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.

Rationale: More patients with chronic obstructive pulmonary disease (COPD) die of cardiovascular causes than of respiratory causes, and patients with COPD have increased morbidity and mortality from stroke and coronary heart disease. Arterial stiffness independently predicts cardiovascular risk, is associated with atheromatous plaque burden, and is increased in patients with COPD compared with control subjects matched for cardiovascular risk factors. Elastin fragmentation and changes in collagen are found in the connective tissue of both emphysematous lungs and stiff arteries, but it is not known whether the severity of arterial stiffness in patients with COPD is associated with the severity of emphysema. Objectives: To identify whether the extent of arterial stiffness is associated with emphysema severity. Methods: We performed a cross-sectional study in 157 patients with COPD. Measurements and Main Results: We measured pulse wave velocity (a validated measure of arterial stiffness), blood pressure, smoking pack-years, glucose, cholesterol, and C-reactive protein in 157 patients with COPD. We assessed emphysema using quantitative computed tomography scanning in a subgroup of 73 patients. We found that emphysema severity was associated with arterial stiffness (r = 0.471, P < 0.001). The association was independent of smoking, age, sex, FEV1% predicted, highly sensitive C-reactive protein and glucose concentrations, cholesterol–high-density lipoprotein ratio, and pulse oximetry oxygen saturations. Conclusions: Emphysema severity is associated with arterial stiffness in patients with COPD. Similar pathophysiological processes may be involved in both lung and arterial tissue and further studies are now required to identify the mechanism underlying this newly described association.

The Stroke Imaging Research (STIR) Group, the American Society of Neuroradiology, and the Foundation of the American Society of Neuroradiology sponsored a series of working group meetings >12 months, with the final meeting occurring during the Stroke Treatment Academy Industry Roundtable (STAIR) on March 9 to 10, 2013, in Washington, DC. This process brought together vascular neurologists, neuroradiologists, neuroimaging research scientists, members of the National Institute of Neurological Disorders and Stroke, industry representatives, and members of the US Food and Drug Administration to discuss stroke imaging research priorities, especially in the light of the recent negative results of acute stroke clinical trials that tested the concept of penumbral imaging selection. The goal of this process was to propose a research roadmap for the next 5 years. STIR recommendations include (1) the use of standard terminology, aligned with the National Institute of Neurological Disorders and Stroke Common Data Elements. ; (2) a standardized imaging assessment of revascularization in acute ischemic stroke trials, including a modified Treatment In Cerebral Ischemia (mTICI) score. ; (3) a standardized process to assess whether ischemic core and penumbral imaging methods meet the requirements to be considered as an acceptable selection tool in acute ischemic stroke trials. ; (4) the characteristics of a clinical and imaging data repository to facilitate the development and testing process described in recommendation no. 3. ; (5) the optimal study design for a clinical trial to evaluate whether advanced imaging adds value in selecting acute ischemic stroke patients for revascularization therapy. ; (6) the structure of a stroke neuroimaging network to implement and coordinate the recommendations listed above. All of these recommendations pertain to research, not to clinical care

Background and Purpose—: In acute ischemic stroke, the hyperdense artery sign (HAS) on noncontrast computed tomography (CT) is thought to represent intraluminal thrombus and, therefore, is a surrogate of arterial obstruction. We sought to assess the accuracy of HAS as a marker of arterial obstruction by thrombus. Methods—: The Third International Stroke Trial (IST-3) was a randomized controlled trial testing the use of intravenous thrombolysis for acute ischemic stroke in patients who did not clearly meet the prevailing license criteria. Some participating IST-3 centers routinely performed CT or MR angiography at baseline. One reader assessed all relevant scans independently, blinded to all other data; we checked observer reliability. We combined IST-3 data with a systematic review and meta-analysis of all studies that assessed the accuracy of HAS using angiography (any modality). Results—: IST-3 had 273 patients with baseline CT or MR angiography and was the largest study of HAS accuracy. The meta-analysis (n=902+273=1175, including IST-3) found sensitivity and specificity of HAS for arterial obstruction on angiography to be 52% and 95%, respectively. HAS was more commonly identified in proximal than distal arteries (47% versus 37%; P =0.015), and its sensitivity increased with thinner CT slices ( r =−0.73; P =0.001). Neither extent of obstruction nor time after stroke influenced HAS accuracy. Conclusions—: When present in acute ischemic stroke, HAS indicates a high likelihood of arterial obstruction, but its absence indicates only a 50/50 chance of normal arterial patency. Thin-slice CT improves sensitivity of HAS detection. Clinical Trial Registration—: URL: http://www.controlled-trials.com/ISRCTN25765518 . Unique identifier: ISRCTN25765518.

Patients undergo interventions to achieve a ‘normal’ brain temperature; a parameter that remains undefined for humans. The profound sensitivity of neuronal function to temperature implies the brain should be isothermal, but observations from patients and non-human primates suggest significant spatiotemporal variation. We aimed to determine the clinical relevance of brain temperature in patients by establishing how much it varies in healthy adults. We retrospectively screened data for all patients recruited to the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) High Resolution Intensive Care Unit Sub-Study. Only patients with direct brain temperature measurements and without targeted temperature management were included. To interpret patient analyses, we prospectively recruited 40 healthy adults (20 males, 20 females, 20–40 years) for brain thermometry using magnetic resonance spectroscopy. Participants were scanned in the morning, afternoon, and late evening of a single day. In patients (n = 114), brain temperature ranged from 32.6 to 42.3°C and mean brain temperature (38.5 ± 0.8°C) exceeded body temperature (37.5 ± 0.5°C, P < 0.0001). Of 100 patients eligible for brain temperature rhythm analysis, 25 displayed a daily rhythm, and the brain temperature range decreased in older patients (P = 0.018). In healthy participants, brain temperature ranged from 36.1 to 40.9°C; mean brain temperature (38.5 ± 0.4°C) exceeded oral temperature (36.0 ± 0.5°C) and was 0.36°C higher in luteal females relative to follicular females and males (P = 0.0006 and P < 0.0001, respectively). Temperature increased with age, most notably in deep brain regions (0.6°C over 20 years, P = 0.0002), and varied spatially by 2.41 ± 0.46°C with highest temperatures in the thalamus. Brain temperature varied by time of day, especially in deep regions (0.86°C, P = 0.0001), and was lowest at night. From the healthy data we built HEATWAVE—a 4D map of human brain temperature. Testing the clinical relevance of HEATWAVE in patients, we found that lack of a daily brain temperature rhythm increased the odds of death in intensive care 21-fold (P = 0.016), whilst absolute temperature maxima or minima did not predict outcome. A warmer mean brain temperature was associated with survival (P = 0.035), however, and ageing by 10 years increased the odds of death 11-fold (P = 0.0002). Human brain temperature is higher and varies more than previously assumed—by age, sex, menstrual cycle, brain region, and time of day. This has major implications for temperature monitoring and management, with daily brain temperature rhythmicity emerging as one of the strongest single predictors of survival after brain injury. We conclude that daily rhythmic brain temperature variation—not absolute brain temperature—is one way in which human brain physiology may be distinguished from pathophysiology.