An Adaptive Generalized Leaky Integrate-and-Fire Model for Hippocampal CA1 Pyramidal Neurons and Interneurons.

Full-scale morphologically and biophysically realistic model networks, aiming at modeling multiple brain areas, provide an invaluable tool to make significant scientific advances from in-silico experiments on cognitive functions to digital twin implementations. Due to the current technical limitations of supercomputer systems in terms of computational power and memory requirements, these networks must be implemented using (at least) simplified neurons. A class of models which achieve a reasonable compromise between accuracy and computational efficiency is given by generalized leaky integrate-and fire models complemented by suitable initial and update conditions. However, we found that these models cannot reproduce the complex and highly variable firing dynamics exhibited by neurons in several brain regions, such as the hippocampus. In this work, we propose an adaptive generalized leaky integrate-and-fire model for hippocampal CA1 neurons and interneurons, in which the nonlinear nature of the firing dynamics is successfully reproduced by linear ordinary differential equations equipped with nonlinear and more realistic initial and update conditions after each spike event, which strictly depends on the external stimulation current. A mathematical analysis of the equilibria stability as well as the monotonicity properties of the analytical solution for the membrane potential allowed (i) to determine general constraints on model parameters, reducing the computational cost of an optimization procedure based on spike times in response to a set of constant currents injections; (ii) to identify additional constraints to quantitatively reproduce and predict the experimental traces from 85 neurons and interneurons in response to any stimulation protocol using constant and piecewise constant current injections. Finally, this approach allows to easily implement a procedure to create infinite copies of neurons with mathematically controlled firing properties, statistically indistinguishable from experiments, to better reproduce the full range and variability of the firing scenarios observed in a real network.

Early derailment of firing properties in CA1 pyramidal cells of the ventral hippocampus in an Alzheimer's disease mouse model.

Gradual decline in cognitive and non-cognitive functions are considered clinical hallmarks of Alzheimer's Disease (AD). Post-mortem autoptic analysis shows the presence of amyloid ß deposits, neuroinflammation and severe brain atrophy. However, brain circuit alterations and cellular derailments, assessed in very early stages of AD, still remain elusive. The understanding of these early alterations is crucial to tackle defective mechanisms. In a previous study we proved that the Tg2576 mouse model of AD displays functional deficits in the dorsal hippocampus and relevant behavioural AD-related alterations. We had shown that these deficits in Tg2576 mice correlate with the precocious degeneration of dopamine (DA) neurons in the Ventral Tegmental Area (VTA) and can be restored by L-DOPA treatment. Due to the distinct functionality and connectivity of dorsal versus ventral hippocampus, here we investigated neuronal excitability and synaptic functionality in the ventral CA1 hippocampal sub-region of Tg2576 mice. We found an age-dependent alteration of cell excitability and firing in pyramidal neurons starting at 3 months of age, that correlates with reduced levels in the ventral CA1 of tyrosine hydroxylase - the rate-limiting enzyme of DA synthesis. Additionally, at odds with the dorsal hippocampus, we found no alterations in basal glutamatergic transmission and long-term plasticity of ventral neurons in 8-month old Tg2576 mice compared to age-matched controls. Last, we used computational analysis to model the early derailments of firing properties observed and hypothesize that the neuronal alterations found could depend on dysfunctional sodium and potassium conductances, leading to anticipated depolarization-block of action potential firing. The present study depicts that impairment of cell excitability and homeostatic control of firing in ventral CA1 pyramidal neurons is a prodromal feature in Tg2576 AD mice.

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models.

In the last decades, brain modeling has been established as a fundamental tool for understanding neural mechanisms and information processing in individual cells and circuits at different scales of observation. Building data-driven brain models requires the availability of experimental data and analysis tools as well as neural simulation environments and, often, large scale computing facilities. All these components are rarely found in a comprehensive framework and usually require ad hoc programming. To address this, we developed the EBRAINS Hodgkin-Huxley Neuron Builder (HHNB), a web resource for building single cell neural models via the extraction of activity features from electrophysiological traces, the optimization of the model parameters via a genetic algorithm executed on high performance computing facilities and the simulation of the optimized model in an interactive framework. Thanks to its inherent characteristics, the HHNB facilitates the data-driven model building workflow and its reproducibility, hence fostering a collaborative approach to brain modeling.

Analysis of Age-Dependent Alterations in Excitability Properties of CA1 Pyramidal Neurons in an APPPS1 Model of Alzheimer's Disease.

Age-dependent accumulation of amyloid-ß, provoking increasing brain amyloidopathy, triggers abnormal patterns of neuron activity and circuit synchronization in Alzheimer's disease (AD) as observed in human AD patients and AD mouse models. Recent studies on AD mouse models, mimicking this age-dependent amyloidopathy, identified alterations in CA1 neuron excitability. However, these models generally also overexpress mutated amyloid precursor protein (APP) and presenilin 1 (PS1) and there is a lack of a clear correlation of neuronal excitability alterations with progressive amyloidopathy. The active development of computational models of AD points out the need of collecting such experimental data to build a reliable disease model exhibiting AD-like disease progression. We therefore used the feature extraction tool of the Human Brain Project (HBP) Brain Simulation Platform to systematically analyze the excitability profile of CA1 pyramidal neuron in the APPPS1 mouse model. We identified specific features of neuron excitability that best correlate either with over-expression of mutated APP and PS1 or increasing Aß amyloidopathy. Notably, we report strong alterations in membrane time constant and action potential width and weak alterations in firing behavior. Also, using a CA1 pyramidal neuron model, we evidence amyloidopathy-dependent alterations in I h . Finally, cluster analysis of these recordings showed that we could reliably assign a trace to its correct group, opening the door to a more refined, less variable analysis of AD-affected neurons. This inter-disciplinary analysis, bringing together experimentalists and modelers, helps to further unravel the neuronal mechanisms most affected by AD and to build a biologically plausible computational model of the AD brain.

Analysis of normal human retinal vascular network architecture using multifractal geometry.

AIM: To apply the multifractal analysis method as a quantitative approach to a comprehensive description of the microvascular network architecture of the normal human retina. METHODS: Fifty volunteers were enrolled in this study in the Ophthalmological Clinic of Cluj-Napoca, Romania, between January 2012 and January 2014. A set of 100 segmented and skeletonised human retinal images, corresponding to normal states of the retina were studied. An automatic unsupervised method for retinal vessel segmentation was applied before multifractal analysis. The multifractal analysis of digital retinal images was made with computer algorithms, applying the standard box-counting method. Statistical analyses were performed using the GraphPad InStat software. RESULTS: The architecture of normal human retinal microvascular network was able to be described using the multifractal geometry. The average of generalized dimensions (Dq ) for q=0, 1, 2, the width of the multifractal spectrum (Δα=αmax - αmin ) and the spectrum arms' heights difference (|Δf|) of the normal images were expressed as mean±standard deviation (SD): for segmented versions, D0 =1.7014±0.0057; D1 =1.6507±0.0058; D2 =1.5772±0.0059; Δα=0.92441±0.0085; |Δf|= 0.1453±0.0051; for skeletonised versions, D0 =1.6303±0.0051; D1 =1.6012±0.0059; D2 =1.5531±0.0058; Δα=0.65032±0.0162; |Δf|= 0.0238±0.0161. The average of generalized dimensions (Dq ) for q=0, 1, 2, the width of the multifractal spectrum (Δα) and the spectrum arms' heights difference (|Δf|) of the segmented versions was slightly greater than the skeletonised versions. CONCLUSION: The multifractal analysis of fundus photographs may be used as a quantitative parameter for the evaluation of the complex three-dimensional structure of the retinal microvasculature as a potential marker for early detection of topological changes associated with retinal diseases.

Characterisation of human non-proliferative diabetic retinopathy using the fractal analysis.

AIM: To investigate and quantify changes in the branching patterns of the retina vascular network in diabetes using the fractal analysis method. METHODS: This was a clinic-based prospective study of 172 participants managed at the Ophthalmological Clinic of Cluj-Napoca, Romania, between January 2012 and December 2013. A set of 172 segmented and skeletonized human retinal images, corresponding to both normal (24 images) and pathological (148 images) states of the retina were examined. An automatic unsupervised method for retinal vessel segmentation was applied before fractal analysis. The fractal analyses of the retinal digital images were performed using the fractal analysis software ImageJ. Statistical analyses were performed for these groups using Microsoft Office Excel 2003 and GraphPad InStat software. RESULTS: It was found that subtle changes in the vascular network geometry of the human retina are influenced by diabetic retinopathy (DR) and can be estimated using the fractal geometry. The average of fractal dimensions D for the normal images (segmented and skeletonized versions) is slightly lower than the corresponding values of mild non-proliferative DR (NPDR) images (segmented and skeletonized versions). The average of fractal dimensions D for the normal images (segmented and skeletonized versions) is higher than the corresponding values of moderate NPDR images (segmented and skeletonized versions). The lowest values were found for the corresponding values of severe NPDR images (segmented and skeletonized versions). CONCLUSION: The fractal analysis of fundus photographs may be used for a more complete undeTrstanding of the early and basic pathophysiological mechanisms of diabetes. The architecture of the retinal microvasculature in diabetes can be quantitative quantified by means of the fractal dimension. Microvascular abnormalities on retinal imaging may elucidate early mechanistic pathways for microvascular complications and distinguish patients with DR from healthy individuals.

A non-parametric segmentation methodology for oral videocapillaroscopic images.

We aim to describe a new non-parametric methodology to support the clinician during the diagnostic process of oral videocapillaroscopy to evaluate peripheral microcirculation. Our methodology, mainly based on wavelet analysis and mathematical morphology to preprocess the images, segments them by minimizing the within-class luminosity variance of both capillaries and background. Experiments were carried out on a set of real microphotographs to validate this approach versus handmade segmentations provided by physicians. By using a leave-one-patient-out approach, we pointed out that our methodology is robust, according to precision-recall criteria (average precision and recall are equal to 0.924 and 0.923, respectively) and it acts as a physician in terms of the Jaccard index (mean and standard deviation equal to 0.858 and 0.064, respectively).

Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution Hermite model.

We present an algorithm estimating the width of retinal vessels in fundus camera images. The algorithm uses a novel parametric surface model of the cross-sectional intensities of vessels, and ensembles of bagged decision trees to estimate the local width from the parameters of the best-fit surface. We report comparative tests with REVIEW, currently the public database of reference for retinal width estimation, containing 16 images with 193 annotated vessel segments and 5066 profile points annotated manually by three independent experts. Comparative tests are reported also with our own set of 378 vessel widths selected sparsely in 38 images from the Tayside Scotland diabetic retinopathy screening programme and annotated manually by two clinicians. We obtain considerably better accuracies compared to leading methods in REVIEW tests and in Tayside tests. An important advantage of our method is its stability (success rate, i.e., meaningful measurement returned, of 100% on all REVIEW data sets and on the Tayside data set) compared to a variety of methods from the literature. We also find that results depend crucially on testing data and conditions, and discuss criteria for selecting a training set yielding optimal accuracy.

FABC: retinal vessel segmentation using AdaBoost.

This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as well as the additional manual segmentation provided by DRIVE. Training was conducted confined to the dedicated training set from the DRIVE database, and feature-based AdaBoost classifier (FABC) was tested on the 20 images from the test set. FABC achieved an area under the receiver operating characteristic (ROC) curve of 0.9561, in line with state-of-the-art approaches, but outperforming their accuracy ( 0.9597 versus 0.9473 for the nearest performer).