MemScreen: A smartphone application for detection of mild cognitive impairment: A validation study: Smartphone App for MCI Detection.

Background And Objectives: Primary care is often the first point of contact for patients with cognitive complaints, making initial cognitive screening an essential step to avoid delays in diagnosing Alzheimer's disease (AD) at an early stage. We developed MemScreen, a self-administered smartphone application that assesses overall cognition and verbal memory, and evaluated its ability to detect mild cognitive impairment (MCI) in both general and clinical populations.

Methods: We conducted two validation cohort studies: (1) UK-based Whitehall II cohort study (13th wave, 2018-2022) involving a general population (MCI defined by poor performance on a global cognitive score), and (2) five French memory clinics involving patients without dementia (amnestic MCI defined by the Free and Cued Selective Reminding Test).

Depressive Symptoms and Amyloid Pathology.

Importance: Depressive symptoms are associated with cognitive decline in older individuals. Uncertainty about underlying mechanisms hampers diagnostic and therapeutic efforts. This large-scale study aimed to elucidate the association between depressive symptoms and amyloid pathology.

Validation study of "Santé-Cerveau", a digital tool for early cognitive changes identification.

Background: There is a need for a reliable, easy-to-use, widely available, and validated tool for timely cognitive impairment identification. We created a computerized cognitive screening tool (Santé-Cerveau digital tool (SCD-T)) including validated questionnaires and the following neuropsychological tests: 5 Word Test (5-WT) for episodic memory, Trail Making Test (TMT) for executive functions, and a number coding test (NCT) adapted from the Digit Symbol Substitution Test for global intellectual efficiency. This study aimed to evaluate the performance of SCD-T to identify cognitive deficit and to determine its usability.

Forecasting individual progression trajectories in Alzheimer's disease.

The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages.

Differential Patterns of Domain-Specific Cognitive Complaints and Awareness Across the Alzheimer's Disease Spectrum.

: Characterizing self- and informant-reported cognitive complaints, as well as awareness of cognitive decline (ACD), is useful for an early diagnosis of Alzheimer's disease (AD). However, complaints and ACD related to cognitive functions other than memory are poorly studied. Furthermore, it remains unclear which source of information is the most useful to distinguish various groups on the AD spectrum.

Pilot study of repeated blood-brain barrier disruption in patients with mild Alzheimer's disease with an implantable ultrasound device.

Background: Temporary disruption of the blood-brain barrier (BBB) using pulsed ultrasound leads to the clearance of both amyloid and tau from the brain, increased neurogenesis, and mitigation of cognitive decline in pre-clinical models of Alzheimer's disease (AD) while also increasing BBB penetration of therapeutic antibodies. The goal of this pilot clinical trial was to investigate the safety and efficacy of this approach in patients with mild AD using an implantable ultrasound device.

Methods: An implantable, 1-MHz ultrasound device (SonoCloud-1) was implanted under local anesthesia in the skull (extradural) of 10 mild AD patients to target the left supra-marginal gyrus.

Identifying health conditions associated with Alzheimer's disease up to 15 years before diagnosis: an agnostic study of French and British health records.

Background: The identification of modifiable risk factors for Alzheimer's disease is paramount for early prevention and the targeting of new interventions. We aimed to assess the associations between health conditions diagnosed in primary care and the risk of incident Alzheimer's disease over time, up to 15 years before a first Alzheimer's disease diagnosis.

Methods: In this agnostic study of French and British health records, data from 20 214 patients with Alzheimer's disease in the UK and 19 458 patients with Alzheimer's disease in France were extracted from The Health Improvement Network database.

Subtle postoperative cognitive disorder in preclinical Alzheimer's disease.

Background: We examined the association between preclinical Alzheimer's disease (AD) and undergoing anesthesia and surgery ("surgery" henceforth) in a cohort of elderly individuals with a subjective cognitive decline (SCD).

Methods: Individuals with SCD (N = 268) were enrolled in a longitudinal follow-up study. Participants underwent comprehensive yearly cognitive evaluation for a period of 4 years.

A Proposal to Make Biomedical Research into Alzheimer's Disease More Democratic Following an International Survey with Researchers.

Background: Therapeutic research into Alzheimer's disease (AD) has been dominated by the amyloid cascade hypothesis (ACH) since the 1990s. However, targeting amyloid in AD patients has not yet resulted in highly significant disease-modifying effects. Furthermore, other promising theories of AD etiology exist.

Awareness of Cognitive Decline in Patients With Alzheimer's Disease: A Systematic Review and Meta-Analysis.

Identifying a poor degree of awareness of cognitive decline (ACD) could represent an early indicator of Alzheimer's disease (AD). (1) to understand whether there is evidence of poor ACD in the pre-dementia stages of AD; (2) to summarize the main findings obtained investigating ACD in AD; (3) to propose a conceptual framework. We searched Scopus, Pubmed, and the reference lists for studies published up to August 2020.

A machine learning approach to screen for preclinical Alzheimer's disease.

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on F-florbetapir and F-fluorodeoxyglucose PET, respectively.

Clinical and neuropathological diversity of tauopathy in MAPT duplication carriers.

Microduplications of the 17q21.31 chromosomal region encompassing the MAPT gene, which encodes the Tau protein, were identified in patients with a progressive disorder initially characterized by severe memory impairment with or without behavioral changes that can clinically mimic Alzheimer disease. The unique neuropathological report showed a primary tauopathy, which could not be unanimously classified in a given known subtype, showing both 4R- and 3R-tau inclusions, mainly within temporal cortical subregions and basal ganglia, without amyloid deposits.

Efficacy of serious exergames in improving neuropsychiatric symptoms in neurocognitive disorders: Results of the X-TORP cluster randomized trial.

Introduction: The aim of this study was to evaluate the efficacy of a serious exergame in improving the neuropsychiatric symptoms of patients with neurocognitive disorders.

Methods: X-Torp is a serious exergame combining motor and cognitive activities. Ninety-one subjects (mean age = 81.

Primary Progressive Aphasia Associated With Mutations: New Insights Into the Nonamyloid Logopenic Variant.

Objective: To determine relative frequencies and linguistic profiles of primary progressive aphasia (PPA) variants associated with (progranulin) mutations and to study their neuroanatomic correlates.

Methods: Patients with PPA carrying mutations (PPA-) were selected among a national prospective research cohort of 1,696 patients with frontotemporal dementia, including 235 patients with PPA. All patients with amyloid-positive CSF biomarkers were excluded.

Understanding the Variability in Graph Data Sets through Statistical Modeling on the Stiefel Manifold.

Network analysis provides a rich framework to model complex phenomena, such as human brain connectivity. It has proven efficient to understand their natural properties and design predictive models. In this paper, we study the variability within groups of networks, i.