Cerebrovascular reactivity MRI as a biomarker for cerebral small vessel disease-related cognitive decline: Multi-site validation in the MarkVCID Consortium.

INTRODUCTION: Vascular contributions to cognitive impairment and dementia (VCID) represent a major factor in cognitive decline in older adults. The present study examined the relationship between cerebrovascular reactivity (CVR) measured by magnetic resonance imaging (MRI) and cognitive function in a multi-site study, using a predefined hypothesis. METHODS: We conducted the study in a total of three analysis sites and 263 subjects. Each site performed an identical CVR MRI procedure using 5% carbon dioxide inhalation. A global cognitive measure of Montreal Cognitive Assessment (MoCA) and an executive function measure of item response theory (IRT) score were used as outcomes. RESULTS: CVR and MoCA were positively associated, and this relationship was reproduced at all analysis sites. CVR was found to be positively associated with executive function. DISCUSSION: The predefined hypothesis on the association between CVR and a global cognitive score was validated in three independent analysis sites, providing support for CVR as a biomarker in VCID. HIGHLIGHTS: This study measured a novel functional index of small arteries referred to as cerebrovascular reactivity (CVR). CVR was positively associated with global cognition in older adults. This finding was observed in three independent cohorts at three sites. Our statistical analysis plan was predefined before beginning data collection.

Capillary Hemangioma of the Spinal Cord: Case Report and Systematic Review of Literature.

BACKGROUND: Capillary hemangiomas are rare vascular lesions that rarely affect the central nervous system. When they present within the spinal canal, they are typically confined intradurally, with intramedullary extension rare. We present a rare case of spinal intramedullary capillary hemangioma, with a systematic review of the literature. METHODS: Medical records and imaging data were retrospectively reviewed using the health record software EPIC (Verona, Wisconsin, USA) and the radiology management software system RIS/PACS (Radiology Information System/Picture Archiving and Communication System; QREADS). The report was written in accordance with the CARE (case reports) guidelines. We also performed a systematic review of the literature on all cases of intramedullary spinal capillary hemangiomas in accordance with PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines. RESULTS: We report a case of a 54-year-old man who presented with progressive paraplegia and sensory deficits in the lower extremities. Spinal magnetic resonance imaging showed an intramedullary enhancing lesion centered at T11 with associated spinal cord compression. He underwent thoracic laminectomy and gross total resection of the lesion without complications and subsequent improvement on his neurological examination. Histological examination showed findings consistent with a capillary hemangioma. The literature review also documented 21 studies with a combined total of 38 cases of intramedullary spinal capillary hemangioma. CONCLUSIONS: Purely intramedullary capillary hemangiomas are unusual spinal lesions with only a few cases reported in the literature. These should be considered in the differential diagnosis of intramedullary tumors. Surgical management remains the first line of treatment for symptomatic patients.

CO2 cerebrovascular reactivity measured with CBF-MRI in older individuals: Association with cognition, physical function, amyloid, and tau proteins.

Vascular pathology is the second leading cause of cognitive impairment and represents a major contributing factor in mixed dementia. However, biomarkers for vascular cognitive impairment and dementia (VCID) are under-developed. Here we aimed to investigate the potential role of CO2 Cerebrovascular Reactivity (CVR) measured with phase-contrast quantitative flow MRI in cognitive impairment and dementia. Forty-five (69 ± 7 years) impaired (37 mild-cognitive-impairment and 8 mild-dementia by syndromic diagnosis) and 22 cognitively-healthy-control (HC) participants were recruited and scanned on a 3 T MRI. Biomarkers of AD pathology were measured in cerebrospinal fluid. We found that CBF-CVR was lower (p = 0.027) in the impaired (mean±SE, 3.70 ± 0.15%/mmHg) relative to HC (4.28 ± 0.21%/mmHg). After adjusting for AD pathological markers (Aß42/40, total tau, and Aß42/p-tau181), higher CBF-CVR was associated with better cognitive performance, including Montreal Cognitive Assessment, MoCA (p = 0.001), composite cognitive score (p = 0.047), and language (p = 0.004). Higher CBF-CVR was also associated with better physical function, including gait-speed (p = 0.006) and time for five chair-stands (p = 0.049). CBF-CVR was additionally related to the Clinical-Dementia-Rating, CDR, including global CDR (p = 0.026) and CDR Sum-of-Boxes (p = 0.015). CBF-CVR was inversely associated with hemoglobin A1C level (p = 0.017). In summary, CBF-CVR measured with phase-contrast MRI shows associations with cognitive performance, physical function, and disease-severity, independent of AD pathological markers.

On the optimization of 3D inflow-based vascular-space-occupancy (iVASO) MRI for the quantification of arterial cerebral blood volume (CBVa).

PURPOSE: The inflow-based vascular-space-occupancy (iVASO) MRI was originally developed in a single-slice mode to measure arterial cerebral blood volume (CBVa). When vascular crushers are applied in iVASO, the signals can be sensitized predominantly to small pial arteries and arterioles. The purpose of this study is to perform a systematic optimization and evaluation of a 3D iVASO sequence on both 3 T and 7 T for the quantification of CBVa values in the human brain. METHODS: Three sets of experiments were performed in three separate cohorts. (1) 3D iVASO MRI protocols were compared to single-slice iVASO, and the reproducibility of whole-brain 3D iVASO MRI was evaluated. (2) The effects from different vascular crushers in iVASO were assessed. (3) 3D iVASO MRI results were evaluated in arterial and venous blood vessels identified using ultrasmall-superparamagnetic-iron-oxides-enhanced MRI to validate its arterial origin. RESULTS: 3D iVASO scans showed signal-to-noise ratio (SNR) and CBVa measures consistent with single-slice iVASO with reasonable intrasubject reproducibility. Among the iVASO scans performed with different vascular crushers, the whole-brain 3D iVASO scan with a motion-sensitized-driven-equilibrium preparation with two binomial refocusing pulses and an effective TE of 50 ms showed the best suppression of macrovascular signals, with a relatively low specific absorption rate. When no vascular crusher was applied, the CBVa maps from 3D iVASO scans showed large CBVa values in arterial vessels but well-suppressed signals in venous vessels. CONCLUSION: A whole-brain 3D iVASO MRI scan was optimized for CBVa measurement in the human brain. When only microvascular signals are desired, a motion-sensitized-driven-equilibrium-based vascular crusher with binomial refocusing pulses can be applied in 3D iVASO.

Prevalence of Developmental Venous Anomalies in Association with Sporadic Cavernous Malformations on 7T MRI.

BACKGROUND AND PURPOSE: The etiology of sporadic cavernous malformations is not well-understood. However, recent evidence suggests that they may arise from a developmental venous anomaly. The goal of this study was to evaluate the prevalence of developmental venous anomalies associated with sporadic cavernous malformations using 7T MR imaging. MATERIALS AND METHODS: We retrospectively identified patients with a sporadic cavernous malformation imaged with 7T MR imaging between August 2019 and July 2022. Two raters determined whether a developmental venous anomaly was associated with each malformation. RESULTS: The study included 59 patients with a total of 61 cavernous malformations. Of the sixty-one, 44 (72%) had an associated developmental venous anomaly. An associated anomaly was most common for cavernous malformations in the brainstem (88%) compared with the cerebral hemispheres or cerebellum (60%-67%). CONCLUSIONS: By means of high-quality 7T imaging, most patients with a sporadic cavernous malformation were found to have an associated developmental venous anomaly. These findings support the hypothesis that cavernous malformations may arise secondary to hemodynamic abnormalities.

Detection and Mitigation of Neurovascular Uncoupling in Brain Gliomas.

Functional magnetic resonance imaging (fMRI) with blood oxygen level-dependent (BOLD) technique is useful for preoperative mapping of brain functional networks in tumor patients, providing reliable in vivo detection of eloquent cortex to help reduce the risk of postsurgical morbidity. BOLD task-based fMRI (tb-fMRI) is the most often used noninvasive method that can reliably map cortical networks, including those associated with sensorimotor, language, and visual functions. BOLD resting-state fMRI (rs-fMRI) is emerging as a promising ancillary tool for visualization of diverse functional networks. Although fMRI is a powerful tool that can be used as an adjunct for brain tumor surgery planning, it has some constraints that should be taken into consideration for proper clinical interpretation. BOLD fMRI interpretation may be limited by neurovascular uncoupling (NVU) induced by brain tumors. Cerebrovascular reactivity (CVR) mapping obtained using breath-hold methods is an effective method for evaluating NVU potential.

A Multidimensional Connectomics- and Radiomics-Based Advanced Machine-Learning Framework to Distinguish Radiation Necrosis from True Progression in Brain Metastases.

We introduce tumor connectomics, a novel MRI-based complex graph theory framework that describes the intricate network of relationships within the tumor and surrounding tissue, and combine this with multiparametric radiomics (mpRad) in a machine-learning approach to distinguish radiation necrosis (RN) from true progression (TP). Pathologically confirmed cases of RN vs. TP in brain metastases treated with SRS were included from a single institution. The region of interest was manually segmented as the single largest diameter of the T1 post-contrast (T1C) lesion plus the corresponding area of T2 FLAIR hyperintensity. There were 40 mpRad features and 6 connectomics features extracted, as well as 5 clinical and treatment factors. We developed an Integrated Radiomics Informatics System (IRIS) based on an Isomap support vector machine (IsoSVM) model to distinguish TP from RN using leave-one-out cross-validation. Class imbalance was resolved with differential misclassification weighting during model training using the IRIS. In total, 135 lesions in 110 patients were analyzed, including 43 cases (31.9%) of pathologically proven RN and 92 cases (68.1%) of TP. The top-performing connectomics features were three centrality measures of degree, betweenness, and eigenvector centralities. Combining these with the 10 top-performing mpRad features, an optimized IsoSVM model was able to produce a sensitivity of 0.87, specificity of 0.84, AUC-ROC of 0.89 (95% CI: 0.82-0.94), and AUC-PR of 0.94 (95% CI: 0.87-0.97).

Neuroimaging findings in 4342 hospitalized COVID-19 subjects: A multicenter report from the United States and Europe.

BACKGROUND AND PURPOSE: To determine the incidence of acute neuroimaging (NI) findings and comorbidities in the coronavirus disease of 2019 (COVID-19)-infected subjects in seven U.S. and four European hospitals. METHODS: This is a retrospective study of COVID-19-positive subjects with the following inclusion criteria: age >18, lab-confirmed COVID-19 infection, and acute NI findings (NI+) attributed to COVID-19 on CT or MRI brain. NI+ and comorbidities in total hospitalized COVID-19-positive (TN) subjects were assessed. RESULTS: A total of 37,950 COVID-19-positive subjects were reviewed and 4342 underwent NI. NI+ incidence in subjects with NI was 10.1% (442/4342) including 7.9% (294/3701) in the United States and 22.8% (148/647) in Europe. NI+ incidence in TN was 1.16% (442/37,950). In NI (4342), incidence of ischemic stroke was 6.4% followed by intracranial hemorrhage (ICH) (3.8%), encephalitis (0.5%), sinus venous thrombosis (0.2%), and acute disseminated encephalomyelitis (ADEM) (0.2%). White matter involvement was seen in 57% of NI+. Hypertension was the most common comorbidity (54%) before cardiac disease (28.8%) and diabetes mellitus (27.7%). Cardiac disease (p < .025), diabetes (p < .014), and chronic kidney disease (p < .012) were more common in the United States. CONCLUSION: This multicenter, multinational study investigated the incidence and spectrum of NI+ in 37,950 hospitalized adult COVID-19 subjects including regional differences in incidences of NI+, associated comorbidities, and other demographics. NI+ incidence in TN was 1.16% including 0.95% in the United States and 2.09% in Europe. ICH, encephalitis, and ADEM were common in Europe, while ischemic strokes were more common in the United States. In this cohort, incidence and distribution of NI+ helped characterize the neurological complications of COVID-19.

Concurrent measurement of perfusion parameters related to small blood vessels and cerebrospinal fluid circulation in the human brain using dynamic dual-spin-echo perfusion MRI.

Accumulating evidence from recent studies has indicated the importance of studying the interaction between the microvascular and lymphatic systems in the brain. To date, most imaging methods can only measure blood or lymphatic vessels separately, such as dynamic susceptibility contrast (DSC) MRI for blood vessels and DSC MRI-in-the-cerebrospinal fluid (CSF) (cDSC MRI) for lymphatic vessels. An approach that can measure both blood and lymphatic vessels in a single scan offers advantages such as a halved scan time and contrast dosage. This study attempts to develop one such approach by optimizing a dual-echo turbo-spin-echo sequence, termed "dynamic dual-spin-echo perfusion (DDSEP) MRI". Bloch simulations were performed to optimize the dual-echo sequence for the measurement of gadolinium (Gd)-induced blood and CSF signal changes using a short and a long echo time, respectively. The proposed method furnishes a T1-dominant contrast in CSF and a T2-dominant contrast in blood. MRI experiments were performed in healthy subjects to evaluate the dual-echo approach by comparing it with existing separate methods. Based on simulations, the short and long echo time were chosen around the time when blood signals show maximum difference between post- and pre-Gd scans, and the time when blood signals are completely suppressed, respectively. The proposed method showed consistent results in human brains as previous studies using separate methods. Signal changes from small blood vessels occurred faster than from lymphatic vessels after intravenous Gd injection. In conclusion, Gd-induced signal changes in blood and CSF can be detected simultaneously in healthy subjects with the proposed sequence. The temporal difference in Gd-induced signal changes from small blood and lymphatic vessels after intravenous Gd injection was confirmed using the proposed approach in the same human subjects. Results from this proof-of-concept study will be used to further optimize DDSEP MRI in subsequent studies.

Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI.

Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early stratification, but at present lack the sensitivity for personalized prognosis. Resting-state functional magnetic resonance imaging (rs-fMRI), a powerful tool previously used for mapping neural activity, is available in most hospitals. Here we show that rs-fMRI can be used to map cerebral hemodynamic function and delineate impairment. By exploiting time variations in breathing pattern during rs-fMRI, deep learning enables reproducible mapping of cerebrovascular reactivity (CVR) and bolus arrival time (BAT) of the human brain using resting-state CO2 fluctuations as a natural "contrast media". The deep-learning network is trained with CVR and BAT maps obtained with a reference method of CO2-inhalation MRI, which includes data from young and older healthy subjects and patients with Moyamoya disease and brain tumors. We demonstrate the performance of deep-learning cerebrovascular mapping in the detection of vascular abnormalities, evaluation of revascularization effects, and vascular alterations in normal aging. In addition, cerebrovascular maps obtained with the proposed method exhibit excellent reproducibility in both healthy volunteers and stroke patients. Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.

Utility of diffusion-weighted imaging to differentiate benign and malignant solid orbital tumours.

OBJECTIVE: We assessed the utility of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging to differentiate benign and malignant orbital tumours by oculoplastic surgeons in the clinical setting and sought to validate observed ADC cut-off values. DESIGN AND PARTICIPANTS: Retrospective review of patients with benign or malignant biopsy-confirmed orbital tumours. METHODS: Blinded graders including 2 oculoplastic surgeons, 1 neuroradiologist, and 1 medical student located and measured orbital tumour ADCs (10-6 mm2/s) using the Region of Interest tool. OUTCOME MEASURES: Nonradiologist measurements were compared with each other to assess reliability and with an expert neuroradiologist measurement and final pathology to assess accuracy. RESULTS: Twenty-nine orbital tumours met inclusion criteria, consisting of 6 benign tumours and 23 malignant tumours. Mean ADC values for benign orbital tumours were 1430.59 ± 254.81 and 798.68 ± 309.12 mm2/s for malignant tumours. Our calculated optimized ADC cut-off to differentiate benign from malignant orbital tumours was 1120.84 × 10-6 mm2/s (sensitivity 1, specificity 0.9). Inter-rater reliability was excellent (intraclass correlation coefficient = 0.92; 95% CI, 0.86-0.96). Our 3 graders had a combined accuracy of 84.5% (92.3%, 92.3%, and 65.4%). CONCLUSIONS: Our ADC cut-off of 1120.84 × 10-6 mm2/s for benign and malignant orbital tumours agrees with previously established values in literature. Without priming with instructions, training, or access to patient characteristics, most tumours were correctly classified using rapid ADC measurements. Surgeons without radiologic expertise can use the ADC tool to quickly risk stratify orbital tumours during clinic visits to guide patient expectations and further work-up.

MRI assessment of cerebral oxygen extraction fraction in the medial temporal lobe.

The medial temporal lobe (MTL) is a key area implicated in many brain diseases, such as Alzheimer's disease. As a functional biomarker, the oxygen extraction fraction (OEF) of MTL may be more sensitive than structural atrophy of MTL, especially at the early stages of diseases. However, there is a lack of non-invasive techniques to measure MTL-OEF in humans. The goal of this work is to develop an MRI technique to assess MTL-OEF in a clinically practical time without using contrast agents. The proposed method measures venous oxygenation (Yv) in the basal veins of Rosenthal (BVs), which are the major draining veins of the MTL. MTL-OEF can then be estimated as the arterio-venous difference in oxygenation. We developed an MRI sequence, dubbed arterial-suppressed accelerated T2-relaxation-under-phase-contrast (AS-aTRUPC), to quantify the blood T2 of the BVs, which was then converted to Yv through a well-established calibration model. MTL-OEF was calculated as (Ya-Yv)/Ya × 100%, where Ya was the arterial oxygenation. The feasibility of AS-aTRUPC to quantify MTL-OEF was evaluated in 16 healthy adults. The sensitivity of AS-aTRUPC in detecting OEF changes was assessed by a caffeine ingestion (200 mg) challenge. For comparison, T2-relaxation-under-spin-tagging (TRUST) MRI, which is a widely used global OEF technique, was also acquired. The dependence of MTL-OEF on age was examined by including another seven healthy elderly subjects. The results showed that in healthy adults, MTL-OEF of the left and right hemispheres were correlated (P=0.005). MTL-OEF was measured to be 23.9±3.6% (mean±standard deviation) and was significantly lower (P<0.0001) than the OEF of 33.3±2.9% measured in superior sagittal sinus (SSS). After caffeine ingestion, there was an absolute percentage increase of 9.1±4.0% in MTL-OEF. Additionally, OEF in SSS measured with AS-aTRUPC showed a strong correlation with TRUST OEF (intra-class correlation coefficient=0.94 with 95% confidence interval [0.91, 0.96]), with no significant bias (P=0.12). MTL-OEF was found to increase with age (MTL-OEF=20.997+0.100 × age; P=0.02). In conclusion, AS-aTRUPC MRI provides non-invasive assessments of MTL-OEF and may facilitate future clinical applications of MTL-OEF as a disease biomarker.

CVR-MRICloud: An online processing tool for CO2-inhalation and resting-state cerebrovascular reactivity (CVR) MRI data.

Cerebrovascular Reactivity (CVR) provides an assessment of the brain's vascular reserve and has been postulated to be a sensitive marker in cerebrovascular diseases. MRI-based CVR measurement typically employs alterations in arterial carbon dioxide (CO2) level while continuously acquiring Blood-Oxygenation-Level-Dependent (BOLD) images. CO2-inhalation and resting-state methods are two commonly used approaches for CVR MRI. However, processing of CVR MRI data often requires special expertise and may become an obstacle in broad utilization of this promising technique. The aim of this work was to develop CVR-MRICloud, a cloud-based CVR processing pipeline, to enable automated processing of CVR MRI data. The CVR-MRICloud consists of several major steps including extraction of end-tidal CO2 (EtCO2) curve from raw CO2 recording, alignment of EtCO2 curve with BOLD time course, computation of CVR value on a whole-brain, regional, and voxel-wise basis. The pipeline also includes standard BOLD image processing steps such as motion correction, registration between functional and anatomic images, and transformation of the CVR images to canonical space. This paper describes these algorithms and demonstrates the performance of the CVR-MRICloud in lifespan healthy subjects and patients with clinical conditions such as stroke, brain tumor, and Moyamoya disease. CVR-MRICloud has potential to be used as a data processing tool for a variety of basic science and clinical applications.

Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine Learning.

The high-level relationships that form complex networks within tumors and between surrounding tissue is challenging and not fully understood. To better understand these tumoral networks, we developed a tumor connectomics framework (TCF) based on graph theory with machine learning to model the complex interactions within and around the tumor microenvironment that are detectable on imaging. The TCF characterization model was tested with independent datasets of breast, brain, and prostate lesions with corresponding validation datasets in breast and brain cancer. The TCF network connections were modeled using graph metrics of centrality, average path length (APL), and clustering from multiparametric MRI with IsoSVM. The Matthews Correlation Coefficient (MCC), Area Under the Curve-ROC, and Precision-Recall (AUC-ROC and AUC-PR) were used for statistical analysis. The TCF classified the breast and brain tumor cohorts with an IsoSVM AUC-PR and MCC of 0.86, 0.63 and 0.85, 0.65, respectively. The TCF benign breast lesions had a significantly higher clustering coefficient and degree centrality than malignant TCFs. Grade 2 brain tumors demonstrated higher connectivity compared to Grade 4 tumors with increased degree centrality and clustering coefficients. Gleason 7 prostate lesions had increased betweenness centrality and APL compared to Gleason 6 lesions with AUC-PR and MCC ranging from 0.90 to 0.99 and 0.73 to 0.87, respectively. These TCF findings were similar in the validation breast and brain datasets. In conclusion, we present a new method for tumor characterization and visualization that results in a better understanding of the global and regional connections within the lesion and surrounding tissue.

Inflow-based vascular-space-occupancy (iVASO) might potentially predict IDH mutation status and tumor grade in diffuse cerebral gliomas.

PURPOSE: The aim of the study is to assess the diagnostic performance of inflow-based vascular-space-occupancy (iVASO) MR imaging for differentiating glioblastomas (grade IV, GBM) and lower-grade diffuse gliomas (grade II and III, LGG) and its potential to predict IDH mutation status. METHODS: One hundred and two patients with diffuse cerebral glioma (56 males; median age, 43.5 years) underwent iVASO and dynamic susceptibility contrast (DSC) MR imaging. The iVASO-derived arteriolar cerebral blood volume (CBVa), relative CBVa (rCBVa), and the DSC-derived relative cerebral blood volume (rCBV) were obtained, and these measurements were compared between the GBM group (n = 43) and the LGG group (n = 59) and between the IDH-mutation group (n = 54) and the IDH-wild group (n = 48). RESULTS: Significant correlation was observed between rCBV and CBVa (P < 0.001) or rCBVa (P < 0.001). Both CBVa (P < 0.001) and rCBVa (P < 0.001) were higher in the GBM group. Both CBVa (P < 0.001) and rCBVa (P < 0.001) were lower in the IDH-mutation group compared to the IDH-wild group. Receiver operating characteristic analyses showed the area under curve (AUC) of 0.95 with CBVa and 0.97 with rCBVa in differentiating GBM from LGG. The AUCs were 0.82 and 0.85 for CBVa and rCBVa in predicting IDH gene status, respectively, which were lower than that of rCBV (AUC = 0.90). Combined rCBV and rCBVa significantly improved the diagnostic performance (AUC = 0.95). CONCLUSIONS: iVASO MR imaging has the potential to predict IDH mutation and grade in glioma.

Effects of Thresholding on Voxel-Wise Correspondence of Breath-Hold and Resting-State Maps of Cerebrovascular Reactivity.

Functional magnetic resonance imaging for presurgical brain mapping enables neurosurgeons to identify viable tissue near a site of operable pathology which might be at risk of surgery-induced damage. However, focal brain pathology (e.g., tumors) may selectively disrupt neurovascular coupling while leaving the underlying neurons functionally intact. Such neurovascular uncoupling can result in false negatives on brain activation maps thereby compromising their use for surgical planning. One way to detect potential neurovascular uncoupling is to map cerebrovascular reactivity using either an active breath-hold challenge or a passive resting-state scan. The equivalence of these two methods has yet to be fully established, especially at a voxel level of resolution. To quantitatively compare breath-hold and resting-state maps of cerebrovascular reactivity, we first identified threshold settings that optimized coverage of gray matter while minimizing false responses in white matter. When so optimized, the resting-state metric had moderately better gray matter coverage and specificity. We then assessed the spatial correspondence between the two metrics within cortical gray matter, again, across a wide range of thresholds. Optimal spatial correspondence was strongly dependent on threshold settings which if improperly set tended to produce statistically biased maps. When optimized, the two CVR maps did have moderately good correspondence with each other (mean accuracy of 73.6%). Our results show that while the breath-hold and resting-state maps may appear qualitatively similar they are not quantitatively identical at a voxel level of resolution.

Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks.

Localizing the eloquent cortex is a crucial part of presurgical planning. While invasive mapping is the gold standard, there is increasing interest in using noninvasive fMRI to shorten and improve the process. However, many surgical patients cannot adequately perform task-based fMRI protocols. Resting-state fMRI has emerged as an alternative modality, but automated eloquent cortex localization remains an open challenge. In this paper, we develop a novel deep learning architecture to simultaneously identify language and primary motor cortex from rs-fMRI connectivity. Our approach uses the representational power of convolutional neural networks alongside the generalization power of multi-task learning to find a shared representation between the eloquent subnetworks. We validate our method on data from the publicly available Human Connectome Project and on a brain tumor dataset acquired at the Johns Hopkins Hospital. We compare our method against feature-based machine learning approaches and a fully-connected deep learning model that does not account for the shared network organization of the data. Our model achieves significantly better performance than competing baselines. We also assess the generalizability and robustness of our method. Our results clearly demonstrate the advantages of our graph convolution architecture combined with multi-task learning and highlight the promise of using rs-fMRI as a presurgical mapping tool.

Identification of the Somatomotor Network from Language Task-based fMRI Compared with Resting-State fMRI in Patients with Brain Lesions.

Background Resting-state functional MRI (rs-fMRI) is a potential alternative to task-based functional MRI (tb-fMRI) for somatomotor network (SMN) identification. Brain networks can also be generated from tb-fMRI by using independent component analysis (ICA). Purpose To investigate whether the SMN can be identified by using ICA from a language task without a motor component, the sentence completion functional MRI (sc-fMRI) task, compared with rs-fMRI. Materials and Methods The sc-fMRI and rs-fMRI scans in patients who underwent presurgical brain mapping between 2012 and 2016 were analyzed, using the same imaging parameters (other than scanning time) on a 3.0-T MRI scanner. ICA was performed on rs-fMRI and sc-fMRI scans with use of a tool to separate data sets into their spatial and temporal components. Two neuroradiologists independently determined the presence of the dorsal SMN (dSMN) and ventral SMN (vSMN) on each study. Groups were compared by using t tests, and logistic regression was performed to identify predictors of the presence of SMNs. Results One hundred patients (mean age, 40.9 years ± 14.8 [standard deviation]; 61 men) were evaluated. The dSMN and vSMN were identified in 86% (86 of 100) and 76% (76 of 100) of rs-fMRI scans and 85% (85 of 100) and 69% (69 of 100) of sc-fMRI scans, respectively. The concordance between rs-fMRI and sc-fMRI for presence of dSMN and vSMN was 75% (75 of 100 patients) and 53% (53 of 100 patients), respectively. In 10 of 14 patients (71%) where rs-fMRI did not show the dSMN, sc-fMRI demonstrated it. This rate was 67% for the vSMN (16 of 24 patients). Conclusion In the majority of patients, independent component analysis of sentence completion task functional MRI scans reliably demonstrated the somatomotor network compared with resting-state functional MRI scans. Identifying target networks with a single sentence completion scan could reduce overall functional MRI scanning times by eliminating the need for separate motor tasks. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Field and Birn in this issue.

A novel radiographic marker of sarcopenia with prognostic value in glioblastoma.

OBJECTIVE: Sarcopenia is an important prognostic consideration in surgical oncology that has received relatively little attention in brain tumor patients. Temporal muscle thickness (TMT) has recently been proposed as a novel radiographic marker of sarcopenia that can be efficiently obtained within existing workflows. We investigated the prognostic value of TMT in primary and progressive glioblastoma. METHODS: TMT measurements were performed on magnetic resonance images of 384 patients undergoing 541 surgeries for glioblastoma. Relationships between TMT and clinical characteristics were examined on bivariate analysis. Optimal TMT cutpoints were established using maximally selected rank statistics. Predictive value of TMT upon postoperative survival (PS) was assessed using Cox proportional hazards regression adjusted for age, sex, Karnofsky performance status (KPS), Stupp protocol completion, extent of resection, and tumor molecular markers. RESULTS: Average TMT for the primary and progressive glioblastoma cohorts was 9.55 mm and 9.40 mm, respectively. TMT was associated with age (r = -0.14, p = 0.0008), BMI (r = 0.29, p < 0.0001), albumin (r = 0.11, p = 0.0239), and KPS (r = 0.11, p = 0.0101). Optimal TMT cutpoints for the primary and progressive cohorts were ≤ 7.15 mm and ≤ 7.10 mm, respectively. High TMT was associated with increased Stupp protocol completion (p = 0.001). On Cox proportional hazards regression, high TMT predicted increased PS in progressive [HR 0.47 (95% confidence interval (CI)) 0.25-0.90), p = 0.023] but not primary [HR 0.99 (95% CI 0.64-1.51), p = 0.949] glioblastoma. CONCLUSIONS: TMT correlates with important prognostic variables in glioblastoma and predicts PS in patients with progressive, but not primary, disease. TMT may represent a pragmatic neurosurgical biomarker in glioblastoma that could inform treatment planning and perioperative optimization.