Large variability in computed tomography (CT) radiomics feature values due to CT imaging parameters can have subsequent implications on the prognostic or predictive significance of these features. Here, we investigated the impact of pitch, dose, and reconstruction kernel on CT radiomic features. Moreover, we introduced correction factors to reduce feature variability introduced by reconstruction kernels. The credence cartridge radiomics and American College of Radiology (ACR) phantoms were scanned on five different scanners. ACR phantom was used for 3-D noise power spectrum (NPS) measurements to quantify correlated noise. The coefficient of variation (COV) was used as the variability assessment metric. The variability in texture features due to different kernels was reduced by applying the NPS peak frequency and region of interest (ROI) maximum intensity as correction factors. Most texture features were dose independent but were strongly kernel dependent, which is demonstrated by a significant shift in NPS peak frequency among kernels. Percentage improvement in robustness was calculated for each feature from original and corrected %COV values. Percentage improvements in robustness of 19 features were in the range of 30% to 78% after corrections. We show that NPS peak frequency and ROI maximum intensity can be used as correction factors to reduce variability in CT texture feature values due to reconstruction kernels.
Abstract Spontaneous neuronal and astrocytic activity in the neonate forebrain is believed to drive the maturation of individual cells and their integration into complex brain-region-specific networks. The previously reported forms include bursts of electrical activity and oscillations in intracellular Ca 2+ concentration. Here, we use ratiometric Na + imaging to demonstrate spontaneous fluctuations in the intracellular Na + concentration of CA1 pyramidal neurons and astrocytes in tissue slices obtained from the hippocampus of mice at postnatal days 2-4 (P2-4). These occur at very low frequency (∼2/h), can last minutes with amplitudes up to several mM, and mostly disappear after the first postnatal week. To further study the mechanisms that may generate such spontaneous fluctuations in neurons, we model a network consisting of pyramidal neurons and interneurons. Experimentally observed Na + fluctuations are mimicked when GABAergic inhibition in the simulated network is inverted. Both our experiments and computational model show that the application of tetrodotoxin to block voltage-gated Na + channels or of inhibitors targeting GABAergic signaling respectively, significantly diminish the neuronal Na + fluctuations. On the other hand, blocking a variety of other ion channels, receptors, or transporters including glutamatergic pathways, does not have significant effects. In addition, our model shows that the amplitude and duration of Na + fluctuations decrease as we increase the strength of glial K + uptake. Furthermore, neurons with smaller somatic volumes exhibit fluctuations with higher frequency and amplitude. As opposed to this, the larger relative size of the extracellular with respect to intracellular space observed in neonatal brain exerts a dampening effect. Finally, our model also predicts that these periods of spontaneous Na + influx leave neonatal neuronal networks more vulnerable to hyperactivity when compared to mature brain. Taken together, our model thus confirms the experimental observations, and offers additional insight into how the neonatal environment shapes early signaling in the brain. Author Summary Spontaneous neuronal and astrocytic activity during the early postnatal period is crucial to the development and physiology of the neonate forebrain. Elucidating the origin of this activity is key to our understanding of the cell maturation and formation of brain-region-specific networks. This study reports spontaneous, ultraslow, large-amplitude, long-lasting fluctuations in the intracellular Na + concentration of neurons and astrocytes in the hippocampus of mice at postnatal days 2-4 that mostly disappear after the first postnatal week. We combine ratiometric Na + imaging and pharmacological manipulations with a detailed computational model of neuronal networks in the neonatal and adult brain to provide key insights into the origin of these Na + fluctuations. Furthermore, our model predicts that these periods of spontaneous Na + influx leave neonatal neuronal networks more vulnerable to hyperactivity when compared to mature brain.
Abstract Alzheimer’s disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions—middle temporal gyrus, superior frontal gyrus, and entorhinal cortex—we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, indicating that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, highlighting the differential impact of DEGs on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we note an overall downregulation of both astrocyte and microglia modules in AD across all brain regions, suggesting a prevailing trend of functional repression in glial cells across these regions. Notable genes, including those of the CALM and HSP90 family genes emerged as hub genes across neuronal modules in all brain regions, indicating conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems oriented approach combining pathway and network analysis for a comprehensive understanding of the cell-type-specific roles of genes in AD-related biological processes.
Figure 1 Assimilating spontaneous seizure data by whole cell recording from CA1 hippocampal pyramidal neurons.(A) Measured V (red) from single PC during spontaneous seizures.Estimated (black) [K] o (B), [Na] i (C), K + diffusion constant (D), glial buffering strength (E), and K + concentration in bath solution (F).
Cell volume changes are ubiquitous in normal and pathological activity of the brain. Nevertheless, we know little of how cell volume affects neuronal dynamics. We here performed the first detailed study of the effects of cell volume on neuronal dynamics. By incorporating cell swelling together with dynamic ion concentrations and oxygen supply into Hodgkin-Huxley type spiking dynamics, we demonstrate the spontaneous transition between epileptic seizure and spreading depression states as the cell swells and contracts in response to changes in osmotic pressure. Our use of volume as an order parameter further revealed a dynamical definition for the experimentally described physiological ceiling that separates seizure from spreading depression, as well as predicted a second ceiling that demarcates spreading depression from anoxic depolarization. Our model highlights the neuroprotective role of glial K buffering against seizures and spreading depression, and provides novel insights into anoxic depolarization and the relevant cell swelling during ischemia. We argue that the dynamics of seizures, spreading depression, and anoxic depolarization lie along a continuum of the repertoire of the neuron membrane that can be understood only when the dynamic ion concentrations, oxygen homeostasis,and cell swelling in response to osmotic pressure are taken into consideration. Our results demonstrate the feasibility of a unified framework for a wide range of neuronal behaviors that may be of substantial importance in the understanding of and potentially developing universal intervention strategies for these pathological states.
Nonlinear ensemble state estimation offers a paradigm-shifting improvement in our ability to observe, predict, and control the state of spiking neuronal systems. We use an ensemble Kalman filter to predict hidden states and future trajectories in the Hodgkin-Huxley equations, reconstruct ion dynamics, control neuronal activity including a strategy for dynamic conductance clamping, and show the feasibility of controlling pathological cellular activity such as seizures.
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