nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.

To determine whether a variational Bayesian independent component analysis mixture model (vB-ICA-mm), a form of unsupervised machine learning, can be used to identify and quantify areas of progression in standard automated perimetry fields.



In an earlier study, it was shown that a model using vB-ICA-mm can separate normal fields from fields with six different patterns of visual field loss related to glaucomatous optic neuropathy (GON) along maximally independent axes. In the present study, an independent group of 191 patient eyes (66 with ocular hypertension (OHT), 12 with suspected glaucoma by field, 61 with suspected glaucoma by disc, and 52 with glaucoma) with five or more standard visual fields under observation for a mean of 6.24 +/- 2.65 years and 8.11 +/- 2.42 visual fields were evaluated with the vB-ICA-mm. In addition, eyes with progressive GON (PGON) were identified (n = 39). Each participant had a series of fields tested, with each field entered independently and placed along the axes of the previously developed model. This allowed change in one pattern of visual field defect (along one axis) to be assessed relative to results other areas of that same field (no change along other axes). Progression was based on a slope falling outside the 5th and the 95th percentile limits of all slopes, with at least two axes not showing such a deviation in a given individual's series of fields. Fields were also scored using Advanced Glaucoma Intervention Study (AGIS) and the Early Manifest Glaucoma Treatment Trial (EMGT) criteria.



Thirty-two of 191 eyes progressed on vB-ICA-mm by this definition. Of the 32, 22 had field loss at baseline, 7 had only GON, 3 were OHTs and 12 were from the 39 eyes (31%) with PGON. The vB-ICA-mm identified a higher percentage of progressing eyes in each diagnostic category than did AGIS or and the EMGT.



The vB-ICA-mm can quantitatively identify progression in eyes with glaucoma by evaluating change in one or more patterns of the visual field loss while other areas or patterns remain stable. This may enable each eye to contribute to the determination of whether change is caused by true progression or by variability.
INFLUENCE OF GLUTAMATE TRANSPORTERS ON SPILLOVER IN A MONTE CARLO MODEL OF HIPPOCAMPAL NEUROPIL

The goal of this study was to explore the effect of glutamate transporter density on spillover activation of AMPA and NMDA receptors at neighboring synapses by diffusion of glutamate in extracellular space following fast excitatory synaptic release at an active synapse. We used MCell, a Monte Carlo simulator of molecular signaling, to study the release of glutamate and diffusion in 3-D geometrical reconstructions of the hippocampal neuropil. Our 3-D neuropil model contained repeating motifs with the appropriate shape and spacing to match experimentally measured values for hippocampal tortuosity, extracellular volume fraction, and glutamate transporter concentration. The model simulates, on the microsecond time scale, the random walk of every glutamate molecule in a synapse after vesicular release. Here we compare the spillover activation of AMPA and NMDA receptors following 100Hz burst release at an active synapse for two different astrocyte conditions: (1) uniform surface density of glutamate transporters and (2) concentrated glutamate transporters on membrane facing the active synapse. Transporter saturation was analyzed and the average radial diffusion distance of the quanta was calculated for each release in the burst. Preliminary results in a model with uniform glial transporter surface density show that transporter saturation near the active synapse enhanced radial glutamate spillover, and transporter saturation increased with each subsequent vesicular release in a burst. This will be compared with the results from a second model with glutamate transporters concentrated in glial membrane facing the active synapse. Support Contributed By: NIH P01 NS044306 (TJS,TMB,JPK)
G protein activation kinetics and spillover of-y-aminobutyric acid may account for differences between inhibitory responses in the hippocampus and thalamus ( synaptic transmission / neuromodulation / epilepsy )

We have developed a model of r-aminobutyric acid (GABA)ergic synaptic transmission mediated by GABAA and GABAB receptors, including cooperativity in the guanine nucleotide binding protein (G protein) cascade mediating the activation of K+ channels by GABAB receptors. If the binding of several G proteins is needed to activate the K+ channels, then only a prolonged activation of GABAB receptors evoked detectable currents. This could occur if strong stimuli evoked release in adjacent termipals and the spillover resulted in prolonged activatiod4of the receptors, leading to inhibitory responses similar to those observed in hippocampal slices. The same model also reproduced thalamic GABAB responses to high-frequency bursts of stimuli. In this case, prolonged activation of the receptors was due to highfrequency release conditions. This model provides insights into the function of GABAB receptors in normal and epileptic discharges. Two receptor types, GABAA and GABAB, are responsible for most inhibitory postsynaptic potentials (IPSPs) mediated by the release of y-aminobutyric acid (GABA) from presynaptic terminals. These receptors have characteristic differences in their kinetics (reviewed in refs. 1 and 2); GABAA-mediated currents have a relatively fast time course (time constant, 5-20 ms), whereas GABAB receptors induce much slower changes in the excitability of the cell (time constant, 150-200 ms). They differ as well in their activation. Typically, relatively strong stimulation is needed to evoke GABAB responses, whereas GABAA-mediated currents are evoked even for very low levels of presynaptic stimulation. Miniature GABAA IPSPs also occur spontaneously and are thought to arise from the spontaneous release of GABA from a single vesicle, but they never have a GABAB component (2-4). Physiological data on GABAergic responses show marked differences between thalamic and hippocampal slices. In the thalamus, stimulation of the reticular (RE) nucleus or interneurons induces biphasic GABAergic IPSPs in thalamocortical (TC) cells (5). The ratio between peak GABAA and GABAB currents evoked by RE neurons is insensitive to the intensity of the stimulation (6), but it changes markedly if the discharge of RE cells is enhanced by pharmacological means (6-9). In hippocampal slices, GABAergic currents can be elicited in the dendrites of pyramidal cells by stimulating interneurons in the stratum radiatum. Unlike the situation in thalamic cells, the GABAA/GABAB ratio depends critically on the intensity of the stimulation (10, 11) and on the presence of uptake (3, 12, 13). In this paper, we focus on the activation kinetics of GABAB responses, in which K+ channels are activated through a guanine nucleotide binding protein (G protein) cascade (14, 15). We assume that this activation shows some "cooperativity," in the sense that independent binding of several G-protein subunits is needed to open the K+ channels.
Repeated decompositions reveal the stability of infomax decomposition of fMRI data

In this study, we decomposed 12 fMRI data sets from six subjects each 101 times using the infomax algorithm. The first decomposition was taken as a reference decomposition; the others were used to form a component matrix of 100 by 100 components. Equivalence relations between components in this matrix, defined as maximum spatial correlations to the components of the reference decomposition, were found by the Hungarian sorting method and used to form 100 equivalence classes for each data set. We then tested the reproducibility of the matched components in the equivalence classes using uncertainty measures based on component distributions, time courses, and ROC curves. Infomax ICA rarely failed to derive nearly the same components in different decompositions. Very few components per data set were poorly reproduced, even using vector angle uncertainty measures stricter than correlation and detection theory measures
Motion detection and prediction through spike-timing dependent plasticity.

We describe a possible mechanism for the formation of direction- and velocity-selective cells in visual cortex through spike-timing dependent learning. We contrast the case where only feedforward excitation and inhibition signals are provided to visual neurons with the case where both feedforward and feedback signals are provided. In the feedforward-only case, neurons become selective for a broad range of velocities centered around the training velocity. However, we show that direction selectivity in this case is strongly dependent on delayed feedforward inhibition and in contrast to experimental results, becomes dramatically weaker when inhibition is reduced. When feedback connections are introduced, direction selectivity becomes much more robust due to predictive delays encoded in recurrent activity. Direction selectivity persists in the face of decreasing inhibition in a manner similar to experimental findings. The model predicts that direction-selective cells should exhibit anticipatory activity due to recurrent excitation and suggests a pivotal role for spike-timing dependent plasticity in shaping cortical circuits for visual motion detection and prediction.
Exploration Bonuses and Dual Control

Finding the Bayesian balance between exploration and exploitation in adaptive optimal control is in general intractable. This paper shows how to compute suboptimal estimates based on a certainty equivalence approximation (Cozzolino, Gonzalez-Zubieta Miller, 1965) arising from a form of dual control. This systematizes and extends existing uses of exploration bonuses in reinforcement learning (Sutton, 1990). The approach has two components: a statistical model of uncertainty in the world and a way of turning this into exploratory behavior. This general approach is applied to two-dimensional mazes with moveable barriers and its performance is compared with Sutton‘s DYNA system.
Spatially Fixed Patterns Account for the Spike and Wave Features in Absence Seizures

Despite genetic, morphological and experimental in vivo data implying fixed abnormalities in patients with absence seizures, attempts to find highly consistent features in the 3-Hz spike-and-wave pattern recorded during sequential seizures from the same subject have been largely unsuccessful. We used a new data decomposition technique called Independent Component Analysis (ICA) to separate multiple spike-and-wave episodes in the EEG recorded from five subjects with absence seizures into multiple consistent components. Each component corresponded to a temporally-independent waveform and a fixed spatial distribution. Almost all components separated by the ICA algorithm had overlapping, largely frontal spatial distributions. The analysis unmasked 5-8 components from each subject that were consistently activated across all seizures, with no components detected that were selectively activated by one seizure and not another. The “spike” and “wave” features noted in the EEG of every subject were each separated by the ICA algorithm into two or more components. Other components were active only at the beginning of each seizure or were related to ongoing brain activity not directly related to the 3Hz spike-and-wave pattern. By contrast, randomly selected spatial patterns used for data decomposition resulted in components that were uninformative, similar to simply changing the montage for viewing the EEG. Our results suggest that despite previously described variability in the raw EEG, certain highly specific spatial distributions of activation are reproducible across seizures. These may reflect ictal and non-ictal brain activity consistently activating the same group of neurons.
A BIOPHYSICAL NEURONAL MODEL EXPLORING ATTENTION MECHANISMS IN VISUAL CORTEX

Neurons in areas V2 and V4 exhibit stimulus specific tuning. When two stimuli that are differentially preferred by a V4 neuron are simultaneously presented within its receptive field, its response to the pair is intermediate between the responses to either of the two stimuli presented alone. When the animal attends to one of the two stimuli, the neuronal response shifts from the pair response towards what would be its response if the stimulus were presented alone. In addition, the effect of attention when single stimuli are presented is to enhance the response sensitivity of the neuron. The intrinsic and network mechanisms that result in these effects are investigated in a realistic multi-compartmental biophysical model of a reconstructed cat visual cortical neuron with thousands of excitatory and inhibitory dynamical synapses. The neuron received stimulus independent background inputs and feedforward synaptic inputs from two V2 populations that represented its preferred and non-preferred stimuli. We found that the best stimulus tuning was generated when the V2 populations differed in the number of projecting synapses, rather than in their firing rates or synaptic strengths. The pair response when both input populations to the neuron were active could be generated if feedforward inhibition was broadly tuned. Sharply tuned or stimulus specific inhibition devoid of interactions amongst interneuronal populations could not produce a significantly intermediate pair response. Aspects of attention could be simulated in the neuron through correlations amongst its input spike trains and via enhanced variations in the firing of top-down background feedback synapses. Support Contributed By: Howard Hughes Medical Institute
Experimental evidence and modeling studies support a synchronizing role for electrical coupling in the cat thalamic reticular neurons in vivo

Thalamic reticular (RE) neurons are crucially implicated in brain rhythms. Here, we report that RE neurons of adult cats, recorded and stained intracellularly in vivo, displayed spontaneously occurring spikelets, which are characteristic of central neurons that are coupled electrotonically via gap junctions. Spikelets occurred spontaneously during spindles, an oscillation in which RE neurons play a leading role, as well as during interspindle lulls. They were significantly different from excitatory postsynaptic potentials and also distinct from fast prepotentials that are presumably dendritic spikes generated synaptically. Spikelets were strongly reduced by halothane, a blocker of gap junctions. Multi‐site extracellular recordings performed before, during and after administration of halothane demonstrated a role for electrical coupling in the synchronization of spindling activity within the RE nucleus. Finally, computational models of RE neurons predicted that gap junctions between these neurons could mediate the spread of low‐frequency activity at great distances. These experimental and modeling data suggest that electrotonic coupling within the RE nucleus plays an important role in the generation and synchronization of low‐frequency (spindling) activities in the thalamus.
Computational modeling of retinotopic map development to define contributions of EphA-ephrinA gradients, axon-axon interactions, and patterned activity.

The topographic projection of retinal ganglion cell (RGC) axons to mouse superior colliculus (SC) or chick optic tectum (OT) is formed in three phases: RGC axons overshoot their termination zone (TZ); they exhibit interstitial branching along the axon that is topographically biased for the correct location of their future TZ; and branches arborize preferentially at the TZ and the initial exuberant projection refines through axon and branch elimination to generate a precise retinotopic map. We present a computational model of map development that demonstrates that the countergradients of EphAs and ephrinAs in retina and the OT/SC and bidirectional repellent signaling between RGC axons and OT/SC cells are sufficient to direct an initial topographic bias in RGC axon branching. Our model also suggests that a proposed repellent action of EphAs/ephrinAs present on RGC branches and arbors added to that of EphAs/ephrinAs expressed by OT/SC cells is required to progressively restrict branching and arborization to topographically correct locations and eliminate axon overshoot. Simulations show that this molecular framework alone can develop considerable topographic order and refinement, including axon elimination, a feature not programmed into the model. Generating a refined map with a condensed TZ as in vivo requires an additional parameter that enhances branch formation along an RGC axon near sites that it has a higher branch density, and resembles an assumed role for patterned neural activity. The same computational model generates the phenotypes reported in ephrinA deficient mice and Isl2-EphA3 knockin mice. This modeling suggests that gradients of counter-repellents can establish a substantial degree of topographic order in the OT/SC, and that repellents present on RGC axon branches and arbors make a substantial contribution to map refinement. However, competitive interactions between RGC axons that enhance the probability of continued local branching are required to generate precise retinotopy.
Reliable Measurement of Cortical Flow Patterns Using Complex Independent Component Analysis of Electroencephalographic Signals

Complex independent component analysis (ICA) of frequency-domain electroencephalographic (EEG) data  is a generalization of real time-domain ICA to the frequency-domain. Complex ICA aims to model functionally independent sources as representing patterns of spatio-temporal dynamics. Applied to EEG data, it may allow non-invasive measurement of flow trajectories of cortical potentials. As complex ICA has a higher complexity and number of parameters than time-domain ICA, it is important to determine the extent to which complex ICA applied to brain signals is stable across decompositions. This question is investigated for the complex ICA method applied to the 5-Hz frequency band of data from a selective attention EEG experiment.
Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects.

To determine whether an unsupervised machine learning classifier can identify patterns of visual field loss in standard visual fields consistent with typical patterns learned by decades of human experience.



Standard perimetry thresholds for 52 locations plus age from one eye of each of 156 patients with glaucomatous optic neuropathy (GON) and 189 eyes of healthy subjects were clustered with an unsupervised machine classifier, variational Bayesian mixture of factor analysis (vbMFA).



The vbMFA formed five distinct clusters. Cluster 5 held 186 of 189 fields from normal eyes plus 46 from eyes with GON. These fields were then judged within normal limits by several traditional methods. Each of the other four clusters could be described by the pattern of loss found within it. Cluster 1 (71 GON + 3 normal optic discs) included early, localized defects. A purely diffuse component was rare. Cluster 2 (26 GON) exhibited primarily deep superior hemifield defects, and cluster 3 (10 GON) held deep inferior hemifield defects only or in combination with lesser superior field defects. Cluster 4 (6 GON) showed deep defects in both hemifields. In other words, visual fields within a given cluster had similar patterns of loss that differed from the predominant pattern found in other clusters. The classifier separated the data based solely on the patterns of loss within the fields, without being guided by the diagnosis, placing 98.4% of the healthy eyes within the same cluster and spreading 70.5% of the eyes with GON across the other four clusters, in good agreement with a glaucoma expert and pattern standard deviation.



Without training-based diagnosis (unsupervised learning), the vbMFA identified four important patterns of field loss in eyes with GON in a manner consistent with years of clinical experience.
THE EFFECTS OF LESIONS ON DYNAMICS IN NETWORK MODELS OF EPILEPSY

Acute trauma and pathogen-related cell death can result in large scale changes in neural activity. Changes to surviving cells are often postulated as the pathophysiological mechanisms. Using network models we demonstrate that alterations in connectivity may be sufficient to cause system-wide changes in dynamics even if intrinsic properties of surviving cells remain constant. The models consisted of single or two-layer networks of up to 10,000 elements. For a range of parameters, isolated pairs of excitatory and inhibitory cells created oscillatory units with bursting-like behavior; the addition of full lateral local connectivity within excitatory and inhibitory layers resulted in a quiescent state. Under these conditions large-amplitude local stimulations and small-amplitude spatially distributed activations generated short-term bursting that quickly dissipated. However, when a small lesion was introduced the local dampening effects were lost. Removal of excitatory units effectively reduced input to inhibitory cells which led to disinhibition and bursting. Once these oscillations were initiated at the lesion boundary they progressively increased in amplitude spreading to adjacent non-boundary cells and eventually triggered a system-wide hyper-excited oscillatory state. The introduction of the lesion thus caused a change in the bifurcation point effectively reducing discharge threshold. These observations suggest that network connectivity factors may be sufficient to cause post-traumatic epilepsy and may also underlie inherited forms of epilepsy. These principles may similarly account for changes in activity patterns in neurodegenerative diseases as a consequence of cell death and the creation of micro-lesions. Support Contributed By: CIHR, NIH