Altered functional connectivity after pilocarpine-induced seizures revealed by intrinsic optical signals imaging in awake mice.

Significance: Comorbidities such as mood and cognitive disorders are often found in individuals with epilepsy after seizures. Cortex processes sensory, motor, and cognitive information. Brain circuit changes can be studied by observing functional network changes in epileptic mice's cortex. Aim: The cortex is easily accessible for non-invasive brain imaging and electroencephalogram recording (EEG). However, the impact of seizures on cortical activity and functional connectivity has been rarely studied in vivo. Approach: Intrinsic optical signal and EEG were used to monitor cortical activity in awake mice within 4 h after pilocarpine induction. It was divided into three periods according to the behavior and EEG of the mice: baseline, onset of seizures (onset, including seizures and resting in between seizure events), and after seizures (post, without seizures). Changes in cortical activity were compared between the baseline and after seizures. Results: Hemoglobin levels increased significantly, particularly in the parietal association cortex (PT), retrosplenial cortex (RS), primary visual cortex (V1), and secondary visual cortex (V2). The network-wide functional connectivity changed post seizures, e.g., hypoconnectivity between PT and visual-associated cortex (e.g., V1 and V2). In contrast, connectivity between the motor-associated cortex and most other regions increased. In addition, the default mode network (DMN) also changed after seizures, with decreased connectivity between primary somatosensory region (SSp) and visual region (VIS), but increased connectivity involving anterior cingulate cortex (AC) and RS. Conclusions: Our results provide references for understanding the mechanisms behind changes in brain circuits, which may explain the profound effects of seizures on comorbid health conditions.

An efficient reduced-lattice model of IP3R for probing Ca2+ dynamics.

Numerous cellular processes are regulated by Ca2+ signals, and the endoplasmic reticulum (ER) membrane's inositol triphosphate receptor (IP3R) is critical for modulating intracellular Ca2+ dynamics. The IP3Rs are seen to be clustered in a variety of cell types. The combination of IP3Rs clustering and IP3Rs-mediated Ca2+-induced Ca2+ release results in the hierarchical organization of the Ca2+ signals, which challenges the numerical simulation given the multiple spatial and temporal scales that must be covered. The previous methods rather ignore the spatial feature of IP3Rs or fail to coordinate the conflicts between the real biological relevance and the computational cost. In this work, a general and efficient reduced-lattice model is presented for the simulation of IP3Rs-mediated multiscale Ca2+ dynamics. The model highlights biological details that make up the majority of the calcium events, including IP3Rs clustering and calcium domains, and it reduces the complexity by approximating the minor details. The model's extensibility provides fresh insights into the function of IP3Rs in producing global Ca2+ events and supports the research under more physiological circumstances. Our work contributes to a novel toolkit for modeling multiscale Ca2+ dynamics and advances knowledge of Ca2+ signals.

DDeep3M+: adaptive enhancement powered weakly supervised learning for neuron segmentation.

Significance: Robust segmentations of neurons greatly improve neuronal population reconstruction, which could support further study of neuron morphology for brain research. Aim: Precise segmentation of 3D neuron structures from optical microscopy (OM) images is crucial to probe neural circuits and brain functions. However, the high noise and low contrast of images make neuron segmentation challenging. Convolutional neural networks (CNNs) can provide feasible solutions for the task but they require large manual labels for training. Labor-intensive labeling is highly expensive and heavily limits the algorithm generalization. Approach: We devise a weakly supervised learning framework Docker-based deep network plus (DDeep3M+) for neuron segmentation without any manual labeling. A Hessian analysis based adaptive enhancement filter is employed to generate pseudo-labels for segmenting neuron images. The automated segmentation labels are input for training a DDeep3M to extract neuronal features. We mine more undetected weak neurites from the probability map based on neuronal structures, thereby modifying the pseudo-labels. We iteratively refine the pseudo-labels and retrain the DDeep3M model with the pseudo-labels to obtain a final segmentation result. Results: The proposed method achieves promising results with the F1 score of 0.973, which is close to that of the CNN model with manual labels and superior to several segmentation algorithms. Conclusions: We propose an accurate weakly supervised neuron segmentation method. The high precision results achieved on 3D OM datasets demonstrate the superior generalization of our DDeep3M+.

Probing microdomain Ca2+ activity and synaptic transmission with a node-based tripartite synapse model.

Astrocytic fine processes are the most minor structures of astrocytes but host much of the Ca2+ activity. These localized Ca2+ signals spatially restricted to microdomains are crucial for information processing and synaptic transmission. However, the mechanistic link between astrocytic nanoscale processes and microdomain Ca2+ activity remains hazily understood because of the technical difficulties in accessing this structurally unresolved region. In this study, we used computational models to disentangle the intricate relations of morphology and local Ca2+ dynamics involved in astrocytic fine processes. We aimed to answer: 1) how nano-morphology affects local Ca2+ activity and synaptic transmission, 2) and how fine processes affect Ca2+ activity of large process they connect. To address these issues, we undertook the following two computational modeling: 1) we integrated the in vivo astrocyte morphological data from a recent study performed with super-resolution microscopy that discriminates sub-compartments of various shapes, referred to as nodes and shafts to a classic IP3R-mediated Ca2+ signaling framework describing the intracellular Ca2+ dynamics, 2) we proposed a node-based tripartite synapse model linking with astrocytic morphology to predict the effect of structural deficits of astrocytes on synaptic transmission. Extensive simulations provided us with several biological insights: 1) the width of nodes and shafts could strongly influence the spatiotemporal variability of Ca2+ signals properties but what indeed determined the Ca2+ activity was the width ratio between nodes and shafts, 2) the connectivity of nodes to larger processes markedly shaped the Ca2+ signal of the parent process rather than nodes morphology itself, 3) the morphological changes of astrocytic part might potentially induce the abnormality of synaptic transmission by affecting the level of glutamate at tripartite synapses. Taken together, this comprehensive model which integrated theoretical computation and in vivo morphological data highlights the role of the nanomorphology of astrocytes in signal transmission and its possible mechanisms related to pathological conditions.

Using head-mounted eye trackers to explore children's color preferences and perceptions of toys with different color gradients.

This study investigated how color gradients affect the attraction and visual comfort of children aged 4 to 7 years. We analyzed 108 eye-tracking datasets, including the color attraction index (COI), visual comfort index (PUI), and saccade rate (SR). The findings revealed that children are more attracted to colors as saturation decreases and brightness increases within a specific range. Beyond this range, reduced saturation diminishes color appeal. Moderate brightness and contrast enhance visual comfort during play, while extremely low contrast hinders concentration. Warm colors (red, orange, and yellow) slightly dominate preferences; however, the roles of hue, saturation, and brightness in children's color preferences remain inconclusive. These insights have practical implications for age-appropriate toy design and marketing. Future research should explore age-specific color preferences for more targeted design strategies.

Astrocytes exhibit diverse Ca2+ changes at subcellular domains during brain aging.

Astrocytic Ca2+ transients are essential for astrocyte integration into neural circuits. These Ca2+ transients are primarily sequestered in subcellular domains, including primary branches, branchlets and leaflets, and endfeet. In previous studies, it suggests that aging causes functional defects in astrocytes. Until now, it was unclear whether and how aging affects astrocytic Ca2+ transients at subcellular domains. In this study, we combined a genetically encoded Ca2+ sensor (GCaMP6f) and in vivo two-photon Ca2+ imaging to determine changes in Ca2+ transients within astrocytic subcellular domains during brain aging. We showed that aging increased Ca2+ transients in astrocytic primary branches, higher-order branchlets, and terminal leaflets. However, Ca2+ transients decreased within astrocytic endfeet during brain aging, which could be caused by the decreased expressions of Aquaporin-4 (AQP4). In addition, aging-induced changes of Ca2+ transient types were heterogeneous within astrocytic subcellular domains. These results demonstrate that the astrocytic Ca2+ transients within subcellular domains are affected by aging differently. This finding contributes to a better understanding of the physiological role of astrocytes in aging-induced neural circuit degeneration.

Unraveling Aß-Mediated Multi-Pathway Calcium Dynamics in Astrocytes: Implications for Alzheimer's Disease Treatment From Simulations.

The accumulation of amyloid ß peptide (Aß) in the brain is hypothesized to be the major factor driving Alzheimer's disease (AD) pathogenesis. Mounting evidence suggests that astrocytes are the primary target of Aß neurotoxicity. Aß is known to interfere with multiple calcium fluxes, thus disrupting the calcium homeostasis regulation of astrocytes, which are likely to produce calcium oscillations. Ca2+ dyshomeostasis has been observed to precede the appearance of clinical symptoms of AD; however, it is experimentally very difficult to investigate the interactions of many mechanisms. Given that Ca2+ disruption is ubiquitously involved in AD progression, it is likely that focusing on Ca2+ dysregulation may serve as a potential therapeutic approach to preventing or treating AD, while current hypotheses concerning AD have so far failed to yield curable therapies. For this purpose, we derive and investigate a concise mathematical model for Aß-mediated multi-pathway astrocytic intracellular Ca2+ dynamics. This model accounts for how Aß affects various fluxes contributions through voltage-gated calcium channels, Aß-formed channels and ryanodine receptors. Bifurcation analysis of Aß level, which reflected the corresponding progression of the disease, revealed that Aß significantly induced the increasing [Ca2+] i and frequency of calcium oscillations. The influence of inositol 1,4,5-trisphosphate production (IP3) is also investigated in the presence of Aß as well as the impact of changes in resting membrane potential. In turn, the Ca2+ flux can be considerably changed by exerting specific interventions, such as ion channel blockers or receptor antagonists. By doing so, a "combination therapy" targeting multiple pathways simultaneously has finally been demonstrated to be more effective. This study helps to better understand the effect of Aß, and our findings provide new insight into the treatment of AD.

Boosting Multilabel Semantic Segmentation for Somata and Vessels in Mouse Brain.

Deep convolutional neural networks (DCNNs) are widely utilized for the semantic segmentation of dense nerve tissues from light and electron microscopy (EM) image data; the goal of this technique is to achieve efficient and accurate three-dimensional reconstruction of the vasculature and neural networks in the brain. The success of these tasks heavily depends on the amount, and especially the quality, of the human-annotated labels fed into DCNNs. However, it is often difficult to acquire the gold standard of human-annotated labels for dense nerve tissues; human annotations inevitably contain discrepancies or even errors, which substantially impact the performance of DCNNs. Thus, a novel boosting framework consisting of a DCNN for multilabel semantic segmentation with a customized Dice-logarithmic loss function, a fusion module combining the annotated labels and the corresponding predictions from the DCNN, and a boosting algorithm to sequentially update the sample weights during network training iterations was proposed to systematically improve the quality of the annotated labels; this framework eventually resulted in improved segmentation task performance. The microoptical sectioning tomography (MOST) dataset was then employed to assess the effectiveness of the proposed framework. The result indicated that the framework, even trained with a dataset including some poor-quality human-annotated labels, achieved state-of-the-art performance in the segmentation of somata and vessels in the mouse brain. Thus, the proposed technique of artificial intelligence could advance neuroscience research.