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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.

Background: Perivascular Spaces (PVS), visible on brain magnetic resonance imaging (MRI), are thought to be associated with small vessel disease (SVD), neuroinflammation, and to be important for cerebral haemodynamics and interstitial fluid drainage. Aims: To benchmark current knowledge on PVS associations with risk factors, neurological disorders, and neuroimaging lesions, using systematic review and meta-analysis. Summary of review: We searched three databases for PVS publications, calculated odds ratios with 95% confidence interval and performed meta-analyses to assess adjusted associations with PVS. We identified 116 relevant studies (n=36,108) but only 23 (n=12,725) were meta-analysable. PVS assessment, imaging and clinical definitions varied. PVS were associated (n; OR, 95%CI, p) with ageing (8395; 1.47, 1.28-1.69, p=0.00001), hypertension (7872; 1.67, 1.20-2.31, p=0.002), lacunes (4894; 3.56, 1.39-9.14, p=0.008), microbleeds (5015; 2.26, 1.04-4.90, p=0.04) but not WMH (4974; 1.54, 0.71-3.32, p=0.27), stroke or cognitive impairment. There was between-study heterogeneity. Lack of appropriate data on other brain disorders and demographic features such as ethnicity precluded analysis. Conclusions: Despite many studies, more are required to determine potential pathophysiological PVS involvement in cerebrovascular, neurodegenerative and neuroinflammatory disorders.

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