Topic: Alzheimer’s disease

Develop machine-learning methods that integrate multimodal human-brain data and characterise Alzheimer’s disease trajectories. Contribute to mechanistic analysis of disease using large-scale molecular, cellular and neuropathology datasets.

Navigation and memory require coordination between map-like allocentric representations and body-centered egocentric actions. Aaron Wilber examines how the parietal–retrosplenial–anterior thalamic–hippocampal network performs these transformations, and how disrupted coordination contributes to spatial deficits in aging and Alzheimer’s disease. Rat sequence-task experiments reveal bidirectional hippocampal–parietal interactions across route-centered, place and egocentric representations. Because that task does not isolate reference-frame use, the laboratory developed a freely available task se

The Allen Institute has launched a new moonshot initiative on human brain health and disease called the Brain Health accelerator. This initiative aims to dramatically accelerate our understanding of human brain structure and function, identify the molecular, cellular and circuit basis of disease progression, and pioneer new therapeutic strategies targeting vulnerable and affected cell types. Our mission focuses on taking a human-centric approach to understanding and treating disease, combining a large-scale open science discovery approach across multiple diseases, AI-based disease modeling, an

Supports Phase 1–3 trials of combination approaches for Alzheimer’s disease and related dementias, with mechanistic rationale and appropriate biomarkers. Phase 1 support is capped at USD 1 million; Phase 2–3 at USD 2 million over two to three years. Exceptional requests up to USD 3 million require case-by-case consideration. Letters of intent are due 20 October 2026 at 17:00 Eastern time (22 October for ISTAART members); invited full applications follow on 15 December (17 December for members).

Funds experimental advancement of AI-discovered therapeutic leads for Alzheimer’s disease and related dementias. Separate tracks support lead optimization and work enabling an investigational-new-drug submission. Awards are up to USD 1 million over two to three years. Pure algorithm development, general AI platforms, repurposing and clinical trial execution are excluded. Letters of intent are due 20 October 2026 at 17:00 Eastern time (22 October for ISTAART members); invited full applications follow on 15 December (17 December for members).

Human brain is the most dynamic and varied system of the body. The brain is composed of neuron and glia. But how do they interact to generate emergent properties like memory, learning, emotion and sleep is little understood. Many such complex systems that exist in non-linear dynamics are characterized by the fractal nature. The fractal dimension (FD) is a quantitative parameter that has been extensively used to analyse the complexity of structural and functional patterns of the human brain. The fractal dimension (FD) of the human brain quantifies the inherent complexity. Evidences strongly sug

The hippocampus (HC) plays a pivotal role in spatial navigation and memory in the human brain. Place cells, specialised neurons residing within the hippocampus, exhibit selective firing patterns corresponding to specific spatial locations, collectively contributing to the cognitive mapping of the environment. In healthy state, the place fields in the CA1 region exhibits continuous representational turnover [1]. Spatial navigation is disrupted in Alzheimer’s Disease (AD). AD, a neurodegenerative disorder characterised by progressive cognitive decline, is associated with deficits in learning a

Advancing therapies for neurological disorders relies on developing efficient, personalized whole-brain models that can capture mechanistic insights and pathological features. Current challenges in creating such computational models arise from managing a considerable number of adjustable parameters. The conventional manual exploration of this parameter space is a serious limitation for personalizing models to individual subject data [1]. This work aims to address this by approaching the problem as an optimization challenge in the context of developing brain models capable of characterizing in

Alzheimer’s disease (AD) is the most common and progressive type of brain disorder that affects parts of the brain responsible for memory, speaking, thinking, and many other important functions. Apart from its common risk factors such as aging, environment, and lifestyle elements, the risk of developing AD largely depends on gene variants, which present a promising opportunity for identifying novel diagnostic and therapeutic biomarkers. Early studies have revealed numerous SNPs simultaneously associated with AD and other diseases such as Parkinson’s disease, stroke, multiple sclerosis, and mor

Alzheimer’s disease (AD) is a complex disorder and multiple molecular mechanisms are involved in AD onset and progression. Recent evidences have suggested that metabolic alterations are an important pathological feature in AD. Likewise, diabetes and obesity, two metabolic illnesses, are risk factors for AD. These two diseases are associated with an expansion of visceral adipose tissue (VAT). Here, we hypothesize that the visceral adipose tissue may serve as a key communicator organ between the brain and peripheral metabolic disorders and affecting both types of disorders.We used histological s

Alzheimer’s Disease (AD) is a progressive neurological disorder that gradually deteriorates cognitive abilities and leads to the inability to carry out simple tasks. However, the molecular mechanisms and causes of this disease are still unclear. This research aims to use the proteomic signature of the 5xFAD AD mouse model to identify early changes in protein expression in the AD brain compared to the wild type. Lysates from the cerebral cortex of 3 months-old 5xFAD and wild-type mice were analysed by LC-MS/MS and investigated for protein candidates over- or under- expressed in AD mice compared

The liver, responsible for regulating the metabolism of the body; the kidney, the most important organ for waste elimination, and the spleen, in charge of filtering the blood and producing lymphocytes, are now considered to have clearance functions in Alzheimer’s disease (AD) eliminating Aβ from the brain. In the present work, we used different levels of study to examine the status and involvement of these three organs in 16-months-old male and female 3xTg-AD for AD and compared them to C57BL/6 counterparts with physiological aging, in two different social scenarios. All three organs presented

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by early decline in episodic and spatial memory, associated with accumulation of brain markers. Intriguingly, despite similar levels of brain alteration, some elderly subjects show less cognitive impairment than others. To explain this lack of correlation between the degree of brain lesions and the severity of cognitive symptoms, the concept of cognitive resilience (CR) has emerged. Yet, the protective mechanisms supporting CR remain poorly understood. To tackle this issue, we developed a preclinical model of