Arpan eBanerjee

Barry eHorwitz

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Barry Horwitz

Arpan Banerjee

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SYSTEMS NEUROSCIENCE
A role for neural modeling in the study of brain disorders
Barry Horwitz* and Arpan Banerjee
Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA
*Correspondence: horwitzb@mail.nih.gov
Edited by:
Ranulfo Romo, Universidad Nacional Autónoma de México, Mexico
A commentary on
From intracerebral EEG signals to brain 
connectivity: identification of epilepto-
genic networks in partial epilepsy
by Wendling, F., Chauvel, P., Biraben, A., and 
Bartolomei, F. (2010). Front. Syst. Neurosci. 
4:154. doi: 10.3389/fnsys.2010.00154
In the past few decades, clinical research 
on brain disorders has become enriched 
by the addition of a number of new tools 
for characterizing and assessing brain func-
tion. Before the 1960s, clinical examination, 
behavioral observation (including batteries 
of neuropsychological tests), and electro-
encephalography (EEG) were the primary 
research tools. Since then, new additions to 
the repertoire have included genetic analy-
sis, and importantly for our purposes, brain 
imaging (both structural and functional). 
Furthermore, the amount of data that can 
be acquired by the genetic and neuroim-
aging tools has grown to such levels that 
advanced and intensive computational 
methods are required to analyze the data. 
However, computational techniques can be 
used for more than data analysis, as a recent 
paper in this issue by Wendling et al. (2010) 
demonstrates. In an article that reviews a 
variety of computational methods for char-
acterizing functional brain connectivity 
from electrophysiological data, specifically 
in the context of partial epilepsy, Wendling 
et al. also employ a physiologically plau-
sible neural model to generate simulated 
data and illustrate how such modeling can 
be used to help interpret the experimen-
tal data. This Wendling et al. study, along 
with some others (Horwitz et al., 1995; 
Schlosser et al., 2006; Husain, 2007; Alstott 
et al., 2009; Kim and Horwitz, 2009; van 
Albada et al., 2009; Rowe, 2010; Seghier 
et al., 2010), suggests that neural modeling 
may soon become a useful addition to the 
clinical research toolkit for investigating 
brain disorders.
The particular issue addressed by 
Wendling et al. (2010) centers on identi-
fying, prior to brain surgery, the epilepto-
genic zone (EZ) responsible for seizures 
in patients with partial epilepsy. The data 
come from depth electrode EEG recordings 
obtained during pre-surgical evaluation. 
What makes such data hard to interpret 
is that the spatial organization of the EZ 
corresponds to a network of distributed 
neural populations “showing ‘hyperexcit-
ability’ and ‘hypersynchronization’ proper-
ties” (Wendling et al., 2010). In an attempt 
to unravel the relationships between these 
various populations, a variety of signal 
processing techniques have been proposed 
to determine the functional connectivities 
among the epileptogenic network neural 
populations. Wendling et al. illustrate one 
such functional connectivity method in 
detail, non-linear regression analysis.
However, the availability of large num-
bers of potential methods for evaluating 
the statistical relationships between time-
varying signals, each resting on specific 
assumptions about the underlying neural 
relationship between the signals, makes it 
imperative to have a way to see whether 
a given method actually produces a result 
that properly reflects the underlying neu-
ral relationships (for a recent review, see 
Banerjee et al., 2012a). For this purpose, 
Wendling et al. (2010) demonstrate how 
a biologically realistic model of coupled 
neuronal populations can be employed to 
simulate EEG data similar to the data that 
are experimentally acquired. They apply the 
non-linear regression analysis method to 
the simulated data and show that this par-
ticular connectivity method captures the 
“ground truth” of the relationships embed-
ded in the model. Because in an actual brain 
from which data are collected we have no 
way of knowing the relationships among 
the network elements, this use of neural 
modeling thus allows one to partially ver-
ify that a functional connectivity method 
 permits an interpretation that corresponds 
to the underlying relationship between the 
neural elements.
Using neural modeling to help assess 
the interpretability of various functional 
and effective brain connectivity methods 
has started to become an important com-
ponent for analyzing connectivity analysis 
techniques. Examples of previous studies 
that utilized this approach include Kim and 
Horwitz (2009), Wendling et al. (2009), and 
Banerjee et al. (2012b). However, there are 
other uses by which neural modeling can 
play a significant role in the clinical research 
of brain disorders. One type of neural mod-
eling, what has been called systems-level 
neural modeling (Horwitz et al., 1999), 
attempts to evaluate the strengths of the 
direct connections between brain regions 
(i.e., the interregional effective connectiv-
ity), and has been applied to functional 
neuroimaging data in a number of stud-
ies to assess differences between patients 
and healthy subjects (e.g., Horwitz et al., 
1995; Schlosser et al., 2006; Rowe, 2010; 
Seghier et al., 2010). This approach takes 
the acquired data and utilizes it to fit the 
parameters of the model. However, one can 
turn the approach around and simulate data 
(at the brain region level) and explore the 
simulated data to determine how parameter 
modification that may characterize a brain 
disorder alters the simulated neuroimaging 
signals. A good example of this can be found 
in Alstott et al. (2009), where the impact 
of brain lesions on simulated resting fMRI 
data was assessed.
A third way in which neural modeling 
is beginning to contribute as a clinical neu-
roscience tool is in terms of helping under-
stand the neural basis of a disorder. Here, one 
would use a large-scale neural model con-
sisting of multiple neuronal units in several 
brain regions. Specific proposals concern-
ing the neural substrate of a disorder could 
be incorporated into the neural model by 
explicit changes in model parameter values; 
Frontiers in Systems Neuroscience www.frontiersin.org August 2012 | Volume 6 | Article 57 | 1
General Commentary
published: 06 August 2012
doi: 10.3389/fnsys.2012.00057
with functional magnetic resonance imaging and 
structural equation modeling. Neuroscience 139, 
91–103.
Seghier, M. L., Zeidman, P., Neufeld, N. H., Leff, A. P., and 
Price, C. J. (2010). Identifying abnormal connectivity 
in patients using dynamic causal modeling of FMRI 
responses. Front. Syst. Neurosci. 4:142. doi: 10.3389/
fnsys.2010.00142
van Albada, S. J., Gray, R. T., Drysdale, P. M., and 
Robinson, P. A. (2009). Mean-field modeling of the 
basal ganglia-thalamocortical system. II Dynamics 
of parkinsonian oscillations. J. Theor. Biol. 257, 
664–688.
Wendling, F., Ansari-Asl, K., Bartolomei, F., and Senhadji, 
L. (2009). From EEG signals to brain connectivity: A 
model-based evaluation of interdependence meas-
ures. J. Neurosci. Methods 183, 9–18.
Wendling, F., Chauvel, P., Biraben, A., and Bartolomei, 
F. (2010). From intracerebral EEG signals to brain 
connectivity: identification of epileptogenic networks 
in partial epilepsy. Front. Syst. Neurosci. 4:154. doi: 
10.3389/fnsys.2010.00154
Received: 11 June 2012; accepted: 18 July 2012; published 
online: 06 August 2012.
Citation: Horwitz B and Banerjee A (2012) A role for neu-
ral modeling in the study of brain disorders. Front. Syst. 
Neurosci. 6:57. doi: 10.3389/fnsys.2012.00057
Copyright © 2012 Horwitz and Banerjee. This is an 
open-access article distributed under the terms of the 
Creative Commons Attribution License, which permits 
use, distribution and reproduction in other forums, pro-
vided the original authors and source are credited and 
subject to any copyright notices concerning any third-
party graphics etc.
in the human brain. PLoS Comput. Biol. 5, e1000408. 
doi: 10.1371/journal.pcbi.1000408
Banerjee, A., Pillai, A. S., and Horwitz, B. (2012a). Using 
large-scale neural models to interpret connectivity 
measures of cortico-cortical dynamics at millisecond 
temporal resolution. Front. Syst. Neurosci. 5:102. doi: 
10.3389/fnsys.2011.00102
Banerjee, A., Pillai, A. S., Sperling, J. R., Smith, J. F., and 
Horwitz, B. (2012b). Temporal microstructure of 
cortical networks (TMCN) underlying task-related 
differences. Neuroimage 62, 1643–1657.
Coombes, S. (2010). Large-scale neural dynamics: simple 
and complex. Neuroimage 52, 731–739.
Horwitz, B., McIntosh, A. R., Haxby, J. V., Furey, M., 
Salerno, J. A., Schapiro, M. B., Rapoport, S. I., and 
Grady, C. L. (1995). Network analysis of PET-
mapped visual pathways in Alzheimer type dementia. 
Neuroreport 6, 2287–2292.
Horwitz, B., Tagamets, M.-A., and McIntosh, A. R. (1999). 
Neural modeling, functional brain imaging, and cog-
nition. Trends Cogn. Sci. (Regul. Ed.) 3, 91–98.
Husain, F. T. (2007). “Neural network models of tinnitus,” 
in Progress in Brain Research: Tinnitus: Pathphysiology 
and Treatment, eds B. Langguth, G. Jajak, T. Kleinjung, 
A. Cacace, and A. R. Moller (Amsterdam: Elsevier), 
125–140.
Kim, J., and Horwitz, B. (2009). How well does structural 
equation modeling reveal abnormal brain anatomical 
connections? An fMRI simulation study. Neuroimage 
45, 1190–1198.
Rowe, J. B. (2010). Connectivity analysis is essential 
to understand neurological disorders. Front. Syst. 
Neurosci. 4:144. doi: 10.3389/fnsys.2010.00144
Schlosser, R. G., Wagner, G., and Sauer, H. (2006). 
Assessing the working memory network: studies 
simulated data could then be generated and 
compared with corresponding experimental 
data. An fMRI example is found in Husain’s 
(2007) work on tinnitus and an EEG illustra-
tion comes from a study by van Albada et al. 
(2009) related to Parkinson’s disease.
Obviously, neural modeling, like any 
other method, has limitations. For exam-
ple, an important simplification of the class 
of neural models used by Wendling et al. 
(2010) arises from the use of a mean-field 
assumption: the activity of a brain region 
is represented by the summed membrane 
potentials rather than neural spiking activ-
ity. Computational work is currently in 
progress to relate the more realistic neu-
ral spiking approach to the mean-field 
modeling framework (Coombes, 2010). 
Overcoming such limitations should result 
in greater use of neural modeling as a useful 
clinical research tool.
Acknowledgment
This work was supported by the NIDCD 
Intramural Research Program.
RefeRences
Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L., 
and Sporns, O. (2009). Modeling the impact of lesions 
Horwitz and Banerjee Neural modeling and brain disorders
Frontiers in Systems Neuroscience www.frontiersin.org August 2012 | Volume 6 | Article 57 | 2


A Role for Neural Modeling in the Study of Brain Disorders

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A role for neural modeling in the study of brain disorders: Comment on From intracerebral EEG signals to brain connectivity:  identification of epileptogenic networks in partial epilepsy by F. Wendling et al.

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A role for neural modeling in the study of brain disorders: Comment on From intracerebral EEG signals to brain connectivity: identification of epileptogenic networks in partial epilepsy by F. Wendling et al.

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