Supervised and unsupervised models of brain networks for brain decoding

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2018

Öğrenci: ABDULLAH N.A. ALCHIHABI

Danışman: FATOŞ TUNAY YARMAN VURAL

Özet:

In this thesis, we propose computational network models for human brain. The models are estimated from fMRI measurements, recorded while subjects perform a set of cognitive tasks. We employ supervised and unsupervised machine learning techniques to represent high level cognitive tasks of human brain by dynamic networks. In the first part of this thesis, we propose an unsupervised multi-resolution brain network model. First, we decompose the signal into multiple sub-bands using Wavelet transform and estimate a set of local meshes at each sub-band. Then, we use stacked denoising auto-encoders to learn low-dimensional connectivity patterns from constructed mesh networks. Finally, learned connectivity patterns are concatenated across different frequency sub-bands and clustered using a hierarchical clustering method. Results show that our proposed model successfully decodes the cognitive states of Human Connectome Project, yielding high rand and adjusted rand indices. In the second part of this thesis, we propose a supervised dynamic brain network model to decode the cognitive subtasks of complex problem solving. First, the raw fMRI images are passed through a preprocessing pipeline that decreases their spatial resolution while increasing their temporal resolution. Then, dynamic functional brain networks are constructed using neural networks. Constructed networks successfully distinguish the phases of complex problem solving. Finally, we analyze the network properties of constructed brain networks to identify potential hubs and clusters of densely connected anatomic regions during planning and execution subtasks. Results show that there are more potential hubs during planning and that clusters are more strongly connected in planning compared to execution.