Measures of Morphological Complexity of Gray Matter on Magnetic Resonance Imaging for Control Age Grouping

Tuan Pham,Yung-Fu Chen,Ryuichi Oka,Taishi Abe

doi:10.3390/e17127868

Abstract

Current brain-age prediction methods using magnetic resonance imaging (MRI) attempt to estimate the physiological brain age via some kind of machine learning of chronological brain age data to perform the classification task. Such a predictive approach imposes greater risk of either over-estimate or under-estimate, mainly due to limited training data. A new conceptual framework for more reliable MRI-based brain-age prediction is by systematic brain-age grouping via the implementation of the phylogenetic tree reconstruction and measures of information complexity. Experimental results carried out on a public MRI database suggest the feasibility of the proposed concept.

Highlights

The ability of computer methods that can predict healthy chronological age of the brain based on radiological images, such as magnetic resonance imaging (MRI), has recently led to a new research direction in computational neuroscience [1,2,3,4,5,6,7]
If the predicted brain age is older than its chronological age, there is some evidence of its accelerated aging that indicates abnormal cognitive impairment [10,11], or traumatic brain injury [12]
Because Alzheimer’s disease (AD) shares many aspects of abnormal brain aging, by applying pattern recognition methods, structural MRI-based features have been discovered as promising biomarkers to identify the early setting of mild cognitive impairment to AD based on the matching of similarity between constructed computational models of healthy and pathological brain aging patterns [11,16]

Summary

Introduction

The ability of computer methods that can predict healthy chronological age of the brain based on radiological images, such as magnetic resonance imaging (MRI), has recently led to a new research direction in computational neuroscience [1,2,3,4,5,6,7]. This new type of study is important because it holds promise of being able to train computers to identify neurodegenerative disorders at an early onset, where image samples collected for brain diseases are limited for clinical inference [8,9]. Because AD shares many aspects of abnormal brain aging, by applying pattern recognition methods, structural MRI-based features have been discovered as promising biomarkers to identify the early setting of mild cognitive impairment to AD based on the matching of similarity between constructed computational models of healthy and pathological brain aging patterns [11,16]

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Conclusion