Brain And SIgnal Research & Analysis laboratory

Develop your research skills in brain data learning
“My training has given me the tools and the confidence to understand and critically assess the contents of research papers so that I can direct my own research.”
Can Gafuroğlu (Undergrad student)

Applications for internships are open now.


BASIRA (Brain And SIgnal Research & Analysis) aims to infuse advanced computer vision and machine-learning methods into big neuroimaging and signal data analysis for improving healthcare and wellbeing.  Specifically, since one can look at the brain as an image, a shape, or a connectional network, we aspire to develop advanced image-based, shape-based, and network-based medical data analysis techniques, that will provide a foundation for better understanding normal brain development and ageing, as well as how the brain gets affected by neuropsychiatric or neurodegenerative disorders. We aim to develop algorithms and architectures for mapping the healthy brain and computer-aided tools for examining the diseased/disordered brain.
More broadly, we also aim to devise efficient algorithms that perform several medical image and signal analysis tasks such as segmentation/labeling, registration, prediction, classification and regression.​

News and Events

Islem Rekik, Ph.D.


Alaa's review paper on "Graph Neural Networks in Network Neuroscience" is accepted for publication in the journal of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (IF: 24.31)!


September 18, 2022

We have 8 papers accepted for publication in the LNCS Springer proceedings of the main MICCAI 2022 conference & MICCAI workshops (PRIME & GRAIL)! 6 out of 8 are from ITU undergraduate students.

Congratulations everyone!

July 20, 2022

Congratulations to Fatih and Xuesong for their accepted MICCAI 2022 papers on Connectional Brain Template Learning using GNNs and Contrastive Functional Connectivity Graph Learning, respectively!

June 2, 2022

Nada's paper on multigraph classification using learnable integration network with application to gender fingerprinting ​ is accepted for publication in the journal of Neural Networks (IF: 9.65)!

Congratulations to all!

March 29, 2022

Ahmed's paper on investigating the reproducibility of graph neural networks is accepted for publication in the journal of Neural Networks (IF: 9.65)!

Congratulations to all!

January 27, 2022

Seymanur's paper on the machine learning Kaggle competition for predicting the evolution of brain connectivity from a baseline timepoint  is accepted for publication in the journal of Neuroscience Methods (IF: 2.78)!


December 21, 2021


If you are interested, please contact us and send your CV and letter of motivation.

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