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Previous studies have adopted an edge-centric framework to study fine-scale dynamics in human fMRI. To date, however, no studies have applied this same framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of “bursty” dynamics and events – brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.

Purpose: To analyze the difference in the prediction accuracy with an active surveillance (AS) protocol between two eras (pre-International Society of Urological Pathology [pre-ISUP]-2014 vs. post-ISUP2014). Materials and Methods:Materials and Methods: We retrospectively analyzed 118 candidates for AS who underwent radical prostatectomy between 2009 and 2017. We divided our patients into two groups (group 1 [n=57], operation date 2009–2015; group 2 [n=61], op-eration date 2016–2017). Pathologic slides in group 1 were reviewed to distinguish men with cribriform pattern (CP) because the determination of Gleason scores in old era had been based on pre-ISUP2014 classification. Postoperative outcomes in the two eras were analyzed twice: first, all men in group 1 vs. group 2; second, the remaining men after excluding those with CPs in group 1 vs. group 2. Results:Results: The proportion of men with insignificant prostate cancer (iPCa) was significantly lower in group 1 than in group 2 (36.8% vs. 57.4%, p=0.040). After excluding 11 men with CPs from group 1, those remaining (46 men) were compared again with group 2. In this analysis, the proportion of men with iPCa was similar between the two groups (old vs. contempo-rary era: 41.3% vs. 57.4%, p=0.146). Nine of 11 men with CP had violated the criteria for iPCa in the earlier comparison. Conclusions:Conclusions: The accuracy of the AS protocol has been affected by the coexistence of CPs and pure Gleason 6 tumors in the pre-ISUP2014 era. We suggest to use only contemporary (post-ISUP2014) data to analyze the accuracy with AS protocols in future studies.

IgA vasculitis, formerly known as Henoch-Schönlein purpura, is a systemic IgA-mediated vasculitis of the small vessels commonly seen in children. The natural history of IgA vasculitis is generally self-limiting; however, one-third of patients experience symptom recurrence and a refractory course. This systematic review examined the use of dapsone in refractory IgA vasculitis cases. A literature search of PubMed databases retrieved 13 articles published until June 14, 2018. The most common clinical feature was a palpable rash (100% of patients), followed by joint pain (69.2%). Treatment response within 1–2 days was observed in 6 of 26 patients (23.1%) versus within 3–7 days in 17 patients (65.4%). Relapse after treatment discontinuation was reported in 17 patients (65.4%) but not in 3 patients (11.5 %). Four of the 26 patients (15.4%) reported adverse effects of dapsone including arthralgia (7.7%), rash (7.7%), and dapsone hypersensitivity syndrome (3.8%). Our findings suggest that dapsone may affect refractory IgA vasculitis. Multicenter randomized placebo-controlled trials are necessary to determine the standard dosage of dapsone at initial or tapering of treatment in IgA vasculitis patients and evaluate whether dapsone has a significant benefit versus steroids or other medications.

BACKGROUND AND PURPOSE: This work investigated alterations in functional connectivity (FC) and associated structures in patients with Angelman syndrome (AS) by using integrated quantitative imaging analysis and connectivity measures. MATERIALS AND METHODS: We obtained 3T brain MR imaging, including resting-state functional MR imaging, diffusion tensor imaging, and 3D T1-weighted imaging from children with AS (n = 14) and age- and sex-matched controls (n = 28). The brains of patients with AS were analyzed by measuring FC, white matter microstructural analysis, cortical thickness, and brain volumes; these were compared with brains of controls. RESULTS: Interregional FC analysis revealed significantly reduced intra- and interhemispheric FC, especially in the basal ganglia and thalamus, in patients with AS. Significant reductions in fractional anisotropy were found in the corpus callosum, cingulum, posterior limb of the internal capsules, and arcuate fasciculus in patients with AS. Quantitative structural analysis also showed gray matter volume loss of the basal ganglia and diffuse WM volume reduction in AS compared with the control group. CONCLUSIONS: This integrated quantitative MR imaging analysis demonstrated poor functional and structural connectivity, as well as brain volume reduction, in children with AS, which may explain the motor and language dysfunction observed in this well-characterized neurobehavioral phenotype.

We aimed to develop and validate a multiparametric MR radiomics model using conventional, diffusion-, and perfusion-weighted MR imaging for better prognostication in patients with newly diagnosed glioblastoma. A total of 216 patients with newly diagnosed glioblastoma were enrolled from two tertiary medical centers and divided into training (n = 158) and external validation sets (n = 58). Radiomic features were extracted from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. After radiomic feature selection using LASSO regression, an individualized radiomic score was calculated. A multiparametric MR prognostic model was built using the radiomic score and clinical predictors. The results showed that the multiparametric MR prognostic model (radiomics score + clinical predictors) exhibited good discrimination (C-index, 0.74) and performed better than a conventional MR radiomics model (C-index, 0.65, P P

The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and development. There exist many data-driven methods for detecting and comparing modular structure, the most popular of which is modularity maximization. Although modularity maximization is a general framework that can be modified and reparamaterized to address domain-specific research questions, its application to neuroscientific datasets has, thus far, been narrow. Here, we highlight several strategies in which the ``out-of-the-box'' version of modularity maximization can be extended to address questions specific to neuroscience. First, we present approaches for detecting ``space-independent'' modules and for applying modularity maximization to signed matrices. Next, we show that the modularity maximization frame is well-suited for detecting task- and condition-specific modules. Finally, we highlight the role of multi-layer models in detecting and tracking modules across time, tasks, subjects, and modalities. In summary, modularity maximization is a flexible and general framework that can be adapted to detect modular structure resulting from a wide range of hypotheses. This article highlights opens multiple frontiers for future research and applications., 18 pages, 4 figures

Both cortical and subcortical regions can be functionally organized into networks. Regions of the basal ganglia are extensively interconnected with the cortex via reciprocal connections that relay and modulate cortical function. Here we employ an edge-centric approach, which computes co-fluctuations among region pairs in a network to investigate the role and interaction of subcortical regions with cortical systems. By clustering edges into communities, we show that cortical systems and subcortical regions couple via multiple edge communities, with hippocampus and amygdala having a distinct pattern from striatum and thalamus. We show that the edge community structure of cortical networks is highly similar to one obtained from cortical nodes when the subcortex is present in the network. Additionally, we show that the edge community profile of both cortical and subcortical nodes can be estimates solely from cortico-subcortical interactions. Finally, we used a motif analysis focusing on edge community triads where a subcortical region coupled to two cortical regions and found that two community triads where one community couples the subcortex to the cortex were overrepresented. In summary, our results show organized coupling of the subcortex to the cortex that may play a role in cortical organization of primary sensorimotor/attention and heteromodal systems and puts forth the motif analysis of edge community triads as a promising method for investigation of communication patterns in networks.

Primary progressive apraxia of speech (PPAOS), a rare neurodedegenerative disorder, can be subdivided into predominant phonetic or prosodic type. Pure prosodic type of PPAOS as an isolated disorder has been hardly found. We present 2 cases of patients with pure prosodic PPAOS who initially were misdiagnosed as nonfluent variant of primary progressive aphasia and later turned out to be corticobasal syndrome. A 65-year-old woman and a 72-year-old man were referred to our speech-language clinic under the clinical impression of nonfluent variant of primary progressive aphasia. The neurological examinations revealed no definite abnormalities except for slow and effortful speech with the production of simple sentences. However, their receptive and expressive language abilities were normal. Their brain magnetic resonance imaging was unremarkable. We initially entertained the diagnosis of pure prosodic type of PPAOS. During several years of follow up, they gradually developed extrapyramidal symptoms which are compatible with corticobasal syndrome. The characteristics of the patients and the results of neuroimaging studies are discussed.

To investigate whether clinical condition, imaging session, and locations affect repeatability of amide proton transfer–weighted (APTw) magnetic resonance imaging (MRI) in the brain. Three APTw MRI data sets were acquired, involving two intrasession scans and one intersession scan for 19 healthy, 15 glioma, and 12 acute stroke adult participants (mean age 53.8, 54.6, and 68.5, respectively) on a 3T MR scanner. The mean APTw signals from five locations in healthy brain (supratentorial and infratentorial locations) and from entire tumor and stroke lesions (supratentorial location) were calculated. The within-subject coefficient of variation (wCV) and intraclass correlation coefficient (ICC) were calculated for each clinical conditions, image sessions, and anatomic locations. Differences in APTw signals between sessions were analyzed using repeated-measures analysis of variance. The ICC and wCV were 0.96 (95% confidence interval [CI], 0.91–0.99) and 16.1 (12.6–21.3) in glioma, 0.93 (0.82–0.98) and 15.0 (11.4–20.6) in stroke, and 0.84 (0.72–0.91) and 34.0 (28.7–41.0) in healthy brain. There were no significant differences in APTw signal between three sessions, irrespective of disease condition and location. The ICC and wCV were 0.85 (0.68–0.94) and 27.4 (21.8–35.6) in supratentorial, and 0.44 (− 0.18 to 0.76) and 32.7 (25.9 to 42.9) in infratentorial locations. There were significant differences in APTw signal between supra- (mean, 0.49%; 95% CI, 0.38–0.61) and infratentorial locations (1.09%, 0.98–1.20; p

Significance Despite widespread applications, the origins of functional connectivity remain elusive. Here we analyze human functional neuroimaging data. We decompose resting-state functional connectivity across time to assess the contributions of moment-to-moment activity cofluctuations to the overall connectivity pattern. We show that functional connectivity is driven by a small number of high-amplitude frames. We show that these frames are underpinned by a specific mode of brain activity; that the topography of this mode gets modulated during in-scanner tasks; and that high-amplitude frames encode personalized, subject-specific information. In summary, our parameter-free method provides an exact mathematical link between functional connectivity and frame-wise cofluctuations, creating opportunities for studying both static and time-varying functional brain networks., Resting-state functional connectivity is used throughout neuroscience to study brain organization and to generate biomarkers of development, disease, and cognition. The processes that give rise to correlated activity are, however, poorly understood. Here we decompose resting-state functional connectivity using a temporal unwrapping procedure to assess the contributions of moment-to-moment activity cofluctuations to the overall connectivity pattern. This approach temporally resolves functional connectivity at a timescale of single frames, which enables us to make direct comparisons of cofluctuations of network organization with fluctuations in the blood oxygen level-dependent (BOLD) time series. We show that surprisingly, only a small fraction of frames exhibiting the strongest cofluctuation amplitude are required to explain a significant fraction of variance in the overall pattern of connection weights as well as the network’s modular structure. These frames coincide with frames of high BOLD activity amplitude, corresponding to activity patterns that are remarkably consistent across individuals and identify fluctuations in default mode and control network activity as the primary driver of resting-state functional connectivity. Finally, we demonstrate that cofluctuation amplitude synchronizes across subjects during movie watching and that high-amplitude frames carry detailed information about individual subjects (whereas low-amplitude frames carry little). Our approach reveals fine-scale temporal structure of resting-state functional connectivity and discloses that frame-wise contributions vary across time. These observations illuminate the relation of brain activity to functional connectivity and open a number of directions for future research.

Malformations of cortical development (MCD) is associated with a wide range of developmental delay and drug resistant epilepsy in children. By using resting-state functional magnetic resonance imaging (RS-fMRI) and event-related spectral perturbation (ERSP) of cortical electroencephalography (EEG) data, we tried to investigate the neural changes of spatiotemporal functional connectivity (FC) and fast oscillation (FO) dynamics in a rat model of methylazoxymethanol (MAM)-induced MCD. A total of 28 infant rats with prenatal exposure to MAM and those of age matched 28 controls with prenatal saline exposure were used. RS-fMRI were acquired at postnatal day 15 (P15) and 29 (P29), and correlation coefficient analysis of eleven region of interests (ROI) was done to find the differences of functional networks between four groups. Two hour-cortical EEGs were also recorded at P15 and P29 and the ERSP of gamma (30–80 Hz) and ripples (80–200 Hz) were analyzed. The rats with MCD showed significantly delayed development of superior colliculus-brainstem network compared to control rats at P15. In contrast to marked maturation of default mode network (DMN) in controls from P15 to P29, there was no clear development in MCD rats. The MCD rats showed significantly higher cortical gamma and ripples-ERSP at P15 and lower cortical ripples-ERSP at P29 than those of control rats. This study demonstrated delayed development of FC and altered cortical FO dynamics in rats with malformed brain. The results should be further investigated in terms of the epileptogenesis and cognitive dysfunction in patients with MCD.

The human brain is composed of regions that can be grouped into functionally specialized systems. These systems transiently couple and decouple across time to support complex cognitive processes. Recently, we proposed an edge-centric model of brain networks whose elements can be clustered to reveal communities of connections whose co-fluctuations are correlated across time. It remains unclear, however, how these co-fluctuation patterns relate to traditionally-defined brain systems. Here, we address this question using data from the Midnight Scan Club. We show that edge communities transcend traditional definitions of brain systems, forming a multiplexed network in which all pairs of brain systems are linked to one another by at least two distinct edge communities. Mapping edge communities back to individual brain regions and deriving a novel distance metric to describe the similarity of regions’ “edge community profiles”, we then demonstrate that the within-system similarity of profiles is heterogeneous across systems. Specifically, we find that heteromodal association areas exhibit significantly greater diversity of edge communities than primary sensory systems. Next, we cluster the entire cerebral cortex according to the similarity of regions’ edge community profiles, revealing systematic differences between traditionally-defined systems and the detected clusters. Specifically, we find that regions in heteromodal systems exhibit dissimilar edge community profiles and are more likely to form their own clusters. Finally, we show show that edge communities are highly personalized and can be used to identify individual subjects. Collectively, our work reveals the pervasive overlap of edge communities across the cerebral cortex and characterizes their relationship with the brain’s system level architecture. Our work provides clear pathways for future research using edge-centric brain networks to investigate individual differences in behavior, development, and disease.

Purpose To analyze the difference in the prediction accuracy with an active surveillance (AS) protocol between two eras (pre-International Society of Urological Pathology [pre-ISUP]-2014 vs. post-ISUP2014). Materials and Methods We retrospectively analyzed 118 candidates for AS who underwent radical prostatectomy between 2009 and 2017. We divided our patients into two groups (group 1 [n=57], operation date 2009–2015; group 2 [n=61], operation date 2016–2017). Pathologic slides in group 1 were reviewed to distinguish men with cribriform pattern (CP) because the determination of Gleason scores in old era had been based on pre-ISUP2014 classification. Postoperative outcomes in the two eras were analyzed twice: first, all men in group 1 vs. group 2; second, the remaining men after excluding those with CPs in group 1 vs. group 2. Results The proportion of men with insignificant prostate cancer (iPCa) was significantly lower in group 1 than in group 2 (36.8% vs. 57.4%, p=0.040). After excluding 11 men with CPs from group 1, those remaining (46 men) were compared again with group 2. In this analysis, the proportion of men with iPCa was similar between the two groups (old vs. contemporary era: 41.3% vs. 57.4%, p=0.146). Nine of 11 men with CP had violated the criteria for iPCa in the earlier comparison. Conclusions The accuracy of the AS protocol has been affected by the coexistence of CPs and pure Gleason 6 tumors in the pre-ISUP2014 era. We suggest to use only contemporary (post-ISUP2014) data to analyze the accuracy with AS protocols in future studies.

The human brain is composed of regions that can be grouped into functionally specialized systems. These systems transiently couple and decouple across time to support complex cognitive processes. Recently, we proposed an edge-centric model of brain networks whose elements can be clustered to reveal communities of connections whose co-fluctuations are correlated across time. It remains unclear, however, how these co-fluctuation patterns relate to traditionally-defined brain systems. Here, we address this question using data from the Midnight Scan Club. We show that edge communities transcend traditional definitions of brain systems, forming a multiplexed network in which all pairs of brain systems are linked to one another by at least two distinct edge communities. Mapping edge communities back to individual brain regions and deriving a novel distance metric to describe the similarity of regions' "edge community profiles'', we then demonstrate that the within-system similarity of profiles is heterogeneous across systems. Specifically, we find that heteromodal association areas exhibit significantly greater diversity of edge communities than primary sensory systems. Next, we cluster the entire cerebral cortex according to the similarity of regions' edge community profiles, revealing systematic differences between traditionally-defined systems and the detected clusters. Specifically, we find that regions in heteromodal systems exhibit dissimilar edge community profiles and are more likely to form their own clusters. Finally, we show show that edge communities are highly personalized and can be used to identify individual subjects. Collectively, our work reveals the pervasive overlap of edge communities across the cerebral cortex and characterizes their relationship with the brain's system level architecture. Our work provides clear pathways for future research using edge-centric brain networks to investigate individual differences in behavior, development, and disease.

Background Next-generation sequencing (NGS) enables highly sensitive cancer genomics analysis, but its clinical implications for therapeutic options from imaging-based prediction have been limited. Purpose To predict core signaling pathways in isocitrate dehydrogenase (IDH) wild-type glioblastoma by using diffusion and perfusion MRI radiomics and NGS. Materials and Methods The radiogenomics model was developed by using retrospective patients with glioma who underwent NGS and anatomic, diffusion-, and perfusion-weighted imaging between March 2017 and February 2019. For testing model performance in predicting core signaling pathway, patients with IDH wild-type glioblastoma from a retrospective analysis from a registry (

BACKGROUND: Pseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma. METHODS: The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23). RESULTS: Twelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57–0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness. CONCLUSIONS: Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.

Background The bispectral index (BIS) is a valuable indicator for measuring sedation levels and patient consciousness. Recent reports have highlighted its clinical value as an indicator for anesthesia-related cerebral hypoperfusion and ischemic brain damage. Case Report A 55-year-old female patient underwent right breast conservation surgery during general anesthesia. During surgery, the patient experienced abrupt bradycardia (heart rate of 36 bpm) without hypotension. During bradycardia, her BIS was severely reduced from 45 to 20 along with elvated suppression ratio (50). After injection of 0.5mg of atropine, her BIS level was recovered, her heart rate was increased, and her suppression ratio was decreased. Conclusions The patient recovered from anesthesia without showing any signs of neurological sequelae based on BIS level monitoring.

Resting-state functional connectivity is used throughout neuroscience to study brain organization and to generate biomarkers of development, disease, and cognition. The processes that give rise to correlated activity are, however, poorly understood. Here, we decompose resting-state functional connectivity using a “temporal unwrapping” procedure to assess the contributions of moment-to-moment activity co-fluctuations to the overall connectivity pattern. This approach temporally resolves functional connectivity at a timescale of single frames, which enables us to make direct comparisons of co-fluctuations of network organization with fluctuations in the BOLD time series. We show that, surprisingly, only a small fraction of frames exhibiting the strongest co-fluctuation amplitude are required to explain a significant fraction of variance in the overall pattern of connection weights as well as the network’s modular structure. These frames coincide with frames of high BOLD activity amplitude, corresponding to activity patterns that are remarkably consistent across individuals and identify fluctuations in default mode and control network activity as the primary driver of resting-state functional connectivity. Finally, we demonstrate that co-fluctuation amplitude synchronizes across subjects during movie-watching and that high-amplitude frames carry detailed information about individual subjects (whereas low-amplitude frames carry little). Our approach reveals fine-scale temporal structure of resting-state functional connectivity, and discloses that frame-wise contributions vary across time. These observations illuminate the relation of brain activity to functional connectivity and open a number of new directions for future research.

Network neuroscience has relied on a node-centric network model in which cells, populations and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. In this study, we developed an edge-centric network model that generates constructs ‘edge time series’ and ‘edge functional connectivity’ (eFC). Using network analysis, we show that, at rest, eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We show that eFC is systematically modulated by variation in sensory input. In future work, the edge-centric approach could be useful for identifying novel biomarkers of disease, characterizing individual variation and mapping the architecture of highly resolved neural circuits. The authors present an edge-centric model of brain connectivity. Edge networks are stable across datasets, and their structure can be modulated by sensory input. When clustered, edge networks yield pervasively overlapping functional modules.

A combination of T2/FLAIR mismatch sign and advanced imaging parameters may improve the determination of molecular subtypes of diffuse lower-grade glioma. We assessed the diagnostic value of adding the apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) to the T2/FLAIR mismatch sign for differentiation of the IDH mutation or 1p/19q codeletion. Preoperative conventional, diffusion-weighted, and dynamic susceptibility contrast imaging were performed on 110 patients with diffuse lower-grade gliomas. The study population was classified into three groups using molecular subtype, namely IDH mutation and 1p/19q codeletion (IDHmut-Codel), IDH wild type (IDHwt) and IDH mutation and no 1p/19q codeletion (IDHmut-Noncodel). T2/FLAIR mismatch sign and the histogram parameters of apparent diffusion coefficient (ADC) and normalised cerebral blood volume (nCBV) values were assessed. A multivariate logistic regression model was constructed to distinguish IDHmut-Noncodel from IDHmut-Codel and IDHwt and from IDHwt, and the performance was compared with that of single parameters using the area under the receiver operating characteristics curve (AUC). Positive visual T2/FLAIR mismatch sign and higher nCBV skewness were significant variables to distinguish IDHmut-Noncodel from the other two groups (AUC, 0.88; 95% CI, 0.81–0.96). A lower ADC10 was a significant variable for distinguishing IDHmut-Noncodel from the IDHwt group (AUC, 0.75; 95% CI, 0.62–0.89). Adding ADC or CBV histogram parameters to T2/FLAIR mismatch sign improved performance in distinguishing IDHmut-Noncodel from the other two groups (AUC 0.882 vs. AUC 0.810) or from IDHwt (AUC 0.923 vs. AUC 0.868). The combination of the T2/FLAIR mismatch sign with ADC or CBV histogram parameters can improve the identification of IDHmut-Noncodel diffuse lower-grade gliomas, which can be easily applied in clinical practice. • The combination of the T2/FLAIR mismatch sign with the ADC or CBV histogram parameters can improve the identification of IDHmut-Noncodel diffuse lower-grade gliomas. • The multivariable model showed a significantly better performance for distinguishing the IDHmut-Noncodel group from other diffuse lower-grade gliomas than the T2/FLAIR mismatch sign alone or any single parameter. • The IDHmut-Noncodel type was associated with intermediate treatment outcomes; therefore, the identification of IDHmut-Noncodel diffuse lower-grade gliomas could be helpful for determining the clinical approach.