fMRI Methods Lecture1 - Introduction

Slides:



Advertisements
Similar presentations
fMRI Methods Lecture6 – Signal & Noise
Advertisements

FMRI Methods Lecture 10 – Using natural stimuli. Reductionism Reducing complex things into simpler components Explaining the whole as a sum of its parts.
Neuro-Imaging High Resolution Ex-Vivo MRI Ex-Vivo DTI of Brain Stem
fMRI Methods Lecture 9 – The brain at rest
Lab III – Linux at UMBC.
Chapter 2.
Section 1 fMRI for Newbies
Introduction to Functional MRI Last Update: January 14, 2013 Last Course: Psychology 9223, W2013 Jody Culham Brain and Mind.
Research course on functional magnetic resonance imaging (non-invasive brain imaging) Juha Salmitaival.
Introduction to Functional and Anatomical Brain MRI Research Dr. Henk Cremers Dr. Sarah Keedy 1.
Functional Brain Signal Processing: EEG & fMRI Lesson 12 Kaushik Majumdar Indian Statistical Institute Bangalore Center M.Tech.
fMRI introduction Michael Firbank
Opportunity to Participate
ICS 6N Computational Linear Algebra
CS150 Introduction to Computer Science 1 Professor: Chadd Williams.
FMRI: Biological Basis and Experiment Design Lecture 13: BOLD Neurons per voxel Neural signaling Neural/vascular link? HRF –linearity 1 light year = 5,913,000,000,000.
Dr. Tatiana Erukhimova [year] Overview of Today’s Class Folders Syllabus and Course requirements Tricks to survive Mechanics Review and Coulomb’s Law.
Magnetic Resonance Imagining (MRI) Magnetic Fields Protons in atomic nuclei spin on axes –Axes point in random directions across atoms In externally applied.
Retinotopic mapping workshop COSMO Starting materials In the folder ‘COSMO’ you will find raw data and toolboxes – as if you had just finished an.
Lecture 2 – Introduction to Neurophysiology research Ilan Dinstein.
Slide 1 Image Guided Surgery. Slide 2 Conventional Surgery: Seeing surfaces Provided by Nakajima, Atsumi et al.
C O M P U T E R G R A P H I C S Guoying Zhao 1 / 16 Computer Graphics Course Introduction.
Matlab tutorial course Exercises 2:. Exercises Copy the script ‘face_points.m’ from my webpage into your ‘scripts’ folder Create a new folder in your.
National Alliance for Medical Image Computing Slicer fMRI introduction.
COMP 111 Programming Languages 1 First Day. Course COMP111 Dr. Abdul-Hameed Assawadi Office: Room AS15 – No. 2 Tel: Ext. ??
Lecture 2 – Introduction to autism research Ilan Dinstein.
Basics of Functional Magnetic Resonance Imaging. How MRI Works Put a person inside a big magnetic field Transmit radio waves into the person –These "energize"
Signal and noise. Tiny signals in lots of noise RestPressing hands Absolute difference % signal difference.
FINSIG'05 25/8/2005 1Eini Niskanen, Dept. of Applied Physics, University of Kuopio Principal Component Regression Approach for Functional Connectivity.
CSCI 51 Introduction to Computer Science Dr. Joshua Stough January 20, 2009.
Astronomy 114 Lab Section 211, Professor Weigel. Outline for Today About Goals for this class Attendance Syllabus Safety Star Project Apparent vs. Absolute.
The basic story – fMRI in 25 words or less!. fMRI Setup.
ME451 Kinematics and Dynamics of Machine Systems Review of Linear Algebra 2.1 through 2.4 Tu, Sept. 07 © Dan Negrut, 2009 ME451, UW-Madison TexPoint fonts.
FMRI Methods Lecture7 – Review: analyses & statistics.
fMRI Methods Lecture 12 – Adaptation & classification
Basics of fMRI Time-Series Analysis Douglas N. Greve.
References: [1]S.M. Smith et al. (2004) Advances in functional and structural MR image analysis and implementation in FSL. Neuroimage 23: [2]S.M.
WPA Neuroimaging. WPA Basic Principles of Brain Imaging Some technique is used to measure a signal in the brain (e.g., the degree to which an xray beam.
Class 3: Neurons  BOLD 2012 spring fMRI: theory & practice.
FMRI – Week 4 – Contrast Scott Huettel, Duke University MR Contrast FMRI Graduate Course (NBIO 381, PSY 362) Dr. Scott Huettel, Course Director.
September 3, 2013Computer Vision Lecture 1: Human Vision 1 Welcome to CS 675 – Computer Vision Fall 2013 Instructor: Marc Pomplun Instructor: Marc Pomplun.
NA-MIC National Alliance for Medical Image Computing Diffusion Tensor Imaging tutorial Sonia Pujol, PhD Surgical Planning Laboratory.
Functional Brain Signal Processing: EEG & fMRI Lesson 14
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.
The brain at rest. Spontaneous rhythms in a dish Connected neural populations tend to synchronize and oscillate together.
Sonia Pujol, PhD -1- National Alliance for Medical Image Computing Neuroimage Analysis Center Diffusion Tensor Imaging tutorial Sonia Pujol, Ph.D. Surgical.
JRN 302: Introduction to Graphics and Visual Communication -Intro. to InDesign Thursday,
ECE297 TA GUIDE Project supervision. Agenda M0 feedback Project overview M1 overview Project supervision.
FMRI Methods Lecture8 – Electrophysiology & fMRI.
The linear systems model of fMRI: Strengths and Weaknesses Stephen Engel UCLA Dept. of Psychology.
Laboratory 2: Introduction to fMRI Data and Analysis September 18, 2006 HST.583 Divya Bolar.
Psych 204b: Computational Neuroimaging: Data Analysis
Supplement 189: Parametric Blending Presentation State Storage.
The Linear Systems Approach
Group Averaging of fMRI Data
To discuss this week What is a classifier? What is generalisation?
Intro to FreeSurfer Jargon
Slicer fMRI introduction
Classification with Perceptrons Reading:
The General Linear Model (GLM): the marriage between linear systems and stats FFA.
Intro to FreeSurfer Jargon
Freshman Engineering Clinic II
Intro to FreeSurfer Jargon
Gaurav Aggarwal, Mark Shaw, Christian Wolf
Michael S Beauchamp, Kathryn E Lee, Brenna D Argall, Alex Martin 
Intro to FreeSurfer Jargon
Jack Grinband, Joy Hirsch, Vincent P. Ferrera  Neuron 
Michael S Beauchamp, Kathryn E Lee, Brenna D Argall, Alex Martin 
Basics of fMRI and fMRI experiment design
Basics of MRI and MRI safety
Presentation transcript:

fMRI Methods Lecture1 - Introduction

http://www. weizmann. ac. il/neurobiology/labs/malach/ilan ilan http://www.weizmann.ac.il/neurobiology/labs/malach/ilan ilan.dinstein@weizmann.ac.il Leonesco Bldg. room 208

Course overview Go over syllabus Weekly exercises, final project (grades) Office hours Groups Scanning (safety, Helsinki) Matlab (experience, licenses) Huettel et. al.

Imaging Contrast Resolution (spatial, temporal)

Resolution scales

MRI scanner What is the measurement in this image?

Hydrogen atoms

Static magnetic field direction Physics Static magnetic field direction

Physics

The voxel

First anatomical MRI 106 voxels took 4 hours to scan! Damadian et. al. 1977

Anatomy 1T 2T

Anatomical measures Gray/White matter Cortical thickness

Diffusion tensor imaging (DTI)

Blue: up-down Green: fwd-bwd Yellow: right-left Tractography Blue: up-down Green: fwd-bwd Yellow: right-left

Tractography

So far we didn’t care about temporal resolution.

Neurovascular coupling Heeger et. al. 2002

Hemodynamics

Hemodynamic changes Time Heeger et. al. 2002

Hemodynamic response

Experiment

Experiment

scan/volume fMRI R L Front Back 24 slices every 1.5 seconds

fMRI activation maps Motor system When judging about stimulus speed there are differences in motor/decision areas

fMRI activation maps Visual system Motor system

Obsession with localization! fMRI activation maps Visual system Motor system Obsession with localization!

With fMRI we care about temporal resolution. Temporal sampling rate With fMRI we care about temporal resolution. Temporal sampling rate. Limited by Hemodynamics

Break

Matlab A tool for manipulating vectors and matrices. Matlab offers an immense number of functions with which one can do these manipulations quickly. Generally we will assume that our data (neural and hemodynamic responses) are generated by a linear system. What does it mean to be linear?

Linearity A linear system is one that satisfies the following two conditions: 1. Additivity/Superposition – f(x+y) = f(x) + f(y) 2. Homogeneity – f(ax) = af(x) What does this mean?

a*x + b*y + c*z scaling/weighting Example of a linear system a*x + b*y + c*z scaling/weighting Y Stimulus and neural response: X (stimulus) = a*Y (neural response) X

Example of a non-linear system The response of a single neuron at any given time is non-linear. It’s an all or nothing response with a certain threshold – a spike. A linear system has to output “graded” responses of consistently increasing/decreasing amplitudes. However, the summed response of a neuron across time windows of a given length (i.e. compute its firing rate) may be linear….

Example of a non-linear system The response of a single neuron at any given time is non-linear. It’s an all or nothing response with a certain threshold – a spike. A linear system has to output “graded” responses of consistently increasing/decreasing amplitudes. However, the summed response of a neuron across time windows of a given length (i.e. compute its firing rate) may be linear….

Are incredibly useful ways of representing data... Vectors and matrices Are incredibly useful ways of representing data... For example images of the brain

Are incredibly useful ways of representing data... Vectors and matrices Are incredibly useful ways of representing data... Or sound – voltage changes over time

And for manipulating the data... Vectors and matrices And for manipulating the data... How would you increase the volume of this sound segment?

Go over handout Open Matlab getting started section “Geometric” linear algebra Go over handout Open Matlab getting started section

Matlab Tutorials Open a folder for your code on the local computer. Try to keep the path name simple (e.g. “C:\Your_name”). Download code and MRI data from: http://www.weizmann.ac.il/neurobiology/labs/malach/ilan/lecture_notes.html Save Lab1.zip in the folder you’ve created and unzip. Open Matlab. Change the “current directory” to the directory you’ve created. Open: “Lab1_VisualizingBrain.m” When done open: “Lab1_CreatingStimuli.m”

Homework! Read Chapters 1 & 2 of Huettel et. al. (available in library) Review Geometric linear algebra handout Matlab exercise: email me the report as a word document. The report should include answers, figures, and the actual Matlab code used to generate them (copy it into word). This week, don’t forget to also send me the movie you’ve created.