Demirci_EU Neurology 10/03/2010 09:57 Page 106
Imaging
compared with relatives of controls. New insight has been provided into results. Cross-validation tools should be used more effectively and
processes underlying this premise by the recent identification of several applied at every stage of the classification analysis, including feature
putative schizophrenia susceptibility genes. Particular genes received selection to obtain more generalisable results.
increasing attention in chromosomal studies and some protein products
may be involved in regulation of neurotransmission related to Most fMRI studies investigating schizophrenia suffer from the limited
schizophrenia.
3
How these genes might relate to abnormal functional availability of subjects. Generalisation of the results and discussions
activation patterns in schizophrenia, and which genes are expressed in should be provided with tests on larger data sets. Possible
the brain, carries great importance and is currently being investigated. inconsistencies of the findings that may be encountered with the
repetition of experiments should be investigated. Whether the results
Identification of risk genes for schizophrenia and other mental are static (trait-like) abnormalities or dynamic (state-like) phenomena
disorders currently motivates psychiatric research and helps the should be clarified and the repeatability of fMRI tests should be
emergence of more comprehensive and testable models for examined.
18
Combining data, possibly from multiple sites with
psychiatric illnesses. Integration of genetics with brain imaging might collaboration between scientists, and including them in the analysis
have the potential to help us better understand how human brain for larger training sets is necessary for reproducibility of the obtained
functions in schizophrenia through the identification of functional classification accuracies.
imaging tools and genetics. However, combining imaging data with
genetics is sophisticated and requires efficient methods, since both Both genetics and environment play important roles in brain
data types include huge amounts of information.
7
development and function. Image analysis techniques help identification
of image-based biological markers and work towards understanding
In addition, a lack of clear diagnostic boundaries is particularly evident schizophrenia, but integration of genetics with brain imaging should
with respect to schizophrenia and there is extensive overlap with facilitate the understanding of the disease further. n
neurophysiology, imaging, cognition, candidate genes and treatment
response.
7
It is our hope that efficient methods and clear definitions
Oguz Demirci is a Senior Applied Research Engineer
will ultimately translate into improved diagnosis and classification of
for Turkcell Techology in Istanbul, Turkey. Prior to this
psychiatric illnesses, with an impact on clinical practice. he was a Post-doctoral Research Associate with the
Mind Research Network in Albuquerque and a Senior
Conclusion
Applied Research Engineer for Sony Electronics in
San Jose, California. Dr Demirci obtained his PhD in
Recent novel ideas and findings from clinical and molecular genetics, electrical engineering from the University of New
cellular biology, brain structural and functional studies, engineering,
Mexico, Albuquerque, in 2006, his MSc in electrical
engineering from the Ohio State University, Columbus,
statistics and clinical phenomenology have refreshed psychiatric
in 2002 and his BSc in electrical and electronics
research and necessitated strong collaboration and continuous input engineering from Bilkent University, Ankara, in 2000.
from scientists belonging to diverse fields.
Vince D Calhoun is Chief Technology Officer and
Director of Image Analysis and Magnetic Resonance
fMRI is among these fields and it has been a very useful tool in the
(MR) Research at the Mind Research Network and an
investigation of mental illnesses such as schizophrenia, but it still has
Associate Professor in the Departments of Electrical
not reached the point where it is systematically and effectively used
and Computer Engineering, Neurosciences and
Computer Science at the University of New Mexico.
in the diagnosis of schizophrenia. More effective stimulus paradigms
Much of his career has been spent on the
and classification algorithms specific to schizophrenia should be
development of data-driven approaches for the
designed and implemented so that this valuable measurement tool
analysis of functional MR imaging (fMRI) data. He has
won over US$12 million in National Science Foundation (NSF) and National Institutes
can be utilised more expeditiously in clinical settings. This target
of Health (NIH) grants on the incorporation of prior information into independent
requires strong collaboration among researchers from various fields
component analysis (ICA) for fMRI, data fusion of multimodal imaging and genetics
beyond that of psychiatry.
data and the identification of biomarkers for disease. He is the author of more than
100 full journal articles and over 200 technical reports, abstracts and conference
proceedings. Dr Calhoun is an active member of many scientific associations and is
Image analysis techniques that have been used with fMRI data require
a reviewer and editorial board member for several international journals.
crucial attention to detail in order not to give rise to any biased
1. Ogawa S, Lee TM, Kay AR, Tank DW, Proc Natl Acad Sci U S A, 7. Calhoun V, Pearlson GD, Neuroscience Imaging Research Trends, Conference, Houston, TX, October, 2002.
1990;87:9868–72. chapter 6, Nova, 2008:93–108. 14. Ford J, Farid H, Makedon F, et al., Proc. of the 6th Annual
2. Clare S, Functional MRI: Methods and Applications, PhD 8. Demirci O, Stevens MC, Andreasen NC, et al., Neuroimage, International Conference on Medical Image Computing
thesis, University of Nottingham, October, 1997. 2009;46(2):419–31. and Computer Assisted Intervention (MICCAI’03), 2003.
3. Saran M, Sachin P, Kablinger AS, Psychiatric Times, 2007;24:2. 9. Demirci O, Clark V, Calhoun VD, NeuroImage, 15. Job DE, Whalley HC, McIntosh AM, et al., BioMed Central,
4. Demirci O, Clark V, Magnotta VA, et al., Brain Imag Behav, 2008;39:1774–82. 2006;4:29.
2008;2:207–26. 10. Friedman L, Glover GH, FBIRN, NeuroImage, 2006;33:471–81. 16. Georgopoulos AP, Karageorgiou E, Leuthold AC, et al.,
5. Jimenez LO, Landgrebe DA, IEEE Transactions on Systems, Man, 11. Shinkareva SV, Ombao HC, Sutton BP, et al., NeuroImage, J Neural Eng, 2007;4:349–55.
and Cybernetics-Part C, 1998;28:39–54. 2006;33:63–71. 17. Fan Y, Rao H, Hurt H, et al., NeuroImage, 2007;36:1189–99.
6. Jimenez LO, Landgrebe DA, IEEE Transactions on Geoscience 12. Robert P, Escoufier Y, Applied Statistics, 1976;25:257–65. 18. Bullmore E, Brammer M, Williams SCR, et al., Human Brain
and Remote Sensing, 1999;37:2653–67. 13. Ford J, Shen L, Makedon F, et al., Second Joint EMBS/BMES Mapping, 1999;8:86–91.
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