Deficits in naming pictures of objects are associated with glioma infiltration of the inferior longitudinal fasciculus: A study with diffusion MRI tractography, volumetric MRI, and neuropsychology.

It has been suggested that the inferior longitudinal fasciculus (ILF) may play an important role in several aspects of language processing such as visual object recognition, visual memory, lexical retrieval, reading, and specifically, in naming visual stimuli. In particular, the ILF appears to convey visual information from the occipital lobe to the anterior temporal lobe (ATL). However, direct evidence proving the essential role of the ILF in language and semantics remains limited and controversial. The first aim of this study was to prove that patients with a brain glioma damaging the left ILF would be selectively impaired in picture naming of objects; the second aim was to prove that patients with glioma infiltrating the ATL would not be impaired due to functional reorganization of the lexical retrieval network elicited by the tumor. We evaluated 48 right-handed patients with neuropsychological testing and magnetic resonance imaging (MRI) before and after surgery for resection of a glioma infiltrating aspects of the left temporal, occipital, and/or parietal lobes; diffusion tensor imaging (DTI) was acquired preoperatively in all patients. Damage to the ILF, inferior frontal occipital fasciculus (IFOF), uncinate fasciculus (UF), arcuate fasciculus (AF), and associated cortical regions was assessed by means of preoperative tractography and pre-/pos-toperative MRI volumetry. The association of fascicles damage with patients' performance in picture naming and three additional cognitive tasks, namely, verbal fluency (two verbal non-visual tasks) and the Trail Making Test (a visual attentional task), was evaluated. Nine patients were impaired in the naming test before surgery. ILF damage was demonstrated with tractography in six (67%) of these patients. The odds of having an ILF damage was 6.35 (95% CI: 1.27-34.92) times higher among patients with naming deficit than among those without it. The ILF was the only fascicle to be significantly associated with naming deficit when all the fascicles were considered together, achieving an adjusted odds ratio of 15.73 (95% CI: 2.30-178.16, p = .010). Tumor infiltration of temporal and occipital cortices did not contribute to increase the odd of having a naming deficit. ILF damage was found to be selectively associated with picture naming deficit and not with lexical retrieval assessed by means of verbal fluency. Early after surgery, 29 patients were impaired in naming objects. The association of naming deficit with percentage of ILF resection (assessed by 3D-MRI) was confirmed (beta = -56.78 ± 20.34, p = .008) through a robust multiple linear regression model; no significant association was found with damage of IFOF, UF or AF. Crucially, postoperative neuropsychological evaluation showed that naming scores of patients with tumor infiltration of the anterior temporal cortex were not significantly associated with the percentage of ILF damage (rho = .180, p > .999), while such association was significant in patients without ATL infiltration (rho = -.556, p = .004). The ILF is selectively involved in picture naming of objects; however, the naming deficits are less severe in patients with glioma infiltration of the ATL probably due to release of an alternative route that may involve the posterior segment of the AF. The left ILF, connecting the extrastriatal visual cortex to the anterior region of the temporal lobe, is crucial for lexical retrieval on visual stimulus, such as in picture naming. However, when the ATL is also damaged, an alternative route is released and the performance improves.

A discriminative event-based model for subtype diagnosis of sporadic Creutzfeldt-Jakob disease using brain MRI.

INTRODUCTION: Sporadic Creutzfeldt-Jakob disease (sCJD) comprises multiple subtypes (MM1, MM2, MV1, MV2C, MV2K, VV1, and VV2) with distinct disease durations and spatiotemporal cascades of brain lesions. Our goal was to establish the ante mortem diagnosis of sCJD subtype, based on patient-specific estimates of the spatiotemporal cascade of lesions detected by diffusion-weighted magnetic resonance imaging (DWI). METHODS: We included 488 patients with autopsy-confirmed diagnosis of sCJD subtype and 50 patients with exclusion of prion disease. We applied a discriminative event-based model (DEBM) to infer the spatiotemporal cascades of lesions, derived from the DWI scores of 12 brain regions assigned by three neuroradiologists. Based on the DEBM cascades and the prion protein genotype at codon 129, we developed and validated a novel algorithm for the diagnosis of the sCJD subtype. RESULTS: Cascades of MM1, MM2, MV1, MV2C, and VV1 originated in the parietal cortex and, following subtype-specific orderings of propagation, went toward the striatum, thalamus, and cerebellum; conversely, VV2 and MV2K cascades showed a striatum-to-cortex propagation. The proposed algorithm achieved 76.5% balanced accuracy for the sCJD subtype diagnosis, with low rater dependency (differences in accuracy of ± 1% among neuroradiologists). DISCUSSION: Ante mortem diagnosis of sCJD subtype is feasible with this novel data-driven approach, and it may be valuable for patient prognostication, stratification in targeted clinical trials, and future therapeutics. HIGHLIGHTS: Subtype diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) is achievable with diffusion MRI. Cascades of diffusion MRI abnormalities in the brain are subtype-specific in sCJD. We proposed a diagnostic algorithm based on cascades of diffusion MRI abnormalities and demonstrated that it is accurate. Our method may aid early diagnosis, prognosis, stratification in clinical trials, and future therapeutics. The present approach is applicable to other neurodegenerative diseases, enhancing the differential diagnoses.

Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning.

The WHO classification since 2016 confirms the importance of integrating molecular diagnosis for prognosis and treatment decisions of adult-type diffuse gliomas. This motivates the development of non-invasive diagnostic methods, in particular MRI, to predict molecular subtypes of gliomas before surgery. At present, this development has been focused on deep-learning (DL)-based predictive models, mainly with conventional MRI (cMRI), despite recent studies suggesting multi-shell diffusion MRI (dMRI) offers complementary information to cMRI for molecular subtyping. The aim of this work is to evaluate the potential benefit of combining cMRI and multi-shell dMRI in DL-based models. A model implemented with deep residual neural networks was chosen as an illustrative example. Using a dataset of 146 patients with gliomas (from grade 2 to 4), the model was trained and evaluated, with nested cross-validation, on pre-operative cMRI, multi-shell dMRI, and a combination of the two for the following classification tasks: (i) IDH-mutation; (ii) 1p/19q-codeletion; and (iii) three molecular subtypes according to WHO 2021. The results from a subset of 100 patients with lower grades gliomas (2 and 3 according to WHO 2016) demonstrated that combining cMRI and multi-shell dMRI enabled the best performance in predicting IDH mutation and 1p/19q codeletion, achieving an accuracy of 75 ± 9% in predicting the IDH-mutation status, higher than using cMRI and multi-shell dMRI separately (both 70 ± 7%). Similar findings were observed for predicting the 1p/19q-codeletion status, with the accuracy from combining cMRI and multi-shell dMRI (72 ± 4%) higher than from each modality used alone (cMRI: 65 ± 6%; multi-shell dMRI: 66 ± 9%). These findings remain when we considered all 146 patients for predicting the IDH status (combined: 81 ± 5% accuracy; cMRI: 74 ± 5%; multi-shell dMRI: 73 ± 6%) and for the diagnosis of the three molecular subtypes according to WHO 2021 (combined: 60 ± 5%; cMRI: 57 ± 8%; multi-shell dMRI: 56 ± 7%). Together, these findings suggest that combining cMRI and multi-shell dMRI can offer higher accuracy than using each modality alone for predicting the IDH and 1p/19q status and in diagnosing the three molecular subtypes with DL-based models.

In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case.

Glioblastoma extensively infiltrates the brain; despite surgery and aggressive therapies, the prognosis is poor. A multidisciplinary approach combining mathematical, clinical and radiological data has the potential to foster our understanding of glioblastoma evolution in every single patient, with the aim of tailoring therapeutic weapons. In particular, the ultimate goal of biomathematics for cancer is the identification of the most suitable theoretical models and simulation tools, both to describe the biological complexity of carcinogenesis and to predict tumor evolution. In this report, we describe the results of a critical review about different mathematical models in neuro-oncology with their clinical implications. A comprehensive literature search and review for English-language articles concerning mathematical modelling in glioblastoma has been conducted. The review explored the different proposed models, classifying them and indicating the significative advances of each one. Furthermore, we present a specific case of a glioblastoma patient in which our recently proposed innovative mechanical model has been applied. The results of the mathematical models have the potential to provide a relevant benefit for clinicians and, more importantly, they might drive progress towards improving tumor control and patient's prognosis. Further prospective comparative trials, however, are still necessary to prove the impact of mathematical neuro-oncology in clinical practice.

The genera Leucoagaricus and Leucocoprinus in the Dominican Republic.

We studied species of Leucoagaricus and Leucocoprinus collected in the Dominican Republic over the past 10 years using morphological and molecular methods and carefully compared our collections with previously described neotropical taxa. Twelve new species, eight in Leucoagaricus (La. bulbiger, La. caeruleovertens, La. margaritifer, La. pegleri, La. roseovertens, La. silvestris, La. stillatus, La. turgipes) and four in Leucocoprinus (Lc. antillarum, Lc. fuligineopunctatus, Lc. microlepis, Lc. scissus) are described. Additional records of previously described taxa are also discussed, including the first molecularly annotated occurrences of Lepiota guatopoensis, Lepiota mucrocystis, and La. rubroconfusus in their putative natural habitats and of Lc. cretaceus in the neotropics. Lepiota guatopoensis and Lepiota mucrocystis are transferred here to Leucoagaricus based on their phylogenetic placement and morphological characteristics. Color photographs of fresh basidiocarps and line drawings of microscopic characters are provided for all species.

Subtype Diagnosis of Sporadic Creutzfeldt-Jakob Disease with Diffusion Magnetic Resonance Imaging.

OBJECTIVE: Sporadic Creutzfeldt-Jakob disease (sCJD) comprises several subtypes as defined by genetic and prion protein characteristics, which are associated with distinct clinical and pathological phenotypes. To date, no clinical test can reliably diagnose the subtype. We established two procedures for the antemortem diagnosis of sCJD subtype using diffusion magnetic resonance imaging (MRI). METHODS: MRI of 1,458 patients referred to the National Prion Disease Pathology Surveillance Center were collected through its consultation service. One neuroradiologist blind to the diagnosis scored 12 brain regions and generated a lesion profile for each MRI scan. We selected 487 patients with autopsy-confirmed diagnosis of "pure" sCJD subtype and at least one positive diffusion MRI examination. We designed and tested two data-driven procedures for subtype diagnosis: the first procedure-prion subtype classification algorithm with MRI (PriSCA_MRI)-uses only MRI examinations; the second-PriSCA_MRI + Gen-includes knowledge of the prion protein codon 129 genotype, a major determinant of sCJD subtypes. Both procedures were tested on the first MRI and the last MRI follow-up. RESULTS: PriSCA_MRI classified the 3 most prevalent subtypes with 82% accuracy. PriSCA_MRI + Gen raised the accuracy to 89% and identified all subtypes. Individually, the 2 most prevalent sCJD subtypes, MM1 and VV2, were diagnosed with sensitivities up to 95 and 97%, respectively. The performances of both procedures did not change in 168 patients with longitudinal MRI studies when the last examination was used. INTERPRETATION: This study provides the first practical algorithms for antemortem diagnosis of sCJD subtypes. MRI diagnosis of subtype is likely to be attainable at early disease stages to prognosticate clinical course and design future therapeutic trials. ANN NEUROL 2021;89:560-572.

Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations.

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.

Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI.

Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, most existing approaches are based on deterministic models, neglecting the presence of different sources of uncertainty in such problems. Here we introduce methods to characterise different components of uncertainty, and demonstrate the ideas using diffusion MRI super-resolution. Specifically, we propose to account for intrinsic uncertainty through a heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference, and integrate the two to quantify predictive uncertainty over the output image. Moreover, we introduce a method to propagate the predictive uncertainty on a multi-channelled image to derived scalar parameters, and separately quantify the effects of intrinsic and parameter uncertainty therein. The methods are evaluated for super-resolution of two different signal representations of diffusion MR images-Diffusion Tensor images and Mean Apparent Propagator MRI-and their derived quantities such as mean diffusivity and fractional anisotropy, on multiple datasets of both healthy and pathological human brains. Results highlight three key potential benefits of modelling uncertainty for improving the safety of DL-based image enhancement systems. Firstly, modelling uncertainty improves the predictive performance even when test data departs from training data ("out-of-distribution" datasets). Secondly, the predictive uncertainty highly correlates with reconstruction errors, and is therefore capable of detecting predictive "failures". Results on both healthy subjects and patients with brain glioma or multiple sclerosis demonstrate that such an uncertainty measure enables subject-specific and voxel-wise risk assessment of the super-resolved images that can be accounted for in subsequent analysis. Thirdly, we show that the method for decomposing predictive uncertainty into its independent sources provides high-level "explanations" for the model performance by separately quantifying how much uncertainty arises from the inherent difficulty of the task or the limited training examples. The introduced concepts of uncertainty modelling extend naturally to many other imaging modalities and data enhancement applications.

Correction to: Automatic identification of atypical clinical fMRI results.

The above article was published online with an incorrect affiliation.

First Report of a Neotropical Agaric (Lepiota spiculata, Agaricales, Basidiomycota) Containing Lethal α-Amanitin at Toxicologically Relevant Levels.

A recent collection of Lepiota spiculata from the Dominican Republic is presented here. Macro- and micromorphological features of L. spiculata are described in detail, and its evolutionary (phylogenetic) position within Lepiota sect. Ovisporae, in the subincarnata/brunneoincarnata clade, is assessed on the basis of a combined nrLSU + nrITS + rpb2 + tef1 analysis. Additionally, high levels of deadly amatoxins were detected and quantified in L. spiculata for the first time by HPLC analysis; in particular, α-amanitin was found at concentrations up to approximately 4 mg/g dry weight, which render L. spiculata a potentially lethal mushroom, if ingested.

Automatic identification of atypical clinical fMRI results.

PURPOSE: Functional MRI is not routinely used for neurosurgical planning despite potential important advantages, due to difficulty of determining quality. We introduce a novel method for objective evaluation of fMRI scan quality, based on activation maps. A template matching analysis (TMA) is presented and tested on data from two clinical fMRI protocols, performed by healthy controls in seven clinical centers. Preliminary clinical utility is tested with data from low-grade glioma patients. METHODS: Data were collected from 42 healthy subjects from seven centers, with standardized finger tapping (FT) and verb generation (VG) tasks. Copies of these "typical" data were deliberately analyzed incorrectly to assess feasibility of identifying them as "atypical." Analyses of the VG task administered to 32 tumor patients assessed sensitivity of the TMA method to anatomical abnormalities. RESULTS: TMA identified all atypical activity maps for both tasks, at the cost of incorrectly classifying 3.6 (VG)-6.5% (FT) of typical maps as atypical. For patients, the average TMA was significantly higher than atypical healthy scans, despite localized anatomical abnormalities caused by a tumor. CONCLUSION: This study supports feasibility of TMA for objective identification of atypical activation patterns for motor and verb generation fMRI protocols. TMA can facilitate the use and evaluation of clinical fMRI in hospital settings that have limited access to fMRI experts. In a clinical setting, this method could be applied to automatically flag fMRI scans showing atypical activation patterns for further investigation to determine whether atypicality is caused by poor scan data quality or abnormal functional topography.

Evaluation of a New Criterion for Detecting Prion Disease With Diffusion Magnetic Resonance Imaging.

Importance: Early diagnosis is a requirement for future treatment of prion diseases. Magnetic resonance imaging (MRI) with diffusion-weighted images and improved real-time quaking-induced conversion (RT-QuIC) in cerebrospinal fluid (CSF) have emerged as reliable tests. Objectives: To assess the sensitivity and specificity of diffusion MRI for the diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) with a new criterion (index test) of at least 1 positive brain region among the cortex of the frontal, parietal, temporal, and occipital lobes; the caudate; the putamen; and the thalamus. Design, Setting, and Participants: This diagnostic study with a prospective and a retrospective arm was performed from January 1, 2003, to October 31, 2018. MRIs were collected from 1387 patients with suspected sCJD consecutively referred to the National Prion Disease Pathology Surveillance Center as part of a consultation service. Intervention: Magnetic resonance imaging. Four neuroradiologists blinded to the diagnosis scored the MRIs of 200 randomly selected patients. One neuroradiologist scored the MRIs of all patients. Main Outcomes and Measures: Sensitivity and specificity of the index test compared with currently used criteria and CSF diagnostic (improved RT-QuIC, 14-3-3, and tau CSF tests). Results: A total of 872 patients matched the inclusion criteria (diffusion MRI and autopsy-confirmed diagnosis), with 619 having sCJD, 102 having other prion diseases, and 151 having nonprion disease. The primary analysis included 200 patients (mean [SD] age, 63.6 [12.9] years; 100 [50.0%] male). Sensitivity of the index test of 4 neuroradiologists was 90% to 95% and superior to sensitivity of current MRI criteria (69%-76%), whereas specificity was 90% to 100% and unchanged. Interrater reliability of the 4 neuroradiologists was high (κ = 0.81), and individual intrarater reliability was excellent (κ ≥0.87). The sensitivity of the index test of 1 neuroradiologist for 770 patients was 92.1% (95% CI, 89.7%-94.1%) and the specificity was 97.4% (95% CI, 93.4%-99.3%) compared with a sensitivity of 69.8% (95% CI, 66.0%-73.4%; P < .001) and a specificity of 98.0% (95% CI, 94.3%-99.6%; P > .99) according to the current criteria. For 88 patients, index test sensitivity (94.9%; 95% CI, 87.5%-98.6%) and specificity (100%; 95% CI, 66.4%-100%) were similar to those of improved RT-QuIC (86.1% [95% CI, 76.5%-92.8%] and 100% [95% CI, 66.4%-100%], respectively). Lower specificities were found for 14-3-3 and tau CSF tests in 452 patients. Conclusions and Relevance: In this study, the diagnostic performance of diffusion MRI with the new criterion was superior to that of current standard criteria and similar to that of improved RT-QuIC. These results may have important clinical implications because MRI is noninvasive and typically prescribed at disease presentation.

Prion propagation estimated from brain diffusion MRI is subtype dependent in sporadic Creutzfeldt-Jakob disease.

Sporadic Creutzfeldt-Jakob disease (sCJD) is a transmissible brain proteinopathy. Five main clinicopathological subtypes (sCJD-MM(V)1, -MM(V)2C, -MV2K, -VV1, and -VV2) are currently distinguished. Histopathological evidence suggests that the localisation of prion aggregates and spongiform lesions varies among subtypes. Establishing whether there is an initial site with detectable imaging abnormalities (epicentre) and an order of lesion propagation would be informative for disease early diagnosis, patient staging, management and recruitment in clinical trials. Diffusion magnetic resonance imaging (MRI) is the most-used and most-sensitive test to detect spongiform degeneration. This study was designed to identify, in vivo and for the first time, subtype-dependent epicentre and lesion propagation in the brain using diffusion-weighted images (DWI), in the largest known cross-sectional dataset of autopsy-proven subjects with sCJD. We estimate lesion propagation by cross-sectional DWI using event-based modelling, a well-established data-driven technique. DWI abnormalities of 594 autopsy-diagnosed subjects (448 patients with sCJD) were scored in 12 brain regions by 1 neuroradiologist blind to the diagnosis. We used the event-based model to reconstruct sequential orderings of lesion propagation in each of five pure subtypes. Follow-up data from 151 patients validated the estimated sequences. Results showed that epicentre and ordering of lesion propagation are subtype specific. The two most common subtypes (-MM1 and -VV2) showed opposite ordering of DWI abnormality appearance: from the neocortex to subcortical regions, and vice versa, respectively. The precuneus was the most likely epicentre also in -MM2 and -VV1 although at variance with -MM1, abnormal signal was also detected early in cingulate and insular cortices. The caudal-rostral sequence of lesion propagation that characterises -VV2 was replicated in -MV2K. Combined, these data-driven models provide unprecedented dynamic insights into subtype-specific epicentre at onset and propagation of the pathologic process, which may also enhance early diagnosis and enable disease staging in sCJD.

Feasibility of Remote Assessment of Human Prion Diseases for Research and Surveillance.

BACKGROUND: Prion disease research and surveillance can be challenging due to the disease's difficulty to diagnose, rapid progression, and geographic dispersion. Improving accessibility through teleneurology could improve the ability to conduct these activities. OBJECTIVES: The aim of this study was to determine the feasibility of conducting teleneurology assessments for research and surveillance of prion diseases. METHOD: Participants were offered in-person visit, medical record review, or teleneurology assessment. Standardized histories and assessments evaluating cognition, functional ability, and neuropsychiatric symptoms were collected. Data regarding participants' satisfaction with teleneurology were collected. RESULTS: From April 2017 to July 2018, the study received 114 referrals. 45 and 5 participants consented for the teleneurology and medical record review arms of the study, respectively. 29 subjects participated in at least one teleneurology visit. Participants expressed satisfaction with teleneurology and found it easy to participate. Some aspects of the examination were hindered or interrupted due to technological reasons. CONCLUSIONS: We demonstrate the feasibility and preference of teleneurology as a modality in which subjects with prion disease can partake in clinical research. Technological aspects sometimes interfered with research assessments.

Methodological consensus on clinical proton MRS of the brain: Review and recommendations.

Proton MRS (1 H 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.

Consequences of brain tumour resection on emotion recognition.

Emotion processing impairments are common in patients undergoing brain surgery for fronto-temporal tumour resection, with potential consequences on social interactions. However, evidence is controversial concerning side and site of lesions causing such deficits. This study investigates visual and auditory emotion recognition in brain tumour patients with the aim of clarifying which lesion sites are related to impairments in emotion processing from different modalities. Thirty-four patients were evaluated, before and after surgery, on facial expression and emotional prosody recognition; voxel-based lesion-symptom mapping (VLSM) analyses were performed on patients' post-surgery MRI images. Results showed that patients' performance decreased after surgery in both visual and auditory modalities, but, in general, recovered 3 months after surgery. In facial expression recognition, left brain-damaged patients showed greater post-surgery deterioration than right brain-damaged ones, whose performance specifically decreased for sadness and fear. VLSM analysis revealed two segregated areas in the left hemisphere accounting for post-surgery scores for happy (fronto-temporo-insular region) and surprised (middle frontal gyrus and inferior fronto-occipital fasciculus) facial expressions. Our findings demonstrate that surgical removal of tumours in the fronto-temporal region produces impairment in facial emotion recognition with an overall recovery at 3 months, suggesting a partially different representation of positive and negative emotions in the left and right hemispheres for visually - but not auditory - presented emotions; moreover, we show that deficits in specific expression recognition are associated with discrete lesion locations.

Prediction of Isocitrate Dehydrogenase Genotype in Brain Gliomas with MRI: Single-Shell versus Multishell Diffusion Models.

Purpose The primary aim of this prospective observational study was to assess whether diffusion MRI metrics correlate with isocitrate dehydrogenase (IDH) status in grade II and III gliomas. A secondary aim was to investigate whether multishell acquisitions with advanced models such as neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging offer greater diagnostic accuracy than diffusion-tensor imaging (DTI). Materials and Methods Diffusion MRI (b = 700 and 2000 sec/mm2) was performed preoperatively in 192 consecutive participants (113 male and 79 female participants; mean age, 46.18 years; age range, 14-77 years) with grade II (n = 62), grade III (n = 58), or grade IV (n = 72) gliomas. DTI, diffusion kurtosis imaging, and NODDI metrics were measured in regions with or without hyperintensity on diffusion MR images and compared among groups defined according to IDH genotype, 1p/19q codeletion status, and tumor grade by using Mann-Whitney tests. Results In grade II and III IDH wild-type gliomas, the maximum fractional anisotropy, kurtosis anisotropy, and restriction fraction were significantly higher and the minimum mean diffusivity was significantly lower than in IDH-mutant gliomas (P = .011, P = .002, P = .044, and P = .027, respectively); areas under the receiver operating characteristic curve ranged from 0.72 to 0.76. In IDH wild-type gliomas, no difference among grades II, III, and IV was found. In IDH-mutant gliomas, no difference between those with and those without 1p/19q loss was found. Conclusion Diffusion MRI metrics showed correlation with isocitrate dehydrogenase status in grade II and III gliomas. Advanced diffusion MRI models did not add diagnostic accuracy, supporting the inclusion of a single-shell diffusion-tensor imaging acquisition in brain tumor imaging protocols. Published under a CC BY 4.0 license. Online supplemental material is available for this article.