An MEG Study of Response Latency and 
Variability in the Human Visual System 
During a VisualsMotor Integration Task 
Akaysha C. Tang 
Dept. of Psychology 
University of New Mexico 
Albuquerque, NM 87131 
akaysha@unm. edu 
Barak A. Pearlmutter 
Dept. of Computer Science 
University of New Mexico 
Albuquerque, NM 87131 
bap@cs.unm. edu 
Tim A. Hely 
Santa Fe Institute 
1399 Hyde Park Road 
Santa Fe, NM 87501 
timhely@santafe. edu 
Michael Zibulevsky 
Dept. of Computer Science 
University of New Mexico 
Albuquerque, NM 87131 
michael@cs. unto. edu 
Michael P. Weisend 
VA Medical Center 
1501 San Pedro SE 
Albuquerque, NM 87108 
mweisend@unm. edu 
Abstract 
Human reaction times during sensory-motor tasks vary consider- 
ably. To begin to understand how this variability arises, we exam- 
ined neuronal populational response time variability at early versus 
late visual processing stages. The conventional view is that pre- 
cise temporal information is gradually lost as information is passed 
through a layered network of mean-rate "units." We tested in hu- 
mans whether neuronal populations at different processing stages 
behave like mean-rate "units". A blind source separation algorithm 
was applied to MEG signals from sensory-motor integration tasks. 
Response time latency and variability for multiple visual sources 
were estimated by detecting single-trial stimulus-locked events for 
each source. In two subjects tested on four visual reaction time 
tasks, we reliably identified sources belonging to early and late vi- 
sual processing stages. The standard deviation of response latency 
was smaller for early rather than late processing stages. This sup- 
ports the hypothesis that human populational response time vari- 
ability increases from early to late visual processing stages. 
I Introduction 
In many situations, precise timing of a motor output is essential for successful task 
completion. Somehow the reliability in the output timing is related to the reliability 
of the underlying neural systems associated with different stages of processing. Re- 
cent literature from animal studies suggests that individual neurons from different 
brain regions and different species can be surprising reliable [1, 2, 5, 7-9, 14, 17, 18], 
186 A. C. Tang, B. A. Pearlmutter, T. A. Hely, M. Zibulevsky and M. P Weisend 
on the order of a few milliseconds. Due to the low spatial resolution of electroen- 
cephalography (EEG) and the requirement of signal averaging due to noisiness of 
magnetoencephalography (MEG), in vivo measurement of human populational re- 
sponse time variability from different processing stages has not been available. 
In four visual reaction time (RT) tasks, we estimated neuronal response time vari- 
ability at different visual processing stages using MEG. One major obstacle that has 
prevented the analysis of response timing variability using MEG before is the rela- 
tive weakness of the brain's magnetic signals (100fT) compared to noise in a shielded 
environment (magnetized lung contaminants: 106fT; abdominal currents 105fT; car- 
diogram and oculogram: 104fT; epileptic and spontaneous activity: 10afT) and in 
the sensors (10fT) [13]. Consequently, neuronal responses evoked during cognitive 
tasks often require signal averaging across many trials, making analysis of single- 
trial response times unfeasible. 
Recently, Bell-Sejnowski Infomax [1995] and Fast ICA [10] algorithms have been 
used successfully to isolate and remove major artifacts from EEG and MEG data 
[11, 15, 20]. These methods greatly increase the effective signal-to-noise ratio and 
make single-trial analysis of EEG data feasible [12]. Here, we applied a Second- 
Order Blind Identification algorithm (SOBI) [4] (another blind source separation, or 
BSS, algorithm) to MEG data to find out whether populational response variability 
changes from early to late visual processing stages. 
2 Methods 
2.1 Experimental Design 
Two volunteer normal subjects (females, right handed) with normal or corrected- 
to-normal visual acuity and binocular vision participated in four different visual RT 
tasks. Subjects gave informed consent prior to the experimental procedure. During 
each task we recorded continuous MEG signals at a 300Hz sampling rate with a 
band-pass filter of 1-100Hz using a 122 channel Neuromag-122. 
In all four tasks, the subject was presented with a pair of abstract color patterns, 
one in the left and the other in the right visual field. One of the two patterns was a 
target pattern. The subject pressed either a left or right mouse button to indicate on 
which side the target pattern was presented. When a correct response was given, a 
low or high frequency tone was presented binaurally following respectively a correct 
or wrong response. The definition of the target pattern varied in the four tasks and 
was used to control task difficulty which ranged from easy (task 1) to more difficult 
(task 4) with increasing RTs. (The specific differences among the four tasks are not 
important for the analysis which follows and are not discussed further.) 
In this study we focus on the one element that all tasks have in common, i.e. ac- 
tivation of multiple visual areas along the visual pathways. Our goal is to identify 
visual neuronal sources activated in all four visual RT tasks and to measure and 
compare response time variability between neuronal sources associated with early 
and later visual processing stages. Specifically, we test the hypothesis that popula- 
tional neuronal response times increase from early to later visual processing stages. 
2.2 Source Separation Using SOBI 
In MEG, magnetic activity from different neuronal populations is observed by many 
sensors arranged around the subject's head. Each sensor responds to a mixture of 
the signals emitted by multiple sources. We used the Second-Order Blind Identi- 
MEG Study of Response Latency and Variability 187 
fication algorithm (SOBI) [4] (a BSS algorithm) to simultaneously separate neu- 
romagnetic responses from different neuronal populations associated with different 
stages of visual processing. Responses from different neuronal populations will be 
referred to as source responses and the neuronal populations that give rise to these 
responses will be referred to as neuronal sources or simply sources. These neu- 
tonal sources often, but not always, consist of a spatially contiguous population of 
neurons. BSS separates the measured sensor signals into maximally independent 
components, each having its own spatial map. Previously we have shown that some 
of these BSS separated components correspond to noise sources, and many others 
correspond to neuronal sources [19]. 
To establish the identity of the components, we analyzed both temporal and spa- 
tial properties of the BSS separated components. Their temporal properties are 
displayed using MEG images, similar to the ERP images described by [12] but 
without smoothing across trials. These MEG images show stimulus or response 
locked responses across many trials in a map, from which response latencies across 
all displayed trials can be observed with a glance. The spatial properties of the sep- 
arated components are displayed using a field map that shows the sensor projection 
of a given component. The intensity at each point on the field map indicates how 
strongly this component influences the sensor at this location. 
The correspondence between the separated components and neuronal populational 
responses at different visual processing stages were established by considering both 
spatial and temporal properties of the separated components [19]. For example, 
a component was identified as an early visual neuronal source if and only if (1) 
the field pattern, or the sensor projection, of the separated component showed 
a focal response over the occipital lobe, and (2) the ERP image showed visual 
stimulus locked responses with latencies shorter than all other visual components 
and falling within the range of early visual responses reported in studies using 
other methods. Only those components consistent both spatially and temporally 
with known neurophysiology and neuroanatomy were identified as neuronal sources. 
2.3 Single Event Detection and Response Latency Estimation 
For all established visual components we calculated the single-trial response latency 
as follows. First, a detection window was defined using the stimulus-triggered av- 
erage (STA). The beginning of the detection window was defined by the time at 
which the STA first exceeded the range of baseline fluctuation. Baseline fluctuation 
was estimated from the time of stimulus onset for approximately 50ms (the visual 
response occurred no earlier than 60ms after stimulus onset.) The detection win- 
dow ended when the STA first returned to the same level as when the detection 
window began. The detection threshold was determined using a control window 
with the same width as the detection window, but immediately preceding the de- 
tection window. The threshold was adjusted until no more than five false detections 
occurred within the control window for each ninety trials. We estimated RTs using 
the leading edge of the response, rather than the time of the peak as this is more 
robust against noise. 
3 Results 
In both subjects across all four visual RT tasks, SOBI generated components that 
corresponded to neuronal populational responses associated with early and late 
stages of visual processing. In both subjects, we identified a single component with 
a sensor projection at the occipital lobe whose latency was the shortest among all 
188 A. C. Tang, B. A. Pearlmutter, T. A. Hely, M. Zibulevsky and M. P. Weisend 
task 
1 
2 
3 
4 
early source late source 
1 
2 
3 
4 
Figure 1: MEG images and field maps for an early and a late source from each 
task, for subject 1 (top) and subject 2 (bottom). MEG image pixels are brightness- 
coded source strength. Each row of a bitmap is one trial, running 1170ms from left 
to right. Vertical bars mark stimulus onset, and 333ms of pre-stimulus activity is 
shown. Each panel contains 90 trials. Field map brightness indicates the strength 
with which a source activates each of the 61 sensor pairs. 
visual stimulus locked components within task and subject (Fig. 1 left). We iden- 
tified multiple components that had sensor projections either at occipital-parietal, 
occipital-temporal, or temporal lobes, and whose response latencies are longer than 
early-stage components within task and subject (Fig. 1 right). 
Fig. 2a shows examples of detected single-trial responses for one early and one late 
visual component (left: early; right: late) from one task. To minimize false positives, 
the detection threshold was set high (allowing 5 false detections out of 90 trials) 
at the expense of a low detection rate (15%-67%.) When Gaussian filters were 
applied to the raw separated data, the detection rates were increased to 22-91% 
(similar results hold but not shown). Fig. 2b shows such detected response time 
histograms superimposed on the stimulus triggered average using raw separated 
data. One early (top row) and two late visual components (middle and bottom 
rows) are plotted for each of the four experiments in subject one. The histogram 
width is smallest for early visual components (short mean response latency) and 
larger for late visual components (longer latency.) 
We computed the standard deviation of component response times as a measure of 
response variability. Fig. 2c shows the response variability as a function of mean 
response latency for subject one. Early components (solid boxes, shorter mean 
latency) have smaller variability (height of the boxes) while late components (dashed 
boxes, longer mean latency) have larger variability (height of the boxes). Multiple 
MEG Study of Response Latency and Variability 189 
lme (ms) 
 20 
I I I I i I o 
o 1oo o 1oo 
Tim (ms) 
1Or 
Time (ms) 
o 
True (ms) 
Time (n') Time 
0 100 200 300 400 0 100 200 300 400 
True (ms) True (ms) 
Time 
o 
Time (m) 
I 
33me (m) 
100 150 200 
latency (ms) 
Figure 2: (a, left) Response onset was estimated for each trial via threshold crossing 
within a window of eligibility. (b, top right) The stimulus-locked averages for a 
number of sources overlaid on histograms of response onset times. (c, bottom 
right) Scatter plot of visual components from all experiments on subject 1 showing 
the standard deviation of the latency (y axis) versus the mean latency (x axis), 
with the error bars in each direction indicating one standard error in the respective 
measurement. Lines connect sources from each task. 
visual components from each task are connected by a line. Four tasks were shown 
here. There is a general trend of increasing standard deviation of response times 
as a function of early-to-late processing stages (increasing mean latency from left 
to right). For the early visual components the standard deviation ranges from 
6.6+0.63ms to 13.4+1.23ms, and for the late visual components, from 9.9+0.86ms 
to 38.85:3.73ms (t = 3.565, p = 0.005.) 
4 Discussion 
By applying SOBI to MEG data from four visual RT tasks, we separated compo- 
nents corresponding to neuronal populational responses associated with early and 
190 A. C. Tang, B. A. Pearlmutter, T. A. Hely, M. Zibulevs19, and M. P Weisend 
later stage visual processing in both subjects across all tasks. We performed single- 
trial RT detection on these early- and late-stage components and estimated both 
the mean and stdev of their response latency. We found that variability of the 
populational response latency increased from early to late processing stages. 
These results contrast with single neuron recordings obtained previously. In early 
and late visual processing stages, the rise time of mean firing rate in single units 
remained constant, suggesting an invariance in RT variability [16]. Characterizing 
the precise relationship between single neuron and populational response reliability 
is difficult without careful simulations or simultaneous single unit and MEG record- 
ing. However, some major differences exist between the two types of studies. While 
MEG is more likely to sample a larger neuronal population, single unit studies are 
more likely to be selective to those neurons that are already highly reliable in their 
responses to stimulus presentation. It is possible that the most reliable neurons at 
both the early and late processing stages are equally reliable while large differences 
exist between the early and late stages for the low reliability neurons. 
Previously, ICA algorithms have been used successfully to separate out various noise 
and neuronal sources in MEG data [19, 20]. Here we show that SOBI can also be 
used to separate different neuronal sources, particularly those associated with dif- 
ferent processing stages. The SOBI algorithm assumes that the components are 
independent across multiple time scales and attempts to minimize the temporal 
correlation at these time scales. Although neuronal sources at different stages of 
processing are not completely independent as assumed in SOBI's derivation, BSS 
algorithms of this sort are quite robust even when the underlying assumptions are 
not fully met [6], i.e. the goodness of the separation is not significantly affected. 
The ultimate reality check should come from satisfying physiological and anatom- 
ical constraints derived from prior knowledge of the neural system under study. 
This was carried out for our analysis. Firstly, the average response latencies of the 
separated components fell within the range of latencies reported in MEG studies us- 
ing conventional source modeling methods. Secondly, the spatial patterns of sensor 
responses to these separated components are consistent with the known functional 
anatomy of the visual system. 
We have attempted to rule out many confounding factors. Our observed results 
cannot be accounted for by a higher signal to noise ratio in the early visual re- 
sponses. The increase in measured onset response time variability from early to 
late visual processing stages was actually accompanied by an slightly lower signal- 
to-noise ratio among the early components. The number of events detected for the 
later components were also slightly greater than the earlier components. The higher 
signal-to-noise ratio at later components should reduce noise-induced variability in 
the later components, which would bias against the hypothesis that later visual re- 
sponses have greater response time variability. We also found that response duration 
and detection window size cannot account for the observed differential variabilities. 
Later visual responses also had gentler onset slopes (as measured by the stimulus- 
triggered average). Sensor noise unavoidably introduces noise into the response 
onset detection process. We cannot rule out the possibility that the interaction of 
the noise with the response onset profiles might give rise to the observed differ- 
ential variabilities. Similarly, we cannot rule out the possibility that even greater 
control of the experimental situation, such as better fixation and more effective 
head restraints, would differentially reduce the observed variabilities. In general, all 
measured variabilities can only be upper bounds, subject to downward revision as 
improved instrumentation and experiments become available. It is with this cau- 
tion in mind that we conclude that response time variability of neuronal populations 
increases from early to late processing stages in the human visual system. 
MEG Study of Response Latency and Variability 191 
Acknowledgments 
This research was supported by NSF CAREER award 97-02-311, and by the Na- 
tional Foundation for Functional Brain Imaging. 
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