Dynamic network model reveals distinct tau spreading patterns in early- and late-onset Alzheimer disease.

BACKGROUND: The clinical features of Alzheimer's disease (AD) vary substantially depending on whether the onset of cognitive deficits is early or late. The amount and distribution patterns of tau pathology are thought to play a key role in the clinical characteristics of AD, which spreads throughout the large-scale brain network. Here, we describe the differences between tau-spreading processes in early- and late-onset symptomatic individuals on the AD spectrum. METHODS: We divided 74 cognitively unimpaired (CU) and 68 cognitively impaired (CI) patients receiving 18F-flortaucipir positron emission tomography scans into two groups by age and age at onset. Members of each group were arranged in a pseudo-longitudinal order based on baseline tau pathology severity, and potential interregional tau-spreading pathways were defined following the order using longitudinal tau uptake. We detected a multilayer community structure through consecutive tau-spreading networks to identify spatio-temporal changes in the propagation hubs. RESULTS: In each group, ordered tau-spreading networks revealed the stage-dependent dynamics of tau propagation, supporting distinct tau accumulation patterns. In the young CU/early-onset CI group, tau appears to spread through a combination of three independent communities with partially overlapped territories, whose specific driving regions were the basal temporal regions, left medial and lateral temporal regions, and left parietal regions. For the old CU/late-onset CI group, however, continuation of major communities occurs in line with the appearance of hub regions in the order of bilateral entorhinal cortices, parahippocampal and fusiform gyri, and lateral temporal regions. CONCLUSION: Longitudinal tau propagation depicts distinct spreading pathways of the early- and late-onset AD spectrum characterized by the specific location and appearance period of several hub regions that dominantly provide tau.

Regional Aß-tau interactions promote onset and acceleration of Alzheimer's disease tau spreading.

Amyloid-beta and tau are key molecules in the pathogenesis of Alzheimer's disease, but it remains unclear how these proteins interact to promote disease. Here, by combining cross-sectional and longitudinal molecular imaging and network connectivity analyses in living humans, we identified two amyloid-beta/tau interactions associated with the onset and propagation of tau spreading. First, we show that the lateral entorhinal cortex, an early site of tau neurofibrillary tangle formation, is subject to remote, connectivity-mediated amyloid-beta/tau interactions linked to initial tau spreading. Second, we identify the inferior temporal gyrus as the region featuring the greatest local amyloid-beta/tau interactions and a connectivity profile well suited to accelerate tau propagation. Taken together, our data address long-standing questions regarding the topographical dissimilarity between early amyloid-beta and tau deposition.

Effects of Alzheimer's and Vascular Pathologies on Structural Connectivity in Early- and Late-Onset Alzheimer's Disease.

Early- and late-onset Alzheimer's disease (AD) patients often exhibit distinct features. We sought to compare overall white matter connectivity and evaluate the pathological factors (amyloid, tau, and vascular pathologies) that affect the disruption of connectivity in these two groups. A total of 50 early- and 38 late-onset AD patients, as well as age-matched cognitively normal participants, were enrolled and underwent diffusion-weighted magnetic resonance imaging to construct fractional anisotropy-weighted white matter connectivity maps. [18F]-THK5351 PET, [18F]-Flutemetamol PET, and magnetic resonance imaging were used for the evaluation of tau and related astrogliosis, amyloid, and small vessel disease markers (lacunes and white matter hyperintensities). Cluster-based statistics was performed for connectivity comparisons and correlation analysis between connectivity disruption and the pathological markers. Both patient groups exhibited significantly disrupted connectivity compared to their control counterparts with distinct patterns. Only THK retention was related to connectivity disruption in early-onset AD patients, and this disruption showed correlations with most cognitive scores, while late-onset AD patients had disrupted connectivity correlated with amyloid deposition, white matter hyperintensities, and lacunes in which only a few cognitive scores showed associations. These findings suggest that the pathogenesis of connectivity disruption and its effects on cognition are distinct between EOAD and LOAD.

Microstructural Connectivity is More Related to Cognition than Conventional MRI in Parkinson's Disease.

BACKGROUND: The different effects of white matter hyperintensity (WMH) severity and WMH-associated microstructural connectivity on cognition in the early stages of Parkinson's disease (PD) have not been investigated. OBJECTIVE: To investigate the differential effect of WMH severity and WMH-associated microstructural connectivity on cognition in early stages of PD. METHODS: A total of 136 de novo PD patients were enrolled and divided into groups based on total WMH visual rating scores as follows: mild, moderate, and severe. Microstructural connectivity was measured using graph theoretical analysis according to WMH severity. Additionally, correlation coefficients between WMH-associated microstructural connectivity or WMH scores and cognitive performance were assessed. RESULTS: Patients with severe WMHs demonstrated poorer performance in language function than those with moderate WMHs, and in frontal/executive and visual memory function than those with mild WMHs. Areas of microstructural connectivity were more extensive in patients with severe WMHs compared to those with mild and moderate WMHs, involving frontal and parieto-temporal regions. WMH-associated right fronto-temporo-parietal microstructural disintegration was correlated with cognitive dysfunction in attention, frontal/executive, and memory domains, whereas there was no correlation between WMH scores and any cognitive domains. CONCLUSION: These data suggest that disruption of microstructural networks by WMHs, rather than WMH burden itself, contributed more to cognitive impairment in PD.

Distinct Patterns of Rich Club Organization in Alzheimer's Disease and Subcortical Vascular Dementia: A White Matter Network Study.

Recent advances in neuroimaging technology have shown that rich club organization in human brain networks plays a crucial role in global communication and cognitive functionality. In this study, we investigated rich club organization within white matter structural brain networks in two common types of dementia, Alzheimer's disease (AD) and subcortical vascular dementia (SVaD). We recruited 30 AD patients ([11C] Pittsburgh compound-B (PiB) PET positive), 39 SVaD patients (PiB negative), and 72 age-, gender-, and education-matched cognitively normal (CN) subjects. Rich club organization was significantly disrupted in both dementia patient groups, which exhibited higher rich club coefficients than the CN group. Rich club organization in the patient groups was primarily disrupted over the left frontal and left middle temporal areas when compared to the CN group. The number of rich club nodes was significantly reduced in the dementia groups, which was more severe in SVaD (p = 0.0107, permutation-based t-test). Although rich club organization was disrupted both in the patient groups, its disruption pattern is different between them. The rich-club connections normalized by degree-and-strength preserved random networks were significantly increased in the dementia groups with SVaD more severely, and feeder connections were reduced more significantly than in AD. Furthermore, SVaD patients exhibited more sporadic disruption in white matter connectivity than AD patients, with local connections showing a more significant degree of deterioration. Combined with the distinct disruption in rich club nodes, these findings may imply a differing role for rich club organization in AD and SVaD, due to different pathological mechanisms.

miTarget: microRNA target gene prediction using a support vector machine.

BACKGROUND: MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes. RESULTS: We implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human miR-1, miR-124a, and miR-373 using Gene Ontology (GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program. CONCLUSION: miTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples.