Showing papers by "Peter A. Robinson published in 2020"

Modeling melanopsin‐mediated effects of light on circadian phase, melatonin suppression, and subjective sleepiness

Abstract: A physiologically based model of arousal dynamics is improved to incorporate the effects of the light spectrum on circadian phase resetting, melatonin suppression, and subjective sleepiness. To account for these nonvisual effects of light, melanopic irradiance replaces photopic illuminance that was used previously in the model. The dynamic circadian oscillator is revised according to the melanopic irradiance definition and tested against experimental circadian phase resetting dose-response and phase response data. Melatonin suppression function is recalibrated against melatonin dose-response data for monochromatic and polychromatic light sources. A new light-dependent term is introduced into the homeostatic weight component of subjective sleepiness to represent the direct alerting effect of light; the new term responds to light change in a time-dependent manner and is calibrated against experimental data. The model predictions are compared to a total of 14 experimental studies containing 26 data sets for 14 different spectral light profiles. The revised melanopic model shows on average 1.4 times lower prediction error for circadian phase resetting compared to the photopic-based model, 3.2 times lower error for melatonin suppression, and 2.1 times lower error for subjective sleepiness. Overall, incorporating melanopic irradiance allowed simulation of wavelength-dependent responses to light and could explain the majority of the observations. Moving forward, models of circadian phase resetting and the direct effects of light on alertness and sleep need to use nonvisual photoreception-based measures of light, for example, melanopic irradiance, instead of the traditionally used illuminance based on the visual system.

Evoked response activity eigenmode analysis in a convoluted cortex via neural field theory.

Abstract: Neural field theory of the corticothalamic system is used to explore evoked response potentials (ERPs) caused by spatially localized impulse stimuli on the convoluted cortex and on a spherical cortex. Eigenfunctions are calculated analytically on the spherical cortex and numerically on the convoluted cortex via eigenfunction expansions. Eigenmodes on a convoluted cortex are similar to those of the spherical cortex, and a few such modes are found to be sufficient to reproduce the main ERP features. It is found that the ERP peak is stronger in spherical cortex than convoluted cortex, but in both cases the peak decreases monotonically with increasing distance from the stimulus point. In the convoluted case, cortical folding causes ERPs to differ between locations at the same distance from the stimulus point and spherical symmetries are only approximately preserved.

Importance of self-connections for brain connectivity and spectral connectomics

Abstract: Spectral analysis and neural field theory are used to investigate the role of local connections in brain connectivity matrices (CMs) that quantify connectivity between pairs of discretized brain regions. This work investigates how the common procedure of omitting such self-connections (i.e., the diagonal elements of CMs) in published studies of brain connectivity affects the properties of functional CMs (fCMs) and the mutually consistent effective CMs (eCMs) that correspond to them. It is shown that retention of self-connections in the fCM calculated from two-point activity covariances is essential for the fCM to be a true covariance matrix, to enable correct inference of the direct total eCMs from the fCM, and to ensure their compatibility with it; the deCM and teCM represent the strengths of direct connections and all connections between points, respectively. When self-connections are retained, inferred eCMs are found to have net inhibitory self-connections that represent the local inhibition needed to balance excitation via white matter fibers at longer ranges. This inference of spatially unresolved connectivity exemplifies the power of spectral connectivity methods, which also enable transformation of CMs to compact diagonal forms that allow accurate approximation of the fCM and total eCM in terms of just a few modes, rather than the full $$N^2$$ CM entries for connections between N brain regions. It is found that omission of fCM self-connections affects both local and long-range connections in eCMs, so they cannot be omitted even when studying the large-scale. Moreover, retention of local connections enables inference of subgrid short-range inhibitory connectivity. The results are verified and illustrated using the NKI-Rockland dataset from the University of Southern California Multimodal Connectivity Database. Deletion of self-connections is common in the field; this does not affect case-control studies but the present results imply that such fCMs must have self-connections restored before eCMs can be inferred from them.

Effects of physiological parameter evolution on the dynamics of tonic-clonic seizures.

Abstract: The temporal and spectral characteristics of tonic-clonic seizures are investigated using a neural field model of the corticothalamic system in the presence of a temporally varying connection strength between the cerebral cortex and thalamus. Increasing connection strength drives the system into ∼ 10 Hz seizure oscillations once a threshold is passed and a subcritical Hopf bifurcation occurs. In this study, the spectral and temporal characteristics of tonic-clonic seizures are explored as functions of the relevant properties of physiological connection strengths, such as maximum strength, time above threshold, and the ramp rate at which the strength increases or decreases. Analysis shows that the seizure onset time decreases with the maximum connection strength and time above threshold, but increases with the ramp rate. Seizure duration and offset time increase with maximum connection strength, time above threshold, and rate of change. Spectral analysis reveals that the power of nonlinear harmonics and the duration of the oscillations increase as the maximum connection strength and the time above threshold increase. A secondary limit cycle at ∼ 18 Hz, termed a saddle-cycle, is also seen during seizure onset and becomes more prominent and robust with increasing ramp rate. If the time above the threshold is too small, the system does not reach the 10 Hz limit cycle, and only exhibits 18 Hz saddle-cycle oscillations. It is also seen that the time to reach the saturated large amplitude limit-cycle seizure oscillation from both the instability threshold and from the end of the saddle-cycle oscillations is inversely proportional to the square root of the ramp rate.

Neural Field Theory of Evoked Response Potentials With Attentional Gain Dynamics.

Abstract: A generalized neural field model of large-scale activity in the corticothalamic system is used to predict standard evoked potentials. This model embodies local feedbacks that modulate the gains of neural activity as part of the response to incoming stimuli and thus enables both activity changes and effective connectivity changes to be calculated as parts of a generalized evoked response, and their relative contributions to be determined. The results show that incorporation of gain modulations enables a compact and physically justifiable description of the differences in gain between background-EEG and standard-ERP conditions, with the latter able to be initiated from the background state, rather than requiring distinct parameters as in earlier work. In particular, top-down gains are found to be reduced during an ERP, consistent with recent theoretical suggestions that the role of internal models is diminished in favor of external inputs when the latter change suddenly. The static-gain and modulated-gain system transfer functions are analyzed via control theory in terms of system resonances that were recently shown to implement data filtering whose gain adjustments can be interpreted as attention. These filters are shown to govern early and late features in standard evoked responses and their gain parameters are shown to be dynamically adjusted in a way that implements a form of attention. The results show that dynamically modulated resonant filters responsible for the low-frequency oscillations in an evoked potential response have different parameters than those responsible for low-frequency resting EEG responses, while both responses share similar mid- and high-frequency resonant filters. These results provide a biophysical mechanism by which oscillatory activity in the theta, alpha, and beta frequency ranges of an evoked response are modulated as reflections of attention; notably theta is enhanced and alpha suppressed during the latter parts of the ERP. Furthermore, the model enables the part of the ERP response induced by gain modulations to be estimated and interpreted in terms of attention.

Effects of parcellation and threshold on brainconnectivity measures.

Abstract: It is shown that the statistical properties of connections between regions of the brain and their dependence on coarse-graining and thresholding in published data can be reproduced by a simple distance-based physical connectivity model. This allows studies with differing parcellation and thresholding to be interrelated objectively, and for the results of future studies on more finely grained or differently thresholded networks to be predicted. As examples of the implications, it is shown that the dependences of network measures on thresholding and parcellation imply that chosen brain regions can appear to form a small world network, even though the network at finer scales, or ultimately of individual neurons, may not be small world networks themselves.

Gamma-band correlations in the primary visual cortex.

Abstract: This paper generalizes and extends previous work on using neural field theory to quantitatively analyze the two-dimensional (2D) spatiotemporal correlation properties of gamma-band (30-70 Hz) oscillations evoked by stimuli arriving at the primary visual cortex, and modulated by patchy connectivities that depend on orientation preference (OP). Correlation functions are derived analytically for general stimulus and measurement conditions. The theoretical results reproduce a range of published experimental results. These include (i) the existence of two-point oscillatory temporal cross correlations with zero time lag between neurons with similar OP; (ii) the influence of spatial separation of neurons on the strength of the correlations; and (iii) the effects of differing stimulus orientations. They go beyond prior work by incorporating experimentally observed patchy projection patterns to predict the 2D correlation structure including both OP and ocular dominance effects, thereby relaxing assumptions of translational invariance implicit in prior one-dimensional analysis.

Cortical Depth-Dependent Modeling of Visual Hemodynamic Responses

Abstract: A physiologically based three-dimensional (3D) hemodynamic model is used to predict the experimentally observed blood oxygen level dependent (BOLD) responses versus the cortical depth induced by visual stimuli. Prior 2D approximations are relaxed in order to analyze 3D blood flow dynamics as a function of cortical depth. Comparison of the predictions with experimental data for typical stimuli demonstrates that the full 3D model matches at least as well as previous approaches while requiring significantly fewer assumptions and model parameters.