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Purpose: Static and dynamic B0$$ {\mathrm{B}}_0 $$ field imperfections are detrimental to functional MRI (fMRI) applications, especially at ultra‐high magnetic fields (UHF). In this work, a field camera is used to assess the benefits of retrospectively correcting B0$$ {\mathrm{B}}_0 $$ field perturbations on Blood Oxygen Level Dependent (BOLD) sensitivity in non‐Cartesian three‐dimensional (3D)‐SPARKLING fMRI acquisitions. Methods: fMRI data were acquired at 1 mm3$$ {}^3 $$ and for a 2.4s‐TR while concurrently monitoring in real‐time field perturbations using a Skope Clip‐on field camera in a novel experimental setting involving a shorter TR than the required minimal TR of the field probes. Measurements of the dynamic field deviations were used along with a static ΔB0$$ \Delta {\mathrm{B}}_0 $$ map to retrospectively correct static and dynamic field imperfections, respectively. In order to evaluate the impact of such a correction on fMRI volumes, a comparative study was conducted on healthy volunteers. Results: Correction of B0$$ {\mathrm{B}}_0 $$ deviations improved image quality and yielded between 20% and 30% increase in median temporal signal‐to‐noise ratio (tSNR).Using fMRI data collected during a retinotopic mapping experiment, we demonstrated a significant increase in sensitivity to the BOLD contrast and improved accuracy of the BOLD phase maps: 44% (resp., 159%) more activated voxels were retrieved when using a significance control level based on a p‐value of 0.001 without correcting for multiple comparisons (resp., 0.05 with a false discovery rate correction). Conclusion: 3D‐SPARKLING fMRI hugely benefits from static and dynamic B0$$ {\mathrm{B}}_0 $$ imperfections correction. However, the proposed experimental protocol is flexible enough to be deployed on a large spectrum of encoding schemes, including arbitrary non‐Cartesian readouts. [ABSTRACT FROM AUTHOR]

Background: One of the clinical problems for biomarkers' clinical use is the ability to differentiate between Alzheimer's disease (AD), frontotemporal dementia (FTD), and healthy subjects (CTR). This clearly challenges diagnosis and prognosis. We implemented a ML algorithm that provides individual probabilistic diagnoses for these dementias based on magnetic resonance imaging (MRI) and we correlated the results with biochemical markers. Method: We studied 3T‐T1w MRI of 432 subjects. A subset of these participants had cerebrospinal fluid (CSF) and plasma biomarkers (Table 1). We obtained regional subcortical gray matter volumes and cortical thickness measures using Freesurfer. We implemented a calibrated classifier with a Support Vector Machine with only the MRI data. We tested paired‐wise classification and classification across the 3 groups. We obtained individual probabilities associated with group correspondence. We studied the correlation between these probabilities and CSF and plasma biomarkers. For this, we subdivided the groups into true‐group (subjects with classification according to clinical diagnosis) and false‐group (subjects which did not coincide with clinical diagnosis). Finally, we implemented a permutation test to find the importance of each region in the classification. Result: We obtained accuracies of 90.7 ± 6.7% in the CTR vs AD classification, 88.6 ± 4.5% for CTR vs FTD, 79.3 ± 8.8% for AD vs FTD, and 79.9 ± 5.1% when discriminating the 3 groups. We obtained a significant positive correlation for plasma p‐tau181 for the false‐AD in the comparison AD vs CTR (Figure 1). The correlation of the false‐CTR was significantly positive for the CSF and plasma NfL. Finally, in the AD vs FTD, the true‐FTD had a significant negative correlation with CSF NfL. The other biochemical biomarkers did not provide additional information. The most important regions for classification are shown in Figure 2. Conclusion: The ML algorithm gave high accuracies. Within wrongly classified AD patients, probabilities correlated positively with plasma p‐tau181, suggesting hidden pathological processes associated with subjects clinically classified as CTR. Finally, the group probability within well‐classified FTD patients in comparison with AD negatively correlated with CSF NfL. We suggest that this approach can be used as a tool to try to develop personalized diagnoses. [ABSTRACT FROM AUTHOR]

Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships. [ABSTRACT FROM AUTHOR]

Purpose: A calibration‐free pulse design method is introduced to alleviate B1+ artifacts in clinical routine with parallel transmission at high field, dealing with significant inter‐subject variability, found for instance in the abdomen. Theory and Methods: From a dual‐transmit 3T scanner, a database of B1+ and off‐resonance abdominal maps from 50 subjects was first divided into 3 clusters based on mutual affinity between their respective tailored kT‐points pulses. For each cluster, a kT‐points pulse was computed, minimizing normalized root‐mean‐square flip angle deviations simultaneously for all subjects comprised in it. Using 30 additional subjects' field distributions, a machine learning classifier was trained on this 80‐labeled‐subject database to recognize the best pulse from the 3 ones available, relying only on patient features accessible from the preliminary localizer sequence present in all protocols. This so‐called SmartPulse process was experimentally tested on an additional 53‐subject set and compared with other pulse types: vendor's hard calibration‐free dual excitation, tailored static radiofrequency shimming, universal and tailored kT‐points pulses. Results: SmartPulse outperformed both calibration‐free approaches. Tailored static radiofrequency shimming yielded similar flip angle homogeneity for most patients but broke down for some while SmartPulse remained robust. Although flip angle homogeneity was systematically better with tailored kT‐points, the difference was barely noticeable on in vivo images. Conclusion: The proposed method paves the way toward an efficient trade‐off between tailored and universal pulse design approaches for large inter‐subject variability. With no need for on‐line field mapping or pulse design, it can fit seamlessly into a clinical protocol. [ABSTRACT FROM AUTHOR]

Schizophrenia is a devastating brain disorder that disturbs sensory perception, motor action, and abstract thought. Its clinical phenotype implies dysfunction of various mental domains, which has motivated a series of theories regarding the underlying pathophysiology. Aiming at a predictive benchmark of a catalog of cognitive functions, we developed a data‐driven machine‐learning strategy and provide a proof of principle in a multisite clinical dataset (n = 324). Existing neuroscientific knowledge on diverse cognitive domains was first condensed into neurotopographical maps. We then examined how the ensuing meta‐analytic cognitive priors can distinguish patients and controls using brain morphology and intrinsic functional connectivity. Some affected cognitive domains supported well‐studied directions of research on auditory evaluation and social cognition. However, rarely suspected cognitive domains also emerged as disease relevant, including self‐oriented processing of bodily sensations in gustation and pain. Such algorithmic charting of the cognitive landscape can be used to make targeted recommendations for future mental health research. [ABSTRACT FROM AUTHOR]

Abstract: Schizophrenia is a devastating mental disease with an apparent disruption in the highly associative default mode network (DMN). Interplay between this canonical network and others probably contributes to goal‐directed behavior so its disturbance is a candidate neural fingerprint underlying schizophrenia psychopathology. Previous research has reported both hyperconnectivity and hypoconnectivity within the DMN, and both increased and decreased DMN coupling with the multimodal saliency network (SN) and dorsal attention network (DAN). This study systematically revisited network disruption in patients with schizophrenia using data‐derived network atlases and multivariate pattern‐learning algorithms in a multisite dataset (n = 325). Resting‐state fluctuations in unconstrained brain states were used to estimate functional connectivity, and local volume differences between individuals were used to estimate structural co‐occurrence within and between the DMN, SN, and DAN. In brain structure and function, sparse inverse covariance estimates of network coupling were used to characterize healthy participants and patients with schizophrenia, and to identify statistically significant group differences. Evidence did not confirm that the backbone of the DMN was the primary driver of brain dysfunction in schizophrenia. Instead, functional and structural aberrations were frequently located outside of the DMN core, such as in the anterior temporoparietal junction and precuneus. Additionally, functional covariation analyses highlighted dysfunctional DMN‐DAN coupling, while structural covariation results highlighted aberrant DMN‐SN coupling. Our findings reframe the role of the DMN core and its relation to canonical networks in schizophrenia. We thus underline the importance of large‐scale neural interactions as effective biomarkers and indicators of how to tailor psychiatric care to single patients. [ABSTRACT FROM AUTHOR]

Regional specialization and functional integration are often viewed as two fundamental principles of human brain organization. They are closely intertwined because each functionally specialized brain region is probably characterized by a distinct set of long-range connections. This notion has prompted the quickly developing family of connectivity-based parcellation (CBP) methods in neuroimaging research. CBP assumes that there is a latent structure of parcels in a region of interest (ROI). First, connectivity strengths are computed to other parts of the brain for each voxel/vertex within the ROI. These features are then used to identify functionally distinct groups of ROI voxels/vertices. CBP enjoys increasing popularity for the in-vivo mapping of regional specialization in the human brain. Due to the requirements of different applications and datasets, CBP has diverged into a heterogeneous family of methods. This broad overview critically discusses the current state as well as the commonalities and idiosyncrasies of the main CBP methods. We target frequent concerns faced by novices and veterans to provide a reference for the investigation and review of CBP studies. [ABSTRACT FROM AUTHOR]

Computerized anatomical atlases play an important role in medical image analysis. While an atlas usually refers to a standard or mean image also called template, which presumably represents well a given population, it is not enough to characterize the observed population in detail. A template image should be learned jointly with the geometric variability of the shapes represented in the observations. These two quantities will in the sequel form the atlas of the corresponding population. The geometric variability is modeled as deformations of the template image so that it fits the observations. In this paper, we provide a detailed analysis of a new generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. Our atlas contains both an estimation of probability maps of each tissue (called class) and the deformation metric. We use a stochastic algorithm for the estimation of the probabilistic atlas given a dataset. This atlas is then used for atlas-based segmentation method to segment the new images. Experiments are shown on brain T1 MRI datasets. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Copyright of Annals of Neurology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The analysis of functional magnetic resonance imaging (fMRI) data recorded on several subjects resorts to the so-called spatial normalization in a common reference space. This normalization is usually carried out on a voxel-by-voxel basis, assuming that after coregistration of the functional images with an anatomical template image in the Talairach reference system, a correct voxel-based inference can be carried out across subjects. Shortcomings of such approaches are often dealt with by spatially smoothing the data to increase the overlap between subject-specific activated regions. This procedure, however, cannot adapt to each anatomo-functional subject configuration. We introduce a novel technique for intra-subject parcellation based on spectral clustering that delineates homogeneous and connected regions. We also propose a hierarchical method to derive group parcels that are spatially coherent across subjects and functionally homogeneous. We show that we can obtain groups (or cliques) of parcels that well summarize inter-subject activations. We also show that the spatial relaxation embedded in our procedure improves the sensitivity of random-effect analysis. Hum Brain Mapp, 2005. © 2005 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Copyright of Human Brain Mapping is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)