Precision Movement Analysis and Brain Mapping (AG Friedrich)

From barely perceptible oscillations of the eyes interfering with reading to mildly slowed finger movements impairing guitar play, balance and movement disorders profoundly impact the way humans engage with the world.

To diagnose, track, and treat these disorders, neurologists have traditionally cultivated highly specialized visual expertise to condense clinical observation of a patient’s movement into conceptual patterns, a process known as phenomenology.

Historically, these patterns were then localized to discrete brain structures to reach an anatomical diagnosis, a process termed brain mapping.

Yet, these paradigms face inherent challenges: human motion perception is limited in clinically relevant granularity, while a one-structure-one-function model falls short of capturing the distributed network nature of many neurological signs.

Recent advances in visual AI and connectomic neuroimaging now offer solutions to both limitations: computer vision enables objective, high-resolution quantification of movement that surpasses human perceptual capacity, while network-based brain mapping reveals the distributed circuits underlying neurological symptoms.

Building on these foundations, the Precision Movement Analysis and Brain Mapping group aims to integrate AI-based movement analytics with multidimensional brain imaging to

1. objectively quantify neurological signs,
2. map them to causal and therapeutically relevant brain circuitry, and
3. translate these digital biomarkers into meaningful and patient-centered clinical applications: from pixels to voxels to meaning.

Research areas and methods

• Video-based movement analysis (video-oculography, computer vision, pose tracking)
• Multimodal neuroimaging (voxel- & connectome-based lesion and deep brain stimulation mapping)
• Brain stimulation (deep brain stimulation, non-invasive vestibular stimulation)
• Translational Neuroanatomy (mesoscale brain imaging, histological correlation)
• Development and validation of digital biomarkers of neurological disorders

Group members

The complexity of neuroscientific problems requires interdisciplinary collaboration and integration of multiple perspectives. Our group fosters this exchange by bringing together researchers from diverse backgrounds, including medicine, neuroscience, computer science, and biomedical engineering, united by a shared interest in quantitative approaches to brain–behavior relationships.

Collaborations & Funding