Harmonizing T1-Weighted Images to Improve Consistency of Brain Morphology Among Different Scanner Manufacturers in Alzheimer's disease.

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.

Higher Blood-brain barrier permeability in patients with major depressive disorder identified by DCE-MRI imaging.

BACKGROUND: Studies from animal models and clinical trials of blood and cerebrospinal fluid have proposed that blood-brain barrier (BBB) dysfunction in depression (MDD). But there are no In vivo proves focused on BBB dysfunction in MDD patients. The present study aimed to identify whether there was abnormal BBB permeability, as well as the association with clinical status in MDD patients using dynamic contrast-enhanced magnetic resonance (DCE-MRI) imaging. METHODS: Patients with MDD and healthy adults were recruited and underwent DCE-MRI and structural MRI scans. The mean volume transfer constant (Ktrans) values were calculated for a quantitative assessment of BBB leakage. For each subject, the mean Ktrans values were calculated for the whole gray matter, white matter, and 90 brain regions of the anatomical automatic labeling template (AAL). The differences in Ktrans values between patients and controls and between treated and untreated patients were compared. RESULTS: 23 MDD patients (12 males and 11 females, mean age 28.09 years) and 18 healthy controls (HC, 8 males and 10 females, mean age 30.67 years) were recruited in the study. We found that the Ktrans values in the olfactory, caudate, and thalamus were higher in MDD patients compared to healthy controls (p<0.05). The Ktrans values in the orbital lobe, anterior cingulate gyrus, putamen, and thalamus in treated patients were lower than the patients never treated. There were positive correlations between HAMD total score with Ktrans values in whole brain WM, hippocampus and thalamus. The total HAMA score was positively correlated with the Ktrans of hippocampus. CONCLUSION: These findings supported a link between blood-brain barrier leakage and depression and symptom severity. The results also suggested a role for non-invasive DCE-MRI in detecting blood-brain barrier dysfunction in depression patients.

Dopaminergic damage pattern predicts phenoconversion time in isolated rapid eye movement sleep behavior disorder.

PURPOSE: The exact phenoconversion time from isolated rapid eye movement (REM) sleep behavior disorder (iRBD) to synucleinopathies remains unpredictable. This study investigated whole-brain dopaminergic damage pattern (DDP) with disease progression and predicted phenoconversion time in individual patients. METHODS: Age-matched 33 iRBD patients and 20 healthy controls with 11C-CFT-PET scans were enrolled. The patients were followed up 2-10 (6.7 ± 2.0) years. The phenoconversion year was defined as the base year, and every 2 years before conversion was defined as a stage. Support vector machine with leave-one-out cross-validation strategy was used to perform prediction. RESULTS: Dopaminergic degeneration of iRBD was found to occur about 6 years before conversion and then abnormal brain regions gradually expanded. Using DDP, area under curve (AUC) was 0.879 (90% sensitivity and 88.3% specificity) for predicting conversion in 0-2 years, 0.807 (72.7% sensitivity and 83.3% specificity) in 2-4 years, 0.940 (100% sensitivity and 84.6% specificity) in 4-6 years, and 0.879 (100% sensitivity and 80.7% specificity) over 6 years. In individual patients, predicted stages correlated with whole-brain dopaminergic levels (r = - 0.740, p < 0.001). CONCLUSION: Our findings suggest that DDP could accurately predict phenoconversion time of individual iRBD patients, which may help to screen patients for early intervention.

Efficient Hardware Accelerator Design of Non-Linear Optimization Correlative Scan Matching Algorithm in 2D LiDAR SLAM for Mobile Robots.

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.

Automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging of rat brain.

Aims: To construct an automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging (MEMRI) of rat brain with high accuracy, which could preserve the inherent voxel intensity and Regions of interest (ROI) morphological characteristics simultaneously. Methods and results: The transformation relationship from standardized space to individual space was obtained by firstly normalizing individual image to the Paxinos space and then inversely transformed. On the other hand, all the regions defined in the atlas image were separated and resaved as binary mask images. Then, transforming the mask images into individual space via the inverse transformations and reslicing using the 4th B-spline interpolation algorithm. The boundary of these transformed regions was further refined by image erosion and expansion operator, and finally combined together to generate the individual parcellations. Moreover, two groups of MEMRI images were used for evaluation. We found that the individual parcellations were satisfied, and the inherent image intensity was preserved. The statistical significance of case-control comparisons was further optimized. Conclusions: We have constructed a new automatic method for individual parcellation of rat brain MEMRI images, which could preserve the inherent voxel intensity and further be beneficial in case-control statistical analyses. This method could also be extended to other imaging modalities, even other experiments species. It would facilitate the accuracy and significance of ROI-based imaging analyses.

Increased Blood-Brain Barrier Permeability of the Thalamus Correlated With Symptom Severity and Brain Volume Alterations in Patients With Schizophrenia.

BACKGROUND: While direct in vivo data from patients is insufficient, cumulative evidence of microvascular dysfunction has shown that the blood-brain barrier (BBB) is disrupted in schizophrenia. In this study, we attempted to test the hypothesis that greater BBB permeability in patients with schizophrenia was associated with clinical characteristics and brain volumetric alterations using dynamic contrast-enhanced magnetic resonance imaging techniques. METHODS: Structural magnetic resonance imaging and dynamic contrast-enhanced magnetic resonance imaging data from 29 patients with schizophrenia and 18 age- and sex-matched control subjects were obtained. We calculated the volume transfer constant (Ktrans) value and compared the difference between the 2 groups. The regions with an abnormal Ktrans value were extracted as regions of interest (thalamus), and the correlations with clinical characteristics and gray matter volume were analyzed. RESULTS: The results revealed that Ktrans value of the bilateral thalamus was higher in the schizophrenia group as compared to the healthy control group (p < .001). There were significant positive correlations between thalamic mean Ktrans value with disease duration (p < .05) and symptom severity (p < .001). Analysis of the thalamic subregions revealed that BBB disruption was significant in the pulvinar, especially the medial pulvinar nucleus and lateral pulvinar nucleus (p < .001). The correlation between the Ktrans values and the corresponding volumes was negative for the whole brain, the thalamus, and the thalamic subregions. CONCLUSIONS: These results provide the first in vivo evidence of BBB disruption of thalamus in patients with schizophrenia and suggest that BBB dysfunction might contribute to the pathological brain structural alterations in schizophrenia.

Retrosplenial Cortex Effects Contextual Fear Formation Relying on Dysgranular Constituent in Rats.

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. [18F]-fluorodeoxyglucose positron emission tomography/computed tomography (18F-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.

Deep Learning With 18F-Fluorodeoxyglucose-PET Gives Valid Diagnoses for the Uncertain Cases in Memory Impairment of Alzheimer's Disease.

Objectives: Neuropsychological tests are an important basis for the memory impairment diagnosis in Alzheimer's disease (AD). However, multiple memory tests might be conflicting within-subjects and lead to uncertain diagnoses in some cases. This study proposed a framework to diagnose the uncertain cases of memory impairment. Methods: We collected 2,386 samples including AD, mild cognitive impairment (MCI), and cognitive normal (CN) using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and three different neuropsychological tests (Mini-Mental State Examination, Alzheimer's Disease Assessment Scale-Cognitive Subscale, and Clinical Dementia Rating) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). A deep learning (DL) framework using FDG-PET was proposed to diagnose uncertain memory impairment cases that were conflicting between tests. Subsequent ANOVA, chi-squared, and t-test were used to explain the potential causes of uncertain cases. Results: For certain cases in the testing set, the proposed DL framework outperformed other methods with 95.65% accuracy. For the uncertain cases, its positive diagnoses had a significant (p < 0.001) worse decline in memory function than negative diagnoses in a longitudinal study of 40 months on average. In the memory-impaired group, uncertain cases were mainly explained by an AD metabolism pattern but mild in extent (p < 0.05). In the healthy group, uncertain cases were mainly explained by a non-energetic mental state (p < 0.001) measured using a global deterioration scale (GDS), with a significant depression-related metabolism pattern detected (p < 0.05). Conclusion: A DL framework for diagnosing uncertain cases of memory impairment is proposed. Proved by longitudinal tracing of its diagnoses, it showed clinical validity and had application potential. Its valid diagnoses also provided evidence and explanation of uncertain cases based on the neurodegeneration and depression mental state.

Tau PET Distributional Pattern in AD Patients with Visuospatial Dysfunction.

BACKGROUND: 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. OBJECTIVE: We investigated the brain regions particularly affected by tau accumulation in patients with visuospatial dysfunction to explore its neural correlates. METHODS: 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: 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. CONCLUSION: 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.