Abstract
Neuroimaging-based diagnosis could help clinicians in making accurate diagnosis, accessing accurate prognosis, and deciding faster, more effective, and personalized treatment on an individual person basis. In this research work, we aim to develop a neuro-imaging, i.e. functional magnetic resonance imaging (fMRI), based method to detect attention deficit hyper-activity disorder (ADHD), which is a psychiatric disorder categorized by the impulsive nature, lack of attention, and hyper activeness. We utilized fMRI scans as well as personal characteristic features (PCF) data provided as part of ADHD-200 challenge. We aim to train a machine learning classifier by using fMRI and PCF data to classify each participant into one of the following three classes: healthy control (HC), combined-type ADHD (ADHD-C), or inattentive-type ADHD (ADHD-I). We used participants’ PCF and fMRI data separately, and then evaluated the combined use of both the datasets in detecting different classes. Support vector machine classifier with linear kernel was used for the training. The experiments were conducted under two different configurations: (i) 2-way configuration where classification was conducted between HC and ADHD (ADHD-C+ADHD-I) patients, and between ADHD-C and ADHD-I, and (ii) 3-way configuration where data of all the categories (HC, ADHD-C and ADHD-I) was combined together for classification. The 2-way classification approach achieved the diagnostic accuracy of 86.52% and 82.43% in distinguishing HC from ADHD patients, and ADHD-C and ADHD-I, respectively. The 3-way classification revealed classification success rate of 78.59% when both fMRI and PCF data were used together. These results demonstrate the importance of utilizing fMRI data and PCF for the detection of psychiatric disorders.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Polanczyk, G., et al.: The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am. J. Psychiatry 164, 942–948 (2007)
King, J.A., et al.: Neural substrates underlying impulsivity. Ann. N. Y. Acad. Sci. 1008, 160–169 (2003)
Liston, C., et al.: Atypical prefrontal connectivity in attention-deficit/hyperactivity disorder: pathway to disease or pathological end point? Biol. Psychiat. 69, 1168–1177 (2011)
Bush, G., et al.: Functional neuroimaging of attention-deficit/hyperactivity disorder: a review and suggested future directions. Biol. Psychiat. 57, 1273–1284 (2005)
Fan, Y., et al.: Discriminant analysis of functional connectivity patterns on Grassmann manifold. NeuroImage 56, 2058–2067 (2011)
Shen, H., et al.: Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. NeuroImage 49, 3110–3121 (2010)
Nouretdinov, I., et al.: Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. NeuroImage 56, 809–813 (2011)
Arribas, J.I., et al.: Automatic Bayesian classification of healthy controls, bipolar disorder, and schizophrenia using intrinsic connectivity maps from fMRI data. IEEE Trans. Biomed. Eng. 57, 2850–2860 (2010)
Rathore, S., et al.: A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. NeuroImage 155, 530–548 (2017)
Rathore, S., et al.: Analysis of MRI data in diagnostic neuroradiology. Annu. Rev. Biomed. Data Sci. 3, 365–390 (2020)
Fox, M.D., et al.: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U.S.A. 102, 9673–9678 (2005)
Friston, K.J., et al.: Dynamic causal modelling revisited. NeuroImage 199, 730–744 (2019)
Calhoun, V.D., et al.: Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Hum. Brain Mapp. 13, 43–53 (2001)
Cao, X., et al.: Abnormal resting-state functional connectivity patterns of the putamen in medication-naïve children with attention deficit hyperactivity disorder. Brain Res. 1303, 195–206 (2009)
Cao, Q., et al.: Abnormal neural activity in children with attention deficit hyperactivity disorder: a resting-state functional magnetic resonance imaging study. NeuroReport 17, 1033–1036 (2006)
Tian, L., et al.: Enhanced resting-state brain activities in ADHD patients: a fMRI study. Brain Develop. 30, 342–348 (2008)
Zang, Y.F., et al.: Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Develop. 29, 83–91 (2007)
Sun, L., et al.: Abnormal functional connectivity between the anterior cingulate and the default mode network in drug-naïve boys with attention deficit hyperactivity disorder. Psychiatry Res. 201, 120–127 (2012)
Qiu, M.G., et al.: Changes of brain structure and function in ADHD children. Brain Topogr. 24, 243–252 (2011)
Tian, L., et al.: Altered resting-state functional connectivity patterns of anterior cingulate cortex in adolescents with attention deficit hyperactivity disorder. Neurosci. Lett. 400, 39–43 (2006)
Zhu, C.Z., et al.: Discriminative analysis of brain function at resting-state for attention-deficit/hyperactivity disorder. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 468–475. Springer, Heidelberg (2005). https://doi.org/10.1007/11566489_58
Sidhu, G., et al.: Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD. Front. Syst. Neurosci. 6, 74 (2012)
Zhu, C.Z., et al.: Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. NeuroImage 40, 110–120 (2008)
Iftikhar, M.A., et al.: Robust brain MRI denoising and segmentation using enhanced non-local means algorithm. Int. J. Imaging Syst. Technol. 24, 52–66 (2014)
Iftikhar, M.A., et al.: Brain MRI denoizing and segmentation based on improved adaptive nonlocal means. Int. J. Imaging Syst. Technol. 23, 235–248 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Rathore, B., Awais, M., Usman, M.U., Shafi, I., Ahmed, W. (2020). Using Functional Magnetic Resonance Imaging and Personal Characteristics Features for Detection of Neurological Conditions. In: Kia, S.M., et al. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. MLCN RNO-AI 2020 2020. Lecture Notes in Computer Science(), vol 12449. Springer, Cham. https://doi.org/10.1007/978-3-030-66843-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-66843-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66842-6
Online ISBN: 978-3-030-66843-3
eBook Packages: Computer ScienceComputer Science (R0)
