Altered static and dynamic cerebellar-cerebral functional connectivity in acute pontine infarction.

This study investigates abnormalities in cerebellar-cerebral static and dynamic functional connectivity among patients with acute pontine infarction, examining the relationship between these connectivity changes and behavioral dysfunction. Resting-state functional magnetic resonance imaging was utilized to collect data from 45 patients within seven days post-pontine infarction and 34 normal controls. Seed-based static and dynamic functional connectivity analyses identified divergences in cerebellar-cerebral connectivity features between pontine infarction patients and normal controls. Correlations between abnormal functional connectivity features and behavioral scores were explored. Compared to normal controls, left pontine infarction patients exhibited significantly increased static functional connectivity within the executive, affective-limbic, and motor networks. Conversely, right pontine infarction patients demonstrated decreased static functional connectivity in the executive, affective-limbic, and default mode networks, alongside an increase in the executive and motor networks. Decreased temporal variability of dynamic functional connectivity was observed in the executive and default mode networks among left pontine infarction patients. Furthermore, abnormalities in static and dynamic functional connectivity within the executive network correlated with motor and working memory performance in patients. These findings suggest that alterations in cerebellar-cerebral static and dynamic functional connectivity could underpin the behavioral dysfunctions observed in acute pontine infarction patients.

Novel, pathogenic insertion variant of GSDME associates with autosomal dominant hearing loss in a large Chinese pedigree.

Nonsyndromic hearing loss (NSHL) is a genetically diverse, highly heterogeneous condition characterised by deafness, and Gasdermin E (GSDME) variants have been identified as directly inducing autosomal dominant NSHL. While many NSHL cases associated with GSDME involve the skipping of exon 8, there is another, less understood pathogenic insertion variant specifically found in Chinese pedigrees that causes deafness, known as autosomal dominant 5 (DFNA5) hearing loss. In this study, we recruited a large Chinese pedigree, conducted whole-exome and Sanger sequencing to serve as a comprehensive clinical examination, and extracted genomic DNA samples for co-segregation analysis of the members. Conservation and expression analyses for GSDME were also conducted. Our clinical examinations revealed an autosomal dominant phenotype of hearing loss in the family. Genetic analysis identified a novel insertion variant in GSDME exon 8 (GSDME: NM_004403.3: c.1113_1114insGGGGTGCAGCTTACAGGGTGGGTGT: p. P372fs*36). This variant is segregated with the deafness phenotype of this pedigree. The GSDME gene was highly conserved in the different species we analysed, and its mRNA expression was ubiquitously low in different human tissues. In conclusion, we have successfully identified a novel pathogenic insertion variant of GSDME in a Chinese pedigree that causes deafness, shedding light on the genetic basis of hearing loss within this specific family. Our findings expand the spectrum of known variants associated with GSDME-related deafness and may further support both the underlying gain-of-function mechanism and functional associations of GSDME hearing loss variants.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities.

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

Comprehensive analysis, diagnosis, prognosis, and cordycepin (CD) regulations for GSDME expressions in pan-cancers.

The Gasdermin E gene (GSDME) plays roles in deafness and cancers. However, the roles and mechanisms in cancers are complex, and the same gene exhibits different mechanisms and actions in different types of cancers. Online databases, such as GEPIA2, cBioPortal, and DNMIVD, were used to comprehensively analyze GSDME profiles, DNA methylations, mutations, diagnosis, and prognosis in patients with tumor tissues and matched healthy tissues. Western blotting and RT-PCR were used to monitor the regulation of GSDME by Cordycepin (CD) in cancer cell lines. We revealed that GSDME expression is significantly upregulated in eight cancers (ACC, DLBC, GBM, HNSC, LGG, PAAD, SKCM, and THYM) and significantly downregulated in seven cancers (COAD, KICH, LAML, OV, READ, UCES, and UCS). The overall survival was longer only in ACC, but shorter in four cancers, including COAD, KIRC, LIHC, and STAD, when GSDME was highly expressed in cancers compared with the corresponding normal tissues. Moreover, the high expression of GSDME was negatively correlated with the poor prognosis of ACC, while the low expression of GSDME was negatively correlated with the poor prognosis of COAD, suggesting that GSDME might serve as a good prognostic factor in these two cancer types. Accordingly, results indicated that the DNA methylations of those 7 CpG sites constitute a potentially effective signature to distinguish different tumors from adjacent healthy tissues. Gene mutations for GSDME were frequently observed in a variety of tumors, with UCES having the highest frequency. Moreover, CD treatment inhibited GSDME expression in different cancer cell lines, while overexpression of GSDME promoted cell migration and invasion. Thus, we have systematically and successfully clarified the GSDME expression profiles, diagnostic values, and prognostic values in pan-cancers. Targeting GSDME with CD implies therapeutic significance and a mechanism for antitumor roles in some types of cancers via increasing the sensitivity of chemotherapy. Altogether, our study may provide a strategy and biomarker for clinical diagnosis, prognostics, and treatment of cancers by targeting GSDME.

Noninvasive prediction of BRAF V600E mutation status of pleomorphic xanthoastrocytomas with MRI morphologic features and diffusion-weighted imaging.

OBJECTIVES: Seeking a noninvasive predictor for BRAF V600E mutation status of pleomorphic xanthoastrocytomas (PXAs) is essential for their prognoses and therapeutic use of BRAF inhibitors. We aimed to noninvasively diagnose BRAF V600E-mutated PXAs using MRI morphologic, DWI and clinical parameters. METHODS: The clinical findings, anatomical MRI characteristics, and diffusion parameters of 36 pathologically confirmed PXAs were retrospectively analyzed, and BRAF V600E-mutated (n = 16) and wild-type (n = 20) groups were compared. A binary logistic-regression analysis was performed, and a ROC curve was calculated to determine the independent predictors of BRAF V600E mutation status, diagnostic accuracy, and optimal cut-off value. RESULTS: A comparison of findings between groups showed that BRAF V600E-mutated PXAs were more frequent in children and young adults (≤ 35 years; P = 0.042) who often had histories of seizures (P = 0.004). Furthermore, BRAF V600E-mutated PXAs generally presented as solitary masses (P = 0.024), superficial locations with meningeal attachment (P < 0.001), predominantly cystic with mural nodules (P = 0.005), and had greater minimal ADC ratio (ADCratio) values of the tumor and peritumoral edema (P < 0.001). Binary logistic regression showed that age ≤ 35 years, solitary mass, superficial locations with meningeal attachment, and a greater minimal ADCratio of the tumor were independent predictors of BRAF V600E-mutated PXAs. The combination of all four independent predictors resulted in the highest sensitivity (100%) and specificity (90%), with AUC = 0.984. CONCLUSION: The BRAF V600E mutation status of PXAs could be noninvasively predicted using clinical and MRI characteristics. CRITICAL RELEVANCE STATEMENT: The noninvasive diagnostic criteria for BRAF V600E-mutated PXAs could offer guidance for the administration of BRAF V600E mutation inhibitors in the future.

Abnormal resting-state effective connectivity in large-scale networks among obsessive-compulsive disorder.

BACKGROUND: Obsessive-compulsive disorder (OCD) is a chronic mental illness characterized by abnormal functional connectivity among distributed brain regions. Previous studies have primarily focused on undirected functional connectivity and rarely reported from network perspective. METHODS: To better understand between or within-network connectivities of OCD, effective connectivity (EC) of a large-scale network is assessed by spectral dynamic causal modeling with eight key regions of interests from default mode (DMN), salience (SN), frontoparietal (FPN) and cerebellum networks, based on large sample size including 100 OCD patients and 120 healthy controls (HCs). Parametric empirical Bayes (PEB) framework was used to identify the difference between the two groups. We further analyzed the relationship between connections and Yale-Brown Obsessive Compulsive Scale (Y-BOCS). RESULTS: OCD and HCs shared some similarities of inter- and intra-network patterns in the resting state. Relative to HCs, patients showed increased ECs from left anterior insula (LAI) to medial prefrontal cortex, right anterior insula (RAI) to left dorsolateral prefrontal cortex (L-DLPFC), right dorsolateral prefrontal cortex (R-DLPFC) to cerebellum anterior lobe (CA), CA to posterior cingulate cortex (PCC) and to anterior cingulate cortex (ACC). Moreover, weaker from LAI to L-DLPFC, RAI to ACC, and the self-connection of R-DLPFC. Connections from ACC to CA and from L-DLPFC to PCC were positively correlated with compulsion and obsession scores (r = 0.209, p = 0.037; r = 0.199, p = 0.047, uncorrected). CONCLUSIONS: Our study revealed dysregulation among DMN, SN, FPN, and cerebellum in OCD, emphasizing the role of these four networks in achieving top-down control for goal-directed behavior. There existed a top-down disruption among these networks, constituting the pathophysiological and clinical basis.

Gray matter atrophy is constrained by normal structural brain network architecture in depression.

BACKGROUND: There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression. METHODS: In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis. RESULTS: In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset. CONCLUSIONS: These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

MRI Assessment of Intrinsic Neural Timescale and Gray Matter Volume in Parkinson's Disease.

BACKGROUND: Numerous studies have indicated altered temporal features of the brain function in Parkinson's disease (PD), and the autocorrelation magnitude of intrinsic neural signals, called intrinsic neural timescales, were often applied to estimate how long neural information stored in local brain areas. However, it is unclear whether PD patients at different disease stages exhibit abnormal timescales accompanied with abnormal gray matter volume (GMV). PURPOSE: To assess the intrinsic timescale and GMV in PD. STUDY TYPE: Prospective. POPULATION: 74 idiopathic PD patients (44 early stage (PD-ES) and 30 late stage (PD-LS), as determined by the Hoehn and Yahr (HY) severity classification scale), and 73 healthy controls (HC). FIELD STRENGTH/SEQUENCE: 3.0 T MRI scanner; magnetization prepared rapid acquisition gradient echo and echo planar imaging sequences. ASSESSMENT: The timescales were estimated by using the autocorrelation magnitude of neural signals. Voxel-based morphometry was performed to calculate GMV in the whole brain. Severity of motor symptoms and cognitive impairments were assessed using the unified PD rating scale, the HY scale, the Montreal cognitive assessment, and the mini-mental state examination. STATISTICAL TEST: Analysis of variance; two-sample t-test; Spearman rank correlation analysis; Mann-Whitney U test; Kruskal-Wallis' H test. A P value <0.05 was considered statistically significant. RESULTS: The PD group had significantly abnormal intrinsic timescales in the sensorimotor, visual, and cognitive-related areas, which correlated with the symptom severity (ρ = -0.265, P = 0.022) and GMV (ρ = 0.254, P = 0.029). Compared to the HC group, the PD-ES group had significantly longer timescales in anterior cortical regions, whereas the PD-LS group had significantly shorter timescales in posterior cortical regions. CONCLUSION: This study suggested that PD patients have abnormal timescales in multisystem and distinct patterns of timescales and GMV in cerebral cortex at different disease stages. This may provide new insights for the neural substrate of PD. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Preoperative Subtyping of WHO Grade 1 Meningiomas Using a Single-Shot Ultrafast MR T2 Mapping.

BACKGROUND: Meningioma subtype is crucial in treatment planning and prognosis delineation, for grade 1 meningiomas. T2 relaxometry could provide detailed microscopic information but is often limited by long scanning times. PURPOSE: To investigate the potential of T2 maps derived from multiple overlapping-echo detachment imaging (MOLED) for predicting meningioma subtypes and Ki-67 index, and to compare the diagnostic efficiency of two different region-of-interest (ROI) placements (whole-tumor and contrast-enhanced, respectively). STUDY TYPE: Prospective. PHANTOM/SUBJECTS: A phantom containing 11 tubes of MnCl2 at different concentrations, eight healthy volunteers, and 75 patients with grade 1 meningioma. FIELD STRENGTH/SEQUENCE: 3 T scanner. MOLED, T2-weighted spin-echo sequence, T2-dark-fluid sequence, and postcontrast T1-weighted gradient echo sequence. ASSESSMENT: Two ROIs were delineated: the whole-tumor area (ROI1) and contrast-enhanced area (ROI2). Histogram parameters were extracted from T2 maps. Meningioma subtypes and Ki-67 index were reviewed by a neuropathologist according to the 2021 classification criteria. STATISTICAL TESTS: Linear regression, Bland-Altman analysis, Pearson's correlation analysis, independent t test, Mann-Whitney U test, Kruskal-Wallis test with Bonferroni correction, and multivariate logistic regression analysis with the P-value significance level of 0.05. RESULTS: The MOLED T2 sequence demonstrated excellent accuracy for phantoms and volunteers (Meandiff = -1.29%, SDdiff = 1.25% and Meandiff = 0.36%, SDdiff = 2.70%, respectively), and good repeatability for volunteers (average coefficient of variance = 1.13%; intraclass correlation coefficient = 0.877). For both ROI1 and ROI2, T2 variance had the highest area under the curves (area under the ROC curve = 0.768 and 0.761, respectively) for meningioma subtyping. There was no significant difference between the two ROIs (P = 0.875). Significant correlations were observed between T2 parameters and Ki-67 index (r = 0.237-0.374). DATA CONCLUSION: MOLED T2 maps can effectively differentiate between meningothelial, fibrous, and transitional meningiomas. Moreover, T2 histogram parameters were significantly correlated with the Ki-67 index. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits.

Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18-30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.

Enhanced vessel wall magnetic resonance imaging in the follow-up of intracranial aneurysms treated with flow diversion.

OBJECTIVE: This study aimed to compare the efficacy of enhanced 3D T1-weighted black-blood fast-spin-echo vessel wall magnetic resonance imaging (eVW-MRI) and time-of-flight magnetic resonance angiography (TOF MRA) for follow-up evaluation of aneurysms treated with flow diversion (FD). METHODS: Our study enrolled 77 patients harboring 84 aneurysms treated with FD. Follow-up was by MRI (eVW-MRI and TOF MRA) and digital subtraction angiography (DSA). Two radiologists, blinded to DSA examination results, independently evaluated the images of aneurysm occlusion and parent artery patency using the Kamran-Byrne Scale. Interobserver diagnostic agreement and intermodality diagnostic agreement were acquired. Pretreatment and follow-up aneurysm wall enhancement (AWE) patterns were collected. RESULTS: Based on the Kamran-Byrne Scale, the intermodality agreement between eVW-MRI and DSA was better than TOF MRA versus DSA for aneurysm remnant detection (weighted ĸ = 0.891 v. 0.553) and parent artery patency (ĸ = 0.950 v. 0.221). Even with the coil artifact, the consistency of eVW-MRI with DSA for aneurysm remnant detection was better than that of TOF MRA (weighted ĸ = 0.891 v. 0.511). The artifact of adjunctive coils might be more likely to affect the accuracy in evaluating parent artery patency with TOF MRA than with eVW-MRI (ĸ = 0.077 v. 0.788). The follow-up AWE patterns were not significantly associated with pretreatment AWE patterns and aneurysm occlusion. CONCLUSIONS: The eVW-MRI outperforms TOF MRA as a reliable noninvasive and nonionizing radioactive imaging method for evaluating aneurysm remnants and parent artery patency after FD. The significance of enhancement patterns on eVW-MRI sequences needs more exploration. CLINICAL RELEVANCE STATEMENT: The application of enhanced vessel wall magnetic resonance imaging has proven to be a promising tool to depict aneurysm remnant and parent artery stenosis in order to tailor the antiplatelet therapy strategy in patients after flow diversion. KEY POINTS: • Enhanced vessel wall magnetic resonance imaging has an emerging role in depicting aneurysm remnant and parent artery patency after flow diversion. • With or without the artifact from adjunctive coils, enhanced vessel wall magnetic resonance imaging was better than TOF MRA in detecting aneurysm residual and parent artery stenosis by using DSA imaging as the standard. • Enhanced vessel wall magnetic resonance imaging holds potential to be used as an alternative to DSA for routine aneurysm follow-up after flow diversion.

Wall permeability on magnetic resonance imaging is associated with intracranial aneurysm symptoms and wall enhancement.

OBJECTIVES: Wall remodeling and inflammation accompany symptomatic unruptured intracranial aneurysms (UIAs). The volume transfer constant (Ktrans) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) reflects UIA wall permeability. Aneurysmal wall enhancement (AWE) on vessel wall MRI (VWI) is associated with inflammation. We hypothesized that Ktrans is related to symptomatic UIAs and AWE. METHODS: Consecutive patients with UIAs were prospectively recruited for 3-T DCE-MRI and VWI from January 2018 to March 2023. UIAs were classified as asymptomatic and symptomatic if associated with sentinel headache or oculomotor nerve palsy. Ktrans and AWE were assessed on DCE-MRI and VWI, respectively. AWE was evaluated using the AWE pattern and wall enhancement index (WEI). Spearman's correlation coefficient and univariate and multivariate analyses were used to assess correlations between parameters. RESULTS: We enrolled 82 patients with 100 UIAs (28 symptomatic and 72 asymptomatic). The median Ktrans (2.1 versus 0.4 min-1; p < 0.001) and WEI (1.5 versus 0.4; p < 0.001) were higher for symptomatic aneurysms than for asymptomatic aneurysms. Ktrans (odds ratio [OR]: 1.60, 95% confidence interval [95% CI]: 1.01-2.52; p = 0.04) and WEI (OR: 3.31, 95% CI: 1.05-10.42; p = 0.04) were independent risk factors for symptomatic aneurysms. Ktrans was positively correlated with WEI (Spearman's coefficient of rank correlation (rs) = 0.41, p < 0.001). The combination of Ktrans and WEI achieved an area under the curve of 0.81 for differentiating symptomatic from asymptomatic aneurysms. CONCLUSIONS: Ktrans may be correlated with symptomatic aneurysms and AWE. Ktrans and WEI may provide an additional value than the PHASES score for risk stratification of UIAs. CLINICAL RELEVANCE STATEMENT: The volume transfer constant (Ktrans) from DCE-MRI perfusion is associated with symptomatic aneurysms and provides additional value above the clinical PHASES score for risk stratification of intracranial aneurysms. KEY POINTS: • The volume transfer constant is correlated with intracranial aneurysm symptoms and aneurysmal wall enhancement. • Dynamic contrast-enhanced and vessel wall MRI facilitates understanding of the pathophysiological characteristics of intracranial aneurysm walls. • The volume transfer constant and wall enhancement index perform better than the traditional PHASES score in differentiating symptomatic aneurysms.

High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics.

OBJECTIVES: To evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM). MATERIALS AND METHODS: In this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison. RESULTS: The validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar. CONCLUSION: The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM. CLINICAL RELEVANCE STATEMENT: The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity. KEY POINTS: • The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis. • Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors. • The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.

Role of endogenous T1ρ and its dispersion imaging in differential diagnosis of cardiac amyloidosis.

BACKGROUND: Cardiovascular magnetic resonance (CMR) has demonstrated excellent performance in the diagnosis of cardiac amyloidosis (CA). However, misdiagnosis occasionally occurs because the morphological and functional features of CA are non-specific. This study was performed to determine the value of non-contrast CMR T1ρ in the diagnosis of CA. METHODS: This prospective study included 45 patients with CA, 30 patients with hypertrophic cardiomyopathy (HCM), and 10 healthy controls (HCs). All participants underwent cine (whole heart), T1ρ mapping, pre- and post-contrast T1 mapping imaging (three slices), and late gadolinium enhancement using a 3T whole-body MRI system. All participants underwent T1ρ at two spin-locking frequencies: 0Hz and 298Hz. ECV maps were obtained using pre- and post-contrast T1 maps. The myocardial T1ρ dispersion map, termed myocardial dispersion index (MDI), was also calculated. All parameters were measured in the left ventricular myocardial wall. Participants in the HC group were scanned twice on different days to assess the reproducibility of T1ρ measurements. RESULTS: Excellent reproducibility was observed upon evaluation of the coefficient of variation between two scans (T1ρ [298Hz]: 3.1%; T1ρ [0Hz], 2.5%). The ECV (HC: 27.4 ± 2.8% vs. HCM: 32.6 ± 5.8% vs. CA: 46 ± 8.9%; p < 0.0001), T1ρ [0Hz] (HC: 35.8 ± 1.7 ms vs. HCM: 40.0 ± 4.5 ms vs. CA: 51.4 ± 4.4 ms; p < 0.0001) and T1ρ [298Hz] (HC: 41.9 ± 1.6 ms vs. HCM: 48.8 ± 6.2 ms vs. CA: 54.4 ± 5.2 ms; p < 0.0001) progressively increased from the HC group to the HCM group, and then the CA group. The MDI progressively decreased from the HCM group to the HC group, and then the CA group (HCM: 8.8 ± 2.8 ms vs. HC: 6.1 ± 0.9 ms vs. CA: 3.4 ± 2.1 ms; p < 0.0001). For differential diagnosis, the combination of MDI and T1ρ [298Hz] showed the greatest sensitivity (98.3%) and specificity (95.5%) between CA and HCM, compared with the native T1 and ECV. CONCLUSIONS: The T1ρ and MDI approaches can be used as non-contrast CMR imaging biomarkers to improve the differential diagnosis of patients with CA.

Comparision of spontaneous brain activity between hippocampal sclerosis and MRI-negative temporal lobe epilepsy.

BACKGROUND: Hippocampal sclerosis (HS) is a prevalent cause of temporal lobe epilepsy (TLE). However, up to 30% of individuals with TLE present negative magnetic resonance imaging (MRI) findings. A comprehensive grasp of the similarities and differences in brain activity among distinct TLE subtypes holds significant clinical and scientific importance. OBJECTIVE: To comprehensively examine the similarities and differences between TLE with HS (TLE-HS) and MRI-negative TLE (TLE-N) regarding static and dynamic abnormalities in spontaneous brain activity (SBA). Furthermore, we aimed to determine whether these alterations correlate with epilepsy duration and cognition, and to determine a potential differential diagnostic index for clinical utility. METHODS: We measured 12 SBA metrics in 38 patients with TLE-HS, 51 with TLE-N, and 53 healthy volunteers. Voxel-wise analysis of variance (ANOVA) and post-hoc comparisons were employed to compare these metrics. The six static metrics included amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), degree centrality (DC), and global signal correlation (GSCorr). Additionally, six corresponding dynamic metrics were assessed: dynamic ALFF (dALFF), dynamic fALFF (dfALFF), dynamic ReHo (dReHo), dynamic DC (dDC), dynamic VMHC (dVMHC), and dynamic GSCorr (dGSCorr). Receiver operating characteristic (ROC) curve analysis of abnormal indices was employed. Spearman correlation analyses were also conducted to examine the relationship between the abnormal indices, epilepsy duration and cognition scores. RESULTS: Both TLE-HS and TLE-N presented as extensive neural network disorders, sharing similar patterns of SBA alterations. The regions with increased fALFF, dALFF, and dfALFF levels were predominantly observed in the mesial temporal lobe, thalamus, basal ganglia, pons, and cerebellum, forming a previously proposed mesial temporal epilepsy network. Conversely, decreased SBA metrics (fALFF, ReHo, dReHo, DC, GSCorr, and VMHC) consistently appeared in the lateral temporal lobe ipsilateral to the epileptic foci. Notably, SBA alterations were more obvious in patients with TLE-HS than in those with TLE-N. Additionally, patients with TLE-HS exhibited reduced VMHC in both mesial and lateral temporal lobes compared with patients with TLE-N, with the hippocampus displaying moderate discriminatory power (AUC = 0.759). Correlation analysis suggested that alterations in SBA indicators may be associated with epilepsy duration and cognitive scores. CONCLUSIONS: The simultaneous use of static and dynamic SBA metrics provides evidence supporting the characterisation of both TLE-HS and TLE-N as complex network diseases, facilitating the exploration of mechanisms underlying epileptic activity and cognitive impairment. Overall, SBA abnormality patterns were generally similar between the TLE-HS and TLE-N groups, encompassing networks related to TLE and auditory and occipital visual functions. These changes were more pronounced in the TLE-HS group, particularly within the mesial and lateral temporal lobes.

Resolving heterogeneity in obsessive-compulsive disorder through individualized differential structural covariance network analysis.

BACKGROUND: The high heterogeneity of obsessive-compulsive disorder (OCD) denies attempts of traditional case-control studies to derive neuroimaging biomarkers indicative of precision diagnosis and treatment. METHODS: To handle the heterogeneity, we uncovered subject-level altered structural covariance by adopting individualized differential structural covariance network (IDSCN) analysis. The IDSCN measures how structural covariance edges in a patient deviated from those in matched healthy controls (HCs) yielding subject-level differential edges. One hundred patients with OCD and 106 HCs were recruited and whose T1-weighted anatomical images were acquired. We obtained individualized differential edges and then clustered patients into subtypes based on these edges. RESULTS: Patients presented tremendously low overlapped altered edges while frequently shared altered edges within subcortical-cerebellum network. Two robust neuroanatomical subtypes were identified. Subtype 1 presented distributed altered edges while subtype 2 presented decreased edges between default mode network and motor network compared with HCs. Altered edges in subtype 1 predicted the total Yale-Brown Obsessive Compulsive Scale score while that in subtype 2 could not. CONCLUSIONS: We depict individualized structural covariance aberrance and identify that altered connections within subcortical-cerebellum network are shared by most patients with OCD. These 2 subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of OCD.

Mapping the neuroanatomical heterogeneity of OCD using a framework integrating normative model and non-negative matrix factorization.

Obsessive-compulsive disorder (OCD) is a spectrum disorder with high interindividual heterogeneity. We propose a comprehensible framework integrating normative model and non-negative matrix factorization (NMF) to quantitatively estimate the neuroanatomical heterogeneity of OCD from a dimensional perspective. T1-weighted magnetic resonance images of 98 first-episode untreated patients with OCD and matched healthy controls (HCs, n = 130) were acquired. We derived individualized differences in gray matter morphometry using normative model and parsed them into latent disease factors using NMF. Four robust disease factors were identified. Each patient expressed multiple factors and exhibited a unique factor composition. Factor compositions of patients were significantly correlated with severity of symptom, age of onset, illness duration, and exhibited sex differences, capturing sources of clinical heterogeneity. In addition, the group-level morphological differences obtained with two-sample t test could be quantitatively derived from the identified disease factors, reconciling the group-level and subject-level findings in neuroimaging studies. Finally, we uncovered two distinct subtypes with opposite morphological differences compared with HCs from factor compositions. Our findings suggest that morphological differences of individuals with OCD are the unique combination of distinct neuroanatomical patterns. The proposed framework quantitatively estimating neuroanatomical heterogeneity paves the way for precision medicine in OCD.

Dynamic network reorganization underlying neuroplasticity: the deficits-severity-related language network dynamics in patients with left hemispheric gliomas involving language network.

Brain network dynamics not only endow the brain with flexible coordination for various cognitive processes but also with a huge potential of neuroplasticity for development, skill learning, and after cerebral injury. Diffusive and progressive glioma infiltration triggers the neuroplasticity for functional compensation, which is an outstanding pathophysiological model for the investigation of network reorganization underlying neuroplasticity. In this study, we employed dynamic conditional correlation to construct framewise language networks and investigated dynamic reorganizations in 83 patients with left hemispheric gliomas involving language networks (40 patients without aphasia and 43 patients with aphasia). We found that, in healthy controls (HCs) and patients, the language network dynamics in resting state clustered into 4 temporal-reoccurring states. Language deficits-severity-dependent topological abnormalities of dFCs were observed. Compared with HCs, suboptimal language network dynamics were observed for those patients without aphasia, while more severe network disruptions were observed for those patients with aphasia. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the 4 states significantly predicted individual patients' language scores. These findings shed light on our understanding of metaplasticity in glioma. Glioma-induced language network reorganizations were investigated under a dynamic "meta-networking" (network of networks) framework. In healthy controls and patients with glioma, the framewise language network dynamics in resting-state robustly clustered into 4 temporal-reoccurring states. The spatial but not temporal language deficits-severity-dependent abnormalities of dFCs were observed in patients with left hemispheric gliomas involving language network. Language network dynamics significantly predicted individual patients' language scores.

Facile synthesis of Gd/Ru-doped fluorescent carbon dots for fluorescent/MR bimodal imaging and tumor therapy.

Functional metal doping endows fluorescent carbon dots with richer physical and chemical properties, greatly expanding their potential in the biomedical field. Nonetheless, fabricating carbon dots with integrated functionality for diagnostic and therapeutic modalities remains challenging. Herein, we develop a simple strategy to prepare Gd/Ru bimetallic doped fluorescent carbon dots (Gd/Ru-CDs) via a one-step microwave-assisted method with Ru(dcbpy)3Cl2, citric acid, polyethyleneimine, and GdCl3 as precursors. Multiple techniques were employed to characterize the morphology and properties of the obtained carbon dots. The Gd/Ru-CDs are high mono-dispersity, uniform spherical nanoparticles with an average diameter of 4.2 nm. Moreover, X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared (FTIR) confirmed the composition and surface properties of the carbon dots. In particular, the successful doping of Gd/Ru enables the carbon dots not only show considerable magnetic resonance imaging (MRI) performance but also obtain better fluorescence (FL) properties, especially in the red emission area. More impressively, it has low cytotoxicity, excellent biocompatibility, and efficient reactive oxygen species (ROS) generation ability, making it an effective imaging-guided tumor treatment reagent. In vivo experiments have revealed that Gd/Ru-CDs can achieve light-induced tumor suppression and non-invasive fluorescence/magnetic resonance bimodal imaging reagents to monitor the treatment process of mouse tumor models. Thus, this simple and efficient carbon dot manufacturing strategy by doping functional metals has expanded avenues for the development and application of multifunctional all-in-one theranostics.

Global, interhemispheric and intrahemispheric functional connection patterns in male adults with alcohol use disorder.

A growing body of evidence indicates the existence of abnormal local and long-range functional connection patterns in patients with alcohol use disorder (AUD). However, it has yet to be established whether AUD is associated with abnormal interhemispheric and intrahemispheric functional connection patterns. In the present study, we analysed resting-state functional magnetic resonance imaging data from 55 individuals with AUD and 32 healthy nonalcohol users. For each subject, whole-brain functional connectivity density (FCD) was decomposed into ipsilateral and contralateral parts. Correlation analysis was performed between abnormal FCD and a range of clinical measurements in the AUD group. Compared with healthy controls, the AUD group exhibited a reduced global FCD in the anterior and middle cingulate gyri, prefrontal cortex and thalamus, along with an enhanced global FCD in the temporal, parietal and occipital cortices. Abnormal interhemispheric and intrahemispheric FCD patterns were also detected in the AUD group. Furthermore, abnormal global, contralateral and ipsilateral FCD data were correlated with the mean amount of pure alcohol and the severity of alcohol addiction in the AUD group. Collectively, our findings indicate that global, interhemispheric and intrahemispheric FCD may represent a robust method to detect abnormal functional connection patterns in AUD; this may help us to identify the neural substrates and therapeutic targets of AUD.