Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

BRAIN WHITE MATTER ABNORMALITIES ACROSS THE ALZHEIMER'S DISEASE SPECTRUM - A LONGITUDINAL PERSPECTIVE

Ph.DDOCTOR OF PHILOSOPHY (DUKE

Association between white matter hyperintensity load and grey matter atrophy in mild cognitive impairment is not unidirectional

Neuroimaging measures of Alzheimer’s disease (AD) include grey matter volume (GMV) alterations in the Default Mode Network (DMN) and Executive Control Network (ECN). Small-vessel cerebrovascular disease, often visualised as white matter hyperintensities (WMH) on MRI, is often seen in AD. However, the relationship between WMH load and GMV needs further examination. We examined the load-dependent influence of WMH on GMV and cognition in 183 subjects. T1-MRI data from 93 Mild Cognitive Impairment (MCI) and 90 cognitively normal subjects were studied and WMH load was categorized into low, medium and high terciles. We examined how differing loads of WMH related to whole-brain voxel-wise and regional DMN and ECN GMV. We further investigated how regional GMV moderated the relationship between WMH and cognition. We found differential load-dependent effects of WMH burden on voxel-wise and regional atrophy in only MCI. At high load, as expected WMH negatively related to both ECN and DMN GMV, however at low load, WMH positively related to ECN GMV. Additionally, negative associations between WMH and memory and executive function were moderated by regional GMV. Our results demonstrate non-unidirectional relationships between WMH load, GMV and cognition in MCI.National Medical Research Council (NMRC)Published versionThis study was supported by the National Medical Research Council [NMRC/CIRG/1416/2015], Singapore and under its Clinician Scientist Individual Research Grant (NMRC/CIRG/14MAY025) and the National Neuroscience Institute, Singapore

Effect of isoflurane on somatosensory evoked potentials in a rat model

10.1109/EMBC.2014.69445722014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 20144286-428

APOE4 carrier status determines association between white matter disease and grey matter atrophy in early-stage dementia

White matter hyperintensities, a neuroimaging marker of small-vessel cerebrovascular disease and apolipoprotein ε4 (APOE4) allele, are important dementia risk factors. However, APOE4 as a key effect modifier in the relationship between white matter hyperintensities and grey matter volume needs further exploration.Ministry of Education (MOE)Nanyang Technological UniversityNational Medical Research Council (NMRC)Published versionThis study is supported by the Ministry of Education, Singapore, under its MOE AcRF Tier 3 Award MOE2017-T3-1–002, National Medical Research Council (NMRC/CIRG/1415/2015 and NMRC/CSA/063/2014), Singapore, and under its Clinician Scientist Award (MOH-CSAINV18nov-0007) and Clinician Scientist Individual Research Grant (NMRC/CIRG/14MAY025), Strategic Academic Initiative grant from the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, and National Neuroscience Institute-Health Research Endowment Fund (NNI-HREF), Singapore (Reference Number: 991016)

Dementia in Southeast Asia: influence of onset-type, education, and cerebrovascular disease

Background: Southeast Asia represents 10% of the global population, yet little is known about regional clinical characteristics of dementia and risk factors for dementia progression. This study aims to describe the clinico-demographic profiles of dementia in Southeast Asia and investigate the association of onset-type, education, and cerebrovascular disease (CVD) on dementia progression in a real-world clinic setting. Methods: In this longitudinal study, participants were consecutive series of 1606 patients with dementia from 2010 to 2019 from a tertiary memory clinic from Singapore. The frequency of dementia subtypes stratified into young-onset (YOD; <65 years age-at-onset) and late-onset dementia (LOD; ≥65 years age-at-onset) was studied. Association of onset-type (YOD or LOD), years of lifespan education, and CVD on the trajectory of cognition was evaluated using linear mixed models. The time to significant cognitive decline was investigated using Kaplan-Meier analysis. Results: Dementia of the Alzheimer’s type (DAT) was the most common diagnosis (59.8%), followed by vascular dementia (14.9%) and frontotemporal dementia (11.1%). YOD patients accounted for 28.5% of all dementia patients. Patients with higher lifespan education had a steeper decline in global cognition (p<0.001), with this finding being more pronounced in YOD (p=0.0006). Older patients with a moderate-to-severe burden of CVD demonstrated a trend for a faster decline in global cognition compared to those with a mild burden. Conclusions: There is a high frequency of YOD with DAT being most common in our Southeast Asian memory clinic cohort. YOD patients with higher lifespan education and LOD patients with moderate-to-severe CVD experience a steep decline in cognition.Published versionThis study was supported by the National Neuroscience Institute, Singapore

Amyloid-tau-neurodegeneration profiles and longitudinal cognition in sporadic young-onset dementia

We examined amyloid-tau-neurodegeneration biomarker effects on cognition in a Southeast-Asian cohort of 84 sporadic young-onset dementia (YOD; age-at-onset <65 years) patients. They were stratified into A+N+, A- N+, and A- N- profiles via cerebrospinal fluid amyloid-β1-42 (A), phosphorylated-tau (T), MRI medial temporal atrophy (neurodegeneration- N), and confluent white matter hyperintensities cerebrovascular disease (CVD). A, T, and CVD effects on longitudinal Mini-Mental State Examination (MMSE) were evaluated. A+N+ patients demonstrated steeper MMSE decline than A- N+ (β = 1.53; p = 0.036; CI 0.15:2.92) and A- N- (β = 4.68; p = 0.001; CI 1.98:7.38) over a mean follow-up of 1.24 years. Within A- N+, T- CVD+ patients showed greater MMSE decline compared to T+CVD- patients (β = - 2.37; p = 0.030; CI - 4.41:- 0.39). A+ results in significant cognitive decline, while CVD influences longitudinal cognition in the A- sub-group.Ministry of Education (MOE)Ministry of Health (MOH)National Medical Research Council (NMRC)Published versionThis study is supported by the Ministry of Education, Singapore, under its MOE AcRF Tier 3 Award MOE2017-T3-1-002, National Medical Research Council (NMRC), Singapore, under its Clinician Scientist Award (MOH-CSAINV18nov0007) and Clinician Scientist Individual Research Grant (NMRC/CIRG/14MAY025)

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

IntroductionArtificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. MethodsWe systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. ResultsA total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DiscussionThe literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HighlightsThere has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

Funder: Alzheimer's Research UKFunder: National Institute for Health and Care Research (NIHR)Funder: National Institute for Health Research (NIHR)Funder: National Health and Medical Research Council (NHMRC)INTRODUCTION: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias