Workshop
MLINI 2014 - 4th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner
Irina Rish · Georg Langs · Brian Murphy · Guillermo Cecchi · Kai-min K Chang · Leila Wehbe
Level 5, room 513 e, f
Sat 13 Dec, 5:30 a.m. PST
- Aim
MLINI workshop focuses on machine learning approaches in neuroscience, neuroimaging, with a specific extension to behavioral experiments and psychology. The special topic of this year is "Going Beyond the Scanner", which includes making inference about the subject's mental states from ''cheap'' data such as subject's speech and/or text, audio, video, EEG and other wearable devices.
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 parallel, there is an intense interest in learning more about brain function in the context of rich naturalistic environments and scenes. Efforts to go beyond highly specific paradigms that pinpoint a single function, towards schemes for measuring the interaction with natural and more varied scene are made. 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 the previous workshops in this series, MLINI ‘11, MLINI’12, and MLINI’13, 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 neuroimaging scope and investigate the role of behavioral models and psychology, including topics such as psycholinguistics.
Besides interpretation, and the shift of paradigms, many open questions remain at the intersection of machine learning, neuroimaging and psychology. Among them:
- How can we move towards more naturalistic stimuli, tasks and paradigms in neuroimaging and neuro-signal analysis?
- What kind of mental states can be inferred from cheaper and easier to collect data sources (as an alternative to fMRI scanner) such as text, speech, audio, video, EEG, and wearable devices?
- How can we leave the lab when acquiring neuroimaging data, towards exploiting mobile acquisition (EEG and NIRS)?
- What type of features should be extracted from naturalistic stimuli such as text, voice, etc., to detect specific mental states and/or mental disorders?
- How can we combine traditional neuroimaging with naturalistic data collected from a subject or group of subjects?
- In general, can we characterize situations when multivariate predictive analysis (MVPA) and inference methods are better suited for brain imaging analysis than more traditional techniques?
- 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?
- 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?
2. Overview
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 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 modelled 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. 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.
3. New topic this year: Beyond the scanner - capturing behavior, cognition and psychology
This year, we propose to shift the focus of the workshop towards mental state detection and prediction ‘’beyond the scanner”, and focus on making inferences not only from imaging data, but also from relatively cheap data sources such as text of interviews with the patients, as well as voice and other behavioral data. 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., several recent papers that demonstrated the possibility of accurate classification of various mental conditions, including schizophrenia, mania, as well as drug influence (ecstasy, meth), based on syntactic graph features as well as semantic features extracted from interviews with subject; moreover, acoustic features of speech were shown to allow for an accurate discrimination of Altzheimer’s patients from MCI and from controls.
Our aim this year is to explore scientific and practical applications of computational linguistics and other approaches to mental state inference from relatively cheap behavioral data that can be collected on everyday basis, from multiple sources such as smartphones, emails, blogs etc, as well as the data collectible from wearable devices such as inexpensive EEG devices (e.g., NeuroSky), and other wearables (e.g., smart watches etc), in order to analyze, track and potentially improve people's mental state via ''smart personal assistants'' (less seriously, we refer to this approach as to "Freud in a box").
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