Dataset Information

Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review.

ABSTRACT:

Introduction

Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear.

Methods

We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries.

Results

Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1-weighted sequences. Accuracy was highest for differentiating Alzheimer's disease from healthy controls and poor for differentiating healthy controls versus mild cognitive impairment versus Alzheimer's disease or mild cognitive impairment converters versus nonconverters. Accuracy increased using combined data types, but not by data source, sample size, or machine learning method.

Discussion

Machine learning does not differentiate clinically relevant disease categories yet. More diverse data sets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field.

SUBMITTER: Pellegrini E

PROVIDER: S-EPMC6197752 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review.

Pellegrini Enrico E Ballerini Lucia L Hernandez Maria Del C Valdes MDCV Chappell Francesca M FM González-Castro Victor V Anblagan Devasuda D Danso Samuel S Muñoz-Maniega Susana S Job Dominic D Pernet Cyril C Mair Grant G MacGillivray Tom J TJ Trucco Emanuele E Wardlaw Joanna M JM

Alzheimer's & dementia (Amsterdam, Netherlands) 20180811

<h4>Introduction</h4>Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear.<h4>Methods</h4>We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries.<h4>Results</h4>Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, us ...[more]

PMID: 30364671

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Project description:Mild cognitive impairment (MCI) is a multifactorial and complex central neurodegenerative disease. Acupuncture appears to be an effective method for cognitive function improvement in MCI patients. Neural plasticity remaining in the MCI brain implies that acupuncture-associated benefits may not be limited to the cognitive function. Instead, neurological alternations in the brain play a vital role in corresponding to the cognitive improvement. However, previous studies have mainly focused on the effects of cognitive function, leaving neurological findings relatively unclear. This systematic review summarized existing studies that used various brain imaging techniques to explore the neurological effect regarding acupuncture use for MCI treatment. Potential neuroimaging trials were searched, collected, and identified independently by two researchers. Four Chinese databases, four English databases, and additional sources were searched to identify studies reporting the use of acupuncture for MCI from the inception of databases until 1 June 2022. Methodological quality was appraised using the Cochrane risk-of-bias tool. In addition, general, methodological, and brain neuroimaging information was extracted and summarized to investigate the potential neural mechanisms by which acupuncture affects patients with MCI. In total, 22 studies involving 647 participants were included. The methodological quality of the included studies was moderate to high. The methods used included functional magnetic resonance imaging, diffusion tensor imaging, functional near-infrared spectroscopy, and magnetic resonance spectroscopy. Acupuncture-induced brain alterations observed in those patients with MCI tended to be observable in the cingulate cortex, prefrontal cortex, and hippocampus. The effect of acupuncture on MCI may play a role in regulating the default mode network, central executive network, and salience network. Based on these studies, researchers could extend the recent research focus from the cognitive domain to the neurological level. Future researches should develop additional relevant, well-designed, high-quality, and multimodal neuroimaging researches to detect the effects of acupuncture on the brains of MCI patients.

Project description:BackgroundMild cognitive impairment and cognitive impairment, no dementia, are emerging terms that encompass the clinical state between normal cognition and dementia in elderly people. Controversy surrounds their characterization, definition and application in clinical practice. In this article, we provide physicians with practical guidance on the definition, diagnosis and treatment of mild cognitive impairment and cognitive impairment, no dementia, based on recommendations from the Third Canadian Consensus Conference on the Diagnosis and Treatment of Dementia, held in March 2006.MethodsWe developed evidence-based guidelines using systematic literature searches, with specific criteria for study selection and quality assessment, and a clear and transparent decision-making process. We selected studies published from January 1996 to December 2005 that had mild cognitive impairment or cognitive impairment, no dementia, as the outcome. Subsequent to the conference, we searched for additional articles published between January 2006 and January 2008. We graded the strength of evidence using the criteria of the Canadian Task Force on Preventive Health Care.ResultsWe identified 2483 articles, of which 314 were considered to be relevant and of good or fair quality. From a synthesis of the evidence in these studies, we made 16 recommendations. In brief, family physicians should be aware that most types of dementia are preceded by a recognizable phase of mild cognitive decline. They should be familiar with the concepts of mild cognitive impairment and of cognitive impairment, no dementia. Patients with these conditions should be closely monitored because of their increased risk for dementia. Leisure activities, cognitive stimulation and physical activity could be promoted as part of a healthy lifestyle in elderly people and those with mild cognitive impairment. Vascular risk factors should be treated optimally. No other specific therapies can yet be recommended.InterpretationPhysicians will increasingly see elderly patients with mild memory loss, and learning an approach to diagnosing states such as mild cognitive impairment is now warranted. Close monitoring for progression to dementia, promotion of a healthy lifestyle and treatment of vascular risk factors are recommended for the management of patients with mild cognitive impairment.

Dataset Information

Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review.

Introduction

Methods

Results

Discussion

Publications

Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review.

Similar Datasets

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