Humans walk, run, and change their speed in accordance with circumstances. These gaits are rhythmic motions generated by multi-articulated movements, which have specific spatiotemporal patterns. The kinematic characteristics depend on the gait and speed. In this study, we focused on the kinematic coordination of locomotor behavior to clarify the underlying mechanism for the effect of speed on the spatiotemporal kinematic patterns for each gait. In particular, we used seven elevation angles for the whole-body motion and separated the measured data into different phases depending on the foot-contact condition, that is, single-stance phase, double-stance phase, and flight phase, which have different physical constraints during locomotion. We extracted the spatiotemporal kinematic coordination patterns with singular value decomposition and investigated the effect of speed on the coordination patterns. Our results showed that most of the whole-body motion could be explained by only two sets of temporal and spatial coordination patterns in each phase. Furthermore, the temporal coordination patterns were invariant for different speeds, while the spatial coordination patterns varied. These findings will improve our understanding of human adaptation mechanisms to tune locomotor behavior for changing speed.
Category: Frontiers in Computational Neuroscience
Recurrence Resonance” in Three-Neuron Motifs
Stochastic Resonance (SR) and Coherence Resonance (CR) are non-linear phenomena, in which an optimal amount of noise maximizes an objective function, such as the sensitivity for weak signals in SR, or the coherence of stochastic oscillations in CR. Here, we demonstrate a related phenomenon, which we call “Recurrence Resonance” (RR): noise can also improve the information flux in recurrent neural networks. In particular, we show for the case of three-neuron motifs with ternary connection strengths that the mutual information between successive network states can be maximized by adding a suitable amount of noise to the neuron inputs. This striking result suggests that noise in the brain may not be a problem that needs to be suppressed, but indeed a resource that is dynamically regulated in order to optimize information processing.
Metastable Resting State Brain Dynamics
Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. Beim Graben and Hutt suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. for the segmentation of system's trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al., and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation ---BOLD--- signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence towards an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively.
Controlling Synchronization of Spiking Neuronal Networks by Harnessing Synaptic Plasticity
Disrupting the pathological synchronous firing patterns of neurons with high frequency stimulation is a common treatment for Parkinsonian symptoms and epileptic seizures when pharmaceutical drugs fail. In this paper, our goal is to design a desynchronization strategy for large networks of spiking neurons such that the neuronal activity of the network remains in the desynchronized regime for a long period of time after the removal of the stimulation. We develop a novel `` Forced Temporal-Spike Time Stimulation’’ (FTSTS) strategy that harnesses the spike-timing dependent plasticity to control the synchronization of neural activity in the network by forcing the neurons in the network to artificially fire in a specific temporal pattern. Our strategy modulates the synaptic strengths of selective synapses to achieve a desired synchrony of neural activity in the network. Our simulation results show that the FTSTS strategy can effectively synchronize or desynchronize neural activity in large spiking neuron networks and keep them in the desired state for a long period of time after the removal of the external stimulation. Using simulations, we demonstrate the robustness of our strategy in desynchronizing neural activity of networks against uncertainties in the designed stimulation pulses and network parameters. Additionally, we show in simulation, how our strategy could be incorporated within the existing desynchronization strategies to improve their overall efficacy in desynchronizing large networks. Our proposed strategy provides complete control over the synchronization of neurons in large networks and can be used to either synchronize or desynchronize neural activity based on specific applications. Moreover, it can be incorporated within other desynchronization strategies to improve the efficacy of existing therapies for numerous neurological and psychiatric disorders associated with pathological synchronization.
On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data
The Multiple-Network Poroelastic Theory (MPET) is a numerical model to characterise the transport of multiple fluid networks in the brain, which overcomes the problem of conducting separate analyses on individual fluid compartments and losing the interactions between tissue and fluids, in addition to the interaction between the different fluids themselves. In this paper, the blood perfusion results from MPET modelling are partially validated using cerebral blood flow (CBF) data obtained from arterial spin labelling (ASL) magnetic resonance imaging (MRI), which uses arterial blood water as an endogenous tracer to measure CBF. Two subjects – one healthy control and one patient with unilateral middle cerebral artery (MCA) stenosis are included in the validation test. The comparison shows several similarities between CBF data from ASL and blood perfusion results from MPET modelling, such as higher blood perfusion in the grey matter than in the white matter, higher perfusion in the periventricular region for both the healthy control and the patient, and asymmetric distribution of blood perfusion for the patient. Although the partial validation is mainly conducted in a qualitative way, it is one important step towards the full validation of the MPET model, which has the potential to be used as a testing bed for hypotheses and new theories in neuroscience research.
Distinct Mechanism of Audiovisual Integration With Informative and Uninformative Sound in a Visual Detection Task: A DCM Study
Previous studies have shown that task-irrelevant auditory information can provide temporal clues for the detection of visual targets and improve visual perception; such sounds are called informative sounds. The neural mechanism of the integration of informative sound and visual stimulus has been investigated extensively, using behavioral measurement or neuroimaging methods such as functional magnetic resonance imaging (fMRI) and event-related potential (ERP), but the dynamic processes of audiovisual integration cannot be characterized formally in terms of directed neuronal coupling. The present study adopts dynamic causal modelling (DCM) of fMRI data to identify changes in effective connectivity in the hierarchical brain networks that underwrite audiovisual integration and memory. This allows us to characterize context-sensitive changes in neuronal coupling and show how visual processing is contextualized by the processing of informative and uninformative sounds. Our results show that audiovisual integration with informative and uninformative sounds conforms to different optimal models in the two conditions, indicating distinct neural mechanisms of audiovisual integration. The findings also reveal that a sound is uninformative owing to low-level automatic audiovisual integration and informative owing to integration in high-level cognitive processes.
Cellular and Network Mechanisms for Temporal Signal Propagation in a Cortical Network Model
Effective (fast, reliable, and accurate) information propagation through multiple brain regions underlies cognitive processes. However, it remains unclear how neural circuits support the propagation, particularly over a background of irregular firing and response latency. Here, we propose a temporal coding model for the cellular and network mechanisms of the cortical propagation through the anatomical-functional integration across different scales of neural systems via dynamic, probabilistic, and statistical analyses. We hypothesize that synchronous spike events are a cortical population response to high-intensity thalamic input by which a high-density spike train is segregated into many low-density spike trains to avoid the lengthened latency caused by the high-intensity signal, thereby enhancing transfer speed. Moreover, cortical minicolumns prevent repeated activation of synchronous spiking events and facilitate transfer speed by parallel propagation via neurons with a column stereotypically interconnected in the vertical dimension, while columnar segregation avoids information loss in disassembly-parallel propagation. Under the Synchronous Spiking-Cortical Columns hypothesis, effective signal transfer in neural circuits relies on interneuron signal transfer with temporal-complete fidelity. We elicit a single-neuron encoder by modeling the membrane potential in response to stimulation as a resilience system in the nonlinear autoregressive integrated process derived by applying Newton's second law to stochastic resilience systems. A decoder is introduced to correct the response error of the encoder based on all-or-none law and backpropagation. Using the encoder-decoder as a signal propagator in interneurons, simulation studies are conducted where the input spike trains are generated by the right parietal 4 neuron and by a sound wave simulator, respectively. Statistical analysis and and simulations indicate that the encoder--decoder can effectively reproduce intracellular recordings from the right parietal 4 snail neuron and predict that interneuron transfer can achieve temporal-complete fidelity via regulations of ionic homeostasis and all-or-none law/backpropagation. Disfunctions of synchronous spiking and minicolumns may be the proximate cause of epilepsy or cognitive disease.
A Multi-parametric MRI-Based Radiomics Signature and a Practical ML Model for Stratifying Glioblastoma Patients Based on Survival Toward Precision Oncology
Purpose: Predicting patient survival outcome is recognized as key importance to clinicians in oncology toward determining an ideal course of treatment and patient management. This study applies radiomics analysis on pre-operative multi-parametric MRI of patients with glioblastoma from multi-institution to identify a signature and a practical machine learning model for stratifying patients into groups based on overall survival.
Methods: This study included 163 patients’ data with glioblastoma, collected by BRATS 2018 Challenge from multiple institutions. In this proposed method, a set of 147 radiomics image features were extracted locally from three tumor sub-regions on standardized pre-operative multi-parametric MR images. LASSO regression was applied for identifying an informative subset of chosen features whereas a Cox model used to obtain the coefficients of those selected features. Then, a radiomics signature model of 9 features was constructed on the discovery set and it performance was evaluated for patients stratification into short- (< 10 months), medium- (10 – 15 months), and long-survivors (> 15 months) groups. Eight ML classification models, trained and then cross-validated, were tested to assess a range of survival prediction performance as a function of the choice of features.
Results: The proposed mpMRI radiomics signature model had a statistically significant association with survival (P < 0.001) in the training set, but was not confirmed (P = 0.110) in the validation cohort. Its performance in the validation set had a sensitivity of 0.476 (short-), 0.231 (medium-), and 0.600 (long-survivors), and specificity of 0.667 (short-), 0.732 (medium-), and 0.794 (long-survivors). Among the tested ML classifiers, the ensemble learning model’s results showed superior performance in predicting the survival classes, with an overall accuracy of 57.8 % and AUC of 0.81 for short-, 0.47 for medium-, and 0.72 for long-survivors using the LASSO selected features combined with clinical factors.
Conclusion: A derived GLCM feature, representing intra-tumoral inhomogeneity, was found to have a high association with survival. Clinical factors, when added to the radiomics image features, boosted the performance of the ML classification model in predicting individual glioblastoma patient’s survival prognosis, which can improve prognostic quality a further step towards precision oncology.