The gut microbiome in end-stage lung disease and lung transplantation

Gut dysbiosis has been associated with impaired outcomes in liver and kidney transplant recipients, but the gut microbiome of lung transplant recipients has not been extensively explored. We assessed the gut microbiome in 64 fecal samples from end-stage lung disease patients before transplantation and 219 samples from lung transplant recipients after transplantation using metagenomic sequencing. To identify dysbiotic microbial signatures, we analyzed 243 fecal samples from age-, sex-, and BMI-matched healthy controls. By unsupervised clustering, we identified five groups of lung transplant recipients using different combinations of immunosuppressants and antibiotics and analyzed them in relation to the gut microbiome. Finally, we investigated the gut microbiome of lung transplant recipients in different chronic lung allograft dysfunction (CLAD) stages and longitudinal gut microbiome changes after transplantation. We found 108 species (58.1%) in end-stage lung disease patients and 139 species (74.7%) in lung transplant recipients that were differentially abundant compared with healthy controls, with several species exhibiting sharp longitudinal increases from before to after transplantation. Different combinations of immunosuppressants and antibiotics were associated with specific gut microbial signatures. We found that the gut microbiome of lung transplant recipients in CLAD stage 0 was more similar to healthy controls compared to those in CLAD stage 1. Finally, the gut microbial diversity of lung transplant recipients remained lower than the average gut microbial diversity of healthy controls up to more than 20 years post-transplantation. Gut dysbiosis, already present before lung transplantation was exacerbated following lung transplantation.IMPORTANCEThis study provides extensive insights into the gut microbiome of end-stage lung disease patients and lung transplant recipients, which warrants further investigation before the gut microbiome can be used for microbiome-targeted interventions that could improve the outcome of lung transplantation.

Mucosal host-microbe interactions associate with clinical phenotypes in inflammatory bowel disease

Disrupted host-microbe interactions at the mucosal level are key to the pathophysiology of IBD. This study aimed to comprehensively examine crosstalk between mucosal gene expression and microbiota in patients with IBD. To study tissue-specific interactions, we perform transcriptomic (RNA-seq) and microbial (16S-rRNA-seq) profiling of 697 intestinal biopsies (645 derived from 335 patients with IBD and 52 from 16 non-IBD controls). Mucosal gene expression patterns in IBD are mainly determined by tissue location and inflammation, whereas the mucosal microbiota composition shows a high degree of individual specificity. Analysis of transcript-bacteria interactions identifies six distinct groups of inflammation-related pathways that are associated with intestinal microbiota (adjusted P < 0.05). An increased abundance of Bifidobacterium is associated with higher expression of genes involved in fatty acid metabolism, while Bacteroides correlates with increased metallothionein signaling. In patients with fibrostenosis, a transcriptional network dominated by immunoregulatory genes is associated with Lachnoclostridium bacteria in non-stenotic tissue (adjusted P < 0.05), while being absent in CD without fibrostenosis. In patients using TNF-α-antagonists, a transcriptional network dominated by fatty acid metabolism genes is linked to Ruminococcaceae (adjusted P < 0.05). Mucosal microbiota composition correlates with enrichment of intestinal epithelial cells, macrophages, and NK-cells. Overall, these data demonstrate the presence of context-specific mucosal host-microbe interactions in IBD, revealing significantly altered inflammation-associated gene-taxa modules, particularly in patients with fibrostenotic CD and patients using TNF-α-antagonists. This study provides compelling insights into host-microbe interactions that may guide microbiota-directed precision medicine and fuels the rationale for microbiota-targeted therapeutics as a strategy to alter disease course in IBD.

Health-related quality of life is linked to the gut microbiome in kidney transplant recipients

Kidney transplant recipients (KTR) have impaired health-related quality of life (HRQoL) and suffer from intestinal dysbiosis. Increasing evidence shows that gut health and HRQoL are tightly related in the general population. Here, we investigate the association between the gut microbiome and HRQoL in KTR, using metagenomic sequencing data from fecal samples collected from 507 KTR. Multiple bacterial species are associated with lower HRQoL, many of which have previously been associated with adverse health conditions. Gut microbiome distance to the general population is highest among KTR with an impaired physical HRQoL (R = -0.20, P = 2.3 × 10-65) and mental HRQoL (R = -0.14, P = 1.3 × 10-3). Physical and mental HRQoL explain a significant part of variance in the gut microbiome (R2 = 0.58%, FDR = 5.43 × 10-4 and R2 = 0.37%, FDR = 1.38 × 10-3, respectively). Additionally, multiple metabolic and neuroactive pathways (gut brain modules) are associated with lower HRQoL. While the observational design of our study does not allow us to analyze causality, we provide a comprehensive overview of the associations between the gut microbiome and HRQoL while controlling for confounders.

Unsuitability of the Oxidation-Reduction Potential Measurement for the Quantification of Fecal Redox Status in Inflammatory Bowel Disease

Oxidative stress is a key pathophysiological process associated with the development and progression of inflammatory bowel disease (IBD). Biomarkers for oxidative stress, however, are scarce, as are diagnostic tools that can interrogate an individual's gut redox status. This proof-of-concept study aimed to evaluate the potential utility of an oxidation-reduction potential (ORP) measurement probe, to quantify redox status in the feces of both patients with IBD and healthy controls. Previous studies using this ORP measurement probe demonstrated promising data when comparing ORP from severely malnourished individuals with that of healthy controls. To date, ORP analyses have not been performed in the context of IBD. We hypothesized that measuring the ORP of fecal water in patients with IBD might have diagnostic value. The current study, however, did not show significant differences in ORP measurement values between patients with IBD (median [IQR] 46.5 [33.0-61.2] mV) and healthy controls (25 [8.0-52.0] mV; p = 0.221). Additionally, ORP measurements were highly unstable and rapidly fluctuated throughout time, with ORP values varying from +24 to +303 mV. Due to potential biological processes and limitations of the measuring equipment, this study was unable to reliably measure ORP. As a result, our findings indicate that ORP quantification may not be a suitable method for assessing fecal redox status and, therefore, does not currently support further exploration as a diagnostic or monitoring tool.

Faecal metabolome and its determinants in inflammatory bowel disease

OBJECTIVE

Inflammatory bowel disease (IBD) is a multifactorial immune-mediated inflammatory disease of the intestine, comprising Crohn's disease and ulcerative colitis. By characterising metabolites in faeces, combined with faecal metagenomics, host genetics and clinical characteristics, we aimed to unravel metabolic alterations in IBD.

DESIGN

We measured 1684 different faecal metabolites and 8 short-chain and branched-chain fatty acids in stool samples of 424 patients with IBD and 255 non-IBD controls. Regression analyses were used to compare concentrations of metabolites between cases and controls and determine the relationship between metabolites and each participant's lifestyle, clinical characteristics and gut microbiota composition. Moreover, genome-wide association analysis was conducted on faecal metabolite levels.

RESULTS

We identified over 300 molecules that were differentially abundant in the faeces of patients with IBD. The ratio between a sphingolipid and L-urobilin could discriminate between IBD and non-IBD samples (AUC=0.85). We found changes in the bile acid pool in patients with dysbiotic microbial communities and a strong association between faecal metabolome and gut microbiota. For example, the abundance of Ruminococcus gnavus was positively associated with tryptamine levels. In addition, we found 158 associations between metabolites and dietary patterns, and polymorphisms near NAT2 strongly associated with coffee metabolism.

CONCLUSION

In this large-scale analysis, we identified alterations in the metabolome of patients with IBD that are independent of commonly overlooked confounders such as diet and surgical history. Considering the influence of the microbiome on faecal metabolites, our results pave the way for future interventions targeting intestinal inflammation.

Gut microbiome dysbiosis is associated with increased mortality after solid organ transplantation

Organ transplantation is a life-saving treatment for patients with end-stage disease, but survival rates after transplantation vary considerably. There is now increasing evidence that the gut microbiome is linked to the survival of patients undergoing hematopoietic cell transplant, yet little is known about the role of the gut microbiome in solid organ transplantation. We analyzed 1370 fecal samples from 415 liver and 672 renal transplant recipients using shotgun metagenomic sequencing to assess microbial taxonomy, metabolic pathways, antibiotic resistance genes, and virulence factors. To quantify taxonomic and metabolic dysbiosis, we also analyzed 1183 age-, sex-, and body mass index-matched controls from the same population. In addition, a subset of 78 renal transplant recipients was followed longitudinally from pretransplantation to 24 months after transplantation. Our data showed that both liver and kidney transplant recipients suffered from gut dysbiosis, including lower microbial diversity, increased abundance of unhealthy microbial species, decreased abundance of important metabolic pathways, and increased prevalence and diversity of antibiotic resistance genes and virulence factors. These changes were found to persist up to 20 years after transplantation. Last, we demonstrated that the use of immunosuppressive drugs was associated with the observed dysbiosis and that the extent of dysbiosis was associated with increased mortality after transplantation. This study represents a step toward potential microbiome-targeted interventions that might influence the outcomes of recipients of solid organ transplantation.

HIF1α-Dependent Induction of TFRC by a Combination of Intestinal Inflammation and Systemic Iron Deficiency in Inflammatory Bowel Disease

Background and Aims: Iron deficiency (ID) is a frequent extra-intestinal manifestation in patients with Inflammatory Bowel Disease (IBD), who often do not respond to iron supplementation. Iron is a cofactor for hydroxylases that suppress the hypoxia-inducible factor-1α (HIF1α), a transcription factor regulating iron homeostasis. We hypothesized that iron deficiency affects mucosal HIF1α activity in IBD. Methods: IBD patients (n = 101) were subdivided based on iron status (ferritin levels or transferrin saturation) and systemic inflammation (C-reactive protein levels). 154 corresponding ileal and colonic biopsies were analyzed for differential expression of 20 HIF1α pathway-associated genes and related to iron and inflammation status. In vitro expression of selected HIF1α pathway genes were analyzed in wild-type and HIF1A-null Caco-2 cells. Results: Gene expression of the mucosal HIF1α pathway was most affected by intestinal location and inflammatory status. Especially, ileal mucosal TFRC expression, encoding the transferrin receptor TFR1, was increased in inflamed tissue (p < 0.001), and further enhanced in ID. Accordingly, TFRC expression in inflamed tissue associated negatively with serum iron levels, which was not observed in the non-inflamed mucosa. The HIF1α pathway agonist DMOG increased TFRC expression in Caco-2 cells, which was blunted in HIF1A-null cells. Conclusion: We demonstrate that inflammation and anatomical location primarily determine HIF1α pathway activation and downstream TFRC expression in the intestinal mucosa. IBD patients with ID may benefit from treatment with HIF1α-agonists by 1) increasing TFRC-mediated iron absorption in non-inflamed tissue and 2) decreasing mucosal inflammation, thereby improving their responsiveness to oral iron supplementation.

Environmental factors shaping the gut microbiome in a Dutch population

The gut microbiome is associated with diverse diseases^1-3, but a universal signature of a healthy or unhealthy microbiome has not been identified, and there is a need to understand how genetics, exposome, lifestyle and diet shape the microbiome in health and disease. Here we profiled bacterial composition, function, antibiotic resistance and virulence factors in the gut microbiomes of 8,208 Dutch individuals from a three-generational cohort comprising 2,756 families. We correlated these to 241 host and environmental factors, including physical and mental health, use of medication, diet, socioeconomic factors and childhood and current exposome. We identify that the microbiome is shaped primarily by the environment and cohabitation. Only around 6.6% of taxa are heritable, whereas the variance of around 48.6% of taxa is significantly explained by cohabitation. By identifying 2,856 associations between the microbiome and health, we find that seemingly unrelated diseases share a common microbiome signature that is independent of comorbidities. Furthermore, we identify 7,519 associations between microbiome features and diet, socioeconomics and early life and current exposome, with numerous early-life and current factors being significantly associated with microbiome function and composition. Overall, this study provides a comprehensive overview of gut microbiome and the underlying impact of heritability and exposures that will facilitate future development of microbiome-targeted therapies.

Donor genetic variants as risk factors for thrombosis after liver transplantation: A genome-wide association study

Thrombosis after liver transplantation substantially impairs graft- and patient survival. Inevitably, heritable disorders of coagulation originating in the donor liver are transmitted by transplantation. We hypothesized that genetic variants in donor thrombophilia genes are associated with increased risk of posttransplant thrombosis. We genotyped 775 donors for adult recipients and 310 donors for pediatric recipients transplanted between 1993 and 2018. We determined the association between known donor thrombophilia gene variants and recipient posttransplant thrombosis. In addition, we performed a genome-wide association study (GWAS) and meta-analyzed 1085 liver transplantations. In our donor cohort, known thrombosis risk loci were not associated with posttransplant thrombosis, suggesting that it is unnecessary to exclude liver donors based on thrombosis-susceptible polymorphisms. By performing a meta-GWAS from children and adults, we identified 280 variants in 55 loci at suggestive genetic significance threshold. Downstream prioritization strategies identified biologically plausible candidate genes, among which were AK4 (rs11208611-T, p = 4.22 × 10^-05 ) which encodes a protein that regulates cellular ATP levels and concurrent activation of AMPK and mTOR, and RGS5 (rs10917696-C, p = 2.62 × 10^-05 ) which is involved in vascular development. We provide evidence that common genetic variants in the donor, but not previously known thrombophilia-related variants, are associated with increased risk of thrombosis after liver transplantation.

Inflammation status modulates the effect of host genetic variation on intestinal gene expression in inflammatory bowel disease

More than 240 genetic risk loci have been associated with inflammatory bowel disease (IBD), but little is known about how they contribute to disease development in involved tissue. Here, we hypothesized that host genetic variation affects gene expression in an inflammation-dependent way, and investigated 299 snap-frozen intestinal biopsies from inflamed and non-inflamed mucosa from 171 IBD patients. RNA-sequencing was performed, and genotypes were determined using whole exome sequencing and genome wide genotyping. In total, 28,746 genes and 6,894,979 SNPs were included. Linear mixed models identified 8,881 independent intestinal cis-expression quantitative trait loci (cis-eQTLs) (FDR < 0.05) and interaction analysis revealed 190 inflammation-dependent intestinal cis-eQTLs (FDR < 0.05), including known IBD-risk genes and genes encoding immune-cell receptors and antibodies. The inflammation-dependent cis-eQTL SNPs (eSNPs) mainly interact with prevalence of immune cell types. Inflammation-dependent intestinal cis-eQTLs reveal genetic susceptibility under inflammatory conditions that can help identify the cell types involved in and the pathways underlying inflammation, knowledge that may guide future drug development and profile patients for precision medicine in IBD.

Inflammatory bowel diseases are heterogeneous, and little is known about how underlying genetic variation can affect their development. Here, the authors report that intestinal inflammation modulates the effect of host genetics on the gut mucosal expression of 190 genes in the context of inflammatory bowel diseases.

The Composition and Metabolic Potential of the Human Small Intestinal Microbiota Within the Context of Inflammatory Bowel Disease

BACKGROUND AND AIMS

The human gastrointestinal tract harbours distinct microbial communities essential for health. Little is known about small intestinal communities, despite the small intestine playing a fundamental role in nutrient absorption and host-microbe immune homeostasis. We aimed to explore the small intestine microbial composition and metabolic potential, in the context of inflammatory bowel disease [IBD].

METHODS

Metagenomes derived from faecal samples and extensive phenotypes were collected from 57 individuals with an ileostomy or ileoanal pouch, and compared with 1178 general population and 478 IBD faecal metagenomes. Microbiome features were identified using MetaPhAn2 and HUMAnN2, and association analyses were performed using multivariate linear regression.

RESULTS

Small intestinal samples had a significantly lower bacterial diversity, compared with the general population and, to a lesser extent, IBD samples. Comparing bacterial composition, small intestinal samples clustered furthest from general population samples and closest to IBD samples with intestinal resections. Veillonella atypica, Streptococcus salivarius, and Actinomyces graevenitzii were among the species significantly enriched in the small intestine. Predicted metabolic pathways in the small intestine are predominantly involved in simple carbohydrate and energy metabolism, but also suggest a higher pro-inflammatory potential.

CONCLUSIONS

We described the bacterial composition and metabolic potential of the small intestinal microbiota. The colonic microbiome of IBD patients, particularly with intestinal resections, showed resemblance to that of the small intestine. Moreover, several features characterising the small intestinal microbiome have been previously associated with IBD. These results highlight the importance of studying the small intestinal microbiota to gain new insight into disease pathogenesis.

Genetic Risk Scores Identify Genetic Aetiology of Inflammatory Bowel Disease Phenotypes

BACKGROUND AND AIMS

Inflammatory bowel disease [IBD] phenotypes are very heterogeneous between patients, and current clinical and molecular classifications do not accurately predict the course that IBD will take over time. Genetic determinants of disease phenotypes remain largely unknown but could aid drug development and allow for personalised management. We used genetic risk scores [GRS] to disentangle the genetic contributions to IBD phenotypes.

METHODS

Clinical characteristics and imputed genome-wide genetic array data of patients with IBD were obtained from two independent cohorts [cohort A, n = 1097; cohort B, n = 2156]. Genetic risk scoring [GRS] was used to assess genetic aetiology shared across traits and IBD phenotypes. Significant GRS-phenotype (false-discovery rate [FDR] corrected p <0.05) associations identified in cohort A were put forward for replication in cohort B.

RESULTS

Crohn's disease [CD] GRS were associated with fibrostenotic CD [R2 = 7.4%, FDR = 0.02] and ileocaecal resection [R2 = 4.1%, FDR = 1.6E-03], and this remained significant after correcting for previously identified clinical and genetic risk factors. Ulcerative colitis [UC] GRS [R2 = 7.1%, FDR = 0.02] and primary sclerosing cholangitis [PSC] GRS [R2 = 3.6%, FDR = 0.03] were associated with colonic CD, and these two associations were largely driven by genetic variation in MHC. We also observed pleiotropy between PSC genetic risk and smoking behaviour [R2 = 1.7%, FDR = 0.04].

CONCLUSIONS

Patients with a higher genetic burden of CD are more likely to develop fibrostenotic disease and undergo ileocaecal resection, whereas colonic CD shares genetic aetiology with PSC and UC that is largely driven by variation in MHC. These results further our understanding of specific IBD phenotypes.

Riboflavin Supplementation in Patients with Crohn's Disease [the RISE-UP study]

BACKGROUND AND AIMS

Crohn's disease [CD] is characterised by chronic intestinal inflammation and dysbiosis in the gut. Riboflavin [vitamin B2] has anti-inflammatory, antioxidant and microbiome-modulatory properties. Here, we analysed the effect of riboflavin on oxidative stress, markers of inflammation, clinical symptoms, and faecal microbiome in patients with CD.

METHODS

In this prospective clinical intervention study, patients received 100 mg riboflavin [DSM, Nutritional Products Ltd] daily for 3 weeks. Clinical disease activity [Harvey-Bradshaw Index: HBI], serum biomarkers of inflammation and redox status [plasma free thiols], and faecal microbiome taxonomical composition and functionality [fluorescent in situ hybridisation: FISH; and metagenomic shotgun sequencing: MGS], were analysed before and after riboflavin intervention.

RESULTS

In total, 70 patients with CD with varying disease activity were included. Riboflavin supplementation significantly decreased serum levels of inflammatory markers. In patients with low faecal calprotectin [FC] levels, IL-2 decreased, and in patients with high FC levels, C-reactive protein [CRP] was reduced and free thiols significantly increased after supplementation. Moreover, HBI was significantly decreased by riboflavin supplementation. Riboflavin supplementation led to decreased Enterobacteriaceae in patients with low FC levels as determined by FISH; however, MGS analysis showed no effects on diversity, taxonomy, or metabolic pathways of the faecal microbiome.

CONCLUSIONS

Three weeks of riboflavin supplementation resulted in a reduction in systemic oxidative stress, mixed anti-inflammatory effects, and a reduction in clinical symptoms [HBI]. FISH analysis showed decreased Enterobacteriaceae in patients with CD with low FC levels, though this was not observed in MGS analysis. Our data demonstrate that riboflavin supplementation has a number of anti-inflammatory and anti-oxidant effects in CD.

Pirfenidone Inhibits Cell Proliferation and Collagen I Production of Primary Human Intestinal Fibroblasts

Intestinal fibrosis is a common complication of inflammatory bowel disease. So far, there is no safe and effective drug for intestinal fibrosis. Pirfenidone is an anti-fibrotic compound available for the treatment of idiopathic pulmonary fibrosis. Here, we explored the anti-proliferative and anti-fibrotic properties of pirfenidone on primary human intestinal fibroblasts (p-hIFs). p-hIFs were cultured in the absence and presence of pirfenidone. Cell proliferation was measured by a real-time cell analyzer (xCELLigence) and BrdU incorporation. Cell motility was monitored by live cell imaging. Cytotoxicity and cell viability were analyzed by Sytox green, Caspase-3 and Water Soluble Tetrazolium Salt-1 (WST-1) assays. Gene expression of fibrosis markers was determined by quantitative reverse transcription PCR (RT-qPCR). The mammalian target of rapamycin (mTOR) signaling was analyzed by Western blotting and type I collagen protein expression additionally by immunofluorescence microscopy. Pirfenidone dose-dependently inhibited p-hIF proliferation and motility, without inducing cell death. Pirfenidone suppressed mRNA levels of genes that contribute to extracellular matrix production, as well as basal and TGF-β1-induced collagen I protein production, which was associated with inhibition of the rapamycin-sensitive mTOR/p70S6K pathway in p-hIFs. Thus, pirfenidone inhibits the proliferation of intestinal fibroblasts and suppresses collagen I production through the TGF-β1/mTOR/p70S6K signaling pathway, which might be a novel and safe anti-fibrotic strategy to treat intestinal fibrosis.

A Combined Set of Four Serum Inflammatory Biomarkers Reliably Predicts Endoscopic Disease Activity in Inflammatory Bowel Disease

Introduction: Blood C-reactive protein (CRP) and fecal calprotectin levels are routinely measured as surrogate markers of disease activity in Inflammatory Bowel Disease (IBD), but often do not correlate well with the degree of mucosal inflammation in the intestine as established by endoscopy. Therefore, novel predictive biomarkers are urgently needed that better reflect mucosal disease activity in IBD. The aim of this study was to identify a combination of serum inflammatory biomarkers predictive for endoscopic disease activity. Methods: Serum concentrations of 10 inflammatory biomarkers were analyzed in 118 IBD patients [64 Crohn's disease (CD), 54 ulcerative colitis (UC)] and 20 healthy controls. In a subset of 71 IBD patients, endoscopic disease activity was established. Non-parametric ROC estimation with bootstrap inference was used to establish the best combination of inflammatory biomarkers predicting endoscopic disease activity. Results: Six (6) inflammatory biomarkers (serum amyloid A (SAA), Eotaxin-1, IL-6, IL-8, IL-17A, and TNF-α) showed better prediction of IBD disease activity than routine measures (CRP, fecal calprotectin and HBI/SCCAI scores). The best combination of predictive inflammatory biomarkers consisted of serum SAA, IL-6, IL-8, and Eotaxin-1, showing an optimism-adjusted area under the ROC (AuROC) curve of 0.84 (95% CI: 0.73-0.94, P < 0.0001), which predicted significantly better (P = 0.002) than serum CRP levels with an AuROC of 0.57 (95% CI: 0.43-0.72, P = 0.32). Conclusion: The combination of SAA, IL-6, IL-8, and Eotaxin-1 reliably predicts endoscopic disease activity in IBD and might be valuable for monitoring disease activity and management of the disease.

A neural network model of normal and abnormal auditory information processing

The ability of the brain to attenuate the response to irrelevant sensory stimulation is referred to as sensory gating. A gating deficiency has been reported in schizophrenia. To study the neural mechanisms underlying sensory gating, a neuroanatomically inspired model of auditory information processing has been developed. The mathematical model consists of lumped parameter modules representing the thalamus (TH), the thalamic reticular nucleus (TRN), auditory cortex (AC), and prefrontal cortex (PC). It was found that the membrane potential of the pyramidal cells in the PC module replicated auditory evoked potentials, recorded from the scalp of healthy individuals, in response to pure tones. Also, the model produced substantial attenuation of the response to the second of a pair of identical stimuli, just as seen in actual human experiments. We also tested the viewpoint that schizophrenia is associated with a deficit in prefrontal dopamine (DA) activity, which would lower the excitatory and inhibitory feedback gains in the AC and PC modules. Lowering these gains by less than 10% resulted in model behavior resembling the brain activity seen in schizophrenia patients, and replicated the reported gating deficits. The model suggests that the TRN plays a critical role in sensory gating, with the smaller response to a second tone arising from a reduction in inhibition of TH by the TRN.

Evoked potential enhancement using a neurophysiologically-based model

OBJECTIVE

Single trial evoked potentials (EP) are generally obscured by the much larger spontaneous or background electroencephalogram (EEG). A novel method was developed to enhance single trial EPs. The potential of this approach was explored using actual flash evoked visual EPs.

METHOD

The basic procedure is a variant of the adaptive filtering approach. At the core of our method is a mathematical, but neurophysiologically-realistic, nonlinear model of the cortical structures involved in generating EEG and EP activity. The model parameters are adjusted by a genetic algorithm in such a way that the model output resembles the actually observed pre-stimulus EEG activity. When post-stimulus EEG is passed through the inverse model, enhancement of the single trial EP should, theoretically, occur.

RESULTS

Evidence was found that, in case of visual evoked potentials obtained by flashing light through closed eyelids, alpha activity continues to around 150 ms post-stimulus, at which point a low frequency potential arises, cresting 100 ms later and disappearing after another 100 ms or so. Also, it was found that an individual's response varies considerably from trial to trial.

CONCLUSION

The inverse modeling approach presented here is effective at enhancing single trial EP activity. One potential application is to distinguish trials that contain a response from those that do not, which could result in improved ensemble averages.

Development and evaluation of the piecewise Prony method for evoked potential analysis

A new method is presented to decompose nonstationary signals into a summation of oscillatory components with time varying frequency, amplitude, and phase characteristics. This method, referred to as piecewise Prony method (PPM), is an improvement over the classical Prony method, which can only deal with signals containing components with fixed frequency, amplitude and phase, and monotonically increasing or decreasing rate of change. PPM allows the study of the temporal profile of post-stimulus signal changes in single-trial evoked potentials (EPs), which can lead to new insights in EP generation. We have evaluated this method on simulated data to test its limitations and capabilities, and also on single-trial EPs. The simulation experiments showed that the PPM can detect amplitude changes as small as 10%, rate changes as small as 10%, and 0.15 Hz of frequency changes. The capabilities of the PPM were demonstrated using single electroencephalogram/EP trials of flash visual EPs recorded from one normal subject. The trial-by-trial results confirmed that the stimulation drastically attenuates the alpha activity shortly after stimulus presentation, with the alpha activity returning about 0.5 s later. The PPM results also provided evidence that delta activity undergoes phase alignment following stimulus presentation.

A fuzzy clustering approach to EP estimation

The problem of extracting a useful signal (a response) buried in relatively high amplitude noise has been investigated, under the conditions of low signal-to-noise ratio. In particular, we present a method for detecting the "true" response of the brain resulting from repeated auditory stimulation, based on selective averaging of single-trial evoked potentials. Selective averaging is accomplished in two steps. First, an unsupervised fuzzy-clustering algorithm is employed to identify groups of trials with similar characteristics, using a performance index as an optimization criterion. Then, typical responses are obtained by ensemble averaging of all trials in the same group. Similarity among the resulting estimates is quantified through a synchronization measure, which accounts for the percentage of time that the estimates are in phase. The performance of the classifier is evaluated with synthetic signals of known characteristics, and its usefulness is demonstrated with real electrophysiological data obtained from normal volunteers.

A fuzzy clustering approach to study the auditory P50 component in schizophrenia

We have recently provided evidence that selective evoked response averaging based on a fuzzy clustering approach is a useful way to increase the signal-to-noise ratio, particularly when recording low-amplitude components, such as the auditory P50. We have also reported that, when stimuli are delivered in pairs (S1 followed by S2) with a short interstimulus interval, the first stimulus (S1) results in synchronization of the EEG producing a large-amplitude evoked response, whereas the second stimulus (S2) causes phase opposition resulting in a lower amplitude average evoked response. In the current study we reanalyzed data previously obtained from 13 normal volunteers and 17 chronic schizophrenia patients. Our results show that the partial EPs corresponding to the S1 stimulus are highly synchronized in normal subjects but not in schizophrenia patients. However, such a synchronization is not present after delivery of the S2 stimulus, neither in normal controls nor in patients. These findings are in agreement with previous reports of decreased amplitude of the S1 response without a significant further decrease in the amplitude of the S2 response in schizophrenia patients.

Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns

This study deals with neurophysiologically based models simulating electrical brain activity (i.e., the electroencephalogram or EEG, and evoked potentials or EPs). A previously developed lumped-parameter model of a single cortical column was implemented using a more accurate computational procedure. Anatomically acceptable values for the various model parameters were determined, and a multi-dimensional exploration of the model parameter-space was conducted. It was found that the model could produce a large variety of EEG-like waveforms and rhythms. Coupling two models, with delays in the interconnections to simulate the synaptic connections within and between cortical areas, made it possible to replicate the spatial distribution of alpha and beta activity. EPs were simulated by presenting pulses to the input of the coupled models. In general, the responses were more realistic than those produced using a single model. Our simulations also suggest that the scalp-recorded EP is at least partially due to a phase reordering of the ongoing activity.

K-complex detection using multi-layer perceptrons and recurrent networks

The feasibility of using a multi-layer perceptron and Elman's recurrent network for the detection of specific waveforms (K-complexes) in electroencephalograms (EEGs), regardless of their location in the signal segment, is explored. Experiments with simulated and actual EEG data were performed. In case of the perceptron, the input consisted of the magnitude and/or phase values obtained from 10-s signal intervals, whereas the recurrent net operated on the digitized data samples directly. It was found that both nets performed well on the simulated data, but not on the actual EEG data. The reasons for the failure of both nets are discussed.

Selective averaging of evoked potentials using trajectory-based clustering

A clustering method has been developed to group evoked potentials that display similar prestimulus dynamic behavior. The procedure involves using the method of time delay embedding to construct a trajectory in state space from a time series. Certain features that characterize the geometry of the trajectory have been defined. The trajectory-based clustering algorithm has been applied to visual evoked potentials to determine relationships between prestimulus EEG and evoked potential shape.

Sleep staging with movement-related signals

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.

A neurophysiologically-based mathematical model of flash visual evoked potentials

Evidence is presented that a neurophysiologically-inspired mathematical model, originally developed for the generation of spontaneous EEG (electroencephalogram) activity, can produce VEP (visual evoked potential)-like waveforms when pulse-like signals serve as input. It was found that the simulated VEP activity was mainly due to intracortical excitatory connections rather than direct thalamic input. Also, the model-generated VEPs exhibited similar relationships between prestimulus EEG characteristics and subsequent VEP morphology, as seen in human data. Specifically, the large correlation between the N1 amplitude and the prestimulus alpha phase angle, and the insensitivity of P2 to the latter feature, as observed in actual VEPs to low intensity flashes, was also found in the model-generated data. These findings provide support for the hypothesis that the spontaneous EEG and the VEP are generated by some of the same neural structures and that the VEP is due to distributed activity, rather than dipolar sources.

The relationship between prestimulus-alpha amplitude and visual evoked potential amplitude

Root-mean-square (RMS) amplitude derived from power spectral measures in the alpha band of the 1 s prestimulus EEG were related to the peak-to-peak amplitude of the N1 and P2 components (N1P2PP) of the visual evoked potential (VEP) in 7 male subjects. Stimuli were low intensity flashes delivered randomly between 2 and 6 whole seconds. Trials were rank ordered according to the levels of prestimulus alpha amplitude and were partitioned into groups of 40 trials each (25 groups per data set). Averaged VEPs were computed from these groups and scattergrams of N1P2PP and enhancement factor (following the approach by Başar, 1980) vs. prestimulus alpha amplitude were produced. There was a correlation of 0.74 (p less than .0001) between prestimulus alpha amplitude and N1P2PP, and all seven subjects displayed a general inverse relationship between VEP enhancement and prestimulus alpha amplitude, replicating the results of Başar. However, we observed an exponential relationship, rather than the linear relationship reported by Başar.

Monitoring of the ballistocardiogram with the static charge sensitive bed

The static charge sensitive bed (SCSB) consists of a sensitive movement detector embedded in a mattress. When a subject rests on the bed, a single electrical signal, containing components reflective of cardiac, respiratory, and body movement related motion, is produced. This paper describes a digital signal processing technique to separate the BCG from the SCSB signal. An evaluation of this algorithm was conducted using recordings from normal volunteers. Comparisons with simultaneously recorded reference signals indicated that the algorithm performed satisfactorily in a laboratory environment.

The effect of the phase of prestimulus alpha activity on the averaged visual evoked response

The relationship between the latencies and amplitudes of the N1 and P2 components of the averaged visual evoked potential (EP) and the phase of the alpha activity immediately preceding the time of the stimulus, has been investigated in 7 male subjects. Low intensity flashes, delivered randomly between 2 and 6 whole seconds, were used as the stimuli. The phase angle of the EEG at the moment of stimulation was computed for all trials containing more than 100 microV2 of prestimulus alpha power. The single trials were grouped into 8 classes on the basis of the phase angle value, and averaged EPs for each individual were computed from these groups. In addition, averaged EPs were computed in 3 ways: (1) a grand average consisting of all artifact-free trials, (2) an 'alpha average' consisting of all trials containing more than 100 microV2 of prestimulus alpha power, and (3) a 'non-alpha average' consisting of all trials with less than 100 microV2 of prestimulus alpha power. Each of these 3 averages were cross-correlated with the phase-selective averages. It was found that the particular N1 component assessed in this experiment may possibly be entrained alpha activity, and that the measured P2 component is not an alpha process, yet it is influenced by the amount of prestimulus alpha activity.

Quantitative analysis of electroencephalograms: is there chaos in the future?

The history of quantitative, computerized electroencephalogram (EEG) analysis is reviewed. It is shown that, until very recently, the basic approach to EEG analysis involved the assumption that the EEG is stochastic. Consequently, statistical pattern recognition techniques, segmentation procedures, syntactic methods, knowledge-based approaches, and even artificial neural network methods have been developed with different levels of success. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of non-linear dynamics, and especially the theory of chaos, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing non-linearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. Evidence supporting the non-linear dynamics paradigm is reviewed, and possible research paths are indicated.

Pre-stimulus spectral EEG patterns and the visual evoked response

The relationship between the latencies and amplitudes of the N1 and P2 components of the visual evoked potential (VEP) and the psychophysiological state of the brain immediately preceding the time of the stimulus has been investigated in 7 male subjects. Power spectral measures in the delta, theta, alpha and beta bands of the 1 sec pre-stimulus EEG were used to assess the brain state, and low intensity flashes, delivered randomly between 2 and 6 whole seconds, were used as the stimuli. Trials were ranked separately according to the relative amounts of pre-stimulus power in each EEG band and were partitioned into groups by an equal pre-stimulus spectral power criterion. Averaged EPs were computed from these groups and multiple regression analysis was used to relate pre-stimulus spectral power values to EP features. Five of the 7 subjects displayed consistent increases in N1-P2 amplitude as a function of increasing pre-stimulus relative alpha power. The between-subjects effect of pre-stimulus EEG on N1 latency was small, but was moderate for P2 latency (both significant). Both N1 and P2 latency were found to decrease with increasing amounts of pre-stimulus relative delta and theta power.

Knowledge-based approach to sleep EEG analysis--a feasibility study

A knowledge-based approach to automated sleep EEG (electroencephalogram) analysis is described. In this system, an object-oriented approach is followed in which specific waveforms and sleep stages ("objects") are represented in terms of frames. The latter capture the morphological and spatio-temporal information for each object. An object detection module ("frame matcher"), operating on the frames, is employed to identify what features need to be extracted from the EEG and to trigger the appropriate "specialist"--specialized signal processing modules--to obtain values for these features. This leads to an opportunistic approach to EEG interpretation with quantitative information being extracted from the signal only when needed by the reasoning processes. The system has been tested on the detection of K complexes and sleep spindles. Its performance indicates that the approach followed is feasible and can become a powerful tool for automated EEG interpretation.

Structural EEG analysis: an explorative study

A method is described to detect (subtle) changes in an EEG (electroencephalogram) by means of a Markovian modeling approach. This method, termed structural EEG analysis, treats the non-stationary EEG as a sequence of a finite number of short elementary patterns. Subtle changes in an EEG may be detected by studying the transition probabilities between the different patterns. By viewing the patterns as states in a Markov chain, a representation of the EEG structure based on a state transition probability matrix emerges. Various techniques to estimate the state transition probability matrices have been investigated. A number of experiments were performed with artificially generated data to determine the data length required to obtain a reliable estimate of the transition matrices. It appeared that a data length of approximately five to eight times the number of entries in the matrices is needed to accurately estimate the matrices. It was determined that the data length required to reliably estimate the transition probability matrix is dependent on the number of states and the number of non-zero entries of the matrix. Also, the data length appears independent of the values of the probabilities. The structural analysis approach was applied to actual EEG data, recorded from normal volunteers and epileptic subjects. It was demonstrated that visually confirmable changes in the EEG could be detected by the structural analysis method more accurately than by a more conventional approach.

Classification of sleep patterns by means of markov modeling and correspondence analysis

Shown is how correspondence analysis can be used to track changes in an individuals' sleep pattern. Correspondence analysis was applied to sleep stage transition matrices computed from all-night sleep of normal, obese, and apnoetic subjects. Differences between the groups, and intraindividual changes in sleep patterns could be visualized better than with a x2-based clustering approach.

EEG waveform analysis by means of dynamic time-warping

The feasibility of using dynamic time-warping (DTW) to cluster EEG waveforms was studied. DTW compresses and extends the time axes of pairs of digitized waveforms to reduce the effects of minor differences in shape due to noise and normal, random shape fluctuations. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity index in a clustering procedure. Experiments with simulated data revealed that DTW based clustering could distinguish between waves only slightly different in frequency, amplitude, peak location, or initial phase. DTW clustering was also applied to sharp waves and spikes taken from actual EEG data and compared with an approach based on features extracted from the waveforms, and one based on computing the peak-aligned difference between waveforms. The results indicated that the DTW approach yielded more homogeneous clusters than the other two methods.

Automated morphological analysis by means of dynamic time-warping

A new technique for the clustering of EEG wave forms is proposed. This method, termed dynamic time-warping (DTW) based clustering, involves the determination of a distance measure by allowing a certain degree of flexibility in the time axes of the two waves to be compared. Sharp waves and spikes, taken from actual EEG data, were subjected to the DTW-clustering approach. The results were compared with an approach based on features extracted from the wave forms and one based on computing the peak-aligned difference between wave forms. It was found that the DTW approach resulted in more homogeneous clusters than the other two approaches. These results, although preliminary, clearly indicate the feasibility of applying this new method for wave form clustering.

Identification and labeling of EEG graphic elements using autoregressive spectral estimates

Syntactic EEG analysis requires descriptive labeling of short (1 s) epochs in an EEG. While discriminant analysis methods are useful for this purpose, significant improvement in label correctness can be achieved using the heuristic method described in this paper. The method is based on the estimation of frequency spectra by autoregressive (AR) modeling. The location of the peak frequencies and the power of these peaks are used to assign labels to 1 s epochs. Artefacts and epochs with exceptionally high or low amplitude and/or frequency values are identified as well. The assignment of labels is accomplished by comparing parameters, extracted from the power spectra estimated for 1 s epochs, with thresholds. These thresholds are automatically adapted to each individual EEG lead. In this paper, the method is outlined and its performance is compared with a discriminant analysis approach and visual labeling.

Evaluation of a syntactic pattern recognition approach to quantitative electroencephalographic analysis

This paper reports an experiment designed to evaluate the usefulness of a syntactic pattern recognition approach to quantitative EEG analysis. This approach provides a systematic way of organizing and interpreting spatially and temporally distributed activity in an EEG record. Many automated scoring systems evaluate only background activity, ignoring the significant transient events that may occur in an EEG. In contrast, syntactic methods allow evaluation of background activity in combination with transient events. In this paper, 454 EEGs recorded from a population of renal patients are evaluated using (1) the syntactic approach, and (2) a method based on discriminant analysis. The main finding is that the syntactic approach outperforms the discriminant analysis method when each technique is compared to visual scoring.

Piece-wise EEG analysis: an objective evaluation

In this paper, a method is described to evaluate EEGs by means of a piece-wise analysis. The procedure involves the recursive computation of a 5th-order autoregressive model by means of a Kalman filter. As an illustration the result of applying this method to sleep recordings is described in this paper. Also, an objective comparison of this method with a more conventional approach (based on analyzing 30-s intervals), and with adaptive segmentation (Praetorius et al., 1977) was carried out using the same data. The results obtained indicate that our method is useful in extracting elementary patterns from an EEG and that the piece-wise analysis approach is to be favored over more conventional techniques.

Quantification of EEG variability

This paper presents a method of segmenting the EEG based on the well-known power spectrum analysis. This procedure is applied to the EEG recordings of two normal subjects in order to determine the temporal EEG variability. These results are compared with a more classical approach.

Demonstration of segmentation techniques for EEG records

In this paper three different techniques for segmenting EEG's are presented. The principles of these techniques (the Kalman filter approach, the power spectrum analysis and the texture matrix approach) are explained and the results obtained summarised. The segmentation is used in an interactive EEG interpretation system.