2 Tiny signals in lots of noise RestPressing hands% signal differenceAbsolute difference
3 What’s the signalThe signal we’re really measuring is tiny changes of current induced in our detector coils.What induces the current?What makes the signal in a voxel stronger (larger image intensity)?What is the “signal” we’re really interested in?
4 What’s the noise Thermal noise System noise Head motion, respiration, heart beat (physiological) noiseHemodynamics variabilityNeural variabilityBehavioral/Cognitive variabilityAre 5&6 really noise?
5 Thermal noiseThermal motion of electrons, collisions, random exchange of energy, larger at higher temperatures…It is generally considered homogeneous and random and so can be reduced by averaging across multiple samples.It increases linearly withstatic field strength.
6 System noiseVariability in the function of the imaging hardware across space and time.Static field inhomogeneities Scanner drift
7 Susceptibility artifacts Field inhomogeneities are particularly strong at tissue/air boundaries (sinuses). Increase with field strength.1.5 T4 T
8 Comparing “extrinsic” noise Thermal and system noise can be measured and estimated using a phantom made of a known material.
9 Head motion Moving the head during a scan causes two types of noise: Spatial changes throughout the scan.
10 Now even done online by the scanner! Rather than post-hoc Head motionSpatial changes can be estimated and fixed by locating brain edges and moving/rotating them appropriately.Now even done online by the scanner! Rather than post-hoc
11 Head motionImage intensity artifacts in time (intensity “spikes”).
12 Head motion Intensity artifacts are more difficult to correct. Can either be “projected out”, interpolated over, or cut out.What happens when head motion and task are correlated?
13 Add motion parameters to model Add 6 predictors (3 translation and 3 rotation) to the model and hope they “soak” up the relevant variability.*+a1 a2 a3 a4 …error=
14 Or project/regress out Ensure zero correlation between the noise estimate (x) and the data (y).a = y*xy(after) = y(before) – a*x
16 Physiological noiseThere are non-neural mechanisms causing hemodynamic or inhomogeneity changes during a scan. Luckily they are periodical…
17 Respiration artifacts The lungs create a changing susceptibility artifact, similar to that seen below in the sinuses (stronger in larger fields).1.5 T4 TOnly the lungs effect the signal throughout the brain…
18 Physiological noiseIncreases at higher static magnetic fields for the same reason the signal increases…
19 Fourier transformDecompose complex signals into sinusoidal componentsFrequency domainFrequencypower++a*b*c*FrequencyphaseTemporal domain
20 Temporal filteringGet rid of very low frequencies (drift, respiration). Others?Fourier transform++a*b*c*Noise? multiply by zero
21 Temporal filteringHigh pass filter – lets the high frequencies pass, stops the low frequencies.Low pass filter – lets the low frequencies pass, stops the high frequencies.Band pass filter – lets a particular range of frequencies through (often by sequentially running a low high and low pass filter).
22 Hemodynamics variability Different subjects exhibit different HRFs
23 Hemodynamics variability HRFs vary across sessionsAcross brain areas?
24 Hemodynamics variability To address this we can estimate the subject’s HIRF in a separate run and use it to model the responses.
25 Neural variabilityThe brain is never at “rest”, spontaneous neural activity fluctuations are as large as stimulus evoked responses.
26 Neural variabilitySome think the stimulus evoked responses “ride” on top of spontaneous cortical fluctuations, others think stimulus evoked responses replace spontaneous fluctuations.We typically get rid of them by averaging across multiple trials.
27 Behavioral/Cognitive variability The more complex an experiment, the more variable the behavioral responses:Subjects can choose different strategies.Changes in attention/arousal (caffeine).Response time distributions oftwo subjects performing asimple decision task.
28 Behavioral/Cognitive variability Again, variability is typically handled by averaging across trials.However, this variability also offers an opportunity:Does neural responseamplitude predict reactiontime or accuracy?fMRI responseReaction time
30 Intra-subject variability Generate random numbers
31 Improve SNR by averaging The main approach to canceling out noise is to average across multiple trials.This assumes that the neural response is constant (locked to the stimulus/task) and that the noise is randomly distributed.Are they?
32 Improve SNR by averaging Estimating HRF using different trial numbers:
33 Improve SNR by averaging Estimating voxel significance using different trial numbers:Never compare statistics across conditions/groups.A difference in statistical significance does not equal a difference in signal strength!
34 Higher fieldsThe signal is dependant on the magnetization of the hydrogen atoms, which increases with field strength (more atoms align with the static field).The gain in signal is quadratic.The increase in noise is linear.So the signal/noise ratioscales linearly with scannerstrength.
35 Higher fieldsStronger signal = finer spatial resolution (smaller voxels).But remember that we are limited to the resolution of the vasculature. There is already a lot of correlation among neighboring 3*3*3 mm voxels.Larger susceptibility artifacts.Shorter T2*Longer T1
36 PreprocessingStandard steps everyone does to reduce noise/variability:
44 Lab #6Open 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: Save Lab6.zip in the folder you’ve created and unzip. Open Matlab. Change the “current directory” to the directory you’ve created. Open: “Lab6_Randomization.m” Then continue with: “Lab6_ProjectingOutNoise.m”