In recent decades, neuroscience has advanced with increasingly sophisticated strategies for recording and analysing brain activity, enabling detailed investigations into the roles of functional units, such as individual neurons, brain regions and their interactions. Recently, new strategies for the investigation of cognitive functions regard the study of higher order interactions-that is, the interactions involving more than two brain regions or neurons. Although methods focusing on individual units and their interactions at various levels offer valuable and often complementary insights, each approach comes with its own set of limitations. In this context, a conceptual map to categorize and locate diverse strategies could be crucial to orient researchers and guide future research directions. To this end, we define the spectrum of orders of interaction, namely, a framework that categorizes the interactions among neurons or brain regions based on the number of elements involved in these interactions. We use a simulation of a toy model and a few case studies to demonstrate the utility and the challenges of the exploration of the spectrum. We conclude by proposing future research directions aimed at enhancing our understanding of brain function and cognition through a more nuanced methodological framework.
- MeSH
- kognice fyziologie MeSH
- lidé MeSH
- modely neurologické MeSH
- mozek * fyziologie MeSH
- neurony fyziologie MeSH
- neurovědy * metody MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Spontaneously fluctuating brain activity patterns that emerge at rest have been linked to the brain's health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high-amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability, and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function. In addition, it shifts the focus from the single recordings towards the brain's capacity to generate certain dynamics characteristic of health and pathology.
- MeSH
- dospělí MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- mapování mozku metody MeSH
- modely neurologické * MeSH
- mozek * fyziologie diagnostické zobrazování MeSH
- nervová síť fyziologie MeSH
- odpočinek * fyziologie MeSH
- počítačová simulace MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- MeSH
- inteligence MeSH
- kybernetika MeSH
- lidé MeSH
- modely neurologické MeSH
- mozek fyziologie MeSH
- nervová síť MeSH
- neuronové sítě MeSH
- pud MeSH
- robotika MeSH
- umělá inteligence * MeSH
- vědomí MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
Single-photon optogenetics enables precise, cell-type-specific modulation of neuronal circuits, making it a crucial tool in neuroscience. Its miniaturization in the form of fully implantable wide-field stimulator arrays enables long-term interrogation of cortical circuits and bears promise for brain-machine interfaces for sensory and motor function restoration. However, achieving selective activation of functional cortical representations poses a challenge, as studies show that targeted optogenetic stimulation results in activity spread beyond one functional domain. While recurrent network mechanisms contribute to activity spread, here we demonstrate with detailed simulations of isolated pyramidal neurons from cats of unknown sex that already neuron morphology causes a complex spread of optogenetic activity at the scale of one cortical column. Since the shape of a neuron impacts its optogenetic response, we find that a single stimulator at the cortical surface recruits a complex spatial distribution of neurons that can be inhomogeneous and vary with stimulation intensity and neuronal morphology across layers. We explore strategies to enhance stimulation precision, finding that optimizing stimulator optics may offer more significant improvements than the preferentially somatic expression of the opsin through genetic targeting. Our results indicate that, with the right optical setup, single-photon optogenetics can precisely activate isolated neurons at the scale of functional cortical domains spanning several hundred micrometers.
- MeSH
- kočky MeSH
- modely neurologické MeSH
- mozková kůra fyziologie cytologie MeSH
- neurony fyziologie MeSH
- optogenetika * metody MeSH
- pyramidové buňky fyziologie MeSH
- světelná stimulace metody MeSH
- zvířata MeSH
- Check Tag
- kočky MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Age-related brain changes affect sleep and are reflected in properties of sleep slow-waves, however, the precise mechanisms behind these changes are still not completely understood. Here, we adapt a previously established whole-brain model relating structural connectivity changes to resting state dynamics, and extend it to a slow-wave sleep brain state. In particular, starting from a representative connectome at the beginning of the aging trajectory, we have gradually reduced the inter-hemispheric connections, and simulated sleep-like slow-wave activity. We show that the main empirically observed trends, namely a decrease in duration and increase in variability of the slow waves are captured by the model. Furthermore, comparing the simulated EEG activity to the source signals, we suggest that the empirically observed decrease in amplitude of the slow waves is caused by the decrease in synchrony between brain regions.
- MeSH
- dospělí MeSH
- elektroencefalografie * MeSH
- konektom * MeSH
- lidé středního věku MeSH
- lidé MeSH
- modely neurologické * MeSH
- mozek * fyziologie MeSH
- počítačová simulace MeSH
- senioři MeSH
- spánek pomalých vln * fyziologie MeSH
- stárnutí * fyziologie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration: NCT03946618.
- MeSH
- algoritmy MeSH
- elektroencefalografie metody MeSH
- elektrokortikografie metody MeSH
- epilepsie * patofyziologie diagnóza MeSH
- hipokampus patofyziologie fyziologie MeSH
- lidé MeSH
- modely neurologické MeSH
- počítačové zpracování signálu MeSH
- výpočetní biologie metody MeSH
- záchvaty patofyziologie diagnóza MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
Applications of causal techniques to neural time series have increased extensively over last decades, including a wide and diverse family of methods focusing on electroencephalogram (EEG) analysis. Besides connectivity inferred in defined frequency bands, there is a growing interest in the analysis of cross-frequency interactions, in particular phase and amplitude coupling and directionality. Some studies show contradicting results of coupling directionality from high frequency to low frequency signal components, in spite of generally considered modulation of a high-frequency amplitude by a low-frequency phase. We have compared two widely used methods to estimate the directionality in cross frequency coupling: conditional mutual information (CMI) and phase slope index (PSI). The latter, applied to infer cross-frequency phase-amplitude directionality from animal intracranial recordings, gives opposite results when comparing to CMI. Both metrics were tested in a numerically simulated example of unidirectionally coupled Rössler systems, which helped to find the explanation of the contradictory results: PSI correctly estimates the lead/lag relationship which, however, is not generally equivalent to causality in the sense of directionality of coupling in nonlinear systems, correctly inferred by using CMI with surrogate data testing.
- MeSH
- elektroencefalografie * metody MeSH
- lidé MeSH
- modely neurologické MeSH
- mozek fyziologie MeSH
- nelineární dynamika * MeSH
- počítačová simulace MeSH
- počítačové zpracování signálu MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- srovnávací studie MeSH
Shared input to a population of neurons induces noise correlations, which can decrease the information carried by a population activity. Inhibitory feedback in recurrent neural networks can reduce the noise correlations and thus increase the information carried by the population activity. However, the activity of inhibitory neurons is costly. This inhibitory feedback decreases the gain of the population. Thus, depolarization of its neurons requires stronger excitatory synaptic input, which is associated with higher ATP consumption. Given that the goal of neural populations is to transmit as much information as possible at minimal metabolic costs, it is unclear whether the increased information transmission reliability provided by inhibitory feedback compensates for the additional costs. We analyze this problem in a network of leaky integrate-and-fire neurons receiving correlated input. By maximizing mutual information with metabolic cost constraints, we show that there is an optimal strength of recurrent connections in the network, which maximizes the value of mutual information-per-cost. For higher values of input correlation, the mutual information-per-cost is higher for recurrent networks with inhibitory feedback compared to feedforward networks without any inhibitory neurons. Our results, therefore, show that the optimal synaptic strength of a recurrent network can be inferred from metabolically efficient coding arguments and that decorrelation of the input by inhibitory feedback compensates for the associated increased metabolic costs.
OBJECTIVE: We hypothesized that spatio-temporal dynamics of interictal spikes reflect the extent and stability of epileptic sources and determine surgical outcome. METHODS: We studied 30 consecutive patients (14 good outcome). Spikes were detected in prolonged stereo-electroencephalography recordings. We quantified the spatio-temporal dynamics of spikes using the variance of the spike rate, line length and skewness of the spike distribution, and related these features to outcome. We built a logistic regression model, and compared its performance to traditional markers. RESULTS: Good outcome patients had more dominant and stable sources than poor outcome patients as expressed by a higher variance of spike rates, a lower variance of line length, and a lower variance of positive skewness (ps < 0.05). The outcome was correctly predicted in 80% of patients. This was better or non-inferior to predictions based on a focal lesion (p = 0.016), focal seizure-onset zone, or complete resection (ps > 0.05). In the five patients where traditional markers failed, spike distribution predicted the outcome correctly. The best results were achieved by 18-h periods or longer. CONCLUSIONS: Analysis of spike dynamics shows that surgery outcome depends on strong, single and stable sources. SIGNIFICANCE: Our quantitative method has the potential to be a reliable predictor of surgical outcome.
- MeSH
- dospělí MeSH
- elektroencefalografie MeSH
- epilepsie parciální patofyziologie chirurgie MeSH
- lidé středního věku MeSH
- lidé MeSH
- mapování mozku MeSH
- mladý dospělý MeSH
- modely neurologické MeSH
- mozek patofyziologie chirurgie MeSH
- mozkové vlny fyziologie MeSH
- neurochirurgické výkony MeSH
- prognóza MeSH
- refrakterní epilepsie patofyziologie chirurgie MeSH
- výsledek terapie 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
- práce podpořená grantem MeSH
