Cognitive Science Symposium on Modeling and Brain Imaging

[Stephen Jose Hanson]

[ Moderator's note:

Steve Hanson would like to suggest that a critical aspect of Brain
Imaging in the future will be Neural Network (or system level)
modeling.  In order to stimulate discussion of this topic on the
Connectionists list, he submitted the program for a symposium he's
organized on the subject for next week's Cognitive Science conference.
There are some intereting ideas here.  Perhaps we'll also have a
workshop on the topic at NIPS this year.  Persons seeking more
information about the Cognitive Science symposium may contact Steve at
jose at psychology.rutgers.edu.

  -- Dave Touretzky ]

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19th Annual Cognitive Science Society, Stanford 8/7-10/97

Brain Imaging:  Models, Methods and High Level Cognition (8/8/97, 2pm - 4pm)
(Organizer: Stephen Hanson)

Brain Imaging methods hold the promise of being the new "brass
instrument" for Psychology.  These methods provide tantalizing
snapshots of mental activity and function.  Nonetheless, basic
measurement questions arise as more complex mental functions are being
inferred.  Tensions arise in determining what is being measured during
blood flow changes in the brain ? And what are the role of
computational models in representing, interpreting and understanding
the nature of the mental function which brain imaging methods probe?

The idea behind this symposium is to examine the tension between
measurement and modeling in Brain Imaging especially against the
backdrop of high level cognitive processes, such as reasoning,
categorization and language.  An important compenent of these
techniques in the future might be in how they may utilize
computational and mathematical models that are initally biased with
prior beliefs about the relevant location estimators and temporal
structure of the underlying mental process.

       "Functional Neuroimaging: A bridge between Cognitive and Neuro
Sciences?".

Tomas Paus
MNI

I will start by posing a question whether one can marry cognitive and
neuro-sciences, and what role functional neuroimaging can play here. I
will ask, with Tulving, whether it is true that "we lack the requisite
background knowledge to appreciate each others excitement", and what
can be done about it.
I will then go on to outline the basic principles and the techniques of
the research that deals with the brain/behavior relationship, pointing
out crucial distinctions between "disruption" (i.e. lesion, stimulation,
etc.) and "correlate" (i.e. unit activity, EEG, PET, fMRI) studies. I
will review the basic principles of the current neuroimaging methods
(concentrating on PET and fMRI, but mentioning also NIRS). At the end of
this methodological section, I will again stress that, using
neuroimaging, we measure brain correlates of behavior and, as such, we
are limited in drawing any causal inferences about the brain/behavior
relationship. This does not mean that we shouldnt be doing this kind of
research though. It only means, in my mind, that we may need to focus on
fairly simple cognitive processes, and that we absolutely need to
constrain the interpretation of  imaging data by specific a priori
hypotheses based on the knowledge of brain anatomy, physiology, etc. In
this context, I will also make a distinction between directed (or
predicted) and exploratory search in the entire brain volume for
significant changes in the signal.
In the second half of the talk, I will concentrate on the issue of
functional connectivity and how we can study it using PET (and fMRI). I
will briefly mention results of our research on corollary discharges and
on combining transcranial magnetic stimulation with PET.



"Methods and Models in interpreting fMRI:  The case of Independent
Components of fMRI Images"

Martin J. McKeown
The Salk Institute

Many current fMRI experiments use a block design in which the subject is
requested to sequentially perform experimental and control tasks in an
alternating sequence of 20-40-s blocks. The bulk of the fluctuations in
the resultant time series recorded from each brain region (a "voxel")
arise not from local task-related activations, but rather from machine
noise, subtle subject movements, and heart and breathing rhythms. This
tangled mixture of signals presents a formidable challenge for
analytical methods attempting to tease apart task-related changes in the
time courses of 5,000 - 25,000 voxels.  Correlational and ANOVA-like
analytical methods technically require narrow \a priori\ assumptions
that may not be valid in fMRI data.  Moreover, activations arising from
important cognitive processes like changes in subject task strategy or
decreasing stimulus novelty cannot typically be tested for, as their
time courses are not easily predicted in advance.
Signal-processing strategies for analyzing fMRI experiments monitoring
cognition are generally used without regard to basic neuropsychological
principles, such as localization or connectionism.  We propose that an
appropriate criteria for the separation of fMRI data into cognitively
and physiologically meaningful components is the determination of the
separate groups of multi-focal anatomical brain areas that are activated
synchronously during an fMRI trial. With this view, each scan obtained
during an fMRI experiment can be considered as the mean activity plus
the sum of enhancements (or suppressions) of activity from the possibly
overlapping individual components.
Using an Independent Component Analysis (ICA) algorithm, we demonstrate
how the fMRI data from Stroop color-naming and attention orienting
experiments can be separated into numerous spatially independent
components, some of which demonstrate transient and sustained
task-related activation during the behavioral experiment.  Active areas
of these task-related components correspond with regions implicated from
PET and neuropsychological studies.  Other components relate to machine
noise, subtle head movements and presumed cardiac and breathing
pulsations.
Considering fMRI data to be the sum of independent areas activated with
different time courses enables, with minimal \a priori\ assumptions, the
separation of artifacts from transient and sustained task-related
activations. Determining the independent components of fMRI data appears
to be a promising method for the analysis of cognitive experiments in
normal and clinical populations.


Sometimes Weak is Strong: Functional Imaging Analysis with Minimal
Assumptions
     Benjamin Martin Bly and Mark Griswold

Brain-Imaging Studies of Categorization by Rule or Family Membership
     Andrea Patalano and Edward Smith

 A PET Study of Deductive Versus Probabilistic Reasoning
     Stefano F. Cappa, Daniela Perani, Daniel Osherson, Tatiana Schnur,
and Ferruccio Fazio

Deductive versus probabilistic inferences are distinguished by normative
theories, but it is still unknown whether these two forms of reasoning
engage similar brain areas. In order to investigate the neurological
correlates of reasoning, we have performed an activation study using
positron emission tomography and 15O-water in normal subjects. Cerebral
perfusion was assessed during a "logic task", in which they had to
distinguish between valid and invalid arguments; a "probability task",
in which they had to judge whether the conclusion had a greater chance
of being true or false, supposing the truth of the premises; and a
"meaning task", in which they had to evaluate the premises and the
conclusions to determine whether any had anomalous content. The latter
was used as "baseline" task: identical arguments were evaluated either
for validity, probability or anomaly. In the direct comparison of the
two reasoning tasks, probabilistic reasoning increased regional cerebral
blood flow (rCBF) in dorsolateral frontal regions, whereas deductive
reasoning enhanced rCBF in associative occipital and parietal regions,
with a right hemispheric prevalence. Compared to the meaning condition,
which involved the same stimuli, both probabilistic and deductive
reasoning increased rCBF in the cerebellum. These results are compatible
with the idea that deductive reasoning has a geometrical character
requiring visuo-spatial processing, while the involvement of the frontal
lobe in probabilistic tasks is in agreement with neuropsychological
evidence of impairment in cognitive estimation in patients with frontal
lesions. The cerebellar activation found in both reasoning tasks may be
related to the involvement of working memory.


 Neural Correlates of Mathematical Reasoning: An fMRI Study of
Word-Problem Solving
     Bart Rypma, Vivek Prabhakaran, Jennifer A. L. Smith, John E.
Desmond, Gary H. Glover,
     and John D. E. Gabrieli