Machine Learning and Interpretation in Neuroimaging (day 1)
Irina Rish · Leila Wehbe · Brian Murphy · Georg Langs · Guillermo Cecchi · Moritz Grosse-Wentrup

Fri Dec 11th 08:30 AM -- 06:30 PM @ Room 515 a
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Modern multivariate statistical methods have been increasingly applied to various problems in neuroimaging, including “mind reading”, “brain mapping”, clinical diagnosis and prognosis. Multivariate pattern analysis (MVPA) methods are designed to examine complex relationships between large-dimensional signals, such as brain MRI images, and an outcome of interest, such as the category of a stimulus, with a limited amount of data. The MVPA approach is in contrast with the classical mass-univariate (MUV) approach that treats each individual imaging measurement in isolation.

Recent work in neuroscience has started to move away from conventional lab-based studies, towards more naturalistic behavioral tasks (e.g. normal reading, movie watching), with mobile neuroimaging technologies (EEG, NIRS), and real-world applications (e.g. in psychiatry, or education) that make use of other available data sources.

This trend presents challenges and opportunities for machine learning. Real world applications typically involve much larger quantities of data, which can be continuously recorded in natural environments like the classroom, home or workplace. But this data is more noisy due to the lower-spec hardware and less controlled environment. And gathering data from much broader swathes of the population, whether healthy or dealing with a condition, results in more uncontrolled variation.

ML techniques have already revolutionized analysis of well-controlled lab data, and are even more necessary for these new applications. Richer stimuli and behavioral tasks provide opportunities for complex modeling (e.g. the psychological experience of perceiving and acting in a computer game), and non-lab contexts may provide much

As in previous editions in this series (2011, 2012, 2013,2014) we will center this workshop around in-depth invited talks, and two panel discussions. Original contributions will be considered for a small number of additional talks, but the majority will be hosted during extended poster sessions. As in previous years this format will give ample opportunity for genuine discussion and exchange of ideas.

Since this is an emerging area we will not be prescriptive on the precise topics within this theme. However we envisage receiving submissions on topics including:
- modelling of more naturalistic stimuli, tasks and paradigms
- real-world applications, e.g. neurological disease, education
- neuroimaging outside the lab, mobile acquisition (EEG and NIRS)
- comparing informativity of lab and mobile neuro-data, trade-off between data quality and quantity
- ‘pervasive’ behavioural data gathered incidentally from personal computing devices (e.g. audio, movement, location, touch screen and typing inputs)
- multi-modal analysis of mental state inference from imaging and/or behavioral data
- machine learning and pattern recognition methodology
- linking machine learning, neuroimaging and neuroscience
- given recent advances of deep learning in image analysis and other applications, a natural question to ask is whether neuroimaging analysis can benefit from such approaches?
- given the enriched context, how does the brain's representation of an individual concept varies as a function of semantic context (e.g., word in sentences) and how combinations of multiple individual concepts are represented in the brain (e.g., sentence decoding)?

Author Information

Irina Rish (IBM T.J. Watson Research Center)
Leila Wehbe (UC Berkeley)
Brian Murphy (Queen's University Belfast)
Georg Langs (Medical University of Vienna)
Guillermo Cecchi (IBM Research)
Moritz Grosse-Wentrup (MPG Tuebingen)

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