The logic behind neural control of breathing pattern.
Ontology highlight
ABSTRACT:
SUBMITTER: Ben-Tal A
PROVIDER: S-EPMC6591426 | biostudies-literature | 2019 Jun
REPOSITORIES: biostudies-literature
Publications
The logic behind neural control of breathing pattern.
Ben-Tal Alona A Wang Yunjiao Y Leite Maria C A MCA
Scientific reports 20190624 1
The respiratory rhythm generator is spectacular in its ability to support a wide range of activities and adapt to changing environmental conditions, yet its operating mechanisms remain elusive. We show how selective control of inspiration and expiration times can be achieved in a new representation of the neural system (called a Boolean network). The new framework enables us to predict the behavior of neural networks based on properties of neurons, not their values. Hence, it reveals the logic b ...[more]
Similar Datasets
Project description:Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.
| S-EPMC7781484 | biostudies-literature
Project description:Breathing is maintained and controlled by a network of automatic neurons in the brainstem that generate respiratory rhythm and receive regulatory inputs. Breathing complexity therefore arises from respiratory central pattern generators modulated by peripheral and supra-spinal inputs. Very little is known on the brainstem neural substrates underlying breathing complexity in humans. We used both experimental and theoretical approaches to decipher these mechanisms in healthy humans and patients with chronic obstructive pulmonary disease (COPD). COPD is the most frequent chronic lung disease in the general population mainly due to tobacco smoke. In patients, airflow obstruction associated with hyperinflation and respiratory muscles weakness are key factors contributing to load-capacity imbalance and hence increased respiratory drive. Unexpectedly, we found that the patients breathed with a higher level of complexity during inspiration and expiration than controls. Using functional magnetic resonance imaging (fMRI), we scanned the brain of the participants to analyze the activity of two small regions involved in respiratory rhythmogenesis, the rostral ventro-lateral (VL) medulla (pre-Bötzinger complex) and the caudal VL pons (parafacial group). fMRI revealed in controls higher activity of the VL medulla suggesting active inspiration, while in patients higher activity of the VL pons suggesting active expiration. COPD patients reactivate the parafacial to sustain ventilation. These findings may be involved in the onset of respiratory failure when the neural network becomes overwhelmed by respiratory overload We show that central neural activity correlates with airflow complexity in healthy subjects and COPD patients, at rest and during inspiratory loading. We finally used a theoretical approach of respiratory rhythmogenesis that reproduces the kernel activity of neurons involved in the automatic breathing. The model reveals how a chaotic activity in neurons can contribute to chaos in airflow and reproduces key experimental fMRI findings.
| S-EPMC3789752 | biostudies-literature
Project description:Humans are highly skilled in social reasoning, e.g., inferring thoughts of others. This mentalizing ability systematically recruits brain regions such as Temporo-Parietal Junction (TPJ), Precuneus (PC) and medial Prefrontal Cortex (mPFC). Further, posterior mPFC is associated with allocentric mentalizing and conflict monitoring while anterior mPFC is associated with self-reference (egocentric) processing. Here we extend this work to how we reason not just about what one person thinks but about the abstract shared social norm. We apply functional magnetic resonance imaging to investigate neural representations while participants judge the social congruency between emotional auditory utterances in relation to visual scenes according to how 'most people' would perceive it. Behaviorally, judging according to a social norm increased the similarity of response patterns among participants. Multivoxel pattern analysis revealed that social congruency information was not represented in visual and auditory areas, but was clear in most parts of the mentalizing network: TPJ, PC and posterior (but not anterior) mPFC. Furthermore, interindividual variability in anterior mPFC representations was inversely related to the behavioral ability to adjust to the social norm. Our results suggest that social norm inferencing is associated with a distributed and partially individually specific representation of social congruency in the mentalizing network.
| S-EPMC6113313 | biostudies-literature
Project description:Protein stability is affected at different hierarchies - gene, RNA, amino acid sequence and structure. Gene is the first level which contributes via varying codon compositions. Codon selectivity of an organism differs with normal and extremophilic milieu. The present work attempts at detailing the codon usage pattern of six extremophilic classes and their harmony. Homologous gene datasets of thermophile-mesophile, psychrophile-mesophile, thermophile-psychrophile, acidophile-alkaliphile, halophile-nonhalophile and barophile-nonbarophile were analysed for filtering statistically significant attributes. Relative abundance analysis, 1-9 scale ranking, nucleotide compositions, attribute weighting and machine learning algorithms were employed to arrive at findings. AGG in thermophiles and barophiles, CAA in mesophiles and psychrophiles, TGG in acidophiles, GAG in alkaliphiles and GAC in halophiles had highest preference. Preference of GC-rich and G/C-ending codons were observed in halophiles and barophiles whereas, a decreasing trend was reflected in psychrophiles and alkaliphiles. GC-rich codons were found to decrease and G/C-ending codons increased in thermophiles whereas, acidophiles showed equal contents of GC-rich and G/C-ending codons. Codon usage patterns exhibited harmony among different extremophiles and has been detailed. However, the codon attribute preferences and their selectivity of extremophiles varied in comparison to non-extremophiles. The finding can be instrumental in codon optimization application for heterologous expression of extremophilic proteins.
| S-EPMC6195531 | biostudies-literature
Project description:Breathing is a vital rhythmic motor behavior with a surprisingly broad influence on the brain and body. The apparent simplicity of breathing belies a complex neural control system, the breathing central pattern generator (bCPG), that exhibits diverse operational modes to regulate gas exchange and coordinate breathing with an array of behaviors. In this review, we focus on selected advances in our understanding of the bCPG. At the core of the bCPG is the preBötzinger complex (preBötC), which drives inspiratory rhythm via an unexpectedly sophisticated emergent mechanism. Synchronization dynamics underlying preBötC rhythmogenesis imbue the system with robustness and lability. These dynamics are modulated by inputs from throughout the brain and generate rhythmic, patterned activity that is widely distributed. The connectivity and an emerging literature support a link between breathing, emotion, and cognition that is becoming experimentally tractable. These advances bring great potential for elucidating function and dysfunction in breathing and other mammalian neural circuits.
| S-EPMC9840384 | biostudies-literature
Project description:The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM) effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf's law and Heaps' law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.
| S-EPMC5747447 | biostudies-literature
Project description:The neural crest is an ectodermal cell population that gives rise to over 30 cell types during vertebrate embryogenesis. These stem cells are formed at the border of the developing central nervous system and undergo extensive migration before differentiating into components of multiple tissues and organs. Neural crest formation and differentiation is a multistep process, as these cells transition through sequential regulatory states before adopting their adult phenotype. Such changes are governed by a complex gene regulatory network (GRN) that integrates environmental and cell-intrinsic inputs to regulate cell identity. Studies of neural crest cells in a variety of vertebrate models have elucidated the function and regulation of dozens of the molecular players that are part of this network. The neural crest GRN has served as a platform to explore the molecular control of multipotency, cell differentiation, and the evolution of vertebrates. In this review, we employ this genetic program as a stepping-stone to explore the architecture and the regulatory principles of developmental GRNs. We also discuss how modern genomic approaches can further expand our understanding of genetic networks in this system and others. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Biological Mechanisms > Cell Fates Developmental Biology > Lineages Models of Systems Properties and Processes > Cellular Models.
| S-EPMC7236752 | biostudies-literature
Project description:Optical logic operations lie at the heart of optical computing, and they enable many applications such as ultrahigh-speed information processing. However, the reported optical logic gates rely heavily on the precise control of input light signals, including their phase difference, polarization, and intensity and the size of the incident beams. Due to the complexity and difficulty in these precise controls, the two output optical logic states may suffer from an inherent instability and a low contrast ratio of intensity. Moreover, the miniaturization of optical logic gates becomes difficult if the extra bulky apparatus for these controls is considered. As such, it is desirable to get rid of these complicated controls and to achieve full logic functionality in a compact photonic system. Such a goal remains challenging. Here, we introduce a simple yet universal design strategy, capable of using plane waves as the incident signal, to perform optical logic operations via a diffractive neural network. Physically, the incident plane wave is first spatially encoded by a specific logic operation at the input layer and further decoded through the hidden layers, namely, a compound Huygens' metasurface. That is, the judiciously designed metasurface scatters the encoded light into one of two small designated areas at the output layer, which provides the information of output logic states. Importantly, after training of the diffractive neural network, all seven basic types of optical logic operations can be realized by the same metasurface. As a conceptual illustration, three logic operations (NOT, OR, and AND) are experimentally demonstrated at microwave frequencies.
| S-EPMC7154031 | biostudies-literature
Project description:Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty age-matched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing pattern was quantified by calculating detrended fluctuation analysis, largest Lyapunov exponents, sample entropy, and cross-sample entropy. The IBI as well as LV fluctuations showed decreased long-range correlation, increased regularity and reduced sensitivity to initial conditions in patients with asthma, particularly in uncontrolled state. Our results also showed a strong synchronization between the IBI and LV in patients with uncontrolled asthma. Receiver operating characteristic (ROC) curve analysis showed that nonlinear analysis of breathing pattern has a diagnostic value in asthma and can be used in differentiating uncontrolled from controlled and non-atopic from atopic asthma. We suggest that complexity analysis of breathing dynamics may represent a novel physiologic marker to facilitate diagnosis and management of patients with asthma. However, future studies are needed to increase the validity of the study and to improve these novel methods for better patient management.
| S-EPMC4732950 | biostudies-literature
Project description:Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.
| S-EPMC5504564 | biostudies-literature