A hybrid computational approach for efficient Alzheimer’s disease classification based on heterogeneous data

Abstract

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer’s disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.

Cluster-permutation statistical analysis for high-dimensional brain-wide functional connectivity mapping

“…Furthermore, both DRM and IRM scores exhibited very similar trend information, as the Spearman rank-correlation analysis between both scores produced an almost perfect relationship ( = 0.94, with negligible p-value). To some extent, this is also consistent with our previous analysis (albeit using a different dataset) that showed a probabilistic causal relationship between immediate and delayed recall memory scores 34 .…”

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

“…In contrast to MMSE, MoCA, and ADAS13, the FAQ is not used in everyday clinical routine (Ritter et al, 2015). However, its relevance for determining impairment in everyday functioning and ensuring accurate early diagnosis of AD has been well-documented (Devanand et al, 2008, Ding et al, 2018, Ritter et al, 2015). For instance, studies found the use of FAQ can significantly contribute to discerning MCI versus AD cases with MoCA scores overlapping in the MCI range (Trzepacz et al, 2015).…”

“…While most of deep learning models show great performance in diagnostic classification, their interpretation remains an emerging field of research (Che, Purushotham, Khemani, & Liu, 2016). Other machine learning approaches for assisted diagnosis of cognitive impairment and dementia include linear regression (Agosta et al, 2012, Bauer et al, 2018, Koch et al, 2012), penalized regression (Wang, Liu, & Shen, 2018), Bayesian networks (Ding et al, 2018), hidden Markov models (Wang et al, 2014), and probabilistic multiple kernel learning (MKL) classifiers (Korolev et al, 2016, Youssofzadeh et al, 2017). Despite the common use of machine learning techniques for the disease diagnostics, controversy still exists regarding the effects of different combinations of explanatory variables, hyper-parameter tuning, sample size and class balance on the performance of predictive models (Du, Fu, & Calhoun, 2018, Finch & Schneider, 2007, Michie, Spiegelhalter, & Taylor, 1994).…”

“…Despite the fact that advanced computational approaches for AD classification and progression have been applied to large sets of patient data, including magnetic resonance imaging (MRI) (Karas et al, 2008, Lebedeva et al, 2017, Moradi et al, 2015), positron emission tomography (PET) (Higdon et al, 2004, Grimmer et al, 2016, Sanchez-Catasus et al, 2017), cerebrospinal fluid (CSF) biomarkers (Forlenza et al, 2015, Handels et al, 2017, Mattsson et al, 2009), combination of the neuroimaging modalities (Youssofzadeh, McGuinness, Maguire, & Wong-Lin., 2017), and cognitive and functional assessments (CFA) (Ding et al, 2018, Chapman et al, 2011, Korolev, Symonds, Bozoki & Alzheimer’s Disease Neuroimaging Initiative, 2016, Maroco et al, 2011), there is a significant gap between research outputs and their actual utilization in daily clinical practice. In contrast to other disease areas, the integration of machine learning methodologies into CDSSs and their deployment for a routine use in AD diagnostics is still very rare.…”

Computational Neurology: Computational Modeling Approaches in Dementia

“…With the complexity of AD, data analytical approaches can holistically capture the relationship across multiple data features, for example, using probabilistic graphical modeling [30]. In particular, Bayesian network modeling, a specific type of directed acyclic graphical modeling of conditional dependences/probabilities (reflected as edges in a graph), is easy to use and interpret without too many assumptions [30,5] and Lifestyle Study of Ageing (AIBL), [16] made use of Bayesian network modeling to capture the probabilistic causal relationship between very different data types as diverse as age, genotype, cognitive and functional assessments, and coarse-grained (total volume) neuroimaging data.…”