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Register a new userA comment on paper of Kim et al. on mechanisms of hysteresis in human brain networks: comparing with theoretical m-adic model
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This comment is aimed to point out that the recent work due to Kim, et al. in which the clinical and experiential assessment of a brain network model suggests that asymmetry of synchronization suppression is the key mechanism of hysteresis has coupling with our theoretical hysteresis model of unconscious-conscious interconnection based on dynamics on m-adic trees.
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The claim in ref.1 [M. Conte et al: Stern-Gerlach Force on a Precessing Magnetic Moment, Proceedings of PAC07 (http://cern.ch/AccelConf/p07/PAPER/THPAS105.pdf)] that the Stern-Gerlach force on a charged particle with a magnetic moment causes a change in longitudinal momentum proportional to gamma-squared when it traverses a specially configured localized electromagnetic field, contradicts the prediction based on the established interaction Lagrangian. It is shown that extending the calculation in ref. 1 to include the entire spin motion eliminates the gamma-squared term and thus the inconsistency.
The human brain is a complex dynamical system that gives rise to cognition through spatiotemporal patterns of coherent and incoherent activity between brain regions. As different regions dynamically interact to perform cognitive tasks, variable patterns of partial synchrony can be observed, forming chimera states. We propose that the emergence of such states plays a fundamental role in the cognitive organization of the brain, and present a novel cognitively-informed, chimera-based framework to explore how large-scale brain architecture affects brain dynamics and function. Using personalized brain network models, we systematically study how regional brain stimulation produces different patterns of synchronization across predefined cognitive systems. We then analyze these emergent patterns within our novel framework to understand the impact of subject-specific and region-specific structural variability on brain dynamics. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles.
Multimodal fusion benefits disease diagnosis by providing a more comprehensive perspective. Developing algorithms is challenging due to data heterogeneity and the complex within- and between-modality associations. Deep-network-based data-fusion models have been developed to capture the complex associations and the performance in diagnosis has been improved accordingly. Moving beyond diagnosis prediction, evaluation of disease mechanisms is critically important for biomedical research. Deep-network-based data-fusion models, however, are difficult to interpret, bringing about difficulties for studying biological mechanisms. In this work, we develop an interpretable multimodal fusion model, namely gCAM-CCL, which can perform automated diagnosis and result interpretation simultaneously. The gCAM-CCL model can generate interpretable activation maps, which quantify pixel-level contributions of the input features. This is achieved by combining intermediate feature maps using gradient-based weights. Moreover, the estimated activation maps are class-specific, and the captured cross-data associations are interest/label related, which further facilitates class-specific analysis and biological mechanism analysis. We validate the gCAM-CCL model on a brain imaging-genetic study, and show gCAM-CCLs performed well for both classification and mechanism analysis. Mechanism analysis suggests that during task-fMRI scans, several object recognition related regions of interests (ROIs) are first activated and then several downstream encoding ROIs get involved. Results also suggest that the higher cognition performing group may have stronger neurotransmission signaling while the lower cognition performing group may have problem in brain/neuron development, resulting from genetic variations.
The generation of action potential brings into play specific mechanosensory stimuli manifest in the variation of membrane capacitance, resulting from the selective membrane permeability to ions exchanges and testifying to the central role of electromechanical processes in the buildup mechanism of nerve impulse. As well established [See e.g. D. Gross et al, Cellular and Molecular Neurobiology vol. 3, p. 89 (1983)], in these electromechanical processes the net instantaneous charge stored in the membrane is regulated by the rate of change of the net fluid density through the membrane, orresponding to the difference in densities of extacellular and intracellular fluids. An electromechanical model is proposed for which mechanical forces are assumed to result from the flow of ionic liquids through the nerve membrane, generating pressure waves stimulating the membrane and hence controlling the net charge stored in the membrane capacitor. The model features coupled nonlinear partial differential equations: the familiar Hodgkin-Huxleys cable equation for the transmembrane voltage in which the membrane capacitor is now a capacitive diode, and the Heimburg-Jacksons nonlinear hydrodynamic equation for the pressure wave controlling the total charge in the membrane capacitor. In the stationary regime, the Hodgkin-Huxley cable equation with variable capacitance reduces to a linear operator problem with zero eigenvalue, the bound states of which can be obtained exactly for specific values of characteristic parameters of the model. In the dynamical regime, numerical simulations of the modified Hodgkin-Huxley equation lead to a variety of typical figures for the transmembrane voltage, reminiscent of action potentials observed in real physiological contexts.
Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets or the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurf
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