Diffusion kurtosis imaging detects the time-dependent progress of pathological changes in the oral rotenone mouse model of Parkinson's disease

Clinical diagnosis of Parkinson's disease (PD) occurs typically when a substantial proportion of dopaminergic neurons in the substantia nigra (SN) already died, and the first motor symptoms appear. Therefore, tools enabling the early diagnosis of PD are essential to identify early-stage PD patients in which neuroprotective treatments could have a significant impact. Here, we test the utility and sensitivity of the diffusion kurtosis imaging (DKI) in detecting progressive microstructural changes in several brain regions of mice exposed to chronic intragastric administration of rotenone, a mouse model that mimics the spatiotemporal progression of PD-like pathology from the ENS to the SN as described by Braak's staging. Our results show that DKI, especially kurtosis, can detect the progression of pathology-associated changes throughout the CNS. Increases in mean kurtosis were first observed in the dorsal motor nucleus of the vagus (DMV) after 2 months of exposure to rotenone and before the loss of dopaminergic neurons in the SN occurred. Remarkably, we also show that limited exposure to rotenone for 2 months is enough to trigger the progression of the disease in the absence of the environmental toxin, thus suggesting that once the first pathological changes in one region appear, they can self-perpetuate and progress within the CNS. Overall, our results show that DKI can be a useful radiological marker for the early detection and monitoring of PD pathology progression in patients with the potential to improve the clinical diagnosis and the development of neuroprotective treatments. (Figure presented.). © 2021 International Society for Neurochemistr

Heteronuclear broadband spin-flip decoupling with adiabatic pulses

NRC publication: Ye

Water removal in MR spectroscopic imaging with Casorati singular value decomposition.

PURPOSE Water removal is one of the computational bottlenecks in the processing of high-resolution MRSI data. The purpose of this work is to propose an approach to reduce the computing time required for water removal in large MRS data. METHODS In this work, we describe a singular value decomposition-based approach that uses the partial position-time separability and the time-domain linear predictability of MRSI data to reduce the computational time required for water removal. Our approach arranges MRS signals in a Casorati matrix form, applies low-rank approximations utilizing singular value decomposition, removes residual water from the most prominent left-singular vectors, and finally reconstructs the water-free matrix using the processed left-singular vectors. RESULTS We have demonstrated the effectiveness of our proposed algorithm for water removal using both simulated and in vivo data. The proposed algorithm encompasses a pip-installable tool ( https://pypi.org/project/CSVD/), available on GitHub ( https://github.com/amirshamaei/CSVD), empowering researchers to use it in future studies. Additionally, to further promote transparency and reproducibility, we provide comprehensive code for result replication. CONCLUSIONS The findings of this study suggest that the proposed method is a promising alternative to existing water removal methods due to its low processing time and good performance in removing water signals

Diffusion Kurtosis Imaging Detects Microstructural Changes in a Methamphetamine-Induced Mouse Model of Parkinson's Disease

Methamphetamine (METH) abuse is known to increase the risk of Parkinson's disease (PD) due to its dopaminergic neurotoxicity. This is the rationale for the METH model of PD developed by toxic METH dosing (10 mg/kg four times every 2 h) which features robust neurodegeneration and typical motor impairment in mice. In this study, we used diffusion kurtosis imaging to reveal microstructural brain changes caused by METH-induced neurodegeneration. The METH-treated mice and saline-treated controls underwent diffusion kurtosis imaging scanning using the Bruker Avance 9.4 Tesla MRI system at two time-points: 5 days and 1 month to capture both early and late changes induced by METH. At 5 days, we found a decrease in kurtosis in substantia nigra, striatum and sensorimotor cortex, which is likely to indicate loss of DAergic neurons. At 1 month, we found an increase of kurtosis in striatum and sensorimotor cortex and hippocampus, which may reflect certain recovery processes. Furthermore, we performed tract-based spatial statistics analysis in the white matter and at 1 month, we observed increased kurtosis in ventral nucleus of the lateral lemniscus and some of the lateral thalamic nuclei. No changes were present at the early stage. This study confirms the ability of diffusion kurtosis imaging to detect microstructural pathological processes in both grey and white matter in the METH model of PD. The exact mechanisms underlying the kurtosis changes remain to be elucidated but kurtosis seems to be a valuable biomarker for tracking microstructural brain changes in PD and potentially other neurodegenerative disorders

Early and progressive microstructural brain changes in mice overexpressing human α-Synuclein detected by diffusion kurtosis imaging

Diffusion kurtosis imaging (DKI) is sensitive in detecting α-Synuclein (α-Syn) accumulation-associated microstructural changes at late stages of the pathology in α-Syn overexpressing TNWT-61 mice. The aim of this study was to perform DKI in young TNWT-61 mice when α-Syn starts to accumulate and to compare the imaging results with an analysis of motor and memory impairment and α-Syn levels. Findings indicate that α-Syn accumulation-associated changes may start in areas with a high density of dopaminergic nerve terminals. We also found TBSS changes in white matter only at 6mo, suggesting α-Syn accumulation-associated changes start in the gray matter and later progress to the white matter. MK is able to detect microstructural changes induced by α-Syn overexpression in TNWT-61 mice and could be a useful clinical tool for detecting early-stage Parkinson's disease in human patients

Diffusion Kurtosis Imaging Detects Microstructural Alterations in Brain of α-Synuclein Overexpressing Transgenic Mouse Model of Parkinson’s Disease: A Pilot Study

This thesis investigates the economic viability of a grid connected PV system integrated with battery storage in a multifamily home in Sweden. In addition, a fleet of electric cars is added to the system and its economic feasibility is analyzed. The analysis is further classified based on the roof area available for PV installation, wherein system 1 considers nearly the entire roof area of 908 m2 and system 2 is assumed to have less than half the roof area of 360 m2 for PV installation. To help with the assessment, five scenarios are created; where scenario one represents a baseline Swedish cooperative without PV, scenario two includes a PV system; scenario three incorporates battery storage; four considers an electric vehicle fleet embedded into the system and scenario five has a fleet of gasoline cars. These scenarios are applied to the two systems and their results compared. To address the question of this thesis both scenarios 2 and 3 are simulated in System Advisor Model (SAM) and scenario 4 is modeled in Matlab. The outputs are exported to Excel in order to obtain the Net Present Value (NPV), which is the economic indicator for this assessment. In none of the tested scenarios the NPVs’ are positive and the best result is observed in a PV system installed with battery storage in a roof area of 360 m2, which has a NPV of -82,000 SEK. A sensitivity analysis is done to assess the changes in NPV by varying the input parameters. It is concluded that battery storage is not yet economically viable in a Swedish multifamily house