrated that cultivating of Massilia sp. in R2A medium resulted in the accumulation of metabolites, including branched-chain amino acid (BCAA) intermediates, compared to fresh medium. By combining genome-scale metabolic modeling with experimental validation using cell-free culture supernatant supplementation assays, we demonstrate that BCAA intermediates, particularly 2-ketoisocaproic acid (4-methyl-2-oxopentanoate; 4MOP), a leucine biosynthetic intermediate produced by Massilia sp., enhance Leptospira growth. To investigate the metabolic role of 4MOP, we incorporated transcriptomic data into a genome-scale metabolic network model to generate condition-specific models. Resulted flux distributions indicated that Leptospira catabolized imported 4MOP to produce acetyl-CoA. Our results reveal a previously unrecognized metabolic interaction where metabolites produced by environmental bacteria support the growth of pathogenic Leptospira, offering mechanistic insight into its metabolic requirement. These findings have implications to understand the environmental persistence of Leptospira through its metabolic dependencies on coexisting microbes, and they also help develop better strategies for this pathogen. ImportancePathogenic Leptospira persist in environmental reservoirs, yet the mechanisms supporting their growth remain poorly defined. Here, we find that metabolites produced by common environmental bacteria, Massilia sp., can promote Leptospira growth, suggesting a previously unrecognized metabolic dependency on coexisting microbes. Importantly, this study indicates that combining genome-scale metabolic modeling with experimental validation provides a useful framework for identifying metabolic interactions that are otherwise difficult to resolve using conventional culture-based approaches. Current strategy may facilitate the systematic identification of growth-supporting metabolites and provide a basis for improving selective cultivation for uncultured or difficult to culture organisms. Determination of growth promoting metabolites advances our understanding of pathogen persistence in natural environments and offers a generalized framework to study metabolically dependent microorganisms.

microbiology↗

Population-scale chemical response revealed by a barcoded yeast collection

Natural genetic variation shapes how microbial populations adapt to environmental and chemical challenges, but scalable approaches to map genotype-phenotype relationships across diverse genetic backgrounds remain limited. Here, we developed a systematically barcoded collection of 520 Saccharomyces cerevisiae natural isolates that captures the ecological, geographical and genetic diversity of the species. Using pooled barcode sequencing, we profiled fitness responses to over 600 bioactive and natural compounds, revealing broader and more polarized bioactivity than the standard yeast gene-deletion collection. Fitness-based clustering defined six major compound groups with reproducible, population-structured sensitivity patterns. Genome-wide association analysis identified significant genetic variants across 107 compounds, linking natural polymorphisms to chemical responses and involving genes in genome maintenance, ribosome biogenesis, vesicular trafficking and stress tolerance. Together, our barcoded natural population provides a scalable framework for chemical-genetic screening, enabling systematic dissection of how genetic diversity shapes microbial fitness and adaptation.

evolutionary biology↗

Genomic characterization of sub-populations in human pluripotent stem cell-derived retinal progenitor cells that drive retinal layer structure

The mechanism underlying retinal spheroid layer formation was investigated using Rax::GFP-positive retinal progenitor cells from human embryonic stem cell-derived retinal organoids. Single-cell RNA sequencing revealed that well-layered spheroids transiently activated canonical WNT2B-FZD7 signaling followed by temporary expression of non-canonical WNT5A. Despite structural differences in vitro, non- layered and well-layered retinal spheroids on differentiation Day 60 successfully developed into cone and rod subtypes of photoreceptors and established synaptic connections with host bipolar cells after transplantation in a retinal degeneration rat model, resulting in light-evoked functional recovery. Additionally, part of the presumable Rax::GFP-positive retinal progenitor cells differentiated into non- retinal lineages, including ciliary marginal zone-like, retinal pigment epithelium, and spinal cord-like tissues in vitro, reflecting the developmental plasticity of RAX-positive cells. These findings suggest that canonical and non-canonical WNT signaling pathways sequentially orchestrate early retinal morphogenesis, whereas environmental factors within the host retina strongly drive the alignment and functional integration of graft photoreceptors.

cell biology↗

Brilacidin, a novel antifungal agent against Cryptococcus neoformans

Cryptococcus neoformans causes cryptococcosis, one of the most prevalent fungal diseases, generally characterized by meningitis. There is a limited and not very effective number of drugs available to combat this disease. In this manuscript, we show the host defense peptide mimetic brilacidin (BRI) as a promising antifungal drug against C. neoformans. BRI is able to affect the organization of the cell membrane, increasing fungal cell permeability. We also investigated the effects of BRI against the model system Saccharomyces cerevisiae by analyzing libraries of mutants grown in the presence of BRI. In S. cerevisiae, BRI also affects the cell membrane organization, but in addition the cell wall integrity pathway and calcium metabolism. In vivo experiments show BRI significantly reduces C. neoformans survival inside macrophages and partially clears C. neoformans lung infection in an immunocompetent murine model of invasive pulmonary cryptococcosis. We also observed that BRI interacts with caspofungin (CAS) and amphotericin (AmB), potentiating their mechanism of action against C. neoformans. BRI+CAS affects endocytic movement, calcineurin, and mitogen activated protein kinases. Our results indicate that BRI is a novel antifungal drug against cryptococcosis. ImportanceInvasive fungal infections have a high mortality rate causing more deaths annually than tuberculosis or malaria. Cryptococcosis, one of the most prevalent fungal diseases, is generally characterized by meningitis and is mainly caused by two closely related species of basidiomycetous yeasts, Cryptococcus neoformans and Cryptococcus gattii. There are few therapeutic options for treating cryptococcosis and searching for new antifungal agents against this disease is very important. Here, we present brilacidin (BRI) as a potential antifungal agent against C. neoformans. BRI is a small molecule host defense peptide mimetic that has previously exhibited broad-spectrum immunomodulatory/anti-inflammatory activity against bacteria and viruses. BRI alone was shown to inhibit the growth of C. neoformans, acting as a fungicidal drug, but surprisingly also potentiated the activity of caspofungin (CAS) against this species. We investigated the mechanism of action of BRI and BRI+CAS against C. neoformans. We propose BRI as a new antifungal agent against cryptococcosis.

microbiology↗

A Deep Learning-Based Evaluation for Microscopic Agglutination Test

The Microscopic Agglutination Test (MAT) is widely recognized as the gold standard for diagnosing zoonosis leptospirosis. However, its reliance on examiners subjective evaluations often leads to inconsistent results. To address this limitation, we propose a deep neural network replicating the agglutination rate estimation in MAT. By leveraging a pre-trained DenseNet121, the network parameters are optimized efficiently during training. We validated our approach using an in-house dataset, and experimental results show that the proposed network achieves accurate agglutination rate estimates. In addition, we employed a standard visualization technique to elucidate the decision-making process, revealing that the network captures image features indicative of leptospire abundance. Overall, our findings suggest that deep learning effectively estimates agglutination rates and that enhancing interpretability supports medical experts in understanding the underlying functionality of deep learning models.

microbiology↗

Transcriptomic Signatures of WNT-Driven Pathways and Granulosa Cell-Oocyte Interactions during Primordial Follicle Activation

Primordial follicle activation (PFA) is a pivotal event in female reproductive biology, coordinating the transition from quiescent to growing follicles. This study employed comprehensive single-cell RNA sequencing to gain insights into the detailed regulatory mechanisms governing the synchronized dormancy and activation between granulosa cells (GCs) and oocytes with the progression of the PFA process. Wntless (Wls) conditional knockout (cKO) mice served as a unique model, suppressing the transition from pre-GCs to GCs, and disrupting somatic cell-derived WNT signaling in the ovary. Our data revealed immediate transcriptomic changes in GCs post-PFA in Wls cKO mice, leading to a divergent trajectory, while oocytes exhibited modest transcriptomic alterations. Subpopulation analysis identified the molecular pathways affected by WNT signaling on GC maturation, along with specific gene signatures linked to dormant and activated oocytes. Despite minimal evidence of continuous up-regulation of dormancy-related genes in oocytes, the loss of WNT signaling in (pre-)GCs impacted gene expression in oocytes even before PFA, subsequently influencing them globally. The infertility observed in Wls cKO mice was attributed to compromised GC-oocyte molecular crosstalk and the microenvironment for oocytes. Our study highlights the pivotal role of the WNT-signaling pathway and its molecular signature, emphasizing the importance of intercellular crosstalk between (pre-)GCs and oocytes in orchestrating folliculogenesis. Author summaryIn mammalian ovaries, primordial follicles protect dormant oocytes from wasteful consumption. We explored the complex molecular dynamics of the critical process of primordial follicle activation (PFA) that underlies female reproductive health, with a focus on the role of WNT signaling. By utilizing a unique mouse model with disrupted WNT signaling, our study reveals novel insights into the regulatory dynamics of granulosa cells (GCs) and oocytes during PFA. Single-cell transcriptome profiling uncovered distinct cell populations and highlighted the impact of WNT signaling on ovarian cell types. This study reveals distinct transcriptomic changes in GCs post-PFA, which affects their differentiation trajectories and highlights the critical role of WNT signaling in establishing GC function. We further examined the consequences of disrupted microenvironments on oocytes and GC-oocyte interactions, which elucidated altered oocyte subclusters and potential links to infertility. These findings provide a foundation for understanding the connections between WNT signaling and ovarian function. Our comprehensive analysis contributes valuable knowledge to reproductive biology, emphasizing the clinical relevance of WNT signaling in female fertility.

bioinformatics↗

Chronic BCR signaling generates and maintains age-associated B cells from anergic B cells

Accumulation of age-associated B cells (ABCs) with autoreactive properties contributes to the pathogenesis of autoimmune diseases1-5. However, the mechanisms whereby ABCs are generated and maintained are not understood1, 2, 4. Here, we show that continuous stimulation of the B-cell receptor (BCR) with self-antigens plays a crucial role in ABC generation from anergic B cells and that this signal is vital for sustaining ABCs during aging and autoimmunity. In ABCs, BCR signaling was constitutively activated and the surface BCR was internalized in vivo, as occurs in autoreactive B cells chronically exposed to self-antigens6. With aging, ABCs were generated from autoreactive anergic B cells, but not from B cells expressing non-self-reactive BCR. In vitro stimulation of anergic B cells with self-antigen, interleukin-21, and Toll-like receptor 7/9 agonists promoted their differentiation to ABCs. Furthermore, the cellular phenotype of ABCs in Bm12-induced lupus mice7, 8 resembled that of ABCs in aged mice, showing activation of BCR signaling, expression of activation markers, and BCR internalization. Importantly, Btk was persistently activated in ABCs of aged/autoimmune mice and humans with lupus. Pharmacological Btk inhibition resulted in a marked reduction in the number of ABCs and pathogenicity in lupus mice. Our findings have implications for accumulating ABCs and developing therapies for autoimmune diseases.

immunology↗

Breakdown in seasonal dynamics of ant communities with land-cover change

Concerns about widespread human-induced declines in insect populations are mounting, yet little is known about how land-use change modifies the dynamics of insect communities, particularly in understudied biomes. Here, we examine how the seasonal patterns of ant activity--key drivers of terrestrial ecosystem functioning--vary with human-induced land cover change on a subtropical island landscape. Using trap captures sampled biweekly from a biodiversity monitoring network covering Okinawa Island, Japan, we processed 1.2 million individuals and reconstructed activity patterns within and across habitat types. Forest communities exhibited greater variability than those in more developed areas. Using time-series decomposition to deconstruct this pattern, we found that ant communities at sites with greater human development exhibited diminished seasonality, reduced synchrony, and higher stochasticity compared to those at sites with greater forest cover. Our results cannot be explained by variation in either regional or in situ temperature patterns, or by differences in species richness or composition among sites. We conclude that the breakdown of natural seasonal patterns of functionally key insect communities may comprise an important and underappreciated consequence of global environmental change that must be better understood across Earths biomes.

ecology↗

Habitat degradation homogenizes ecological responses to typhoons across a subtropical island

Climate change is increasing the frequency, intensity, and duration of extreme weather events across the globe. Understanding the capacity for ecological communities to withstand and recover from such events is critical. Typhoons are extreme weather events that are expected to broadly homogenise ecological dynamics through structural damage to vegetation and longer-term effects of salinization. Given their unpredictable nature, monitoring ecological responses to typhoons is challenging, particularly for mobile animals such as birds. Here, we report spatially variable ecological responses to typhoons across terrestrial landscapes. Using a high temporal resolution passive acoustic monitoring network across 24 sites on the subtropical island of Okinawa, Japan, we found that typhoons elicit divergent ecological responses among Okinawas diverse terrestrial habitats, as indicated by increased spatial variability of biological sound production (biophony) and individual species detections. This suggests that soniferous communities are capable of a diversity of different responses to typhoons. That is, spatial insurance effects among local ecological communities provide resilience to typhoons at the landscape scale. Even though site-level typhoon impacts on soundscapes and bird detections were not particularly strong, monitoring at scale with high temporal resolution across a broad spatial extent nevertheless enabled detection of spatial heterogeneity in typhoon responses. Further, species-level responses mirrored those of acoustic indices, underscoring the utility of such indices for revealing insight into fundamental questions concerning disturbance and stability. Our findings demonstrate the significant potential of landscape-scale acoustic sensor networks to capture the understudied ecological impacts of unpredictable extreme weather events.

ecology↗

Quantitative analysis of calcium oxalate monohydrate and dihydrate for elucidating the formation mechanism of calcium oxalate kidney stones

We aimed to identify and quantitatively analyze calcium oxalate (CaOx) kidney stones on the order of micrometers, with a focus on the quantitative identification of calcium oxalate monohydrate (COM) and dihydrate (COD). Fourier Transform Infrared (FTIR) spectroscopy, powder X-ray diffraction (PXRD), and microfocus X-ray CT measurements (micro-CT) were performed, and the results were compared. The extended analysis method of the FTIR spectrum, focusing on the 780 cm-1 peak, made it possible to achieve a reliable analysis of the COM/COD ratio. We succeeded in the quantitative analysis of COM/COD in the region of 50 x 50 m by microscopic FTIR for thin sections of kidney stones, and by the micro-CT for bulk samples. The extended analysis method of the FTIR spectrum focusing on the 780 cm-1 peak was introduced to analyze the COM/COD ratio. The analysis results based on PXRD measurement with micro sampling, microscopic FTIR analysis of a thin section, and micro-CT observation of a bulk sample of a kidney stone showed roughly consistent results, indicating that all methods can be used complementarily. This quantitative analysis method evaluates the detailed CaOx composition on the preserved stone surface and provides information on the stone formation processes and interactions with organic molecules.

biophysics↗

Circadian regulation of hippocampal function is disrupted with chronic corticosteroid treatment

Neuropsychiatric disorders such as major depression and schizophrenia are highly prevalent and contribute substantially to disease burden worldwide. Despite this, progress understanding the pathophysiology has remained largely elusive, yet these disorders often exhibit a loss of regulation of biological rhythms, such as sleep/wake cycles and hormonal rhythms. Cushings disease, a condition characterized by chronic corticosteroid (cortisol) hypersecretion is associated with psychiatric and neurocognitive disorders and disruption to the circadian release of cortisol can result in depression and neurocognitive impairment. In rats, we report that circadian regulation of the hippocampal transcriptome integrates crucial functional networks that link corticosteroid-inducible gene regulation to synaptic plasticity regulation via an intra-hippocampal circadian transcriptional clock. During the early active period, when corticosteroid availability is high, CA1 region excitatory and inhibitory post-synaptic currents were augmented along with long-term potentiation. In contrast, chronic corticosteroid exposure disturbed hippocampal function. The hippocampal transcriptome, as well as circadian regulation of synaptic plasticity were ablated, resulting in memory loss during hippocampal-dependent behavior. These findings identify how exposure to elevated levels of corticosteroid, that is often seen in neuropsychiatric illness, results in adverse critical hippocampal function. These data provide novel insights into the molecular mechanisms of neurocognitive disorders and provides evidence for corticosteroid-mediated intervention in disabling mental illnesses.

neuroscience↗

Pervasive false brain connectivity from electrophysiological signals

1.Signals of brain electric neuronal activity, either invasively measured or non-invasively estimated, are commonly used for connectivity inference. One popular methodology assumes that the neural dynamics follow a multivariate autoregression, where the autoregressive coefficients represent the couplings among regions. If observation noise is present and ignored, as is common in practice, the estimated couplings are biased, affecting all forms of Granger-causality inference, both in time and in frequency domains. Significant nonsense coupling, i.e., nonsense connectivity, can appear when in reality there is none, since there is always observation noise in two possible forms: measurement noise, and activity from other brain regions due to volume conduction and low spatial resolution. This problem is critical, and is currently not being addressed, calling into question the validity of many Granger-causality reports in the literature. An estimation method that accounts for noise is based on an overdetermined system of high-order multivariate Yule-Walker equations, which give reduced variance estimators for the coupling coefficients of the unobserved signals. Simulation-based comparisons to other published methods are presented, demonstrating its adequate performance. In addition, simulation results are presented for a zero connectivity case with noisy observations, where the new method correctly reports no connectivity while classical analyses (as found in most software packages) report nonsense connectivity. For the sake of reproducible research, the supplementary material includes, in human readable format, all the time series data used here.

neuroscience↗

Genome-wide analysis of transcriptional bursting-induced noise in mammalian cells

Transcriptional bursting is stochastic activation and inactivation of promoters, leading to discontinuous production of mRNA, and is considered to be a contributing factor to cell-to-cell heterogeneity in gene expression. However, it remains elusive how the kinetic properties of transcriptional bursting (e.g., burst size, burst frequency, and noise induced by transcriptional bursting) are regulated in mammalian cells. In this study, we performed a genome-wide analysis of transcriptional bursting in mouse embryonic stem cells (mESCs) using single-cell RNA-sequencing. We found that the kinetics of transcriptional bursting was determined by a combination of promoter and gene body binding proteins, including polycomb repressive complex 2 and transcription elongation-related factors. Furthermore, large-scale CRISPR-Cas9-based screening and functional analysis revealed that the Akt/MAPK signaling pathway regulated bursting kinetics by modulating transcription elongation efficiency. These results uncover key molecular mechanisms underlying transcriptional bursting and cell-to-cell gene expression noise in mammalian cells.

systems biology↗

Measuring Granger-causal effects in multivariate time series by system editing

1.What is the role of each node in a system of many interconnected nodes? This can be quantified by comparing the dynamics of the nodes in the intact system, with their modified dynamics in the edited system, where one node is deleted. In detail, the spectra are calculated from a causal multivariate autoregressive model for the intact system. Next, without re-estimation, one node is deleted from the model and the modified spectra at all other nodes are re-calculated. The change in spectra from the edited system to the intact system quantifies the role of the deleted node, giving a measure of its Granger-causal effects (CFX) on the system. A generalization of this novel measure is available for networks (i.e. for groups of nodes), which quantifies the role of each network in a system of many networks. For the sake of reproducible research, program codes (PASCAL), executable file, and toy data in human readable format are included in the supplementary material.

neuroscience↗

A comparison of bivariate frequency domain measures of electrophysiological connectivity

1The problem of interest here concerns electrophysiological signals from two cortical sites, acquired as invasive intracranial recordings, or from non-invasive estimates of cortical electric neuronal activity computed from EEG or MEG recordings (see e.g. https://doi.org/10.1101/269753). In the absence of other sources, these measured signals consist of an instantaneous linear mixture of the true, actual, unobserved local signals, due to low spatial resolution and volume conduction. A connectivity measure is unreliable as a true indicator of electrophysiological connectivity if it is not invariant to mixing, or if it reports a significant connection for a mixture of independent signals. In (Vinck et al 2011 Neuroimage 55:1548) it was shown that coherence, imaginary coherence, and phase locking value are not invariant to mixing, while the phase lag index (PLI) and the weighted version (wPLI) are invariant to mixing. Here we show that the lagged coherence (LagCoh) measure (2007, https://arxiv.org/abs/0711.1455), not studied in Vinck et al, is invariant to mixing. Additionally, we present here a new mixture-invariant connectivity statistic: the \"standardized imaginary covariance\" (sImCov). We also include in the comparisons the directed PLI (dPLI) by Stam et al (2012 Neuroimage 62:1415). Fourier coefficients for \"N\" trials are generated from a linear unidirectional causal time domain model with electrophysiological delay \"k\" and regression coefficient \"b\". 1000 random data sets of \"N\" trials are simulated, and for each one, and for each connectivity measure, non-parametric randomization tests are performed. The \"true positive detection rate\" is calculated as the fraction of 1000 cases that have significant connectivity at p<0.05, 0.1, and 0.2. The connectivity methods were compared in terms of detection rates, under non-mixed and mixed conditions, for small and large sample sizes \"N\", with and without jitter for \"k\", and for different values of signal to noise. Under mixing, the results show that LagCoh outperforms wPLI, PLI, dPLI, and sImCov. Without mixing, LagCoh and sImCov outperform wPLI, PLI, and dPLI. Finally, it is shown that dPLI is an invalid estimator of flow direction, i.e. it reverses and \"goes against the flow\" by merely changing the sign of one of the time series, a fact that violates the basic definition of Granger causality. For the sake of reproducible research, the supplementary material includes Delphi Pascal source code and all detailed result files in human readable format.

neuroscience↗

Comparing EEG/MEG neuroimaging methods based on localization error, false positive activity, and false positive connectivity

1.EEG/MEG neuroimaging consists of estimating the cortical distribution of time varying signals of electric neuronal activity, for the study of functional localization and connectivity. Currently, many different imaging methods are being used, with very different capabilities of correct localization of activity and of correct localization of connectivity. The aim here is to provide a guideline for choosing the best (i.e. least bad) imaging method. This first study is limited to the comparison of the following methods for EEG signals: sLORETA and eLORETA (standardized and exact low resolution electromagnetic tomography), MNE (minimum norm estimate), dSPM (dynamic statistical parametric mapping), and LCMVBs (linearly constrained minimum variance beamformers). These methods are linear, except for the LCMVBs that make use of the quadratic EEG covariances. To achieve a fair comparison, it is assumed here that the generators are independent and widely distributed (i.e. not few in number), giving a well-defined theoretical population EEG covariance matrix for use with the LCMVBs. Measures of localization error, false positive activity, and false positive connectivity are defined and computed under ideal no-noise conditions. It is empirically shown with extensive simulations that: (1) MNE, dSPM, and all LCMVBs are in general incapable of correct localization, while sLORETA and eLORETA have exact (zero-error) localization; (2) the brain volume with false positive activity is significantly larger for MN, dSPM, and all LCMVBs, as compared to sLORETA and eLORETA; and (3) the number of false positive connections is significantly larger for MN, dSPM, all LCMVBs, and sLORETA, as compared to eLORETA. Non-vague and fully detailed equations are given. PASCAL program codes and data files are available. It is noted that the results reported here do not apply to the LCMVBs based on EEG covariance matrices generated from extremely few generators, such as only one or two independent point sources.

neuroscience↗

Measures of time series coupling based on generalized weighted multiple regression

1.The sharing and the transmission of information between cortical brain regions is carried out by mechanisms that are still not fully understood. A deeper understanding should shed light on how consciousness and cognition are implemented in the brain. Research activity in this field has recently been focusing on the discovery of non-conventional coupling mechanisms, such as all forms of cross-frequency couplings between diverse combinations of amplitudes and phases, applied to measured or estimated cortical signals of electric neuronal activity. However, all coupling measures that involve phase computation have poor statistical properties. In this work, the conventional estimators for the well-known phase-phase (phase synchronization or locking), phase-amplitude, and phaseamplitude-amplitude couplings are generalized by means of the weighted multiple regression model. The choice of appropriate weights produces estimators that bypass the need for computing the complex-valued phase. In addition, a new coupling, denoted as the inhibitory coupling (InhCo), is introduced and defined as the dependence of one complex-valued variable on the inverse and on the conjugate inverse of another complex-valued variable. A weighted version denoted as wInhCo is also introduced, bypassing the need for computing the inverse of a complex variable, which has very poor statistical properties. The importance of this form of inhibitory coupling is that it may capture well- known processes, such as the observed inverse alpha/gamma relation within the same cortical region, or the inverse alpha/alpha relation between distant cortical regions.

neuroscience↗