Learning BOLD response in fMRI by reservoir computing

Paolo Avesani, Hananel Hazann, Ester Koilis, Larry Manevitz, Diego Sona

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

2 اقتباسات (Scopus)

ملخص

This work proposes a model-free approach to fMRI-based brain mapping where the BOLD response is learnt from data rather than assumed in advance. For each voxel, a paired sequence of stimuli and fMRI recording is given to a supervised learning process. The result is a voxel-wise model of the expected BOLD response related to a set of stimuli. Differently from standard brain mapping techniques, where voxel relevance is assessed by fitting an hemodynamic response function, we argue that relevant voxels can be filtered according to the prediction accuracy of a learning model. In this work we present a computational architecture based on reservoir computing which combines a Liquid State Machine with a Multi-Layer Perceptron. An empirical analysis on synthetic data shows how the learning process can be robust with respect to noise artificially added to the signal. A similar investigation on real fMRI data provides a prediction of BOLD response whose accuracy allows for discriminating between relevant and irrelevant voxels.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفProceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011
الصفحات57-60
عدد الصفحات4
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2011
منشور خارجيًانعم
الحدثInternational Workshop on Pattern Recognition in NeuroImaging, PRNI 2011 - Seoul, كوريا الجنوبيّة
المدة: ١٦ مايو ٢٠١١١٨ مايو ٢٠١١

سلسلة المنشورات

الاسمProceedings - International Workshop on Pattern Recognition in NeuroImaging, PRNI 2011

!!Conference

!!ConferenceInternational Workshop on Pattern Recognition in NeuroImaging, PRNI 2011
الدولة/الإقليمكوريا الجنوبيّة
المدينةSeoul
المدة١٦/٠٥/١١١٨/٠٥/١١

بصمة

أدرس بدقة موضوعات البحث “Learning BOLD response in fMRI by reservoir computing'. فهما يشكلان معًا بصمة فريدة.

قم بذكر هذا