Cellular and Network Mechanisms for Temporal Signal Propagation in a Cortical Network Model

Functional Dissection of Ipsilateral and Contralateral Neural Activity Propagation Using Voltage-Sensitive Dye Imaging in Mouse Prefrontal Cortex

Prefrontal cortex (PFC) intrahemispheric activity and the interhemispheric connection have a significant impact on neuropsychiatric disorder pathology. This study aimed to generate a functional map of FC intrahemispheric and interhemispheric connections. Functional dissection of mouse PFCs was performed using the voltage-sensitive dye (VSD) imaging method with high speed (1 ms/frame), high resolution (256 × 256 pixels), and a large field of view (∼10 mm). Acute serial 350 μm slices were prepared from the bregma covering the PFC and numbered 1-5 based on their distance from the bregma (i.e., 1.70, 1.34, 0.98, 0.62, and 0.26 mm) with reference to the Mouse Brain Atlas (Paxinos and Franklin, 2008). The neural response to electrical stimulation was measured at nine sites and then averaged, and a functional map of the propagation patterns was created. Intracortical propagation was observed in slices 3-5, encompassing the anterior cingulate cortex (ACC) and corpus callosum (CC). The activity reached area 33 of the ACC. Direct white matter stimulation activated area 33 in both hemispheres. Similar findings were obtained via DiI staining of the CC. Imaging analysis revealed directional biases in neural signals traveling within the ACC, whereby the signal transmission speed and probability varied based on the signal direction. Specifically, the spread of neural signals from cg2 to cg1 was stronger than that from cingulate cortex area 1(cg1) to cingulate cortex area 2(cg2), which has implications for interhemispheric functional connections. These findings highlight the importance of understanding the PFC functional anatomy in evaluating neuromodulators like serotonin and dopamine, as well as other factors related to neuropsychiatric diseases.

The control mechanisms of heart rate dynamics in a new heart rate nonlinear time series model

The control mechanisms and implications of heart rate variability (HRV) under the sympathetic (SNS) and parasympathetic nervous system (PNS) modulation remain poorly understood. Here, we establish the HR model/HRV responder using a nonlinear process derived from Newton's second law in stochastic self-restoring systems through dynamic analysis of physiological properties. We conduct model validation by testing, predictions, simulations, and sensitivity and time-scale analysis. We confirm that the outputs of the HRV responder can be accepted as the real data-generating process. Empirical studies show that the dynamic control mechanism of heart rate is a stable fixed point, rather than a strange attractor or transitions between a fixed point and a limit cycle; HR slope (amplitude) may depend on the ratio of cardiac disturbance or metabolic demand mean (standard deviation) to myocardial electrical resistance (PNS-SNS activity). For example, when metabolic demands remain unchanged, HR amplitude depends on PNS to SNS activity; when autonomic activity remains unchanged, HR amplitude during resting reflects basal metabolism. HR parameter alterations suggest that age-related decreased HRV, ultrareduced HRV in heart failure, and ultraelevated HRV in ST segment alterations refer to age-related decreased basal metabolism, impaired myocardial metabolism, and SNS hyperactivity triggered by myocardial ischemia, respectively.