Elizabeth
S. Lorenc1*, Kartik Sreenivasan2, Derek E. Nee3,
Annelinde R. E. Vandenbroucke4, & Mark D’Esposito1,5
1Helen Wills
Neuroscience Institute, University of California, Berkeley, California, USA
* Correspondence: Elizabeth S. Lorenc, Helen Wills
Neuroscience Institute, University of California, Berkeley, 132 Barker Hall,
Berkeley, CA, 94720-3190, USA.
Keywords:
Abstract
1.
Introduction
Visual working memory (VWM) allows for
the maintenance of precise visual details of objects no longer in view, and is
supported by activity in many brain regions, including lateral prefrontal
cortex (D’Esposito et al., 1995; D'Esposito, Postle, & Rypma, 2000) and
primary sensory cortices (Harrison & Tong, 2009; Serences 2009).
While “sensory recruitment” models
(D'Esposito, 2007; Postle, 2006) suggest that precise visual details are
maintained in stimulus-selective primary visual regions, it remains unclear
what happens to VWM representations in the face of subsequent visual input.
To this end, the present experiment
__
participants completed the training, ___ participants completed a retinotopic
mapping session, and ultimately a total of twelve healthy young adults (mean
age___, 2 male) completed the entire experiment. Each of the twelve participants
completed a one-hour training session and four two-hour MRI scan sessions. All
procedures were approved by the UC Berkeley Committee for the Protection of
Human Subjects. Participants gave their written informed consent before the
study and were compensated monetarily for their time.
2.2.
Cognitive
task
Timing
(slightly different timing for first two participants), method-of-adjustment
response, feedback at the end of each run, pay for precision.
Orientations
covered the whole 180 degree space – 8 base orientations, with +/- 1 - 10
degrees of jitter: ensured that the same general set of orientations were shown
in each of the three distractor conditions. Broken up between runs, so that
half of the orientation categories were shown on odd runs and half on even
runs. 12 trials per run: one of each of 4 orientation categories and 3
distractor conditions.
One third
of trials did not have a distractor stimulus, and the rest had a distractor
with an orientation that was 40 – 50 degrees clockwise (50%) or
counterclockwise (50%) of the remembered orientation. With the exception of two
of the authors, participants were unaware of the relationship between the
memory and distractor stimulus orientations.
parameters
of the gabors: size, eccentricity, full contrast, degrees of visual angle, phase-alternating
Figure 1. Right-lateralized
delayed-estimation task for orientation, with intervening distractors
Stimuli
were projected onto a screen at the rear of the magnet bore and viewed via a
headcoil-mounted mirror, and responses were collected with an MR-compatible
joystick (Current Designs, Inc.).
Participants
were required to maintain central fixation throughout each scanning run, and eye
position was continuously monitored using an MR-compatible eyetracker (Avotec).
2.3.
Behavioral
analyses
The method-of-adjustment response yielded
a trial-by-trial measure of memory error in degrees for each distractor
condition (no distractor, clockwise distractor, counterclockwise distractor),
for each participant. Using the MemToolbox (citation)
in MATLAB (Mathworks), each error distribution
was fit with a mixture model of a von Mises distribution and a uniform distribution.
As has been previously published (Zhang & Luck, 2006), three free
parameters were estimated: the mean of the von Mises (reflecting any systematic
clockwise or counterclockwise biases in participants’ responses), the standard
deviation of the von Mises (reflecting the average precision of a participant’s
responses), and the height of the uniform distribution (reflecting the rate of
random guesses). This model was fit separately for each
distractor condition, but hierarchically across all twelve participants, which
in addition to yielding participant-specific parameter estimates, also
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2.4.
Functional
MRI acquisition and preprocessing
MRI data were acquired in the UC
Berkeley Henry H. Wheeler, Jr. Brain Imaging Center with a Siemens TIM/Trio 3T MRI scanner
with a 12-channel receive-only head coil. Whole-brain MP Flash T1-weighted
scans were acquired for anatomical localization and normalization. Functional
data were obtained using a one-shot T2*-weighted echoplanar imaging (EPI) sequence
sensitive to blood oxygenation level-dependent (BOLD) contrast. The EPI
sequence parameters for the first two participants (TR = 1.6667s, TE = 30ms,
field of view = ??, matrix size = ??, in-plane resolution = 3 x 3mm, ?? contiguous 3mm-thick axial slices separated by a
0.3mm interslice gap) were slightly different from the sequence used for the
remainder of the participants (TR = 2.0s, TE = 30ms, field of view = ??, matrix size = ??,
in-plane resolution = 3 x 3mm, ?? contiguous
3mm-thick axial slices separated by a 0.3mm interslice gap), as adjustments
were made to improve whole-brain coverage.
Gray/white matter boundary segmentation
and cortical surface reconstruction was performed with Freesurfer’s (citation) recon-all tool, and all surface-based
analyses were then performed in AFNI’s SUMA (citation)
package.
Functional MRI data were
then subject to standard preprocessing with AFNI (Cox, 1996; Cox & Hyde, 1996) and custom Matlab (v2011b, The MathWorks, Inc., Natick, MA)
scripts. Motion correction and volume registration of
each EPI run to the anatomical scan was carried out in a single resampling step
by align_epi_anat.py (Saad et al., 2009), by first aligning the mean of the middle EPI to
the anatomical data and then aligning each volume to that mean EPI with a
12-parameter affine registration. Finally, each run was z-scored temporally, voxel-wise, in
preparation for forward encoding modeling (FEM) and multi-voxel pattern
analysis (MVPA).
2.5.
Region-of-interest creation
Spatial
localizer & retinotopy stimuli
Left and right visual areas V1 – V3, dorsal and ventral, were
delineated on the surface based on separate polar angle and eccentricity
mapping scans, following
standard procedures (cite @Retino_Proc).
2.6.
Forward encoding model analyses
The following analyses were performed separately for the left and
right early visual areas (V1 – V3). Because the stimuli were always presented
in the right hemifield, the left hemisphere was always contralateral to the
memory stimulus, distractor, and probe, and the right hemisphere was always
ipsilateral.
Extract two volumes representing stimulus perception (__ to __s
after stimulus onset), the first memory delay (__ to __s after stimulus onset),
distractor perception, and the second memory delay, and average each pair to
yield a single BOLD intensity pattern for each trial epoch, for every trial. The
forward modeling analysis was then completed separately within each trial
epoch, using a leave-one-run-pair-out cross-validated structure.
Basis set: 8 channels, shape of channels,
How significance was evaluated:
Control: delta functions
2.7.
Multi-voxel pattern classification analyses
Regularized logistic regression classifier – penalty
Look at classifier evidence – provides a more nuanced measure than
raw accuracy
3.
Results
3.1.
Behavior
3.2.
Orientation
reconstruction in early visual areas
3.2.1. Sinusoidal basis functions
Can reconstruct perception only in
ipsilateral hemisphere (do stats to compare directly), but
can reconstruct orientation bilaterally during the memory delay.
Effect of distractor on reconstructions
Any way to see if there is a bias??
3.2.2. Delta basis functions
4.
Discussion
5. Acknowledgments
Funding:
6. References
9. Tables and figures
9.1 Figure legends