Efficient Psychological Disorder Analysis With Multimodal Fusion of Brain Imaging Data

Alagappa Institute of Skill Development & Computer Centre,Alagappa University, Karaikudi, India.15 -16 February 2017. IT Skills Show & International Conference on Advancements In Computing Resources (SSICACR-2017)

Format: Volume 5, Issue 1, No 23, 2017

Copyright: All Rights Reserved ©2017

Year of Publication: 2017

Author: Dr.K.Latha,M.Muthupandi

Reference:IJCS-267

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Abstract

The mental disorders can be defined generally through a combination of features that reflect the feelings of a person or his actions and explain his thinking and perceptions. Mental illnesses include psychological or behavioral configurations that are frequently correlated with distress or disability. Thus, around 80% of Bipolar disorder patients who are going through depressive episodes receive an incorrect diagnosis. Depression and mania are thought to be heterogeneous illnesses that can result from dysfunction of numerous neurotransmitters or metabolic systems. Several neuroimaging studies have directly compared patients with (Bipolar Disorder) BD, (Unipolar Disorder)UD, (Major Depressive Disorder) MDD using magnetic resonance imaging (MRI), which provides noninvasive observation of the structural characteristics and functional states of the brain. Feature selection is often considered necessary for classifying neuroimaging data. For neuroimaging data processing, conventional univariate feature selection approaches ignore the mutual information among features with certain independence or orthogonality assumptions.
[1] K. Cuellar, S. L. Johnson, and R. Winters,“Distinctions between bipolar and unipolar depression,” Clinical Psychol. Rev., vol. 25, no.3, pp. 307–339, 2005. [2] J. R. Cardoso de Almeida and M. L. Phillips, “Distinguishing between unipolar depression and bipolar depression: Current and future clinical and neuroimaging perspectives,” Biol. Psychiatr. , vol. 73, no. 2, pp.111–8, Jan. 15, 2013. [3] D. Dudek, M. Siwek, and D. Zielinska et al., “Diagnostic conversions from major depressive disorder into bipolar disorder in an outpatient setting: Results of a retrospective chart review,” J. Affective Disorders, vol. 144, no. 1–2, pp. 112–5, Jan. 10, 2013. [4] F. P. MacMaster, R. Leslie, and D. R. Rosenberg et al., “Pituitary gland volume in adolescent and young adult bipolar and unipolar depression,”Bipolar Disorder, vol. 10, no. 1, pp. 101–4, Feb. 2008. [5] J. Sui, T. Adali, and Q. Yu et al., “A review of multivariate methods for multimodal fusion of brain imaging data,” J. Neurosci. Methods, vol. 204, no. 1, pp. 68–81, 2012. [6] L. L. Zeng, H. Shen, and L. Liu et al., “Identifying major depression using whole- brain functional connectivity: A multivariate pattern analysis,” Brain, vol. 135, no. Pt 5, pp. 1498–507, May 2012. [7] V. D. Calhoun and T. Adali, “Feature-based fusion of medical imaging data,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 5, pp. 711–720,Sep. 2009. [8] M. P. DelBello, S. M. Strakowski, and M. E. Zimmerman et al., “MRI analysis of the cerebellum in bipolar disorder: A pilot study,” Neuropsychopharmacol.,vol. 21, no. 1, pp. 63–68, 1999. [9] J. Sui, G. Pearlson, and A. Caprihan et al., “Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model,” Neuroimage, vol. 57, no. 3, pp.839–855, 2011. [10] D. Öngür, M. Lundy, and I. Greenhouse et al., “Default mode network abnormalities in bipolar disorder and schizophrenia,” Psychiatr. Res.: Neuroimaging, vol. 183, no. 1, pp. 59–68, 2010. [11] M. Phillips, M. Travis, and A. Fagiolini et al., “Medication effects in neuroimaging studies of bipolar disorder,” Amer. J. Psychiatr., vol. 165, no. 3, pp. 313–320, 2008.

References

[1] K. Cuellar, S. L. Johnson, and R. Winters,“Distinctions between bipolar and unipolar depression,” Clinical Psychol. Rev., vol. 25, no.3, pp. 307–339, 2005. [2] J. R. Cardoso de Almeida and M. L. Phillips, “Distinguishing between unipolar depression and bipolar depression: Current and future clinical and neuroimaging perspectives,” Biol. Psychiatr. , vol. 73, no. 2, pp.111–8, Jan. 15, 2013. [3] D. Dudek, M. Siwek, and D. Zielinska et al., “Diagnostic conversions from major depressive disorder into bipolar disorder in an outpatient setting: Results of a retrospective chart review,” J. Affective Disorders, vol. 144, no. 1–2, pp. 112–5, Jan. 10, 2013. [4] F. P. MacMaster, R. Leslie, and D. R. Rosenberg et al., “Pituitary gland volume in adolescent and young adult bipolar and unipolar depression,”Bipolar Disorder, vol. 10, no. 1, pp. 101–4, Feb. 2008. [5] J. Sui, T. Adali, and Q. Yu et al., “A review of multivariate methods for multimodal fusion of brain imaging data,” J. Neurosci. Methods, vol. 204, no. 1, pp. 68–81, 2012. [6] L. L. Zeng, H. Shen, and L. Liu et al., “Identifying major depression using whole- brain functional connectivity: A multivariate pattern analysis,” Brain, vol. 135, no. Pt 5, pp. 1498–507, May 2012. [7] V. D. Calhoun and T. Adali, “Feature-based fusion of medical imaging data,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 5, pp. 711–720,Sep. 2009. [8] M. P. DelBello, S. M. Strakowski, and M. E. Zimmerman et al., “MRI analysis of the cerebellum in bipolar disorder: A pilot study,” Neuropsychopharmacol.,vol. 21, no. 1, pp. 63–68, 1999. [9] J. Sui, G. Pearlson, and A. Caprihan et al., “Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model,” Neuroimage, vol. 57, no. 3, pp.839–855, 2011. [10] D. Öngür, M. Lundy, and I. Greenhouse et al., “Default mode network abnormalities in bipolar disorder and schizophrenia,” Psychiatr. Res.: Neuroimaging, vol. 183, no. 1, pp. 59–68, 2010. [11] M. Phillips, M. Travis, and A. Fagiolini et al., “Medication effects in neuroimaging studies of bipolar disorder,” Amer. J. Psychiatr., vol. 165, no. 3, pp. 313–320, 2008.

Keywords

Bipolar Disorder and Major Depressive Disorder, Multimodal Fusion, Neuroimaging, MRI Data, Data Driven Techniques.

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