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Evolutionary patterns of retinal-binding pockets of type I rhodopsins and their functions.

Genome sequencing projects resulted in the identification of a large number of new sequence homologs of archaeal rhodopsins in marine bacteria, fungi, and unicellular algae. It is an important task to unambiguously predict the functions of these new rhodopsins, as it is difficult to perform individual experiments on every newly discovered sequence. The transmembrane segments of rhodopsins have similar three-dimensional structures where the seven transmembrane helices form a tightly packed scaffold to accommodate a covalently bound retinal. We use geometric computations to accurately define the retinal-binding pockets in high-resolution structures of rhodopsins and to extract residues forming the wall of the retinal-binding pocket. We then obtain a tree defining the functional relationship of rhodopsins based on the short sequences of residues forming the wall of the retinal-binding pocket concatenated from the primary sequence, and show that these sequence fragments are often sufficient to discriminate phototactic vs transporting function of the bacterial and unicellular algal rhodopsins. We further study the evolutionary history of retinal-binding pockets by estimating the pocket residue substitution rates using a Bayesian Monte Carlo method. Our findings indicate that every functional class of rhodopsins has a specific allowed set of fast-rate amino acid substitutions in the retinal-binding pocket that may contribute to spectral tuning or photocycle modulation. The substitution rates of the amino acid residues in a putative retinal-binding pocket of marine proteorhodopsins together with the clustering of pocket sequences indicate that green-absorbing and blue-absorbing proteorhodopsins have similar function. Our results demonstrate that the evolutionary patterns of the retinal-binding pockets reflect the functional specificity of the rhodopsins. The approach we describe in this paper may be useful for large-scale functional prediction of rhodopsins.

Amino Acid Sequence↗
PubMed2004

Single-trial lambda wave identification using a fuzzy inference system and predictive statistical diagnosis.

The aim of the study was to automate the identification of a saccade-related visual evoked potential (EP) called the lambda wave. The lambda waves were extracted from single trials of electroencephalogram (EEG) waveforms using independent component analysis (ICA). A trial was a set of EEG waveforms recorded from 64 scalp electrode locations while a saccade was performed. Forty saccade-related EEG trials (recorded from four normal subjects) were used in the study. The number of waveforms per trial was reduced from 64 to 22 by pre-processing. The application of ICA to the resulting waveforms produced 880 components (i.e. 4 subjects x 10 trials per subject x 22 components per trial). The components were divided into 373 lambda and 507 nonlambda waves by visual inspection and then they were represented by one spatial and two temporal features. The classification performance of a Bayesian approach called predictive statistical diagnosis (PSD) was compared with that of a fuzzy logic approach called a fuzzy inference system (FIS). The outputs from the two classification approaches were then combined and the resulting discrimination accuracy was evaluated. For each approach, half the data from the lambda and nonlambda wave categories were used to determine the operating parameters of the classification schemes while the rest (i.e. the validation set) were used to evaluate their classification accuracies. The sensitivity and specificity values when the classification approaches were applied to the lambda wave validation data set were as follows: for the PSD 92.51% and 91.73% respectively, for the FIS 95.72% and 89.76% respectively, and for the combined FIS and PSD approach 97.33% and 97.24% respectively (classification threshold was 0.5). The devised signal processing techniques together with the classification approaches provided for an effective extraction and classification of the single-trial lambda waves. However, as only four subjects were included, it will be valuable to further evaluate the methods on a larger group of subjects.

Adult↗
PubMed1998

Population pharmacokinetic parameters of gentamicin in patients with solid tumors: estimation by one- and two-stage methods.

Gentamicin monitoring has been improved with the introduction of Bayesian methods but the usefulness depends on the quality of the population parameters (PP) used. The objective of this study was to determine PP of gentamicin in patients with solid tumors. A total of 198 adult patients with cancer were included in the analysis. Population parameters were estimated by both a two-stage and a one-stage method (NPEM, Non Parametric Expectation Maximization). Individual parameters (IP) were estimated by the Sawchuk-Zaske method and by nonlinear regression. The estimated distribution volume and clearance of gentamicin (mean +/- SD) were 18.37 +/- 5.021 (0.30-0.32 l/kg of dosing weight) and 3.34 +/- 1.6 l/h, respectively. No significant differences between IP or PP obtained by the different methods were found. The results indicated a wide variability of gentamicin pharmacokinetics making monitoring necessary and that patients with solid tumors may have larger gentamicin volume and slower clearance than normal patients. These observations imply that different population pharmacokinetic parameters should be used for this group of patients.

Adult↗
PubMed2006

A two-sample Bayesian t-test for microarray data.

BACKGROUND: Determining whether a gene is differentially expressed in two different samples remains an important statistical problem. Prior work in this area has featured the use of t-tests with pooled estimates of the sample variance based on similarly expressed genes. These methods do not display consistent behavior across the entire range of pooling and can be biased when the prior hyperparameters are specified heuristically. RESULTS: A two-sample Bayesian t-test is proposed for use in determining whether a gene is differentially expressed in two different samples. The test method is an extension of earlier work that made use of point estimates for the variance. The method proposed here explicitly calculates in analytic form the marginal distribution for the difference in the mean expression of two samples, obviating the need for point estimates of the variance without recourse to posterior simulation. The prior distribution involves a single hyperparameter that can be calculated in a statistically rigorous manner, making clear the connection between the prior degrees of freedom and prior variance. CONCLUSION: The test is easy to understand and implement and application to both real and simulated data shows that the method has equal or greater power compared to the previous method and demonstrates consistent Type I error rates. The test is generally applicable outside the microarray field to any situation where prior information about the variance is available and is not limited to cases where estimates of the variance are based on many similar observations.

Algorithms↗
PubMed2003

Bayes or bootstrap? A simulation study comparing the performance of Bayesian Markov chain Monte Carlo sampling and bootstrapping in assessing phylogenetic confidence.

Bayesian Markov chain Monte Carlo sampling has become increasingly popular in phylogenetics as a method for both estimating the maximum likelihood topology and for assessing nodal confidence. Despite the growing use of posterior probabilities, the relationship between the Bayesian measure of confidence and the most commonly used confidence measure in phylogenetics, the nonparametric bootstrap proportion, is poorly understood. We used computer simulation to investigate the behavior of three phylogenetic confidence methods: Bayesian posterior probabilities calculated via Markov chain Monte Carlo sampling (BMCMC-PP), maximum likelihood bootstrap proportion (ML-BP), and maximum parsimony bootstrap proportion (MP-BP). We simulated the evolution of DNA sequence on 17-taxon topologies under 18 evolutionary scenarios and examined the performance of these methods in assigning confidence to correct monophyletic and incorrect monophyletic groups, and we examined the effects of increasing character number on support value. BMCMC-PP and ML-BP were often strongly correlated with one another but could provide substantially different estimates of support on short internodes. In contrast, BMCMC-PP correlated poorly with MP-BP across most of the simulation conditions that we examined. For a given threshold value, more correct monophyletic groups were supported by BMCMC-PP than by either ML-BP or MP-BP. When threshold values were chosen that fixed the rate of accepting incorrect monophyletic relationship as true at 5%, all three methods recovered most of the correct relationships on the simulated topologies, although BMCMC-PP and ML-BP performed better than MP-BP. BMCMC-PP was usually a less biased predictor of phylogenetic accuracy than either bootstrapping method. BMCMC-PP provided high support values for correct topological bipartitions with fewer characters than was needed for nonparametric bootstrap.

Bayes Theorem↗
PubMed2001

Automatic identification of patients eligible for a pneumonia guideline: comparing the diagnostic accuracy of two decision support models.

BACKGROUND: In busy clinical settings, physicians often do not have enough time to identify patients for specific therapeutic guidelines. As a solution, decision support systems could automatically identify eligible patients and trigger computerized guidelines for specific diseases. Applying this idea to community-acquired pneumonia (CAP), we developed a Bayesian network (BN) and an artificial neural network (ANN) for identifying patients who have CAP and are eligible for a pneumonia guideline. OBJECTIVE: The aim of this study was to determine whether the diagnostic accuracy of these two decision support models differs in terms of identifying CAP patients. METHODS: We trained and tested the networks with a data set of 32,662 adult patients. For each network, we (1) calculated the specificity, the positive predictive value (PPV), and the negative predictive value (NPV) at a sensitivity of 95%, and (2) determined the area under the receiver operating characteristic curve (AUC) as a measure of overall accuracy. We tested for statistical difference between the AUCs using the correlated area z statistic. RESULTS: At a sensitivity of 95%, the respective values for specificity, PPV, and NPV were: 92.3%, 15.1%, and 99.9% for the BN, and 94.0%, 18.6%, and 99.9% for the ANN. The BN had an AUC of 0.9795 (95% CI: 0.9736, 0.9843), and the ANN had an AUC of 0.9855 (95% CI: 0.9805, 0.9894). The difference between the AUCs was statistically significant (p=0.0044). CONCLUSIONS: The networks achieved high overall accuracies on the testing data set. Because the difference in accuracies is statistically significant but not clinically significant, both networks are equally suited to drive a guideline.

Area Under Curve↗
PubMed2003

Statistical morphological analysis of hippocampal principal neurons indicates cell-specific repulsion of dendrites from their own cell.

Traditionally, the sources of guidance cues for dendritic outgrowth are mainly associated with external bodies (A) rather than with the same neuron from which dendrites originate (B). To quantify the relationship between factors A and B as determinants of the adult dendritic shape, the morphology of 83 intracellularly characterized, stained, completely reconstructed, and digitized principal neurons of the rat hippocampus was statistically analyzed using Bayesian optimization. It was found that the dominant directional preference (tropism) manifested in dendritic turns is to grow away from the soma rather than toward the incoming fibers or in any other fixed direction; therefore, B is predominant. Results are robust and consistent for all examined morphological classes (dentate gyrus granule cells, basal and apical trees of CA3 and CA1 pyramidal cells). In addition, computer remodeling of neurons based on the measured parameters produced virtual structures consistent with real morphologies, as confirmed by measurement of several global emergent parameters. Thus, the simple description of dendritic shape based on dendrites' tendency to grow straight, away from their own soma, and with additional random deflections, proves remarkably accurate and complete. Although based on adult neurons, these results suggest that dendritic guidance during development may be associated primarily with the host cell. This possibility challenges the traditional concept of dendritic guidance: in that hippocampal cells are densely packed and have highly overlapping dendritic fields, the somatodendritic repulsion must be cell specific. Plausible mechanisms involving extracellular effects of spikes are discussed, together with feasible experimental tests and predicted results.

Animals↗
PubMed2025

Navigating Sampling Bias in Discrete Phylogeographic Analysis: Assessing the Performance of an Adjusted Bayes Factor.

Bayesian phylogeographic inference is widely used in molecular epidemiological studies to reconstruct the dispersal history of pathogens. Discrete phylogeographic analysis treats geographic locations as discrete traits and infers lineage transition events among them, and is typically followed by a Bayes factor (BF) test to assess the statistical support. In the standard BF (BFstd) test, the relative abundance of the involved trait states is not considered, which can be problematic in the case of unbalanced sampling. Existing methods to correct sampling bias in discrete phylogeographic analyses using continuous-time Markov chain (CTMC) model, often require additional epidemiological information to balance the sampling effort among locations. As such data is not necessarily available, alternative approaches that rely solely on available genomic data are needed. In this perspective, we assess the performance of a modification of the BFstd, the adjusted Bayes factor (BFadj), which incorporates information on the relative abundance of samples by location when inferring support for transition events and root location inference without requiring additional data. Using a simulation framework, we assess the statistical performance of BFstd and BFadj under varying levels of sampling bias, estimating their type I and type II error rates. Our results show that BFadj complements the BFstd by reducing type I errors at the cost increasing type II errors for inferred transition events, while improving type I and type II errors in root location inference. Our findings provide guidelines for implementing the complementary BFadj to detect and mitigate sampling bias in discrete phylogeographic inference using CTMC modeling.

Bayes Theorem↗
PubMed2006

Evolution of the human immunodeficiency virus envelope gene is dominated by purifying selection.

The evolution of the human immunodeficiency virus (HIV-1) during chronic infection involves the rapid, continuous turnover of genetic diversity. However, the role of natural selection, relative to random genetic drift, in governing this process is unclear. We tested a stochastic model of genetic drift using partial envelope sequences sampled longitudinally in 28 infected children. In each case the Bayesian posterior (empirical) distribution of coalescent genealogies was estimated using Markov chain Monte Carlo methods. Posterior predictive simulation was then used to generate a null distribution of genealogies assuming neutrality, with the null and empirical distributions compared using four genealogy-based summary statistics sensitive to nonneutral evolution. Because both null and empirical distributions were generated within a coalescent framework, we were able to explicitly account for the confounding influence of demography. From the distribution of corrected P-values across patients, we conclude that empirical genealogies are more asymmetric than expected if evolution is driven by mutation and genetic drift only, with an excess of low-frequency polymorphisms in the population. This indicates that although drift may still play an important role, natural selection has a strong influence on the evolution of HIV-1 envelope. A negative relationship between effective population size and substitution rate indicates that as the efficacy of selection increases, a smaller proportion of mutations approach fixation in the population. This suggests the presence of deleterious mutations. We therefore conclude that intrahost HIV-1 evolution in envelope is dominated by purifying selection against low-frequency deleterious mutations that do not reach fixation.

Base Sequence↗
PubMed1995

An expectation maximization reconstruction algorithm for emission tomography with non-uniform entropy prior.

A Bayesian image reconstruction algorithm is proposed for emission tomography. It incorporates the Poisson nature of the noise in the projection data and uses a non-uniform entropy as an a priori probability distribution of the image in a maximum a posteriori (MAP) approach. The expectation maximization (EM) method was applied to find the MAP estimator. The Newton-Raphson numerical method whose convergence and positive solutions are proven, was used to solve the EM problem. The prior mean at iteration k was determined by smoothing the image obtained at iteration k-1. Comparisons between the ML and the MAP algorithm were carried out with a numerical phantom that contains a narrow valley region. The ML solution after 50 iterations was chosen as the initial solution for the MAP algorithm, since the global performance of the ML algorithm deteriorates with increasing number of iterations while its local performance in the valley region is always improving. The resulting algorithm is a compromise between ML who has the best local performance in the valley region and the MAP who has the best global performance.

Algorithms↗
PubMed2006

Deblurring of breathing motion artifacts in thoracic PET images by deconvolution methods.

In FDG-PET imaging of thoracic tumors, blurring due to breathing motion often significantly degrades the quality of the observed image, which then obscures the tumor boundary. We demonstrate a deblurring technique that combines patient-specific motion estimates of tissue trajectories with image deconvolution techniques, thereby partially eliminating breathing-motion induced artifacts. Two data sets were used to evaluate the methodology including mobile phantoms and clinical images. The clinical images consist of PET/CT co-registered images of patients diagnosed with lung cancer. A breathing motion model was used to locally estimate the location-dependent tissue location probability function (TLP) due to breathing. The deconvolution process is carried by an expectation-maximization (EM) iterative algorithm using the motion-based TLP. Several methods were used to improve the robustness of the deblurring process by mitigating noise amplification and compensating for motion estimate uncertainties. The mobile phantom study with controlled settings demonstrated significant reduction in underestimation error of concentration in high activity case without significant superiority between the different applied methods. In case of medium activity concentration (moderate noise levels), less improvement was reported (10%-15% reduction in underestimation error relative to 15%-20% reduction in high concentration). Residual denoising using wavelets offered the best performance for this case. In the clinical data case, the image spatial resolution was significantly improved, especially in the direction of greatest motion (cranio-caudal). The EM algorithm converged within 15 and 5 iterations in the large and small tumor cases, respectively. A compromise between a figure-of-merit and entropy minimization was suggested as a stopping criterion. Regularization techniques such as wavelets and Bayesian methods provided further refinement by suppressing noise amplification. Our initial results show that the proposed method provides a feasible framework for improving PET thoracic images, without the need for gated/4-D PET imaging, when 4-D CT is available to estimate tumor motion.

Algorithms↗
PubMed2005

Filtering high-throughput protein-protein interaction data using a combination of genomic features.

BACKGROUND: Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies. RESULTS: In this study, we use a combination of 3 genomic features -- structurally known interacting Pfam domains, Gene Ontology annotations and sequence homology -- as a means to assign reliability to the protein-protein interactions in Saccharomyces cerevisiae determined by high-throughput experiments. Using Bayesian network approaches, we show that protein-protein interactions from high-throughput data supported by one or more genomic features have a higher likelihood ratio and hence are more likely to be real interactions. Our method has a high sensitivity (90%) and good specificity (63%). We show that 56% of the interactions from high-throughput experiments in Saccharomyces cerevisiae have high reliability. We use the method to estimate the number of true interactions in the high-throughput protein-protein interaction data sets in Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens to be 27%, 18% and 68% respectively. Our results are available for searching and downloading at http://helix.protein.osaka-u.ac.jp/htp/. CONCLUSION: A combination of genomic features that include sequence, structure and annotation information is a good predictor of true interactions in large and noisy high-throughput data sets. The method has a very high sensitivity and good specificity and can be used to assign a likelihood ratio, corresponding to the reliability, to each interaction.

Animals↗
PubMed2006

Robustness of coalescent estimators to between-lineage mutation rate variation.

Data from HIV and from human neoplastic cells can show substantial between-lineage mutation rate variation even within a single population. Such variation may affect estimators of population quantities such as Theta = 4N(e)mu. Using simulated DNA data, I measured the effect of rate variation on recovery of Theta by the summary-statistic estimator of Watterson (Watterson GA. 1975. On the number of segregating sites in genetical systems without recombination. Theor Popul Biol. 7:256-276) and the coalescent maximum likelihood algorithm LAMARC (Kuhner MK. 2006. LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters. Bioinformatics. Advance Access doi: 10.1093/bioinformatics/btk051). Watterson's estimator showed a downward bias, as expected, with high values of Theta. LAMARC's mean estimate was accurate for all tested values of Theta and rate variation except for a downward bias when rate variation was maximal (i.e., the slow rate was zero). LAMARC had consistently narrower confidence intervals (CIs) than Watterson's estimator. Both methods tended to reject the truth too often when rate variation was 8x or greater and independent among branches, as well as when variation was 4x or greater and correlated among branches. In the case of Watterson's estimate, this excess rejection was fully attributable to variation among genealogies in the amount of total branch length associated with the fast and slow rates. However, in the case of LAMARC, some excess rejection was still observed even when between-genealogy variation was taken into account. Both estimators are robust to modest rate variation; however, their use should be coupled with a statistical test to rule out extreme rate variation as the resulting CIs may not be reliable.

Algorithms↗
PubMed2005

Unsupervised multistage image classification using hierarchical clustering with a Bayesian similarity measure.

A new multistage method using hierarchical clustering for unsupervised image classification is presented. In the first phase, the multistage method performs segmentation using a hierarchical clustering procedure which confines merging to spatially adjacent clusters and generates an image partition such that no union of any neighboring segments has homogeneous intensity values. In the second phase, the segments resulting from the first stage are classified into a small number of distinct states by a sequential merging operation. The region-merging procedure in the first phase makes use of spatial contextual information by characterizing the geophysical connectedness of a digital image structure with a Markov random field, while the second phase employs a context-free similarity measure in the clustering process. The segmentation procedure of region merging is implemented as a hierarchical clustering algorithm whereby a multiwindow approach using a pyramid-like structure is employed to increase computational efficiency while maintaining spatial connectivity in merging. From experiments with both simulated and remotely sensed data, the proposed method was determined to be quite effective for unsupervised analysis. In particular, the region-merging approach based on spatial contextual information was shown to provide more accurate classification of images with smooth spatial patterns.

Algorithms↗
PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗
PubMed2005

A Bayesian approach for discriminating among alternative inheritance hypotheses in plant polyploids: the allotetraploid origin of genus Borderea (Dioscoreaceae).

Polyploidy is a common phenomenon occurring in a vast number of land plants. Investigations of patterns of inheritance and the origins of plants (i.e., autopolyploidy vs. allopolyploidy) usually involve cytogenetic and molecular studies of chromosome pairing, chromosome mapping, and marker segregation analysis through experimental crosses and progeny tests. Such studies are missing for most wild species, for which artificial crosses are difficult, not feasible, or unaffordable. We report here a Bayesian method to discriminate between alternative inheritance patterns in the two extant, tetraploid species of the monocot genus Borderea (Dioscoreaceae), which does not involve progeny array tests. Our approach is based on the screening of a large number of SSR genotypes, which were obtained from successful amplifications of 17 microsatellite regions in individuals of both B. chouardii and B. pyrenaica. We tested for tetrasomic vs. disomic modes of inheritance, using the Bayes factor test. Assignment of genotypes under both alternatives could be unequivocally done for 14 and 11 of the 17 studied microsatellite regions in B. chouardii and B. pyrenaica, respectively, totaling 9502 analyzed genotypes. The comparison of posterior probabilities for the two competing hypotheses across the surveyed loci clearly favored a disomic inheritance pattern. Linkage tests indicated that none of the studied SSR loci were in linkage disequilibrium, thus representing independent samples of the Borderea genome. These results, along with previous allozyme data, support the allotetraploid origin of this paleoendemic genus and reveal the lowest reported chromosome base number for the family of the yams.

Bayes Theorem↗
PubMed2003

A score for Bayesian genome screening.

Bayesian Monte Carlo Markov chain (MCMC) techniques have shown promise in dissecting complex genetic traits. The methods introduced by Heath ([1997], Am. J. Hum. Genet. 61:748-760), and implemented in the program Loki, have been able to localize genes for complex traits in both real and simulated data sets. Loki estimates the posterior probability of quantitative trait loci (QTL) at locations on a chromosome in an iterative MCMC process. Unfortunately, interpretation of the results and assessment of their significance have been difficult. Here, we introduce a score, the log of the posterior placement probability ratio (LOP), for assessing oligogenic QTL detection and localization. The LOP is the log of the posterior probability of linkage to the real chromosome divided by the posterior probability of linkage to an unlinked pseudochromosome, with marker informativeness similar to the marker data on the real chromosome. Since the LOP cannot be calculated exactly, we estimate it in simultaneous MCMC on both real and pseudochromosomes. We investigate empirically the distributional properties of the LOP in the presence and absence of trait genes. The LOP is not subject to trait model misspecification in the way a lod score may be, and we show that the LOP can detect linkage for loci of small effect when the lod score cannot. We show how, in the absence of linkage, an empirical distribution of the LOP may be estimated by simulation and used to provide an assessment of linkage detection significance.

Bayes Theorem↗
PubMed1998

Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells.

Physical variables such as the orientation of a line in the visual field or the location of the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which the physical variables are estimated from observed neural activity. Reconstruction is useful first in quantifying how much information about the physical variables is present in the population and, second, in providing insight into how the brain might use distributed representations in solving related computational problems such as visual object recognition and spatial navigation. Two classes of reconstruction methods, namely, probabilistic or Bayesian methods and basis function methods, are discussed. They include important existing methods as special cases, such as population vector coding, optimal linear estimation, and template matching. As a representative example for the reconstruction problem, different methods were applied to multi-electrode spike train data from hippocampal place cells in freely moving rats. The reconstruction accuracy of the trajectories of the rats was compared for the different methods. Bayesian methods were especially accurate when a continuity constraint was enforced, and the best errors were within a factor of two of the information-theoretic limit on how accurate any reconstruction can be and were comparable with the intrinsic experimental errors in position tracking. In addition, the reconstruction analysis uncovered some interesting aspects of place cell activity, such as the tendency for erratic jumps of the reconstructed trajectory when the animal stopped running. In general, the theoretical values of the minimal achievable reconstruction errors quantify how accurately a physical variable is encoded in the neuronal population in the sense of mean square error, regardless of the method used for reading out the information. One related result is that the theoretical accuracy is independent of the width of the Gaussian tuning function only in two dimensions. Finally, all the reconstruction methods considered in this paper can be implemented by a unified neural network architecture, which the brain feasibly could use to solve related problems.

Action Potentials↗