Fabián Labra-Spröhnle
Research Director
Organisation :
NOOLOGICA
Sector:
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Location:
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Biography
Fabián Labra-Spröhnle is a translational neuroscience researcher and experimental-system developer working at the intersection of neuroscience, psychiatry, cognitive science, and artificial intelligence. At Noologica, he provides scientific and conceptual leadership, directing the development of digital experimental methods for studying executive function and the organisation of behaviour over time.
His role includes experimental design, behavioural phenotyping, analytical development, clinical translation, research strategy, ethics and funding applications, and coordination of international academic and clinical collaborations. He leads Noologica’s work on ADHD assessment, deep phenotyping, clinical triage, longitudinal evaluation, and personalised analysis, translating theoretical and experimental advances into clinically useful tools.
Research Projects and Ideas
Current work focuses on validating behavioural signatures associated with ADHD and related neurodevelopmental conditions across different clinical and international populations. Noologica is also developing applications for clinical triage, deep phenotyping, normative modelling, longitudinal monitoring, and personalised assessment. Its next phase is to conduct multi-site pilot studies, strengthen clinical validation, integrate clinician-in-the-loop decision support, and translate the platform in
Collaborations and Skills
We are seeking collaborators for independent replication, cross-cultural and multi-site studies, clinical and educational applications, development of new experimental tasks, computational and machine-learning analysis, longitudinal and N-of-1 research, joint grant applications, student supervision, and open-source software development.
Connections of interest
We are interested in connecting with researchers, clinicians, universities, health services, and technology partners working in neuroscience, psychiatry, cognitive and developmental science, behavioural phenotyping, AI, complexity science, and digital health. Priority areas include multi-site validation, clinical and educational pilots, new experimental tasks, longitudinal and N-of-1 studies, analytical development, and joint funding applications.
Speciality(s)
AI Speciality(s):
Clinical Decision Support Systems
Machine Learning
Precision Medicine / Genomics
Explainable AI & Interpretability
Model & Tool Development
Predictive Analytics & Risk Stratification
Research Expertise:
Health Speciality(s):
Mental Health
Paediatrics & Child Health
Neurology & Neuroscience
Public & Population Health