DIMACS* Workshop on Data Mining, Systems Analysis and Optimization in Neuroscience

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Oct 20, 2013 (3 years and 9 months ago)


DIMACS* Workshop on Data Mining, Systems
Analysis and Optimization in Neuroscience


W. Art Chaovalitwongse, Rutgers University

Leonidas D. Iasemidis, Arizona State University

Panos Pardalos, University of Florida

University of Florida, Gaine
sville, FL

17 February 2006

Report Author: Onur Seref (University of Florida).

Date of Report: June, 2006

*DIMACS was founded as a National Science Foundation Science and
Technology Center. It is a joint project of Rutgers Universi
ty, Princeton University,
AT&T Labs
Research, Bell Labs, NEC Laboratories America, and Telcordia
Technologies, with affiliated partners Avaya Labs, Georgia Institute of
Technology, HP Labs, IBM Research, Microsoft Research, Rensselaer
Polytechnic Institute
, and Steven Institute of Technology.



University of Florida has been hosting a series of conferences on biocomputing,
biomedicine and neuroscience with a focus on quantitative methods for more
than five years. The last conference , titled

“DIMACS Workshop on Data Mining,
Systems Analysis, and Optimization in Neuroscience”, was held between
February 15
17, 2006 in University of Florida. It was jointly sponsored by the
Center for Discrete Mathematics and Theoretical Computer Science (DIMACS)
the Biological, Mathematical, and Physical Sciences Interfaces Institute for
Quantitative Biology (BioMaPS), and the Rutgers Center for Molecular
Biophysics and Biophysical Chemistry (MB Center) under the auspices of the
DIMACS/BioMaPS/MB Center Special Focus on Information Processing in
. Also Center for Applied Optimization, College of Engineering, Genetics
Institute, Office of the Vice President for Research were among local sponso
from University of Florida.

A large variety of leading research topics in neuroscience were presented.
Plenary talks on fundamental aspects of the scope of the conference addressed
subjects such as the organizational structure of the transition from mi
croscopic to
macroscopic level [1]; probing brain functions at different spatial and temporal
scales through single trial experiments [2]; and the wiring in the brain as optimal
design problems solved by evolution [3]. Each plenary session ended with
ring questions and an extended discussion. Although the talks followed a
disciplinary structure in general, they can be grouped under the main topics
systems analysis

data mining

parallel to the main themes of
the conference. Und
er optimization, the main emphasis was on classification and
clustering methods and statistical approaches.



The data regarding brain functions have a highly non
linear and high
nature, and thus, often require efficient and so
phisticated optimization techniques
for a translation from electrical signals into meaningful interpretation of the
underlying mechanisms. Classification and clustering methods offer a powerful
way to enable such translations. These methods involve optimiz
ation models at
their core, which deliver the desired translation function in the most efficient way
with the most precise results achievable.

Talks that revolved around

introduced novel approaches with
significant results. Improvements ove
r optimization based solutions to
discrimination problems, which involve mixed integer programming formulations
for multiple hyperplane classification, were proposed [4], together with extensions
on decision trees [5] and comparative computational results
[6]. Single trial
based experiments also provide a natural ground for classification. Applications
of kernel based methods were presented for understanding the integration of
visual and sensory
motor cortical areas through intracranial single trial neural

recordings from monkeys [7]. One
class machine learning techniques were
demonstrated on a similar set of experiments involving visual and motor areas of
the human brain, using fMRI [8]. New Bayesian network based classification
techniques for high dimensi
onal data were introduced. The first of the two talks
on this subject featured a score
based structure inference to study language
acquisition in songbirds [9], while the second talk used a graphical model to
combine a tabu search metaheuristic with a Mark
ov blanket method [10].

Being very similar to classification algorithms in practice, novel

methods also found important application areas in mining brain imaging problems.
A new fuzzy hyperplane clustering algorithm that uses a union of hyper
was shown to have direct applications in sparse representation problems, and
successful results on fMRI data analysis were presented [11]. An image
clustering method, which uses level set methods in a hierarchical structure, was
introduced toge
ther with the results of a straightforward parallel implementation
for brain imaging problems [12]. A quadratic optimization model was proposed to
study the brain clustering problem, which has a potential application in
differentiating the normal and pre
eizure states of an epileptic brain through
analysis of EEG data [13].


Systems Analysis

The brain is a very complex system with a high level of uncertainty. Therefore,
one of the most convenient ways of analysis is building statistical models for th
variability of the brain functions over time and their distribution over different
cortexes. An alternative approach to analyze such functions is through simulation
using artificial neural networks. Some other network based approaches such as
coherence m
ethods or finding cliques in the brain also provide means to
understand the brain on different scales as a highly connected system.

Statistical approaches, in general, comprise a large majority of quantitative
studies on neural data, as they constituted t
he core of many talks during the
conference. A generative data model that explains the variability of evoked
responses was introduced in a classical statistical framework [14]. An extension
followed this talk with more focus on current source density analy
sis with results
on simulated data, as well as monkeys performing an intermodal selective
attention task [15]. Different adaptations of stationary analytics methods were
applied on a decision
making task in rats to understand nonstationary neural
[16]. Spatio
Temporal changes are important indicators in predicting
epileptic seizures. A statistical technique that involves a mantel test statistic on a
linear synchronization measure was introduced to detect such changes
before the seizure happens
, up to more than an hour in advance [17].

Intuitively one of the best ways to model the brain is as a complex network, or to
use network based models to solve problems regarding neural data, in different
scales of connectivity in the brain. Using artific
ial neural networks is a well
established data mining technique, however may be slow for large datasets
because of slow convergence. Combining Monte Carlo with artificial neural
network is capable of overcoming this problem with comparable generalization
esults [18]. Exploratory simulation tools that use an integrate and fire model
were shown to be very efficient, even with a massive number of neurons, in
understanding the influence of anatomy on information flow [19].

The existence of networks on varyin
g scales of complexity and magnitude is a
well studied, and yet mostly unexplored research area. One such network is the
brain’s language processing system. Although the basic large scale structure is
well known in individuals without language deficits, la
nguage processing may be
fundamentally different in individuals with dyslexia. A comprehensive study using
magnetoencephalography was presented that shows a clear distinction in the
network structure of a normal and a dyslexic brain [20], with further veri
using a power and a coherence study [21]. The highly connected structure of the
brain sometimes causes more serious problems through synchronization of large
scale networks such as in the case of epileptic seizures. This underlying network
re may be revealed by finding maximum cliques that shows high pair
spectral similarity, and a brain similarity network can be formed to discover the
pattern of epileptic seizures efficiently [22].


Data Mining

As the rising level of functional br
ain imaging and data acquisition technology
enable high
speed and high
resolution data to be available to researchers, major
challenges result from a data mining perspective. The talks under the data
mining topic involved important application areas such a
s epilepsy detection and
control systems and visualization techniques that involve high resolution imaging,
spatiotemporal tracking and monitoring plastic deformation in cortical tissues.

Epilepsy and related problems are one of the central research areas

neuroscience. Aside from the visiting speakers, there was a significant local
contribution from the speakers affiliated with McKnight Brain Institute, a part of
the Shands research hospital at University of Florida. The majority of the talks
evolved ar
ound quantitative techniques in analyzing the underlying mechanisms
of epilepsy and prediction of seizures before they happen. One of the important
results pointed out that seizures serve as dynamical resetting mechanisms of the
brain, which increased the
knowledge base on epileptogenesis, seizure
intervention and control, and even intermittent spatiotemporal state transitions in
other complex systems [23]. Another notable approach was through simulation of
an epileptic brain by modeling it as an interconne
cted network of nonlinear
chaotic oscillators. This study showed that by distributed sensing and stimulation
seizures can be suppressed, which is consistent with clinical studies [24]. A novel
seizure control system was developed that consists of a closed

loop feedback
control system receiving EEG signals and converting them to dynamical indicator
quantities known as the short
term maximum Lyapunov exponent (STL_max),
which in return activates a medication delivery to prevent seizure occurrence.
The succes
s of the technique was demonstrated on rodents with future plans to
extend it to human patients [25]. A similar study incorporates average angular
frequency from EEG signals together with STL_max and it was shown that both
indicators converge preceding sei
zures in the chronic limbic epilepsy in rats [26].
As an extension to this study an electrical stimulation seizure control system
based on state space regional coupling was developed [27]. STL_max was also
used as a new brain mapping method using a Gaussia
n mixture model to
approximate the spatial distribution of STL
max. The results provided important
information on the spatial organization of the epileptogenic focus dynamics [28].

Quantitative methods were especially emphasized in visualization methods,
some of which involved complex mathematical techniques. A variety of geometric
invariants that quantify topological properties of cortical surfaces were introduced
on global and local levels of detail in 3D space [29]. Including the time
component, through

a similar study, a visualization tool was developed to help
spatiotemporal analysis on neural models, voltage
dye imaging and
electrode array experiments. The effectiveness of the tool is shown on a
comparison of structure, timing and sync
hronization properties of neural
populations [30]. Another visualization technique was developed to track
changes in the neural tissue after the insertion of a prosthetic device. A finite
element method based model was used to modulate parameters of the de
with respect to the changing geometry of the neural tissue [31].



Significant results on all of the main topics of optimization, system analysis and
data mining were presented. Contributions ranging from theoretical and
neuroscience to detection and prevention of neural abnormalities
and to novel techniques of visualization on different scales point to new research
directions, while possible interactions between the research areas define new
dimensions to be explored in n
euroscience. With such a great variety of different
approaches, and highly interconnected problems, the conference concluded with
the exchange of many ideas and results, which are promising seeds for fruitful
collaborations in new research directions. The
productivity of this conference has
been encouraging and inspirational to many researchers. Future conferences will
continue to bring together the prominent researchers, who are on the cutting
edge of the growing field of neuroscience.


Open Questions
And Future Directions

Is there an underlying neural signal code that governs brain functions? Or
is there no code at all?

How is the brain organized from micro to macro scale? What are the
different stages of the transition between these scales?

What are
the evolutionary mechanisms that shaped the wiring in the
brain? What are the similarities and differences between wiring structure
across different species?

How are the functions of the brain integrated over time and over different
cortical areas in diffe
rent scales?

Is it possible to detect a specific thought from EEG recordings?

Which channels are most relevant to perform data mining? How can such
channels be chosen in an automatic way?

How can the source of evoked potentials be precisely localized?

Is i
t possible to detect epileptic seizures with 100 percent accuracy? Is it
possible to eliminate seizures by stimulation of the brain.

Can the brain functions be modeled accurately based on statistical
methods only?

How can the brain function be visualized a
t the microscopic level? How
can visualization tools be integrated at different levels of resolution?

How can the brain be simulated accurately to produce similar input/output
even for basic functions?



The author and the DIMACS Cent
er acknowledge the support of the National
Science Foundation under grant number NSF CCF 05
14703 Workshops
Connecting Theoretical Computer Science to Other Fields to Rutgers University.


[1] Walter J. Freeman
, Sourcing organizing concepts
for neocortical dynamic
data from many
body physics.

University of California, Berkeley

[2] Andreas Ioannides,

Probing Brain Function Across Different Spatial and
Temporal Scales with Tomographic Analysis of Magnetoencephalographic
Brain Science
Institute, RIKEN, Japan

[3] Dmitri Chklovskii,
Evolution as the Blind Engineer: Wiring Minimization in the

Cold Spring Harbor Laboratory


Fred Glover,
Discrimination and Classification by Mixed Integer Programming.
University of Colorado

[5] M
ichele Samorani,

Based Decision Trees and Their Optimization

Università degli Studi di Bologna, Italy

[6] Fang Liang,

Computational Evaluation of Mixed Integer Programming Models
for Discrimination and Classification.
University of Colorado


Onur Seref,

Kernel Based Methods Applied to Single Trial Neural Signals.

University of Florida

[8] Larry Manevitz,
Reading the Mind: fMRI Analysis Via One
Class Machine
Learning Techniques.

University of Haifa, Israel

[9] Alexander Hartemink,
Information Flow Networks in Songbirds.

[10] Xue Bai,
Tabu Search Enhanced Graphical Models for Classification of High
Carnegie Mellon University

[11] Pando Georgiev,
Fuzzy Hyperplane Clustering Algorithm and Applications to

Sparse Representations.
University of Cincinnati

[12] Moongu Jeon ,
Parallel Image Clustering using Level Set Methods.
Institute of Science and Technology, Korea

[13] W. Art Chaovalitwongse,
Cluster Analysis of Epileptic Brains.

[14] Mingzhou Ding,
Statistical Modeling of Neurobiological Data
. University of

[15] Mukesh Dhamala,
Current Source Density Analysis of Ongoing Neural
Activity: Theory and Applications.

University of Florida

[16] Linda Hermer
ions of Stationary Analytics Techniques to
Understand the NonstationaryNeural Activity Underlying Performance of a
Complex Cognitive Task.

University of Florida

[17] Anant Hegde,
Tracking Spatio
Temporal Changes in ECOG.
University of

[18] Jeff K
Neural Networks, Monte Carlo Methods, and Real

East Tennessee University

[19] Maya Maimon,
A Simulation Tool Using Discrete Integrate and Fire Neurons:
Modeling the Influence of Anatomy on Information Flow in Very Large Simulated

University of Haifa, Israel

[20] Richard E. Frye,

Dyslexia: An Example of Natural Variation in Large
Neural Network Organization.

University of Florida

[21] Yan Zang,
MEG in Dyslexia: A Power and Coherence Study
. University of

] Wichai Suharitdamrong,
Graph Theory
Based Data Mining Techniques to
Study Similarity of Epileptic Brain Network.

University of Florida

[23] Leonidas D. Iasemidis,
Resetting of Brain Dynamics by Epileptic Seizures
Arizona State University

[24] Kostas T
sakalis, A Feedback Control Systems View of Epileptic Seizures.
Arizona State University

[25] Kevin M. Kelly
, Development of a Rodent Seizure Control System using
IntracerebroventricularInjections of Midazolam
. Drexel University

[26] Paul R. Carney,
mical EEG Properties in the Limbic Epilepsy Rat Model.

University of Florida

[27] Sandeep P. Nair,
Dynamical State Dependent Electrical Stimulation for
Seizure Control in a Chronic Limbic Epilepsy Model.

University of Florida

[28] Nadia Mammone,
A New Br
ain Mapping Based on the Visualization and
Modeling of the Short TermMaximum Lyapunov Exponent.

Reggio Calabria, Italy

[29] Monica K. Hurdal,
Shape Analysis for Automated Sulcal Classification and
Parcellation of MRI Data.
(Florida State University

Kay A. Robbins,
Visual Analysis for Comparing Structure, Timing and
Synchronization Properties of Neural Populations.

Univsersity of Texas at San

[31] Aparna Gupta,
Online Analysis of Device
Tissue Interactions

Tissue Impedance Spectra.

Resselaer Polytechnic Institute