Software development
Software development
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JTrack & JDash
JTrack & JDash
Digital phenotyping and remote study management
JTrack provides smartphone applications for studying daily-life behaviour in health and disease. Passive sensor recordings and ecological momentary assessment offer complementary measures outside the clinic. JTrack-EMA+ supports repeated self-reports, while JDash gives researchers an interface for study setup, survey design, participant enrolment, progress monitoring and data validation.
The platform is intended for research studies with defined protocols and appropriate study access. The repositories provide the mobile, dashboard and backend components for researchers who wish to inspect or deploy the software.
Source repositories · JTrack publication · JTrack-EMA+ publication
Case-E
Case-E
Electronic case report forms and research data capture
Case-E supports the design and conduct of research studies through structured electronic forms. Investigators can define cohorts, visits and schedules of assessments, build reusable forms, monitor data completion and export analysis-ready study packages. The application maintains an audit history and supports controlled revisions to study definitions.
Local workstation and hosted server profiles are available. Access to an existing hosted study, or permission to create one, is arranged with the study team; registering an account does not automatically provide access to study data.
JuSpace
JuSpace
Spatial analysis of imaging and neurotransmitter maps
JuSpace relates spatial patterns in MRI-derived measures to nuclear imaging-derived maps of neurotransmitter receptors and transporters. It provides a practical framework for investigating whether regional imaging differences follow the distribution of particular neurochemical systems. Permutation-based analyses support evaluation of spatial associations.
The toolbox is available for MATLAB, with standalone versions for Windows and Linux. Spatial associations provide biological context and should be interpreted alongside the study design and the original imaging data. The software and the source maps used in an analysis should be cited.
NiSpace
NiSpace
Neuroimaging Spatial Colocalization Environment
NiSpace is a Python framework for comparing spatial patterns in brain maps. Its workflow combines retrieval of reference neuroimaging data, transformations between imaging spaces, univariate and multivariate colocalization methods, non-parametric significance testing and visualization.
Developed by Dr. Leon Lotter and collaborators, it supports analyses across surface and volumetric representations. The package remains under development, and its documentation describes the available methods and installation options. Reference datasets retain their own licenses and citation requirements; users should consult the metadata for each map.
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