Distinct Association of Amyloid and Vascular Pathologies with Enhanced Perivascular Spaces.

Background: Glymphatic system dysfunction as characterized by increased MRI‐visible Perivascular Spaces (PVS) is speculated to play a role in the acceleration of amyloid accumulation in Alzheimer’s Disease (AD). However, while PVS is also prevalent amongst Vascular Dementia (VD), the pathological distinctions between regional PVS in AD‐ and VD‐driven cohorts remain largely unknown. Through a mixed dementia cohort, we examined these pathology‐driven localization patterns via automated PVS segmentations from T2‐weighted MRI.

Addressing Unmet Precision Requirements through Improved Automatic Identification of Cerebral Microbleeds.

Background: Cerebral microbleeds (CMBs) hold significant clinical relevance, linked to elevated risks of hemorrhages, cognitive decline, and mortality. Notably, with the recent advancement in Alzheimer’s treatments, the number of CMBs serves as a crucial safety indicators to assess the risk and occurrence of amyloid‐related imaging abnormalities. However, the commonly utilized manual detection process is time‐consuming and labor‐intensive, prompting the development of various automated detection models.

Counterfactual reasoning‐based intervention effect estimation for longitudinal cognitive decline.

Background: After the landmark approval of the Aβ‐lowering antibody for treatment of mild cognitive impairment and mild dementia due to Alzheimer’s disease (AD), it has intensified the need to stratify patients based on the likelihood that they will benefit from any amyloid‐lowering treatments currently in the pipeline. We therefore seek to identify individuals most likely to benefit from Aβ‐lowering drugs by estimating intervention effect based on counterfactual reasoning for longitudinal cognitive decline at the individual level.

Method: We utilized 3,542 T1‐weighted magnetic resonance images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), involving 3,103 Alzheimer’s patients and 439 cognitively normal individuals.

Mechanistic links of amyloid, atrophy, and cognition profiles for gait disturbance in Alzheimer's disease.

Background: This study delves into the relationship between gait disturbances and the progression of Alzheimer's disease (AD), focusing on how these changes correlate with cognitive impairments and key neuropathological indicators.

Method: We prospectively enrolled 48 patients with AD dementia (ADD), 27 patients with prodromal AD (proAD), and 41 cognitively unimpaired (CU) individuals between January 2022 and May 2023. Participants underwent brain T1‐weighted MRI, F‐florbetaben PET, neuropsychiatric tests, and APOE genotyping, and quantified gait analysis was assessed using a 5.

Latent Representation Learning of Cortical Atrophy using BERT‐based Foundation Model.

Background: The assessment of cortical thickness through structural magnetic resonance imaging (MRI) reveals various cortical atrophy patterns, delivering crucial insights into the extent of neurodegeneration. Numerous studies have focused on these thickness values to construct models for diverse neurodegeneration‐related tasks, such as classifying disease groups, staging progression, or predicting progression speed. However, these tasks require substantial training datasets tailored to each specific task, necessitating the development of new task‐specific models.

Mechanistic links of amyloid, atrophy, and cognition profiles for gait disturbance in Alzheimer’s disease.

Background: This study delves into the relationship between gait disturbances and the progression of Alzheimer’s disease (AD), focusing on how these changes correlate with cognitive impairments and key neuropathological indicators.

Method: We prospectively enrolled 48 patients with AD dementia (ADD), 27 patients with prodromal AD (proAD), and 41 cognitively unimpaired (CU) individuals between January 2022 and May 2023. Participants underwent brain T1‐weighted MRI, 18F‐florbetaben PET, neuropsychiatric tests, and APOE genotyping, and quantified gait analysis was assessed using a 5.

Self-Assembled Peptide-Derived Proteolysis-Targeting Chimera (PROTAC) Nanoparticles for Tumor-Targeted and Durable PD-L1 Degradation in Cancer Immunotherapy.

Proteolysis-targeting chimeras (PROTACs) are a promising technique for the specific and durable degradation of cancer-related proteins via the ubiquitin-proteasome system in cancer treatment. However, the therapeutic efficacy of PROTACs is restricted due to their hydrophobicity, poor cell permeability and insufficient tumor-targeting ability. Herein, we develop the self-assembled peptide-derived PROTAC nanoparticles (PT-NPs) for precise and durable programmed death-ligand 1 (PD-L1) degradation in targeted tumors.

Heterogeneity of factors associated with cognitive decline and cortical atrophy in early- versus late-onset Alzheimer's disease.

The objectives of this study were to investigate the variable factors associated with cognitive function and cortical atrophy and estimated variable importance of those factors in affecting cognitive function and cortical atrophy in patients with EOAD and LOAD. Patients with EOAD (n = 40), LOAD (n = 34), and healthy volunteers with normal cognition were included (n = 65). All of them performed 3T MRI, [F]THK5351 PET (THK), [F]flutemetamol PET (FLUTE), and detailed neuropsychological tests.

Diverging Relationships among Amyloid, Tau, and Brain Atrophy in Early-Onset and Late-Onset Alzheimer's Disease.

Purpose: Alzheimer's disease (AD) dementia may not be a single disease entity. Early-onset AD (EOAD) and late-onset AD (LOAD) have been united under the same eponym of AD until now, but disentangling the heterogeneity according to the age of sonset has been a major tenet in the field of AD research.

Materials And Methods: Ninety-nine patients with AD (EOAD, n=54; LOAD, n=45) and 66 cognitively normal controls completed both [F]THK5351 and [F]flutemetamol (FLUTE) positron emission tomography scans along with structural magnetic resonance imaging and detailed neuropsychological tests.

Disentangling brain atrophy heterogeneity in Alzheimer's disease: A deep self-supervised approach with interpretable latent space.

Alzheimer's disease (AD) is heterogeneous, but existing methods for capturing this heterogeneity through dimensionality reduction and unsupervised clustering have limitations when it comes to extracting intricate atrophy patterns. In this study, we propose a deep learning based self-supervised framework that characterizes complex atrophy features using latent space representation. It integrates feature engineering, classification, and clustering to synergistically disentangle heterogeneity in Alzheimer's disease.

Cancer cell-specific and pro-apoptotic SMAC peptide-doxorubicin conjugated prodrug encapsulated aposomes for synergistic cancer immunotherapy.

Background: Immunogenic cell death (ICD) is a crucial approach to turn immunosuppressive tumor microenvironment (ITM) into immune-responsive milieu and improve the response rate of immune checkpoint blockade (ICB) therapy. However, cancer cells show resistance to ICD-inducing chemotherapeutic drugs, and non-specific toxicity of those drugs against immune cells reduce the immunotherapy efficiency.

Methods: Herein, we propose cancer cell-specific and pro-apoptotic liposomes (Aposomes) encapsulating second mitochondria-derived activator of caspases mimetic peptide (SMAC-P)-doxorubicin (DOX) conjugated prodrug to potentiate combinational ICB therapy with ICD.

Deep learning-based dynamic ventilatory threshold estimation from electrocardiograms.

Background And Objective: The ventilatory threshold (VT) marks the transition from aerobic to anaerobic metabolism and is used to assess cardiorespiratory endurance. A conventional way to assess VT is cardiopulmonary exercise testing, which requires a gas analyzer. Another method for measuring VT involves calculating the heart rate variability (HRV) from an electrocardiogram (ECG) by computing the variability of heartbeats.

Real-world prediction of preclinical Alzheimer's disease with a deep generative model.

Amyloid positivity is an early indicator of Alzheimer's disease and is necessary to determine the disease. In this study, a deep generative model is utilized to predict the amyloid positivity of cognitively normal individuals using proxy measures, such as structural MRI scans, demographic variables, and cognitive scores, instead of invasive direct measurements. Through its remarkable efficacy in handling imperfect datasets caused by missing data or labels, and imbalanced classes, the model outperforms previous studies and widely used machine learning approaches with an AUROC of 0.

Sex differences in the structural rich-club connectivity in patients with Alzheimer's disease.

Background And Objectives: Alzheimer's disease (AD) is more prevalent in women than in men; however, there is a discrepancy in research on sex differences in AD. The human brain is a large-scale network with hub regions forming a central core, the rich-club, which is vital to cognitive functions. However, it is unknown whether alterations in the rich-clubs in AD differ between men and women.

Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorder using a deep learning model.

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are autoimmune inflammatory disorders of the central nervous system (CNS) with similar characteristics. The differential diagnosis between MS and NMOSD is critical for initiating early effective therapy. In this study, we developed a deep learning model to differentiate between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) using brain magnetic resonance imaging (MRI) data.

Effect of Amyloid on Cognitive Performance in Parkinson's Disease and Dementia with Lewy Bodies.

Background: Concomitant amyloid pathology contributes to the clinical heterogeneity of Lewy body diseases (LBDs).

Objective: The objective of this study was to investigate the pattern and effect of amyloid accumulation on cognitive dysfunction in Parkinson's disease (PD) and dementia with Lewy bodies (DLB).

Methods: We retrospectively assessed 205 patients with LBD (91 with DLB and 114 with PD) who underwent F-florbetaben positron emission tomography and divided them into amyloid-positive and amyloid-negative groups depending on global standardized uptake value ratios (SUVRs).

Local striatal volume and motor reserve in drug-naïve Parkinson's disease.

Motor reserve (MR) may explain why individuals with similar pathological changes show marked differences in motor deficits in Parkinson's disease (PD). In this study, we investigated whether estimated individual MR was linked to local striatal volume (LSV) in PD. We analyzed data obtained from 333 patients with drug naïve PD who underwent dopamine transporter scans and high-resolution 3-tesla T1-weighted structural magnetic resonance images.

Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders.

Background: Brain connectivity is useful for deciphering complex brain dynamics controlling interregional communication. Identifying specific brain phenomena based on brain connectivity and quantifying their levels can help explain or diagnose neurodegenerative disorders.

Objective: This study aimed to establish a unified framework to identify brain connectivity-based biomarkers associated with disease progression and summarize them into a single numerical value, with consideration for connectivity-specific structural attributes.

On the reliability of deep learning-based classification for Alzheimer's disease: Multi-cohorts, multi-vendors, multi-protocols, and head-to-head validation.

Structural changes in the brain due to Alzheimer's disease dementia (ADD) can be observed through brain T1-weighted magnetic resonance imaging (MRI) images. Many ADD diagnostic studies using brain MRI images have been conducted with machine-learning and deep-learning models. Although reliability is a key in clinical application and applicability of low-resolution MRI (LRMRI) is a key to broad clinical application, both are not sufficiently studied in the deep-learning area.

White matter tract-specific microstructural disruption is associated with depressive symptoms in isolated RBD.

Objective: White matter (WM) tract-specific changes may precede gray matter loss in isolated rapid eye movement sleep behavior disorder (iRBD). We aimed to evaluate tract-specific WM changes using tract-specific statistical analysis (TSSA) and their correlation with clinical variables in iRBD patients.

Methods: This was a cross-sectional single-center study of 50 polysomnography-confirmed iRBD patients and 20 age- and sex-matched controls.

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.

Machine learning-based automatic estimation of cortical atrophy using brain computed tomography images.

Cortical atrophy is measured clinically according to established visual rating scales based on magnetic resonance imaging (MRI). Although brain MRI is the primary imaging marker for neurodegeneration, computed tomography (CT) is also widely used for the early detection and diagnosis of dementia. However, they are seldom investigated.

Contracted thalamic shape is associated with early development of levodopa-induced dyskinesia in Parkinson's disease.

Levodopa-induced dyskinesia (LID), a long-term motor complication in Parkinson's disease (PD), is attributable to both presynaptic and postsynaptic mechanisms. However, no studies have evaluated the baseline structural changes associated with LID at a subcortical level in PD. A total of 116 right-handed PD patients were recruited and based on the LID latency of 5 years, we classified patients into those vulnerable to LID (PD-vLID, n = 49) and those resistant to LID (PD-rLID, n = 67).