Neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD) and amyotrophic lateral sclerosis (ALS), affect millions of people every year and so far, there are no therapeutic cures available. Even though animal and histological models have been of great aid in understanding disease mechanisms and identifying possible therapeutic strategies, in order to find disease-modifying solutions there is still a critical need for systems that can provide more predictive and physiologically relevant results. One possible avenue is the development of patient-derived models, e.g. by reprogramming patient somatic cells into human induced pluripotent stem cells (hiPSCs), which can then be differentiated into any cell type for modelling. These systems contain key genetic information from the donors, and therefore have enormous potential as tools in the investigation of pathological mechanisms underlying disease phenotype, and progression, as well as in drug testing platforms. hiPSCs have been widely cultured in 2D systems, but in order to mimic human brain complexity, 3D models have been proposed as a more advanced alternative. This review will focus on the use of patient-derived hiPSCs to model AD, PD, HD and ALS. In brief, we will cover the available stem cells, types of 2D and 3D culture systems, existing models for neurodegenerative diseases, obstacles to model these diseases in vitro, and current perspectives in the field.

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Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons in the nigrostriatal pathway. The etiology of PD remains unclear and most cases are sporadic, however genetic mutations in more than 20 proteins have been shown to cause inherited forms of PD. Many of these proteins are linked to mitochondrial function, defects in which are a central characteristic of PD. Post-translational modifications (PTMs) allow rapid and reversible control over protein function. Largely focussing on mitochondrial dysfunction in PD, here we review findings on the PTMs phosphorylation, SUMOylation and ubiquitination that have been shown to affect PDrelated proteins.

The brain is an extraordinarily complex system that facilitates the optimal integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pairwise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses the 1000 Functional Connectomes Project dataset. Moreover, we would like to highlight one part of our notebook dedicated to the realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pairwise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.