Brain tumors impair brain function both locally and across distant regions by disrupting network connectivity, contributing to cognitive deficits and triggering compensatory plasticity. Traditional resting-state fMRI methods average brain activity over time, missing transient, dynamic events critical to cognition. Co-Activation Pattern (CAP) analysis captures these brief brain states, enabling quantification of state engagement and duration. To investigate alterations in transient brain states in patients with brain tumors using CAP analysis of resting-state fMRI, and to assess whether these changes reflect modified engagement of cognitive states compared to healthy controls. This retrospective cross-sectional study included 106 patients with left-hemispheric brain tumors (72 high-grade gliomas, 19 low-grade gliomas, 15 metastases; mean age 61.15 ± 8.95 years; 43 women) and 100 age-matched healthy controls. Resting-state fMRI data were analyzed using a seed-free clustering method (TbCAPs toolbox) to extract CAPs. CAPs were first identified in controls and then matched to patients via spatial similarity. Each CAP was assigned to a canonical brain network using the GIFT toolbox. Dynamic metrics computed included: persistence (duration of a CAP), transitions (switching frequency), in-degree, and out-degree. Group comparisons used two-tailed t-tests with Benjamini–Hochberg correction (p < 0.05). Six CAPs were identified. Compared to controls, patients showed significantly increased transitions, in-degree, and out-degree, and decreased persistence in CAPs linked to the default mode and executive control networks (all p < 0.01), suggesting more frequent but less stable engagement. Visuospatial network CAPs demonstrated lower transition, in/out-degree and persistence in patients (p < 0.05). No significant differences were observed among tumor types. Patients with brain tumors display altered CAP dynamics involving higher-order cognitive networks and perceptual networks. These alterations may reflect the combined effects of tumor-related network damage and potential adaptive reorganization, with potential implications for functional preoperative planning and prognosis. However, in the absence of direct clinical or neuropsychological correlations, these interpretations remain hypothesis-generating. The findings are also limited by the retrospective cross-sectional design preventing causal or longitudinal interpretation, and the restriction to left-hemispheric tumors. Future studies integrating CAP dynamics with cognitive and clinical outcomes will be necessary to determine whether these changes reflect compensatory functional reorganization in brain tumor patients.

We investigated the temporal dynamics of resting-state brain activity associated with language reorganization in patients with left-hemisphere brain tumors using coactivation pattern (CAP) analysis of functional MRI. This retrospective study included 106 right-handed patients who underwent both resting-state and task-based fMRI before surgery. Language dominance was determined using a phonemic fluency task, and CAP analysis identified recurring whole-brain activation patterns. Dynamic properties of CAPs, including persistence, transitions, in-degree, and out-degree, were compared between patients with typical and atypical language dominance and between those with and without postoperative aphasia. Six distinct CAPs corresponding to known functional networks were identified. Patients with atypical language dominance showed higher out-degree (p=0.004) and transitions (p=0.006) in the dorsal default mode network (DMN; CAP2) and lower persistence in the visuospatial network (VSN; CAP3; p=0.002) compared with patients with typical dominance. Patients without postoperative aphasia demonstrated higher transitions in VSN CAP6 (p=0.024). Logistic regression analyses were performed to evaluate the prediction of postoperative aphasia. A model including CAP metrics and the language laterality index (LI) demonstrated good predictive performance (area under the curve [AUC] = 0.851, accuracy = 82.4%, sensitivity = 71.9%, and specificity = 88.1%), outperforming a model based on LI alone (AUC = 0.640 and accuracy = 67.0%). No significant differences were observed by tumor type or volume. These findings indicate that CAP dynamics capture distinct neural states associated with language dominance and postoperative outcomes. Specific CAP metrics may serve as potential imaging biomarkers of functional reorganization, supporting prognostic evaluation and surgical planning in patients with brain tumors.

OBJECTIVE:To characterize functional network alterations associated with aphasia and preserved language function in patients with left-hemispheric brain tumors using resting-state fMRI graph-theoretical analysis, with secondary evaluation of whether whole-brain network metrics are associated with aphasia status within the tumor cohort. MATERIALS AND METHODS:This retrospective IRB-approved study included 120 participants: 40 aphasic patients, 40 non-aphasic patients with left-hemispheric intra-axial tumors, and 40 matched healthy controls. ROI-to-ROI rs-fMRI connectivity across seven canonical networks yielded graph metrics (global/local efficiency, clustering, path length, centrality) at whole-brain, hemispheric, and network levels with FDR-corrected comparisons. Multinomial logistic regression compared healthy controls, non-aphasic patients, and aphasic patients. A secondary exploratory patient-only binary logistic regression examined aphasia status using whole-brain graph metrics and demographic covariates, including age, sex, and handedness. RESULTS:The whole-brain multinomial model did not significantly distinguish healthy controls, non-aphasic patients, and aphasic patients (χ²=21.622, df=18, p = 0.249; classification accuracy=57.5%). Therefore, individual graph-metric coefficients from this model were treated as exploratory rather than confirmatory. In a secondary exploratory patient-only regression, the model did not significantly distinguish aphasic from non-aphasic tumor patients after adjustment for age, sex, and handedness (Omnibus χ²=7.147, df=10, p = 0.712; Nagelkerke R²=0.114; Hosmer-Lemeshow p = 0.508; accuracy=62.5%). Whole-brain graph metrics were not independently associated with aphasia after demographic adjustment. ROI-level analyses showed focal differences in non-aphasic patients, including greater left IFG closeness, posterior parietal centrality, and anterior cerebellar connectivity, but these findings were interpreted as exploratory network correlates rather than definitive evidence of compensation. CONCLUSION:Rs-fMRI graph-theoretical measures characterized network-level differences among aphasic patients, non-aphasic patients, and healthy controls, but the three-group multinomial model did not significantly distinguish the groups and the secondary patient-only regression did not independently distinguish aphasic from non-aphasic tumor patients after demographic adjustment. These findings suggest that graph metrics should be interpreted as exploratory system-level correlates of aphasia status and preserved language rather than stand-alone predictors or definitive markers of compensation.

Despite continuous advancements in cancer treatment, brain metastatic disease remains a significant complication of primary cancer and is associated with an unfavorable prognosis. One approach for improving diagnosis, management, and outcomes is to implement algorithms based on artificial intelligence for the automated segmentation of both pre- and post-treatment MRI brain images. Such algorithms rely on volumetric criteria for lesion identification and treatment response assessment, which are still not available in clinical practice. Therefore, it is critical to establish tools for rapid volumetric segmentations methods that can be translated to clinical practice and that are trained on high quality annotated data. The BraTS-METS 2025 Lighthouse Challenge aims to address this critical need by establishing inter-rater and intra-rater variability in dataset annotation by generating high quality annotated datasets from four individual instances of segmentation by neuroradiologists while being recorded on video (two instances doing "from scratch" and two instances after AI pre-segmentation). This high-quality annotated dataset will be used for testing phase in 2025 Lighthouse challenge and will be publicly released at the completion of the challenge. The 2025 Lighthouse challenge will also release the 2023 and 2024 segmented datasets that were annotated using an established pipeline of pre-segmentation, student annotation, two neuroradiologists checking, and one neuroradiologist finalizing the process. It builds upon its previous edition by including post-treatment cases in the dataset. Using these high-quality annotated datasets, the 2025 Lighthouse challenge plans to test benchmark algorithms for automated segmentation of pre-and post-treatment brain metastases (BM), trained on diverse and multi-institutional datasets of MRI images obtained from patients with brain metastases.

7524 Background: BCMA-targeted CAR T cell therapy is a highly effective treatment for patients with relapsed/refractory multiple myeloma (MM) with side effects such as CRS, ICANS, and movement and neurocognitive toxicities (MNTs). Regional changes on [ 18 F]fluoro-deoxy-2-D-glucose PET (PET) can be used to derive metabolic connectivity, an emerging technique that models brain function from the uptake on a PET, allowing us to investigate alterations of regional metabolism (SUV) and changes in metabolic brain networks (connectivity) peri-CAR T. Methods: Patients were included in this retrospective study if they were treated with commercially available BCMA-directed CAR T cells and had pre-and post-CAR therapy PET imaging. We analyzed the connectivity of the whole brain and parcellated the brain to generate global and regional connectivity matrices and investigated the association of regional metabolic differences and differences in metabolic connectivity with clinical parameters. Results: Of the 108 consecutive patients (65 Cilta-cel, 43 Ide-cel), there were 61 men and 47 women (median age 65), with PET a median of 12 days prior to infusion 28 days post infusion. Toxicities included CRS alone (n=66), CRS + ICANS (n=8), CRS+facial palsy (n=3), and CRS + Parkinsonism + facial palsy (n=2). Within the entire cohort, a significantly higher SUV-mean was noted in putamen (p<0.0004) post-CAR T compared to pre-CAR T, with other brain regions not showing a difference. These regional differences were significantly and inversely associated with the grade of ICANS (Post-Pre: Left: t=-1.76, p=0.08; Right t=-2.1 p=0.04). When comparing patients with (n = 79) and without (n=29) any post-CAR T cell CRS/ICANS/MNT, the post SUV-mean was significantly higher in the bilateral basal ganglia (BG) of patients who experienced toxicity (p<0.05). The SUV-mean was significantly lower in the bilateral inferior frontal opercularis, triangularis, and bilateral Rolandic operculum of those who developed ICANS (Grade 1-2, all with CRS, n=8) vs with CRS alone (all grade 1, n=46) (p<0.05). Globally, the metabolic connectivity network had less efficiency (post-30 ), degree (p=10 -15 ), strengths (p=0.001), clustering coefficient (p=10 -12 ), and higher edge betweenness centrality (p=0.02) compared to the pre-CAR T timepoint. The decreases in network measurement were more severe in the frontal lobe and basal ganglia (p=10 -6 and p=0.004, respectively). Conclusions: Patients with neurotoxicity after BCMA CAR T had an increased SUV in the putamen, but decreased in the frontal regions and basal ganglia at Day 28. Metabolic networks were globally less efficient and less dense and have changes that signify injury or attempts at compensation.

Nearly all glioblastomas recur within two years of initial treatment. While most recurrences occur within or near the resection cavity, a substantial minority of recurrences arise distant to the primary tumor site, representing progression of microscopic disseminated disease, and leading to compromised oncologic outcomes. We hypothesized that glioblastoma distant recurrences preferentially occur near white matter tracts associated with the primary site, and that these high-risk regions can be mapped using probabilistic tractography. We identified glioblastoma patients with distant recurrence ≥ 2 cm from the primary site and diffusion tensor imaging prior to recurrence. Probabilistic tractography connection (PTC) maps were produced, initialized from the primary site plus a 1 cm expansion. Low-probability voxels with values less than the mean were eliminated. The distance from PTC map to the recurrent tumor was then evaluated as the primary metric of model performance. The probability of the distant recurrence occurring within 0.5 cm of the PTC map was evaluated with a one-sided binomial distribution. Of 35 glioblastoma distant recurrences identified at median 15.9 months after resection, the median distance from the primary site was 4.5 cm. The median volume of the PTC map was 139 cc, equivalent to 9.7% of the brain volume. The median distance from the PTC map to the recurrent tumor was 0.0 cm (range: 0.0-1.7 cm). Among 35 distant recurrences, 29 (83%) were within 0.5 cm of the PTC map (p=0.0002). We describe a novel technique that uses probabilistic tractography to identify regions of the brain at high risk for glioblastoma distant recurrence. Nearly all distant recurrences occur along mappable white matter tracts, supporting the hypothesis that white matter tracts are a predominant pathway for microscopic dissemination. Mapping of white matter tracts with probabilistic tractography may aid in personalizing neurosurgical and radiotherapeutic treatment planning, improving oncologic control.

Background:Diffuse intrinsic pontine glioma (DIPG) carries a high mortality rate and lacks effective treatment options with a median overall survival (OS) of 8-12 months. Convection-enhanced delivery (CED) has demonstrated safety in phase I trials, but efficacy is indeterminate. Evaluating anatomic patterns of relapse may aid in determining therapeutic efficacy of local CED drug delivery strategies. Methods:Sixty-three children with DIPG were retrospectively reviewed for first radiographic progression. All patients were treated using conventional external beam radiation (EBRT) and 31 were treated with CED of radiolabeled 124-iodine-omburtamab (NCT01502917). Anatomic patterns of initial progression were coded by independent neuroradiologists. OS and cumulative incidence of progression at each anatomic site were assessed in a competing risk analysis with death as a competing variable and were stratified based on CED treatment. Results:Median OS was 14.67 months for the cohort. Patients receiving CED demonstrated higher rates of progression in general, when considering progression at all anatomical sites (HR 1.79, P = .047); no significant difference was found in OS when stratified by CED treatment (P = .22). However, CED treatment was associated with significantly lower cumulative incidence of local pontine and medullary progression (HR: 0.42, P = .03; HR 0.14, P = .01, respectively) relative to non-CED-treated patients. Conclusions:Anatomically defined patterns of relapse provide evidence for locoregional control in children with DIPG treated with radioimmunotherapy administered by CED. Future CED or local surgical therapy trials can benefit from including detailed patterns of relapse as a prospective outcome.

PURPOSE:Functional magnetic resonance imaging (fMRI) localizes eloquent areas of the brain more accurately than structural imaging alone and minimizes the risk of neurosurgical injury. However, fMRI remains underutilized in stereotactic radiosurgery (SRS). We assessed the neuroanatomic relationship between SRS to brain metastases (BMs), nearby eloquent areas, identified using fMRI, and attributable symptoms of radionecrosis (RN). We then evaluated the advantage of a novel fMRI-guided SRS (fMRI-SRS) planning approach. METHODS AND MATERIALS:Patients with an fMRI study within 3 months of SRS for BMs were included. The nearest eloquent area (NEA) was defined as the motor, language, or visual eloquent area on fMRI nearest to an irradiated BM. The primary outcome was focal symptomatic RN (FSRN), defined as radiographic RN with progressive neurologic symptoms localizable to the NEA. The relationship between dose to the NEA and risk of FSRN was assessed with logistic regression. Cases were replanned using fMRI-SRS, maximizing NEA avoidance while maintaining target coverage. RESULTS:Among 93 patients, 76 (82%) had resection prior to SRS. The most common SRS prescription was 30 Gy in 5 fractions. The NEA was a motor eloquent area in 71 cases (76%) and a language area in 18 cases (19%). Of 20 patients who developed radiographic RN, 4 (20%) were asymptomatic, 4 (20%) had nonlocalizable neurologic symptoms, and 12 (60%) had focal neurologic symptoms directly attributable to the NEA, consistent with FSRN. Among patients who received 5-fraction SRS with modern planning techniques, greater V14Gy (single-fraction equivalent) to the NEA predicted increased risk of FSRN at 18 months (odds ratio, 6.8/mL; P= .05). fMRI-SRS reduced NEA V14Gy in all cases replanned, with a mean reduction of 22.5%. CONCLUSIONS:Focal neurologic symptoms of RN reflect SRS dose to nearby eloquent areas on fMRI. fMRI-SRS planning minimizes dose to eloquent areas without sacrificing target coverage and may mitigate neurologic toxicity.

BACKGROUND AND PURPOSE: The interaction between language and other cognitive networks in patients harboring brain tumors is poorly understood. We studied the modification of the cognitive control network (CCN) induced by brain tumors and its participation in language reorganization. We hypothesized that patients with brain tumors and reorganized language would show a modification of the CCN compared with patients who remain left dominant. MATERIALS AND METHODS: Patients were selected with the criteria: newly diagnosed, pathologically-confirmed left-hemispheric tumor; single lesions; right-handedness; task-based and resting-state fMRI; no artifacts. Age-matched healthy controls (HC) were recruited from open-source databases. Language laterality was calculated by using task-based fMRI. We obtained the CCN through ad hoc independent component analysis on resting-state fMRI. Differences in CCN between patients and HC were characterized by cosine similarity (CS) and earth mover's distance (EMD). Changes related to language reorganization and patients' speech were assessed with the t test (P < .05). Results were corrected for multiple comparisons. RESULTS: One hundred forty-two right-handed patients (35 low-grade and 88 high-grade gliomas; 19 metastases) and 184 HC were included. Two independent components of the CCN were obtained. The t test confirmed significant effects of lateralization on the CCN (P = .004). Modification of CCN was associated with fewer speech deficits 1 week after surgery (P = .005). CONCLUSIONS: This study provides evidence that modifications of CCN occur in the setting of language reorganization. Patients exhibiting these modifications perform better at speech evaluation after surgery, suggesting a role of cognitive control in compensating for speech deficits when language reorganizes.

Congenital malformations of the eye represent a wide and heterogeneous spectrum of abnormalities that may be part of a complex syndrome or be isolated. Ocular malformation severity depends on the timing of the causative event during eye formation, ranging from the complete absence of the eye if injury occurs during the first weeks of gestation, to subtle abnormalities if the cause occurs later on. Knowledge of ocular malformations is crucial to performing a tailored imaging protocol and correctly reporting imaging findings. Together with the ophthalmologic evaluation, imaging may help frame ocular malformations and identify underlying genetic conditions. The purpose of this pictorial review is to describe the imaging features of the main ocular malformations and the related ophthalmologic findings in order to provide a clinico-radiological overview of these abnormalities to the clinical radiologist. Sight is a crucial sense for children to explore the world and relate with their parents from birth. Vision impairment or even blindness secondary to ocular malformations deeply affects children’s growth and quality of life.

Reliable and trustworthy artificial intelligence (AI), particularly in high-stake medical diagnoses, necessitates effective uncertainty quantification (UQ). Existing UQ methods using model ensembles often introduce invalid variability or computational complexity, rendering them impractical and ineffective in clinical workflow. We propose a UQ approach based on deep neuroevolution (DNE), a data-efficient optimization strategy. Our goal is to replicate trends observed in expert-based UQ. We focused on language lateralization maps from resting-state functional MRI (rs-fMRI). Fifty rs-fMRI maps were divided into training/testing (30:20) sets, representing two labels: “left-dominant” and “co-dominant.” DNE facilitated acquiring an ensemble of 100 models with high training and testing set accuracy. Model uncertainty was derived from distribution entropies over the 100 model predictions. Expert reviewers provided user-based uncertainties for comparison. Model (epistemic) and user-based (aleatoric) uncertainties were consistent in the independently and identically distributed (IID) testing set, mainly indicating low uncertainty. In a mostly out-of-distribution (OOD) holdout set, both model and user-based entropies correlated but displayed a bimodal distribution, with one peak representing low and another high uncertainty. We also found a statistically significant positive correlation between epistemic and aleatoric uncertainties. DNE-based UQ effectively mirrored user-based uncertainties, particularly highlighting increased uncertainty in OOD images. We conclude that DNE-based UQ correlates with expert assessments, making it reliable for our use case and potentially for other radiology applications.

Purpose: F-18-FDG PET captures the relationship between glucose metabolism and synaptic activity, allowing for modeling brain function through metabolic connectivity. We investigated tumor-induced modifications of brain metabolic connectivity. Patients and Methods: Forty-three patients with left hemispheric tumors and F-18-FDG PET/MRI were retrospectively recruited. We included 37 healthy controls (HCs) from the database CERMEP-IDB-MRXFDG. We analyzed the whole brain and right versus left hemispheres connectivity in patients and HC, frontal versus temporal tumors, active tumors versus radiation necrosis, and patients with high Karnofsky performance score (KPS = 100) versus low KPS (KPS < 70). Results were compared with 2-sided t test (P < 0.05). Results: Twenty high-grade glioma, 4 low-grade glioma, and 19 metastases were included. The patients' whole-brain network displayed lower connectivity metrics compared with HC (P < 0.001), except assortativity and betweenness centrality (P = 0.001). The patients' left hemispheres showed decreased similarity, and lower connectivity metrics compared with the right (P < 0.01), with the exception of betweenness centrality (P = 0.002). HC did not show significant hemispheric differences. Frontal tumors showed higher connectivity metrics (P < 0.001) than temporal tumors, but lower betweenness centrality (P = 4.5(-7)). Patients with high KPS showed higher distance local efficiency (P = 0.01), rich club coefficient (P = 0.0048), clustering coefficient (P = 0.00032), betweenness centrality (P = 0.008), and similarity (P = 0.0027) compared with low KPS. Patients with active tumor(s) (14/43) demonstrated significantly lower connectivity metrics compared with necroses. Conclusions: Tumors cause reorganization of metabolic brain networks, characterized by formation of new connections and decreased centrality. Patients with frontal tumors retained a more efficient, centralized, and segregated network than patients with temporal tumors. Stronger metabolic connectivity was associated with higher KPS.

2010 Background: High-Grade Gliomas (HGGs) are the most aggressive primary brain tumors in adults and molecular characterization is crucial for diagnosis and optimal disease treatment. Due to the eloquent location of many HGGs, upfront or repeat tumor sampling may be sub-optimal. Hence, there is an urgent need to develop alternative, minimally invasive means to obtain diagnostic information and determine the expected clinical behavior. Here we report that circulating tumor DNA (ctDNA) from cerebrospinal fluid (CSF) can be used to identify disease defining genomic alterations and to track clonal evolution. Additionally, we demonstrate that detection of CSF ctDNA is positively correlated with leptomeningeal disease and overall survival suggesting that CSF ctDNA should be integrated into clinical decision making in the clinic. Methods: Our study includes 313 samples from 253 patients with recurrent glioma treated at Memorial Sloan Kettering Cancer Center who underwent CSF collection for routine clinical care and were sequenced using the MSK-IMPACT targeted clinical sequencing assay (468 and 505 genes). Alterations were classified as oncogenic based on OncoKB. 17% of genomic alterations detected were identified using a secondary bioinformatics analysis, following an informed approach with less stringent mutation calling criteria and Gaussian Mixture Model to call copy number events. CSF ctDNA positivity was determined by the presence of at least one oncogenic disease defining alteration or any shared alteration with the tumor. Results: Within this cohort, we found 193 CSF ctDNA positive (62%) and 120 CSF negative (38%) samples. Of note, CSF ctDNA positivity was the highest amongst histone mutant tumors with 94% CSF ctDNA positivity compared to 56% CSF ctDNA positivity for IDH-WT tumors. We noticed 44% of alterations shared between the tumor and the CSF, additionally, we noted considerable tumor evolution particularly within the growth signaling pathways (e.g. PDGFRA). The majority of the shared alterations were clonal, and we noticed the emergence of new clonal events in the CSF. Conclusions: Our cohort demonstrated that patients with positive CSF ctDNA had a significantly shorter overall survival compared to those who were CSF ctDNA negative (4.83 months vs 11.83 months, HR 2.1, p < 0.001). In tandem with secondary clinical analysis, radiographic findings also correlated with CSF ctDNA positivity. Specifically, the presence of enhancing disease and contact of the tumor with the ventricular space were positively associated with detection of CSF ctDNA. Additionally, CSF ctDNA was associated with positive cytology in 21/21 cases (100%). With our improved bioinformatics pipeline, we hypothesize ctDNA from CSF may be used as a prognostic biomarker for survival, but confirmation requires further validation in a prospective study.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

Abstract BACKGROUND Radiation necrosis (RN) is a common complication of stereotactic radiosurgery (SRS). Functional magnetic resonance imaging (fMRI) localizes eloquent cortical areas for neurosurgical planning but is seldom applied to SRS. We explored the relationship between symptomatic RN and functional neuroanatomy on fMRI. We evaluated the potential benefit of reducing radiation dose to functional areas. METHODS From 2011 to 2022, patients with an fMRI within 3 months of SRS for resected or unresected brain metastases (BM) were included. The BM nearest to the primary motor, language, or visual cortices was designated as the index BM. The functional area on fMRI nearest to the index BM was designated as the fOAR. Focally symptomatic RN (FSRN) was defined as radiographic RN alongside new or worsening focal neurologic symptoms consistent with the fOAR. Patients treated with 5-fraction SRS and modern planning techniques after 2013 were included in the dosimetric analysis. Associations between fOAR dose and FSRN were evaluated with logistic regression. SRS replanning was performed to determine if fOAR dose could be reduced without sacrificing target coverage. RESULTS Among 94 patients, 82% received SRS postoperatively, and 71% received 5-fraction SRS. fOARs included the primary motor (77%), language (19%), and visual (2%) cortices. Of 20 patients with radiographic RN, 20% were neurologically asymptomatic, 20% had anatomically unrelated neurologic symptoms, and 60% had FSRN. The dosimetric analysis included 39 patients. The fOAR volume receiving a single-fraction equivalent dose ≥14 Gy (V14Gy) was associated with FSRN at 18 months post-SRS (OR=6.8/mL, p=0.05). Replanning reduced fOAR V14Gy in all cases. The mean reduction was 28%. CONCLUSION Neurologic symptoms of SRS-induced RN reflect nearby functional neuroanatomy on fMRI. Dose reduction to functional areas while maintaining target coverage is consistently achievable and may mitigate symptomatic RN. Integrated fMRI-based SRS planning may protect neurologic function when treating tumors near eloquent areas.

The gut microbiota emerged as a potential modulator of brain connectivity in health and disease. This systematic review details current evidence on the gut-brain axis and its influence on brain connectivity. The initial set of studies included 532 papers, updated to January 2024. Studies were selected based on employed techniques. We excluded reviews, studies without connectivity focus, studies on non-human subjects. Forty-nine papers were selected. Employed techniques in healthy subjects included 15 functional magnetic resonance imaging studies (fMRI), 5 diffusion tensor imaging, (DTI) 1 electroencephalography (EEG), 6 structural magnetic resonance imaging, 2 magnetoencephalography, 1 spectroscopy, 2 arterial spin labeling (ASL); in patients 17 fMRI, 6 DTI, 2 EEG, 9 structural MRI, 1 transcranial magnetic stimulation, 1 spectroscopy, 2 R2*MRI. In healthy subjects, the gut microbiota was associated with connectivity of areas implied in cognition, memory, attention and emotions. Among the tested areas, amygdala and temporal cortex showed functional and structural differences based on bacteria abundance, as well as frontal and somatosensory cortices, especially in patients with inflammatory bowel syndrome. Several studies confirmed the connection between microbiota and brain functions in healthy subjects and patients affected by gastrointestinal to renal and psychiatric diseases.

In his book 'A Beautiful Question', physicist Frank Wilczek argues that symmetry is 'nature's deep design,' governing the behavior of the universe, from the smallest particles to the largest structures. While symmetry is a cornerstone of physics, it has not yet been found widespread applicability to describe biological systems, particularly the human brain. In this context, we study the human brain network engaged in language and explore the relationship between the structural connectivity (connectome or structural network) and the emergent synchronization of the mesoscopic regions of interest (functional network). We explain this relationship through a different kind of symmetry than physical symmetry, derived from the categorical notion of Grothendieck fibrations. This introduces a new understanding of the human brain by proposing a local symmetry theory of the connectome, which accounts for how the structure of the brain's network determines its coherent activity. Among the allowed patterns of structural connectivity, synchronization elicits different symmetry subsets according to the functional engagement of the brain. We show that the resting state is a particular realization of the cerebral synchronization pattern characterized by a fibration symmetry that is broken in the transition from rest to language. Our findings suggest that the brain's network symmetry at the local level determines its coherent function, and we can understand this relationship from theoretical principles.

AIMBecause the tongue is a midline structure, studies on the neural correlates of lateralized tongue function are challenging and remain limited. Patients with tongue cancer who undergo unilateral partial glossectomy may be a unique cohort to study tongue-associated cortical activation, particularly regarding brain hemispheric lateralization. This longitudinal functional magnetic resonance imaging (fMRI) study investigated cortical activation changes for three tongue tasks before and after left-sided partial glossectomy in patients with squamous cell carcinoma of the tongue.METHODSSeven patients with squamous cell carcinoma involving the left tongue who underwent fMRI before and 6 months after unilateral partial glossectomy were studied. Post-surgical changes in laterality index (LI) values for tongue-associated precentral and postcentral gyri fMRI activation were calculated for the dry swallow, tongue press, and saliva sucking tasks. Group analysis fMRI activation maps were generated for each of the three tasks.RESULTSThere were significant differences in changes in LI values post-surgery between the tongue press (p < 0.005; median: +0.24), saliva sucking (-0.10), and dry swallow tasks (-0.16). Decreased contralateral activation (change in LI ≥+0.20) was observed post-surgery during tongue press in six of seven patients, but only in two patients during saliva sucking and one patient during dry swallow (p < 0.05). There was also increased activation in the supplementary motor area following surgery.CONCLUSIONPost-surgical fMRI changes following left-sided partial glossectomy may suggest task-specific sensitivities to cortical activation changes following unilateral tongue deficits that may reflect the impacts of surgery and adaptive responses to tongue impairment.

Objective:The goal of this work is to show how to implement a mixed reality application (app) for neurosurgery planning based on neuroimaging data, highlighting the strengths and weaknesses of its design. Methods:Our workflow explains how to handle neuroimaging data, including how to load morphological, functional and diffusion tensor imaging data into a mixed reality environment, thus creating a first guide of this kind. Brain magnetic resonance imaging data from a paediatric patient were acquired using a 3 T Siemens Magnetom Skyra scanner. Initially, this raw data underwent specific software pre-processing and were subsequently transformed to ensure seamless integration with the mixed reality app. After that, we created three-dimensional models of brain structures and the mixed reality environment using Unity™ engine together with Microsoft® HoloLens 2™ device. To get an evaluation of the app we submitted a questionnaire to four neurosurgeons. To collect data concerning the performance of a user session we used Unity Performance Profiler. Results:The use of the interactive features, such as rotating, scaling and moving models and browsing through menus, provided by the app had high scores in the questionnaire, and their use can still be improved as suggested by the performance data collected. The questionnaire's average scores were high, so the overall experiences of using our mixed reality app were positive. Conclusion:We have successfully created a valuable and easy-to-use neuroimaging data mixed reality app, laying the foundation for more future clinical uses, as more models and data derived from various biomedical images can be imported.

Recent insights into histopathological and molecular subgroups of glioma have revolutionized the field of neuro-oncology by refining diagnostic categories. An emblematic example in pediatric neuro-oncology is the newly defined diffuse midline glioma (DMG), H3 K27–altered. DMG represents a rare tumor with a dismal prognosis. The diagnosis of DMG is largely based on clinical presentation and characteristic features on conventional magnetic resonance imaging (MRI), with biopsy limited by its delicate neuroanatomic location. Standard MRI remains limited in its ability to characterize tumor biology. Advanced MRI and positron emission tomography (PET) imaging offer additional value as they enable non-invasive evaluation of molecular and metabolic features of brain tumors. These techniques have been widely used for tumor detection, metabolic characterization and treatment response monitoring of brain tumors. However, their role in the realm of pediatric DMG is nascent. By summarizing DMG metabolic pathways in conjunction with their imaging surrogates, we aim to elucidate the untapped potential of such imaging techniques in this devastating disease.