Lin Shi

Background: Bone health and brain function may be closely interconnected through a complex bone–brain axis. The relationship between bone mineral density (BMD), vertebral perfusion, marrow composition, cerebral perfusion, brain volume, and cognitive decline, however, remain incompletely understood. Methods: Ninety-nine female subjects (mean age 65.00 ± 5.00 years) with clinically suspected mild cognitive impairment underwent dual-energy X-ray absorptiometry, carotid ultrasound, and multimodal magnetic resonance imaging (MRI) of the brain and lumbar spine to measure BMD, bone perfusion, marrow fat content as well as cerebral perfusion, cerebral volume, cerebral white matter burden and large vessel atherosclerosis. Cognitive function was assessed using the Hong Kong Montreal Cognitive Assessment (HK-MoCA). Bone, cerebral, vascular, and cognitive measures were correlated using Spearman correlation coefficients and compared in group comparisons. Results: Lower BMD was correlated with reduced subcortical cerebral blood flow (CBF) (r = 0.27, p = 0.031) and lower total brain parenchymal volume (r = 0.25, p = 0.021). Reduced bone marrow perfusion and increased marrow fat content were also associated with lower total brain parenchymal volume (r = 0.24, p = 0.023 and r = −0.26, p = 0.025). Subjects with the lowest L3 vertebral body perfusion or highest marrow fat content had significantly reduced total brain and hippocampal volumes (p = 0.029–0.049) compared with those with the highest perfusion or lowest marrow fat content. Conclusions: This study shows an association between lower BMD, reduced vertebral perfusion, and increased marrow fat with reduced brain parenchymal volumes and reduced brain perfusion. Further studies are warranted to clarify these relationships and explore the underlying shared mechanisms affecting bone health and cerebral microvascular and structural brain changes.

Increasing evidence supported a link between peripheral inflammation and Parkinson’s disease (PD). However, the role of peripheral inflammation in the progression of PD clinical symptoms remained unclear. This study evaluates peripheral inflammation using serum differential leukocyte counts and their derived ratios. A total of 170 PD patients were retrospectively enrolled from Guangdong Provincial People’s Hospital (GDPH) and 68 from PPMI. Partial correlation analysis showed that neutrophil-to-lymphocyte ratio (NLR) negatively correlated with MoCA in GDPH but not in PPMI. Moreover, peripheral inflammation was shown to correlate with white matter integrity. The result of the longitudinal analysis showed that higher baseline NLR predicted worsening in letter number sequencing (LNS) score. Path analysis indicated that white matter integrity significantly mediated the relationship between NLR and cognitive change in the LNS score from Year 5 to baseline. Peripheral inflammation is associated with global cognition and white matter integrity in PD and predicts cognitive decline.

International Press of Boston - publishers of scholarly mathematical and scientific journals and books

Background: Although positron emission tomography (PET) or cerebrospinal fluid measurements of amyloid and tau burden are available for the detection of Alzheimer’s disease pathology, such methods are invasive and not easily accessible. Recent studies show that Alzheimer’s Disease‐resemblance atrophy index (AD‐RAI), an MRI‐based machine learning‐derived biomarker, and plasma‐based biomarkers can be used as accurate biomarkers for Alzheimer’s disease pathology. We aim to evaluate the diagnostic metrics of AD‐RAI and plasma‐based biomarkers for detecting Alzheimer’s disease.Method: We recruited 69 subjects from the CU‐SEEDS (The Chinese University of Hong Kong‐Screening for Early AlzhEimer’s DiseaSe) study who were stroke‐free and had different degrees of cognitive impairment: 8 cognitive unimpaired [CU], 26 with subjective cognitive decline [SCD], 21 with mild cognitive impairment [MCI] and 14 with dementia. All subjects underwent ¹¹C‐ PIB and ¹⁸F‐T807 PET to measure pathological Aβ deposition (A+) and tau burden (T+). Subjects received structural MRI for deriving AD‐RAI. Plasma levels of Aβ40, Aβ42, total tau (t‐tau), phosphorylated tau at 181 (p‐tau181), and neurofilament light chain (NfL) were measured by Single Molecule Array (SiMoA) assays.Result: Among 69 subjects (mean [SD] age, 67.7 [6.8] years; 28 men [40.6%]), 25 (36.23%) subjects were confirmed to be A+ and T+. AD‐RAI, plasma p‐tau 181, and plasma Aβ42 were associated with A+T+ after adjusting for age, gender, and education (p<0.01). AD‐RAI individually outperformed all plasma‐based biomarkers (AUC = 0.911; Sensitivity = 0.826; Specificity = 0.905). The combination of AD‐RAI, p‐tau181 and Aβ42 yielded the best diagnostic metrics for detecting A+T+ subjects (AUC = 0.957; Sensitivity = 0.870; Specificity = 0.929).Conclusion: A panel of AD‐RAI, plasma p‐tau, and plasma Aβ42 might help with screening and diagnosis of Alzheimer’s disease.

Introduction: The diagnosis of Alzheimer’s disease (AD) requires the presence of amyloid and tau pathology, but it remains unclear how they affect structural network in the preclinical stage. We aimed to assess differences of topological properties in cognitively normal individuals with varying levels of amyloid and tau pathologies, as well as their association with AD pathology burden. Methods: A total of 68 cognitively normal individuals were included and stratified by normal/abnormal (−/+) amyloid (A) and tau (T) status based on positron emission tomography results, yielding three groups: A−T− (n = 19), A+T− (n = 28), and A+T+ (n = 21). Topological properties were measured from structural connectivity. Group differences and correlations with A and T were evaluated. Results: Compared with the A−T− group, the A+T+ group exhibited changes in the structural network topology. At the global level, higher assortativity was shown in the A+T+ group and was correlated with greater tau burden (r = 0.29, P = 0.02), while no difference in global efficiency was found across the three groups. At the local level, A+T+ showed disrupted topological properties in the left hippocampus compared with the A−T− group, characterized by lower local efficiency (P < 0.01) and lower clustering coefficient (P = 0.014). Conclusion: The increased linkage in the higher level architecture of the white matter network reflected by assortativity may indicate increased brain resilience in the early pathological state. Our results encourage further investigation on the topological properties of the structural network in preclinical AD.

Chinese character skeletons are key topological description of Chinese character glyphs which provided essential information about structures of the Chinese characters. The Chinese character skeleton can be regarded as sequential points generated using deep neural networks, such as recurrent neural networks (RNN) while time intervals between sequential points which included dynamic information of handwriting process of Chinese characters could not be presented elegantly using RNN. Deep neural networks with long sequence outputs face problems such as gradient vanishing or explosion. Chinese characters had longer sequential points than those of English characters. Here, we generated the Chinese character skeletons using multiple parallel random recurrent networks (RRN) which provided rich dynamic presentation of sequential points. The generation took place at two levels, the stroke level which included a sequence of points, and the character level which included a sequence of strokes. Our method not only generated temporal information of the skeletons but also retained more skeleton feature points with only small-scale training data which made the generated Chinese character skeletons had richer writing detail features than existing methods. Our method avoided the problems of gradient vanishing or explosion, and could be used to quickly generate large-scale and high-quality Chinese character skeletons.

Background: As a tool of automated brain volumetry, AccuBrain has been widely used to identify brain structural changes in different neurological diseases with the older version (IV1.2). As AccuBrain underwent a substantial revision update (IV2.0) for segmentation algorithms recently, a comparison of the two versions would be necessary to identify and characterize any differences that could affect the interpretation and clinical translation of the older research findings.Method: We included 200 Alzheimer’s disease (AD) patients (75.3±7.6 years old, 44.0% women) and 200 normal control (NC) subjects (75.2±5.6 years old, 41.5% women) with 3D T1‐weighted scans processed with AccuBrain IV1.2 and IV2.0. To measure the volumetric difference or agreement of the two versions, we calculated the mean relative absolute error (MRAE, i.e., the mean of |(V2‐V1)/V1| for each volumetric measure) and intraclass correlation coefficient (ICC) between two versions regarding the volumes of typical brain structures, lobar atrophy indices and the quantitative medial temporal lobe atrophy (QMTA) score. In addition, we investigated the difference of the two versions regarding volumetric associations with age using ANCOVA, with the volumetric measures as the dependent variable, the version as fixed variable and age as the covariate. An interaction term between version and age was additionally included in the model to determine whether these two versions were statistically different in this respect.Result: The ICC between the volumetric measures of the two versions were >0.85 for absolute volumes of typical brain structures (e.g., hippocampus) and >0.60 for lobar atrophy indices and QMTA (Table 1), indicating moderate to good agreement between versions. Furthermore, the MRAE indicated substantial difference between versions with >5% for half of the measures, and >10% for QMTA and most lobar atrophy indices, where the MRAE was slightly larger in AD than NC. There were no significant differences between versions regarding their volumetric associations with age (Table 2) for either NC or AD group (regression lines shown for NC in Figure 1∼2).Conclusion: The two versions of AccuBrain are not suggested for a mixed use. Previous research findings with older version may be repeatable with the new version of AccuBrain.

Background: We investigated the ability of MRI, plasma, and cognitive biomarkers in detecting preclinical or prodromal Alzheimer’s Disease (AD) who will progress to the next syndrome stage of the cognitive continuum.Method: 589 subjects with longitudinal cognitive data were recruited from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which included 227 cognitive unimpaired (CU) and 362 mild cognitive impairment (MCI) subjects. Preclinical or prodromal AD was defined by a low amyloid beta (Aβ)₄₂ (A+) and high phosphorylated ‐tau (p‐tau) (T+) on cerebrospinal fluid (CSF) assessment at baseline. The following baseline MRI biomarkers were derived by an automatic segmentation tool (AccuBrain®): AD‐resemblance atrophy index (AD‐RAI), quantitative medial temporal lobe atrophy (QMTA) and hippocampus volume (HV). Plasma biomarkers included p‐tau 181, neurofilament (NFL) and APOE ε4. Montreal Cognitive Assessment (MoCA) score was collected for all subjects at baseline. Conversion (C+) was defined as subjects progressed from CU to MCI and from MCI to dementia within 4 years.Results: Of the 589 subjects (mean [SD] age, 72.2 [6.9] years; 314 men [53.3%]), 96 (16.3%) were A+T+C+ and 180 (30.6%) were A+T+C‐. In the ROC analysis, AD‐RAI achieved the best detection ability than other individual biomarker with a sensitivity of 83.4%, a specificity of 69.4% and an AUC of 87.8%. A combination of AD‐RAI, plasma p‐tau 181, APOE ε4 and MoCA score achieved the best detection ability with AUC of 89.8%, sensitivity of 85.1% and specificity of 80.2%. In the subgroup analysis, the combination of AD‐RAI, plasma p‐tau 181, APOE ε4 and MoCA score also showed good accuracy in identifying A+T+C+ subjects in the CU group (AUC = 87.6%) and in the MCI group (AUC = 89.3%).Conclusion: A panel of MRI, plasma and cognitive biomarkers might help to detect progressive early AD subjects.