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Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast‐enhanced MRI, arterial spin labeling, diffusion‐weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility‐weighted imaging, MRI‐PET, MR elastography, and MR‐based radiomics applications.Evidence Level: 3Technical Efficacy: Stage 2

AbstractPredictability is known to modulate semantic processing in language, but it is unclear to what extent this applies for other modalities. Here we ask whether similar cognitive processes are at play in predicting upcoming events in a non-verbal visual narrative. Typically developing adults viewed comics sequences in which a target panel was highly predictable (“high cloze”), less predictable (“low cloze”), or incongruent with the preceding narrative context (“anomalous”) during EEG recording. High and low predictable sequences were determined by a pretest where participants assessed “what happened next?”, resulting in cloze probability scores for sequence outcomes comparable to those used to measure predictability in sentence processing. Through both factorial and correlational analyses, we show a significant modulation of neural responses by cloze such that N400 effects are diminished as a target panel in a comic sequence becomes more predictable. Predictability thus appears to play a similar role in non-verbal comprehension of sequential images as in language comprehension, providing further evidence for the domain generality of semantic processing in the brain.

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834 ± 0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781 ± 0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514, and the Dana Foundation.

Abstract The cingulate cortex is a functionally and morphologically heterogeneous cortical area comprising a number of interconnected subregions. To date, the exact anatomy of intracingulate connections has not been studied in detail. In the present study we aimed to determine the topographical and laminar characteristics of intrinsic cingulate connections in the rat, using the anterograde tracers Phaseolus vulgaris -leucoagglutinin and biotinylated dextran amine. For assessment of these data we further refined and compared the existing cytoarchitectonic descriptions of the two major cingulate constituents, the anterior cingulate and retrosplenial cortices. The results of this study demonstrate that rostral areas, i.e. the infralimbic and prelimbic cortices and the rostral one third of the dorsal anterior cingulate cortex are primarily interconnected with each other and not with other cingulate areas. The caudal two thirds of the dorsal anterior cingulate cortex project to the caudal part of the ventral anterior cingulate cortex, whereas the entire ventral anterior cingulate cortex projects to only the mid-rostro-caudal part of the dorsal anterior cingulate cortex. Dense reciprocal connections exist between the remaining, i.e. the supracallosal parts of the anterior cingulate and retrosplenial cortices with a general rostro-caudal topography, in the sense that the rostral part of the anterior cingulate cortex and caudal part of the retrosplenial cortex are interconnected and the same holds true for the caudal part of the anterior cingulate cortex and rostral part of the retrosplenial cortex. This topographical pattern of intracingulate connections relates to the results of several functional studies, suggesting that specific cingulate functions depend on a number of interconnected cingulate subregions. Through their intricate associational connections, these subregions form functionally segregated networks.

Abstract The primary electrophysiological marker of feature-based selection is the N2pc, a lateralized posterior negativity emerging around 180-200 ms. As it relies on hemispheric differences, its ability to discriminate the locus of focal attention is severely limited. Here we demonstrate that multivariate analyses of raw EEG data provide a much more fine-grained spatial profile of feature-based target selection. When training a pattern classifier to determine target position from EEG, we were able to decode target positions on the vertical midline, which cannot be achieved using standard N2pc methodology. Next, we used a forward encoding model to construct a channel tuning function that describes the continuous relationship between target position and multivariate EEG in an eight-position display. This model can spatially discriminate individual target positions in these displays and is fully invertible, enabling us to construct hypothetical topographic activation maps for target positions that were never used. When tested against the real pattern of neural activity obtained from a different group of subjects, the constructed maps from the forward model turned out statistically indistinguishable, thus providing independent validation of our model. Our findings demonstrate the power of multivariate EEG analysis to track feature-based target selection with high spatial and temporal precision. Significance Statement Feature-based attentional selection enables observers to find objects in their visual field. The spatiotemporal profile of this process is difficult to assess with standard electrophysiological methods, which rely on activity differences between cerebral hemispheres. We demonstrate that multivariate analyses of EEG data can track target selection across the visual field with high temporal and spatial resolution. Using a forward model, we were able to capture the continuous relationship between target position and EEG measurements, allowing us to reconstruct the distribution of cortical activity for target locations that were never shown during the experiment. Our findings demonstrate the existence of a temporally and spatially precise EEG signal that can be used to study the neural basis of feature-based attentional selection.

Abstract Background Genome-wide association studies have identified several common risk loci for schizophrenia (SCZ). In parallel, neuroimaging studies have shown consistent findings of widespread white matter disconnectivity in patients with SCZ. Methods We examined the role of genes in brain connectivity in patients with SCZ by combining transcriptional profiles of 43 SCZ risk genes identified by the recent genome-wide association study of the Schizophrenia Working Group of the Psychiatric Genomics Consortium with data on macroscale connectivity reductions in patients with SCZ. Expression profiles of 43 Psychiatric Genomics Consortium SCZ risk genes were extracted from the Allen Human Brain Atlas, and their average profile across the cortex was correlated to the pattern of cortical disconnectivity as derived from diffusion-weighted magnetic resonance imaging data of patients with SCZ ( n = 48) and matched healthy controls ( n = 43). Results The expression profile of SCZ risk genes across cortical regions was significantly correlated with the regional macroscale disconnectivity ( r = .588; p = .017). In addition, effects were found to be potentially specific to SCZ, with transcriptional profiles not related to cortical disconnectivity in patients with bipolar I disorder (diffusion-weighted magnetic resonance imaging data; 216 patients, 144 controls). Further examination of correlations across all 20,737 genes present in the Allen Human Brain Atlas showed the set of top 100 strongest correlating genes to display significant enrichment for the disorder, potentially identifying new genes involved in the pathophysiology of SCZ. Conclusions Our results suggest that under disease conditions, cortical areas with pronounced expression of risk genes implicated in SCZ form central areas for white matter disconnectivity.