nils-herrmann/clean_scirepeval huggingface dataset:
Machine Learning Classifiers In The Diagnosis And Follow-up Of Glaucoma Using Short-wavelength Automated Perimetry (SWAP)

To evaluate the performance of machine classifiers in diagnosing glaucoma from short-wavelength automated perimetry (PHASE 1) and to evaluate the ability of machine classifiers to predict the development of abnormal fields at follow-up in eyes with a normal baseline field (PHASE 2).  PHASE 1 Raw thresholds and ages from 158 eyes with glaucomatous optic neuropathy (GON) and 186 normal eyes served as training input to a linear discriminant function (LDF) and two types of support vector machine (SVMl and SVMg), a mixture of Gaussian classifier (MoG), a constrained MoG (QDF) and a multilayer perceptron (MLP). PHASE 2 Serial SWAP fields from 68 eyes with IOP greater than 24, a normal baseline field, at least 4 fields and optic disc stereophotographs were included. Machine classifiers from Phase 1 categorized fields as normal or abnormal. Specificity was set at 95% for the machine classifiers using longitudinal data from 94 normal eyes, matching the specificity of the statpac-like analysis (STAT). Specificity cut-offs required confirmation of abnormality on two successive visual fields.  PHASE 1. Areas under the ROC were 0.77 for LDF, 0.80 for MLP, 0.82 for SVMl, and 0.83 for QDF and SVMg. PHASE 2. A mean±SD of 5.99±1.93 visual fields were completed over 6.67±2.40 years. 19% (13/68) of eyes converted by STAT. Of those eyes, 6 to 9 eyes were found to have converted by SVM (SVMl and SVMg). However, 20 eyes converted by at least 1 machine classifier that did not convert by STAT and 15 of these had GON. LDF identified the most converts (22 eyes) of which 17 had GON.  The sensitivity in Phase 1 was not as good as our previous results with SAP. It is possible that SWAP may be influenced to a greater extent by generalized depression than SAP. We are currently pursuing this hypothesis. However, in Phase 2 the machine classifiers identified a larger number of GON as converts than STAT. None Supported by NIH Grants EY08208 (PAS), LM05759 (MHG), and Research to Prevent Blindness (PAS)
MODULATION OF THE FREQUENCY PREFERENCE AND ATTRACTOR STRUCTURE OF PREFRONTAL CORTICAL NEURONS

Neurons in vitro respond to repetitive injection of the same fluctuating input current with reproducible patterns of precisely timed action potentials. These spike patterns, called attractors, are robust against slow drifts in neuronal response properties that occur during experiments, the effects of intrinsic membrane noise, and small changes in the input. Larger variations in the input lead to new spike patterns that may have different number of spikes (spike insertions or deletions) and different spike times.
Spike propagation synchronized by temporally asymmetric Hebbian learning

Synchronously spiking neurons have been observed in the cerebral cortex and the hippocampus. In computer models, synchronous spike volleys may be propagated across appropriately connected neuron populations. However, it is unclear how the appropriate synaptic connectivity is set up during development and maintained during adult learning. We performed computer simulations to investigate the influence of temporally asymmetric Hebbian synaptic plasticity on the propagation of spike volleys. In addition to feedforward connections, recurrent connections were included between and within neuron populations and spike transmission delays varied due to axonal, synaptic and dendritic transmission. We found that repeated presentations of input volleys decreased the synaptic conductances of intragroup and feedback connections while synaptic conductances of feedforward connections with short delays became stronger than those of connections with longer delays. These adaptations led to the synchronization of spike volleys as they propagated across neuron populations. The findings suggests that temporally asymmetric Hebbian learning may enhance synchronized spiking within small populations of neurons in cortical and hippocampal areas and familiar stimuli may produce synchronized spike volleys that are rapidly propagated across neural tissue.
INDEPENDENT SOURCES OF QUANTAL VARIABILITY AT A CENTRAL GLUTAMATERGIC SYNAPSE

Repeated stimulation at a single glutamatergic synapse typically produces highly variable postsynaptic responses. Bekkers et al (1) proposed that jitter in vesicle diameter, and thus, quantal content (q) could account for this variability. We used MCell, a Monte Carlo simulation program, to determine the relative contributions of different sources of this variance. The model consisted of a synapse containing 80 AMPA receptors (AMPARs) and randomly assigned release locations across the active zone (AZ) embedded within a spatially complex 3-D neuropil. Quantal release resulted in an average of 14 AMPA receptors (AMPARs) opening at peak (CV 0.46). Three sources of variability were then examined in isolation: Channel noise, variability in q, and eccentricity of the release location within the active zone ( x). Release with fixed q and x=0 isolated channel noise and produced a normal distribution of peaks ( , 22; CV, 0.17). Jitter in vesicle size (diam: 35nm 3.8nm) did not affect the mean but increased variability (CV 0.34) and skewed the distribution of peaks as predicted (1). Quantal size systematically decreased with increasing x, a 2-fold difference between release in the center and edge of the AZ. Release from 50 locations, with fixed q, gave a CV of 0.34. The variances from the three different sources sum to the total,consistent with their independent contribution to experimentally observed variance. Increased potency of clustered receptors and a non-linear transformation relation between receptor number and quantal response follow from release-location dependence.
Predicting Development Of Abnormal Standard Visual Fields In Ocular Hypertensive Eyes: Machine Learning Classifiers And Statpac-like Analysis

To compare the ability of several learning machine classifiers to identify development of abnormal fields at follow-up in ocular hypertensive eyes that had had normal fields at baseline.  The visual fields of 114 eyes of 114 ocular hypertensive patients followed for a minimum of four years with standard automated perimetry (SAP) and stereo optic disc photographs were assessed. Fields were classified as normal or abnormal based on Statpac-like analysis (STAT). Several machine classifiers that previously successfully separated SAP 24-2 fields from normal eyes and eyes with glaucomatous optic neuropathy were evaluated, including support vector machines with linear (SVMl) and Gaussian (SVMg) kernels, a mixture of Gaussian classifier (MoG), a constrained MoG (QDF) and a mixture of generalized Gaussian (MGG). Specificity was set to 96% for STAT determination and each of the classifiers using data from 94 normal eyes evaluated longitudinally. Confirmation of abnormality on two successive visual fields was required.  The mean number (± sd) of fields was 7.89 ± 3.04 over 5.89 ± 2.40 years. 32% (36/114) of the eyes converted to abnormal fields based on STAT. Of these, all were identified by one or more of the machine classifiers earlier by an average gap in years of 4.12 ±1.78 (QDF), 4.39±2.92 (SVMl), 4.43±2.75 (SVMg), 3.39±1.55 (MoG), and 3.28±1.59 (MGG). SVMg showed the best agreement with the presence of GON at 94% (32/34 converts).  The attractive aspect of machine classifiers is their ability to adapt to the data without the constraints imposed by statistical classifiers. This adaptation allowed the machine classifiers to identify abnormality in visual field converts much earlier than traditional methods. Supported by NIH Grants EY08208 (PAS), LM05759 (MHG), EY03424 (CJ), and Research to Prevent Blindness (PAS)
Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry.

To determine which machine learning classifier learns best to interpret standard automated perimetry (SAP) and to compare the best of the machine classifiers with the global indices of STATPAC 2 and with experts in glaucoma.



Multilayer perceptrons (MLP), support vector machines (SVM), mixture of Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers were trained and tested by cross validation on the numerical plot of absolute sensitivity plus age of 189 normal eyes and 156 glaucomatous eyes, designated as such by the appearance of the optic nerve. The authors compared performance of these classifiers with the global indices of STATPAC, using the area under the ROC curve. Two human experts were judged against the machine classifiers and the global indices by plotting their sensitivity-specificity pairs.



MoG had the greatest area under the ROC curve of the machine classifiers. Pattern SD (PSD) and corrected PSD (CPSD) had the largest areas under the curve of the global indices. MoG had significantly greater ROC area than PSD and CPSD. Human experts were not better at classifying visual fields than the machine classifiers or the global indices.



MoG, using the entire visual field and age for input, interpreted SAP better than the global indices of STATPAC. Machine classifiers may augment the global indices of STATPAC.
Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields.

To compare the ability of several machine learning classifiers to predict development of abnormal fields at follow-up in ocular hypertensive (OHT) eyes that had normal visual fields in baseline examination.



The visual fields of 114 eyes of 114 patients with OHT with four or more visual field tests with standard automated perimetry over three or more years and for whom stereophotographs were available were assessed. The mean (+/-SD) number of visual field tests was 7.89 +/- 3.04. The mean number of years covered (+/-SD) was 5.92 +/- 2.34 (range, 2.81-11.77). Fields were classified as normal or abnormal based on Statpac-like methods (Humphrey Instruments, Dublin, CA) and by several machine learning classifiers. The machine learning classifiers were two types of support vector machine (SVM), a mixture of Gaussian (MoG) classifier, a constrained MoG, and a mixture of generalized Gaussian (MGG). Specificity was set to 96% for all classifiers, using data from 94 normal eyes evaluated longitudinally. Specificity cutoffs required confirmation of abnormality.



Thirty-two percent (36/114) of the eyes converted to abnormal fields during follow-up based on the Statpac-like methods. All 36 were identified by at least one machine classifier. In nearly all cases, the machine learning classifiers predicted the confirmed abnormality, on average, 3.92 +/- 0.55 years earlier than traditional Statpac-like methods.



Machine learning classifiers can learn complex patterns and trends in data and adapt to create a decision surface without the constraints imposed by statistical classifiers. This adaptation allowed the machine learning classifiers to identify abnormality in visual field converts much earlier than the traditional methods.
Bayesian learning in classification and density estimation

The Bayes' theorem is central to probability manipulation. Use of Bayes' theorem in supervised classification transforms the problem into density estimation and is called the generative approach. Discriminative classifiers on the other hand find direct mappings from input data to class label. Various classifiers from the two categories were compared in a glaucoma standard automated perimetry (SAP) dataset. It showed that when an appropriate density model is employed, the generative classifiers can outperform their discriminative counterparts. In addition, their decisions are easier to understand and interpret through the intermediate steps in probability manipulation. 
Besides classification, many other machine learning problems involves building probabilistic models for data, such as clustering, finding hidden correlations and estimating missing values. Independent Component Analysis (ICA) describes data as linear combinations of independent hidden factors. It finds non-trivial projections that could uncover interesting characteristics in the data. A new density model that fits data with clusters of ICA structure was developed. Unlike mixture of factor analysis, the ICA clusters model avoids shattering of clusters into subpieces. The Bayes' theorem is applied again to perform automatic model selection. This makes identifying the number of clusters and component dimensionality possible. Variational method was employed to solve the problem of intractability usually associated with full Bayesian treatment. When applied to the glaucoma SAP dataset, the proposed algorithm discovered interesting patterns that were supported by physiological evidence. Finally, the variational Bayesian ICA was extended to incorporate incomplete data. This helps to analyze small high-dimensional datasets containing missing entries which is of great concern in many machine learning techniques trying to analyze challenging real-world data.
Comparison of machine learning and traditional classifiers in glaucoma diagnosis

Glaucoma is a progressive optic neuropathy with characteristic structural changes in the optic nerve head reflected in the visual field. The visual-field sensitivity test is commonly used in a clinical setting to evaluate glaucoma. Standard automated perimetry (SAP) is a common computerized visual-field test whose output is amenable to machine learning. We compared the performance of a number of machine learning algorithms with STATPAC indexes mean deviation, pattern standard deviation, and corrected pattern standard deviation. The machine learning algorithms studied included multilayer perceptron (MLP), support vector machine (SVM), and linear (LDA) and quadratic discriminant analysis (QDA), Parzen window, mixture of Gaussian (MOG), and mixture of generalized Gaussian (MGG). MLP and SVM are classifiers that work directly on the decision boundary and fall under the discriminative paradigm. Generative classifiers, which first model the data probability density and then perform classification via Bayes' rule, usually give deeper insight into the structure of the data space. We have applied MOG, MGG, LDA, QDA, and Parzen window to the classification of glaucoma from SAP. Performance of the various classifiers was compared by the areas under their receiver operating characteristic curves and by sensitivities (true-positive rates) at chosen specificities (true-negative rates). The machine-learning-type classifiers showed improved performance over the best indexes from STATPAC. Forward-selection and backward-elimination methodology further improved the classification rate and also has the potential to reduce testing time by diminishing the number of visual-field location measurements.
A Prototype for Automatic Recognition of Spontaneous Facial Actions

We present ongoing work on a project for automatic recognition of spontaneous facial actions. Spontaneous facial expressions differ substantially from posed expressions, similar to how continuous, spontaneous speech differs from isolated words produced on command. Previous methods for automatic facial expression recognition assumed images were collected in controlled environments in which the subjects deliberately faced the camera. Since people often nod or turn their heads, automatic recognition of spontaneous facial behavior requires methods for handling out-of-image-plane head rotations. Here we explore an approach based on 3-D warping of images into canonical views. We evaluated the performance of the approach as a front-end for a spontaneous expression recognition system using support vector machines and hidden Markov models. This system employed general purpose learning mechanisms that can be applied to recognition of any facial movement. The system was tested for recognition of a set of facial actions defined by the Facial Action Coding System (FACS). We showed that 3D tracking and warping followed by machine learning techniques directly applied to the warped images, is a viable and promising technology for automatic facial expression recognition. One exciting aspect of the approach presented here is that information about movement dynamics emerged out of filters which were derived from the statistics of images.
The initiation of bursts in thalamic neurons and the cortical control of thalamic sensitivity.

Thalamic neurons generate high-frequency bursts of action potentials when a low-threshold (T-type) calcium current, located in soma and dendrites, becomes activated. Computational models were used to investigate the bursting properties of thalamic relay and reticular neurons. These two types of thalamic cells differ fundamentally in their ability to generate bursts following either excitatory or inhibitory events. Bursts generated with excitatory inputs in relay cells required a high degree of convergence from excitatory inputs, whereas moderate excitation drove burst discharges in reticular neurons from hyperpolarized levels. The opposite holds for inhibitory rebound bursts, which are more difficult to evoke in reticular neurons than in relay cells. The differences between the reticular neurons and thalamocortical neurons were due to different kinetics of the T-current, different electrotonic properties and different distribution patterns of the T-current in the two cell types. These properties enable the cortex to control the sensitivity of the thalamus to inputs and are also important for understanding states such as absence seizures.
Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc.

To determine whether neural network techniques can improve differentiation between glaucomatous and nonglaucomatous eyes, using the optic disc topography parameters of the Heidelberg Retina Tomograph (HRT; Heidelberg Engineering, Heidelberg, Germany).



With the HRT, one eye was imaged from each of 108 patients with glaucoma (defined as having repeatable visual field defects with standard automated perimetry) and 189 subjects without glaucoma (no visual field defects with healthy-appearing optic disc and retinal nerve fiber layer on clinical examination) and the optic nerve topography was defined by 17 global and 66 regional HRT parameters. With all the HRT parameters used as input, receiver operating characteristic (ROC) curves were generated for the classification of eyes, by three neural network techniques: linear and Gaussian support vector machines (SVM linear and SVM Gaussian, respectively) and a multilayer perceptron (MLP), as well as four previously proposed linear discriminant functions (LDFs) and one LDF developed on the current data with all HRT parameters used as input.



The areas under the ROC curves for SVM linear and SVM Gaussian were 0.938 and 0.945, respectively; for MLP, 0.941; for the current LDF, 0.906; and for the best previously proposed LDF, 0.890. With the use of forward selection and backward elimination optimization techniques, the areas under the ROC curves for SVM Gaussian and the current LDF were increased to approximately 0.96.



Trained neural networks, with global and regional HRT parameters used as input, improve on previously proposed HRT parameter-based LDFs for discriminating between glaucomatous and nonglaucomatous eyes. The performance of both neural networks and LDFs can be improved with optimization of the features in the input. Neural network analyses show promise for increasing diagnostic accuracy of tests for glaucoma.
ATTENTIONAL MODULATION OF SYNCHRONY IN CORTICAL NETWORKS

Attention can modulate the synchrony of neurons in extrastriate cortex without changing their mean activity. We investigated using two different model networks how cortical neurons synchronize when a stimulus was attended and how they desynchronize when attention was switched off. Network 1 consisted of interneurons that were coupled by fast GABAergic synapses and electrical gap junctions. The effects of attention were mediated by activation of cholinergic or glutamatergic projections and was modeled as a depolarizing current to part of the network. This resulted in synchronized oscillations in the gamma frequency range (40 Hz) within a 100 ms. The firing rate of the activated neurons increased, but their inhibitory projection decreased the activity of non-activated neurons, keeping the mean activity of the network approximately constant. Synaptic background activity -noise -was critical to attentional modulation: Networks of inhibitory neurons did not synchronize when the noise was too strong; for noise that was too weak the networks did not desynchronize when attention was switched off. Hence, there was an optimal noise level for attentional modulation. Network 2 consisted of excitatory and inhibitory neurons at a 4:1 ratio. A parameter range was identified where by modulating the drive to the interneuron network the synchrony of the excitatory neurons could be varied without changing their mean activity. In studying how synchrony was manipulated by neuromodulatory substances we used a novel synchrony measure: The spike times of all neurons in the network were sorted and synchrony was quantified as the standard deviation of the interspike intervals divided by the mean. Supported by: HHMI, Sloan-Swartz
Persistent calcium microdomains in dendritic spines

Calcium is a ubiquitous signaling molecule in the nervous system. Selective induction of a particular pathway may depend on activation of a sensor that reads highly local Ca2+ levels. However, if Ca2+ equilibration is rapid compared to the kinetics of the sensor the latter strategy would be precluded. To test whether Ca2+ gradients persist for sufficiently long to differentially activate calmodulin (CaM) we developed a Monte Carlo model of intracellular Ca2+ dynamics in a dendritic spine. The model quantitatively reproduced experimental measures of fluorescent Ca2+ transients, and suggests a large, rapid spike in free intracellular Ca2+ concentration ([Ca]i) not predicted by the fluorescent signal. Because of the differential distribution of different Ca2+ sources, excitatory postsynaptic potentials (EPSPs), but not action potentials, induced large gradients across the spine. This differential distribution of Ca2+ produced location-specific activation of CaM following an EPSP, but a largely location-insensitive activation of CaM following an action potential. These simulations suggest functional Ca2+ microdomains can exist in dendritic spines, that Ca2+ sensors in spines can be sensitive to the mode of Ca2+ entry, and that these can lead to specificity in activation of Ca2+-dependent signaling pathways. Franks et al, 2002.