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Background: Several studies have applied resting-state functional MRI to examine whether functional brain connectivity is altered in migraine with aura patients. These studies had multiple limitations, including small sample sizes, and reported conflicting results. Here, we performed a large, cross-sectional brain imaging study to reproduce previous findings. Methods: We recruited women aged 30–60 years from the nationwide Danish Twin Registry. Resting-state functional MRI of women with migraine with aura, their co-twins, and unrelated migraine-free twins was performed at a single centre. We carried out an extensive series of brain connectivity data analyses. Patients were compared to migraine-free controls and to co-twins. Results: Comparisons were based on data from 160 patients, 30 co-twins, and 136 controls. Patients were similar to controls with regard to age, and several lifestyle characteristics. We replicated clear effects of age on resting-state networks. In contrast, we failed to detect any differences, and to replicate previously reported differences, in functional connectivity between migraine patients with aura and non-migraine controls or their co-twins in any of the analyses. Conclusion: Given the large sample size and the unbiased population-based design of our study, we conclude that women with migraine with aura have normal resting-state brain connectivity outside of migraine attacks.

The development of appropriate models for dynamic functional connectivity is imperative to gain a better understanding of the brain both during rest and while performing a task. Leading eigenvector dynamics analysis is among the favored methods for assessing frame-wise connectivity, but eigenvectors are distributed on the sign-symmetric unit hypersphere, which is typically disregarded during modeling. Here we develop both mixture model and Hidden Markov model formulations for two sign-symmetric spherical statistical distributions and display their performance on synthetic data and task-fMRI data involving a finger-tapping task.

Objective: In the field of radiation oncology, the benefit of MRI goes beyond that of providing high soft-tissue contrast images for staging and treatment planning. With the recent clinical introduction of hybrid MRI linear accelerators (MR-Linacs) it has become feasible to map physiological parameters describing diffusion, perfusion, and relaxation during the entire course of radiotherapy, for example. However, advanced data analysis tools are required for extracting qualified prognostic and predictive imaging biomarkers from longitudinal MRI data. In this study, we propose a new prediction framework tailored to exploit temporal dynamics of tissue features from repeated measurements. We demonstrate the framework using a newly developed decomposition method for tumor characterization. Approach: Two previously published MRI datasets with multiple measurements during and after radiotherapy, were used for development and testing: T$_2$-weighted multi-echo images obtained for two mouse models of pancreatic cancer, and diffusion-weighted images for patients with brain metastases. Initially, the data was decomposed using the novel monotonous slope non-negative matrix factorization (msNMF) tailored for MR data. The following processing consisted of a tumor heterogeneity assessment using descriptive statistical measures, robust linear modelling to capture temporal changes of these, and finally logistic regression analysis for stratification of tumors and volumetric outcome. Main Results: The framework was able to classify the two pancreatic tumor types with an \textit{area under curve} AUC of 0.999, $P

Purpose: The diffusion-weighted SPLICE (split acquisition of fast spin-echo signals) sequence employs split-echo rapid acquisition with relaxation enhancement (RARE) readout to provide images almost free of geometric distortions. However, due to the varying T (Formula presented.) -weighting during k-space traversal, SPLICE suffers from blurring. This work extends a method for controlling the spatial point spread function (PSF) while optimizing the signal-to-noise ratio (SNR) achieved by adjusting the flip angles in the refocusing pulse train of SPLICE. Methods: An algorithm based on extended phase graph (EPG) simulations optimizes the flip angles by maximizing SNR for a flexibly chosen predefined target PSF that describes the desired k-space density weighting and spatial resolution. An optimized flip angle scheme and a corresponding post-processing correction filter which together achieve the target PSF was tested by healthy subject brain imaging using a clinical 1.5 T scanner. Results: Brain images showed a clear and consistent improvement over those obtained with a standard constant flip angle scheme. SNR was increased and apparent diffusion coefficient estimates were more accurate. For a modified Hann k-space weighting example, considerable benefits resulted from acquisition weighting by flip angle control. Conclusion: The presented flexible method for optimizing SPLICE flip angle schemes offers improved MR image quality of geometrically accurate diffusion-weighted images that makes the sequence a strong candidate for radiotherapy planning or stereotactic surgery.

Low-intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation, applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional MRI (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies. The objective of this work was to develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency and reproducibility (ContES checklist). A two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists through the International Network of the tES-fMRI Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC on the basis of a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed by using the checklist. Experts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (i) technological factors, (ii) safety and noise tests and (iii) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-f

Purpose: To demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients. Theory and Methods: Gradient switching during simultaneous EEG–fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high-impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. We introduce head tracking based on these signals (CapTrack) involving 3 steps: (1) phantom scanning is used to characterize the target sequence and a fast calibration sequence; (2) a linear relation between changes of induced signals and head pose is established using the calibration sequence; and (3) induced signals recorded during target sequence scanning are used for tracking and retrospective correction of head movement without prolonging the scan time of the target sequence. Performance of CapTrack is compared directly to interleaved navigators. Results: Head-pose tracking at 27.5 Hz during echo planar imaging (EPI) was demonstrated with close resemblance to rigid body alignment (mean absolute difference: [0.14 0.38 0.15]-mm translation, [0.30 0.27 0.22]-degree rotation). Retrospective correction of 3D gradient-echo imaging shows an increase of average edge strength of 12%/−0.39% for instructed/uninstructed motion with CapTrack pose estimates, with a tracking interval of 1561 ms and high similarity to interleaved navigator estimates (mean absolute difference: [0.13 0.33 0.12] mm, [0.28 0.15 0.22] degrees). Conclusion: Motion can be estimated from recordings of gradient switching with little or no sequence modification, optionally in real time at low computational burden and synchronized to image acquisition, using EEG equipment already found at many research institutions

Background: Parkinson's disease (PD) causes a loss of neuromelanin-positive, noradrenergic neurons in the locus coeruleus (LC), which has been implicated in nonmotor dysfunction. Objectives: We used "neuromelanin sensitive" magnetic resonance imaging (MRI) to localize structural disintegration in the LC and its association with nonmotor dysfunction in PD. Methods: A total of 42 patients with PD and 24 age-matched healthy volunteers underwent magnetization transfer weighted (MTw) MRI of the LC. The contrast-to-noise ratio of the MTw signal (CNRMTw) was used as an index of structural LC integrity. We performed slicewise and voxelwise analyses to map spatial patterns of structural disintegration, complemented by principal component analysis (PCA). We also tested for correlations between regional CNRMTw and severity of nonmotor symptoms. Results: Mean CNRMTw of the right LC was reduced in patients relative to controls. Voxelwise and slicewise analyses showed that the attenuation of CNRMTw was confined to the right mid-caudal LC and linked regional CNRMTw to nonmotor symptoms. CNRMTw attenuation in the left mid-caudal LC was associated with the orthostatic drop in systolic blood pressure, whereas CNRMTw attenuation in the caudal most portion of right LC correlated with apathy ratings. PCA identified a bilateral component that was more weakly expressed in patients. This component was characterized by a gradient in CNRMTw along the rostro-caudal and dorso-ventral axes of the nucleus. The individual expression score of this component reflected the overall severity of nonmotor symptoms. Conclusion: A spatially heterogeneous disintegration of LC in PD may determine the individual expression of specific nonmotor symptoms such as orthostatic dysregulation or apathy. © 2022 International Parkinson and Movement Disorder Society

Metastable microstates in electro- and magnetoencephalographic (EEG and MEG) measurements are usually determined using modified k-means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous fashion. We present multimodal, multisubject directional archetypal analysis as a scale and polarity invariant extension to archetypal analysis using a loss function based on the Watson distribution. With this method, EEG/MEG microstates are modeled using subject- and modality-specific archetypes that are representative, distinct topographic maps between which the brain continuously traverses. Archetypes are specified as convex combinations of unit norm input data based on a shared generator matrix, thus assuming that the timing of neural responses to stimuli is consistent across subjects and modalities. The input data is reconstructed as convex combinations of archetypes using a subject- and modality-specific continuous archetypal mixing matrix. We showcase the model on synthetic data and an openly available face perception event-related potential data set with concurrently recorded EEG and MEG. In synthetic and unimodal experiments, we compare our model to conventional Euclidean multisubject archetypal analysis. We also contrast our model to a directional clustering model with discrete state assignments to highlight the advantages of modeling state trajectories rather than hard assignments. We find that our approach successfully models scale and polarity invariant data, such as microstates, accounting for intersubject and intermodal variability. The model is readily extendable to other modalities ensuring component correspondence while elucidating spatiotemporal signal variability.

Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspo