Connectionists: New publication of computational modelling of antisaccade performance in early Huntington's disease

[Vassilis Cutsuridis]

Dear colleagues,

I would like to bring to your attention a recent paper of ours entitled
"NEURAL MODELLING OF ANTISACCADE PERFORMANCE OF HEALTHY CONTROLS AND EARLY
HUNTINGTON'S DISEASE PATIENTS" published in Chaos journal.

Questions, comments, criticism are most welcome. A brief description of our
research work and its findings follow:

Antisaccade task, a behavioral response inhibition paradigm, involves
suppression of the reflex to look towards a newly presented target (error
prosaccade response) and instead directs the eyes to a position diametrically
opposite to target’s position (correct antisaccade response). Failure to
suppress the error prosaccade response results in a direction error. Τwo
processes usually take place during this task: (1) suppression of an error
prosaccade towards the peripheral stimulus, and (2) generation of an
antisaccade to the diametrically opposite direction. Participants have been
observed to express any of the following three eye movement behaviors
during a trial: (1) Participant fails to suppress the error prosaccade
resultingin a direction error, (2) Participant makes an antisaccade, or (3)
Participant makes an error prosaccade and corrects with a corrected antisaccade
in the same trial.

The current accepted dogma in the antisaccade task is that a third top-down
inhibitory signal is needed to suppress the error prosaccade in favor of
the antisaccade. In line with this dogma past modelling studies of the
antisaccade task in health and Huntington disease (HD) required the
presence of a third STOP decision signal to suppress in trials the
erroneous response. These models although they provided a successful
mechanistic
view of decision making in the antisaccade task, they failed to
capture all aspects
of antisaccade performance.

Our research work described in our paper offers an alternative view, which
succeeds for the first time to:

1.  Capture all aspects of the antisaccade performance of both healthy
controls and early HD patients

2.  Offer a mechanistic view of processes taking place in the antisaccade
paradigm

3.  Decipher the mechanisms which give rise to the observed slowed and more
variable antisaccade latencies and increased error rates in HD patients
relative to healthycontrols.


The model shows that the poor HD antisaccade performance is not due  to a
deficit in the top-down inhibitory control of the erroneous responseas many
speculated, but instead is a productof a competition between two different
neuronal populations each coding for a different decision signal: one coding
for the erroneous prosaccade decision and the other one for antisaccade
decision.

The model accurately reproduces the error rates, response latencies and
latency distributions of antisaccades, error prosaccades and corrected
antisaccades of both healthy controls and HD participants. Our model shows
that the increased variability in the antisaccade and corrected antisaccade RT
distributions of HD participants are due to a slower and noisier accumulation
of information (μ and σ), but the HD patients’ confidence level required
before commitment to a particular course of action is not affected by the
disease.

Our results have major implications in clinical and pharmaceutical
research. Furthermore, our results illustrate the benefits of tightly
integrating psychophysical studies with computational neural modelling,
because the two methods complement each other and they may provide together
a strong basis for hypothesis generation and theory testing regarding the
neural basis of decision making in health and in disease.



Kind regards,
Vassilis Cutsuridis
University of Lincoln
UK
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