Neurological update: neuroimaging in dementia

Rittman, Timothy

doi:10.1007/s00415-020-10040-0

Cited by 15 publications

(8 citation statements)

References 85 publications

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“…The finding of selective early hippocampal atrophy is well established in AD [ 64 , 65 ] and is an accepted biomarker for clinical trials [ 66 , 67 ], yet translating this into detecting early AD for clinical use requires further work [ 68 ]. However, there are a number of potential methodological developments in artificial intelligence, PET, and MRI technology that may lead to more specific and biologically relevant neuroimaging biomarkers [ 69 ].…”

Section: Assessments In the Clinicmentioning

confidence: 99%

Modifiable risk factors for dementia and dementia risk profiling. A user manual for Brain Health Services—part 2 of 6

et al. 2021

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We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39–64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions.

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Section: Assessments In the Clinicmentioning

confidence: 99%

Modifiable risk factors for dementia and dementia risk profiling. A user manual for Brain Health Services—part 2 of 6

et al. 2021

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“…Neuroimaging is a non‐invasive investigation used in routine clinical practice to support the diagnosis of dementia 7,8 . A range of neuroimaging methods are used in dementia and magnetic resonance imaging (MRI) is one of the most widely used to examine brain structure, 9,10 longitudinal patterns of atrophy, 11 and changes in brain function 12–14 .…”

Section: Introductionmentioning

confidence: 99%

“…6 Neuroimaging is a non-invasive investigation used in routine clinical practice to support the diagnosis of dementia. 7,8 A range of neuroimaging methods are used in dementia and magnetic resonance imaging (MRI) is one of the most widely used to examine brain structure, 9,10 longitudinal patterns of atrophy, 11 and changes in brain function. [12][13][14] Positron emission tomography (PET) is available in specialist centers and is more expensive; it is used to measure metabolic activity, or using protein-specific ligands to identify underlying pathologies.…”

Section: Introductionmentioning

confidence: 99%

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

Borchert

Azevedo

Badhwar

et al. 2023

Alzheimer's & Dementia

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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.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

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“…The past few decades have seen growing interest in the field of biomarkers for neurodegenerative conditions. The neuroimaging community has led the way in open data 1 , facilitating an explosion of research in neuroimaging biomarkers for dementia 2 . This interest is in the context of an increasing global burden of neurodegenerative disorders, particularly in relation to the impact of Alzheimer's disease and other dementias on an increasingly ageing population 3 .…”

Section: Introductionmentioning

confidence: 99%

Understanding ethnic diversity in open dementia neuroimaging datasets

Heng

Rittman

2023

Preprint

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Introduction Ethnic differences in dementia are increasingly recognised in epidemiological measures and diagnostic biomarkers. Nonetheless, ethnic diversity remains limited in many study populations Here we provide insights into ethnic diversity in open access neuroimaging dementia datasets. Methods Datasets comprising dementia populations who underwent neuroimaging assessment with available data on ethnicity were included. Statistical analyses of sample and effect sizes were based on the Cochrane Handbook. Results 14 databases were included, with 12 studies of healthy and MCI groups, and 11 of dementia groups. Combining all studies, the largest ethnic group was Caucasian (21,512 participants) with the next most common being Afro-Caribbean (1,960), followed by Asian (780). The smallest effect size detectable within the Caucasian group was 0.03, compared to Afro-Caribbean (0.1) and Asian (0.16). Discussion Our findings quantify the lack of ethnic diversity in openly available neuroimaging dementia datasets. More representative data would facilitate the development and validation of neuroimaging biomarkers relevant across ethnicities.

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Neurological update: neuroimaging in dementia

Cited by 15 publications

References 85 publications

Modifiable risk factors for dementia and dementia risk profiling. A user manual for Brain Health Services—part 2 of 6

Modifiable risk factors for dementia and dementia risk profiling. A user manual for Brain Health Services—part 2 of 6

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

Understanding ethnic diversity in open dementia neuroimaging datasets

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