Background Brain MRI scanner variability can introduce bias in measurements. Harmonizing scanner variability is crucial. Purpose To develop a harmonization method aimed at removing scanner variability, and to evaluate the consistency of results in multicenter studies. Study Type Retrospective. Population Multicenter data from 170 healthy participants (males/females = 98/72; age = 73.8 ± 7.3) and 170 Alzheimer's disease patients (males/females = 98/72; age = 76.2 ± 8.5) were compared with reference data from another 340 participants. Field Strength/Sequence 3‐T, magnetization prepared rapid gradient echo and turbo field echo; 1.5‐T, inversion recovery prepared fast spoiled gradient echo T1‐weighted sequences. Assessment Gray matter (GM) brain images, obtained through segmentation of T1‐weighted images, were utilized to evaluate the performance of the harmonization method using common orthogonal basis extraction (HCOBE) and four other methods (removal of artificial voxel effect by linear regression, RAVEL; Z_score; general linear model, GLM; ComBat). Linear discriminant analysis (LDA) was used to access the effectiveness of different methods in reducing scanner variability. The performance of harmonization methods in preserving GM volumes heterogeneity was evaluated by the similarity of the relationship between GM proportion and age in the reference and multicenter data. Furthermore, the consistency of the harmonized multicenter data with the reference data were evaluated based on classification results (train/test = 7/3) and brain atrophy. Statistical Tests Two‐sample t ‐tests, area under the curve (AUC), and Dice coefficients were used to analyze the consistency of results from the reference and harmonized multicenter data. A P ‐value <0.01 was considered statistically significant. Results HCOBE reduced the scanner variability from 0.09 before harmonization to 0.003 (ideal: 0, RAVEL/Z_score/GLM/ComBat = 0.087/0.003/0.006/0.013). GM volumes showed no significant difference ( P = 0.52) between the reference and HCOBE‐harmonized multicenter data. Consistency evaluation showed that AUC values of 0.95 for both reference and HCOBE‐harmonized multicenter data (RAVEL/Z_score/GLM/ComBat = 0.86/0.86/0.84/0.89), and the Dice coefficient increased from 0.73 before harmonization to 0.82 (ideal: 1, RAVEL/Z_score/GLM/ComBat = 0.39/0.64/0.59/0.74). Data Conclusion HCOBE may help to remove scanner variability and could improve the consistency of results in multicenter studies. Level of Evidence 2 Technical Efficacy Stage 1
Visuospatial dysfunction is one predominant symptom in many atypical Alzheimer's disease (AD) patients, however, until now its neural correlates still remain unclear. For the accumulation of intracellular hyperphosphorylated tau proteins is a major pathogenic factor in neurodegeneration of AD, the distributional pattern of tau could highlight the affected brain regions associated with specific cognitive deficits.We investigated the brain regions particularly affected by tau accumulation in patients with visuospatial dysfunction to explore its neural correlates.Using 18F-AV-1451 tau positron emission tomography (PET), voxel-wise two-sample t-tests were performed between AD patients with obvious visuospatial dysfunction (VS-AD) and cognitively normal subjects, AD patients with little-to-no visuospatial dysfunction (non VS-AD) and cognitively normal subjects, respectively.Results showed increased tau accumulations mainly located in occipitoparietal cortex, posterior cingulate cortex, precuneus, inferior and medial temporal cortex in VS-AD patients, while increased tau accumulations mainly occurred in the inferior and medial temporal cortex in non VS-AD patients.These findings suggested that occipitoparietal cortex, posterior cingulate cortex and precuneus, which were particularly affected by increased tau accumulation in VS-AD patients, may associate with visuospatial dysfunction of AD.
The dopamine D3 receptor (D3R) is important in the pathophysiology of various neuropsychiatric disorders, such as depression, bipolar disorder, schizophrenia, drug addiction, and Parkinson's disease. Positron emission tomography (PET) with innovative radioligands provides an opportunity to assess D3R in vivo and to elucidate D3R-related disease mechanisms. Herein, we present the synthesis of eight
Animal contextual fear conditioning (CFC) models are the most-studied forms used to explore the neural substances of posttraumatic stress disorder (PTSD). In addition to the well-recognized hippocampal–amygdalar system, the retrosplenial cortex (RSC) is getting more and more attention due to substantial involvement in CFC, but with a poor understanding of the specific roles of its two major constituents—dysgranular (RSCd) and granular (RSCg). The current study sought to identify their roles and underlying brain network mechanisms during the encoding processing of the rat CFC model. Rats with pharmacologically inactivated RSCd, RSCg, and corresponding controls underwent contextual fear conditioning. [ 18 F]-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) scanning was performed for each animal. The 5-h and 24-h retrieval were followed to test the formation of contextual memory. Graph theoretic tools were used to identify the brain metabolic network involved in encoding phase, and changes of nodal (brain region) properties linked, respectively, to disturbed RSCd and RSCg were analyzed. Impaired retrieval occurred in disturbed RSCd animals, not in RSCg ones. The RSC, hippocampus (Hip), amygdala (Amy), piriform cortex (Pir), and visual cortex (VC) are hub nodes of the brain-wide network for contextual fear memory encoding in rats. Nodal degree and efficiency of hippocampus and its connectivity with amygdala, Pir, and VC were decreased in rats with disturbed RSCd, while not in those with suppressed RSCg. The RSC plays its role in contextual fear memory encoding mainly relying on its RSCd part, whose condition would influence the activity of the hippocampal–amygdalar system.
Simultaneous localization and mapping (SLAM) is the major solution for constructing or updating a map of an unknown environment while simultaneously keeping track of a mobile robot's location. Correlative Scan Matching (CSM) is a scan matching algorithm for obtaining the posterior distribution probability for the robot's pose in SLAM. This paper combines the non-linear optimization algorithm and CSM algorithm into an NLO-CSM (Non-linear Optimization CSM) algorithm for reducing the computation resources and the amount of computation while ensuring high calculation accuracy, and it presents an efficient hardware accelerator design of the NLO-CSM algorithm for the scan matching in 2D LiDAR SLAM. The proposed NLO-CSM hardware accelerator utilizes pipeline processing and module reusing techniques to achieve low hardware overhead, fast matching, and high energy efficiency. FPGA implementation results show that, at 100 MHz clock, the power consumption of the proposed hardware accelerator is as low as 0.79 W, while it performs a scan match at 8.98 ms and 7.15 mJ per frame. The proposed design outperforms the ARM-A9 dual-core CPU implementation with a 92.74% increase and 90.71% saving in computing speed and energy consumption, respectively. It has also achieved 80.3% LUTs, 84.13% FFs, and 20.83% DSPs saving, as well as an 8.17× increase in frame rate and 96.22% improvement in energy efficiency over a state-of-the-art hardware accelerator design in the literature. ASIC implementation in 65 nm can further reduce the computing time and energy consumption per scan to 5.94 ms and 0.06 mJ, respectively, which shows that the proposed NLO-CSM hardware accelerator design is suitable for resource-limited and energy-constrained mobile and micro robot applications.
We study the reliability of stochastically excited and controlled single first-integral systems and present an approximate analytical solution for the first-passage rate (FPR). By introducing the stochastic averaging method (SAM), we reduce the dimension of the original system to an averaged one-dimensional controlled Ito^ differential equation. We then modify the classic Laplace integral method (LIM) and apply it to deal with the arduous integrals in the expression of reliability function. The procedure of acquiring the analytical solution for the reliability is illuminated in detail as well. In addition, we provide two controlled single first-integral nonlinear vibration systems, namely, the classical bistable model and the two coupled nonlinear oscillators, as examples. By comparing the results obtained from the modified Laplace integral method (MLIM) to Monte Carlo simulations (MCS), we verify the effectiveness and exactness of the proposed procedure. We identified two properties in the obtained analytical solution: One is that the solutions are independent of the initial system state. The other is that they are only effective in the high passage threshold range. Finally, a reasonable explanation has been given to explain these two properties.
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