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Irina Rish, Researcher, IBM Watson, at MLconf NYC 2017

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Irina Rish is a researcher at the AI Foundations department of the IBM T.J. Watson Research Center. She received MS in Applied Mathematics from Moscow Gubkin Institute, Russia, and PhD in Computer Science from the University of California, Irvine. Her areas of expertise include artificial intelligence and machine learning, with a particular focus on probabilistic graphical models, sparsity and compressed sensing, active learning, and their applications to various domains, ranging from diagnosis and performance management of distributed computer systems (“autonomic computing”) to predictive modeling and statistical biomarker discovery in neuroimaging and other biological data. Irina has published over 60 research papers, several book chapters, two edited books, and a monograph on Sparse Modeling, taught several tutorials and organized multiple workshops at machine-learning conferences, including NIPS, ICML and ECML. She holds 24 patents and several IBM awards. Irina currently serves on the editorial board of the Artificial Intelligence Journal (AIJ). As an adjunct professor at the EE Department of Columbia University, she taught several advanced graduate courses on statistical learning and sparse signal modeling.

Abstract Summary:

Learning About the Brain and Brain-Inspired Learning:
Quantifying mental states and identifying statistical biomarkers of mental disorders from neuroimaging data is an exciting and rapidly growing research area at the intersection of neuroscience and machine learning, with the particular focus on interpretability and reproducibility of learned models. We will discuss promises and limitations of machine-learning methods in such applications, focusing on recent applications of deep learning methods such as recurrent convnets to the analysis of “brain movies” (EEG) data. On the other hand, besides the above “AI to Brain” direction, we will also discuss the “Brain to AI”, namely, borrowing ideas from neuroscience to improve machine learning, with specific focus on adult neurogenesis and online model adaptation in representation learning.

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Irina Rish, Researcher, IBM Watson, at MLconf NYC 2017

  1. 1. minx ||y - Ax||2 + λ ||x||1 Irina Rish IBM T.J. Watson Research Center Learning About the Brain and Brain-Inspired Learning
  2. 2. Collaborators (an incomplete list) IBM T.J. Watson Research: Guillermo Cecchi Steve Heisig Aurelie Lozano Google: Mt Sinai: Northwestern U. Melissa Carroll Rita Goldstein A. Vania Apkarian INRIA: Neurospin/UC Berkeley Bertrand Thirion MIT: Pouya Bashivan Purdue: Jean Honorio Lehigh U. Katya Scheinberg SUNY Stony Brook Dimitris Samaras St. Johns U. Genady GrabarnikJB Poline USC: Sahil Garg
  3. 3. AI Brain2 Brain 2 AI: Brain-inspired AI Algorithms AI 2 Brain: Mental-State Prediction and Statistical Biomarker Discovery
  4. 4. Mental State Recognition to Improve Mental Function Detecting emotional & cognitive changes to predict response to different types of input, e.g. music, video, news, ads, emails (both for mental health and for neuromarketing) Safety: detecting changes in driver’s alertness level (drowsiness, microsleeps) to prevent accidents Computational psychiatry: data-analytic approach to diagnosis based on objective measurements (new Research Domain Criteria (RDoC) initiative by NIMH) Our current focus: schizophrenia, addiction, Huntington’s, Alzheimer’s, Parkinson’s “Psychiatric research is in crisis” [Wiecki et al. 2015] AI 2 Brain: Health & Productivity: mental-state-sensitive software monitoring cognitive load, focus/attention; monitoring stress/anxiety
  5. 5. Overview: Machine Learning in Neuroimaging “Statistical biomarkers”: [Cecchi et al, NIPS 2009] [Rish et al, PLOS One, 2013] [Carroll et al, Neuroimage 2009] [Scheinberg&Rish, ECML 2010] Schizophrenia classification: 86% to 93% accuracy [Rish et al, Brain Informatics 2010] [Rish et al, SPIE Med.Imaging 2012] [Cecchi et al, PLOS Comp Bio 2012] Cognitive state prediction in videogames: 70-95% Pain perception: 70-80%, distributed activation patterns [Honorio et al, AISTATS 2012] [Rish et al, SPIE Med.Imaging 2016] Cocaine addiction: evaluating potential treatments [Bashivan et al, ICLR 2016] EEG-cognitive load prediction: 91% w/ recurrent ConvNets + ++ + - - --- Predictive Model mental disorder healthy
  6. 6. Example 1: Cocaine Addiction fMRI Study Cocaine: Mechanism of Action • Cocaine affects the reward pathway in the brain (blocks the dopamine transporter) • May lead to addiction: cocaine use disorder (CUD)
  7. 7. MPH: A Stimulant for a Stimulant? A potential therapeutic agent for CUD? (e.g., similarly to nicotine patch and using methadone for heroin addiction) Methylphenidate Hydrochloride (MPH) • Common ADHD treatment (Ritalin) • Similarity to cocaine: • chemical structure • mechanism of action (blocks dopamine transporter) • Difference: slower rate of clearance (90 vs 20 min), and thus a lower dependence and abuse potential • MPH has shown positive behavioral effects on CUD subjects [Levin 2007] • MPH tends to normalize both task-related [Goldstein 2010] and resting-state functional activity in certain areas [Konova 2013]
  8. 8. Resting-state Functional MRI Image courtesy of fMRI Research Center at Columbia University Resting-state fMRI experiment: MPH vs. placebo [Konova et al 2013] Features: functional network degrees • Network link (i,j)  correlation between BOLD signals of voxels i and j exceeds a given threshold (e.g., > 0.6) • Feature selection: univariate ranking based on p-values; multiple subsets of top K features, with increasing K, are used to train classifiers
  9. 9. Classification Results: MPH Normalizes CUD’s Networks [Rish, Bashivan, Cecchi, Goldstein, SPIE 2016] MPH ‘normalizes’ CUD networks: CUD’s are harder to discriminate from controls (10-20% increase in classification error) under MPH vs under placebo MPH has stronger effect on CUDs: MPH (M2) vs Placebo (P2) condition is much easier to discriminate for CUDs rather than for controls Leave-one-out CV with Nearest Neighbor (NN), Linear SVM, Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Naïve Bayes (NB), Linear Discriminant Analysis (LDA)
  10. 10. Example 2: Working Memory Load Classification from EEG) EEG Experiment:  64-electrode EEG  Working Memory task, 4 levels of difficulty: 2,4,6, or 8 symbols to remember  13 subjects, 240 trials each (=3120 trial) [Bashivan, Rish, Yeasin, Codella, ICLR 2016] Classification Problem: given time-series recorded during each trial of WM task, predict WM load level Data Samples: 2670 correctly answered trials (a subset of total 3120) Feature Extraction: FFT to find spectral power within each electrode at three frequency bands - theta (4-8Hz), alpha (8-13Hz), and beta (13-30Hz). Evaluation: leave-one-subject out (i.e., 13-fold) CV
  11. 11. Brain ‘Movie’ Classification with Recurrent ConvNets • Idea: combine spatial, temporal and frequency – make EEG ‘movies’ • EEG images: project 3D electrode locations (64) into a 2D via distance-preserving Azimuthal Equidistant Projection, then interpolate the activity • RGB colores = Theta, Alpha, Beta frequences • Each trial = 7 frames (RGB images)  short “movies” as samples
  12. 12. • FFT over the complete trial  single image for each trial • VGG style ConvNets [Simonyan & Zisserman, 2015] • Conv layers with 3 x 3 receptive fields • 4 architectures, increasing depth; deeper is (slightly) better Baseline: Non-temporal Approach with ConvNets ConvNet Configurations A B C D input (32 x 32 3-channel image) Conv3-32 Conv3-32 Conv3-32 Conv3-32 Conv3-32 Conv3-32 Conv3-32 Conv3-32 Conv3-32 Conv3-32 maxpool - Conv3-64 Conv3-64 Conv3-64 Conv3-64 Conv3-64 Conv3-64 - maxpool - - Conv3- 128 Conv3- 128 - - maxpool Architecture Number of parameters Test Error A ~10k 13.05 B ~65.5k 13.17 C ~139.4k 13.91 D ~158k 12.39
  13. 13. Adding Time is Better: Recurrent ConvNets Best result: 8.9% error discriminating among 4 levels of cognitive load achieved by recurrent Conv Nets with LSTM + time convolution • EEG times series for each trial split into 7 windows (0.5 sec). FFT on each time window to get an image as before • Best ConvNet (7-layer) used as C component • All 7 ConvNets shared parameters • video classification architectures from [Ng et al, CVPR 2015] • Temporal Maxpool: Max pool over time frames • Temporal Convolution: 1D convolution over time frames • LSTM - sequence mapping over times frames • Mixed LSTM/1D-Conv: Combination of both LSTM and 1D-Conv architectures Architecture Test Error (%) Validation Error (%) Number of parameters RBF SVM 15.34 - - L1-logistic regression 15.32 - - Random Forest 12.59 - - DBN 14.96 8.37 1.02 mil ConvNet+Maxpoo l 14.80 8.48 1.21 mil ConvNet+1D- Conv 11.32 9.28 441 k ConvNet+LSTM 10.54 6.10 1.34 mil ConvNet+LSTM/1 D-Conv 8.89 8.39 1.62 mil [Bashivan, Rish, Yeasin, Codella, ICLR 2016]
  14. 14. Interpretability via Deconvolution Code: https://github.com/pbashivan/EEGLearn Using deconvnet of [Zeiler et al] to map features back to the brain images Back Projections: maps obtained by deconvnet on the feature map displaying structures in the input image that excite that particular feature map. Some of these features correspond to well-known electrophysiological markers of cognitive load. First-layer features (1st stack, kernel 7) captured wide-spread theta (1st stack output-kernel7) and another (1st stack, kernel 23) frontal beta activity Second- and third-layer features – frontal theta/beta (2nd stack,kernel7) and 3rd stack kernel60, 112) as well as parietal alpha (2nd stack kernel29) . Frontal theta and beta activity as well as parietal alpha are most prominent markers of cognitive/memory load in neuroscience literature [Bashivan et al., 2015; Jensen et al., 2002; Onton et al., 2005; Tallon-Baudry et al., 1999] Input EEG images: top 9 images with highest feature activations across the training set Layer4Layer6Layer7
  15. 15. • Current theories: the hippocampus functions as an autoenconder to evoke memories; similar encoding function is suggested in the olfactory bulb • Our computational model: sparse linear autoencoder (online dictionary learning of Mairal et al) + dynamic addition (birth) abnd deletion (death) of hidden nodes Adult Neurogenesis: Inspiration for Adaptive Representation Learning • Predominant in the dentate gyrus of the hippocampus and in the olfactory bulb Olfactory bulb Dentate gyrus [Garg, Rish, Cecchi, Lozano, ICLR 2017] nsamples p variables ~~ mbasisvectors (dictionary)sparse representation input x output x’  reconstructed x hidden nodes c  encoded x link weights  ‘dictionary’ D c c Brain 2 AI:
  16. 16. Better Adaptation in Non-Stationary Environment Learned dictionary size ‘Old’ domain reconstruction ‘New’ domain reconstruction non-stationary visual input Outperforms fixed-size autoencoder on non-stationary input: improved accuracy + more compact representation Adapts to a new domain without forgetting the old one (via ‘memory’ matrices, part of original Mairal’s method)
  17. 17. Some Lessons  In brain imaging applications  Datasets are relatively small (e.g., few 1000 samples)  Model interpretability is important  Brain-inspired algorithms:  Neurogenesis, attention, memory and many other brain phenomena can serve as inspiration for better AI algorithms  Challenge: deeper understanding and better modeling of such phenomena  Deep learning faces specific challenges in neuroimaging  Need for stronger regularization  Need for interpretability (e.g., deconvolution, sparsity)
  18. 18. References [Garg, Rish, Cecchi, Lozano 2016; submitted] S. Garg, I. Rish, G. Cecchi, A. Lozano. Neurogenesis-inspired Dictionary Learning: Online Model Adaptation in a changing world, submitted to ICLR-2017 [Bashivan et al, ICLR 2016] P. Bashivan, I. Rish, M. Yeasin, N. Codella. Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks. ICLR 2016 : International Conference on Learning Representations. [Bashivan et al, 2015] Mental State Recognition via Wearable EEG, in Proc. of MLINI-2015 workshop at NIPS-2015. [Heisig et al, 2014] S. Heisig, G. Cecchi, R. Rao and I. Rish. Augmented Human: Human OS for Improved Mental Function. AAAI 2014 Workshop on Cognitive Computing and Augmented Human Intelligence. [Neuropsychopharmacology, 2014] A Window into the Intoxicated Mind? Speech as an Index of Psychoactive Drug Effects. Bedi G, Cecchi G A, Fernandez Slezak D, Carrillo F, Sigman M, de Wit H. Neuropsychopharmacology, 2014 [NPJ 2015] G. Bedi, F. Carrillo, G. A Cecchi, D. F. Slezak, M. Sigman, N. B Mota, S. Ribeiro, D C Javitt, M. Copelli, C M Corcoran. Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophrenia 2015. [PLoS ONE, 2013] Schizophrenia as a Network Disease: Disruption of Emergent Brain Function in Patients with Auditory Hallucinations, I Rish, G Cecchi, B Thyreau, B Thirion, M Plaze, M-L Paillere-Martinot, C Martelli, J-L Martinot, J-B Poline. PloS ONE 8(1), e50625, Public Library of Science, 2013. [PLoS One, 2012] Speech Graphs Provide a Quantitative Measure of Thought Disorder in Psychosis. N.B. Mota, N.A.P. Vasconcelos, N. Lemos, A.C. Pieretti, O. Kinouchi, G.A. Cecchi, M. Copelli, S. Ribeiro. PLoS One, 2012 [Rish et al, SPIE 2016] I.Rish, P. Bashivan, G. A. Cecchi, R.Z. Goldstein, Evaluating Effects of Methylphenidate on Brain Activity in Cocaine Addiction: A Machine-Learning Approach. SPIE Medical Imaging, 2016 [SPIE Med.Imaging 2012] Sparse regression analysis of task-relevant information distribution in the brain. Irina Rish, Guillermo A Cecchi, Kyle Heuton, Marwan N Baliki, A Vania Apkarian, SPIE Medical Imaging, 2012. [AISTATS 2012] J. Honorio, D. Samaras, I. Rish, G.A. Cecchi. Variable Selection for Gaussian Graphical Models. AISTATS, 2012. [PLoS Comp Bio 2012] Predictive Dynamics of Human Pain Perception, GA Cecchi, L Huang, J Ali Hashmi, M Baliki, MV Centeno, I Rish, AV Apkarian, PLoS Comp Bio 8(10), e1002719, Public Library of Science, 2012. [Brain Informatics 2010] I. Rish, G. Cecchi, M.N. Baliki and A.V. Apkarian. Sparse Regression Models of Pain Perception, in Proc. of Brain Informatics (BI- 2010), Toronto, Canada, August 2010. [NeuroImage, 2009] Prediction and interpretation of distributed neural activity with sparse models. Melissa K Carroll, Guillermo A Cecchi, Irina Rish, Rahul Garg, A Ravishankar Rao. NeuroImage 44(1), 112--122, Elsevier, 2009. [NIPS, 2009] Discriminative network models of schizophrenia, GA Cecchi, I Rish, B Thyreau, B Thirion, M Plaze, M-L Paillere-Martinot, C Martelli, J-L Martinot, J-B Poline. Advances in Neural Information Processing Systems (NIPS 2009) , pp. 252--260, 2009.

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