Frontiers in neuroimaging最新文献

{"title":"DORIS: A diffusion MRI-based 10 tissue class deep learning segmentation algorithm tailored to improve anatomically-constrained tractography.","authors":"Guillaume Theaud, Manon Edde, Matthieu Dumont, Clément Zotti, Mauro Zucchelli, Samuel Deslauriers-Gauthier, Rachid Deriche, Pierre-Marc Jodoin, Maxime Descoteaux","doi":"10.3389/fnimg.2022.917806","DOIUrl":"10.3389/fnimg.2022.917806","url":null,"abstract":"<p><p>Modern tractography algorithms such as anatomically-constrained tractography (ACT) are based on segmentation maps of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). These maps are generally estimated from a T1-weighted (T1w) image and then registered in diffusion weighted images (DWI) space. Registration of T1w to diffusion space and partial volume estimation are challenging and rarely voxel-perfect. Diffusion-based segmentation would, thus, potentially allow not to have higher quality anatomical priors injected in the tractography process. On the other hand, even if FA-based tractography is possible without T1 registration, the literature shows that this technique suffers from multiple issues such as holes in the tracking mask and a high proportion of generated broken and anatomically implausible streamlines. Therefore, there is an important need for a tissue segmentation algorithm that works directly in the native diffusion space. We propose <b>DORIS</b>, a DWI-based deep learning segmentation algorithm. DORIS outputs 10 different tissue classes including WM, GM, CSF, ventricles, and 6 other subcortical structures (putamen, pallidum, hippocampus, caudate, amygdala, and thalamus). DORIS was trained and validated on a wide range of subjects, including 1,000 individuals from 22 to 90 years old from clinical and research DWI acquisitions, from 5 public databases. In the absence of a \"true\" ground truth in diffusion space, DORIS used a silver standard strategy from Freesurfer output registered onto the DWI. This strategy is extensively evaluated and discussed in the current study. Segmentation maps provided by DORIS are quantitatively compared to Freesurfer and FSL-fast and the impacts on tractography are evaluated. Overall, we show that DORIS is fast, accurate, and reproducible and that DORIS-based tractograms produce bundles with a longer mean length and fewer anatomically implausible streamlines.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"917806"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9957254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the contribution of cytoarchitecture to diffusion MRI measures in gray matter using histology.","authors":"Madhura Baxi, Suheyla Cetin-Karayumak, George Papadimitriou, Nikos Makris, Andre van der Kouwe, Bruce Jenkins, Tara L Moore, Douglas L Rosene, Marek Kubicki, Yogesh Rathi","doi":"10.3389/fnimg.2022.947526","DOIUrl":"10.3389/fnimg.2022.947526","url":null,"abstract":"<p><p>Postmortem studies are currently considered a gold standard for investigating brain structure at the cellular level. To investigate cellular changes in the context of human development, aging, or disease treatment, non-invasive <i>in-vivo</i> imaging methods such as diffusion MRI (dMRI) are needed. However, dMRI measures are only indirect measures and require validation in gray matter (GM) in the context of their sensitivity to the underlying cytoarchitecture, which has been lacking. Therefore, in this study we conducted direct comparisons between <i>in-vivo</i> dMRI measures and histology acquired from the same four rhesus monkeys. Average and heterogeneity of fractional anisotropy and trace from diffusion tensor imaging and mean squared displacement (MSD) and return-to-origin-probability from biexponential model were calculated in nine cytoarchitectonically different GM regions using dMRI data. DMRI measures were compared with corresponding histology measures of regional average and heterogeneity in cell area density. Results show that both average and heterogeneity in trace and MSD measures are sensitive to the underlying cytoarchitecture (cell area density) and capture different aspects of cell composition and organization. Trace and MSD thus would prove valuable as non-invasive imaging biomarkers in future studies investigating GM cytoarchitectural changes related to development and aging as well as abnormal cellular pathologies in clinical studies.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"947526"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9968649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extracting default mode network based on graph neural network for resting state fMRI study.","authors":"Donglin Wang, Qiang Wu, Don Hong","doi":"10.3389/fnimg.2022.963125","DOIUrl":"10.3389/fnimg.2022.963125","url":null,"abstract":"<p><p>Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant information to explain a wide range of pathological conditions and behavioral characteristics. In this paper, we propose the use of a graph neural network, a deep learning technique called graphSAGE, to investigate resting state fMRI (rs-fMRI) and extract the default mode network (DMN). Comparing typical methods such as seed-based correlation, independent component analysis, and dictionary learning, real data experiment results showed that the graphSAGE is more robust, reliable, and defines a clearer region of interests. In addition, graphSAGE requires fewer and more relaxed assumptions, and considers the single subject analysis and group subjects analysis simultaneously.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"963125"},"PeriodicalIF":0.0,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9966266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory Network.","authors":"Xueqi Guo, Sule Tinaz, Nicha C Dvornek","doi":"10.3389/fnimg.2022.952084","DOIUrl":"10.3389/fnimg.2022.952084","url":null,"abstract":"<p><p>Parkinson's disease (PD) is a common and complex neurodegenerative disorder with five stages on the Hoehn and Yahr scaling. Characterizing brain function alterations with progression of early stage disease would support accurate disease staging, development of new therapies, and objective monitoring of disease progression or treatment response. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. While fMRI and FC data have been utilized for diagnosis of PD through application of machine learning approaches such as support vector machine and logistic regression, the characterization of FC changes in early-stage PD has not been investigated. Given the complexity and non-linearity of fMRI data, we propose the use of a long short-term memory (LSTM) network to distinguish the early stages of PD and understand related functional brain changes. The study included 84 subjects (56 in stage 2 and 28 in stage 1) from the Parkinson's Progression Markers Initiative (PPMI), the largest-available public PD dataset. Under a repeated 10-fold stratified cross-validation, the LSTM model reached an accuracy of 71.63%, 13.52% higher than the best traditional machine learning method and 11.56% higher than a CNN model, indicating significantly better robustness and accuracy compared with other machine learning classifiers. Finally, we used the learned LSTM model weights to select the top brain regions that contributed to model prediction and performed FC analyses to characterize functional changes with disease stage and motor impairment to gain better insight into the brain mechanisms of PD.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"952084"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10420717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment and Alzheimer's Disease.","authors":"Ruaridh A Clark, Keith Smith, Javier Escudero, Agustín Ibáñez, Mario A Parra","doi":"10.3389/fnimg.2022.924811","DOIUrl":"10.3389/fnimg.2022.924811","url":null,"abstract":"<p><p>The prevalence of dementia, including Alzheimer's disease (AD), is on the rise globally with screening and intervention of particular importance and benefit to those with limited access to healthcare. Electroencephalogram (EEG) is an inexpensive, scalable, and portable brain imaging technology that could deliver AD screening to those without local tertiary healthcare infrastructure. We study EEG recordings of subjects with sporadic mild cognitive impairment (MCI) and prodromal familial, early-onset, AD for the same working memory tasks using high- and low-density EEG, respectively. A challenge in detecting electrophysiological changes from EEG recordings is that noise and volume conduction effects are common and disruptive. It is known that the imaginary part of coherency (iCOH) can generate functional connectivity networks that mitigate against volume conduction, while also erasing true instantaneous activity (zero or π-phase). We aim to expose topological differences in these iCOH connectivity networks using a global network measure, eigenvector alignment (EA), shown to be robust to network alterations that emulate the erasure of connectivities by iCOH. Alignments assessed by EA capture the relationship between a pair of EEG channels from the similarity of their connectivity patterns. Significant alignments-from comparison with random null models-are seen to be consistent across frequency ranges (delta, theta, alpha, and beta) for the working memory tasks, where consistency of iCOH connectivities is also noted. For high-density EEG recordings, stark differences in the control and sporadic MCI results are observed with the control group demonstrating far more consistent alignments. Differences between the control and pre-dementia groupings are detected for significant correlation and iCOH connectivities, but only EA suggests a notable difference in network topology when comparing between subjects with sporadic MCI and prodromal familial AD. The consistency of alignments, across frequency ranges, provides a measure of confidence in EA's detection of topological structure, an important aspect that marks this approach as a promising direction for developing a reliable test for early onset AD.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"924811"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406240/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9963614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abnormal Functional Hierarchies of EEG Networks in Familial and Sporadic Prodromal Alzheimer's Disease During Visual Short-Term Memory Binding.","authors":"Keith M Smith, John M Starr, Javier Escudero, Agustin Ibañez, Mario A Parra","doi":"10.3389/fnimg.2022.883968","DOIUrl":"10.3389/fnimg.2022.883968","url":null,"abstract":"<p><p>Alzheimer's Disease (AD) shows both complex alterations of functional dependencies between brain regions and a decreased ability to perform Visual Short-Term Memory Binding (VSTMB) tasks. Recent advances in network neuroscience toward understanding the complexity of hierarchical brain function here enables us to establish a link between these two phenomena. Here, we study data on two types of dementia at Mild Cognitive Impairment (MCI) stage-familial AD patients (E280A mutation of the presenilin-1 gene) and elderly MCI patients at high risk of sporadic AD, both with age-matched controls. We analyzed Electroencephalogram (EEG) signals recorded during the performance of Visual Short-Term Memory (VSTM) tasks by these participants. Functional connectivity was computed using the phase-lag index in Alpha and Beta; and network analysis was employed using network indices of hierarchical spread (degree variance) and complexity. Hierarchical characteristics of EEG functional connectivity networks revealed abnormal patterns in familial MCI VSTMB function and sporadic MCI VSTMB function. The middle-aged familial MCI binding network displayed a larger degree variance in lower Beta compared to healthy controls (<i>p</i> = <i>0.0051</i>, Cohen's <i>d</i> = 1.0124), while the elderly sporadic MCI binding network displayed greater hierarchical complexity in Alpha (<i>p</i> = <i>0.0140</i>, Cohen's <i>d</i> = 1.1627). Characteristics in healthy aging were not shown to differ. These results indicate that activity in MCI exhibits cross-frequency network reorganization characterized by increased heterogeneity of node roles in the functional hierarchy. Aging itself is not found to cause VSTM functional hierarchy differences.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"883968"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10338072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Sectional and Longitudinal Hippocampal Atrophy, Not Cortical Thinning, Occurs in Amyloid-Negative, p-Tau-Positive, Older Adults With Non-Amyloid Pathology and Mild Cognitive Impairment.","authors":"Swati Rane Levendovszky","doi":"10.3389/fnimg.2022.828767","DOIUrl":"10.3389/fnimg.2022.828767","url":null,"abstract":"<p><strong>Introduction: </strong>Alzheimer's disease (AD) is a degenerative disease characterized by pathological accumulation of amyloid and phosphorylated tau. Typically, the early stage of AD, also called mild cognitive impairment (MCI), shows amyloid pathology. A small but significant number of individuals with MCI do not exhibit amyloid pathology but have elevated phosphorylated tau levels (A-T+ MCI). We used CSF amyloid and phosphorylated tau to identify the individuals with A+T+ and A-T+ MCI as well as cognitively normal (A-T-) controls. To increase the sample size, we leveraged the Global Alzheimer's Association Interactive Network and identified 137 MCI+ and 61 A-T+ MCI participants. We compared baseline and longitudinal, hippocampal, and cortical atrophy between groups.</p><p><strong>Methods: </strong>We applied ComBat harmonization to minimize site-related variability and used FreeSurfer for all measurements.</p><p><strong>Results: </strong>Harmonization reduced unwanted variability in cortical thickness by 3.4% and in hippocampal volume measurement by 10.3%. Cross-sectionally, widespread cortical thinning with age was seen in the A+T+ and A-T+ MCI groups (<i>p</i> < 0.0005). A decrease in the hippocampal volume with age was faster in both groups (<i>p</i> < 0.05) than in the controls. Longitudinally also, hippocampal atrophy rates were significant (<i>p</i> < 0.05) when compared with the controls. No longitudinal cortical thinning was observed in A-T+ MCI group.</p><p><strong>Discussion: </strong>A-T+ MCI participants showed similar baseline cortical thickness patterns with aging and longitudinal hippocampal atrophy rates as participants with A+T+ MCI, but did not show longitudinal cortical atrophy signature.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"828767"},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10319942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients.","authors":"Sara Ranjbar, Kyle W Singleton, Lee Curtin, Cassandra R Rickertsen, Lisa E Paulson, Leland S Hu, Joseph Ross Mitchell, Kristin R Swanson","doi":"10.3389/fnimg.2022.832512","DOIUrl":"10.3389/fnimg.2022.832512","url":null,"abstract":"<p><p>Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-based methodologies for MRI segmentation, few works have focused on tackling MRI skull stripping of brain tumor patient data. This gap in literature can be associated with the lack of publicly available data (due to concerns about patient identification) and the labor-intensive nature of generating ground truth labels for model training. In this retrospective study, we assessed the performance of Dense-Vnet in skull stripping brain tumor patient MRI trained on our large multi-institutional brain tumor patient dataset. Our data included pretreatment MRI of 668 patients from our in-house institutional review board-approved multi-institutional brain tumor repository. Because of the absence of ground truth, we used imperfect automatically generated training labels using SPM12 software. We trained the network using common MRI sequences in oncology: T1-weighted with gadolinium contrast, T2-weighted fluid-attenuated inversion recovery, or both. We measured model performance against 30 independent brain tumor test cases with available manual brain masks. All images were harmonized for voxel spacing and volumetric dimensions before model training. Model training was performed using the modularly structured deep learning platform NiftyNet that is tailored toward simplifying medical image analysis. Our proposed approach showed the success of a weakly supervised deep learning approach in MRI brain extraction even in the presence of pathology. Our best model achieved an average Dice score, sensitivity, and specificity of, respectively, 94.5, 96.4, and 98.5% on the multi-institutional independent brain tumor test set. To further contextualize our results within existing literature on healthy brain segmentation, we tested the model against healthy subjects from the benchmark LBPA40 dataset. For this dataset, the model achieved an average Dice score, sensitivity, and specificity of 96.2, 96.6, and 99.2%, which are, although comparable to other publications, slightly lower than the performance of models trained on healthy patients. We associate this drop in performance with the use of brain tumor data for model training and its influence on brain appearance.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"832512"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9966264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A training program for researchers in population neuroimaging: Early experiences.","authors":"Caterina Rosano","doi":"10.3389/fnimg.2022.896350","DOIUrl":"https://doi.org/10.3389/fnimg.2022.896350","url":null,"abstract":"<p><p>Recent advances in neuroimaging create groundbreaking opportunities to better understand human neurological and psychiatric diseases, but also bring new challenges. With the advent of more and more sophisticated and efficient multimodal image processing software, we can now study much larger populations and integrate information from multiple modalities. In consequence, investigators that use neuroimaging techniques must also understand and apply principles of population sampling and contemporary data analytic techniques. The next generation of neuroimaging researchers must be skilled in numerous previously distinct disciplines and so a new integrated model of training is needed. This tutorial presents the rationale for such a new training model and presents the results from the first years of the training program focused on population neuroimaging of Alzheimer's Disease. This approach is applicable to other areas of population neuroimaging.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"896350"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10338070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic photobiomodulation with 808-nm and 1064-nm lasers to reduce the β-amyloid neurotoxicity in the <i>in vitro</i> Alzheimer's disease models.","authors":"Renlong Zhang,&nbsp;Ting Zhou,&nbsp;Soham Samanta,&nbsp;Ziyi Luo,&nbsp;Shaowei Li,&nbsp;Hao Xu,&nbsp;Junle Qu","doi":"10.3389/fnimg.2022.903531","DOIUrl":"https://doi.org/10.3389/fnimg.2022.903531","url":null,"abstract":"<p><strong>Background: </strong>In Alzheimer's disease (AD), the deposition of β-amyloid (Aβ) plaques is closely associated with the neuronal apoptosis and activation of microglia, which may result in the functional impairment of neurons through pro-inflammation and over-pruning of the neurons. Photobiomodulation (PBM) is a non-invasive therapeutic approach without any conspicuous side effect, which has shown promising attributes in the treatment of chronic brain diseases such as AD by reducing the Aβ burden. However, neither the optimal parameters for PBM treatment nor its exact role in modulating the microglial functions/activities has been conclusively established yet.</p><p><strong>Methods: </strong>An inflammatory stimulation model of Alzheimer's disease (AD) was set up by activating microglia and neuroblastoma with fibrosis β-amyloid (fAβ) in a transwell insert system. SH-SY5Y neuroblastoma cells and BV2 microglial cells were irradiated with the 808- and 1,064-nm lasers, respectively (a power density of 50 mW/cm² and a dose of 10 J/cm²) to study the PBM activity. The amount of labeled fAβ phagocytosed by microglia was considered to assess the microglial phagocytosis. A PBM-induced neuroprotective study was conducted with the AD model under different laser parameters to realize the optimal condition. Microglial phenotype, microglial secretions of the pro-inflammatory and anti-inflammatory factors, and the intracellular Ca²⁺ levels in microglia were studied in detail to understand the structural and functional changes occurring in the microglial cells of AD model upon PBM treatment.</p><p><strong>Conclusion: </strong>A synergistic PBM effect (with the 808- and 1,064-nm lasers) effectively inhibited the fAβ-induced neurotoxicity of neuroblastoma by promoting the viability of neuroblastoma and regulating the intracellular Ca²⁺ levels of microglia. Moreover, the downregulation of Ca²⁺ led to microglial polarization with an M2 phenotype, which promotes the fAβ phagocytosis, and resulted in the upregulated expression of anti-inflammatory factors and downregulated expression of inflammatory factors.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"1 ","pages":"903531"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10338076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}