Multidimensional compressed sensing to advance 23 Na multi-quantum coherences MRI.

PURPOSE: Sodium (23 Na) multi-quantum coherences (MQC) MRI was accelerated using three-dimensional (3D) and a dedicated five-dimensional (5D) compressed sensing (CS) framework for simultaneous Cartesian single (SQ) and triple quantum (TQ) sodium imaging of in vivo human brain at 3.0 and 7.0 T. THEORY AND METHODS: 3D 23 Na MQC MRI requires multi-echo paired with phase-cycling and exhibits thus a multidimensional space. A joint reconstruction framework to exploit the sparsity in all imaging dimensions by extending the conventional 3D CS framework to 5D was developed. 3D MQC images of simulated brain, phantom and healthy brain volunteers obtained from 3.0 T and 7.0 T were retrospectively and prospectively undersampled. Performance of the CS models were analyzed by means of structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR) and signal quantification of tissue sodium concentration and TQ/SQ ratio. RESULTS: It was shown that an acceleration of three-fold, leading to less than 2 × 10 $$ 2\times 10 $$ min of scan time with a resolution of 8 × 8 × 20 mm 3 $$ 8\times 8\times 20\;{\mathrm{mm}}^3 $$ at 3.0 T, are possible. 5D CS improved SSIM by 3%, 5%, 1% and reduced RMSE by 50%, 30%, 8% for in vivo SQ, TQ, and TQ/SQ ratio maps, respectively. Furthermore, for the first time prospective undersampling enabled unprecedented high resolution from 8 × 8 × 20 mm 3 $$ 8\times 8\times 20\;{\mathrm{mm}}^3 $$ to 6 × 6 × 10 mm 3 $$ 6\times 6\times 10\;{\mathrm{mm}}^3 $$ MQC images of in vivo human brain at 7.0 T without extending acquisition time. CONCLUSION: 5D CS proved to allow up to three-fold acceleration retrospectively on 3.0 T data. 2-fold acceleration was demonstrated prospectively at 7.0 T to reach higher spatial resolution of 23 Na MQC MRI.

Low-rank reconstruction for simultaneous double half-echo 23Na and undersampled 23Na multi-quantum coherences MRI.

PURPOSE: To develop a new sequence to simultaneously acquire Cartesian sodium (23Na) MRI and accelerated Cartesian single (SQ) and triple quantum (TQ) sodium MRI of in vivo human brain at 7 T by leveraging two dedicated low-rank reconstruction frameworks. THEORY AND METHODS: The Double Half-Echo technique enables short echo time Cartesian 23Na MRI and acquires two k-space halves, reconstructed by a low-rank coupling constraint. Additionally, three-dimensional (3D) 23Na Multi-Quantum Coherences (MQC) MRI requires multi-echo sampling paired with phase-cycling, exhibiting a redundant multidimensional space. Simultaneous Autocalibrating and k-Space Estimation (SAKE) were used to reconstruct highly undersampled 23Na MQC MRI. Reconstruction performance was assessed against five-dimensional (5D) CS, evaluating structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR), and quantification of tissue sodium concentration and TQ/SQ ratio in silico, in vitro, and in vivo. RESULTS: The proposed sequence enabled the simultaneous acquisition of fully sampled 23Na MRI while leveraging prospective undersampling for 23Na MQC MRI. SAKE improved TQ image reconstruction regarding SSIM by 6% and reduced RMSE by 35% compared to 5D CS in vivo. Thanks to prospective undersampling, the spatial resolution of 23Na MQC MRI was enhanced from 8 × 8 × 15 $$ 8\times 8\times 15 $$ mm3 to 8 × 8 × 8 $$ 8\times 8\times 8 $$ mm3 while reducing acquisition time from 2 × 31 $$ 2\times 31 $$ min to 2 × 23 $$ 2\times 23 $$ min. CONCLUSION: The proposed sequence, coupled with low-rank reconstructions, provides an efficient framework for comprehensive whole-brain sodium MRI, combining TSC, T2*, and TQ/SQ ratio estimations. Additionally, low-rank matrix completion enables the reconstruction of highly undersampled 23Na MQC MRI, allowing for accelerated acquisition or enhanced spatial resolution.

Multimodal mapping of regional brain vulnerability to focal cortical dysplasia.

Focal cortical dysplasia (FCD) type II is a highly epileptogenic developmental malformation and a common cause of surgically treated drug-resistant epilepsy. While clinical observations suggest frequent occurrence in the frontal lobe, mechanisms for such propensity remain unexplored. Here, we hypothesized that cortex-wide spatial associations of FCD distribution with cortical cytoarchitecture, gene expression and organizational axes may offer complementary insights into processes that predispose given cortical regions to harbour FCD. We mapped the cortex-wide MRI distribution of FCDs in 337 patients collected from 13 sites worldwide. We then determined its associations with (i) cytoarchitectural features using histological atlases by Von Economo and Koskinas and BigBrain; (ii) whole-brain gene expression and spatiotemporal dynamics from prenatal to adulthood stages using the Allen Human Brain Atlas and PsychENCODE BrainSpan; and (iii) macroscale developmental axes of cortical organization. FCD lesions were preferentially located in the prefrontal and fronto-limbic cortices typified by low neuron density, large soma and thick grey matter. Transcriptomic associations with FCD distribution uncovered a prenatal component related to neuroglial proliferation and differentiation, likely accounting for the dysplastic makeup, and a postnatal component related to synaptogenesis and circuit organization, possibly contributing to circuit-level hyperexcitability. FCD distribution showed a strong association with the anterior region of the antero-posterior axis derived from heritability analysis of interregional structural covariance of cortical thickness, but not with structural and functional hierarchical axes. Reliability of all results was confirmed through resampling techniques. Multimodal associations with cytoarchitecture, gene expression and axes of cortical organization indicate that prenatal neurogenesis and postnatal synaptogenesis may be key points of developmental vulnerability of the frontal lobe to FCD. Concordant with a causal role of atypical neuroglial proliferation and growth, our results indicate that FCD-vulnerable cortices display properties indicative of earlier termination of neurogenesis and initiation of cell growth. They also suggest a potential contribution of aberrant postnatal synaptogenesis and circuit development to FCD epileptogenicity.

Multi-scale structural alterations of the thalamus and basal ganglia in focal epilepsy using 7T MRI.

Focal epilepsy is characterized by repeated spontaneous seizures that originate from cortical epileptogenic zone networks (EZN). Analysis of intracerebral recordings showed that subcortical structures, and in particular the thalamus, play an important role in seizure dynamics as well, supporting their structural alterations reported in the neuroimaging literature. Nonetheless, between-patient differences in EZN localization (e.g., temporal vs. non-temporal lobe epilepsy) as well as extension (i.e., number of epileptogenic regions) might impact the magnitude as well as spatial distribution of subcortical structural changes. Here we used 7 Tesla MRI T1 data to provide an unprecedented description of subcortical morphological (volume, tissue deformation, and shape) and longitudinal relaxation (T1 ) changes in focal epilepsy patients and evaluate the impact of the EZN and other patient-specific clinical features. Our results showed variable levels of atrophy across thalamic nuclei that appeared most prominent in the temporal lobe epilepsy group and the side ipsilateral to the EZN, while shortening of T1 was especially observed for the lateral thalamus. Multivariate analyses across thalamic nuclei and basal ganglia showed that volume acted as the dominant discriminator between patients and controls, while (posterolateral) thalamic T1 measures looked promising to further differentiate patients based on EZN localization. In particular, the observed differences in T1 changes between thalamic nuclei indicated differential involvement based on EZN localization. Finally, EZN extension was found to best explain the observed variability between patients. To conclude, this work revealed multi-scale subcortical alterations in focal epilepsy as well as their dependence on several clinical characteristics.

Combining sodium MRI, proton MR spectroscopic imaging, and intracerebral EEG in epilepsy.

Whole brain ionic and metabolic imaging has potential as a powerful tool for the characterization of brain diseases. We combined sodium MRI (23 Na MRI) and 1 H-MR Spectroscopic Imaging (1 H-MRSI), assessing changes within epileptogenic networks in comparison with electrophysiologically normal networks as defined by stereotactic EEG (SEEG) recordings analysis. We applied a multi-echo density adapted 3D projection reconstruction pulse sequence at 7 T (23 Na-MRI) and a 3D echo-planar spectroscopic imaging sequence at 3 T (1 H-MRSI) in 19 patients suffering from drug-resistant focal epilepsy who underwent presurgical SEEG. We investigated 23 Na MRI parameters including total sodium concentration (TSC) and the sodium signal fraction associated with the short component of T2 * decay (f), alongside the level of metabolites N-acetyl aspartate (NAA), choline compounds (Cho), and total creatine (tCr). All measures were extracted from spherical regions of interest (ROIs) centered between two adjacent SEEG electrode contacts and z-scored against the same ROI in controls. Group comparison showed a significant increase in f only in the epileptogenic zone (EZ) compared to controls and compared to patients' propagation zone (PZ) and non-involved zone (NIZ). TSC was significantly increased in all patients' regions compared to controls. Conversely, NAA levels were significantly lower in patients compared to controls, and lower in the EZ compared to PZ and NIZ. Multiple regression analyzing the relationship between sodium and metabolites levels revealed significant relations in PZ and in NIZ but not in EZ. Our results are in agreement with the energetic failure hypothesis in epileptic regions associated with widespread tissue reorganization.

Optimized interferometric encoding of presaturated TurboFLASH B1 mapping for parallel transmission MRI at 7 T: Preliminary application for quantitative T1 mapping in the spinal cord.

PURPOSE: The acquisition of accurate B1 maps is critical for parallel transmit techniques (pTx). The presaturated turboFLASH (satTFL) method has been widely used in combination with interferometric encoding to provide robust and fast B1 maps. However, typical encodings, mostly evaluated on brain, do not necessarily fit all coils and organs. In this work, we evaluated and improved the accuracy of the satTFL for cervical spine at 7 T, proposing a novel interferometric encoding optimization. The benefits of such improvements were investigated in an exploratory study of quantitative T1 mapping with pTx-MP2RAGE. METHODS: Global optimization of interferometric encoding was implemented by simulating the ability of the satTFL to reconstruct B1 maps, with varying encoding and inclusion of complex noise, inside a region of interest covering the cervical spine. The performance of satTFL before and after optimization was compared to actual flip angle imaging. Optimized and non-optimized B1 maps were then used to calculate pTx pulses for MP2RAGE T1 mapping. RESULTS: Interferometric encoding optimization resulted in satTFL closer to actual flip angle imaging, with substantial gain of signal in regions where non-optimized satTFL could fail. T1 maps measured with non-adiabatic pTx pulses were closer to standard non-pTx results (which used adiabatic pulses) when using optimized-satTFL, with substantially lower specific absorption rate. CONCLUSION: Optimization of the satTFL interferometric encoding improves B1 maps in the spinal cord, in particular in low SNR regions. A linear correction of the satTFL was additionally shown to be required. The method was successfully used for quantitative phantom and in vivo T1 mapping, showing improved results compared to non-optimized satTFL thanks to improved pTx-pulse generation.

Quantitative magnetization transfer MRI unbiased by on-resonance saturation and dipolar order contributions.

PURPOSE: To demonstrate the bias in quantitative MT (qMT) measures introduced by the presence of dipolar order and on-resonance saturation (ONRS) effects using magnetization transfer (MT) spoiled gradient-recalled (SPGR) acquisitions, and propose changes to the acquisition and analysis strategies to remove these biases. METHODS: The proposed framework consists of SPGR sequences prepared with simultaneous dual-offset frequency-saturation pulses to cancel out dipolar order and associated relaxation (T1D ) effects in Z-spectrum acquisitions, and a matched quantitative MT (qMT) mathematical model that includes ONRS effects of readout pulses. Variable flip angle and MT data were fitted jointly to simultaneously estimate qMT parameters (macromolecular proton fraction [MPF], T2,f , T2,b , R, and free pool T1 ). This framework is compared with standard qMT and investigated in terms of reproducibility, and then further developed to follow a joint single-point qMT methodology for combined estimation of MPF and T1 . RESULTS: Bland-Altman analyses demonstrated a systematic underestimation of MPF (-2.5% and -1.3%, on average, in white and gray matter, respectively) and overestimation of T1 (47.1 ms and 38.6 ms, on average, in white and gray matter, respectively) if both ONRS and dipolar order effects are ignored. Reproducibility of the proposed framework is excellent (ΔMPF = -0.03% and ΔT1 = -19.0 ms). The single-point methodology yielded consistent MPF and T1 values with respective maximum relative average bias of -0.15% and -3.5 ms found in white matter. CONCLUSION: The influence of acquisition strategy and matched mathematical model with regard to ONRS and dipolar order effects in qMT-SPGR frameworks has been investigated. The proposed framework holds promise for improved accuracy with reproducibility.

The Impact of Fatty Infiltration on MRI Segmentation of Lower Limb Muscles in Neuromuscular Diseases: A Comparative Study of Deep Learning Approaches.

BACKGROUND: Deep learning methods have been shown to be useful for segmentation of lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on neuromuscular disease (NDM) patients. PURPOSE: Evaluate the influence of fat infiltration on convolutional neural network (CNN) segmentation of MRIs from NMD patients. STUDY TYPE: Retrospective study. SUBJECTS: Data were collected from a hospital database of 67 patients with NMDs and 14 controls (age: 53 ± 17 years, sex: 48 M, 33 F). Ten individual muscles were segmented from the thigh and six from the calf (20 slices, 200 cm section). FIELD STRENGTH/SEQUENCE: A 1.5 T. Sequences: 2D T1 -weighted fast spin echo. Fat fraction (FF): three-point Dixon 3D GRE, magnetization transfer ratio (MTR): 3D MT-prepared GRE, T2: 2D multispin-echo sequence. ASSESSMENT: U-Net 2D, U-Net 3D, TransUNet, and HRNet were trained to segment thigh and leg muscles (101/11 and 95/11 training/validation images, 10-fold cross-validation). Automatic and manual segmentations were compared based on geometric criteria (Dice coefficient [DSC], outlier rate, absence rate) and reliability of measured MRI quantities (FF, MTR, T2, volume). STATISTICAL TESTS: Bland-Altman plots were chosen to describe agreement between manual vs. automatic estimated FF, MTR, T2 and volume. Comparisons were made between muscle populations with an FF greater than 20% (G20+) and lower than 20% (G20-). RESULTS: The CNNs achieved equivalent results, yet only HRNet recognized every muscle in the database, with a DSC of 0.91 ± 0.08, and measurement biases reaching -0.32% ± 0.92% for FF, 0.19 ± 0.77 for MTR, -0.55 ± 1.95 msec for T2, and - 0.38 ± 3.67 cm3 for volume. The performances of HRNet, between G20- and G20+ decreased significantly. DATA CONCLUSION: HRNet was the most appropriate network, as it did not omit any muscle. The accuracy obtained shows that CNNs could provide fully automated methods for studying NMDs. However, the accuracy of the methods may be degraded on the most infiltrated muscles (>20%). EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Prognostic value of cardiovascular magnetic resonance T1 mapping and extracellular volume fraction in nonischemic dilated cardiomyopathy.

BACKGROUND: Heart failure- (HF) and arrhythmia-related complications are the main causes of morbidity and mortality in patients with nonischemic dilated cardiomyopathy (NIDCM). Cardiovascular magnetic resonance (CMR) imaging is a noninvasive tool for risk stratification based on fibrosis assessment. Diffuse interstitial fibrosis in NIDCM may be a limitation for fibrosis assessment through late gadolinium enhancement (LGE), which might be overcome through quantitative T1 and extracellular volume (ECV) assessment. T1 and ECV prognostic value for arrhythmia-related events remain poorly investigated. We asked whether T1 and ECV have a prognostic value in NIDCM patients. METHODS: This prospective multicenter study analyzed 225 patients with NIDCM confirmed by CMR who were followed up for 2 years. CMR evaluation included LGE, native T1 mapping and ECV values. The primary endpoint was the occurrence of a major adverse cardiovascular event (MACE) which was divided in two groups: HF-related events and arrhythmia-related events. Optimal cutoffs for prediction of MACE occurrence were calculated for all CMR quantitative values. RESULTS: Fifty-eight patients (26%) developed a MACE during follow-up, 42 patients (19%) with HF-related events and 16 patients (7%) arrhythmia-related events. T1 Z-score (p = 0.008) and global ECV (p = 0.001) were associated with HF-related events occurrence, in addition to left ventricular ejection fraction (p < 0.001). ECV > 32.1% (optimal cutoff) remained the only CMR independent predictor of HF-related events occurrence (HR 2.15 [1.14-4.07], p = 0.018). In the arrhythmia-related events group, patients had increased native T1 Z-score and ECV values, with both T1 Z-score > 4.2 and ECV > 30.5% (optimal cutoffs) being independent predictors of arrhythmia-related events occurrence (respectively, HR 2.86 [1.06-7.68], p = 0.037 and HR 2.72 [1.01-7.36], p = 0.049). CONCLUSIONS: ECV was the sole independent predictive factor for both HF- and arrhythmia-related events in NIDCM patients. Native T1 was also an independent predictor in arrhythmia-related events occurrence. The addition of ECV and more importantly native T1 in the decision-making algorithm may improve arrhythmia risk stratification in NIDCM patients. Trial registration NCT02352129. Registered 2nd February 2015-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02352129.

Association between brain and upper cervical spinal cord atrophy assessed by MRI and disease aggressiveness in amyotrophic lateral sclerosis.

PURPOSE: To study the relative contributions of brain and upper cervical spinal cord compartmental atrophy to disease aggressiveness in amyotrophic lateral sclerosis (ALS). METHODS: Twenty-nine ALS patients and 24 age- and gender-matched healthy controls (HC) were recruited. Disease duration and the Revised-ALS Functional Rating Scale (ALSFRS-R) at baseline, 3- and 6-months follow-up were assessed. Patients were clinically differentiated into fast (n=13) and slow (n=16) progressors according to their ALSFRS-R progression rate. Brain grey (GM) and white matter, brainstem sub-structures volumes and spinal cord cross-sectional area (SC-CSA) at C1-C2 vertebral levels were measured from a 3D-T1-weighted MRI. RESULTS: Fast progressors showed significant GM, medulla oblongata and SC atrophy compared to HC (p<0.001, p=0.013 and p=0.008) and significant GM atrophy compared to slow progressors (p=0.008). GM volume correlated with the ALSFRS-R progression rate (Rho/p=-0.487/0.007), the ALSFRS-R at 3-months (Rho/p=0.622/0.002), and ALSFRS-R at 6-months (Rho/p=0.407/0.039). Medulla oblongata volume and SC-CSA correlated with the ALSFRS-R at 3-months (Rho/p=0.510/0.015 and Rho/p=0.479/0.024). MRI measures showed high performance to discriminate between fast and slow progressors. CONCLUSION: Our study suggests an association between compartmental atrophy and disease aggressiveness. This result is consistent with the combination of upper and lower motor neuron degeneration as the main driver of disease worsening and severity in ALS. Our study highlights the potential of brain and spinal cord atrophy measured by MRI as biomarker of disease aggressiveness signature.

Ultra-high field magnetic resonance imaging of the quadriceps tendon enthesis in healthy subjects.

PURPOSE: Although enthesitis is a hallmark of several rheumatologic conditions, current imaging methods are still unable to characterize entheses changes because of the corresponding short transverse relaxation times (T2). A growing number of MR studies have used Ultra-High Field (UHF) MRI in order to assess low-T2 tissues e.g., tendon but never in humans. The purpose of the present study was to assess in vivo the enthesis of the quadriceps tendon in healthy subjects using UHF MRI. METHODS: Eleven healthy subjects volunteered in an osteoarthritis imaging study. The inclusion criteria were: no knee trauma, Lequesne index = 0, less than 3 h of sport activities per week, and Kellgren and Lawrence grade = 0. 3D MR images were acquired at 7 T using GRE sequences and a T2* mapping. Regions of interest i.e., trabecular bone, subchondral bone, enthesis, and tendon body were identified, and T2* values were quantified and compared. RESULTS: Quadriceps tendon enthesis was visible as a hyper-intense signal. The largest and the lowest T2* values were quantified in the subchondral bone region and the tendon body respectively. T2* value within subchondral bone was significantly higher than T2* value within the enthesis. T2* in subchondral bone region was significantly higher than the whole tendon body T2*. CONCLUSION: A T2* gradient was observed along the axis from the enthesis toward the tendon body. It illustrates different water biophysical properties. These results provide normative values which could be used in the field of inflammatory rheumatologic diseases and mechanical disorders affecting the tendon.

Automatic segmentation of deep grey nuclei using a high-resolution 7T magnetic resonance imaging atlas-Quantification of T1 values in healthy volunteers.

We present a new consensus atlas of deep grey nuclei obtained by shape-based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (.6 mm)3 in 60 healthy subjects. A group-wise normalization method was used to build a high-contrast and high-resolution T1 -weighted brain template (.5 mm)3 using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and 12 thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data-derived, Morel and CIT168 atlases were all applied at the individual level using non-linear registration to fit the subject reference and to extract absolute mean quantitative T1 values derived from the 3D-MP2RAGE volumes, after correction for residual B1+ biases. Three metrics (the Dice and the volumetric similarity coefficients and a novel Hausdorff distance) were used to estimate the inter-rater agreement of manual MRI segmentation and inter-atlas variability, and these metrics were measured to quantify biases due to image registration, and their impact on the measurements of the quantitative T1 values was highlighted. This represents a fully automated segmentation process permitting the extraction of unbiased normative T1 values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases.

Respiratory-triggered quantitative MR spectroscopy of the human cervical spinal cord at 7 T.

PURPOSE: Ultra-high field 1 H MR spectroscopy (MRS) is of great interest to help characterizing human spinal cord pathologies. However, very few studies have been reported so far in this small size structure at these fields due to challenging experimental difficulties caused by static and radiofrequency field heterogeneities, as well as physiological motion. In this work, in line with the recent developments proposed to strengthen spinal cord MRS feasibility at 7 T, a respiratory-triggered acquisition approach was optimized to compensate for dynamic B 0 field heterogeneities and to provide robust cervical spinal cord MRS data. METHODS: A semi-LASER sequence was purposely used, and a dedicated raw data processing algorithm was developed to enhance MR spectral quality by discarding corrupted scans. To legitimate the choices done during the optimization stage, additional tests were carried out to determine the impact of breathing, voluntary motion, body mass index, and fitting algorithm. An in-house quantification tool was concomitantly designed for accurate estimation of the metabolite concentration ratios for choline, N-acetyl-aspartate (NAA), myo-inositol and glutathione. The method was tested on a cohort of 14 healthy volunteers. RESULTS: Average water linewidth and NAA signal-to-noise ratio reached 0.04 ppm and 11.01, respectively. The group-average metabolic ratios were in good agreement with previous studies and showed intersession reproducibility variations below 30%. CONCLUSION: The developed approach allows a rise of the acquired MRS signal quality and of the quantification robustness as compared to previous studies hence offering strengthened possibilities to probe the metabolism of degenerative and traumatic spinal cord pathologies.

A strategy to reduce the sensitivity of inhomogeneous magnetization transfer (ihMT) imaging to radiofrequency transmit field variations at 3 T.

PURPOSE: To minimize the sensitivity of inhomogeneous magnetization transfer gradient-echo (ihMT-GRE) imaging to radiofrequency (RF) transmit field ( B1+ ) inhomogeneities at 3 T. METHODS: The ihMT-GRE sequence was optimized by varying the concentration of the RF saturation energy over time, obtained by increasing the saturation pulse power while extending the sequence repetition time (TR). Different protocols were tested using numerical simulations and human in vivo experiments in the brain white matter (WM) of healthy subjects at 3 T. The sensitivity of the ihMT ratio (ihMTR) to B1+ variations was investigated by comparing measurements obtained at nominal transmitter adjustments and following a 20% global B1+ drop. The resulting relative variations (δihMTR ) were evaluated voxelwise as a function of the local B1+ distribution. The reproducibility of the protocol providing minimal B1+ bias was assessed in a test-retest experiment. RESULTS: In line with simulations, ihMT-GRE experiments conducted at high concentration of the RF energy over time demonstrated strong reduction of the B1+ inhomogeneity effects in the human WM. Under the optimal conditions of 350-ms TR and 3-µT root mean square (RMS) saturation power, 73% of all WM voxels presented δihMTR below 10%. Reproducibility analysis yielded a close-to-zero systematic bias (ΔihMTR = -0.081%) and a high correlation (ρ² = 0.977) between test and retest experiments. CONCLUSION: Concentrating RF saturation energy in ihMT-GRE sequences mitigates the sensitivity of the ihMTR to B1+ variations and allows for clinical-ready ihMT imaging at 3 T. This feature is of particular interest for high and ultra-high field applications.

Computational modeling of seizure spread on a cortical surface.

In the field of computational epilepsy, neural field models helped to understand some large-scale features of seizure dynamics. These insights however remain on general levels, without translation to the clinical settings via personalization of the model with the patient-specific structure. In particular, a link was suggested between epileptic seizures spreading across the cortical surface and the so-called theta-alpha activity (TAA) pattern seen on intracranial electrographic signals, yet this link was not demonstrated on a patient-specific level. Here we present a single patient computational study linking the seizure spreading across the patient-specific cortical surface with a specific instance of the TAA pattern recorded in the patient. Using the realistic geometry of the cortical surface we perform the simulations of seizure dynamics in The Virtual Brain platform, and we show that the simulated electrographic signals qualitatively agree with the recorded signals. Furthermore, the comparison with the simulations performed on surrogate surfaces reveals that the best quantitative fit is obtained for the real surface. The work illustrates how the patient-specific cortical geometry can be utilized in The Virtual Brain for personalized model building, and the importance of such approach.

Grey-matter sodium concentration as an individual marker of multiple sclerosis severity.

OBJECTIVE: Quantification of brain injury in patients with variable disability despite similar disease duration may be relevant to identify the mechanisms underlying disability in multiple sclerosis (MS). We aimed to compare grey-matter sodium abnormalities (GMSAs), a parameter reflecting neuronal and astrocyte dysfunction, in MS patients with benign multiple sclerosis (BMS) and non-benign multiple sclerosis (NBMS). METHODS: We identified never-treated BMS patients in our local MS database of 1352 patients. A group with NBMS was identified with same disease duration. All participants underwent 23Na magnetic resonance imaging (MRI). The existence of GMSA was detected by statistical analysis. RESULTS: In total, 102 individuals were included (21 BMS, 25 NBMS and 56 controls). GMSA was detected in 10 BMS and 19 NBMS (11/16 relapsing-remitting multiple sclerosis (RRMS) and 8/9 secondary progressive multiple sclerosis (SPMS) patients) (p = 0.05). On logistic regression including the presence or absence of GMSA, thalamic volume, cortical grey-matter volume and T2-weighted lesion load, thalamic volume was independently associated with BMS status (odds ratio (OR) = 0.64 for each unit). Nonetheless, the absence of GMSA was independently associated when excluding patients with significant cognitive alteration (n = 7) from the BMS group (OR = 4.6). CONCLUSION: Detection of GMSA in individuals and thalamic volume are promising to differentiate BMS from NBMS as compared with cortical or whole grey-matter atrophy and T2-weighted lesions.

Virtual epileptic patient brain modeling: Relationships with seizure onset and surgical outcome.

OBJECTIVE: The virtual epileptic patient (VEP) is a large-scale brain modeling method based on virtual brain technology, using stereoelectroencephalography (SEEG), anatomical data (magnetic resonance imaging [MRI] and connectivity), and a computational neuronal model to provide computer simulations of a patient's seizures. VEP has potential interest in the presurgical evaluation of drug-resistant epilepsy by identifying regions most likely to generate seizures. We aimed to assess the performance of the VEP approach in estimating the epileptogenic zone and in predicting surgical outcome. METHODS: VEP modeling was retrospectively applied in a cohort of 53 patients with pharmacoresistant epilepsy and available SEEG, T1-weighted MRI, and diffusion-weighted MRI. Precision recall was used to compare the regions identified as epileptogenic by VEP (EZVEP ) to the epileptogenic zone defined by clinical analysis incorporating the Epileptogenicity Index (EI) method (EZC ). In 28 operated patients, we compared the VEP results and clinical analysis with surgical outcome. RESULTS: VEP showed a precision of 64% and a recall of 44% for EZVEP detection compared to EZC . There was a better concordance of VEP predictions with clinical results, with higher precision (77%) in seizure-free compared to non-seizure-free patients. Although the completeness of resection was significantly correlated with surgical outcome for both EZC and EZVEP , there was a significantly higher number of regions defined as epileptogenic exclusively by VEP that remained nonresected in non-seizure-free patients. SIGNIFICANCE: VEP is the first computational model that estimates the extent and organization of the epileptogenic zone network. It is characterized by good precision in detecting epileptogenic regions as defined by a combination of visual analysis and EI. The potential impact of VEP on improving surgical prognosis remains to be exploited. Analysis of factors limiting the performance of the actual model is crucial for its further development.

Evidence for spreading seizure as a cause of theta-alpha activity electrographic pattern in stereo-EEG seizure recordings.

Intracranial electroencephalography is a standard tool in clinical evaluation of patients with focal epilepsy. Various early electrographic seizure patterns differing in frequency, amplitude, and waveform of the oscillations are observed. The pattern most common in the areas of seizure propagation is the so-called theta-alpha activity (TAA), whose defining features are oscillations in the θ - α range and gradually increasing amplitude. A deeper understanding of the mechanism underlying the generation of the TAA pattern is however lacking. In this work we evaluate the hypothesis that the TAA patterns are caused by seizures spreading across the cortex. To do so, we perform simulations of seizure dynamics on detailed patient-derived cortical surfaces using the spreading seizure model as well as reference models with one or two homogeneous sources. We then detect the occurrences of the TAA patterns both in the simulated stereo-electroencephalographic signals and in the signals of recorded epileptic seizures from a cohort of fifty patients, and we compare the features of the groups of detected TAA patterns to assess the plausibility of the different models. Our results show that spreading seizure hypothesis is qualitatively consistent with the evidence available in the seizure recordings, and it can explain the features of the detected TAA groups best among the examined models.

On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.

Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.

Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography.

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.