Editorial: Brain Modeling of Neurogenerative Disorders

1 Facultad CITEC, Centro de Estudios de Matemática Computacional (CEMC), Universidad de las Ciencias Informáticas (UCI), Havana, Cuba, 2 The Clinical Hospital of Chengdu Brain Sciences Institute, MoE Key Lab for Neuroinformation, University of Electronic Sciences and Technology of China, Chengdu, China, Cuban Neuroscience Center, Havana, Cuba, 4 Section Brain Stimulation, Charité Universitätsmedizin Berlin, Berlin, Germany, 5 School of Behavioral and Brain Sciences,

models for AD that aim to provide a mechanistic understanding of the disease and they conceptualize how multi-scale modeling can link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using TVB platform. In the other review, González et al. explore some of the most relevant models of astrocyte metabolism, including genome-scale reconstructions and astrocyte-neuron interactions developed in previous research (Schuster et al., 2000;Agutter, 2005). The subject of this review contrasts with preceding ones focused on computational models regarding calcium signaling in astrocytes and its modulation in synaptic transmission, gliotransmitter release, and related processes (Oschmann et al., 2016;Manninen et al., 2019). Additionally, and not less importantly, the authors discuss strategies from the multi-omics viewpoint and computational models of other glial cell types, thus increasing our understanding of brain metabolism and its association with neurodegenerative diseases.
In the Original Research by Castellazzi et al., ML is leveraged to elucidate a critical aspect of neurodegenerative diseases, namely, the correct disease diagnosis. The study concerns two frequent dementias, AD and vascular dementia (VD). The authors employed three kinds of ML algorithms (artificial neural network, support vector machine, and adaptive neurofuzzy inference system) combined with advanced magnetic resonance imaging features to distinguish VD from AD. They also developed approaches that might help predict the prevalent disease in subjects with an unclear profile of AD or VD. The approach showed a high discriminant power to classify both diseases.
Parkinson's disease (PD) is a motor disorder affecting millions of people worldwide, mostly older adults. PD is caused by loss of dopaminergic neurons in substantia nigra pars compacta (SNc), located in the midbrain region (Fu et al., 2018). However, the precise cause of cell death has not been established. For this purpose, Muddapu and Chakravarthy developed a multiscale computational model of the subsystem of the basal ganglia comprising the subthalamic nucleus, globus pallidus externa, and SNc. They simulated the molecular pathways involved in cell death of SNc neurons in terms of detailed chemical kinetics. The authors showed that energy deficiencies occurring at cellular and network levels could precipitate the excitotoxic loss of SNc neurons in PD. Therapeutic effects of neuroprotective interventions were simulated.
Brain rhythms are critical in coordinating brain networks. Rhythms of different frequencies have specific coupling properties, such as cross-frequency coupling (CFC), which potentially provides synchronization and interaction mechanisms between local and global processes across cortical networks. Thus, CFC is a novel feature of brain activity correlated with brain function and dysfunction. Measures for CFC exist in the form of the cross-bispectrum or its normalized version, which are third-order statistical moments in the frequency domain (Chella et al., 2014). The investigation of Bartz et al. makes two contributions to analyzing brain oscillations with CFC techniques. First, a new bispectral CFC measure selective to couplings between three or more brain sources is introduced. Secondly, they present the correct empirical distribution for the coupling measure, which is necessary to assess the significance of coupling results properly. The corrected statistic is not limited to their particular measure but holds for all complex-valued coupling estimators.
Tuladhar et al. present a fascinating study focused on a paradigm for modeling neural diseases in silico with DL methods. The developed model is used in modeling posterior cortical atrophy (PCA), an atypical form of AD affecting the visual cortex. The authors simulated PCA in deep convolutional neural networks trained for visual object recognition by randomly injuring connections between artificial neurons. They reported that injured networks progressively lost their object recognition capability. This paradigm can be used to study the impact of neural plasticity in partially recovering the object recognition capability and can also be applied to other cognitive domains (e.g., motor control, language processing, and decision making). Thus, this research opens possibilities for DL applications in neurodegenerative brain disorders and aging.
We want to thank all authors for their cutting-edge contributions. The Research Topic provides to readers an opportunity to update their knowledge in clinically relevant computational neurosciences research.

AUTHOR CONTRIBUTIONS
JG-G, EM-M, PR, and PV-S wrote and edited the text. All authors contributed to the article and approved this version. a review. Brain Res. Bull. 136, 76-84. doi: 10.1016/j.brainresbull.2017. 01.027 Schuster, S., Fell,D. A., and Dandekar, T. (2000.A general definition of metabolic path ways useful for systematic organization and analysis of complex metabolic networks. Nat. Biotechnol. 18, 326-332. doi: 10.1038/73786 Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Copyright © 2022 Gulín-González, Bringas-Vega, Martínez-Montes, Ritter, Solodkin, Valdes-Sosa and Valdes-Sosa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.