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Complex neurologic and psychiatric syndromes cannot be understood on the basis of focal brain lesions. Functional neuroimaging, maps of interrelated regions called the connectome, and the combination of lesion analysis with networks of the connectome offer a new way to understand neurologic function and disease.

Objectives The purpose of this study was to compare cranial CT (CCT) image quality (IQ) of the MBIR algorithm with standard iterative reconstruction (ASiR). Methods In this institutional review board (IRB)-approved study, raw data sets of 100 unenhanced CCT examinations (120 kV, 50–260 mAs, 20 mm collimation, 0.984 pitch) were reconstructed with both ASiR and MBIR. Signal-to-noise (SNR) and contrast-to-noise (CNR) were calculated from attenuation values measured in caudate nucleus, frontal white matter, anterior ventricle horn, fourth ventricle, and pons. Two radiologists, who were blinded to the reconstruction algorithms, evaluated anonymized multiplanar reformations of 2.5 mm with respect to depiction of different parenchymal structures and impact of artefacts on IQ with a five-point scale (0: unacceptable, 1: less than average, 2: average, 3: above average, 4: excellent). Results MBIR decreased artefacts more effectively than ASiR ( p  < 0.01). The median depiction score for MBIR was 3, whereas the median value for ASiR was 2 ( p  < 0.01). SNR and CNR were significantly higher in MBIR than ASiR ( p  < 0.01). Conclusions MBIR showed significant improvement of IQ parameters compared to ASiR. As CCT is an examination that is frequently required, the use of MBIR may allow for substantial reduction of radiation exposure caused by medical diagnostics. Key Points • Model-Based iterative reconstruction (MBIR) effectively decreased artefacts in cranial CT . • MBIR reconstructed images were rated with significantly higher scores for image quality . • Model-Based iterative reconstruction may allow reduced-dose diagnostic examination protocols .

Neuroimaging studies of patients with treatment-resistant depression (TRD) have reported abnormalities in the frontal and temporal regions. We sought to determine whether metabolism in these regions might be related to response to repetitive transcranial magnetic stimulation (TMS) in patients with TRD. Magnetic resonance images and baseline resting-state cerebral glucose uptake index (gluMI) obtained using 18 F-fluorodeoxyglucose positron emission tomography were analyzed in TRD patients who had participated in a double-blind, randomized, sham-controlled trial of prefrontal 10 Hz TMS. Among the patients randomized to active TMS, 17 responders, defined as having 50% depression score decrease, and 14 nonresponders were investigated for prestimulation glucose metabolism and compared with 39 healthy subjects using a voxel-based analysis. In nonresponders relative to responders, gluMI was lower in left lateral orbitofrontal cortex (OFC), and higher in left amygdala and uncinate fasciculus. OFC and amygdala gluMI negatively correlated in nonresponders, positively correlated in responders, and did not correlate in healthy subjects. Relative to healthy subjects, both responders and nonresponders displayed lower gluMI in right dorsolateral prefrontal (DLPFC), right anterior cingulate (ACC), and left ventrolateral prefrontal cortices. Additionally, nonresponders had lower gluMI in left DLPFC, ACC, left and right insula, and higher gluMI in left amygdala and uncus. Hypometabolisms were partly explained by gray matter reductions, whereas hypermetabolisms were unrelated to structural changes. The findings suggest that different patterns of frontal–temporal–limbic abnormalities may distinguish responders and nonresponders to prefrontal magnetic stimulation. Both preserved OFC volume and amygdala metabolism might precondition response to TMS.

Perivascular spaces include a variety of passageways around arterioles, capillaries and venules in the brain, along which a range of substances can move. Although perivascular spaces were first identified over 150 years ago, they have come to prominence recently owing to advances in knowledge of their roles in clearance of interstitial fluid and waste from the brain, particularly during sleep, and in the pathogenesis of small vessel disease, Alzheimer disease and other neurodegenerative and inflammatory disorders. Experimental advances have facilitated in vivo studies of perivascular space function in intact rodent models during wakefulness and sleep, and MRI in humans has enabled perivascular space morphology to be related to cognitive function, vascular risk factors, vascular and neurodegenerative brain lesions, sleep patterns and cerebral haemodynamics. Many questions about perivascular spaces remain, but what is now clear is that normal perivascular space function is important for maintaining brain health. Here, we review perivascular space anatomy, physiology and pathology, particularly as seen with MRI in humans, and consider translation from models to humans to highlight knowns, unknowns, controversies and clinical relevance.In this Review, Wardlaw et al. discuss the anatomy, physiology and pathology of perivascular spaces, particularly as seen with MRI in humans, and consider translation from models to humans to highlight knowns, unknowns, controversies and clinical relevance.

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead. •Past efforts on classification of brain disorders are comprehensively reviewed.•The common pitfalls from machine learning point of view are discussed.•Emerging trends related to single-subject prediction are reviewed and discussed.

Using structural MRI data from 45,615 individuals aged 3–96 years, Kaufmann and colleagues reveal that common brain disorders are associated with heritable patterns of apparent aging of the brain.

Objectives To investigate whether moderate alcohol consumption has a favourable or adverse association or no association with brain structure and function.Design Observational cohort study with weekly alcohol intake and cognitive performance measured repeatedly over 30 years (1985-2015). Multimodal magnetic resonance imaging (MRI) was performed at study endpoint (2012-15).Setting Community dwelling adults enrolled in the Whitehall II cohort based in the UK (the Whitehall II imaging substudy).Participants 550 men and women with mean age 43.0 (SD 5.4) at study baseline, none were “alcohol dependent” according to the CAGE screening questionnaire, and all safe to undergo MRI of the brain at follow-up. Twenty three were excluded because of incomplete or poor quality imaging data or gross structural abnormality (such as a brain cyst) or incomplete alcohol use, sociodemographic, health, or cognitive data.Main outcome measures Structural brain measures included hippocampal atrophy, grey matter density, and white matter microstructure. Functional measures included cognitive decline over the study and cross sectional cognitive performance at the time of scanning.Results Higher alcohol consumption over the 30 year follow-up was associated with increased odds of hippocampal atrophy in a dose dependent fashion. While those consuming over 30 units a week were at the highest risk compared with abstainers (odds ratio 5.8, 95% confidence interval 1.8 to 18.6; P≤0.001), even those drinking moderately (14-21 units/week) had three times the odds of right sided hippocampal atrophy (3.4, 1.4 to 8.1; P=0.007). There was no protective effect of light drinking (1-<7 units/week) over abstinence. Higher alcohol use was also associated with differences in corpus callosum microstructure and faster decline in lexical fluency. No association was found with cross sectional cognitive performance or longitudinal changes in semantic fluency or word recall.Conclusions Alcohol consumption, even at moderate levels, is associated with adverse brain outcomes including hippocampal atrophy. These results support the recent reduction in alcohol guidance in the UK and question the current limits recommended in the US.

The measurement of cerebral blood volume (CBV) has been the topic of numerous neuroimaging studies. To date, however, most in vivo imaging approaches can only measure CBV summed over all types of blood vessels, including arterial, capillary and venous vessels in the microvasculature (i.e. total CBV or CBVtot). As different types of blood vessels have intrinsically different anatomy, function and physiology, the ability to quantify CBV in different segments of the microvascular tree may furnish information that is not obtainable from CBVtot, and may provide a more sensitive and specific measure for the underlying physiology. This review attempts to summarize major efforts in the development of MRI techniques to measure arterial (CBVa) and venous CBV (CBVv) separately. Advantages and disadvantages of each type of method are discussed. Applications of some of the methods in the investigation of flow-volume coupling in healthy brains, and in the detection of pathophysiological abnormalities in brain diseases such as arterial steno-occlusive disease, brain tumors, schizophrenia, Huntington's disease, Alzheimer's disease, and hypertension are demonstrated. We believe that the continual development of MRI approaches for the measurement of compartment-specific CBV will likely provide essential imaging tools for the advancement and refinement of our knowledge on the exquisite details of the microvasculature in healthy and diseased brains. •To date, most CBV MRI methods measure total CBV.•Arterial and venous blood vessels are intrinsically different.•Here, MRI methods for CBVa and CBVv measurement are reviewed.•Compartment specific flow-volume coupling is investigated using the methods.•Applications of the methods in brain diseases are demonstrated.

Mind wandering represents the human capacity for internally focused thought and relies upon the brain’s default network and its interactions with attentional networks. Studies have characterized mind wandering in healthy people, yet there is limited understanding of how this capacity is affected in clinical populations. This paper used a validated thought-sampling task to probe mind wandering capacity in two neurodegenerative disorders: behavioral variant frontotemporal dementia [(bvFTD); n = 35] and Alzheimer’s disease [(AD); n = 24], compared with older controls (n = 37). These patient groups were selected due to canonical structural and functional changes across sites of the default and frontoparietal networks and well-defined impairments in cognitive processes that support mind wandering. Relative to the controls, bvFTD patients displayed significantly reduced mind wandering capacity, offset by a significant increase in stimulus-bound thought. In contrast, AD patients demonstrated comparable levels of mind wandering to controls, in the context of a relatively subtle shift toward stimulus-/task-related forms of thought. In the patient groups, mind wandering was associated with gray matter integrity in the hippocampus/parahippocampus, striatum, insula, and orbitofrontal cortex. Restingstate functional connectivity revealed associations between mind wandering capacity and connectivity within and between regions of the frontoparietal and default networks with distinct patterns evident in patients vs. controls. These findings support a relationship between altered mind wandering capacity in neurodegenerative disorders and structural and functional integrity of the default and frontoparietal networks. This paper highlights a dimension of cognitive dysfunction not well documented in neurodegenerative disorders and validates current models of mind wandering in a clinical population.

Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle's algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.