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2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Xi Feng ◽  
Elma S. Frias ◽  
Maria S. Paladini ◽  
David Chen ◽  
Zoe Boosalis ◽  
...  

Abstract Background Brain-resident microglia have a distinct origin compared to macrophages in other organs. Under physiological conditions, microglia are maintained by self-renewal from the local pool, independent of hematopoietic progenitors. Pharmacological depletion of microglia during whole-brain radiotherapy prevents synaptic loss and long-term recognition memory deficits. However, the origin or repopulated cells and the mechanisms behind these protective effects are unknown. Methods CD45low/int/CD11b+ cells from naïve brains, irradiated brains, PLX5622-treated brains and PLX5622 + whole-brain radiotherapy-treated brains were FACS sorted and sequenced for transcriptomic comparisons. Bone marrow chimeras were used to trace the origin and long-term morphology of repopulated cells after PLX5622 and whole-brain radiotherapy. FACS analyses of intrinsic and exotic synaptic compartments were used to measure phagocytic activities of microglia and repopulated cells. In addition, concussive brain injuries were given to PLX5622 and brain-irradiated mice to study the potential protective functions of repopulated cells after PLX5622 + whole-brain radiotherapy. Results After a combination of whole-brain radiotherapy and microglia depletion, repopulated cells are brain-engrafted macrophages that originate from circulating monocytes. Comparisons of transcriptomes reveal that brain-engrafted macrophages have an intermediate phenotype that resembles both monocytes and embryonic microglia. In addition, brain-engrafted macrophages display reduced phagocytic activity for synaptic compartments compared to microglia from normal brains in response to a secondary concussive brain injury. Importantly, replacement of microglia by brain-engrafted macrophages spare mice from whole-brain radiotherapy-induced long-term cognitive deficits, and prevent concussive injury-induced memory loss. Conclusions Brain-engrafted macrophages prevent radiation- and concussion-induced brain injuries and cognitive deficits.


Author(s):  
Charles W. Bradberry

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giorgio Arcara ◽  
Rachele Pezzetta ◽  
S. Benavides-Varela ◽  
G. Rizzi ◽  
S. Formica ◽  
...  

AbstractDespite decades of studies, it is still an open question on how and where simple multiplications are solved by the brain. This fragmented picture is mostly related to the different tasks employed. While in neuropsychological studies patients are asked to perform and report simple oral calculations, neuroimaging and neurophysiological studies often use verification tasks, in which the result is shown, and the participant must verify the correctness. This MEG study aims to unify the sources of evidence, investigating how brain activation unfolds in time using a single-digit multiplication production task. We compared the participants' brain activity—focusing on the parietal lobes—based on response efficiency, dividing their responses in fast and slow. Results showed higher activation for fast, as compared to slow, responses in the left angular gyrus starting after the first operand, and in the right supramarginal gyrus only after the second operand. A whole-brain analysis showed that fast responses had higher activation in the right dorsolateral prefrontal cortex. We show a timing difference of both hemispheres during simple multiplications. Results suggest that while the left parietal lobe may allow an initial retrieval of several possible solutions, the right one may be engaged later, helping to identify the solution based on magnitude checking.


Author(s):  
Caglar Cakan ◽  
Nikola Jajcay ◽  
Klaus Obermayer

Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.


Author(s):  
Takahisa Mori ◽  
Kazuhiro Yoshioka ◽  
Yuhei Tanno ◽  
Shigen Kasakura ◽  
Yuichi Miyazaki

Abstract Objectives Angiographic “slow flow” in the middle cerebral artery (MCA), caused by carotid stenosis, may be associated with high oxygen extraction fraction (OEF). If the MCA slow flow is associated with a reduced relative signal intensity (rSI) of the MCA on MR angiography, the reduced rSI may be associated with a high OEF. We investigated whether the MCA slow flow ipsilateral to carotid stenosis was associated with a high OEF and aimed to create a practical index to estimate the high OEF. Methods We included patients who underwent digital subtraction angiography (DSA) and MRA between 2015 and 2019 to evaluate carotid stenosis. MCA slow flow by image count using DSA, MCA rSI, minimal luminal diameter (MLD) of the carotid artery, carotid artery stenosis rate (CASr), and whole-brain OEF (wb-OEF) was evaluated. When MCA slow flow was associated with a high wb-OEF, the determinants of MCA slow flow were identified, and their association with high wb-OEF was evaluated. Results One hundred and twenty-seven patients met our inclusion criteria. Angiographic MCA slow flow was associated with high wb-OEF. We identified MCA rSI and MLD as determinants of angiographic MCA slow flow. The upper limits of MCA rSI and MLD for angiographic MCA slow flow were 0.89 and 1.06 mm, respectively. The wb-OEF was higher in patients with an MCA rSI ≤ 0.89 and ipsilateral MLD ≤ 1.06 mm than patients without this combination. Conclusions The combination of reduced MCA rSI and ipsilateral narrow MLD is a straightforward index of high wb-OEF. Key Points • The whole-brain OEF in patients with angiographic slow flow in the MCA ipsilateral to high-grade carotid stenosis was higher than in patients without it. • Independent determinants of MCA slow flow were MCA relative signal intensity (rSI) on MRA or minimal luminal diameter (MLD) of the carotid stenosis. • The wb-OEF was higher in patients with an MCA rSI ≤ 0.89 and ipsilateral MLD ≤ 1.06 mm than patients without this combination.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ahana Priyanka ◽  
Kavitha Ganesan

Abstract The diagnostic and clinical overlap of early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and Alzheimer disease (AD) is a vital oncological issue in dementia disorder. This study is designed to examine Whole brain (WB), grey matter (GM) and Hippocampus (HC) morphological variation and identify the prominent biomarkers in MR brain images of demented subjects to understand the severity progression. Curve evolution based on shape constraint is carried out to segment the complex brain structure such as HC and GM. Pre-trained models are used to observe the severity variation in these regions. This work is evaluated on ADNI database. The outcome of the proposed work shows that curve evolution method could segment HC and GM regions with better correlation. Pre-trained models are able to show significant severity difference among WB, GM and HC regions for the considered classes. Further, prominent variation is observed between AD vs. EMCI, AD vs. MCI and AD vs. LMCI in the whole brain, GM and HC. It is concluded that AlexNet model for HC region result in better classification for AD vs. EMCI, AD vs. MCI and AD vs. LMCI with an accuracy of 93, 78.3 and 91% respectively.


2021 ◽  
pp. 1-11
Author(s):  
Yi Liu ◽  
Zhuoyuan Li ◽  
Xueyan Jiang ◽  
Wenying Du ◽  
Xiaoqi Wang ◽  
...  

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p <  0.001). The above results appeared in both the whole brain and DMN. Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.


2021 ◽  
pp. 1-14
Author(s):  
Youjin Jung ◽  
Raymond P. Viviano ◽  
Sanneke van Rooden ◽  
Jeroen van der Grond ◽  
Serge A.R.B. Rombouts ◽  
...  

Background: White matter hyperintensities (WMH) show a robust relationship with arterial pressure as well as objective and subjective cognitive functioning. In addition, APOE ɛ4 carriership may influence how arterial pressure affects cognitive functioning. Objective: To determine the role of region-specific WMH burden and APOE ɛ4 carriership on the relationship between mean arterial pressure (MAP) and cognitive function as well as subjective cognitive decline (SCD). Methods: The sample consisted of 87 cognitively unimpaired middle-aged to older adults aged 50–85. We measured WMH volume for the whole brain, anterior thalamic radiation (ATR), forceps minor, and superior longitudinal fasciculus (SLF). We examined whether WMH burden mediated the relationship between MAP and cognition (i.e., TMT-A score for processing speed; Stroop performance for executive function) as well as SCD (i.e., Frequency of Forgetting (FoF)), and whether APOE ɛ4 carriership moderated that mediation. Results: WMH burden within SLF mediated the effect of MAP on Stroop performance. Both whole brain and ATR WMH burden mediated the effect of MAP on FoF score. In the MAP–WMH–Stroop relationship, the mediation effect of SLF WMH and the effect of MAP on SLF WMH were significant only in APOE ɛ4 carriers. In the MAP–WMH–FoF relationship, the effect of MAP on whole brain WMH burden was significant only in ɛ4 carriers. Conclusion: WMH burden and APOE genotype explain the link between blood pressure and cognitive function and may enable a more accurate assessment of the effect of high blood pressure on cognitive decline and risk for dementia.


2021 ◽  
Author(s):  
Marie Karam ◽  
Guy Malkinson ◽  
Isabelle V BRUNET

Brain perivascular macrophages (PVMs) belong to border-associated macrophages. PVMs are situated along blood vessels in the Virchow-Robin space and are thus found at a unique anatomical position between the endothelium and the parenchyma. Owing to their location and phagocytic capabilities, PVMs are regarded as important components that regulate various aspects of brain physiology in health and pathophysiological states. Here, used LYVE-1 to identify PVMs in the mouse brain. We used brain-tissue sections and cleared whole-brain to learn how they are distributed within the brain and across different developmental postnatal stages. We find that LYVE-1+ PVMs associate with the vasculature in a brain-region-dependent manner, where the hippocampus shows the highest density of LYVE-1+ PVMs. We show that their postnatal distribution is developmentally dynamic and peaks at P10-P20 depending on the brain region. We further demonstrate that their density is reduced in the APP/PS1 mouse model of Alzheimers Disease. In conclusion, our results show an unexpected heterogeneity and dynamics of LYVE-1+ PVMs, and support an important role for this population of PVMs during development and in regulating brain functions in steady-state and disease conditions.


2021 ◽  
Author(s):  
G. Allan Johnson ◽  
Gary Cofer ◽  
James Cook ◽  
James Gee ◽  
Adam Hall ◽  
...  

Paul Lauterbur closed his seminal paper on MRI with the statement that "zeugmatographic (imaging) techniques should find many useful applications in studies of the internal structures, states and composition of microscopic objects" {Lauterbur, 1973 #967}. Magnetic resonance microscopy was subsequently demonstrated in 1986 by three groups{Aguayo, 1986 #968}{Eccles, 1986 #969}{Johnson, 1986 #970}. The application of MRI to the study of tissue structure, i.e. magnetic resonance histology (MRH) was suggested in 1993 {Johnson, 1993 #957}. MRH, while based on the same physical principals as MRI is something fundamentally different than the clinical exams which are typically limited to voxel dimensions of ~ 1 mm3. Preclinical imaging systems can acquire images with voxels ~ 1000 times smaller. The MR histology images presented here have been acquired at yet another factor of 1000 increase in spatial resolution. Figure S1 in the supplement shows a comparison of a state-of-the-art fractional anisotropy images of a C57 mouse brain in vivo @ 150 um resolution (voxel volume of 3.3 x10-3 mm3) with the atlas we have generated for this work at 15 um spatial resolution (voxel volume of 3.3 x 10-6 mm3). In previous work, we have demonstrated the utility of MR histology in neurogenetics at spatial/angular resolution of 45 um /46 angles {Wang N, 2020 #972}. At this spatial/angular resolution it is possible to map whole brain connectivity with high correspondence to retroviral tracers {Calabrese, 2015 #895}. But the MRH derived connectomes can be derived in less than a day where the retroviral tracer studies require months/years {Oh, 2014 #971}. The resolution index (angular samples/voxel volume) for this previous work was >500,000 {Johnson, 2018 #894}. Figure S2 shows a comparison between that previous work and the new atlas presented in this paper with a resolution index of 32 million. Light sheet microscopy (LSM) has undergone similar rapid evolution over the last 20 years. The invention of tissue clearing, advances in immuno histochemistry and development of selective plane illumination microscopy (SPIM) now make it possible to acquire whole mouse brain images at submicron spatial resolution with a vast array of cell specific markers{Ueda, 2020 #974}{Park, 2018 #953}{Murray, 2015 #952}{Gao, 2014 #973}. And these advantages can be realized in scan times of < 6hrs. The major limitation from these studies is the distortion in the tissue from dissection from the cranium, swelling from clearing and staining, and tissue damage from handling. We report here the merger of these two methods: 1. MRH with the brain in the skull to provide accurate geometry, cytoarchitectural measures using scalar imaging metrics and whole brain connectivity at 15 um isotropic spatial resolution with super resolution track density images @ 5 um isotropic resolution; 2. whole brain multichannel LSM @ 1.8x1.8x4.0 um; 3. a big image data infrastructure that enables label mapping from the atlas to the MR image, geometric correction to the light sheet data, label mapping to the light sheet volumes and quantitative extraction of regional cell density. These methods make it possible to generate a comprehensive collection of image derived phenotypes (IDP) of cells and circuits covering the whole mouse brain with throughput that can be scaled for quantitative neurogenetics.


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