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Background and PurposeThe lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging.MethodsIn this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects.ResultsWe obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis.ConclusionBased on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity., BACKGROUND AND PURPOSE: The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging.METHODS: In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects.RESULTS: We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis.CONCLUSION: Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.

PURPOSE: Ischemic injury in the kidney is a common pathophysiological event associated with both acute kidney injury and chronic kidney disease; however, regional ischemia-reperfusion as seen in thromboembolic renal disease is often undetectable and thus subclinical. Here, we assessed the metabolic alterations following subclinical focal ischemia-reperfusion injury with hyperpolarized [1- 13 C]pyruvate MRI in a porcine model. METHODS: Five pigs were subjected to 60 min of focal kidney ischemia. After 90 min of reperfusion, a multiparametric proton MRI protocol was performed on a clinical 3T scanner system. Metabolism was evaluated using 13 C MRI following infusion of hyperpolarized [1- 13 C]pyruvate. Ratios of pyruvate to its detectable metabolites (lactate, bicarbonate, and alanine) were used to quantify metabolism. RESULTS: The focal ischemia-reperfusion injury resulted in injured areas with a mean size of 0.971 cm 3 (±1.019). Compared with the contralateral kidney, the injured areas demonstrated restricted diffusion (1269 ± 83.59 × 10 -6 mm 2 /s vs. 1530 ± 52.73 × 10 -6 mm 2 /s; p = 0.006) and decreased perfusion (158.8 ± 29.4 mL/100 mL/min vs. 274 ± 63.1 mL/100 mL/min; p = 0.014). In the metabolic assessment, the injured areas displayed increased lactate/pyruvate ratios compared with the entire ipsilateral and the contralateral kidney (0.35 ± 0.13 vs. 0.27 ± 0.1 vs. 0.25 ± 0.1; p = 0.0086). Alanine/pyruvate ratio was unaltered, and we were unable to quantify bicarbonate due to low signal. CONCLUSION: MRI with hyperpolarized [1- 13 C]pyruvate in a clinical setup is capable of detecting the acute, subtle, focal metabolic changes following ischemia. This may prove to be a valuable future addition to the renal MRI suite.

Learning to read impacts the way the ventral occipitotemporal cortex (VOT) reorganizes. The postulated underlying mechanism of neuronal recycling was recently revisited. Neuroimaging data showed that voxels weakly specialized for visual processing keep their initial category selectivity (i.e., object or face processing) while acquiring an additional and stronger responsivity to written words. Here, we examined a large and diverse group of six-year-olds prior to formal literacy training (N = 72) using various data analysis techniques (univariate, multivariate, rapid adaptation) and types of stimuli (print, false fonts, houses, faces) to further explore how VOT changes and adapts to the novel skill of reading. We found that among several visual stimuli categories only print activated a wide network of language related areas outside of the bilateral visual cortex, and the level of reading skill was related to the strength of this activation, showing the development of the reading circuit. Rapid adaptation was not directly related to the level of reading skill in the young children studied here, but it clearly revealed the emergence of the reading network in readers. Most importantly, we found that the reorganization of the VOT is not in fact an "invasion" by reading acquisition-voxels previously activated for faces started to respond more for print, while at the same time keeping their previous function. We can thus conclude that the revised hypothesis of neuronal recycling is supported by our data.

Background Alzheimer’s disease (AD) and frontotemporal dementia (FTD) show network dysfunctions linked with cognitive deficits. Within this framework, network abnormalities between AD and FTD show both convergent and divergent patterns. However, these functional patterns are far from being established and their relevance to cognitive processes remains to be elucidated. Methods We investigated the relationship between cognition and functional connectivity of major cognitive networks in these diseases. Twenty-three bvFTD (age: 71±10), 22 AD (age: 72±6), and 20 controls (age: 72±6) underwent cognitive evaluation and resting-state functional MRI. Principal component analysis was used to describe cognitive variance across participants. Brain network connectivity was estimated with connectome analysis. Connectivity matrices were created assessing correlations between parcels within each functional network. The following cognitive networks were considered: default mode (DMN), dorsal attention (DAN), ventral attention (VAN), and frontoparietal (FPN) networks. The relationship between cognition and connectivity was assessed using a bootstrapping correlation and interaction analyses. Results Three principal cognitive components explained more than 80% of the cognitive variance: the first component (cogPC1) loaded on memory, the second component (cogPC2) loaded on emotion and language, and the third component (cogPC3) loaded on the visuo-spatial and attentional domains. Compared to HC, AD and bvFTD showed impairment in all cogPCs (pp=0.031). At the network level, the DMN showed a significant association in the whole group with cogPC1 and cogPC2 and the VAN with cogPC2. By contrast, DAN and FPN showed a divergent pattern between diagnosis and connectivity for cogPC2. We confirmed these results by means of a multivariate analysis (canonical correlation). Conclusions A low-dimensional representation can account for a large variance in cognitive scores in the continuum from normal to pathological aging. Moreover, cognitive components showed both convergent and divergent patterns with connectivity across AD and bvFTD. The convergent pattern was observed across the networks primarily involved in these diseases (i.e., the DMN and VAN), while a divergent FC-cognitive pattern was mainly observed between attention/executive networks and the language/emotion cognitive component, suggesting the co-existence of compensatory and detrimental mechanisms underlying these components.

This article reviews recent developments in designing and testing new types of materials which can be: (i) placed around the body for in vivo imaging, (ii) be integrated into a conventional RF coil, or (iii) form the resonator itself. These materials can improve the quality of MRI scans for both in vivo and magnetic resonance microscopy applications. The methodological section covers the basic operation and design of two different types of materials, namely high permittivity materials constructed from ceramics and artificial dielectrics/metasurfaces formed by coupled conductive subunits, either in air or surrounded by dielectric material. Applications of high permittivity materials and metasurfaces placed next to the body to neuroimaging and extremity imaging at 7 T, body and neuroimaging at 3 T, and extremity imaging at 1.5 T are shown. Results using ceramic resonators for both high field in vivo imaging and magnetic resonance microscopy are also shown. The development of new materials to improve MR image quality remains an active area of research, but has not yet found significant use in clinical applications. This is mainly due to practical issues such as specific absorption rate modelling, accurate and reproducible placement, and acceptable size/weight of such materials. The most successful area has been simple “dielectric pads” for neuroimaging at 7 T which were initially developed somewhat as a stop-gap while parallel transmit technology was being developed, but have continued to be used at many sites. Some of these issues can potentially be overcome using much lighter metasurfaces and artificial dielectrics, which are just beginning to be assessed.

Objective As preservation of cognitive functioning increasingly becomes important in the light of ameliorated survival after intracranial tumor treatments, identification of eloquent brain areas would enable optimization of these treatments. Methods This cohort study enrolled adult intracranial tumor patients who received neuropsychological assessments pre-irradiation, estimating processing speed, verbal fluency and memory. Anatomical magnetic resonance imaging scans were used for multivariate voxel-wise lesion-symptom predictions of the test scores (corrected for age, gender, educational level, histological subtype, surgery, and tumor volume). Potential effects of histological and molecular subtype and corresponding WHO grades on the risk of cognitive impairment were investigated using Chi square tests. P-values were adjusted for multiple comparisons (p Results A cohort of 179 intracranial tumor patients was included [aged 19–85 years, median age (SD) = 58.46 (14.62), 50% females]. In this cohort, test-specific impairment was detected in 20–30% of patients. Higher WHO grade was associated with lower processing speed, cognitive flexibility and delayed memory in gliomas, while no acute surgery-effects were found. No grading, nor surgery effects were found in meningiomas. The voxel-wise analyses showed that tumor locations in left temporal areas and right temporo-parietal areas were related to verbal memory and processing speed, respectively. Interpretation Patients with intracranial tumors affecting the left temporal areas and right temporo-parietal areas might specifically be vulnerable for lower verbal memory and processing speed. These specific patients at-risk might benefit from early-stage interventions. Furthermore, based on future validation studies, imaging-informed surgical and radiotherapy planning could further be improved.

Self-control is of vital importance for human wellbeing. Hare et al. (2009) were among the first to provide empirical evidence on the neural correlates of self-control. This seminal study profoundly impacted theory and empirical work across multiple fields. To solidify the empirical evidence supporting self-control theory, we conducted a preregistered replication of this work. Further, we tested the robustness of the findings across analytic strategies. Participants underwent functional magnetic resonance imaging while rating 50 food items on healthiness and tastiness and making choices about food consumption. We closely replicated the original analysis pipeline and supplemented it with additional exploratory analyses to follow-up on unexpected findings and to test the sensitivity of results to key analytical choices. Our replication data provide support for the notion that decisions are associated with a value signal in ventromedial prefrontal cortex (vmPFC), which integrates relevant choice attributes to inform a final decision. We found that vmPFC activity was correlated with goal values regardless of the amount of self-control and it correlated with both taste and health in self-controllers but only taste in non-self-controllers. We did not find strong support for the hypothesized role of left dorsolateral prefrontal cortex (dlPFC) in self-control. The absence of statistically significant group differences in dlPFC activity during successful self-control in our sample contrasts with the notion that dlPFC involvement is required in order to effectively integrate longer-term goals into subjective value judgments. Exploratory analyses highlight the sensitivity of results (in terms of effect size) to the analytical strategy, for instance, concerning the approach to region-of-interest analysis.

Purpose: To develop a denoising strategy leveraging redundancy in high-dimensional data. Theory and Methods: The SNR fundamentally limits the information accessible by MRI. This limitation has been addressed by a host of denoising techniques, recently including the so-called MPPCA: principal component analysis of the signal followed by automated rank estimation, exploiting the Marchenko-Pastur distribution of noise singular values. Operating on matrices comprised of data patches, this popular approach objectively identifies noise components and, ideally, allows noise to be removed without introducing artifacts such as image blurring, or nonlocal averaging. The MPPCA rank estimation, however, relies on a large number of noise singular values relative to the number of signal components to avoid such ill effects. This condition is unlikely to be met when data patches and therefore matrices are small, for example due to spatially varying noise. Here, we introduce tensor MPPCA (tMPPCA) for the purpose of denoising multidimensional data, such as from multicontrast acquisitions. Rather than combining dimensions in matrices, tMPPCA uses each dimension of the multidimensional data's inherent tensor-structure to better characterize noise, and to recursively estimate signal components. Results: Relative to matrix-based MPPCA, tMPPCA requires no additional assumptions, and comparing the two in a numerical phantom and a multi-TE diffusion MRI data set, tMPPCA dramatically improves denoising performance. This is particularly true for small data patches, suggesting that tMPPCA can be especially beneficial in such cases. Conclusions: The MPPCA denoising technique can be extended to high-dimensional data with improved performance for smaller patch sizes.

BACKGROUND: Patients with symptomatic carotid stenosis are at high risk for recurrent stroke. The decision for carotid endarterectomy currently mainly relies on degree of stenosis (cutoff value >50% or 70%). Nevertheless, also, patients with mild-to-moderate stenosis still have a considerable recurrent stroke risk. Increasing evidence suggests that carotid plaque composition rather than degree of stenosis determines plaque vulnerability; however, it remains unclear whether this also provides additional information to improve clinical decision making. OBJECTIVES: The PARISK (Plaque At RISK) study aimed to improve the identification of patients at increased risk of recurrent ischemic stroke using multimodality carotid imaging. METHODS: The authors included 244 patients (71% men; mean age, 68 years) with a recent symptomatic mild-to-moderate carotid stenosis in a prospective multicenter cohort study. Magnetic resonance imaging (carotid and brain) and computed tomography angiography (carotid) were performed at baseline and after 2 years. The clinical endpoint was a recurrent ipsilateral ischemic stroke or transient ischemic attack (TIA). Cox proportional hazards models were used to assess whether intraplaque hemorrhage (IPH), ulceration, proportion of calcifications, and total plaque volume in ipsilateral carotid plaques were associated with the endpoint. Next, the authors investigated the predictive performance of these imaging biomarkers by adding these markers (separately and simultaneously) to the ECST (European Carotid Surgery Trial) risk score. RESULTS: During 5.1 years follow-up, 37 patients reached the clinical endpoint. IPH presence and total plaque volume were associated with recurrent ipsilateral ischemic stroke or TIA (HR: 2.12 [95% CI: 1.02-4.44] for IPH; HR: 1.07 [95% CI: 1.00-1.15] for total plaque volume per 100 µL increase). Ulcerations and proportion of calcifications were not statistically significant determinants. Addition of IPH and total plaque volume to the ECST risk score improved the model performance (C-statistics increased from 0.67 to 0.75-0.78). CONCLUSIONS: IPH and total plaque volume are independent risk factors for recurrent ipsilateral ischemic stroke or TIA in patients with mild-to-moderate carotid stenosis. These plaque characteristics improve current decision making. Validation studies to implement plaque characteristics in clinical scoring tools are needed. (PARISK: Validation of Imaging Techniques [PARISK]; NCT01208025).

Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.

Background Diabetic kidney disease (DKD) accounts for ∼50% of end-stage kidney disease. Renal hypoxia is suggested as a main driver in the pathophysiology underlying chronic DKD. Blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI) has made noninvasive investigations of renal oxygenation in humans possible. Whether diabetes per se contributes to measurable changes in renal oxygenation by BOLD-MRI remains to be elucidated. We investigated whether renal oxygenation measured with BOLD-MRI differs between people with type 2 diabetes (T2DM) with normal to moderate chronic kidney disease (CKD) (Stages 1–3A) and matched controls. The repeatability of the BOLD-MRI method was also assessed. Methods In this matched cross-sectional study, 20 people with T2DM (age 69.2 ± 4.7 years, duration of diabetes 10.5 ± 6.7 years, male 55.6%) and 20 matched nondiabetic controls (mean age 68.8 ± 5.4 years, male 55.%) underwent BOLD-MRI analysed with the 12-layer concentric object method (TLCO). To investigate the repeatability, seven in the T2DM group and nine in the control group were scanned twice. Results A significant reduction in renal oxygenation from the cortex to medulla was found in both groups (P Conclusion T2DM patients with normal to moderate CKD do not seem to have lower renal oxygenation when measured with BOLD-MRI and TLCO. BOLD-MRI has a low intraindividual CV and seems like a reliable method for investigation of renal oxygenation in T2DM.

Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging-based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network-based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.

Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging-based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network-based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.

BackgroundDuring resonance frequency (RF) hyperthermia treatment, the temperature of the tumor tissue is elevated to the range of 39–44°C. Accurate temperature monitoring is essential to guide treatments and ensure precise heat delivery and treatment quality. Magnetic resonance (MR) thermometry is currently the only clinical method to measure temperature noninvasively in a volume during treatment. However, several studies have shown that this approach is not always sufficiently accurate for thermal dosimetry in areas with motion, such as the pelvic region. Model-based temperature estimation is a promising approach to correct and supplement 3D online temperature estimation in regions where MR thermometry is unreliable or cannot be measured. However, complete 3D temperature modeling of the pelvic region is too complex for online usage.PurposeThis study aimed to evaluate the use of proper orthogonal decomposition (POD) model reduction combined with Kalman filtering to improve temperature estimation using MR thermometry. Furthermore, we assessed the benefit of this method using data from hyperthermia treatment where there were limited and unreliable MR thermometry measurements.MethodsThe performance of POD–Kalman filtering was evaluated in several heating experiments and for data from patients treated for locally advanced cervical cancer. For each method, we evaluated the mean absolute error (MAE) concerning the temperature measurements acquired by the thermal probes, and we assessed the reproducibility and consistency using the standard deviation of error (SDE). Furthermore, three patient groups were defined according to susceptibility artifacts caused by the level of intestinal gas motion to assess if the POD–Kalman filtering could compensate for missing and unreliable MR thermometry measurements.ResultsFirst, we showed that this method is beneficial and reproducible in phantom experiments. Second, we demonstrated that the combined method improved the match between temperature prediction and temperature acquired by intraluminal thermometry for patients treated for locally advanced cervical cancer. Considering all patients, the POD–Kalman filter improved MAE by 43% (filtered MR thermometry = 1.29°C, POD–Kalman filtered temperature = 0.74°C). Moreover, the SDE was improved by 47% (filtered MR thermometry = 1.16°C, POD–Kalman filtered temperature = 0.61°C). Specifically, the POD–Kalman filter reduced the MAE by approximately 60% in patients whose MR thermometry was unreliable because of the great amount of susceptibilities caused by the high level of intestinal gas motion.ConclusionsWe showed that the POD–Kalman filter significantly improved the accuracy of temperature monitoring compared to MR thermometry in heating experiments and hyperthermia treatments. The results demonstrated that POD–Kalman filtering can improve thermal dosimetry during RF hyperthermia treatment, especially when MR thermometry is inaccurate.

Background and ObjectivesWe assessed whether novelty-related fMRI activity in medial temporal lobe regions and the precuneus follows an inverted U-shaped pattern across the clinical spectrum of increased Alzheimer disease (AD) risk as previously suggested. Specifically, we tested for potentially increased activity in individuals with a higher AD risk due to subjective cognitive decline (SCD) or mild cognitive impairment (MCI). We further tested whether activity differences related to diagnostic groups were accounted for by CSF markers of AD or brain atrophy.MethodsWe studied 499 participants aged 60–88 years from the German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study (DELCODE) who underwent task-fMRI. Participants included 163 cognitively normal (healthy control, HC) individuals, 222 SCD, 82 MCI, and 32 patients with clinical diagnosis of mild AD. CSF levels of β-amyloid 42/40 ratio and phosphorylated-tau181 were available from 232 participants. We used region-based analyses to assess novelty-related activity (novel > highly familiar scenes) in entorhinal cortex, hippocampus, and precuneus as well as whole-brain voxel-wise analyses. First, general linear models tested differences in fMRI activity between participant groups. Complementary regression models tested quadratic relationships between memory impairment and activity. Second, relationships of activity with AD CSF biomarkers and brain volume were analyzed. Analyses were controlled for age, sex, study site, and education.ResultsIn the precuneus, we observed an inverted U-shaped pattern of novelty-related activity across groups, with higher activity in SCD and MCI compared with HC, but not in patients with AD who showed relatively lower activity than MCI. This nonlinear pattern was confirmed by a quadratic relationship between memory impairment and precuneus activity. Precuneus activity was not related to AD biomarkers or brain volume. In contrast to the precuneus, hippocampal activity was reduced in AD dementia compared with all other groups and related to AD biomarkers.DiscussionNovelty-related activity in the precuneus follows a nonlinear pattern across the clinical spectrum of increased AD risk. Although the underlying mechanism remains unclear, increased precuneus activity might represent an early signature of memory impairment. Our results highlight the nonlinearity of activity alterations that should be considered in clinical trials using functional outcome measures or targeting hyperactivity.

Crohn’s disease is an inflammatory bowel disease that can affect any segment of the gastrointestinal tract. It has a variable clinical course, with alternating periods of disease activity and remission. Because the incidence and prevalence of Crohn’s disease have been increasing, evaluation by imaging methods has become more important. The most widely used methods are computed tomography enterography, magnetic resonance enterography (as an elective examination), and contrast-enhanced computed tomography (in the context of emergency). Computed tomography enterography and magnetic resonance enterography are useful for diagnosis, follow-up, evaluation of complications, and prognosis. Both can be used in order to evaluate the small bowel loops and the associated mesenteric findings, as well as to evaluate other abdominal organs. They both also can detect signs of disease activity, fibrosis, penetrating disease, and complications. The interpretation of such changes is essential to the multidisciplinary approach, as is the standardization of the nomenclature employed in the reports. In this paper, we review and illustrate the imaging findings of Crohn’s disease, using the standardized nomenclature proposed in the multidisciplinary consensus statement issued by the Society of Abdominal Radiology, the Society of Pediatric Radiology, and the American Gastroenterology Association, with recommendations for descriptions, interpretations, and impressions related to those findings. Resumo Doença de Crohn é uma doença inflamatória intestinal que pode acometer qualquer segmento do trato gastrointestinal, com curso clínico variado, alternando períodos de atividade de doença e remissão. Sua incidência e prevalência vêm aumentando, tornando a avaliação por métodos de imagem mais importante. Os métodos mais utilizados são enterotomografia computadorizada, enterorressonância magnética eletivamente e tomografia computadorizada com contraste no contexto da emergência. A enterotomografia computadorizada e a enterorressonância magnética são úteis para diagnóstico, seguimento, avaliação de complicações e para prognóstico. Avaliam as alças intestinais delgadas e os achados mesentéricos associados, além dos demais órgãos abdominais. Detectam sinais de atividade de doença, sinais de cronicidade e doença penetrante e complicações, importantes para a escolha do melhor tratamento. A interpretação dessas alterações é fundamental para que a equipe multidisciplinar trabalhe conjuntamente com compreensão da linguagem utilizada nos relatórios. Neste artigo, apresentamos uma revisão dos achados de imagem da doença de Crohn e ilustramos os possíveis achados dessa doença utilizando a nomenclatura padronizada proposta no consenso multidisciplinar composto pelas Sociedade de Radiologia Abdominal, Sociedade de Radiologia Pediátrica e a Associação Americana de Gastroenterologia, com recomendações de descrição, interpretação dos achados e recomendações de impressão.

Objective: The perfusion profile of vestibular schwannomas (VSs) and the factors that influence it have yet to be determined. Materials and Methods: Twenty patients with sporadic VS were analyzed by calculating parameters related to the extravascular extracellular space (EES)—the volume transfer constant between a vessel and the EES (Ktrans); the EES volume per unit of tissue volume (Ve); and the rate transfer constant between EES and blood plasma (Kep)—as well as the relative cerebral blood volume (rCBV), and by correlating those parameters with the size of the tumor and its structure (solid, cystic, or heterogeneous). Results: Although Ktrans, Ve, and Kep were measurable in all tumors, rCBV was measurable only in large tumors. We detected a positive correlation between Ktrans and rCBV (r = 0.62, p = 0.031), a negative correlation between Ve and Kep (r = –0.51, p = 0.021), and a positive correlation between Ktrans and Ve only in solid VSs (r = 0.64, p = 0.048). Comparing the means for small and large VSs, we found that the former showed lower Ktrans (0.13 vs. 0.029, p < 0.001), higher Kep (0.68 vs. 0.46, p = 0.037), and lower Ve (0.45 vs. 0.83, p < 0.001). The mean Ktrans was lower in the cystic portions of cystic VSs than in their solid portions (0.14 vs. 0.32, p < 0.001), as was the mean Ve (0.37 vs. 0.78, p < 0.001). There were positive correlations between the solid and cystic portions for Ktrans (r = 0.71, p = 0.048) and Kep (r = 0.74, p = 0.037). Conclusion: In VS, tumor size appears to be consistently associated with perfusion values. In cystic VS, the cystic portions seem to have lower Ktrans and Ve than do the solid portions. Resumo Objetivo: O perfil de perfusão do schwannoma vestibular (SV) não tem sido estudado, nem os fatores que o influenciam. Materiais e Métodos: Vinte pacientes com SV esporádico foram analisados usando Ktrans, Ve, Kep e rCBV e correlacionados com tamanho e estádio cístico. Resultados: Ktrans, Ve e Kep foram medidos em todos os casos. rCBV só foi possível em tumores grandes. Ktrans e rCBV estavam correlacionados positivamente (r = 0,62, p = 0, 0 31 ) . Ve e Kep estavam negativamente correlacionados (r = –0,51, p = 0,021). Ktrans estava correlacionado positivamente com Ve em SVs sólidos (r = 0,64, p = 0,048). Em SVs pequenos, Ktrans foi menor (0,13 vs 0,029, p < 0,001), Kep foi maior (0,68 vs 0,46, p = 0,037) e Ve foi menor (0,45 vs 0,83, p < 0,001) que nos SVs grandes. Ktrans e Ve foram menores dentro dos cistos que nas porções solidas dos SVs císticos (0,14 vs 0,32, p < 0,001; 0,37 vs 0,78, p < 0.001, respectivamente). Foi encontrada correlação positiva em Ktrans (r = 0,71, p = 0,048) e Kep (r = 0,74, p = 0,037) entre as áreas sólidas e císticas. Conclusão: Nos SVs, o tamanho está consistentemente associado com os valores da perfusão. Nos SVs císticos, as porções císticas parecem ter valores menores de Ktrans e Ve do que nas porções sólidas.

Introduction: Functional MRI with spatial resolution in the submillimeter domain enables measurements of activation across cortical layers in humans. This is valuable as different types of cortical computations, e.g., feedforward versus feedback related activity, take place in different cortical layers. Laminar fMRI studies have almost exclusively employed 7T scanners to overcome the reduced signal stability associated with small voxels. However, such systems are relatively rare and only a subset of those are clinically approved. In the present study, we examined if the feasibility of laminar fMRI at 3T could be improved by use of NORDIC denoising and phase regression.Methods: 5 healthy subjects were scanned on a Siemens MAGNETOM Prisma 3T scanner. To assess across-session reliability, each subject was scanned in 3-8 sessions on 3-4 consecutive days. A 3D gradient echo EPI (GE-EPI) sequence was used for BOLD acquisitions (voxel size 0.82 mm isotopic, TR = 2.2 s) using a block design finger tapping paradigm. NORDIC denoising was applied to the magnitude and phase time series to overcome limitations in temporal signal-to-noise ratio (tSNR) and the denoised phase time series were subsequently used to correct for large vein contamination through phase regression.Results and conclusion: NORDIC denoising resulted in tSNR values comparable to or higher than commonly observed at 7T. Layer-dependent activation profiles could thus be extracted robustly, within and across sessions, from regions of interest located in the hand knob of the primary motor cortex (M1). Phase regression led to substantially reduced superficial bias in obtained layer profiles, although residual macrovascular contribution remained. We believe the present results support an improved feasibility of laminar fMRI at 3T. Introduction: Functional MRI with spatial resolution in the submillimeter domain enables measurements of activation across cortical layers in humans. This is valuable as different types of cortical computations, e.g., feedforward versus feedback related activity, take place in different cortical layers. Laminar fMRI studies have almost exclusively employed 7T scanners to overcome the reduced signal stability associated with small voxels. However, such systems are relatively rare and only a subset of those are clinically approved. In the present study, we examined if the feasibility of laminar fMRI at 3T could be improved by use of NORDIC denoising and phase regression. Methods: 5 healthy subjects were scanned on a Siemens MAGNETOM Prisma 3T scanner. To assess across-session reliability, each subject was scanned in 3–8 sessions on 3–4 consecutive days. A 3D gradient echo EPI (GE-EPI) sequence was used for BOLD acquisitions (voxel size 0.82 mm isotopic, TR = 2.2 s) using a block design finger tapping paradigm. NORDIC denoising was applied to the magnitude and phase time series to overcome limitations in temporal signal-to-noise ratio (tSNR) and the denoised phase time series were subsequently used to correct for large vein contamination through phase regression. Results and conclusion: NORDIC denoising resulted in tSNR values comparable to or higher than commonly observed at 7T. Layer-dependent activation profiles could thus be extracted robustly, within and across sessions, from regions of interest located in the hand knob of the primary motor cortex (M1). Phase regression led to substantially reduced superficial bias in obtained layer profiles, although residual macrovascular contribution remained. We believe the present results support an improved feasibility of laminar fMRI at 3T.

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.

INTRODUCTION: Placental magnetic resonance imaging (MRI) may be a valuable tool in the prediction of small for gestational age (SGA) at birth. MRI provides reliable estimates of placental volume and thickness. In addition, placental transverse relaxation time (T2*) may be directly related to placental function. This study aimed to explore and compare the predictive performance of three placental MRI parameters - volume, thickness and T2* - in relation to SGA at birth.METHODS: A mixed cohort of 85 pregnancies was retrieved from the placental MRI database at the study hospital. MRI was performed in a 1.5 T system at gestational weeks 15-41. In normal birthweight (BW) pregnancies [BW > -22 % of expected for gestational age (GA)], the correlation between each of the MRI parameters and GA was investigated by linear regression. The prediction of SGA was investigated by logistic regression analysis adjusted for GA at MRI.RESULTS: In normal BW pregnancies, a significant linear correlation was found between GA and each of the MRI parameters. Univariate analysis demonstrated that placental volume [odds ratio (OR) 0.97, p = 0.001] and placental T2* (OR 0.79, p = 0.003), but not placental thickness (OR 0.92, p = 0.862) were significant predictors of SGA. A multi-variate model including all three MRI parameters found that placental T2* was the only independent predictor of SGA (OR 0.81, p = 0.04).CONCLUSION: Among the MRI parameters investigated in this study, placental T2* was the only independent predictor of SGA in a multi-variate model. This finding underlines the strong position of T2*-weighted placental MRI in the prediction of SGA.