Dr Fani Deligianni


Fani Deligianni
PhD, MSc, MSc, MEng
Website under development


The raw data associated with two of our publications below are available for download from the Open Science Framework: https:osf.io94c5t

  • F. Deligianni et al. NODDI and tensor-based microstructural indices as predictors of functional connectivity, PLoS ONE, 11(4), 2016.

  • Deligianni et al. Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands, Frontiers in Neuroscience, 8(258), 2014.

Short Bio

Dr Fani Deligianni’s holds a PhD in Medical Image Computing at ICL (2002-2006), an MSc in Advanced Computing (awarded project distinction) at ICL (2001-2002), an MSc in Neuroscience at UCL (awarded project distinction) (2008-2010) and a MEng (equivalent) in Electrical and Computer Engineering at Aristotle University (1995-2000), Greece.

Her interests lie within medical image computing, machine learning, statistics, neuroimage analysis and neuroscience. In 2008, she was awarded an MRC Special Research Training Fellowship in Biomedical Informatics to explore links between structural connectivity as it is measured with Diffusion Weighted Imaging (DWI) and functional brain connectivity captured with resting-state (rs)-fMRI. She was based at the Biomedical Image Analysis group in Computing Department of Imperial College London. Her research work suggests a prediction framework to study the link between structural brain connectivity and functional brain connectivity. She developed sophisticate computational approaches in machine learning, statistics and network analysis for the investigation of human brain structure and function. In 2012 she moved to UCL and continued her work on the link between functional and structural brain connectomes. She applied her approach in functional data derived from simultaneous resting-state EEG-fMRI and microstructural indices obtained from neurite orientation dispersion and density imaging of the human brain.

Fani is also an affiliated member of the Centre for Brain and Cognitive Development (CBCD) at Birkbeck. She worked at CBCD from 2006 to 2008 and she developed a toolbox for contingent eyetracking, Talk2Tobii, to investigate the development of social skills in toddlers. The toolbox is used in several labs and is now integrated in T2T Package and SMART-T.

Her PhD work was on augmenting 3D reconstructed models of the bronchial tree with 2D video images acquired during bronchoscopy. Bronchial deformation was modeled based on Active Shape Models (ASM) and a predictive tracking algorithm was incorporated to improve tracking of the endoscopic camera.

She is an active reviewer of Journals such as IEEE Trans Med Imaging, NeuroImage, Human Brain Mapping, Frontiers, IEEE Journal of Biomedical Informatics, IEEE signal processing letters, Neurobiology of Aging and international conferences such as MICCAI and IEEE ISBI. Fani is also a UCL alumni careers mentor.

Contact Details

Email: fadel at gmail dot com
Twitter: @fd301
LinkedIn Profile