Integration of Cognitive Tests and Resting State fMRI for the Individual Identification of Mild Cognitive Impairment

Beltrachini, Leandro; Marco, Matteo De; Taylor, Zeike A.; Lötjönen, Jyrki; Frangi, Alejandro F.; Venneri, Annalena

doi:10.2174/156720501206150716120332

Cited by 10 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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.).

show abstract

Section: Related Workmentioning

confidence: 99%

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Fraser

Fors

Eckerström

et al. 2019

Front. Aging Neurosci.

View full text Add to dashboard Cite

show abstract

“…We then ranked the features according to their absolute Kendall's tau. Here we selected 100 FC features (the criterion of retaining top 100 features was based on previous literatures (Beltrachini et al 2015;Challis et al 2015)).…”

Section: Cross-validationmentioning

confidence: 99%

Identify a shared neural circuit linking multiple neuropsychiatric symptoms with Alzheimer’s pathology

Wang

Ren

Mapstone

et al. 2017

Brain Imaging and Behavior

View full text Add to dashboard Cite

Neuropsychiatric symptoms (NPS) are common in Alzheimer's disease (AD)-associated neurodegeneration. However, NPS lack a consistent relationship with AD pathology. It is unknown whether any common neural circuits can link these clinically disparate while mechanistically similar features with AD pathology. Here, we explored the neural circuits of NPS in AD-associated neurodegeneration using multivariate pattern analysis (MVPA) of resting-state functional MRI data. Data from 98 subjects (70 amnestic mild cognitive impairment and 28 AD subjects) were obtained. The top 10 regions differentiating symptom presence across NPS were identified, which were mostly the fronto-limbic regions (medial prefrontal cortex, caudate, etc.). These 10 regions' functional connectivity classified symptomatic subjects across individual NPS at 69.46-81.27%, and predicted multiple NPS (indexed by Neuropsychiatric Symptom Questionnaire-Inventory) and AD pathology (indexed by baseline and change of beta-amyloid/pTau ratio) all above 70%. Our findings suggest a fronto-limbic dominated neural circuit that links multiple NPS and AD pathology. With further examination of the structural and pathological changes within the circuit, the circuit may shed light on linking behavioral disturbances with AD-associated neurodegeneration.

show abstract

“…A number of recent studies have implemented these classificatory techniques to identify MCI patients using RS-fMRI as a single source of diagnostic information, [15][16][17][18] or in combination with sMRI. [19][20][21] In this study we used machine-learning methods to carry out classifications of participants with a diagnosis of MCI based on features extracted from cognitive performance, sMRI, and RS-fMRI, with a series of single-type and mixed classifiers. No specific hypothesis was formulated in association with cognitive classifiers as the diagnostic status was heavily dependent on cognitive performance.…”

mentioning

confidence: 99%

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

Marco¹,

Beltrachini

Biancardi

et al. 2017

Alzheimer Disease &Amp; Associated Disorders

Self Cite

View full text Add to dashboard Cite

ReuseThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request.

show abstract

Integration of Cognitive Tests and Resting State fMRI for the Individual Identification of Mild Cognitive Impairment

Cited by 10 publications

References 0 publications

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Identify a shared neural circuit linking multiple neuropsychiatric symptoms with Alzheimer’s pathology

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

Contact Info

Product

Resources

About