The state of the field regarding the diagnosis and treatment of major depressive disorder (MDD) is discussed. Current challenges and opportunities facing the research and clinical communities are outlined, including appropriate quantitative and qualitative analyses of the heterogeneity of biological, social, and psychiatric factors which may contribute to MDD.

This lesson gives a description of the BrainHealth Databank, a repository of many types of health-related data, whose aim is to accelerate research, improve care, and to help better understand and diagnose mental illness, as well as develop new treatments and prevention strategies. 

This lesson corresponds to slides 46-78 of the PDF below. 

This lesson describes not only the need for precision medicine, but also the current state of the methods, pharmacogenetic approaches, utility and implementation of such care today.

This lesson corresponds to slides 1-50 of the PowerPoint below. 

This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling. 

This lecture covers the needs and challenges involved in creating a FAIR ecosystem for neuroimaging research.

This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.

This lecture focuses on the structured validation process within computational neuroscience, including the tools, services, and methods involved in simulation and analysis.

This session provides users with an introduction to tools and resources that facilitate the implementation of FAIR in their research.

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 lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity. 

This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

This is the second of three lectures around current challenges and opportunities facing neuroinformatic infrastructure for handling sensitive data. 

In this lesson you will learn about current efforts towards integrating multimodal human brain data using the open source SCORE HED library schema. 

This lecture aims to help researchers, students, and health care professionals understand the place for neuroinformatics in the patient journey using the exemplar of an epilepsy patient. 

The lesson introduces the Brain Imaging Data Structure (BIDS), the community standard for organizing, curating, and sharing neuroimaging and associated data. The session focuses on understanding the BIDS framework, learning its data structure and validation processes.

This session moves from BIDS basics into analysis workflows, focusing on how to turn raw, BIDS-organized data into derivatives using BIDS Apps and containers for reproducible processing. It compares end-to-end pipelines across fMRI and PET (and notes EEG/MEG), explains typical preprocessing choices, and shows how standardized inputs plus containerized tools (Docker/AppTainer) yield consistent, auditable outputs.

The session explains GDPR rules around data sharing for research in Europe, the distinction between law and ethics, and introduces practical solutions for securely sharing sensitive datasets. Researchers have more flexibility than commonly assumed: scientific research is considered a public interest task, so explicit consent for data sharing isn’t legally required, though transparency and informing participants remain ethically important. The talk also introduces publicneuro.eu, a controlled-access platform that enables sharing neuroimaging datasets with open metadata, DOIs, and customizable access restrictions while ensuring GDPR compliance.

This session introduces the PET-to-BIDS (PET2BIDS) library, a toolkit designed to simplify the conversion and preparation of PET imaging datasets into BIDS-compliant formats. It supports multiple data types and formats (e.g., DICOM, ECAT7+, nifti, JSON), integrates seamlessly with Excel-based metadata, and provides automated routines for metadata updates, blood data conversion, and JSON synchronization. PET2BIDS improves human readability by mapping complex reconstruction names into standardized, descriptive labels and offers extensive documentation, examples, and video tutorials to make adoption easier for researchers.

This session introduces the PET-to-BIDS (PET2BIDS) library, a toolkit designed to simplify the conversion and preparation of PET imaging datasets into BIDS-compliant formats. It supports multiple data types and formats (e.g., DICOM, ECAT7+, nifti, JSON), integrates seamlessly with Excel-based metadata, and provides automated routines for metadata updates, blood data conversion, and JSON synchronization. PET2BIDS improves human readability by mapping complex reconstruction names into standardized, descriptive labels and offers extensive documentation, examples, and video tutorials to make adoption easier for researchers.

This session dives into practical PET tooling on BIDS data—showing how to run motion correction, register PET↔MRI, extract time–activity curves, and generate standardized PET-BIDS derivatives with clear QC reports. It introduces modular BIDS Apps (head-motion correction, TAC extraction), a full pipeline (PETPrep), and a PET/MRI defacer, with guidance on parameters, outputs, provenance, and why Dockerized containers are the reliable way to run them at scale.