PurposeIn this study we develop undersampled echo-volumar imaging (EVI) using multi-band/simultaneous multi-slab encoding in conjunction with multi-shot slab-segmentation to accelerate 3D encoding and to reduce the duration of EVI encoding within slabs. This approach combines the sampling efficiency of single-shot 3D encoding with the sensitivity advantage of multi-echo acquisition. We describe the pulse sequence development and characterize the spatial–temporal resolution limits and BOLD sensitivity of this approach for high-speed task-based and resting-state fMRI at 3 T. We study the feasibility of further acceleration using compressed sensing (CS) and assess compatibility with NORDIC denoising.MethodsMulti-band echo volumar imaging (MB-EVI) combines multi-band encoding of up to 6 slabs with CAIPI shifting, accelerated EVI encoding within slabs using up to 4-fold GRAPPA accelerations, 2-shot kz-segmentation and partial Fourier acquisitions along the two phase-encoding dimensions. Task-based and resting-state fMRI at 3 Tesla was performed across a range of voxel sizes (between 1 and 3 mm isotropic), repetition times (118–650 ms), and number of slabs (up to 12). MB-EVI was compared with multi-slab EVI (MS-EVI) and multi-band-EPI (MB-EPI).ResultsImage quality and temporal SNR of MB-EVI was comparable to MS-EVI when using 2–3 mm spatial resolution. High sensitivity for mapping task-based activation and resting-state connectivity at short TR was measured. Online deconvolution of T2* signal decay markedly reduced spatial blurring and improved image contrast. The high temporal resolution of MB-EVI enabled sensitive mapping of high-frequency resting-state connectivity above 0.3 Hz with 3 mm isotropic voxel size (TR: 163 ms). Detection of task-based activation with 1 mm isotropic voxel size was feasible in scan times as short as 1 min 13 s. Compressed sensing with up to 2.4-fold retrospective undersampling showed negligible loss in image quality and moderate region-specific losses in BOLD sensitivity. NORDIC denoising significantly enhanced fMRI sensitivity without introducing image blurring.ConclusionCombining MS-EVI with multi-band encoding enables high overall acceleration factors and provides flexibility for maximizing spatial–temporal resolution and volume coverage. The high BOLD sensitivity of this hybrid MB-EVI approach and its compatibility with online image reconstruction enables high spatial–temporal resolution real-time task-based and resting state fMRI.

[This corrects the article DOI: 10.3389/fnins.2025.1543206.].

Functional connectivity (FC) is the degree of synchrony of time series between distinct, spatially separated brain regions. While traditional FC analysis assumes the temporal stationarity throughout a brain scan, there is growing recognition that connectivity can change over time and is not stationary, leading to the concept of dynamic FC (dFC). Resting-state functional magnetic resonance imaging (fMRI) can assess dFC using the sliding window method with the correlation analysis of fMRI signals. Accurate statistical inference of sliding window correlation must consider the autocorrelated nature of the time series. Currently, the dynamic consideration is mainly confined to the point estimation of sliding window correlations. Using in vivo resting-state fMRI data, we first demonstrate the non-stationarity in both the cross-correlation function (XCF) and the autocorrelation function (ACF). Then, we propose the variance estimation of the sliding window correlation considering the nonstationary of XCF and ACF. This approach provides a means to dynamically estimate confidence intervals in assessing dynamic connectivity. Using simulations, we compare the performance of the proposed method with other methods, showing the impact of dynamic ACF and XCF on connectivity inference. Accurate variance estimation can help in addressing the critical issue of false positivity and negativity.

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

Background/Introduction: There is considerable interest in using real-time functional magnetic resonance imaging (fMRI) for monitoring functional connectivity dynamics. To date, the majority of real-time resting-state fMRI studies have examined a limited number of brain regions. This is, in part, due to the computational demands of traditional seed- and independent component analysis-based methods, in particular when using increasingly available high-speed fMRI methods. Methods: This study describes a computationally efficient, real-time, seed-based, resting-state fMRI analysis pipeline using moving averaged sliding-windows (ASW) with partial correlations and regression of motion parameters and signals from white matter and cerebrospinal fluid. Results: Analytical and numerical analyses of ASW correlation and sliding-window regression as a function of window width show selectable bandpass filter characteristics and effective suppression of artifactual correlations resulting from signal drifts and transients. The analysis pipeline is compatible with multislab echo-volumar imaging and simultaneous multislice echo-planar imaging with repetition times as short as 136 msec. High-speed, resting-state fMRI data in healthy controls demonstrate the effectiveness of this approach for minimizing artifactual correlations in white and gray matter, which was comparable to conventional regression across the entire scan. Integrating sliding-window averaging (width: W1) within a second-level sliding-window (width: W2) enabled monitoring of intra- and internetwork correlation dynamics of up to 12 resting-state networks with bandpass filter characteristics determined by the first-level sliding-window and temporal resolution W1 + W2. Conclusions: The computational performance and confound tolerance make this seed-based, resting-state fMRI approach suitable for real-time monitoring of data quality and resting-state connectivity dynamics in neuroscience and clinical research studies. Impact statement Using averaged sliding-windows for seed-based correlation and regression of confounding signals provides a powerful model-free approach to increase tolerance to artifactual signal transients in resting-state analysis. The algorithmic efficiency of this sliding-window approach enables real-time, seed-based, resting-state functional magnetic resonance imaging (fMRI) of multiple networks with computation of connectivity matrices and online monitoring of data quality. Integration of a second-level sliding-window enables mapping of resting-state connectivity dynamics. Sensitivity and tolerance to confounding signals compare favorably with conventional correlation and confound regression across the entire scan. This methodological advance has the potential to enhance the clinical utility of resting-state fMRI and facilitate neuroscience applications.

This study evaluated the utility of concurrent water signal acquisition as part of the water suppression in MR spectroscopic imaging (MRSI), to allow simultaneous water referencing for metabolite quantification, and to concurrently acquire functional MRI (fMRI) data. We integrated a spatial-spectral binomial water excitation RF pulse and a short spatial-spectral echo-planar readout into the water suppression module of 2D and 3D proton-echo-planar-spectroscopic-imaging (PEPSI) with a voxel size as small as 4 x 4 x 6 mm3 . Metabolite quantification in reference to tissue water was validated in healthy controls for different prelocalization methods (spin-echo, PRESS and semi-LASER) and the clinical feasibility of a 3-minute 3D semi-Laser PEPSI scan (TR/TE: 1250/32 ms) with water referencing in patients with brain tumors was demonstrated. Spectral quality, SNR, Cramer-Rao-lower-bounds and water suppression efficiency were comparable with conventional PEPSI. Metabolite concentration values in reference to tissue water, using custom LCModel-based spectral fitting with relaxation correction, were in the range of previous studies and independent of the prelocalization method used. Next, we added a phase-encoding undersampled echo-volumar imaging (EVI) module during water suppression to concurrently acquire metabolite maps with water referencing and fMRI data during task execution and resting state in healthy controls. Integration of multimodal signal acquisition prolongated minimum TR by less than 50 ms on average. Visual and motor activation in concurrent fMRI/MRSI (TR: 1250-1500 ms, voxel size: 4 x 4 x 6 mm3 ) was readily detectable in single-task blocks with percent signal change comparable with conventional fMRI. Resting-state connectivity in sensory and motor networks was detectable in 4 minutes. This hybrid water suppression approach for multimodal imaging has the potential to significantly reduce scan time and extend neuroscience research and clinical applications through concurrent quantitative MRSI and fMRI acquisitions.

•Trichotillomania (TTM) neurochemistry is understudied, with no prior research using magnetic resonance spectroscopy (MRS).•This study examined associations of TTM diagnosis, severity, and behavioral treatment response with MRS neurometabolites glutamate (Glu) and γ-aminobutyric acid (GABA) in cortico-striato-thalamo-cortical (CSTC) structures•Higher pretreatment levels of Glu in pACC and thalamus were associated with more severe TTM symptoms, while higher GABA levels in pACC were associated with less severe symptoms•Levels of GABA in putamen rose notably after treatment•Higher pretreatment levels of Glu in multiple regions (caudate, putamen, globus pallidus, thalamus, and white matter) predicted greater post-treatment decrease in TTM symptoms, while worse response was predicted by higher GABA in caudate and lower GABA in globus pallidus.•Together, these results link TTM symptoms with higher excitatory metabolism in pACC and thalamus and link behavioral therapy-derived symptom alleviation with suppression of CSTC direct-pathway activity.

Magnetic resonance spectroscopic imaging (MRSI) offers considerable promise for monitoring metabolic alterations associated with disease or injury; however, to date, these methods have not had a significant impact on clinical care, and their use remains largely confined to the research community and a limited number of clinical sites. The MRSI methods currently implemented on clinical MRI instruments have remained essentially unchanged for two decades, with only incremental improvements in sequence implementation. During this time, a number of technological developments have taken place that have already greatly benefited the quality of MRSI measurements within the research community and which promise to bring advanced MRSI studies to the point where the technique becomes a true imaging modality, while making the traditional review of individual spectra a secondary requirement. Furthermore, the increasing use of biomedical MR spectroscopy studies has indicated clinical areas where advanced MRSI methods can provide valuable information for clinical care. In light of this rapidly changing technological environment and growing understanding of the value of MRSI studies for biomedical studies, this article presents a consensus from a group of experts in the field that reviews the state-of-the-art for clinical proton MRSI studies of the human brain, recommends minimal standards for further development of vendor-provided MRSI implementations, and identifies areas which need further technical development.

With a 40‐year history of use for in vivo studies, the terminology used to describe the methodology and results of magnetic resonance spectroscopy (MRS) has grown substantially and is not consistent in many aspects. Given the platform offered by this special issue on advanced MRS methodology, the authors decided to describe many of the implicated terms, to pinpoint differences in their meanings and to suggest specific uses or definitions. This work covers terms used to describe all aspects of MRS, starting from the description of the MR signal and its theoretical basis to acquisition methods, processing and to quantification procedures, as well as terms involved in describing results, for example, those used with regard to aspects of quality, reproducibility or indications of error. The descriptions of the meanings of such terms emerge from the descriptions of the basic concepts involved in MRS methods and examinations. This paper also includes specific suggestions for future use of terms where multiple conventions have emerged or coexisted in the past.

Reorganization of eloquent cortex due to neighboring stressors, like tumors and epilepsy, has been previously documented. Here we report reorganization of a subregion of the primary motor cortex due to a slow-growing primary brain tumor. We report the case of a 22-year-old, right-handed male with long-standing focal partial epilepsy involving the right leg who, on imaging, had a small contrast enhancing lesion within the left leg primary motor area. He underwent resective surgery with awake motor mapping. During mapping, a specific subregion of the primary leg motor area subserving proximal leg muscles was found on a gyrus posterior to the precentral gyrus. In addition, the foot/toe area had relocated supero-medially, but remained within the pre-central gyrus. The remainder of the motor homunculus was appropriately mapped on the same gyrus. To our knowledge, this is the first report of segmental reorganization of primary motor cortex subserving an isolated muscle group. Pre-operative functional imaging does not substitute for intra-operative real-time mapping with direct electrical stimulation. This case highlights the importance of careful mapping in order to achieve gross total resection of tumors within the primary motor cortex.

Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with real-time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45±8.9%, sensitivity 0.74±0.11, specificity 0.79±0.11) than STR (accuracy 67.45±8.13%, sensitivity 0.65±0.11, specificity 0.71±0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.

Proton MRS (1H MRS) provides noninvasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Although most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges toward obtaining reliable good‐quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the PRESS localization sequence was found to be unacceptably high at 3 T, and use of the semi‐adiabatic localization by adiabatic selective refocusing sequence is a recommended solution. Incorporation of simulated metabolite basis sets into analysis routines is recommended for reliably capturing the full spectral detail available from short TE acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B0) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. Implementation of these recommendations should strengthen current clinical applications and advance progress toward developing and validating new MRS biomarkers for clinical use.

Stroke, either ischemic or hemorrhagic, accounts for significantly high morbidity and mortality rates around the globe effecting millions of lives annually. For the past few decades, ultrasound has been extensively investigated to promote clot lysis for the treatment of stroke, myocardial infarction, and acute peripheral arterial occlusions, with or without the use of tPA or contrast agents. In the age of modern minimal invasive techniques, magnetic resonance imaging-guided high-intensity focused ultrasound is a new emerging modality that seems to promise therapeutic utilities for both ischemic and hemorrhagic stroke. High-intensity focused ultrasound causes thermal heating as the tissue absorbs the mechanical energy transmitted by the ultrasonic waves leading to tissue denaturation and coagulation. Several in-vitro and in-vivo studies have demonstrated the viability of this technology for sonothrombolysis in both types of stroke and have warranted clinical trials. Apart from safety and efficacy, initiation of trials would further enable answers regarding its practical application in a clinical setup. Though this technology has been under study for treatment of various brain diseases for some decades now, relatively very few neurologists and even neurosurgeons seem to be acquainted with it. The aim of this review is to provide basic understanding of this powerful technology and discuss its clinical application and potential role as an emerging viable therapeutic option for the future management of stroke.

Here, we developed a symmetric echo-planar spectroscopic imaging (EPSI) sequence for hyperpolarized 13C imaging on a clinical hybrid positron emission tomography/magnetic resonance imaging system. The pulse sequence uses parallel reconstruction pipelines to separately reconstruct data from odd-and-even gradient echoes to reduce artifacts from gradient imbalances. The ramp-sampled data in the spatiotemporal frequency space are regridded to compensate for the chemical-shift displacements. Unaliasing of nonoverlapping peaks outside of the sampled spectral width was performed to double the effective spectral width. The sequence was compared with conventional phase-encoded chemical-shift imaging (CSI) in phantoms, and it was evaluated in a canine cancer patient with ameloblastoma after injection of hyperpolarized [1-13C]pyruvate. The relative signal-to-noise ratio of EPSI with respect to CSI was 0.88, which is consistent with the decrease in sampling efficiency due to ramp sampling. Data regridding in the spatiotemporal frequency space significantly reduced spatial blurring compared with direct fast Fourier transform. EPSI captured the spatial distributions of both metabolites and their temporal dynamics in vivo with an in-plane spatial resolution of 5 × 9 mm2 and a temporal resolution of 3 seconds. Significantly higher spatial and temporal resolution for delineating anatomical structures in vivo was achieved for EPSI metabolic maps than for CSI maps, which suffered spatiotemporal blurring. The EPSI sequence showed promising results in terms of short acquisition time and sufficient spectral bandwidth of 500 Hz, allowing to adjust the trade-off between signal-to-noise ratio and encoding speed.

Wilson, Martin; Andronesi, Ovidiu; Barker, Peter B; Bartha, Robert; Bizzi, Alberto; Bolan, Patrick J; Brindle, Kevin M; Choi, In-Young; Cudalbu, Cristina; Dydak, Ulrike; Emir, Uzay E; Gonzalez, Ramon G; Gruber, Stephan; Gruetter, Rolf; Gupta, Rakesh K; Heerschap, Arend; Henning, Anke; Hetherington, Hoby P; Huppi, Petra S; Hurd, Ralph E; Kantarci, Kejal; Kauppinen, Risto A; Klomp, Dennis W J; Kreis, Roland; Kruiskamp, Marijn J; Leach, Martin O; Lin, Alexander P; Luijten, Peter R; Marjaska, Magorzata; Maudsley, Andrew A; Meyerhoff, Dieter J; Mountford, Carolyn E; Mullins, Paul G; Murdoch, James B; Nelson, Sarah J; Noeske, Ralph; Öz, Gülin; Pan, Julie W; Peet, Andrew C; Poptani, Harish; Posse, Stefan; Ratai, Eva-Maria; Salibi, Nouha; Scheenen, Tom W J; Smith, Ian C P; Soher, Brian J; Tká, Ivan; Vigneron, Daniel B; Howe, Franklyn A

Purpose Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality. Materials and methods The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18–70 years) and 13 patients with epilepsy (8 males, age range 21–67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients). Results RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG recorded during high-speed fMRI and during conventional EPI (p = 0.78). Residual ballistocardiographic artifacts resulted in 58% of EEG data being rated as poor quality. Conclusion This study demonstrates that high-density EEG can be safely implemented in conjunction with high-speed fMRI and that high-speed fMRI does not adversely affect EEG data quality. However, the deterioration of the EEG quality due to residual ballistocardiographic artifacts remains a significant constraint for routine clinical applications of concurrent EEG-fMRI.

Current studies of resting-state connectivity rely on coherent signal fluctuations at frequencies below 0.1 Hz, however, recent studies using high-speed fMRI have shown that fluctuations above 0.5 Hz may exist. This study replicates the feasibility of measuring high frequency (HF) correlations in six healthy controls and a patient with a brain tumor while analyzing non-physiological signal sources via simulation. Resting-state data were acquired using a high-speed multi-slab echo-volumar imaging pulse sequence with 136 ms temporal resolution. Bandpass frequency filtering in combination with sliding window seed-based connectivity analysis using running mean of the correlation maps was employed to map HF correlations up to 3.7 Hz. Computer simulations of Rician noise and the underlying point spread function were analyzed to estimate baseline spatial autocorrelation levels in four major networks (auditory, sensorimotor, visual, and default-mode). Using seed regions based on Brodmann areas, the auditory and default-mode networks were observed to have significant frequency band dependent HF correlations above baseline spatial autocorrelation levels. Correlations in the sensorimotor network were at trend level. The auditory network was still observed using a unilateral single voxel seed. In the patient, HF auditory correlations showed a spatial displacement near the tumor consistent with the displacement seen at low frequencies. In conclusion, our data suggest that HF connectivity in the human brain may be observable with high-speed fMRI, however, the detection sensitivity may depend on the network observed, data acquisition technique, and analysis method.

In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network’s collaborative informatics and neuroimaging suite (COINS).