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Groden M, Moessinger HM, Schaffran B, DeFelipe J, Benavides-Piccione R, Cuntz H, Jedlicka P. A biologically inspired repair mechanism for neuronal reconstructions with a focus on human dendrites. PLoS Comput Biol 2024; 20:e1011267. [PMID: 38394339 PMCID: PMC10917450 DOI: 10.1371/journal.pcbi.1011267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 03/06/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024] Open
Abstract
Investigating and modelling the functionality of human neurons remains challenging due to the technical limitations, resulting in scarce and incomplete 3D anatomical reconstructions. Here we used a morphological modelling approach based on optimal wiring to repair the parts of a dendritic morphology that were lost due to incomplete tissue samples. In Drosophila, where dendritic regrowth has been studied experimentally using laser ablation, we found that modelling the regrowth reproduced a bimodal distribution between regeneration of cut branches and invasion by neighbouring branches. Interestingly, our repair model followed growth rules similar to those for the generation of a new dendritic tree. To generalise the repair algorithm from Drosophila to mammalian neurons, we artificially sectioned reconstructed dendrites from mouse and human hippocampal pyramidal cell morphologies, and showed that the regrown dendrites were morphologically similar to the original ones. Furthermore, we were able to restore their electrophysiological functionality, as evidenced by the recovery of their firing behaviour. Importantly, we show that such repairs also apply to other neuron types including hippocampal granule cells and cerebellar Purkinje cells. We then extrapolated the repair to incomplete human CA1 pyramidal neurons, where the anatomical boundaries of the particular brain areas innervated by the neurons in question were known. Interestingly, the repair of incomplete human dendrites helped to simulate the recently observed increased synaptic thresholds for dendritic NMDA spikes in human versus mouse dendrites. To make the repair tool available to the neuroscience community, we have developed an intuitive and simple graphical user interface (GUI), which is available in the TREES toolbox (www.treestoolbox.org).
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Affiliation(s)
- Moritz Groden
- 3R Computer-Based Modelling, Faculty of Medicine, ICAR3R, Justus Liebig University Giessen, Giessen, Germany
| | - Hannah M. Moessinger
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt am Main, Germany
| | - Barbara Schaffran
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt am Main, Germany
- Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Spain
- Instituto Cajal (CSIC), Madrid, Spain
| | - Ruth Benavides-Piccione
- Laboratorio Cajal de Circuitos Corticales (CTB), Universidad Politécnica de Madrid, Spain
- Instituto Cajal (CSIC), Madrid, Spain
| | - Hermann Cuntz
- 3R Computer-Based Modelling, Faculty of Medicine, ICAR3R, Justus Liebig University Giessen, Giessen, Germany
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
| | - Peter Jedlicka
- 3R Computer-Based Modelling, Faculty of Medicine, ICAR3R, Justus Liebig University Giessen, Giessen, Germany
- Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, Frankfurt am Main, Germany
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Karperien AL, Jelinek HF. Morphology and Fractal-Based Classifications of Neurons and Microglia in Two and Three Dimensions. ADVANCES IN NEUROBIOLOGY 2024; 36:149-172. [PMID: 38468031 DOI: 10.1007/978-3-031-47606-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Microglia and neurons live physically intertwined, intimately related structurally and functionally in a dynamic relationship in which microglia change continuously over a much shorter timescale than do neurons. Although microglia may unwind and depart from the neurons they attend under certain circumstances, in general, together both contribute to the fractal topology of the brain that defines its computational capabilities. Both neuronal and microglial morphologies are well-described using fractal analysis complementary to more traditional measures. For neurons, the fractal dimension has proved valuable for classifying dendritic branching and other neuronal features relevant to pathology and development. For microglia, fractal geometry has substantially contributed to classifying functional categories, where, in general, the more pathological the biological status, the lower the fractal dimension for individual cells, with some exceptions, including hyper-ramification. This chapter provides a review of the intimate relationships between neurons and microglia, by introducing 2D and 3D fractal analysis methodology and its applications in neuron-microglia function in health and disease.
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Affiliation(s)
- Audrey L Karperien
- School of Community Health, Charles Sturt University, Albury, NSW, Australia
| | - Herbert F Jelinek
- Department of Medical Sciences and Biotechnology Center, Khalifa University, Abu Dhabi, UAE
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Fuster-Parra P, Yañez AM, López-González A, Aguiló A, Bennasar-Veny M. Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network. Front Public Health 2023; 10:1035025. [PMID: 36711374 PMCID: PMC9878341 DOI: 10.3389/fpubh.2022.1035025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/15/2022] [Indexed: 01/14/2023] Open
Abstract
Background It is known that people with prediabetes increase their risk of developing type 2 diabetes (T2D), which constitutes a global public health concern, and it is associated with other diseases such as cardiovascular disease. Methods This study aimed to determine those factors with high influence in the development of T2D once prediabetes has been diagnosed, through a Bayesian network (BN), which can help to prevent T2D. Furthermore, the set of features with the strongest influences on T2D can be determined through the Markov blanket. A BN model for T2D was built from a dataset composed of 12 relevant features of the T2D domain, determining the dependencies and conditional independencies from empirical data in a multivariate context. The structure and parameters were learned with the bnlearn package in R language introducing prior knowledge. The Markov blanket was considered to find those features (variables) which increase the risk of T2D. Results The BN model established the different relationships among features (variables). Through inference, a high estimated probability value of T2D was obtained when the body mass index (BMI) was instantiated to obesity value, the glycosylated hemoglobin (HbA1c) to more than 6 value, the fatty liver index (FLI) to more than 60 value, physical activity (PA) to no state, and age to 48-62 state. The features increasing T2D in specific states (warning factors) were ranked. Conclusion The feasibility of BNs in epidemiological studies is shown, in particular, when data from T2D risk factors are considered. BNs allow us to order the features which influence the most the development of T2D. The proposed BN model might be used as a general tool for prevention, that is, to improve the prognosis.
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Affiliation(s)
- Pilar Fuster-Parra
- Department of Mathematics and Computer Sciences, Balearic Islands University, Palma, Spain,Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma, Spain
| | - Aina M. Yañez
- Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma, Spain,Department of Nursing and Physiotherapy, Balearic Islands University, Palma, Spain,Research Group on Global Health and Human Development, Balearic Islands University, Palma, Spain,*Correspondence: Aina M. Yañez ✉
| | - Arturo López-González
- Escuela Universitaria ADEMA, Palma, Spain,Prevention of Occupational Risk in Health Services, Balearic Islands Health Service, Palma, Spain
| | - A. Aguiló
- Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma, Spain,Department of Nursing and Physiotherapy, Balearic Islands University, Palma, Spain
| | - Miquel Bennasar-Veny
- Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma, Spain,Department of Nursing and Physiotherapy, Balearic Islands University, Palma, Spain,CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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Kagan BJ, Kitchen AC, Tran NT, Habibollahi F, Khajehnejad M, Parker BJ, Bhat A, Rollo B, Razi A, Friston KJ. In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron 2022; 110:3952-3969.e8. [PMID: 36228614 DOI: 10.1016/j.neuron.2022.09.001] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 06/21/2022] [Accepted: 08/31/2022] [Indexed: 11/06/2022]
Abstract
Integrating neurons into digital systems may enable performance infeasible with silicon alone. Here, we develop DishBrain, a system that harnesses the inherent adaptive computation of neurons in a structured environment. In vitro neural networks from human or rodent origins are integrated with in silico computing via a high-density multielectrode array. Through electrophysiological stimulation and recording, cultures are embedded in a simulated game-world, mimicking the arcade game "Pong." Applying implications from the theory of active inference via the free energy principle, we find apparent learning within five minutes of real-time gameplay not observed in control conditions. Further experiments demonstrate the importance of closed-loop structured feedback in eliciting learning over time. Cultures display the ability to self-organize activity in a goal-directed manner in response to sparse sensory information about the consequences of their actions, which we term synthetic biological intelligence. Future applications may provide further insights into the cellular correlates of intelligence.
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Affiliation(s)
| | | | - Nhi T Tran
- The Ritchie Centre, Hudson Institute of Medical Research, Clayton, VIC, Australia
| | - Forough Habibollahi
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
| | - Moein Khajehnejad
- Department of Data Science and AI, Monash University, Melbourne, Australia
| | - Bradyn J Parker
- Department of Materials Science and Engineering, Monash University, Melbourne, VIC, Australia
| | - Anjali Bhat
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Ben Rollo
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Adeel Razi
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK; Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia; Monash Biomedical Imaging, Monash University, Clayton, VIC, Australia; CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Canada
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
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Acute gut inflammation reduces neural activity and spine maturity in hippocampus but not basolateral amygdala. Sci Rep 2022; 12:20169. [PMID: 36418891 PMCID: PMC9684565 DOI: 10.1038/s41598-022-24245-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 11/11/2022] [Indexed: 11/25/2022] Open
Abstract
Gastrointestinal tract (gut) inflammation increases stress and threat-coping behaviors, which are associated with altered activity in fear-related neural circuits, such as the basolateral amygdala and hippocampus. It remains to be determined whether inflammation from the gut affects neural activity by altering dendritic spines. We hypothesized that acute inflammation alters dendritic spines in a brain region-specific manner. Here we show that acute gut inflammation (colitis) evoked by dextran sodium sulfate (DSS) did not affect the overall spine density in the CA1 region of hippocampus, but increased the relative proportion of immature spines to mature spines on basal dendrites of pyramidal neurons. In contrast, in animals with colitis, no changes in spine density or composition on dendrites of pyramidal cells was observed in the basolateral amygdala. Rather, we observed decreased spine density on dendrites of stellate neurons, but not the relative proportions of mature vs immature spines. We used cFos expression evoked by the forced swim task as a measure of neural activity during stress and found no effect of DSS on the density of cFos immunoreactive neurons in basolateral amygdala. In contrast, fewer CA1 neurons expressed cFos in mice with colitis, relative to controls. Furthermore, CA1 cFos expression negatively correlated with active stress-coping in the swim task and was negatively correlated with gut inflammation. These data reveal that the effects of acute gut inflammation on synaptic remodeling depend on brain region, neuronal phenotype, and dendrite location. In the hippocampus, a shift to immature spines and hypoactivity are more strongly related to colitis-evoked behavioral changes than is remodeling in basolateral amygdala.
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Mihaljević B, Larrañaga P, Bielza C. Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks. Front Neuroinform 2021; 15:580873. [PMID: 33679362 PMCID: PMC7930221 DOI: 10.3389/fninf.2021.580873] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 01/14/2021] [Indexed: 11/13/2022] Open
Abstract
Pyramidal neurons are the most common neurons in the cerebral cortex. Understanding how they differ between species is a key challenge in neuroscience. We compared human temporal cortex and mouse visual cortex pyramidal neurons from the Allen Cell Types Database in terms of their electrophysiology and dendritic morphology. We found that, among other differences, human pyramidal neurons had a higher action potential threshold voltage, a lower input resistance, and larger dendritic arbors. We learned Gaussian Bayesian networks from the data in order to identify correlations and conditional independencies between the variables and compare them between the species. We found strong correlations between electrophysiological and morphological variables in both species. In human cells, electrophysiological variables were correlated even with morphological variables that are not directly related to dendritic arbor size or diameter, such as mean bifurcation angle and mean branch tortuosity. Cortical depth was correlated with both electrophysiological and morphological variables in both species, and its effect on electrophysiology could not be explained in terms of the morphological variables. For some variables, the effect of cortical depth was opposite in the two species. Overall, the correlations among the variables differed strikingly between human and mouse neurons. Besides identifying correlations and conditional independencies, the learned Bayesian networks might be useful for probabilistic reasoning regarding the morphology and electrophysiology of pyramidal neurons.
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Affiliation(s)
- Bojan Mihaljević
- Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Boadilla del Monte, Spain
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