Table Of ContentSpringer Series in Computational Neuroscience
Volume 14
Series Editors
Alain Destexhe
CNRS, UPR -2191, UNIC, Gif-sur-Yvette, France
Romain Brette
Institut de la Vision, Paris, France
Computational Neuroscience gathers monographs and edited volumes on all as-
pects of computational neuroscience, including theoretical and mathematical neuro-
science, biophysics of the brain, models of neurons and neural networks, and meth-
ods of data analysis (e.g. information theory). The editors welcome suggestions and
projects for inclusion in the series.
About the Editors:
Alain Destexhe is Research Director at the Centre National de la Recherche Sci-
entifique (CNRS), France, and Romain Brette is Research Director at the Institut
National de la Santé et de la Recherche Médicale (INSERM), France.
More information about this series at http://www.springer.com/series/8164
Basabdatta Sen Bhattacharya
Fahmida N. Chowdhury
Editors
Validating Neuro-
Computational Models
of Neurological and
Psychiatric Disorders
1 3
Editors
Basabdatta Sen Bhattacharya Fahmida N. Chowdhury
School of Engineering Office of Multidisciplinary Activities
University of Lincoln National Science Foundation
Lincoln Arlington
United Kingdom Virginia
USA
ISSN 2197-1900 ISSN 2197-1919 (electronic)
Springer Series in Computational Neuroscience
ISBN 978-3-319-20036-1 ISBN 978-3-319-20037-8 (eBook)
DOI 10.1007/978-3-319-20037-8
Library of Congress Control Number: 2015951468
Springer Cham Heidelberg New York Dordrecht London
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Foreword
There is a long and successful tradition of using mathematical modelling to ad-
vance understanding in the neurosciences. The seminal example being the work
of Alan Hodgkin and Andrew Huxley on the action potential dating back to the
1950s. As well as being seen as a tool for complementing our understanding of
fundamental biological processes, computational modelling is now a widely ac-
cepted practice for gaining knowledge when direct experimental probing is either
infeasible, impractical or ethically unsound. A case in point is the understanding
of human diseases of the brain and psychiatric disorders. Hand-in-hand with the
use of computational modelling and model validation comes the use of other tools
from the arsenal of theoretical neuroscience, including dynamical systems theory,
time-series analysis and multi-scale modelling. In much the same way that these
essentially mathematical tools have helped shed light on understanding single cell
and small network behaviour, they are now beginning to be wielded effectively at
the whole brain level. This compilation exemplifies such an approach, and one sees
much use of so-called neural mass models to develop frameworks that can encom-
pass not only forward modelling of neuroimaging signals but the utilisation of the
growing wealth of data from imaging modalities, such as EEG, MEG, and fMRI to
construct biologically meaningful models for real brain states. It is a delight to see
this framework being pushed to tackle issues in epilepsy, deep brain stimulation,
and Alzheimer’s disease, with a concomitant emphasis on the development of next
generation whole brain models and their integration with statistical methods to de-
velop powerful new approaches relevant to clinical applications. Indeed this book
is an important step along a path that also poses plenty of challenges for the wider
modelling community, including the incorporation of extracellular space, models
of non-excitable glial cells, neurovascular coupling, and metabolic regulation, to
name but a few processes to incorporate within the next generation of whole brain
models.
Centre for Mathematical Medicine and Biology, Stephen Coombes
School of Mathematical Sciences,
University of Nottingham
v
Preface
This book originated from a curious and serendipitous mix of personal and profes-
sional factors several years ago. We (Basabdatta Sen Bhattacharya and Fahmida N
Chowdhury) first met in 2010, during a luncheon at the WCCI (World Congress
in Computational Intelligence) in Barcelona, Spain. At that conference, Fahmida
had co-organized and presented an NSF-NIH (US National Science Foundation
and US National Institutes of Health) grant-writing workshop with NIH scientist
Patricia Mabry. Some of the potential funding areas from both of those US agen-
cies could involve neuro-computational modeling of various disorders. Basabdatta
was already working on using computational models in understanding biomark-
ers in brain-signals corresponding to neurological disorders. Against this backdrop,
our conversation turned to health-related applications of mathematical and com-
putational modeling; and it is during these discussions that we questioned whether
there were any uniform standards for validating models with real data obtained
from experimental psychology (or from other sources). We continued this discus-
sion via email, which eventually led to the development of a workshop at the 2013
OCNS (Organisation for Computational Neurosciences) Annual Meeting in Paris,
France; the workshop was titled “Validating neuro-computational models of neuro-
logical and psychiatric disorders”. The workshop was run jointly by Basabdatta Sen
Bhattacharya and Rosalyn Moran, and was an interesting mix of talks—speakers
included Piotr Suffczyński, Dimitris Pinotsis, Udo Ernst, Andre Marreiros, Ingo
Bojak and Rosalyn Moran with an opening overall introductory talk by Basabdatta
Sen Bhattacharya. Post-workshop, the idea was presented to Springer, who has a
‘tie-up’ with the OCNS annual meetings and offers interested parties to forward
proposals for edited compilations on the workshops organised for its series on Com-
putational Neurosciences, eds. A. Destexhe, R. Brette. We do not claim that this
book represents a comprehensive overview or analysis of existing model validation
procedures used by research groups and laboratories across the field of computa-
tional neuroscience. Rather, the book is a snapshot of some of the major work in
modeling and validation, coming from a diverse group of researchers in the field.
We are convinced that this effort is very timely, because the field of computa-
tional neuroscience has now matured to the point where proper validation of the
various models proposed by different research groups would be required in order
vii
viii Preface
for the field to grow in a more cohesive and meaningful fashion. Otherwise, we run
the risk of getting stagnated by the incompatibility of data formats, problems with
reproducibility, and, most importantly, lack of any real impact in terms of usability
in the fields of health and medicine.
The issue of model validation has received attention from many traditional fields
of science and engineering; indeed, there is general agreement that validation is
the process of determining to what extent a model represents the real world from
the perspective of the intended uses of the model. A look at a 1978 report entitled
“Methods and Examples of Model Validation—An Annotated Bibliography”, by J.
Gruhl and N. Gruhl from MIT Energy Laboratory, reveals just how extensive the
topic is: even 36 years ago, the bibliography in this report was 74 pages long. It is
worth quoting a short paragraph from this report, where the authors provide a list
for validation approaches:
• Comparison with other empirical models
• Comparison with theoretical or analytical models
• Comparison with hand calculations or reprogrammed versions of model compo-
nents
• Data splitting on observed data, by time or region
• Obtain new estimation/prediction data with time, new experiments, or in-simu-
lated environments
• Examination of reasonableness and accuracy, that is, comparison with under-
standing
• Examination of appropriateness and detail.
This list was generated by and for engineers, but the concept is quite applicable to
other fields, including computational neuroscience. Indeed, many researchers are
already using these ideas, although we did not find in the published literature any
collective presentation and explicit discussion of the issues involved. Perhaps this
book will fill that gap. To that effect, the Introductory Chapter of this book (by
Rosalyn Moran) captures the essence and the connections between the various mod-
eling and validation approaches presented in the book. Thus, it is strongly recom-
mended that the Introductory Chapter be read first, to set the context for the rest of
the book. The sequence of the chapters follows the exposition presented in the in-
troduction; however, each chapter is self-contained, and can be read independently.
It is worth mentioning—at the risk of stating the obvious—that ultimately, valida-
tion of these models will have to come from the user communities, and therefore,
validation techniques must be developed in close cooperation with the experimental
scientists, with clear understanding of end-user goals. Our sincere and humble hope
is that this book will bring model validation issues to the forefront and serve as a
significant step toward generating a more unified and coordinated effort toward this
crucial issue in our research enterprise.
We could not have done this work without support and encouragement from our
employers, the US National Science Foundation and the University of Lincoln, UK.
We are grateful to Rosalyn Moran, who not only played a leading part in the CNS
workshop but provided immense help, support and guidance during planning and
Preface ix
editing of the book; suffice it to say that the book would not be what it is without her
input. Basabdatta Sen Bhattacharya would like to thank Péter Érdi for his review of
the first draft of the workshop proposal; the final title as well as the ‘body’ of the
workshop theme was a modified version based on his feedback. We are grateful to
our families, friends and colleagues for their support and understanding.
Basabdatta Sen Bhattacharya
Fahmida N. Chowdhury
Acknowledgements
This book was produced while one of the Editors (Chowdhury) was employed by
the US National Science Foundation. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the authors and do not
necessarily reflect the views of the National Science Foundation.
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