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Modeling the Evolution of Neurophysiological Signals Mark Fiecas Hernando Ombao
Data Characteristics Small signal-to-noise ratios 2
Data Characteristics Nonstationary time series data 3
Data Characteristics Evolving over time within a replicate Nonidentical replicates across the experiment 4
A Learning Association Experiment 6 Time
A Learning Association Experiment 7
Evolving Evolutionary Coherence 8
Evolving Evolutionary Spectrum 10
Evolving Evolutionary Spectrum 11
The Time Series Models Weakly stationary time series (Brillinger, 1981): 12
The Time Series Models Locally stationary time series (Dahlhaus, 2000): 13
The Time Series Models Locally stationary time series with slowly evolving replicates: 14
The Time Series Models 1. Replicates are uncorrelated. For each replicate, use existing methods to address nonstationarity over time. 2. Smooth the estimates over time and replicate-time. 15
Hippocampus Log Periodogram 17
Nucleus Accumbens Log Periodogram 18
A Relevant Scientific Question Is the power in a frequency band of interest the same between “familiar” and “novel” trials? 19
Log Periodogram Models Weakly stationary data (Krafty et al, 2011): 20
Log Periodogram Models Weakly stationary data (Krafty et al, 2011): where 21
The Log Periodogram Models Locally stationary data (Krafty, 2007; Qin and Guo, 2009): 22
The Log Periodogram Models Locally stationary data (Krafty et al, 2007): where 23
The Proposed Log Periodogram Model 24
The Proposed Log Periodogram Model 25
The Proposed Log Periodogram Model 26
Calling All Statisticians “Understanding how the brain works is arguably one of the greatest scientific challenges of our time.” - Alivisatos et al, 2013 27
Calling All Statisticians The BRAIN Initiative (USA) The Human Brain Project (European Union) –86 Institutions in Europe involved –€1 billion in funding / year 28
Calling All Statisticians Very rich data sets –High temporal resolution (EEG, MEG, LFP) –High spatial resolution (PET, fMRI) –300k spatial locations in fMRI –Imaging genetics Many open problems 29
Calling All Statisticians Handbook of Modern Statistical Methods: Neuroimaging Data Analysis (eds: H. Ombao, M. Lindquist, W. Thompson, and J. Aston) 30
Acknowledgments Shaun Patel, Boston University Emad Eskandar, MGH 31
Functional Brain Signal Processing: EEG & fMRI Lesson 10 Kaushik Majumdar Indian Statistical Institute Bangalore Center M.Tech.
Resolving the paradox of stasis: models with stabilizing selection explain evolutionary divergence on all timescales Suzanne Estes & Stevan J. Arnold.
SPM for EEG/MEG SPM Course London, May 2013 Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London.
Contrasts & Inference - EEG & MEG Himn Sabir 1. Topics 1 st level analysis 2 nd level analysis Space-Time SPMs Time-frequency analysis Conclusion 2.
Multiple comparisons in M/EEG analysis Gareth Barnes Wellcome Trust Centre for Neuroimaging University College London SPM M/EEG Course London, May 2013.
Spectral analysis II: Applications Bijan Pesaran Center for Neural Science New York University.
Reverse engineering the brain Prof. Jan Lauwereyns Advanced Engineering A.
Functional Brain Signal Processing: Current Trends and Future Directions Kaushik Majumdar Indian Statistical Institute Bangalore Center
Frontal Lobes The Immune System A healthy brain and a healthy body.
Stochastic Process Theory and Spectral Estimation Bijan Pesaran Center for Neural Science New York University.
Statistical analysis and modeling of neural data Lecture 17 Bijan Pesaran 12 November, 2007.
Spatial Smoothing and Multiple Comparisons Correction for Dummies Alexa Morcom, Matthew Brett Acknowledgements.
Alternative Neuroimaging Techniques PET TMS SPECT EEG.
Chapter 4: Local integration 2: Neural correlates of the BOLD signal.
Multimodal Brain Imaging Wellcome Trust Centre for Neuroimaging, University College, London Guillaume Flandin, CEA, Paris Nelson Trujillo-Barreto, CNC,
IMAGING THE MIND Direct methods –Electrical activity (EEG, MEG) –Metabolic activity (EROS) Indirect methods –Changes in regional Cerebral Blood Flow (rCBF)
Session IV Electro-Vascular Coupling and Brain Energy Budget Barry Horwitz, NIH (Bethesda) – Chair David Attwell, Univ College London The Brain’s Energy.
1 Dept. of Neurophysiology and Pathophysiology – MEG Project Proposal Please do not use more than 20 min for your presentation. Discussion time following.
Mental visual imagery – can a language encoded object generate depictive imagery? Igor Bascandziev Harvard Graduate School of Education.
Functional Brain Signal Processing: EEG & fMRI Lesson 11 Kaushik Majumdar Indian Statistical Institute Bangalore Center M.Tech.
HELSINKI UNIVERSITY OF TECHNOLOGY LABORATORY OF COMPUTER AND INFORMATION SCIENCE NEURAL NETWORKS RESEACH CENTRE Variability of Independent Components.
Study of Change Blindness EEG Synchronization using Wavelet Coherence Analysis Professor: Liu Student: Ruby.
Functional Brain Signal Processing: EEG & fMRI Lesson 13 Kaushik Majumdar Indian Statistical Institute Bangalore Center M.Tech.
Estimating Dynamics of Information Processing from EEG and MEG Measurements Philippe G. Schyns Centre of Cognitive Neuroimaging (CCNi) University of Glasgow.
Possible thesis projects Anders M. Fjell / Kristine B Walhovd – main research interests Related to the use of event-related potentials (ERPs) Related to.
Introduction Electroencephalography correlated functional Magnetic Resonance Imaging (EEG-fMRI) is a multi-modal imaging technique with growing application.
FMRI and MR Spectroscopy. BOLD BOLD=Blood Oxygenation Level Dependant contrast Neurons at work use oxygen (carried by hemoglobin) 1-5 s. delay, peaks.
Methods for Dummies M/EEG Analysis: Contrasts, Inferences and Source Localisation Diana Omigie Stjepana Kovac.
Recording of electrical activity / electrical stimulation of brain tissue Spike trains Spikes.
Network modelling using resting-state fMRI: effects of age and APOE Lars T. Westlye University of Oslo CAS kickoff meeting 23/
Topological Inference Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London SPM Course London, May 2014 Many thanks to Justin.
Methods of Studying the Brain Mrs. Joseph AP Psychology Solon High School.
Functional Brain Signal Processing: EEG & fMRI Lesson 9 Kaushik Majumdar Indian Statistical Institute Bangalore Center M.Tech.
5. Spectrograms and non- stationary signals Kenneth D. Harris 25/2/15.
Brain Science Awareness Workshop, 15 March 2010 THE ELECTRIC BRAIN Kaushik Majumdar Systems Science and Informatics Unit Indian Statistical Institute Bangalore.
Overview Contrast in fMRI v contrast in MEG 2D interpolation 1 st level 2 nd level Which buttons? Other clever things with SPM for MEG Things to bear in.
Being Comoplex is Simpler: Event Related Dynamics Pedro Valdes-Sosa Eduardo Martínez-Montes Cuban Neurosciences Centre Wael El-Deredy School of Psychological.
Spectral analysis Kenneth D. Harris 18/2/15. Continuous processes A continuous process defines a probability distribution over the space of possible signals.
Arpan Banerjee Research Fellow National Institute of Deafness and Other Communication Disorders National Institutes of Health, USA.
Using Imaging Tools to Track the Phenotype Changes to Capture Potential Genotype Changes Mei Xiao The Jackson Laboratory.
Ongoing BIRN-GCRC Collaborations Medical College Wisconsin (non BIRN site) –Functional MRI acquisition calibration University of Texas (non BIRN site)
Brain Innovation BVTurbo BrainVoyager Training Course January, 2011 Real-time Independent Component Analysis of functional MRI time-series A new TBV (3.0)
Analysis Techniques for Weak Brain Activity Alain de Cheveigné CNRS / Université Paris Descartes / École normale supérieure / UCL Ear Institute AIM Isolate.
Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation Rendong Yang and Zhen Su Division of Bioinformatics,
Center for Brain and Cognitive Science Mind Reading for Cognitive Systems Yong-Ho Lee Center for Brain & Cognitive Research Korea Research Institute of.
Types of Scaling Session scaling; global mean scaling; block effect; mean intensity scaling Purpose – remove intensity differences between runs (i.e.,
Experimental Design in fMRI A real example of fMRI block design done well: – alternate moving, blank and stationary visual input MovingBlankStationaryBlank.
The t-Test for Differences Between Groups. The t-Test Tests whether the means of two groups are statistically different from each other.
The Analysis of Non-Stationary Time Series with Time Varying Frequencies using Time Deformation The Analysis of Non-Stationary Time Series with Time Varying.
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