This book was written with the goal of introducing researchers and students in a variety of research fields to the intersection of data science and neuroimaging. This book reflects our own experience of doing research at the intersection of data science and neuroimaging and it is based on our experience working with students and collaborators who come from a variety of backgrounds and have a variety of reasons for wanting to use data science approaches in their work. The tools and ideas that we chose to write about are all tools and ideas that we have used in some way in our own research. Many of them are tools that we use on a daily basis in our work. This was important to us for a few reasons: the first is that we want to teach people things that we ourselves find useful. Second, it allowed us to write the book with a focus on solving specific analysis tasks. For example, in many of the chapters you will see that we walk you through ideas while implementing them in code, and with data. We believe that this is a good way to learn about data analysis, because it provides a connecting thread from scientific questions through the data and its representation to implementing specific answers to these questions. Finally, we find these ideas compelling and fruitful. That’s why we were drawn to them in the first place. We hope that our enthusiasm about the ideas and tools described in this book will be infectious enough to convince the readers of their value.

This talks discusses data sharing in the context of dementia. It explains why data sharing in dementia is important, how data is usually shared in the field and illustrates two examples: the Netherlands Consortium Dementia cohorts and the European Platform for Neurodegenerative Diseases.

The Medical Informatics Platform (MIP) Dementia had been installed in several memory clinics across Europe allowing them to federate their real-world databases. Research open access databases had also been integrated such as ADNI (Alzheimer’s Dementia Neuroimaging Initiative), reaching a cumulative case load of more than 5,000 patients (major cognitive disorder due to Alzheimer’s disease, other major cognitive disorder, minor cognitive disorder, controls). The statistic and machine learning tools implemented in the MIP allowed researchers to conduct easily federated analyses among Italian memory clinics (Redolfi et al. 2020) and also across borders between the French (Lille), the Swiss (Lausanne) and the Italian (Brescia) datasets.

The number of patients with dementia is estimated to increase given the aging population. This will lead to a number of challenges in the future in terms of diagnosis and care for patients with dementia. To meet these needs such as early diagnsosis and development of prognostic biomarkers, large datasets, such as the federated datasets on dementia. The EAN Dementia and cognitive disorders scientific panel can play an important role as coordinator and connecting panel members who wish to participate in e.g. consortia.

This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

This tutorial demonstrates how to perform cell-type deconvolution in order to estimate how proportions of cell-types in the brain change in response to various conditions. While these techniques may be useful in addressing a wide range of scientific questions, this tutorial will focus on the cellular changes associated with major depression (MDD). 

This lesson explains the fundamental principles of neuronal communication, such as neuronal spiking, membrane potentials, and cellular excitability, and how these electrophysiological features of the brain may be modelled and simulated digitally. 

This is an in-depth guide on EEG signals and their interaction within brain microcircuits. Participants are also shown techniques and software for simulating, analyzing, and visualizing these signals.

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health. 

This lecture covers the emergence of cognitive science after the Second World War as an interdisciplinary field for studying the mind, with influences from anthropology, cybernetics, and artificial intelligence.

In this lesson, you will learn about the connectome, the collective system of neural pathways in an organism, with a closer look at the neurons, synapses, and connections of particular species. 

This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein. 

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved. 

In this lesson you will learn about the motivation behind manipulating neural activity, and what forms that may take in various experimental designs. 

This lesson provides an introduction to neurons, synaptic transmission, and ion channels.

This lecture consists of the second half of the introduction to signal transduction, here focusing on cell receptors and signalling cascades.

This lecture gives an introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.

Followers of this lesson wilul learn about the origin and differentiation of myelinating cell types, molecular mechanisms defining onset and progression of myelination, as well as demyelination and remyelination after injury.

This lecture covers integrating information within a network, modulating and controlling networks, functions and dysfunctions of hippocampal networks, and the integrative network controlling sleep and arousal.

This lecture focuses on the comprehension of nociception and pain sensation, highlighting how the somatosensory system and different molecular partners are involved in nociception.