Machine Learning SIG

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Objectives

Advance general knowledge of machine learning techniques within the MEG community. Discuss journal articles, replicate techniques on NIH data, develop new ML techniques at NIH.

Format

  1. Specific Projects (Weekly)
    1. Code Review
    2. Project updates
    3. Question and Answer Clinic for users
  2. General (Monthly)
    1. Journal Club
    2. Hackathons – implement novel technique from JC with provided data
    3. Tutorial Workshops - instruct worked out examples with provided code/data
    4. General ML training
      1. Parameter tuning
      2. Model optimization
      3. Techniques
      4. Toolbox tutorials (Scikit-learn / keras)

Analysis Types

  1. Decoding[1][2]
    1. Multivariate time series classification between conditions [3][4][5]
      1. Filtering Issues[6]
    2. Temporal Generalization [7]
    3. Realtime - Brain computer interface / neurofeedback [8][9][10] [11][12]
    4. Language Phoneme Decoding [13]
  2. Subject classification
    1. eg. Healthy Control vs Major Depressive Disorder
    2. What are the significant features (brain regions, Hz)
    3. Automated diagnosis of TBI from MEG low frequency signals [14]
  3. Prediction of future condition / Biomarkers
    1. What signals predict conversion from mild cognitive impairment to Alzheimer’s Disease [15] [16]
    2. What signals predict recovery from traumatic brain injury [17]
    3. What signals predict poor outcome for epilepsy surgery
  4. Multimodal Integration [18]
    1. Timing derived from MEG / localization from fMRI [19]
    2. Signal comparison between naturalistic viewing data MEG/fMRI
    3. Combining DTI and MEG [20]
    4. Multimodal imaging classification [21]
  5. Signal classification
    1. Artifact [22] [23]
    2. Signal of interest – Spike detection for epilepsy [24] [25] [26]
    3. Signal/sequence predicts correct response
  6. Temporal learning models - Sequence learning
    1. Recurrent Neural Networks
    2. Markov Models[27]
  7. Inferences from deep learning models
    1. Convolutional Neural Networks (CNN) on images vs. CNN of brain responses to same images [28][29]

Resources

General

SciKit Learn
Keras

MEG

MNE Python Decoding
MNE Python Decoding at Source
RealTime MEG
Deep Learning Decoding
MNE Representational Similarity Analysis
Toolbox for Trial-Masked Robust Detrending to overcome decoding issues from filtering

NIH

Biowulf Deep Learning Course
Biowulf DeepLearning Tools

NIMH Machine Learning in Neuroimaging
Center for Multimodal Neuroimaging
NIH-AI

Relevant Papers

  1. ↑ [1] Deconstructing multivariate decoding for the study of brain function: Martin N.Hebart, Chris I.Baker
  2. ↑ [2] An introduction to time-resolved decoding analysis for M/EEG:Thomas A. Carlson, Tijl Grootswagers, Amanda K. Robinson
  3. ↑ [3] Encoding and Decoding Neuronal Dynamics: Methodological Framework to Uncover the Algorithms of Cognition
  4. ↑ [4] Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data
  5. ↑ [5] Multivariate pattern analysis for MEG: A comparison of dissimilarity measures - Matthias Guggenmos, Philipp Sterzer, Radoslaw Martin Cichy
  6. ↑ [6] High-pass filtering artifacts in multivariate classification of neural time series data
  7. ↑ [7] Characterizing the dynamics of mental representations: the temporal generalization method - J-R.King, S.Dehaene
  8. ↑ [8] Targeted Reinforcement of Neural Oscillatory Activity with Real-time Neuroimaging Feedback : Esther Florin, Elizabeth Bock, Sylvain Baillet
  9. ↑ [9] Real-time MEG neurofeedback training of posterior alpha activity modulates subsequent visual detection performance: Yuka O.Okazaki, Jörn M.Horschig, Lisa Luther, Robert Oostenveld, Ikuya Murakami, OleJensen
  10. ↑ [10] Alpha Synchrony and the Neurofeedback Control of Spatial Attention: Yasaman Bagherzadeh, Daniel Baldauf, Dimitrios Pantazis, Robert Desimone
  11. ↑ [11] Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance Benedikt Zoefel, René J.Huster, Christoph S.Herrmann
  12. ↑ [12] Implementation of a beam forming technique in real-time magnetoencephalography: Hiroki Ora, Kouji Takano, Toshihiro Kawase, Sunao Iwaki,Lauri Parkkonen Kenji Kansaku
  13. ↑ [13] Laura Gwilliams, Jean-Remi King,Alec Marantz & David Poeppel
  14. ↑ [14] An automatic MEG low-frequency source imaging approach for detecting injuries in mild and moderate TBI patients with blast and non-blast causes
  15. ↑ [15] Aberrant MEG multi-frequency phase temporal synchronization predictsconversion from mild cognitive impairment-to-Alzheimer's disease: Sandra Pusil, Stavros I. Dimitriadis, María Eugenia López, Ernesto Pereda, Fernando Maestú
  16. ↑ [16] Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography: Antonio Giovannetti, Gianluca Susi, Paola Casti, Arianna Mencattini, Sandra Pusil, María Eugenia López, Corrado Di Natale & Eugenio Martinelli
  17. ↑ [17] Assessing Recovery from Mild Traumatic Brain Injury (Mtbi) using Magnetoencephalography (MEG): An Application of the Synchronous Neural Interactions (SNI) Test : Don R. Thorpe, Brian E. Engdahl, Arthur Leuthold, Apostolos P. Georgopoulos
  18. ↑ [18] A M/EEG-fMRI Fusion Primer: Resolving Human Brain Responses in Space and Time: Radoslaw M.Cichy Aude Oliva
  19. ↑ [19] Resolving human object recognition in space and time: Radoslaw Martin Cichy, Dimitrios Pantazis & Aude Oliva Nature Neuroscience volume 17, pages455–462 (2014)
  20. ↑ [20] Using joint ICA to link function and structure using MEG and DTI in schizophrenia J.M.Stephen, B.A.Coffman, R.E.Jung, J.R.Bustillo, C.J.Aine, V.D.Calhoun
  21. ↑ [21] Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures: Mustafa S. Cetin, Jon M. Houck, Barnaly Rashid, Oktay Agacoglu, Julia M. Stephen, Jing Sui, Jose Canive, Andy Mayer, Cheryl Aine, Juan R. Bustillo and Vince D. Calhoun
  22. ↑ [22] MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks Alex H.Treacher et al.
  23. ↑ [23] ICLabel: An automated electroencephalographic independent component classifier, dataset, and website: Luca Pion-Tonachini, Ken Kreutz-Delgado, Scott Makeig
  24. ↑ [24] EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes: Li Zheng, Pan Liao, Shen Luo, Jingwei Sheng, Pengfei Teng, Guoming Luan, Jia-Hong Gao
  25. ↑ [25] Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data: Miguel C. Soriano, Guiomar Niso, Jillian Clements, Silvia Ortín, Sira Carrasco, María Gudín, Claudio R. Mirasso and Ernesto Pereda
  26. ↑ [26] A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations: A. V. Medvedev, G. I. Agoureeva & A. M. Murro
  27. ↑ [27] Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling: Andrew J. Quinn, Diego Vidaurre, Romesh Abeysuriya, Robert Becker,Anna C. Nobre1, and Mark W. Woolrich
  28. ↑ [28] Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence: Radoslaw Martin Cichy 1,2 , Aditya Khosla 1 , Dimitrios Pantazis 3 , Antonio Torralba 1 & Aude Oliva 1
  29. ↑ [29] Performance-optimized hierarchical models only partially predict neural responses during perceptual decision making: Laura Gwilliams, Jean-Rémi King
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