Combined Quantitative amyloid-β PET and Structural MRI Features Improve Alzheimer's Disease Classification in Random Forest Model - A Multicenter Study.

Rationale And Objectives: Prior to clinical presentations of Alzheimer's Disease (AD), neuropathological changes, such as amyloid-β and brain atrophy, have accumulated at the earlier stages of the disease. The combination of such biomarkers assessed by multiple modalities commonly improves the likelihood of AD etiology. We aimed to explore the discriminative ability of Aβ PET features and whether combining Aβ PET and structural MRI features can improve the classification performance of the machine learning model in older healthy control (OHC) and mild cognitive impairment (MCI) from AD.

Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.

Background: MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications.

The structural-functional-connectivity coupling of the aging brain.

Aging primarily affects memory and executive functions, a relationship that may be underpinned by the fact that almost all adults over 60 years old develop small vessel disease (SVD). The fact that a wide range of neuropathologies could only explain up to 43% of the variation in age-related cognitive impairment suggests that other factors, such as cognitive reserve, may play a role in the brain's resilience against aging-related cognitive decline. This study aims to examine the relationship between structural-functional-connectivity coupling (SFC), and aging, cognitive abilities and reserve, and SVD-related neuropathologies using a cohort of n = 176 healthy elders from the Harvard Aging Brain Study.

Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging.

Background And Purpose: DTI can be used to derive conventional diffusion measurements, which can measure WM abnormalities in multiple sclerosis. DTI can also be used to construct structural brain networks and derive network measurements. However, few studies have compared their sensitivity in detecting brain alterations, especially in longitudinal studies.

Cuprizone drives divergent neuropathological changes in different mouse models of Alzheimer's disease.

Unlabelled: Myelin degradation is a normal feature of brain aging that accelerates in Alzheimer's disease (AD). To date, however, the underlying biological basis of this correlation remains elusive. The amyloid cascade hypothesis predicts that demyelination is caused by increased levels of the β-amyloid (Aβ) peptide.

A Pilot Study of Short-Course Oral Vitamin A and Aerosolised Diffuser Olfactory Training for the Treatment of Smell Loss in Long COVID.

Olfactory dysfunction (OD) is a common neurosensory manifestation in long COVID. An effective and safe treatment against COVID-19-related OD is needed. This pilot trial recruited long COVID patients with persistent OD.

Disrupted Structural White Matter Network in Alzheimer's Disease Continuum, Vascular Dementia, and Mixed Dementia: A Diffusion Tensor Imaging Study.

Background: Dementia presents a significant burden to patients and healthcare systems worldwide. Early and accurate diagnosis, as well as differential diagnosis of various types of dementia, are crucial for timely intervention and management. However, there is currently a lack of clinical tools for accurately distinguishing between these types.

The Use of Diffusion Kurtosis Imaging for the Differential Diagnosis of Alzheimer's Disease Spectrum.

Structural and diffusion kurtosis imaging (DKI) can be used to assess hippocampal macrostructural and microstructural alterations respectively, in Alzheimer's disease (AD) spectrum, spanning from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and AD. In this study, we explored the diagnostic performance of structural imaging and DKI of the hippocampus in the AD spectrum. Eleven SCD, thirty-seven MCI, sixteen AD, and nineteen age- and sex-matched normal controls (NCs) were included.

Deep learning-regularized, single-step quantitative susceptibility mapping quantification.

The purpose of the current study was to develop deep learning-regularized, single-step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning-regularized, single-step QSM quantification model, named SS-POCSnet, was trained with datasets created using the QSM synthesis approach in QSM reconstruction challenge 2.0.

Level of Amyloid-β (Aβ) Binding Leading to Differential Effects on Resting State Functional Connectivity in Major Brain Networks.

Introduction: Amyloid-β protein (Aβ) is one of the biomarkers for Alzheimer's disease (AD). The recent application of interhemispheric functional connectivity (IFC) in resting-state fMRI has been used as a non-invasive diagnostic tool for early dementia. In this study, we focused on the level of Aβ accumulated and its effects on the major functional networks, including default mode network (DMN), central executive network (CEN), salience network (SN), self-referential network (SRN) and sensory motor network (SMN).

Associations of sleep apnea risk and oxygen desaturation indices with cerebral small vessel disease burden in patients with stroke.

Background: Obstructive sleep apnea (OSA) is associated with cerebral small vessel disease (CSVD). Nonetheless, whether OSA-risk determined by a simple screening questionnaire or indices quantifying nocturnal hypoxemia other than the conventional apnea-hypopnea index (AHI) by the home sleep apnea test (HSAT) associated with CSVD burden remains uncertain.

Methods: From 2018 to 2021, we recruited patients with transient ischemic attack (TIA)/minor stroke from the Queen Mary Hospital Acute Stroke Unit and TIA/Stroke Outpatient Clinics.

The fractional amplitude of low-frequency fluctuations signals related to amyloid uptake in high-risk populations-A pilot fMRI study.

Background: Patients with type 2 diabetes mellitus (T2DM) and subjective cognitive decline (SCD) have a higher risk to develop Alzheimer's Disease (AD). Resting-state-functional magnetic resonance imaging (rs-fMRI) was used to document neurological involvement in the two groups from the aspect of brain dysfunction. Accumulation of amyloid-β (Aβ) starts decades ago before the onset of clinical symptoms and may already have been associated with brain function in high-risk populations.

Incremental diagnostic value of 18F-Fluetemetamol PET in differential diagnoses of Alzheimer's Disease-related neurodegenerative diseases from an unselected memory clinic cohort.

To evaluate the incremental diagnostic value of 18F-Flutemetamol PET following MRI measurements on an unselected prospective cohort collected from a memory clinic. A total of 84 participants was included in this study. A stepwise study design was performed including initial analysis (based on clinical assessments), interim analysis (revision of initial analysis post-MRI) and final analysis (revision of interim analysis post-18F-Flutemetamol PET).

Changes in the Intranetwork and Internetwork Connectivity of the Default Mode Network and Olfactory Network in Patients with COVID-19 and Olfactory Dysfunction.

Olfactory dysfunction (OD) is a common symptom in coronavirus disease 2019 (COVID-19) patients. Moreover, many neurological manifestations have been reported in these patients, suggesting central nervous system involvement. The default mode network (DMN) is closely associated with olfactory processing.

Striatal dopamine synthesis capacity and its association with negative symptoms upon resolution of positive symptoms in first-episode schizophrenia and delusional disorder.

Rationale: How striatal dopamine synthesis capacity (DSC) contributes to the pathogenesis of negative symptoms in first-episode schizophrenia (SZ) and delusional disorder (DD) has seldom been explored. As negative symptoms during active psychotic episodes can be complicated by secondary influences, such as positive symptoms, longitudinal investigations may help to clarify the relationship between striatal DSC and negative symptoms and differentiate between primary and secondary negative symptoms.

Objective: A longitudinal study was conducted to examine whether baseline striatal DSC would be related to negative symptoms at 3 months in first-episode SZ and DD patients.

A data-driven deep learning pipeline for quantitative susceptibility mapping (QSM).

Purpose: This study developed a data-driven optimization to improve the accuracy of deep learning QSM quantification.

Methods: The proposed deep learning QSM pipeline consisted of two projections onto convex set (POCS) models designed to decouple trainable network components with the spherical mean value (SMV) filters and dipole kernel in the data-driven optimization. They were a background field removal network (named POCSnet1) and a dipole inversion network (named POCSnet2).

Mapping brain structural differences and neuroreceptor correlates in Parkinson's disease visual hallucinations.

Parkinson's psychosis (PDP) describes a spectrum of symptoms that may arise in Parkinson's disease (PD) including visual hallucinations (VH). Imaging studies investigating the neural correlates of PDP have been inconsistent in their findings, due to differences in study design and limitations of scale. Here we use empirical Bayes harmonisation to pool together structural imaging data from multiple research groups into a large-scale mega-analysis, allowing us to identify cortical regions and networks involved in VH and their relation to receptor binding.