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The STRAT-PARK initiative aims to provide a platform for stratifying Parkinson's disease (PD) into biological subtypes, using a bottom-up, multidisciplinary biomarker-based and data-driven approach. PD is a heterogeneous entity, exhibiting high interindividual clinicopathological variability. This diversity suggests that PD may encompass multiple distinct biological entities, each driven by different molecular mechanisms. Molecular stratification and identification of disease subtypes is therefore a key priority for understanding and treating PD. STRAT-PARK is a multi-center longitudinal cohort aiming to recruit a total of 2000 individuals with PD and neurologically healthy controls from Norway and Canada, for the purpose of identifying molecular disease subtypes. Clinical assessment is performed annually, whereas biosampling, imaging, and digital and neurophysiological phenotyping occur every second year. The unique feature of STRAT-PARK is the diversity of collected biological material, including muscle biopsies and platelets, tissues particularly useful for mitochondrial biomarker research. Recruitment rate is ∼150 participants per year. By March 2023, 252 participants were included, comprising 204 cases and 48 controls. STRAT-PARK is a powerful stratification initiative anticipated to become a global research resource, contributing to personalized care in PD.

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Dysfunction within large-scale brain networks as the basis for movement disorders is an accepted hypothesis. The treatment options for restoring network function are limited. Non-invasive brain stimulation techniques such as repetitive transcranial magnetic stimulation are now being studied to modify the network. Transcranial electrical stimulation (tES) is also a portable, cost-effective, and non-invasive way of network modulation. Transcranial direct current stimulation and transcranial alternating current stimulation have been studied in Parkinson's disease, dystonia, tremor, and ataxia. Transcranial pulsed current stimulation and transcranial random noise stimulation are not yet studied enough. The literature in the use of these techniques is intriguing, yet many unanswered questions remain. In this review, we highlight the studies using these four potential tES techniques and their electrophysiological basis and consider the therapeutic implication in the field of movement disorders. The objectives are to consolidate the current literature, demonstrate that these methods are feasible, and encourage the application of such techniques in the near future.

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BACKGROUND: Complexity measures for time series have been used in many applications to quantify the regularity of one dimensional time series, however many dynamical systems are spatially distributed multidimensional systems. NEW METHOD: We introduced Dynamic Cross-Entropy (DCE) a novel multidimensional complexity measure that quantifies the degree of regularity of EEG signals in selected frequency bands. Time series generated by discrete logistic equations with varying control parameter r are used to test DCE measures. RESULTS: Sliding window DCE analyses are able to reveal specific period doubling bifurcations that lead to chaos. A similar behavior can be observed in seizures triggered by electroconvulsive therapy (ECT). Sample entropy data show the level of signal complexity in different phases of the ictal ECT. The transition to irregular activity is preceded by the occurrence of cyclic regular behavior. A significant increase of DCE values in successive order from high frequencies in gamma to low frequencies in delta band reveals several phase transitions into less ordered states, possible chaos in the human brain. COMPARISON WITH EXISTING METHOD: To our knowledge there are no reliable techniques able to reveal the transition to chaos in case of multidimensional times series. In addition, DCE based on sample entropy appears to be robust to EEG artifacts compared to DCE based on Shannon entropy. CONCLUSIONS: The applied technique may offer new approaches to better understand nonlinear brain activity.

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Spike directivity, a new measure that quantifies the transient charge density dynamics within action potentials provides better results in discriminating different categories of visual object recognition. Specifically, intracranial recordings from medial temporal lobe (MTL) of epileptic patients have been analyzed using firing rate, interspike intervals and spike directivity. A comparative statistical analysis of the same spikes from a local ensemble of four selected neurons shows that electrical patterns in these neurons display higher separability to input images compared to spike timing features. If the observation vector includes data from all four neurons then the comparative analysis shows a highly significant separation between categories for spike directivity (p=0.0023) and does not display separability for interspike interval (p=0.3768) and firing rate (p=0.5492). Since electrical patterns in neuronal spikes provide information regarding different presented objects this result shows that related information is intracellularly processed in neurons and carried out within a millisecond-level time domain of action potential occurrence. This significant statistical outcome obtained from a local ensemble of four neurons suggests that meaningful information can be electrically inferred at the network level to generate a better discrimination of presented images.

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The mechanisms of generating epileptic seizures are still unknown. To identify the mechanisms that underlie the transition to seizure a combination of features that include firing rate, power spectrum and complexity measures were simultaneously analyzed. Pre-ictal periods are characterized by large fluctuations of firing rate which reflect local dysfunctional regulation of neuronal activity. This local dysfunction in neuronal activity is translated in changes of endogenous electric field within clustered regions with high frequency oscillations (HFO) that act at fundamental level of charge dynamics and lead to chaotic dynamics followed by electrical resonances. Right before the onset of seizures the presence of chaotic behavior becomes persistent and leads all types of cells to fire simultaneously and generate the transition to ictal state. The alteration in neuronal regulation and the nature of physical phenomena involved in this transition supports some models of seizure generation and rules out others.

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The electrodynamics of action potentials represents the fundamental level where information is integrated and processed in neurons. The Hodgkin-Huxley model cannot explain the non-stereotyped spatial charge density dynamics that occur during action potential propagation. Revealed in experiments as spike directivity, the non-uniform charge density dynamics within neurons carry meaningful information and suggest that fragments of information regarding our memories are endogenously stored in structural patterns at a molecular level and are revealed only during spiking activity. The main conceptual idea is that under the influence of electric fields, efficient computation by interaction occurs between charge densities embedded within molecular structures and the transient developed flow of electrical charges. This process of computation underlying electrical interactions and molecular mechanisms at the subcellular level is dissimilar from spiking neuron models that are completely devoid of physical interactions. Computation by interaction describes a more powerful continuous model of computation than the one that consists of discrete steps as represented in Turing machines.

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Learning is important for humans and can be disrupted by disease. However, the essence of how learning may be represented within a neuronal network is still elusive. Spike trains generated by neurons have been demonstrated to carry information which is relevant for learning. The present study uses well-established mutual information (MI) analysis techniques to better understand learning within neuronal ensembles. Spike trains in tetrode recordings from the dorso-lateral striatum were used for computing MI as rats learnt a T-maze procedural task. We demonstrate that in in-vivo recordings the growth of MI is reflected in the behavioral response as learning proceeds. These changes in MI are seen to correspond to three phases, a low MI value, namely early learning, a rapid increase in MI value, task-acquisition and stabilization of MI, over-training. Over multiple training sessions, small changes in MI within the neuronal network suddenly produce a big change in ensemble MI during the task acquisition phase. This phase represents the "tipping point" in the neuronal network where the MI growth builds habits during motor learning in the striatum.

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This paper presents a new technique for analyzing the recorded information from tetrodes in freely behaving rats, based on independent component analysis (ICA). The ion-specific pumps and channels allow fast transfer of charges such as Na+, K+, Cl- and eventually Ca2+ during each action potential (AP). These groups of charges under an electrical field have distinct spatial trajectories. Therefore, the generated signals within a tetrode are considered to be composed mainly by statistically independent signal sources that can be obtained by performing ICA. In order to compute the position of independent sources during AP generation, the triangulation method uses an iterative Newton-Raphson algorithm. The representation of the independent signal sources in three-dimensional tetrode space is then obtained. Since the charge movements are extensively spread on the neuron's surface, the representation in tetrode space reveals electrical spatial patterns of activation during each AP. The analysis of several spikes coming from the same neuron reveals small changes from spike to spike in the 3D shape. Since information within spikes is highly transferred by ionic fluxes these electrical patterns of activation reflect neuronal computation occurring during each AP.

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The ability of neurons to generate electrical signals is strongly dependent on the evolution of ion-specific pumps and channels that allow the transfer of charges under the influence of electric fields and concentration gradients. This paper presents a novel method by which flow of these charge fluxes may be computed to provide directivity of charge movement. Simulations of charge flow as well as actual electrophysiological data recorded by tetrodes are used to demonstrate the method. The propagation of charge fluxes in space in data from simulation and actual recordings during action potential can be analyzed using signals recorded by tetrodes. Variation in spike directivity can be estimated by computing singular value decomposition of the estimated 3D trajectory data. The analysis of the spike model can be accomplished by performing simulations of presumed equivalent moving charges recorded by the tetrode tips. For in vivo spike recordings, the variation of spike directivity could be obtained using several spikes of selected neurons considering the charge movement model (CMM). The relationship between computer simulation results and tetrode data recordings is examined. The paper concludes by showing that the method for calculating directivity in actual spike recordings is robust. The method allows for improved filtering of data and more importantly may shed light on furthering the study of spatio-temporal encoding in neurons.

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