Samir: Difference between revisions

From Psych 221 Image Systems Engineering
Jump to navigation Jump to search
imported>Psych204B
Created page with '==Probing BOLD Responses For Motor Tasks With High-Resolution fMRI== ''Investigating how analysis methods change the significance of neural correlates of planning and motion whil…'
 
imported>Psych204B
mNo edit summary
Line 21: Line 21:


===Stimulus Presentation===
===Stimulus Presentation===
The task description text was displayed on a modified Samsung SyncMaster 305T 30 inch diagonal display (76 cm, 16:10 aspect ratio), built by Resonance Technology (www.mrivideo.com) at a resolution of 1280x800 pixels. Subjects viewed the display through a double mirror at a distance of about 190cm from the head-coil, and the display was inverted so text appeared normal to the subjects.  
The task description text was displayed on a modified Samsung SyncMaster 305T 30 inch diagonal display (76 cm, 16:10 aspect ratio), built by Resonance Technology (www.mrivideo.com) at a resolution of 1280x800 pixels. Subjects viewed the display through a double mirror at a distance of about 190cm from the head-coil, and the display was inverted so text appeared normal to the subjects. The text was rendered on the screen using VisionEgg, a freely available python based stimulus presentation software.


===Subject Details===
===Subject Details===
Line 29: Line 29:
We acquired images GE MR 750 3-Tesla scanner and a Nova 32 channel head coil at the Stanford Center for Cognitive and Neurobiological Imaging (CNI). Two series of 510 functional volumes were acquired using a gradient echo, echoplanar sequence with a 134 square matrix, 27 oblique slices, 2mm thick, with a 0.5mm gap between slices. The voxel size was 1.5mm x 1.5mm x 2.0mm, repetition time was 2 sec, echo time was 33ms and flip angle was 75degrees. The slices were adjusted for each subject to include cerebellum, posterior striatum, and motor cortex.
We acquired images GE MR 750 3-Tesla scanner and a Nova 32 channel head coil at the Stanford Center for Cognitive and Neurobiological Imaging (CNI). Two series of 510 functional volumes were acquired using a gradient echo, echoplanar sequence with a 134 square matrix, 27 oblique slices, 2mm thick, with a 0.5mm gap between slices. The voxel size was 1.5mm x 1.5mm x 2.0mm, repetition time was 2 sec, echo time was 33ms and flip angle was 75degrees. The slices were adjusted for each subject to include cerebellum, posterior striatum, and motor cortex.


The functional images were overlaid on a co-aligned, high-resolution anatomical scan of the whole brain taken at the end of each session (BRAVO sequence; TR �7,8 sec; TE 3.1 ms, flip angle 12 degrees; matrix, 300x300; 0.8 mm, isotropic).
The functional images were overlaid on a co-aligned, high-resolution anatomical scan of the whole brain taken at the end of each session (BRAVO sequence; TR = 7,8 sec; TE = 3.1 ms, flip angle = 12 degrees; matrix, 300x300; 0.8 mm, isotropic).


==How do data smoothing and inflated surface maps might influence the interpretation of motor fMRI data?==
==How do data smoothing and inflated surface maps might influence the interpretation of motor fMRI data?==

Revision as of 02:41, 13 March 2012

Probing BOLD Responses For Motor Tasks With High-Resolution fMRI

Investigating how analysis methods change the significance of neural correlates of planning and motion while writing

Summary

This project explores how high resolution fMRI can be used to probe activity in the brain's motor regions, the motor cortex, the basal ganglia and the cerebellum. A first step towards designing experiments that can probe their motor representation is to validate different choices made while processing data to get statistical significance maps. Here, I investigated:

1. How data smoothing and inflated surface maps might influence the interpretation of motor fMRI data
2. How activity varied across subjects 
3. Whether mapping data on to inflated brains made it easier to interpret

Motivation

The ability to coordinate muscles and resolve inter-task conflicts is fundamental to human motor control, and underlies even simple motions like holding a cup with a hand and flipping a switch with the same arm’s elbow. The arm muscles that help hold the cup, one task, must also flip the switch, another task, creating a conflict that the brain must resolve while coordinating the muscles. Past research has not elucidated the brain’s coordination strategy largely because it has focused on tasks in isolation [cite]. Moreover such experiments are confounded by the fact that at a fine spatial scale, motor related neurons correlate with all observable movement parameters [cite] , while at a higher spatial scale, fMRI experiments have been unable to delineate any generalizable motor organization apart from fractured somatotopic sensorimotor maps [cite].

Probing the motor control regions in a manner that elucidates the structure of the underlying motor controller thus requires novel experiments, whose results might be hard to interpret under existing statistical analysis pipelines. Here, I will highlight how the same data might be interpreted in multiple ways with minor changes to the processing pipeline.

Methods

Task Details

A novel motor control dataset was acquired for this study. Subjects were asked to perform writing tasks like drawing a square while holding a pencil at three linearly spaced grasp locations They were provided visual text stimuli indicating when to plan and execute motions, and when to rest. For instance, one stimulus sequence would be: `Plan : Square : Tip' (yellow, 5 sec), `Execute : Square : Tip' (green, 8 sec), and `Rest' (red, 3-11 sec, randomized). During the plan phase subjects would plan their motion and possibly adjust grasp position, but would not make any whole arm motions. During the execute phase, subjects would move their entire arm to draw a square, without any finger movement. And during the rest phase, they would place their arm on their torso and rest. Subjects were asked to abandon tasks midway if they could not complete them in time.

Stimulus Presentation

The task description text was displayed on a modified Samsung SyncMaster 305T 30 inch diagonal display (76 cm, 16:10 aspect ratio), built by Resonance Technology (www.mrivideo.com) at a resolution of 1280x800 pixels. Subjects viewed the display through a double mirror at a distance of about 190cm from the head-coil, and the display was inverted so text appeared normal to the subjects. The text was rendered on the screen using VisionEgg, a freely available python based stimulus presentation software.

Subject Details

As part of our study, we scanned XXX healthy right-handed volunteers (XXX males; 19–28 yr of age). All subjects were informed about the experiment's details in advance, and gave their informed written consent to a protocol approved by the Institutional Review Board of Stanford University. The subjects were healthy and did not have any psychiatric or neurological disorders at the time or in the past. Subjects were asked to lie supine on the scanner bed, and their heads were surrounded by cloth to comfort and reduce head movements. In addition, they bit down on a bite bar customized to their dental structure with putty.

Scan Sequence Details

We acquired images GE MR 750 3-Tesla scanner and a Nova 32 channel head coil at the Stanford Center for Cognitive and Neurobiological Imaging (CNI). Two series of 510 functional volumes were acquired using a gradient echo, echoplanar sequence with a 134 square matrix, 27 oblique slices, 2mm thick, with a 0.5mm gap between slices. The voxel size was 1.5mm x 1.5mm x 2.0mm, repetition time was 2 sec, echo time was 33ms and flip angle was 75degrees. The slices were adjusted for each subject to include cerebellum, posterior striatum, and motor cortex.

The functional images were overlaid on a co-aligned, high-resolution anatomical scan of the whole brain taken at the end of each session (BRAVO sequence; TR = 7,8 sec; TE = 3.1 ms, flip angle = 12 degrees; matrix, 300x300; 0.8 mm, isotropic).

How do data smoothing and inflated surface maps might influence the interpretation of motor fMRI data?

Data

Discussion and Future Work

Appendix

Presentation

Script

REFERENCES

 [1]