Parkinson's disease (PD) is the second most common neurodegenerative disease and affects approximately 2-3% of the population over the age of 65. PD is characterised by the loss of dopaminergic neurons from the substantia nigra, leading to debilitating motor symptoms including bradykinesia, tremor, rigidity, and postural instability. PD also results in a host of non-motor symptoms such as cognitive decline, sleep disturbances and depression. Although existing therapies can successfully manage some motor symptoms for several years, there is still no means to halt progression of this severely debilitating disorder. Animal models used to replicate aspects of PD have contributed greatly to our current understanding but do not fully replicate pathological mechanisms as they occur in patients. Because of this, there is now great interest in the use of human brain-based models to help further our understanding of disease processes. Human brain-based models include those derived from embryonic stem cells, patient-derived induced neurons, induced pluripotent stem cells and brain organoids, as well as post-mortem tissue. These models facilitate analysis of disease mechanisms and it is hoped they will help bridge the existing gap between bench and bedside. This review will discuss the various human brain-based models utilised in PD research today and highlight some of the key breakthroughs they have facilitated. Furthermore, the potential caveats associated with the use of human brain-based models will be detailed.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149087PMC
http://dx.doi.org/10.3389/fnins.2022.851058DOI Listing

Publication Analysis

Top Keywords

human brain-based
20
brain-based models
20
parkinson's disease
8
motor symptoms
8
stem cells
8
models
7
human
5
disease
5
models provide
4
provide powerful
4

Similar Publications

Word problems are essential for math learning and education, bridging numerical knowledge with real-world applications. Despite their importance, the neural mechanisms underlying word problem solving, especially in children, remain poorly understood. Here, we examine children's cognitive and brain response profiles for arithmetic word problems (AWPs), which involve one-step mathematical operations, and compare them with nonarithmetic word problems (NWPs), structured as parallel narratives without numerical operations.

View Article and Find Full Text PDF

Introduction: Current brain-based visual prostheses pose significant challenges impeding adoption such as the necessarily complex surgeries and occurrence of more substantial side effects due to the sensitivity of the brain. This has led to much effort toward vision restoration being focused on the more approachable part of the brain - the retina. Here we introduce a novel, parameterized simulation platform that enables study of human retinal degeneration and optimization of stimulation strategies.

View Article and Find Full Text PDF

Generative dynamical models for classification of rsfMRI data.

Netw Neurosci

December 2024

Department of Psychology, Stanford University, Stanford, CA, USA.

The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data.

View Article and Find Full Text PDF

Background: Low-intensity transcranial focused ultrasound (tFUS) is a brain stimulation approach that holds promise for the treatment of brain-based disorders. Studies in humans have shown that tFUS can successfully modulate perfusion in focal sonication targets, including the amygdala; however, limited research has explored how tFUS impacts large-scale neural networks.

Objective: The aim of the current study was to address this gap and examine changes in resting-state connectivity between large-scale network nodes using a randomized, double-blind, within-subjects crossover study design.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!