fmri Analysis Sackler Ins2tute 2011

Transcription

1 fmri Analysis Sackler Ins2tute 2011

2 How do we get from this to this?

3 How do we get from this to this? And what are those colored blobs we re all trying to see, anyway?

4 Raw fmri data straight from the scanner Volume Slice Voxel

5 Raw fmri data straight from the scanner Volume Slice Voxel

6 fmri Signal What does real data look like?

7 fmri Signal What does real data look like?

8 So what s in our signal? Task-Related Variability Non-task-related Variability

9 Task-Related Variability SIGNAL Non-task-related Variability NOISE

10 Signal to Noise Ra2o (SNR) Task-Related Variability Non-task-related Variability

11 SNR = 4.0 SNR = 2.0 SNR = 1.0 SNR =.5

12 Task related variability (SIGNAL) Difference in response related to experimental manipula2on

13 Task related variability (SIGNAL) Difference in response related to experimental manipula2on Non task related variability (NOISE) Thermal noise, e.g., scanner hea2ng Variability in scanner func2on Head mo2on effects Temporal ar2facts from acquisi2on Physiological changes (heart rate, breathing) Ar2fact induced problems Subject induced noise unexpected changes in awen2on, alertness, performance

14 Task related variability (SIGNAL) Difference in response related to experimental manipula2on Non task related variability (NOISE) Thermal noise, e.g., scanner hea2ng Variability in scanner func2on Head mo2on effects Temporal ar2facts from acquisi2on Physiological changes (heart rate, breathing) Ar2fact induced problems Subject induced noise unexpected changes in awen2on, alertness, performance GOAL OF SNR PREPROCESSING

15 Preprocessing Separate steps to deal with separate, known sources of noise 1. Deal with temporal ar2facts of acquisi2on Slice Timing Correc.on 2. Dealing with movement related noise Realignment 3. Decreasing influence of noise in data generally Smoothing 4. Dealing with the fact that each person s brain is a different shape and size Alignment and Normaliza.on

16 Slice Timing Correc2on

17 The slice 2ming problem TR = 4s Collecting 4 slices Collected at: 0s 1s 2s 3s

18 The slice 2ming problem EPI images collected at varying points in 2me a^er TR onset Correc2on shi^s each voxel's 2me series so that they "appear" to have been captured at exactly the same 2me

19 The slice 2ming solu2on The slices of one functional volume are shifted in time. This is corrected by sinc (or linear) interpolation in time.

20 Realignment/ Mo2on correc2on

21 Subject BK

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45 Transla2ons X Y Z Rota2ons Pitch Roll Yaw Yaw Pitch Roll

46 Head movement: The problems 1. All movements Images are not lined up When data is combined across un-aligned scans, it can create artificial activations on the edges of the brain

47 Head movement: The problems 2. Sudden movements/movement during scans: Blurry images

48 Head movement: The solu2on Realignment Algorithm that detects and corrects movement across scans Realigns images to a reference image Uses rigid body transforma.on in the 6 planes Writes out mo2on parameters

49 Reference Image In theory

50 Reference Image In practice

51 Movement: The Good

52 Movement: The Bad

53 Movement: The Ugly

54 Smoothing

55 Smoothing Reasons to smooth Increases signal to noise ra2o Improves ability to make comparisons across subjects Smoothing tool of choice: Gaussian kernel

56 How does smoothing work? Replaces voxel value with a weighted average of itself and its neighbors Before smoothing After smoothing Gaussian kernel

57 How does smoothing work? Smoothing decreases signal value in highintensity voxels, while increasing the spatial extent of this signal

58 Benefit #1 of smoothing Increases probability that across subjects, clusters will line up Subject A activation Subject B activation Unsmoothed Version Composite Intensity : 50 Intensity : 50 Max intensity : 50

59 Benefit #1 of smoothing Increases probability that across subjects, clusters will line up Subject A activation Subject B activation Smoothed Version Composite Intensity : 40 Intensity : 40 Max intensity : >50

60 Benefit #2 of smoothing Real signal should consist of voxels with a high intensity value, clustered together Probable real signal

61 Benefit #2 of smoothing Noise should consist of high intensity values located randomly across space Probable noise

62 Benefit #2 of smoothing Smoothing increases SNR by enhancing impact of clustered signal and suppressing impact of random noise Probable signal overlaps with others Probable noise drops out

63 Unsmoothed Smoothed

64 Alignment and normaliza2on

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66 One subject s dataset

67 2 problems 1. Within a single subject s scan session, there is no guarantee that each of their scans will be lined up in the same space. 2. Everyone s brain is a different shape and size compared to everyone else s but we eventually want to combine data from several individuals to perform group sta2s2cs. Goal: We want everything to line up!

68 2 solu2ons 1. Within a single subject s scan session, there is no guarantee that each of their scans will be lined up in the same space. SOLUTION: ALIGNMENT / COREGISTRATION 2. Everyone s brain is a different shape and size compared to everyone else s but we eventually want to combine data from several individuals to perform group sta2s2cs. SOLUTION: NORMALIZATION

69 Alignment

70 Spa2ally align scans within each subject

71 Normaliza2on Talairaching

72 Recap of what we ve done so far 1. Corrected for acquisi2on delays 2. Corrected as much as possible for mo2on 3. Increased SNR by smoothing 4. Aligned scans within each person 5. Warped data into a common space

73 Are we any closer to this? Yes, but we have to do a lot of math. Tool of choice: GENERAL LINEAR MODEL

74 General linear model in layman terms Signal in a voxel is a conglomera2on of TASK INDUCED CHANGE and noise.

75 General linear model in layman terms Signal in a voxel is a conglomera2on of TASK INDUCED CHANGE and noise. The GLM allows us to es2mate what por2on of the signal is best explained by our task (as well as by noise).

76 General linear model in layman terms The GLM itself is the math used to accomplish this.

77 General linear model in layman terms The GLM itself is the math used to accomplish this. During the GLM we are crea2ng maps of PARAMETER ESTIMATES which represent how well our task explains signal.

78 How it works in layman terms We tell the GLM everything we can about sources of variability in our signal. THE MODEL

79 How it works in layman terms We tell the GLM everything we can about sources of variability in our signal. THE MODEL The GLM tells us how much variability each of these sources explains. THE SOLUTION

80 A very simple experiment 2 condi2ons block design 1st Condi2on: Watching FACES Healthy Volunteer + Scanner Bed Screen

81 A very simple experiment 2 condi2ons block design 1st Condi2on: Watching FACES 2nd Condi2on: Watching TAXIs Healthy Volunteer + Scanner Bed Screen

82 A very simple experiment 2 condi2ons block design 1st Condi2on: Watching FACES 2nd Condi2on: Watching TAXIs And, there s rest Healthy Volunteer + Scanner Bed Screen F T + F T + F T + F T +

83 Hypothe2cal Responses Faces Taxis Rest Time

84 Hypothe2cal Responses Face Responsive Faces Taxis Rest Time

85 Hypothe2cal Responses Face Responsive Face + House Responsive Faces Taxis Rest Time

86 Hypothe2cal Responses Face Responsive Face + House Responsive Task insensi2ve Faces Taxis Rest Time

87 It d be good to tell the GLM about our task A model consists of a set of assump2ons of the type: I think a voxel that is into faces might have a 2me series looking like this and Time I think a voxel that is into taxis might have a 2me series looking like this

88 The Model: A Set of Hypothe2cal Time series For a given voxel (2me series) we try to figure out just what type that is by modelling it as a linear combina2on of the hypothe2cal 2me series. β 1 + β 2 + β 3 Measured

89 The Es2ma2on: Finding the best parameter values The es2ma2on entails finding the parameter values such that the linear combina2on best fits the data β 1 + β 2 + β 3

90 The Es2ma2on: Finding the best parameter values The es2ma2on entails finding the parameter values such that the linear combina2on best fits the data β 1 + β 2 + β 3 Not brilliant 0 0 3

91 The Es2ma2on: Finding the best parameter values The es2ma2on entails finding the parameter values such that the linear combina2on best fits the data β 1 + β 2 + β

92 The Es2ma2on: Finding the best parameter values The es2ma2on entails finding the parameter values such that the linear combina2on best fits the data β 1 + β 2 + β 3 Cool!

93 The Es2ma2on: Finding the best parameter values And the nice thing is that the same model fits all the 2meseries, only with different parameters β 1 + β 2 + β

94 The Es2ma2on: Finding the best parameter values And the nice thing is that the same model fits all the 2meseries, only with different parameters β 1 + β 2 + β 3 Doesn t care

95 The Es2ma2on: The format of data, model and parameters Same model for all voxels. Different parameter es2mates for each voxel. beta_0001. beta_0002. beta_0003.

96 GLM converts the units of your data One 2meseries per voxel One beta es2mate for each input of the GLM per voxel beta_0001. beta_0002. beta_0003.

97 β 1 + β 2 + β errorts Whatever we do not explain in our model becomes part of the error term

98 Group analysis Runs sta2s2cal tests on the betas Each par2cipant contributes one beta value to the analysis per voxel per condi2on (much easier to handle than 2meseries) Random effects group analysis (GOOD!)

99 Contrasts/GLTs are simple math Houses Taxis Per subject Is the houses taxis difference score consistently different from zero? This is the output of a one sample t test If significant in an area, then region X is significantly more ac2ve to Houses than Taxis