Dementia and Cognitive Impairment Research

The paper uses machine learning on multi-cohort transcriptomic and proteomic data to identify molecular subtypes of cognitive resilience in Alzheimer's disease and finds two major subgroups with distinct proteomic and neuroimaging features.

The paper introduces DunedinPACNI, a brain MRI-based aging measure that predicts dementia conversion, morbidity, and mortality.

This study explores cognitive tests sensitive to sleep quantity and early dementia-related cognitive changes in middle-aged and older adults.

This paper uses multi-sensory-evoked potentials and a support vector machine to classify aMCI with high accuracy, showing better performance when combining multiple modalities.

This paper introduces a tensor kernel learning method to integrate multimodal data for Alzheimer's disease classification, achieving improved accuracy compared to single-modality approaches.

This paper compares deep learning models for predicting cognitive performance trajectories using real-world data and finds CNNs to be the most robust for pattern recognition and overfitting resistance.

The paper examines the relationship between cardiometabolic factors, blood beta-amyloid, and white matter hyperintensities in dementia-free individuals with cognitive complaints, finding stronger associations with cardiometabolic markers.

This paper develops an AI-ensembled network to classify Alzheimer's disease stages and predict progression using MRI data from ADNI.

The paper examines amygdalar tau binding and connectivity in preclinical Alzheimer's disease, finding associations with anxiety symptoms.

This study links cardiometabolic state, measured by an allostatic load index, with neurovascular burden and brain structure/function changes across age using EEG and MRI data.

This study finds that frontotemporal dementia patients show higher delay discounting, linked to impulsivity symptoms and pulvinar atrophy.

This study shows that poor cerebrovascular health slows processing speed through anterior white matter changes in middle-aged adults, with effects specific to cognition and risk factor duration.

The paper identifies brain structure signatures linked to Alzheimer's genetic risk across disease stages using compositional data analysis.

This study uses MD-MRI to map cortical astrogliosis and distinguishes cognitive resilience from dementia in Alzheimer's disease despite significant neuropathology.

The paper examines scale-free dynamics in cerebrospinal fluid regions and finds higher Hurst exponents in Alzheimer's Disease patients compared to cognitively normal individuals, correlating with AD biomarkers.

This study uses fMRI to examine empathy processing in bvFTD patients and controls, finding reduced BOLD signal in key regions and altered connectivity patterns.

This paper introduces re-PLS Learning to predict multiple AD outcomes from brain cortical thickness, identifying joint predictive regions across cohorts.

The paper identifies an AD-like subtype in brain aging across multiple regions, showing overlap with AD gene signatures and regional differences in gene expression.

The paper examines functional connectivity differences in cerebellar dentate nucleus networks in Alzheimer's disease and mild cognitive impairment compared to cognitively normal individuals, finding altered connectivity patterns and behavioral associations.

The paper identifies joint signatures of microstructural and morphological changes in the Alzheimer's spectrum using MRI metrics and non-negative matrix factorization, linking them to risk factors and disease progression.

The paper investigates the relationship between tau PET, glucose metabolism, and beta-amyloid in MCI, finding that tau scores are strongly correlated with reduced glucose metabolism independently of APOE ε4 and modulated by beta-amyloid.

The paper investigates the relationship between regional amyloid burden and tau pathology spread, finding that functional connectivity mediates the spatial relationship between these pathologies.

This paper introduces a decision tree to improve the identification of dementia patients with pathogenic genetic variants by optimizing clinical selection criteria for genetic testing.

This study identifies novel plasma biomarkers for Alzheimer's disease and related dementias using unbiased proteomics in a large cohort.

The paper examines the effect of APOE ε4 and its interaction with sociodemographic factors on cognitive measures in South Asians from LASI-DAD, finding stronger associations in older adults and females.

The paper identifies diagnostic images that correlate with Alzheimer's biomarkers and show differential neural processing in MCI patients compared to healthy controls.

The paper proposes a threshold-crossing model to explain the sigmoid shape of logistic curves in neuroscience, particularly in neurodegenerative diseases, suggesting it reflects biological systems approaching and exceeding an effect threshold rather than an infection-like mechanism.

The paper demonstrates that ignoring sampling time points in statistical analyses can lead to misinterpretation of interaction effects between biomarkers and cognitive reserve in Alzheimer's Disease.