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MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 2)
Georg Langs · Brian Murphy · Kai-min K Chang · Paolo Avesani · James Haxby · Nikolaus Kriegeskorte · Susan Whitfield-Gabrieli · Irina Rish · Guillermo Cecchi · Raif Rustamov · Marius Kloft · Jonathan Young · Sina Ghiassian · Michael Coen

Tue Dec 10 07:30 AM -- 06:30 PM (PST) @ Harvey's Sierra
Event URL: https://sites.google.com/site/mlininips2013/ »

Aim of the workshop

We propose a two-day workshop on the topic of machine learning approaches in neuroscience, neuroimaging, with a specific extension to behavioral experiments and psychology. We believe that machine learning has a prominent role in shaping how questions in neuroscience are framed, and that the machine-learning mind set is now entering modern psychology and behavioral studies. It is also equally important that practical applications in these fields motivate a rapidly evolving line or research in the machine learning community. In this context, many controversies and open questions exist.

The goal of the workshop is to pinpoint the most pressing issues and common challenges across the fields, and to sketch future directions and open questions in the light of novel methodology. The proposed workshop is aimed at offering a forum that joins machine learning, neuroscience, and psychology community, and should facilitate formulating and discussing the issues at their interface.

Motivated by two previous workshops, MLINI ‘11 and MLINI’12, we will center this workshop around invited talks, and two panel discussions. Triggered by these discussions, this year we plan to adapt the workshop topics to a less traditional scope that investigates the role of machine learning in neuroimaging of both animals and humans, as well as in behavioral models and psychology.

The first two workshops of this series at NIPS 2011 and NIPS 2012 built upon earlier neuroscience-centered NIPS workshops in 2006 and 2008. The last two MLINI workshops included many invited speakers, and were centered around panel discussions, discussing the key questions on the intersection of machine learning and neuroimaging: the interpretability of machine learning findings, and the shift of paradigms in the neuroscience community. Peer reviewed contributions of the participants were the basis of more detailed discussions of recent ideas. All discussions were inspiring, and made clear, that there is a tremendous amount the two communities can learn from each other benefiting from communication across the disciplines.

The aim of the workshop is to offer a forum for the overlap of these communities. Besides interpretation, and the shift of paradigms, many open questions remain. Among them:

  • How suitable are multivariate predictive analysis (MVPA) and inference methods for brain mapping?

  • How can we assess the specificity and sensitivity?

  • What is the role of decoding vs. embedded or separate feature selection?

  • How can we use these approaches for a flexible and useful representation of neuroimaging data?

  • What can we accomplish with generative vs. discriminative modelling?

  • How well can functional networks and dynamical models capture the brain activity, and when using network and dynamics information is superior to standard task-based brain activations?

  • How much information about mental state can be extracted from (cheaper’’) behavioral data vs (more expensive’’) neuroimaging data?

Background and Current Trends

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.

Moreover, recent results on applying multivariate statistical techniques to behavioral data, such as text/voice data from interviews with the psychiatric patients, open new exciting opportunities on objectively quantifying mental states from subject’s behavior, i.e. extending the traditional, and rather subjective, diagnostic approaches to the ones based on objective measures computed from behavioral data (i.e., ``computational psychiatry’’). For example, recent exciting directions along these lines include mental state classification using behavioral data such as voice and/or text from interviews with subjects; e.g., a recent paper by Mota et al accurately discriminates schizophrenic, manic and control subjects based on simple syntactic analysis of their interview texts, while another recent paper by Satt et al discriminates Altzheimer’s patients from MCI and from controls based on voice features.

Also, recent multivariate methods give researchers more latitude in their choice of intricate models of behaviour and psychological state, beyond traditional cognitive and clinical neuroscience studies often limited to binary classification (e.g., healthy vs schizophrenic, etc), and traditionally driven by staitisical tools such as SPM oriented towards contrastive analysis. For example ‘zero-shot-learning’ methods (Mitchell 2008) managed to generalize predictions of brain activity beyond training data, by using a modeled descriptive latent space (in this case a vector space of word meaning). Work by John Anderson predicts variations in local processing load with a general model of cognitive function, instantiated with very specific operations, such as mental arithmetic.

Finally, an important and rapidly growing area of brain imaging is the study of brain’s functional connectivity, i.e. focusing on brain as a network of functionally dependent areas, as well as brain’s dynamical models (Granger causality, etc). It was demonstrated that functional networks can be very informative about particular mental states and/or diseases even when standard activation-based MUV approaches fail (e.g., see Rish et al, PloS One 2013, schizophrenia study). Modern machine-learning approaches to network analysis, including large-scale (sparse) probabilistic graphical models, such as Gaussian MRFs, that go beyond standard correlation-based functional network, can advance our understanding of brain activity even further (e.g., see Honorio et al, and other work). Finally, dynamical models (from differential equations to dynamic graphical models) should provide even more accurate tools for capturing the activity of the brain, perhaps the most complicated dynamical system, and relating it to mental states and behavior.

In this workshop, we intend to investigate the implications that follow from adopting multivariate machine-learning methods for studying brain function. In particular, this concerns the question how these methods may be used to represent cognitive states, and what ramifications this has for consequent theories of cognition. Besides providing a rationale for the use of machine-learning methods in studying brain function, a further goal of this workshop is to identify shortcomings of state-of-the-art approaches and initiate research efforts that increase the impact of machine learning on cognitive neuroscience. Open questions and possible topics for contribution will be structured around the following 4 main topics: I) machine learning and pattern recognition methodology in brain research, II) functional connectivity and dynamical models of brain activity, III) multi-modal analysis including mental state inference from behavioral data, and IV) linking machine learning, neuroimaging and neuroscience.

Author Information

Georg Langs (Medical University of Vienna)
Brian Murphy (BrainWaveBank)
Kai-min K Chang (CMU)
Paolo Avesani (FBK)
James Haxby
Nikolaus Kriegeskorte (Cognition and Brain Sciences Unit, UK Medical Research Council)
Susan Whitfield-Gabrieli (Massachusetts Institute of Technology)
Irina Rish (Mila/UdeM/LAION)
Guillermo Cecchi (IBM Research)
Raif Rustamov (AT&T Chief Data Office)
Marius Kloft (TU Kaiserslautern)
Jonathan Young (University College London)
Sina Ghiassian (University of Alberta)
Michael Coen (University of Wisconsin-Madison)

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