Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments

Marco, Matteo De; Beltrachini, Leandro; Biancardi, Anna; Frangi, Alejandro F.; Venneri, Annalena

doi:10.1097/wad.0000000000000208

Cited by 28 publications

(25 citation statements)

References 42 publications

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“…Linear discriminant analysis (LDA) is another widely used classifier (Dai Z. et al, 2012 ; Cetin et al, 2016 ; De Marco et al, 2017 ; Qureshi et al, 2017a ; Wang et al, 2017 ), which projects features into a lower-dimensional space in which different groups of data can be maximally separately (Altman et al, 1994 ). LDA is a generalization of Fisher's linear discriminant and is based on the concept of searching for a linear combination of features that separate two groups (Mika et al, 1999 ).…”

Section: Classification or Prediction Strategiesmentioning

confidence: 99%

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging

2018

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Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.

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Section: Classification or Prediction Strategiesmentioning

confidence: 99%

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging

2018

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“…With respect to dementia detection, multimodal approaches have been most effective in the medical imaging domain, where such methodologies have been used to combine information from various brain imaging technologies (Suk et al, 2014; Thung et al, 2017). For example, work from Beltrachini et al (2015) and De Marco et al (2017) has shown that the detection of MCI can be improved when combining features from MRI images with cognitive test scores in a multimodal machine learning classifier, compared to learning from either data source individually.…”

Section: Related Workmentioning

confidence: 99%

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Fraser

Fors

Eckerström

et al. 2019

Front. Aging Neurosci.

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Recent work has indicated the potential utility of automated language analysis for the detection of mild cognitive impairment (MCI). Most studies combining language processing and machine learning for the prediction of MCI focus on a single language task; here, we consider a cascaded approach to combine data from multiple language tasks. A cohort of 26 MCI participants and 29 healthy controls completed three language tasks: picture description, reading silently, and reading aloud. Information from each task is captured through different modes (audio, text, eye-tracking, and comprehension questions). Features are extracted from each mode, and used to train a series of cascaded classifiers which output predictions at the level of features, modes, tasks, and finally at the overall session level. The best classification result is achieved through combining the data at the task level (AUC = 0.88, accuracy = 0.83). This outperforms a classifier trained on neuropsychological test scores (AUC = 0.75, accuracy = 0.65) as well as the “early fusion” approach to multimodal classification (AUC = 0.79, accuracy = 0.70). By combining the predictions from the multimodal language classifier and the neuropsychological classifier, this result can be further improved to AUC = 0.90 and accuracy = 0.84. In a correlation analysis, language classifier predictions are found to be moderately correlated (ρ = 0.42) with participant scores on the Rey Auditory Verbal Learning Test (RAVLT). The cascaded approach for multimodal classification improves both system performance and interpretability. This modular architecture can be easily generalized to incorporate different types of classifiers as well as other heterogeneous sources of data (imaging, metabolic, etc.).

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“…In fact, recent studies have also been show great promises for improving the accuracy of MCI identification by combining multiple structural and functional metrics, such as grey matter volume (GMV) and shortest path length (SPL). For example, Wee et al [ 18 ] first used both structural MRI and functional MRI data of each subject to construct multiple brain networks for each subject, and then extracted local clustering coefficient from each brain network of each subject as feature representation to perform the MCI identification task by using a multi-kernel learning algorithm; De Marco et al [ 19 ] used multiple machine learning models based on different metrics from both structural MRI and functional MRI data to investigate the performance of MCI identification; Tripathi et al [ 20 ] proposed an unsupervised framework for the classification of EMCI and LMCI by combining shape and voxel-based features from 12 brain regions; Jie et al [ 21 ] proposed a feature combination framework to combine both temporal and spatial features of dynamic functional networks to perform automatic identification of EMCI and LMCI. So far, although some results have been achieved for the identification of MCI subjects based on structural and functional MRI data, extracting which features and how to combine multiple features to improve MCI identification accuracy have always been a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks

et al. 2020

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Background The identification of early mild cognitive impairment (EMCI), which is an early stage of Alzheimer’s disease (AD) and is associated with brain structural and functional changes, is still a challenging task. Recent studies show great promises for improving the performance of EMCI identification by combining multiple structural and functional features, such as grey matter volume and shortest path length. However, extracting which features and how to combine multiple features to improve the performance of EMCI identification have always been a challenging problem. To address this problem, in this study we propose a new EMCI identification framework using multi-modal data and graph convolutional networks (GCNs). Firstly, we extract grey matter volume and shortest path length of each brain region based on automated anatomical labeling (AAL) atlas as feature representation from T1w MRI and rs-fMRI data of each subject, respectively. Then, in order to obtain features that are more helpful in identifying EMCI, a common multi-task feature selection method is applied. Afterwards, we construct a non-fully labelled subject graph using imaging and non-imaging phenotypic measures of each subject. Finally, a GCN model is adopted to perform the EMCI identification task. Results Our proposed EMCI identification method is evaluated on 210 subjects, including 105 subjects with EMCI and 105 normal controls (NCs), with both T1w MRI and rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed framework achieves an accuracy of 84.1% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.856 for EMCI/NC classification. In addition, by comparison, the accuracy and AUC values of our proposed framework are better than those of some existing methods in EMCI identification. Conclusion Our proposed EMCI identification framework is effective and promising for automatic diagnosis of EMCI in clinical practice.

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Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments

Cited by 28 publications

References 42 publications

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks

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