network function Dotaz Zobrazit nápovědu
The salience network (SN), ventral attention network (VAN), dorsal attention network (DAN) and default mode network (DMN) have shown significant interactions and overlapping functions in bottom-up and top-down mechanisms of attention. In the present study, we tested if the SN, VAN, DAN and DMN connectivity can infer the gestational age (GA) at birth in a study group of 88 healthy neonates, scanned at 40 weeks of post-menstrual age, and with GA at birth ranging from 28 to 40 weeks. We also ascertained whether the connectivity within each of the SN, VAN, DAN and DMN was able to infer the average functional connectivity of the others. The ability to infer GA at birth or another network's connectivity was evaluated using a multivariate data-driven framework. The VAN, DAN and the DMN inferred the GA at birth (p < 0.05). The SN, DMN and VAN were able to infer the average connectivity of the other networks (p < 0.05). Mediation analysis between VAN's and DAN's inference on GA at birth found reciprocal transmittance of change with GA at birth of VAN's and DAN's connectivity (p < 0.05). Our findings suggest that the VAN has a prominent role in bottom-up salience detection in early infancy and that the role of the VAN and the SN may overlap in the bottom-up control of attention.
- Klíčová slova
- Bottom-up salience detection, Data-driven analysis, Default mode network, Dorsal attention network, Mediation analysis, Salience network, Ventral attention network,
- MeSH
- default mode network * MeSH
- gestační stáří MeSH
- kojenec MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- mapování mozku * MeSH
- mozek diagnostické zobrazování MeSH
- nervová síť diagnostické zobrazování MeSH
- novorozenec MeSH
- předškolní dítě MeSH
- Check Tag
- kojenec MeSH
- lidé MeSH
- novorozenec MeSH
- předškolní dítě MeSH
- Publikační typ
- časopisecké články MeSH
Artificial neural network based modeling is a generic approach to understand and correlate different complex parameters of biological systems for improving the desired output. In addition, some new inferences can also be predicted in a shorter time with less cost and labor. As terpenoid indole alkaloid pathway in Vinca minor is very less investigated or elucidated, a strategy of elicitation with hydroxylase and acetyltransferase along with incorporation of various precursors from primary shikimate and secoiridoid pools via simultaneous employment of cyclooxygenase inhibitor was performed in the hairy roots of V. minor. This led to the increment in biomass accumulation, total alkaloid concentration, and vincamine production in selected treatments. The resultant experimental values were correlated with algorithm approaches of artificial neural network that assisted in finding the yield of vincamine, alkaloids, and growth kinetics using number of elicits. The inputs were the hydroxylase/acetyltransferase elicitors and cyclooxygenase inhibitor along with various precursors from shikimate and secoiridoid pools and the outputs were growth index (GI), alkaloids, and vincamine. The approach incorporates two MATLAB codes; GRNN and FFBPNN. Growth kinetic studies revealed that shikimate and tryptophan supplementation triggers biomass accumulation (GI = 440.2 to 540.5); while maximum alkaloid (3.7 % dry wt.) and vincamine production (0.017 ± 0.001 % dry wt.) was obtained on supplementation of secologanin along with tryptophan, naproxen, hydrogen peroxide, and acetic anhydride. The study shows that experimental and predicted values strongly correlate each other. The correlation coefficient for growth index (GI), alkaloids, and vincamine was found to be 0.9997, 0.9980, 0.9511 in GRNN and 0.9725, 0.9444, 0.9422 in FFBPNN, respectively. GRNN provided greater similarity between the target and predicted dataset in comparison to FFBPNN. The findings can provide future insights to calculate growth index, alkaloids, and vincamine in combination to different elicits.
- Klíčová slova
- Artificial neural network, Generalized regression neural network, MATLAB, Vinca minor,
- MeSH
- alkaloidy biosyntéza MeSH
- kořeny rostlin metabolismus MeSH
- neuronové sítě * MeSH
- Vinca metabolismus MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- alkaloidy MeSH
Schizophrenia is a psychiatric disorder with heterogeneous clinical manifestations and complex aetiology. Notably, the triple-network model proposes an interesting framework for investigating abnormal neurocircuit activity at rest in schizophrenia. The present study on 30 chronic schizophrenia individuals and 30 controls aimed to explore the differences in EEG resting state effective connectivity within a triple-network model using source-localization-based Directed Transfer Function. Our findings revealed multiband effective connectivity disturbances within default mode (DMN), central executive (CEN), and salience (SN) networks in schizophrenia. The most significant difference was manifested in a global DMN hyperconnectivity, accompanied by low-band hyperconnectivity and high-band hypoconnectivity in CEN, along with the aberrant information flows in SN. In conclusion, our study presents novel insights into schizophrenia neuropathology, with a particular emphasis on the reversed directionality in information flows between hubs of SN, DMN, and CEN. This may be suggested as a promising biomarker of schizophrenia.
- Klíčová slova
- Central executive network, Default mode network, EEG, Effective connectivity, Resting-state, Schizophrenia,
- MeSH
- default mode network * patofyziologie MeSH
- dospělí MeSH
- elektroencefalografie MeSH
- konektom * metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- nervová síť * patofyziologie diagnostické zobrazování MeSH
- schizofrenie * patofyziologie diagnostické zobrazování MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. RESULTS: Here we present ENNGene-Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. CONCLUSIONS: As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.
Investigating the effect of changes in neuronal connectivity on the brain's behavior is of interest in neuroscience studies. Complex network theory is one of the most capable tools to study the effects of these changes on collective brain behavior. By using complex networks, the neural structure, function, and dynamics can be analyzed. In this context, various frameworks can be used to mimic neural networks, among which multi-layer networks are a proper one. Compared to single-layer models, multi-layer networks can provide a more realistic model of the brain due to their high complexity and dimensionality. This paper examines the effect of changes in asymmetry coupling on the behaviors of a multi-layer neuronal network. To this aim, a two-layer network is considered as a minimum model of left and right cerebral hemispheres communicated with the corpus callosum. The chaotic model of Hindmarsh-Rose is taken as the dynamics of the nodes. Only two neurons of each layer connect two layers of the network. In this model, it is assumed that the layers have different coupling strengths, so the effect of each coupling change on network behavior can be analyzed. As a result, the projection of the nodes is plotted for several coupling strengths to investigate how the asymmetry coupling influences the network behaviors. It is observed that although no coexisting attractor is present in the Hindmarsh-Rose model, an asymmetry in couplings causes the emergence of different attractors. The bifurcation diagrams of one node of each layer are presented to show the variation of the dynamics due to coupling changes. For further analysis, the network synchronization is investigated by computing intra-layer and inter-layer errors. Calculating these errors shows that the network can be synchronized only for large enough symmetric coupling.
- Klíčová slova
- asymmetry coupling, attractor, multi-layer networks, neuronal network, synchronization,
- MeSH
- modely neurologické MeSH
- mozek * fyziologie MeSH
- neuronové sítě MeSH
- neurony * fyziologie MeSH
- shluková analýza MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Alcohol Use Disorder (AUD) adversely affects the lives of millions of people, but still lacks effective treatment options. Recent advancements in psychedelic research suggest psilocybin to be potentially efficacious for AUD. However, major knowledge gaps remain regarding (1) psilocybin's general mode of action and (2) AUD-specific alterations of responsivity to psilocybin treatment in the brain that are crucial for treatment development. Here, we conducted a randomized, placebo-controlled crossover pharmaco-fMRI study on psilocybin effects using a translational approach with healthy rats and a rat model of alcohol relapse. Psilocybin effects were quantified with resting-state functional connectivity using data-driven whole-brain global brain connectivity, network-based statistics, graph theory, hypothesis-driven Default Mode Network (DMN)-specific connectivity, and entropy analyses. Results demonstrate that psilocybin induced an acute wide-spread decrease in different functional connectivity domains together with a distinct increase of connectivity between serotonergic core regions and cortical areas. We could further provide translational evidence for psilocybin-induced DMN hypoconnectivity reported in humans. Psilocybin showed an AUD-specific blunting of DMN hypoconnectivity, which strongly correlated to the alcohol relapse intensity and was mainly driven by medial prefrontal regions. In conclusion, our results provide translational validity for acute psilocybin-induced neural effects in the rodent brain. Furthermore, alcohol relapse severity was negatively correlated with neural responsivity to psilocybin treatment. Our data suggest that a clinical standard dose of psilocybin may not be sufficient to treat severe AUD cases; a finding that should be considered for future clinical trials.
- MeSH
- alkoholismus * diagnostické zobrazování farmakoterapie MeSH
- default mode network MeSH
- ethanol MeSH
- halucinogeny * farmakologie MeSH
- krysa rodu Rattus MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- mozek diagnostické zobrazování MeSH
- psilocybin farmakologie MeSH
- recidiva MeSH
- zvířata MeSH
- Check Tag
- krysa rodu Rattus MeSH
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- randomizované kontrolované studie MeSH
- Názvy látek
- ethanol MeSH
- halucinogeny * MeSH
- psilocybin MeSH
BACKGROUND: Social networks are associated with better cognitive health in older people, but the role of specific aspects of the social network remains unclear. This is especially the case in Central and Eastern Europe. This study examined associations between three aspects of the social network (network size of friends and relatives, contact frequency with friends and relatives, and social activity participation) with cognitive functions (verbal memory, learning ability, verbal fluency, processing speed, and global cognitive function) in older Czech, Polish, and Russian adults. METHODS: Linear regression estimated associations between baseline social networks and cognitive domains measured at both baseline and follow-up (mean duration of follow-up, 3.5 ± 0.7 years) in 6691 participants (mean age, 62.2 ± 6.0 years; 53.7% women) from the Health, Alcohol and Psychosocial factors In Eastern Europe (HAPIEE) study. RESULTS: Cross-sectional analyses, adjusted for country, age, and sex, showed positive associations of global cognitive function with social activity participation and network size of friends and relatives, but not with contact frequency in either network. Further adjustment for sociodemographic, behavioural, and health characteristics attenuated the associations with network size of relatives (P-trend = 0.074) but not with network size of friends (P-trend = 0.036) or social activities (P-trend< 0.001). In prospective analyses, network size and social activity participation were also linked with better cognition in simple models, but the associations were much stronger for social activities (P-trend< 0.001) than for network size of friends (P-trend = 0.095) and relatives (P-trend = 0.425). Adjustment for baseline cognition largely explained the prospective associations with network size of friends (P-trend = 0.787) and relatives (P-trend = 0.815), but it only slightly attenuated the association with social activities (P-trend< 0.001). The prospective effect of social activities was largely explained by sociodemographic, health behavioural, and health covariates (P-trend = 0.233). Analyses of specific cognitive domains generally replicated the cross-sectional and prospective findings for global cognitive function. CONCLUSIONS: Older Central and Eastern European adults with larger social networks and greater social activities participation had better cognitive function, but these associations were stronger at baseline than over the short-term follow-up.
- Klíčová slova
- Ageing, Cognitive decline, Cognitive function, Czech Republic, Poland, Russia, Social networks, Social relationships,
- MeSH
- kognice * MeSH
- lidé MeSH
- přátelé MeSH
- průřezové studie MeSH
- senioři MeSH
- sociální sítě * MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Geografické názvy
- Polsko MeSH
A lower-extremity exoskeleton can facilitate the lower limbs' rehabilitation by providing additional structural support and strength. This article discusses the design and implementation of a functional prototype of lower extremity brace actuation and its wireless communication control system. The design provides supportive torque and increases the range of motion after complications reducing muscular strength. The control system prototype facilitates elevating a leg, gradually followed by standing and slow walking. The main control modalities are based on an Artificial Neural Network (ANN). The prototype's functionality was tested by time-angle graphs. The final prototype demonstrates the potential application of the ANN in the control system of exoskeletons for joint impairment therapy.
- Klíčová slova
- Keras, Lower-extremity, control system, exoskeleton, neural network, walking,
- MeSH
- chůze MeSH
- dolní končetina MeSH
- exoskeleton * MeSH
- neuronové sítě MeSH
- točivý moment MeSH
- Publikační typ
- časopisecké články MeSH
Optimization of neural network topology, weights and neuron transfer functions for given data set and problem is not an easy task. In this article, we focus primarily on building optimal feed-forward neural network classifier for i.i.d. data sets. We apply meta-learning principles to the neural network structure and function optimization. We show that diversity promotion, ensembling, self-organization and induction are beneficial for the problem. We combine several different neuron types trained by various optimization algorithms to build a supervised feed-forward neural network called Group of Adaptive Models Evolution (GAME). The approach was tested on a large number of benchmark data sets. The experiments show that the combination of different optimization algorithms in the network is the best choice when the performance is averaged over several real-world problems.
Many cell control processes consist of networks of interacting elements that affect the state of each other over time. Such an arrangement resembles the principles of artificial neural networks, in which the state of a particular node depends on the combination of the states of other neurons. The lambda bacteriophage lysis/lysogeny decision circuit can be represented by such a network. It is used here as a model for testing the validity of a neural approach to the analysis of genetic networks. The model considers multigenic regulation including positive and negative feedback. It is used to simulate the dynamics of the lambda phage regulatory system; the results are compared with experimental observation. The comparison proves that the neural network model describes behavior of the system in full agreement with experiments; moreover, it predicts its function in experimentally inaccessible situations and explains the experimental observations. The application of the principles of neural networks to the cell control system leads to conclusions about the stability and redundancy of genetic networks and the cell functionality. Reverse engineering of the biochemical pathways from proteomics and DNA micro array data using the suggested neural network model is discussed.
