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Individual Differences in the Localization of Visual

Perception within Low Signal/Noise Environments:

An fMRI Study

Farzana Z. Ali

Final Progress Report

HBY 591

Research Advisor: Joshua M. Carlson, Ph.D.

State University of New York at Stony Brook School of Medicine

Departments of Biomedical Engineering, Neurobiology and Behavior, and Psychiatry

May 7th, 2013

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INTRODUCTION

This project investigates individual’s ability to

detect the coherence in the motion of objects

by evaluating brain areas associated with the

task. The visual and visual association areas,

frontal eye field (FEF)/dorsolateral prefrontal

cortex (DLPFC), and the supplemental motor

area (SMA) have been implicated in task-

related brain activation during a random-dot

visual stimuli perception task.1 DLPFC is

involved in both task and response specificity

in perceptual decision making,2 and disruption

of the left DLPFC leads to reduced accuracy

and increased response times in speeded

perceptual categorization task.3 Moreover, the

left posterior DLPFC responds more to high-

than to low-coherence stimuli, along with the

left posterior cingulate cortex, left inferior

parietal lobule, and left fusiform / para-

hippocampal gyrus.2

In addition, under degraded or low

signal/noise contexts, the posterior parietal

cortex (PPC) also participates in signal

detection, possibly via enhancement of visual

attentional resources. Selective visual attention

allows an individual to concentrate on relevant

stimuli while ignoring irrelevant stimuli

through the involvement of the parietal

cortex.4,5 The intraparietal sulcus helps

establish attentional priority maps and

calibrate attentional weights.6 In particular, the

right parietal cortex controls attention in both

visual hemifields, whereas the left hemisphere

only deals with the contralateral hemispace.7

Many fMRI studies have extensively

researched different brain areas related to

visual perception of random dot motion.

However, the novelty of our study lies in the

application of the acquired knowledge on

localization of visual perception to identify

individual’s capability of motion detection.

Our experimental model manipulated the

signal/noise ratio by incrementally varying the

levels of coherence between dots. We

hypothesized that the individual’s sensitivity

to signal/noise ratio will affect their visual

perception, which can be illustrated through

their differential localization of signal

detection.

The results of our study can be applied to

test and train military personnel, who are often

required to detect, identify and respond to

visual signals, associated with their unit’s

control, coordination and potential threats.

Early detection of threatening cues, established

through appropriate cognitive training, can

give the soldier enough time to organize

defenses and to intercept attacking planes.8 In

the battlefield, visual communications, such as

arm and hand signals, flags, and pyrotechnics,

play a vital role for transmitting orders,

information or request for aid and support.9

There are 5 standard US Army and Marine arm

and hand signals for the military commands,

namely: “Attention”, “Halt”, “Rally”, “Move

Out”, and “Nuclear Biological or Chemical

event (NBC)”.10 These signals include arm and

hand motions in varying directions that need to

be recognized rapidly and accurately.9

However, the noise in these visual signals may

increase due to poor visibility such as at night

or in dense terrain, and due to interception by

the enemy, and that may endanger their health

and survival.11 Our findings on localization of

brain areas responsible for an individual’s

visual perception in different signal/noise

environments can be helpful in identifying the

differential visual perception of soldiers, and

ensure its improvement through proper

training of the appropriate regions of interest in

the brain.

The primary aim of this study is to analyze

individual brain responses to see whether an

individual is able to identify a pattern or not.

During visual motion perception, the left

dorsolateral prefrontal cortex, posterior

cingulate and inferior parietal cortex are

expected to be more active for the "easy"

compared to the "hard" trials, while during

response-related phases, the bilateral

precuneus and inferior parietal cortex as well

as the bilateral superior medial gyrus should

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display such pattern of activation.12 The

individual’s perceptual state can be predicted

accurately by using data from dorsal visual

areas V3A, V4D, V7, MT+ and the appropriate

parietal areas.13 A higher utilization of the

attentional posterior parietal cortex is expected

in individuals who demonstrate optimal visual

perception in low signal / noise environ-

ment.4,5,14,15

METHODS Data Acquisition

The study utilized fMRI data on the iterative

performance of 15 (male and female) healthy

participants between the ages of 18-45 in the

“Random Dot-Motion” task including visual

motion information sensitive to the magno-

cellular pathway. The subjects were required to

identify the net motion of dots presented on the

screen as either leftward or rightward,

followed by a jittered inter-trial interval. The

net coherence of the dots were varied to

manipulate the signal/noise ratio by

establishing 4 levels of ambiguity: difficult,

medium difficult, medium easy, and easy

(Figure 1).16 30 repeated trials were performed

for each subject at each coherence level, and

the entire experiment lasted for 24 minutes.

During the tasks, whole-brain coverage was

obtained using 36 slices with 3.5mm thickness

through no-gap ascending interleaved oblique

transaxial acquisition, with TR=2000 ms,

TE=22 ms, Flip ∠=83°, FOV=224 x 224 mm

in a 96 x 96 matrix. The recorded data were

analyzed via the Statistical Parametric

Mapping (SPM) toolbox in a Matlab

platform.17

Data Pre-processing

At first, the acquired functional images were

run through spatial preprocessing steps,

including realignment, slight timing

correction, normalization and smoothing

procedures. The realignment process removed

movement-related artifacts, and provided with

a mean image as well as regressors for fitting

General Linear Models (GLMs). The

differences in slice acquisition times were

corrected afterwards. The slice-time corrected,

realigned functional images were normalized

into a 2×2×2 mm standardized Talairach

space, and smoothed with a Gaussian kernel of

8 mm full-width half maximum in each

direction.

Data Analysis

These processed images were subsequently

modelled as categorical responses, and a

canonical haemodynamic response function

(HRF) was utilized for analysis without any

model derivatives. For this event-related

response model, four T-contrasts were defined

and applied to test the one sided main effects

for the active condition (one-sided t-test) for

the Easy > Baseline, Medium Easy > Baseline,

Medium Difficult > Baseline, and Difficult >

Baseline conditions.

RESULTS

Part 1: Spatial Pre-processing

Realignment

All 482 images from each subject were run

through the realign job using a least squares

approach and a 6 parameter (rigid body) spatial

transformation. The details of the

transformation through realignment are shown

as plots of the estimated time series of

translations and rotations of the images (Figure

2). These realignment parameters were also

saved to a text file (Table 1) to be used as

regressors when fitting General Linear Models

(GLMs). This parameter file allowed

movements effects to be discounted when

looking for brain activations. The images were

resliced such that they match the first image

selected voxel-for-voxel.

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Figure 1: Random dot motion (RDM) task for discriminating between ‘Right’ and ‘Left’ motion

directions for each RDM stimulus corresponding to different coherence levels: difficult, medium

difficult, medium easy, and easy.1

Slice Timing Correction

The realigned images were subsequently

corrected for the differences in image

acquisition time between slices.

Normalization

All the brain images were normalized into the

same 3D space defined by the Montreal

Neurological Institute (MNI) template (Figure

3). The figure shows results from computation

of the warp that best registered the mean image

created from the set of functional images in the

realignment process to match the MNI

template.

Smoothing

Smoothed image volumes were acquired in

accordance with a Gaussian kernel of 8mm

full-width half maximum. This resulted in the

suppression of noise and effects due to residual

differences in functional and gyral anatomy.

1 Image source: Pilly, P. K. Random dot motion (RDM) stimuli, http://cns.bu.edu/~advait/RDMstimuli.html.

Figure 2: Realignment of RDMT data:

Movement less than the voxel size of 3mm was

not considered problematic.

Easy Medium Easy

Medium Difficult Difficult

Table 1: A sample subset of realignment parameters used as regressors for fitting GLMs.

Realignment parameters

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Figure 3: Spatial normalization of images

after being placed into a same 3-D space

defined by the MNI template.

Part 2: Categorical Response Model

A representative figure of the experimental

design including 482 fMRI images for each

subject in our study is presented in the design

matrix (Figure 4). The design matrix shows

482 rows representing the scans used in the

study. For these event-related responses, four

T-contrasts were generated to test one sided

main effects for the active condition (one-sided

t-test) for the Easy > Baseline, Medium Easy >

Baseline, Medium Difficult > Baseline, and

Difficult > Baseline hypotheses. The results of

these contrasts are shown in the first four

columns of the design matrix presented on the

right hand side of Figures 5 and 6. These two

figures also illustrate the hemodynamic

response acquired from two different subjects,

where the activated areas of the brain during

the random dot motion task are darkened.

DISCUSSION

This study involved a full factorial experiment

whose design consisted of a single factor, with

four discrete possible levels. The four T-

contrasts generated to test one sided main

effects for the active condition showed

different areas of activation during the random

dot motion task. Of note is the fact that some

of the areas active in the easy task appears to

diminish and is completely absent in the

difficult task, as observed in Figure 6. This

supports the argument that prominently active

areas in the brain changes depending on the

difficulty/coherence level of the task

presented.1,2 However, this phenomenon is not

observed in Figure 7 illustrating the

prominently active areas in another subject.

Therefore, the presentation of these two

different figures representing two different

subjects indicate that the activated areas in the

brain during the same task may not be the same

for every subject.

The study had only considered the main

effects and interactions at the within-subject

level through a first level analysis. From these

findings, only the activated areas for a single Figure 4: The design matrix of fMRI image

files indicated in the rows, and conditions

shown in each column.

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subject can be visualized, and the results of

these analyses can then be presented as case

studies.

However, second-level analysis needs to be

performed on the resulting contrasts to show

the differences in activation of brain areas

between subjects. This subsequent step can be

utilized to make inferences about the

population from which the subjects were

drawn. Moreover, numerical estimates of

sensitivity can be acquired via the sensitivity

index, and the response bias in the model can

be estimated with C and β values.18 The area

under the ROC-curve may also be assessed to

measure the accuracy of the classifier

performance.19

The hemodynamic response observed in the

findings can be further visualized and labeled

to specifically recognize the prominently

active areas during the task involving different

coherence levels. This can be achieved by

using tools such as MRIcron or WFU

Pickatlas. The power of the level of activation

in these notable areas can also be

characterized. This kind of visualization

technique will be helpful in verifying the

research findings against the established

knowledge regarding the activation of brain

areas for processing information regarding the

Figure 5: Statistical Parametric Mapping showing the prominent areas marked during the

random dot motion task for four different coherence levels, as compared to their baseline

values. The design matrix with the selected contrasts are shown on the right of the image

representing each contrast.

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motion of an object. In particular, the level of

activation in the visual and visual association

areas, frontal eye field (FEF)/dorsolateral

prefrontal cortex (DLPFC), and the

supplemental motor area (SMA) will be taken

into account, to analyze how these areas were

implicated during the assigned random dot

motion task, and how their activity varied

depending on the level of coherence.1 The left

posterior DLPFC is expected to be more

responsive to the “easy” condition, compared

to levels with higher difficulty.2 The left

posterior cingulate cortex, left inferior parietal

lobule, and left fusifom/parahippocampal

gyrus are expected to show a similar

characteristic activation depending on the level

of coherence in the stimuli presented.2 The

differences between subjects regarding the

activation levels of the posterior parietal cortex

will be helpful in distinguishing their ability of

selective attention.4,5 The subject’s efficiency

in detecting right- and left- ward signals may

also be analyzed in regards to their differential

activation of the right versus the left parietal

cortex.7

Figure 6: Statistical Parametric Mapping showing the prominent areas marked during the

random dot motion task for four different coherence levels, as compared to their baseline values.

The design matrix with the selected contrasts are shown on the right of the image representing

each contrast.

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