Elucidating the complex organization of neural micro-domains in the locust Schistocerca gregaria using dMRI

AbstractTo understand brain function it is necessary to characterize both the underlying structural connectivity between neurons and the physiological integrity of these connections. Previous research exploring insect brain connectivity has typically used electron microscopy techniques, but this methodology cannot be applied to living animals and so cannot be used to understand dynamic physiological processes. The relatively large brain of the desert locust, Schistercera gregaria (Forksȧl) is ideal for exploring a novel methodology; micro diffusion magnetic resonance imaging (micro-dMRI) for the characterization of neuronal connectivity in an insect brain. The diffusion-weighted imaging (DWI) data were acquired on a preclinical system using a customised multi-shell diffusion MRI scheme optimized to image the locust brain. Endogenous imaging contrasts from the averaged DWIs and Diffusion Kurtosis Imaging (DKI) scheme were applied to classify various anatomical features and diffusion patterns in neuropils, respectively. The application of micro-dMRI modelling to the locust brain provides a novel means of identifying anatomical regions and inferring connectivity of large tracts in an insect brain. Furthermore, quantitative imaging indices derived from the kurtosis model that include fractional anisotropy (FA), mean diffusivity (MD) and kurtosis anisotropy (KA) can be extracted. These metrics could, in future, be used to quantify longitudinal structural changes in the nervous system of the locust brain that occur due to environmental stressors or ageing.

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Elucidating the complex organization of neural micro-domains in the locust Schistocerca gregaria using dMRI

10.1101/2020.01.17.910851 ◽

2020 ◽

Author(s):

S.S. Shahid ◽

C.M. Kerskens ◽

M. Burrows ◽

A.G. Witney

Keyword(s):

Structural Changes ◽

Schistocerca Gregaria ◽

Brain Connectivity ◽

Quantitative Imaging ◽

Structural Connectivity ◽

Mean Diffusivity ◽

Diffusion Kurtosis Imaging ◽

Insect Brain ◽

Neuronal Connectivity ◽

And Diffusion

AbstractTo understand brain function it is necessary to characterize both the underlying structural connectivity between neurons and the physiological integrity of these connections. Previous research exploring insect brain connectivity has used microscopy techniques, but this methodology is time consuming and cannot be applied to living animals and so cannot be used to understand dynamic physiological processes. The relatively large brain of the desert locust, Schistercera gregaria (Forksȧl) is ideal for exploring a novel methodology; diffusion magnetic resonance imaging (dMRI) for the characterization of neuronal connectivity in an insect brain. The diffusion-weighted imaging (DWI) data were acquired on a preclinical system using a customised multi-shell diffusion MRI scheme. Endogenous imaging contrasts from the averaged DWIs and Diffusion Kurtosis Imaging (DKI) scheme were applied to classify various anatomical features and diffusion patterns in neuropils, respectively. The application of micro-MRI and dMRI modelling to the locust brain provides a novel means of identifying anatomical regions and connectivity in an insect brain. Furthermore, quantitative imaging indices derived from the kurtosis model that include fractional anisotropy (FA), mean diffusivity (MD) and kurtosis anisotropy (KA) could, in future, be used to quantify longitudinal structural changes in neuronal connectivity due to environmental stressors or ageing.

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Subcortical nuclei in Alzheimer’s disease: a volumetric and diffusion kurtosis imaging study

Acta Radiologica ◽

10.1177/0284185118758122 ◽

2018 ◽

Vol 59 (11) ◽

pp. 1365-1371 ◽

Cited By ~ 3

Author(s):

Ming-Liang Wang ◽

Xiao-Er Wei ◽

Jian-Liang Fu ◽

Wei Li ◽

Meng-Meng Yu ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Mmse Score ◽

Mean Diffusivity ◽

Volume Reduction ◽

Diffusion Kurtosis Imaging ◽

Left Hippocampus ◽

Subcortical Nuclei ◽

Diffusion Kurtosis ◽

And Diffusion

Background Previous studies revealed that subcortical nuclei were harmed in the process of Alzheimer’s disease (AD). Purpose To investigate the volumetric and diffusion kurtosis imaging (DKI) parameter changes of subcortical nuclei in AD and their relationship with cognitive function. Materials and Methods A total of 17 mild AD patients, 15 moderate to severe AD patients, and 16 controls underwent neuropsychological tests and magnetic resonance imaging (MRI) scans. Volume, mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA) were measured in hippocampus, thalamus, caudate, putamen, pallidum, and amygdala. MRI parameters were compared. Correlation analysis was performed between subcortical nuclei volume, DKI parameters, and MMSE score. Results Significant volume reduction was seen in the left hippocampus in mild AD, and the bilateral hippocampus, thalamus, putamen, left caudate, and right amygdala in moderate to severe AD ( P < 0.05). Increased MD values were observed in the left hippocampus, left amygdala, and right caudate in mild AD, and the bilateral hippocampus and right amygdala in moderate to severe AD ( P < 0.05). Decreased MK values were observed only in the bilateral hippocampus in moderate to severe AD ( P < 0.05). No group significances were found in FA value. MMSE score was positively correlated with the volume of the bilateral hippocampus, thalamus, and putamen, and MK value of the left hippocampus ( P < 0.05). A negative correlation was found with the MD value of the bilateral hippocampus and left amygdala ( P < 0.05). Conclusion Mild AD mainly has microscopic subcortical changes revealed by increased MD value, and moderate to severe AD mainly has macroscopic subcortical changes revealed by volume reduction. MK is more sensitive in severe AD than mild AD.

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The diagnostic accuracy of intravoxel incoherent motion and diffusion kurtosis imaging in the differentiation of malignant and benign soft-tissue masses: which is better?

Acta Radiologica ◽

10.1177/02841851211017511 ◽

2021 ◽

pp. 028418512110175

Author(s):

Yajie Liu ◽

Zhenzhen Yin ◽

Xiangwen Li ◽

Yu Zhang ◽

Yuan Yuan ◽

...

Keyword(s):

Soft Tissue ◽

Mean Diffusivity ◽

Intravoxel Incoherent Motion ◽

Diffusion Kurtosis Imaging ◽

Conventional Mri ◽

Mri Features ◽

Soft Tissue Masses ◽

Mri Sequences ◽

Diffusion Kurtosis ◽

And Diffusion

Background It is difficult for conventional magnetic resonance imaging (MRI) to distinguish benign soft-tissue masses (STMs) from malignant masses. Purpose To quantitatively compare the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) in STMs. Material and Methods The data from 58 patients with STMs were retrospectively analyzed. The GE Discovery 3.0-T MRI scanner was used to acquire conventional MRI sequences, IVIM, and DKI images. The chi-square test, independent sample t-test, and Mann–Whitney U tests were used to compare the differences between conventional MRI features, IVIM, and DKI parameters (Dslow, Dfast, f, mean kurtosis [MK], and mean diffusivity [MD]) between the benign and malignant groups. Receiver-operating characteristic (ROC) curve analysis was also performed. Results Tumor size and depth are statistically different in STTs. Dslow, MK, and MD values in the malignant groups are significantly lower than the benign groups ( P < 0.05). However, Dfast and f values are not statistically different between the two groups. The area under the curve (AUC) of Dslow value (0.859) is higher than MD (0.765) and MK (0.676) values for identifying benign and malignant STMs. The Dslow value showed the best specificity (82.93%). The sensitivity and specificity of IVIM and DKI parameters are higher than that of conventional MRI sequences. Conclusion IVIM and DKI can be used to distinguish between benign and malignant STMs, with Dslow as the most meaningful parameter.

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The diagnostic value of T2 map, diffusion tensor imaging, and diffusion kurtosis imaging in differentiating dermatomyositis from muscular dystrophy

Acta Radiologica ◽

10.1177/0284185121999006 ◽

2021 ◽

pp. 028418512199900

Author(s):

Jun Ran ◽

Bin Dai ◽

Chanyuan Liu ◽

Huayue Zhang ◽

Yitong Li ◽

...

Keyword(s):

Diffusion Tensor Imaging ◽

Muscular Dystrophy ◽

Diffusion Tensor ◽

Mean Diffusivity ◽

Diagnostic Value ◽

Thigh Muscle ◽

Diffusion Kurtosis Imaging ◽

T2 Map ◽

Diffusion Kurtosis ◽

And Diffusion

Background Dermatomyositis (DM) and muscular dystrophy are clinically difficult to differentiate. Purpose To confirm the feasibility and assess the accuracy of conventional magnetic resonance imaging (MRI), T2 map, diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) in the differentiation of DM from muscular dystrophy. Material and Methods Forty-two patients with DM proven by diagnostic criteria were enrolled in the study along with 23 patients with muscular dystrophy. Conventional MR, T2 map, DTI, and DKI images were obtained in the thigh musculature for all patients. Intramuscular T2 value, apparent diffusion coefficient (ADC), fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) values were compared between the patients with DM and muscular dystrophy. Student’s t-tests and receiver operating characteristic (ROC) curve analyses were performed for all parameters. P values < 0.05 were considered statistically significant. Results The intramuscular T2, ADC, FA, MD, and MK values within muscles were statistically significantly different between the DM and muscular dystrophy groups ( P<0.01). The MK value was statistically significantly different between the groups in comparison with T2 and FA value. As a supplement to conventional MRI, the parameters of MD and MK differentiated DM and muscular dystrophy may be valuable. The optimal cut-off value of ADC and MD values (with respective AUC, sensitivity, and specificity) between DM and muscular dystrophy were 1.698 ×10−3mm2/s (0.723, 54.1%, and 78.1%) and 1.80 ×10−3mm2/s (61.9% and 70.2%), respectively. Conclusion Thigh muscle ADC and MD parameters may be useful in differentiating patients with DM from those with muscular dystrophy.

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Characterizing Diaschisis-Related Thalamic Perfusion and Diffusion After Middle Cerebral Artery Infarction

Stroke ◽

10.1161/strokeaha.120.032464 ◽

2021 ◽

Author(s):

Cong Xia ◽

Jiaying Zhou ◽

Chunqiang Lu ◽

Yuancheng Wang ◽

Tianyu Tang ◽

...

Keyword(s):

Middle Cerebral Artery ◽

Cerebral Artery ◽

Arterial Spin Labeling ◽

Mean Diffusivity ◽

Spin Labeling ◽

Diffusion Kurtosis Imaging ◽

Middle Cerebral Artery Infarction ◽

Chronic Group ◽

Diffusion Kurtosis ◽

And Diffusion

Background and Purpose: Ipsilateral thalamic diaschisis (ITD) initially describes functional depression of the thalamus ipsilateral to a supratentorial lesion, but accumulating evidence has shown morphological changes also occur. Therefore, we aimed to characterize thalamic perfusion and diffusion related to ITD over time and their inter-relationships after middle cerebral artery infarction. Methods: Eighty-five patients with middle cerebral artery infarction who underwent diffusion kurtosis imaging and arterial spin labeling were retrospectively included. ITD was diagnosed as ipsilateral thalamic hypoperfusion present on ≥2 cerebral blood flow maps. The thalamic asymmetrical index was calculated as (ipsilateral value−contralateral value)/contralateral value×100%. Finally, the inter-relationships of thalamic perfusion and diffusion were analyzed. Results: ITD was present in 56/85 patients (65.9%, ITD+). In ITD+ patients, larger abnormal perfusion volume, higher perfusion-infarct mismatch and lower rates of focal hyperperfusion were observed than ITD− patients. Infarction affecting the corona radiata were more frequent among ITD+ patients. Mean kurtosis were slightly but significantly increased within the ipsilateral thalamus compared with the contralateral one in ITD+ patients of subacute and chronic groups, while fractional anisotropy was significantly increased in subacute group but decreased in chronic group for both ITD+ and ITD− patients. Mean diffusivity was significantly increased in ITD+ patients of chronic group. Furthermore, the AI CBF was negatively and significantly correlated with AI MK and AI FA in ITD+ patients in subacute group, and AI MD , even after adjustment for abnormal perfusion volume and days from symptoms onset, in chronic group. ITD+ patients had significantly higher National Institutes of Health Stroke Scale and modified Rankin Scale scores at admission and discharge and also showed a trend to independent association with clinical outcome at discharge. Conclusions: The combination of arterial spin labeling and diffusion kurtosis imaging can reveal early, time-specific thalamic perfusion and diffusion changes after middle cerebral artery infarction. ITD-related hypoperfusion was significantly correlated with underlying microstructural alterations.

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Brain Network Connectivity in Adolescents At Risk for Alcohol Use Problems

10.1101/2020.10.22.20217976 ◽

2020 ◽

Author(s):

Simon T. E. Baker ◽

Murat Yücel ◽

Alex Fornito ◽

Andrew Zalesky ◽

Sarah Whittle ◽

...

Keyword(s):

Family History ◽

Alcohol Use ◽

Brain Connectivity ◽

Structural Connectivity ◽

Mean Diffusivity ◽

Network Connectivity ◽

Brain Network ◽

Increased Risk ◽

Brain Network Connectivity ◽

History Of

AbstractImaging studies of young people with a family history of alcohol use disorder (AUD) have found structural and/or functional differences within and between anatomically distributed and functionally specialised systems throughout the brain. Differences in brain connectivity among adolescents with a family history of AUD may account for the increased risk of later alcohol use problems; however, to date, no prospective studies have directly examined this hypothesis across the entire connectome in a regionally unbiased way. Our analysis included 52 adolescents (Mage = 16.5 years ± 0.6 SD) assessed with diffusion-weighted magnetic resonance imaging, of whom 20 had a family history of AUD and 32 did not. All participants were followed-up 2.3 years later and completed a questionnaire measuring past year alcohol use and alcohol-related harms. Subject-specific connectomic maps of structural connectivity were constructed using two parcellation schemes (82-node anatomical and 530-node random) and five measures of connectivity weight (streamline count, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity), and a connectome-wide network-based statistic analysis was used to compare group differences at each and every connection between adolescents with and without a family history of AUD. Baseline connectivity measures did not differentiate these groups, and we did not find an association between baseline connectivity measures and alcohol outcomes at follow-up. These findings suggest that atypical inter-regional structural connectivity may not contribute to the risk of developing alcohol use problems in this particular age group, or during this particular period of development.

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Network curvature as a hallmark of brain structural connectivity

Nature Communications ◽

10.1038/s41467-019-12915-x ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 3

Author(s):

Hamza Farooq ◽

Yongxin Chen ◽

Tryphon T. Georgiou ◽

Allen Tannenbaum ◽

Christophe Lenglet

Keyword(s):

Financial Market ◽

Biological Networks ◽

Healthy Subjects ◽

Structural Changes ◽

Brain Connectivity ◽

Empirical Studies ◽

Structural Connectivity ◽

Brain Regions ◽

Autism Spectrum ◽

Structural Networks

Abstract Although brain functionality is often remarkably robust to lesions and other insults, it may be fragile when these take place in specific locations. Previous attempts to quantify robustness and fragility sought to understand how the functional connectivity of brain networks is affected by structural changes, using either model-based predictions or empirical studies of the effects of lesions. We advance a geometric viewpoint relying on a notion of network curvature, the so-called Ollivier-Ricci curvature. This approach has been proposed to assess financial market robustness and to differentiate biological networks of cancer cells from healthy ones. Here, we apply curvature-based measures to brain structural networks to identify robust and fragile brain regions in healthy subjects. We show that curvature can also be used to track changes in brain connectivity related to age and autism spectrum disorder (ASD), and we obtain results that are in agreement with previous MRI studies.

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Brain Connectivity Affecting Gait Function after Unilateral Supratentorial Stroke

Brain Sciences ◽

10.3390/brainsci11070870 ◽

2021 ◽

Vol 11 (7) ◽

pp. 870

Author(s):

Hyun-Ah Lee ◽

Dae-Hyun Kim

Keyword(s):

Diffusion Tensor Imaging ◽

Corpus Callosum ◽

Fractional Anisotropy ◽

Motor Coordination ◽

Diffusion Tensor ◽

Brain Connectivity ◽

Structural Connectivity ◽

Mean Diffusivity ◽

Medial Lemniscus ◽

Gait Function

Gait dysfunction is a leading cause of long-term disability after stroke. The mechanisms underlying recovery of gait function are unknown. We retrospectively evaluated the association between structural connectivity and gait function in 127 patients with unilateral supratentorial stroke (>1 month after stroke). All patients underwent T1-weighted, diffusion tensor imaging and functional ambulation categorization. Voxel-wise linear regression analyses of the images were conducted using fractional anisotropy, mean diffusivity, and mode of anisotropy mapping as dependent variables, while the functional ambulation category was used as an independent variable with age and days after stroke as covariates. The functional ambulation category was positively associated with increased fractional anisotropy in the lesioned cortico-ponto-cerebellar system, corona radiata of the non-lesioned corticospinal tract pathway, bilateral medial lemniscus in the brainstem, and the corpus callosum. The functional ambulation category was also positively associated with increased mode of anisotropy in the lesioned posterior corpus callosum. In conclusion, structural connectivity associated with motor coordination and feedback affects gait function after stroke. Diffusion tensor imaging for evaluating structural connectivity can help to predict gait recovery and target rehabilitation goals after stroke.

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PPARs as Metabolic Sensors and Therapeutic Targets in Liver Diseases

International Journal of Molecular Sciences ◽

10.3390/ijms22158298 ◽

2021 ◽

Vol 22 (15) ◽

pp. 8298

Author(s):

Hugo Christian Monroy-Ramirez ◽

Marina Galicia-Moreno ◽

Ana Sandoval-Rodriguez ◽

Alejandra Meza-Rios ◽

Arturo Santos ◽

...

Keyword(s):

Liver Diseases ◽

Metabolic Regulation ◽

Structural Changes ◽

Critical Role ◽

The Body ◽

Molecular Characteristics ◽

Signal Pathways ◽

Virus Infections ◽

Physiological Processes ◽

Hepatic Level

Carbohydrates and lipids are two components of the diet that provide the necessary energy to carry out various physiological processes to help maintain homeostasis in the body. However, when the metabolism of both biomolecules is altered, development of various liver diseases takes place; such as metabolic-associated fatty liver diseases (MAFLD), hepatitis B and C virus infections, alcoholic liver disease (ALD), and in more severe cases, hepatocelular carcinoma (HCC). On the other hand, PPARs are a family of ligand-dependent transcription factors with an important role in the regulation of metabolic processes to hepatic level as well as in other organs. After interaction with specific ligands, PPARs are translocated to the nucleus, undergoing structural changes to regulate gene transcription involved in lipid metabolism, adipogenesis, inflammation and metabolic homeostasis. This review aims to provide updated data about PPARs’ critical role in liver metabolic regulation, and their involvement triggering the genesis of several liver diseases. Information is provided about their molecular characteristics, cell signal pathways, and the main pharmacological therapies that modulate their function, currently engaged in the clinic scenario, or in pharmacological development.

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Magnetic resonance diffusion kurtosis imaging in differential diagnosis of benign and malignant renal tumors

Cancer Imaging ◽

10.1186/s40644-020-00369-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jianxiong Fu ◽

Jing Ye ◽

Wenrong Zhu ◽

Jingtao Wu ◽

Wenxin Chen ◽

...

Keyword(s):

Differential Diagnosis ◽

Mean Diffusivity ◽

Threshold Value ◽

Renal Tumors ◽

Diffusion Kurtosis Imaging ◽

Renal Oncocytoma ◽

Renal Angiomyolipoma ◽

Planar Imaging ◽

Cell Renal Cell Carcinoma ◽

Diffusion Kurtosis

Abstract Background Benign and malignant renal tumors share similar some imaging findings. Methods Sixty-six patients with clear cell renal cell carcinoma (CCRCC), 13 patients with renal angiomyolipoma with minimal fat (RAMF) and 7 patients with renal oncocytoma (RO) were examined. For diffusion kurtosis imaging (DKI), respiratory triggered echo-planar imaging sequences were acquired in axial plane (3 b-values: 0, 500, 1000s/mm2). Mean Diffusivity (MD), fractional Anisotropy (FA), mean kurtosis (MK), kurtosis anisotropy (KA) and radial kurtosis (RK) were performed. Results For MD, a significant higher value was shown in CCRCC (3.08 ± 0.23) than the rest renal tumors (2.93 ± 0.30 for RO, 1.52 ± 0.24 for AML, P < 0.05). The MD values were higher for RO than for AML (2.93 ± 0.30 vs.1.52 ± 0.24, P < 0.05), while comparable MD values were found between CCRCC and RO (3.08 ± 0.23 vs. 2.93 ± 0.30, P > 0.05). For MK, KA and RK, a significant higher value was shown in AML (1.32 ± 0.16, 1.42 ± 0.23, 1.41 ± 0.29) than CCRCC (0.43 ± 0.08, 0.57 ± 0.16, 0.37 ± 0.11) and RO (0.81 ± 0.08, 0.86 ± 0.16, 0.69 ± 0.08) (P < 0.05). The MK, KA and RK values were higher for RO than for CCRCC (0.81 ± 0.08 vs. 0.43 ± 0.08, 0.86 ± 0.16 vs. 0.57 ± 0.16, 0.69 ± 0.08 vs. 0.37 ± 0.11, P < 0.05). Using MD values of 2.86 as the threshold value for differentiating CCRCC from RO and AML, the best result obtained had a sensitivity of 76.1%, specificity of 72.6%. Using MK, KA and RK values of 1.19,1.13 and 1.11 as the threshold value for differentiating AML from CCRCC and RO, the best result obtained had a sensitivity of 91.2, 86.7, 82.1%, and specificity of 86.7, 83.2, 72.8%. Conclusion DKI can be used as another noninvasive biomarker for benign and malignant renal tumors’ differential diagnosis.

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