Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review.

Morphometry is fundamental for studying and correlating neuronal morphology with brain functions. With increasing computational power, it is possible to extract morphometric characteristics automatically, including features such as length, volume, and number of neuron branches. However, to the best of our knowledge, there is no mapping of morphometric tools yet. In this context, we conducted a systematic search and review to identify and analyze tools within the scope of neuron analysis. Thus, the work followed a well-defined protocol and sought to answer the following research questions: What open-source tools are available for neuronal morphometric analysis? What morphometric characteristics are extracted by these tools? For this, aiming for greater robustness and coverage, the study was based on the paper analysis as well as the study of documentation and tests with the tools available in repositories. We analyzed 1,586 papers and mapped 23 tools, where NeuroM, L-Measure, and NeuroMorphoVis extract the most features. Furthermore, we contribute to the body of knowledge with the unprecedented presentation of 150 unique morphometric features whose terminologies were categorized and standardized. Overall, the study contributes to advancing the understanding of the complex mechanisms underlying the brain.

Active surveillance is a feasible and safe strategy in selected patients with papillary thyroid cancer and suspicious cervical lymph nodes detected after thyroidectomy.

Objective: After initial treatment, up to 30% of patients with papillary thyroid cancer (PTC) have incomplete response, mainly cervical lymph node (LN) disease. Previous studies have suggested that active surveillance (AS) is a possible option for these patients. Our aim was to report the results of AS in patients with PTC and cervical LN disease. Materials and methods: In this retrospective observational study, we included adult patients treated and followed for PTC, who presented with cervical LN disease and were managed with AS. Growth was defined as an increase ≥ 3mm in either diameter. Results: We included 32 patients: 27 (84.4%) women, age of 39 ± 14 years, all initially treated with total thyroidectomy, and 22 (69%) with therapeutic neck dissection. Cervical LN disease was diagnosed 1 year (0.3-12.6) after initial management, with a diameter of 9.0 mm (6.0-19.0). After a median AS of 4.3 years (0.6-14.1), 4 (12.5%) patients had LNgrowth: 2 (50%) of whom were surgically removed, 1 (25%) was effectively treated with radiotherapy, and 1 (25%) had a scheduled surgery. Tg increase was the only predictive factor of LN growth evaluated as both the delta Tg (p < 0.0366) and percentage of Tg change (p < 0.0140). None of the included patients died, had local complications due to LN growth or salvage therapy, or developed distant metastases during follow-up. Conclusion: In selected patients with PTC and suspicious cervical LNs diagnosed after initial treatment, AS is a feasible and safe strategy as it allows effective identification and treatment of the minority of patients who progress.

Lobectomy in patients with differentiated thyroid cancer: experience of a Chilean tertiary center.

PURPOSE: Thyroid lobectomy (TL) is an appropriate treatment for up to 4 cm intrathyroidal differentiated thyroid cancer (DTC). There is scarce data regarding TL outside first-world centers. Our aim is to report a cohort of patients with DTC treated with TL in Chile. METHODS: We included DTC patients treated with TL, followed for at least 6 months, characterized their clinicopathological features and classified their risk of recurrence and response to treatment. RESULTS: Eighty-two patients followed for a median of 2.3 years (0.5-7.0). Seventy-three (89%) patients had papillary, 8 (9.8%) follicular and 1 (1.2%) high-grade DTC. The risk of recurrence was low in 56 (68.3%) and intermediate in 26 (31.7%). Eight (9.8%) patients required early completion thyroidectomy and radioiodine. At last follow-up, 52 (70.3%) had excellent, 19 (25.7%) had indeterminate, and 1 (1.4%) had structural incomplete response. CONCLUSION: In a developing country, TL is an adequate option for appropriately selected DTC patients.

On the taxonomy of optimization problems under estimation of distribution algorithms.

Understanding the relationship between a search algorithm and the space of problems is a fundamental issue in the optimization field. In this paper, we lay the foundations to elaborate taxonomies of problems under estimation of distribution algorithms (EDAs). By using an infinite population model and assuming that the selection operator is based on the rank of the solutions, we group optimization problems according to the behavior of the EDA. Throughout the definition of an equivalence relation between functions it is possible to partition the space of problems in equivalence classes in which the algorithm has the same behavior. We show that only the probabilistic model is able to generate different partitions of the set of possible problems and hence, it predetermines the number of different behaviors that the algorithm can exhibit. As a natural consequence of our definitions, all the objective functions are in the same equivalence class when the algorithm does not impose restrictions to the probabilistic model. The taxonomy of problems, which is also valid for finite populations, is studied in depth for a simple EDA that considers independence among the variables of the problem. We provide the sufficient and necessary condition to decide the equivalence between functions and then we develop the operators to describe and count the members of a class. In addition, we show the intrinsic relation between univariate EDAs and the neighborhood system induced by the Hamming distance by proving that all the functions in the same class have the same number of local optima and that they are in the same ranking positions. Finally, we carry out numerical simulations in order to analyze the different behaviors that the algorithm can exhibit for the functions defined over the search space [Formula: see text].

Redefining Neural Architecture Search of Heterogeneous Multinetwork Models by Characterizing Variation Operators and Model Components.

With neural architecture search (NAS) methods gaining ground on manually designed deep neural networks-even more rapidly as model sophistication escalates-the research trend is shifting toward arranging different and often increasingly complex NAS spaces. In this conjuncture, delineating algorithms which can efficiently explore these search spaces can result in a significant improvement over currently used methods, which, in general, randomly select the structural variation operator, hoping for a performance gain. In this article, we investigate the effect of different variation operators in a complex domain, that of multinetwork heterogeneous neural models. These models have an extensive and complex search space of structures as they require multiple subnetworks within the general model in order to answer different output types. From that investigation, we extract a set of general guidelines whose application is not limited to that particular type of model and are useful to determine the direction in which an architecture optimization method could find the largest improvement. To deduce the set of guidelines, we characterize both the variation operators, according to their effect on the complexity and performance of the model; and the models, relying on diverse metrics which estimate the quality of the different parts composing it.

Does displacement of lower pole stones during retrograde intrarenal surgery improves stone-free status? A systematic review and meta-analysis.

PURPOSE: Kidney stones are one of the most common urological diseases worldwide. The size and location of the stone are the most important factors in determining the most suitable treatment options. The aim of this review was to evaluate the displacement of lower pole stones. METHODS: Three studies assessing the efficacy of translocating kidney stones from the lower pole of the kidney to other locations during retrograde intrarenal surgery published in the last 20 years were included. A systematic search was conducted in the PubMed, Embase, Latin American and Caribbean Health Sciences Literature (LILACS), and Web of Science databases using the following search terms: "Lower pole," "Lithotripsy." Meta-analysis was performed using Review Manager version 5.4. RESULTS: Stone-free rates were improved through displacement (odds ratio - OR = -0.15; 95% confidence interval-95%CI -0.24--0.05; p = 0.002; I2 = 21%), but at the cost of increased surgical duration (mean difference = -12.50; 95%CI -24.06--0.95; p = 0.03; I2 = 94%). Although this represents a potentially negative outcome, the improvement in clearance rates justifies the additional investment of time and effort. CONCLUSIONS: Displacement of lower pole kidney stones for subsequent lithotripsy brings significant benefits in terms of stone-free rate, with no difference in laser energy usage. However, it results in increased surgical time. Despite these factors, the benefits to patients undergoing the procedure are substantial.

Regularized logistic regression and multiobjective variable selection for classifying MEG data.

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks.

A framework to pinpoint the scope of unconscious processing is critical to improve models of visual consciousness. Previous research observed brain signatures of unconscious processing in visual cortex, but these were not reliably identified. Further, whether unconscious contents are represented in high-level stages of the ventral visual stream and linked parieto-frontal areas remains unknown. Using a within-subject, high-precision functional magnetic resonance imaging approach, we show that unconscious contents can be decoded from multi-voxel patterns that are highly distributed alongside the ventral visual pathway and also involving parieto-frontal substrates. Classifiers trained with multi-voxel patterns of conscious items generalized to predict the unconscious counterparts, indicating that their neural representations overlap. These findings suggest revisions to models of consciousness such as the neuronal global workspace. We then provide a computational simulation of visual processing/representation without perceptual sensitivity by using deep neural networks performing a similar visual task. The work provides a framework for pinpointing the representation of unconscious knowledge across different task domains.

Learning factorizations in estimation of distribution algorithms using affinity propagation.

Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of mainly binary optimization problems. In this paper, we introduce the affinity propagation EDA (AffEDA) which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and nonbinary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased.

Analysis of the transferability and robustness of GANs evolved for Pareto set approximations.

The generative adversarial network (GAN) is a good example of a strong-performing, neural network-based generative model, even though it does have some drawbacks of its own. Mode collapsing and the difficulty in finding the optimal network structure are two of the most concerning issues. In this paper, we address these two issues at the same time by proposing a neuro-evolutionary approach with an agile evaluation method for the fast evolution of robust deep architectures that avoid mode collapsing. The computation of Pareto set approximations with GANs is chosen as a suitable benchmark to evaluate the quality of our approach. Furthermore, we demonstrate the consistency, scalability, and generalization capabilities of the proposed method, which shows its potential applications to many areas. We finally readdress the issue of designing this kind of models by analyzing the characteristics of the best performing GAN specifications, and conclude with a set of general guidelines. This results in a reduction of the many-dimensional problem of structural manual design or automated search.

Correction to 'Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning'.

[This corrects the article DOI: 10.1098/rsos.192043.].

Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning.

How the brain representation of conceptual knowledge varies as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. In the present functional magnetic resonance imaging study, participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models-associated with the image referents of the words-and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.

Active surveillance is a feasible and safe strategy in selected patients with papillary thyroid cancer and suspicious cervical lymph nodes detected after thyroidectomy

ABSTRACT Objective: After initial treatment, up to 30% of patients with papillary thyroid cancer (PTC) have incomplete response, mainly cervical lymph node (LN) disease. Previous studies have suggested that active surveillance (AS) is a possible option for these patients. Our aim was to report the results of AS in patients with PTC and cervical LN disease. Materials and methods: In this retrospective observational study, we included adult patients treated and followed for PTC, who presented with cervical LN disease and were managed with AS. Growth was defined as an increase ≥ 3mm in either diameter. Results: We included 32 patients: 27 (84.4%) women, age of 39 ± 14 years, all initially treated with total thyroidectomy, and 22 (69%) with therapeutic neck dissection. Cervical LN disease was diagnosed 1 year (0.3-12.6) after initial management, with a diameter of 9.0 mm (6.0-19.0). After a median AS of 4.3 years (0.6-14.1), 4 (12.5%) patients had LN growth: 2 (50%) of whom were surgically removed, 1 (25%) was effectively treated with radiotherapy, and 1 (25%) had a scheduled surgery. Tg increase was the only predictive factor of LN growth evaluated as both the delta Tg (p < 0.0366) and percentage of Tg change (p < 0.0140). None of the included patients died, had local complications due to LN growth or salvage therapy, or developed distant metastases during follow-up. Conclusions: In selected patients with PTC and suspicious cervical LNs diagnosed after initial treatment, AS is a feasible and safe strategy as it allows effective identification and treatment of the minority of patients who progress.

Does displacement of lower pole stones during retrograde intrarenal surgery improves stone-free status?: a systematic review and meta-analysis

Purpose: Kidney stones are one of the most common urological diseases worldwide. The size and location of the stone are the most important factors in determining the most suitable treatment options. The aim of this review was to evaluate the displacement of lower pole stones. Methods: Three studies assessing the efficacy of translocating kidney stones from the lower pole of the kidney to other locations during retrograde intrarenal surgery published in the last 20 years were included. A systematic search was conducted in the PubMed, Embase, Latin American and Caribbean Health Sciences Literature (LILACS), and Web of Science databases using the following search terms: "Lower pole," "Lithotripsy." Meta-analysis was performed using Review Manager version 5.4. Results: Stone-free rates were improved through displacement (odds ratio - OR = -0.15; 95% confidence interval-95%CI -0.24--0.05; p = 0.002; I2 = 21%), but at the cost of increased surgical duration (mean difference = -12.50; 95%CI -24.06--0.95; p = 0.03; I2 = 94%). Although this represents a potentially negative outcome, the improvement in clearance rates justifies the additional investment of time and effort. Conclusions: Displacement of lower pole kidney stones for subsequent lithotripsy brings significant benefits in terms of stone-free rate, with no difference in laser energy usage. However, it results in increased surgical time. Despite these factors, the benefits to patients undergoing the procedure are substantial.

Side chain placement using estimation of distribution algorithms.

OBJECTIVE: This paper presents an algorithm for the solution of the side chain placement problem. METHODS AND MATERIALS: The algorithm combines the application of the Goldstein elimination criterion with the univariate marginal distribution algorithm (UMDA), which stochastically searches the space of possible solutions. The suitability of the algorithm to address the problem is investigated using a set of 425 proteins. RESULTS: For a number of difficult instances where inference algorithms do not converge, it has been shown that UMDA is able to find better structures. CONCLUSIONS: The results obtained show that the algorithm can achieve better structures than those obtained with other state-of-the-art methods like inference-based techniques. Additionally, a theoretical and empirical analysis of the computational cost of the algorithm introduced has been presented.

Classification of neocortical interneurons using affinity propagation.

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

Optimizing brain networks topologies using multi-objective evolutionary computation.

The analysis of brain network topological features has served to better understand these networks and reveal particular characteristics of their functional behavior. The distribution of brain network motifs is particularly useful for detecting and describing differences between brain networks and random and computationally optimized artificial networks. In this paper we use a multi-objective evolutionary optimization approach to generate optimized artificial networks that have a number of topological features resembling brain networks. The Pareto set approximation of the optimized networks is used to extract network descriptors that are compared to brain and random network descriptors. To analyze the networks, the clustering coefficient, the average path length, the modularity and the betweenness centrality are computed. We argue that the topological complexity of a brain network can be estimated using the number of evaluations needed by an optimization algorithm to output artificial networks of similar complexity. For the analyzed network examples, our results indicate that while original brain networks have a reduced structural motif number and a high functional motif number, they are not optimal with respect to these two topological features. We also investigate the correlation between the structural and functional motif numbers, the average path length and the clustering coefficient in random, optimized and brain networks.

Multi-marker tagging single nucleotide polymorphism selection using estimation of distribution algorithms.

OBJECTIVES: This paper presents an optimization algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs). METHODS AND MATERIALS: The determination of the set of minimal tagging SNPs is approached as an optimization problem in which each tagged SNP can be covered by a single tagging SNP or by a pair of tagging SNPs. The problem is solved using an estimation of distribution algorithm (EDA) which takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions. The EDA stochastically searches the constrained space of feasible solutions. It is evaluated across HapMap reference panel data sets. RESULTS: The EDA was compared with a SAT solver, able to find the single-marker minimal tagging sets, and with the Tagger program. The percentage of reduction ranged from 10% to 43% in the number of tagging SNPs of the minimal multi-marker tagging set found by the EDA with respect to the other algorithms. CONCLUSIONS: The introduced algorithm is effective for the identification of minimal multi-marker SNP sets, which considerably reduce the dimension of the tagging SNP set in comparison with single-marker sets. Other variants of the SNP problem can be treated following the same approach.

A review of estimation of distribution algorithms in bioinformatics.

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain.