Abstract Chronic back pain often emerges from a transitional period of subacute pain, yet no clinically applicable biomarker exists to identify which patients are at risk for chronification. Evidence suggests that this transition is driven not only by nociceptive input but by changes in brain networks involved in valuation, emotion regulation, and learning. Here, we used resting-state functional magnetic resonance imaging (rs-fMRI) and machine learning to explore whether dysconnectivity in these networks is associated with later development of chronic back pain. We analyzed functional connectivity in 46 patients with subacute back pain and 43 healthy controls from a publicly available longitudinal cohort, classifying patients one year later as either recovered or chronified based on pain outcomes. A data-driven model identified a set of six brain regions whose patterns of dysconnectivity distinguished the two patient trajectories with an area under the curve of 0.87. These regions encompass prefrontal, temporal, and somatosensory hubs implicated in reinforcement learning, avoidance behavior, and pain catastrophizing, suggesting a potential link between dysconnectivity patterns and psychological processes implicated in pain persistence. Based on these features, we introduced an exploratory rs-fMRI–based marker for pain chronification, suggesting potential prognostic relevance that requires independent validation before clinical stratification or targeted intervention can be considered.

Objective:Low grade glioma (LGG) is a disease associated with survival >10 years in most cases. Some patients, however, do not respond well to treatment and exhibit early progression as well as low overall survival. It is a challenge to identify these at-risk patients. Here, we used resting-state functional MRI (rsfMRI) to identify patients with LGG at risk for poor clinical outcome. Methods:Twenty-five patients with suspected LGG were prospectively enrolled. All patients underwent rsfMRI before any invasive procedure. Patient data was compared to a reference cohort of 1000 healthy controls to determine abnormality of functional connectivity on an individual level, resulting in a relative numerical measure called the dysconnectivity index (DCI). A median split was performed in order to divide the cohort into 2 groups with low and high DCI, respectively. Progression-free survival (PFS) as a primary outcome measure was calculated in both groups. Results:Twelve patients were diagnosed with astrocytoma, IDH-mutated, CNS WHO grade 2, and 13 patients were diagnosed with oligodendroglioma, IDH-mutated, 1p/19q-codeleted, CNS WHO grade 2. Eight patients had tumor progression, and 2 patients died during the observation period. 1/12 patients in the low DCI group and 7/13 patients in the high DCI group had tumor progression, resulting in significantly shorter PFS for patients with high DCI (P  = 0.028). No patient in the low DCI group died while 2 patients in the high DCI group died. Malignant transformation occurred in 4 patients with high DCI and in 0 patients with low DCI. There was no statistically significant difference in age, sex, diagnosis, and RANO resect class between the 2 groups. Conclusion:Greater disturbance of functional connectivity at the time of diagnosis as determined by rsfMRI was associated with shorter PFS in our cohort of patients with LGG. This suggests that rsfMRI might be used to identify LGG patients who are at risk of poor clinical outcome.

Functional magnetic resonance imaging studies have demonstrated that back pain, particularly chronic back pain, is associated with altered functional brain connectivity, especially in regions involved in the modulation of pain and emotion regulation. Our study investigated dysconnectivity patterns in subacute back pain patients to distinguish those who develop chronic back pain from those who recover. This work utilized a publicly available longitudinal resting-state functional magnetic resonance imaging dataset from the OpenPain database, including clinical assessments collected across multiple sessions, and recorded pain scores. The dataset consists of 46 subacute back pain patients and 27 healthy controls. Based on longitudinal pain scores, patients were classified into recovery and chronic back pain groups. In contrast to prior analyses of the OpenPain dataset, which primarily relied on specific brain region's connectivity or predictive modeling, this study applies a voxel-wise dysconnectivity count framework combined with data-driven clustering to identify spatially precise, whole-brain signatures of abnormal functional connectivity. This approach enables detection of anatomically specific dysconnectivity patterns at a resolution not achieved in previous work. Our findings therefore extend the OpenPain literature by providing a fine-grained, network-wide characterization of early connectivity disturbances that distinguish recovery from back pain chronification. The results showed that the chronic back pain group exhibited greater dysconnectivity in regions linked to emotion regulation, pain processing, and attentional control. In contrast, the recovery group displayed connectivity deviations in sensorimotor, visual, and cognitive control regions. PERSPECTIVE: Baseline whole-brain dysconnectivity patterns differ between patients who recover and those who develop chronic pain. These findings highlight distributed network alterations associated with pain trajectories and provide insight into neural mechanisms underlying pain chronification.

Background:Brain tumors, especially glioblastomas, remain among the tumor diseases with the worst prognosis. Recent findings in brain tumor research show that neuronal and glial integration of tumors, as well as the formation of glioma cell networks, promote tumor progression and therapy resistance. This highlights the need for innovative imaging techniques that conceptualize brain tumors as systemic central nervous system (CNS) diseases that are deeply integrated in the brain's network architecture. Materials and Methods:This review presents current imaging methods for analyzing tumor-associated functional and structural connectivity with a focus on resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI). Results:Functional connectivity changes in glioma patients can be detected and quantified using fMRI. These changes are associated with tumor biology, as well as prognosis and cognitive performance. Rs-fMRI parameters may support prognostic assessment and the development of new therapeutic strategies. Quantitative structural connectivity analysis at the individual patient level can provide further insight into tumor integration in the brain's connectional architecture. DTI-based tractography is especially relevant in neurosurgical planning, as it maps the spatial relationship between the tumor and white matter tracts. Conclusion:Imaging analysis of tumor-associated network alterations provides deeper insight into brain tumor biology and may support the development of network-targeted therapeutic approaches. Connectivity-based imaging methods, particularly rs-fMRI and DTI, hold great potential to further enhance preoperative planning, prognostic assessment, and personalized treatment strategies for patients with brain tumors. Key Points:· Glioma cells form networks beyond macroscopic tumor boundaries and promote therapy resistance.. · Glioma cells form synapses with neurons and exploit neural signals for growth.. · Network alterations can be visualized and quantified using rs-fMRI and DTI.. · Tumor-associated network alterations in imaging correlate with tumor biology and prognosis.. · Imaging markers optimize patient management and support development of new therapeutic strategies.. Citation Format:· Suvak S, Wunderlich S, Stoecklein V et al. Imaging of Brain Tumor Connectivity. Rofo 2026; DOI 10.1055/a-2779-7718.

BACKGROUND:The dysconnection hypothesis of schizophrenia posits that widespread synaptic inefficiencies lead to altered macroscale brain connectivity, contributing to symptom severity and cognitive deficits in individuals with schizophrenia spectrum disorders (SSD). Emerging evidence suggests that physical exercise may help to ameliorate these connectivity abnormalities and associated clinical impairments. AIMS:This study investigated whether reductions in functional dysconnectivity following exercise therapy were associated with clinical improvements in individuals with SSD. In addition, it explored the genetic underpinnings of these changes using imaging transcriptomics. METHOD:Using data from the ESPRIT C3 trial, we analysed 23 SSD patients (seven female) undergoing aerobic exercise or flexibility, strengthening and balance training over 6 months. Functional dysconnectivity, assessed at baseline and post-intervention relative to a healthy reference sample (n = 200), was evaluated at the whole-brain, network and regional levels. Linear mixed effect models and voxel-wise Pearson's correlations were used to assess exercise-induced changes and clinical relevance. RESULTS:Functional dysconnectivity significantly decreased (d = -2.73, P < 0.001), and this decrease was primarily linked to enhanced oligodendrocyte-related gene expression. Reductions in the default-mode network were correlated with improved global functioning, whereas changes in insular regions were associated with symptom severity and functioning. Dysconnectivity reductions in somatomotor and frontoparietal networks were correlated with total symptom improvements, and changes in language-related regions (e.g. Broca's area) were linked to cognitive benefits. CONCLUSIONS:Our findings support the role of oligodendrocyte pathology in SSD and suggest that targeting dysconnectivity in the default-mode, salience and language networks may enhance global functioning, symptom severity and cognitive impairments.

Functional connectivity magnetic resonance imaging (fcMRI) is a well-established technique for studying brain networks in both healthy and diseased individuals. However, no fcMRI-based biomarker has yet achieved clinical relevance. To establish better understanding of the state of the art in quantifying abnormal connectivity in comparison to a reference distribution, for potential use in individual patients, we have conducted a scoping review over 5672 entries from the last 10 years. We have located five publications proposing metrics of abnormal connectivity quantification, reported these metrics, formalized their computing methods, assessed their technology readiness and estimated their computational efficiency. Building upon our findings, we have discussed the metrics' lesion data handling, region of interest level of detail and potential clinical use cases. We also proposed methodical and computational strategies for improvement of current and emerging abnormality quantification metrics in fcMRI research.

BACKGROUND:Functional connectivity in the context of functional magnetic resonance imaging is typically quantified by Pearson´s or partial correlation between regional time series of the blood oxygenation level dependent signal. However, a recent interdisciplinary methodological work proposes >230 different metrics to measure similarity between different types of time series. OBJECTIVE:Hence, we systematically evaluated how the results of typical research approaches in functional neuroimaging vary depending on the functional connectivity metric of choice. We further explored which metrics most accurately detect presumed reductions in connectivity related to age and malignant brain tumors, aiming to initiate a debate on the best approaches for assessing brain connectivity in functional neuroimaging research. METHODS:We addressed both research questions using four independent neuroimaging datasets, comprising multimodal data from a total of 1187 individuals. We analyzed resting-state functional sequences to calculate functional connectivity using 20 representative metrics from four distinct mathematical domains. We further used T1- and T2-weighted images to compute regional brain volumes, diffusion-weighted imaging data to build structural connectomes, and pseudo-continuous arterial spin labeling to measure regional brain perfusion. RESULTS:First, our findings demonstrate that the results of typical functional neuroimaging approaches differ fundamentally depending on the functional connectivity metric of choice. Second, we show that correlational and distance metrics are most appropriate to cover reductions in connectivity linked to aging. In this context, partial correlation performs worse than other correlational metrics. Third, our findings suggest that the FC metric of choice depends on the utilized scanning parameters, the regions of interest, and the individual investigated. Lastly, beyond the major objective of this study, we provide evidence in favor of brain perfusion measured via pseudo-continuous arterial spin labeling as a robust neural entity mirroring age-related neural and cognitive decline. CONCLUSION:Our empirical evaluation supports a recent theoretical functional connectivity framework. Future functional imaging studies need to comprehensively define the study-specific theoretical property of interest, the methodological property to assess the theoretical property, and the confounding property that may bias the conclusions.

Functional connectivity magnetic resonance imaging (fcMRI) is a widely utilized tool for analyzing functional connectivity (FC) in both healthy and diseased brains. However, patients with brain disorders are particularly susceptible to head movement during scanning, which can introduce substantial noise and compromise data quality. Therefore, identifying optimal denoising strategies is essential to ensure reliable and accurate downstream data analysis for both lesional and non-lesional brain conditions. In this study, we analyzed data from four cohorts: healthy subjects, patients with brain lesions (glioma, meningioma), and patients with a non-lesional encephalopathic condition. Our goal was to evaluate various denoising strategies using quality control (QC) metrics to identify the most effective approach for minimizing noise while preserving the integrity of the blood oxygen level-dependent (BOLD) signal, tailored to each disease type. The effectiveness of denoising strategies varied based on the data quality and whether the data were derived from lesional or non-lesional diseases. At comparable levels of head motion, combinations involving independent component analysis-based automatic removal of motion artifacts (ICA-AROMA) denoising strategies were most effective for data from a non-lesional encephalopathic condition, while combinations including anatomical component correction (CC) yielded the best results for data from lesional conditions. Here, we present the first comparison of denoising pipelines for patients with lesional and non-lesional brain diseases. A key finding was that, at comparable levels of head motion, the optimal denoising strategy varies depending on the nature of the brain disease.

ABSTRACTBackgroundAs a condition of dysconnectivity, schizophrenia spectrum disorders (SSD) are characterized by positive, negative, and cognitive symptoms. To improve these symptoms in SSD, physical exercise interventions show promise. We examined if reductions of functional dysconnectivity following exercise therapy are associated with clinical improvements in SSD and explored potential genetic underpinnings.MethodsThe study utilized data from the ESPRIT C3 trial, investigating the effects of aerobic exercise versus flexibility, strengthening, and balance training on different health outcomes in individuals with SSD. Functional dysconnectivity in 23 patients relative to a healthy reference sample, was assessed both pre- and post-intervention. Changes of functional dysconnectivity after exercise and their clinical relevance were evaluated. An imaging transcriptomics approach was used to study the link between changes in functional dysconnectivity and gene expression profiles.ResultsWe observed substantial reductions of functional dysconnectivity on the whole-brain level linked to enhanced gene expression mainly in oligodendrocytes. With regard to the clinical implications, decreases of dysconnectivity in the default-mode network were associated with improvements in global functioning. Reductions of dysconnectivity within the salience network were linked to improvements in symptom severity. Lastly, reductions of functional dysconnectivity in language regions such as Broca’s area were related to cognitive benefits.ConclusionsOur study supports a recent theory of oligodendrocyte pathology in SSD and suggests that reducing functional dysconnectivity in the default-mode, salience, and language network reflect a potential therapeutic target to improve global functioning, total symptom severity, and cognitive impairments in post-acute SSD.Trial name: ESPRIT C3Registry: International Clinical Trials Database,ClinicalTrials.govRegistration number:NCT03466112URL:https://clinicaltrials.gov/ct2/show/NCT03466112?term=NCT03466112&draw=2&rank=1

Cognitive deficits are a core symptom of schizophrenia, but research on their neural underpinnings has been challenged by the heterogeneity in deficits' severity among patients. Here, we address this issue by combining logistic regression and random forest to classify two neuropsychological profiles of patients with high (HighCog) and low (LowCog) cognitive performance in two independent samples. We based our analysis on the cortical features grey matter volume (VOL), cortical thickness (CT), and mean curvature (MC) of N = 57 patients (discovery sample) and validated the classification in an independent sample (N N = 52). We investigated which cortical feature would yield the best classification results and expected that the 10 most important features would include frontal and temporal brain regions. The model based on MC had the best performance with area under the curve (AUC) values of 76% and 73%, and identified frontotemporal and occipital brain regions as the most important features for the classification. Moreover, subsequent comparison analyses could reveal significant differences in MC of single brain regions between the two cognitive profiles. The present study suggests MC as a promising neuroanatomical parameter for characterizing schizophrenia cognitive subtypes.

BACKGROUND AND PURPOSE: Meningiomas are intracranial tumors that usually carry a benign prognosis. Some meningiomas cause perifocal edema. Resting-state fMRI can be used to assess whole-brain functional connectivity, which can serve as a marker for disease severity. Here, we investigated whether the presence of perifocal edema in preoperative patients with meningiomas leads to impaired functional connectivity and if these changes are associated with cognitive function. MATERIALS AND METHODS: Patients with suspected meningiomas were prospectively included, and resting-state fMRI scans were obtained. Impairment of functional connectivity was quantified on a whole-brain level using our recently published resting-state fMRI–based marker, called the dysconnectivity index. Using uni- and multivariate regression models, we investigated the association of the dysconnectivity index with edema and tumor volume as well as cognitive test scores. RESULTS: Twenty-nine patients were included. In a multivariate regression analysis, there was a highly significant association of dysconnectivity index values and edema volume in the total sample and in a subsample of 14 patients with edema, when accounting for potential confounders like age and temporal SNR. There was no statistically significant association with tumor volume. Better neurocognitive performance was strongly associated with lower dysconnectivity index values. CONCLUSIONS: Resting-state fMRI showed a significant association between impaired functional connectivity and perifocal edema, but not tumor volume, in patients with meningiomas. We demonstrated that better neurocognitive function was associated with less impairment of functional connectivity. This result shows that our resting-state fMRI marker indicates a detrimental influence of peritumoral brain edema on global functional connectivity in patients with meningiomas.

Background Treatment of hematological malignancies with chimeric antigen receptor modified T cells (CART) is highly efficient, but often limited by an immune effector cell-associated neurotoxicity syndrome (ICANS). As conventional MRI is often unremarkable during ICANS, we aimed to examine whether resting-state functional MRI (rsfMRI) is suitable to depict and quantify brain network alterations underlying ICANS in the individual patient.Methods The dysconnectivity index (DCI) based on rsfMRI was longitudinally assessed in systemic lymphoma patients and 1 melanoma patient during ICANS and before or after clinical resolution of ICANS.Results Seven lymphoma patients and 1 melanoma patient (19-77 years; 2 female) were included. DCI was significantly increased during ICANS with normalization after recovery (P = .0039). Higher ICANS grades were significantly correlated with increased DCI scores (r = 0.7807; P = .0222). DCI increase was most prominent in the inferior frontal gyrus and the frontal operculum (ie, Broca's area) and in the posterior parts of the superior temporal gyrus and the temporoparietal junction (ie, Wernicke's area) of the language-dominant hemisphere, thus reflecting the major clinical symptoms of nonfluent dysphasia and dyspraxia.Conclusions RsfMRI-based DCI might be suitable to directly quantify the severity of ICANS in individual patients undergoing CAR T-transfusion. Besides ICANS, DCI seems a promising diagnostic tool to quantify functional brain network alterations during encephalopathies of different etiologies, in general.

Abstract BACKGROUND Meningiomas are common intracranial tumors which usually carry a benign prognosis. Some meningiomas cause perifocal edema which might indicate that this subset could interfere with normal brain function. Resting-state functional MRI (rsfMRI) can be used to assess whole brain functional connectivity (fc) which can be used as a marker for disease severity in patients with intracranial tumors, as was recently shown by our group in a cohort of glioma patients. In this study, we investigated whether the presence of perifocal edema in preoperative patients with meningioma leads to fc. METHODS Patients with suspected meningioma were prospectively included and functional resting state MRI scans were obtained. The resulting data was processed according to our recently published method and abnormality of fc was quantified for each individual patient. Abnormality of fc was then correlated with tumor and edema volume as well as WHO grade. RESULTS 26 patients (23 WHO grade I, 3 WHO grade II) were included. 13 patients had perifocal edema. There was a highly significant correlation between edema volume and higher abnormality of fc both in the lesional and the contra-lesional hemisphere (r=0.51, p=0.008 and r=0.61, p=0.001). Patients with no perifocal edema showed only very low abnormality of fc. Tumor volume was not correlated with abnormal fc in both the lesional and the contralesional hemispheres (r=0.23, p=0.27 and r=0.28, p=0.17). There was also no significant correlation between WHO grade and abnormality of fc. CONCLUSION RsfMRI showed significant abnormal fc in meningioma patients with perifocal edema in contrast to patients without edema, independent of tumor volume. This demonstrates that the presence of edema but not the tumor volume is relevant for disturbances of fc.

The retinal and cerebral microvasculatures share many morphological and physiological properties. In this pilot we study the strength of the associations between morphological measurements of the retinal vasculature, obtained from fundus camera images, and of features of Small Vessel Disease (SVD), as white matter hyperintensities (WMH) and perivascular spaces (PVS), obtained from MRI brain scans. We performed a 500-trial bootstrap analysis with Regularized Gaussian linear regression on a cohort of older community-dwelling subjects (Lothian Birth Cohort 1936, N = 866) in their eighth decade. Arteriolar bifurcation coefficients, vessel tortuosity and fractal dimension predicted WMH volume in 23% of the trials. Arteriolar widths, venular bifurcation coefficients, and venular tortuosity predicted PVS in up to 99.6% of the trials.

Functional connectivity (FC) is known to be individually unique and to reflect cognitive variability. Although FC can serve as a valuable correlate and potential predictor of (patho-) physiological nervous function in high-risk constellations, such as preterm birth, templates for individualized FC analysis are lacking, and knowledge about the capacity of the premature brain to develop FC variability is limited. In a cohort of prospectively recruited, preterm-born infants undergoing magnetic resonance imaging close to term equivalent age, we show that the overall pattern could be reliably detected with a broad range of interindividual FC variability in regions of higher-order cognitive functions (e.g., association cortices) and less interindividual variability in unimodal regions (e.g., visual and motor cortices). However, when comparing the preterm and adult brains, some brain regions showed a marked shift in variability toward adulthood. This shift toward greater variability was strongest in cognitive networks like the attention and frontoparietal networks and could be partially predicted by developmental cortical expansion. Furthermore, FC variability was reflected by brain tissue characteristics indicating cortical maturation. Brain regions with high functional variability (e.g., the inferior frontal gyrus and temporoparietal junction) displayed lower cortical maturation at birth compared with somatosensory cortices. In conclusion, the overall pattern of interindividual variability in FC is already present preterm; however, some brain regions show increased variability toward adulthood, identifying characteristic patterns, such as in cognitive networks. These changes are related to postnatal cortical expansion and maturation, allowing for environmental and developmental factors to translate into marked individual differences in FC.