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The Hidden Markov Brains

The Hidden Markov Brains

Tuan D. Pham
Copyright: © 2013 |Pages: 20
ISBN13: 9781466625068|ISBN10: 1466625066|EISBN13: 9781466625075
DOI: 10.4018/978-1-4666-2506-8.ch009
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MLA

Pham, Tuan D. "The Hidden Markov Brains." Medical Advancements in Aging and Regenerative Technologies: Clinical Tools and Applications, edited by Andriani Daskalaki , IGI Global, 2013, pp. 195-214. https://doi.org/10.4018/978-1-4666-2506-8.ch009

APA

Pham, T. D. (2013). The Hidden Markov Brains. In A. Daskalaki (Ed.), Medical Advancements in Aging and Regenerative Technologies: Clinical Tools and Applications (pp. 195-214). IGI Global. https://doi.org/10.4018/978-1-4666-2506-8.ch009

Chicago

Pham, Tuan D. "The Hidden Markov Brains." In Medical Advancements in Aging and Regenerative Technologies: Clinical Tools and Applications, edited by Andriani Daskalaki , 195-214. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2506-8.ch009

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Abstract

This chapter presents Hidden Markov models (HMM) of the brain on Magnetic Resonance Imaging (MRI) for the inference of white matter hyperintensities and brain age prediction to study the bidirectional vascular depression hypothesis in the elderly and neurodegenerative diseases, respectively. Rating and quantification of cerebral white matter hyperintensities on magnetic resonance imaging are important tasks in various clinical and scientific settings. The authors have proposed that prior knowledge about white matter hyperintensities can be accumulated and utilised to enable a reliable inference of the rating of a new white matter hyperintensity observation. The use of HMM for rating inference of white matter hyperintensities can be used as a computerized rating-assisting tool and can be very economical for diagnostic evaluation of brain tissue lesions. They have also applied HMM for MRI-based brain age prediction. Cortical thinning and intracortical gray matter volume losses are widely observed in normal ageing, while the decreasing rate of the volume loss in subjects with neurodegenerative diseases such as Alzheimer’s disease is reported to be faster than the average speed. Therefore, neurodegenerative disease is considered as accelerated aging. Accurate detection of accelerated ageing of the brain is a relatively new direction of research in computational neuroscience, as it has the potential to offer positive clinical outcome through early intervention.

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