Reading electric minds: How blood reveals the spark Michelle Rigozzi Integrating the evoked response potential (ERP) with the functional magnetic resonance.

Slides:

Advertisements

Similar presentations

PS4529/30 Applications of Cognitive Neuroscience.

Advertisements

Memory and Cognition Lecture 10: Filling in the gaps… ENCODING RETRIEVAL Seeing Word Hearing Word MTL.

Neuroimaging for Cognitive Research

Chapter 4: Local integration 2: Neural correlates of the BOLD signal

Fundamentals of Lifespan Development SEPTEMBER 17 – PHYSICAL DEVELOPMENT IN INFANCY AND TODDLERHOOD.

HST 583 fMRI DATA ANALYSIS AND ACQUISITION Neural Signal Processing for Functional Neuroimaging Emery N. Brown Neuroscience Statistics Research Laboratory.

Principles of NMR Protons are like little magnets

fMRI introduction Michael Firbank

Experimental Design in fMRI

Opportunity to Participate

Opportunity to Participate EEG study of auditory attention – takes about 2 hours Sign up on sheet or

Predictive Modeling of Spatial Properties of fMRI Response Predictive Modeling of Spatial Properties of fMRI Response Melissa K. Carroll Princeton University.

Research Methods Psychology Or, so how do we know this stuff We can look at the whole brain We can look at the whole brain We can look at parts.

COGNITIVE NEUROSCIENCE

How to Explain Why "Unequal Anisotropy Ratios" is Important Using Pictures but No Mathematics Bradley J. Roth Dept. Physics, Oakland University.

Four Main Approaches Experimental cognitive psychology Cognitive neuropsychology Computational cognitive science Cognitive neuroscience.

BOLD fMRI Cheryl Olman 4th year student Department of Neuroscience and

Experiment Design 4: Theoretical + Operational Def’ns Martin Ch. 7.

Principles of MRI Some terms: – Nuclear Magnetic Resonance (NMR) quantum property of protons energy absorbed when precession frequency.

Measuring Blood Oxygenation in the Brain. Functional Imaging Functional Imaging must provide a spatial depiction of some process that is at least indirectly.

HELSINKI UNIVERSITY OF TECHNOLOGY LABORATORY OF COMPUTER AND INFORMATION SCIENCE NEURAL NETWORKS RESEACH CENTRE Variability of Independent Components.

Functional Magnetic Resonance Imaging.  All subatomic particles possess a property called ‘spin’  i.e. like a planet rotating on it’s axis  Magnetic.

IMAGING THE MIND Direct methods –Electrical activity (EEG, MEG) –Metabolic activity (EROS) Indirect methods –Changes in regional Cerebral Blood Flow (rCBF)

Dynamic Causal Modelling THEORY SPM Course FIL, London October 2009 Hanneke den Ouden Donders Centre for Cognitive Neuroimaging Radboud University.

Neural mechanisms of Spatial Learning. Spatial Learning Materials covered in previous lectures Historical development –Tolman and cognitive maps the classic.

Neuroimaging Methods: Visualising the brain & its injuries Structural (brain structure) –X-rays –CT (Computer Tomography) –MRI (Magnetic Resonance Imaging)

Dynamic Causal Modelling (DCM): Theory Demis Hassabis & Hanneke den Ouden Thanks to Klaas Enno Stephan Functional Imaging Lab Wellcome Dept. of Imaging.

Biological Basis for the Blood Oxygenation Level Dependent signal.

ANALYSIS OF fMRI DATA BASED ON NN-ARx MODELING Biscay-Lirio, R: Inst. of Cybernetics, Mathematics and Physics, Cuba Bosch-Bayard, J.: Cuban Neuroscience.

FINSIG'05 25/8/2005 1Eini Niskanen, Dept. of Applied Physics, University of Kuopio Principal Component Regression Approach for Functional Connectivity.

Recording of electrical activity / electrical stimulation of brain tissue Spike trains Spikes.

Attention Loads program into working memory (more about that later) Vast amount of perceptual information available at one Moment How much can be retained.

How well do we understand the neural origins of the fMRI BOLD signal? Owen J Arthurs and Simon Boniface Trends in Neuroscience, 2002 Gillian Elizabeth.

Neural Imaging II: Imaging Brain Function ANA 516: February 13, 2007 Jane E. Joseph, PhD.

Contrasts & Inference - EEG & MEG Himn Sabir 1. Topics 1 st level analysis 2 nd level analysis Space-Time SPMs Time-frequency analysis Conclusion 2.

 Populations: ◦ Animal Models:  advantages: disadvantages:

STRATEGIES OF COGNITIVE NEUROSCIENCE The Coin of the Realm: correlations between psychological and neurophysiological events/structures Establishing two-way.

Cognition, Brain and Consciousness: An Introduction to Cognitive Neuroscience Edited by Bernard J. Baars and Nicole M. Gage 2007 Academic Press Chapter.

Statistical Parametric Mapping

J. Daunizeau ICM, Paris, France ETH, Zurich, Switzerland Dynamic Causal Modelling of fMRI timeseries.

Cognitive Psychology PSYC231 Cognition and the Brain: Basic Principles 2 Dr. Jan Lauwereyns, EA619, ext

 fMRI: functional (nuclear) magnetic resonance imaging  Neuroimaging: get the structure of the brain Want to know how it works: connection brain parts.

Class 3: Neurons  BOLD 2012 spring fMRI: theory & practice.

Announcements a3 is out, due 2/15 11:59pm Please please please start early quiz will be graded in about a week. a1 will be graded shortly—use glookup to.

Ch. 5 Bayesian Treatment of Neuroimaging Data Will Penny and Karl Friston Ch. 5 Bayesian Treatment of Neuroimaging Data Will Penny and Karl Friston 18.

Alternative Neuroimaging Techniques PET TMS SPECT EEG

What are we measuring in fMRI?

Statistical Parametric Mapping Lecture 2 - Chapter 8 Quantitative Measurements Using fMRI BOLD, CBF, CMRO 2 Textbook: Functional MRI an introduction to.

FMRI and MR Spectroscopy. BOLD BOLD=Blood Oxygenation Level Dependant contrast Neurons at work use oxygen (carried by hemoglobin) 1-5 s. delay, peaks.

Dynamic Causal Model for evoked responses in MEG/EEG Rosalyn Moran.

The linear systems model of fMRI: Strengths and Weaknesses Stephen Engel UCLA Dept. of Psychology.

Reverse engineering the brain Prof. Jan Lauwereyns Advanced Engineering A.

Laboratory 2: Introduction to fMRI Data and Analysis September 18, 2006 HST.583 Divya Bolar.

Methodology in the Biological Level of Analysis Learning Objectives: 1.Discuss how and why particular research methods are used at the biological level.

Depth and Surface EEG: Generation and Propagation

Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies Shinji Nishimoto, An T. Vu, Thomas Naselaris, Yuval Benjamini, Bin Yu, Jack.

The General Linear Model Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London SPM fMRI Course London, October 2012.

Methods used for studying brain development

EEG, Event-related potential (ERP), Magnetoencephalography (MEG)

COMPARISON OF OPTICAL AND fMRI MEASURES OF NEUROVASCULAR COUPLING

Ways of investigating the brain

Chapter 4 The Imaged Brain.

How do we study brain/behavior relationships?

Dynamic Causal Model for evoked responses in M/EEG Rosalyn Moran.

? Dynamical properties of simulated MEG/EEG using a neural mass model

Dynamic Causal Modelling

Synchrony & Perception

SPM2: Modelling and Inference

How do we study brain/behavior relationships?

Before Speech: Cerebral Voice Processing in Infants

Presentation transcript:

Reading electric minds: How blood reveals the spark Michelle Rigozzi Integrating the evoked response potential (ERP) with the functional magnetic resonance imaging blood oxygen level dependent (fMRI BOLD) response

In the beginning… Stimulus Neurons fire Oxygen deficiency. Increase in dHb Blood rushes in Oxygen excess. dHb drops

ER P Evoked response potential  reads neuronal activity Advantage: –High temporal resolution

fMRI BOLD Functional magnetic resonance imaging Blood oxygen level dependent  reads dHb levels Advantage: High spatial resolution

What we know Yet we are still to uncover…

…the gap Is one really just a slow version of the other? How is the underlying neuronal activity (ERP) manifested through the haemodynamic BOLD response? New insights for future models?

Super models Robinson & Rennie – neuronal model –P. A. Robinson, C.J. Rennie et al., Neurophysical Modeling of Brain Dynamics. Phys. Rev. E. Friston – haemodynamic model –Friston, K.J., Dynamic Causal Modelling. NeuroImage 0.

Real sparky Friston Robinson & Rennie Neuronal networks Electrical Response (Activity) input BOLD

∫ing models…

Testing, testing… General behaviour, reliability and limits of equations ERP v BOLD response when changing: –Overall amplitude of a biphasic ERP –ERP peak ratio –Width of ERP

Changing ERP amplitude Fix peak ratio ERP = BOLD Hypothesis

Changing ERP peak ratios Vary height of 2 nd peak Hypothesis: –Area of ERP  BOLD

Changing ERP width Amplitude same, width stretched Hypothesis: ERP = BOLD

Log-log Linear-linear

What the future holds… We now know: –The peak amplitudes are linearly related –BOLD reflects integral of ERP –Linearity is lost once stimulation lengthens Shape of ERP is critical – The relationship still has much to reveal!

Got you thinking?