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OBJECTIVES: To investigate the application of the three-compartment restriction spectrum imaging (RSI) model, diffusion kurtosis imaging (DKI), and diffusion-weighted imaging (DWI) in predicting Ki-67 status in rectal carcinoma. METHODS: A total of 80 rectal carcinoma patients, including 47 high-proliferation (Ki-67 > 50%) cases and 33 low-proliferation (Ki-67 ≤ 50%) cases, underwent pelvic MRI were enrolled. Parameters derived from RSI (f1, f2, and f3), DKI (MD and MK), and DWI (ADC) were calculated and compared between the two groups. Logistic regression (LR) analysis was conducted to identify independent predictors and assess combined diagnosis. Area under the receiver operating characteristic curve (AUC), DeLong analysis, and calibration curve analyses were performed to evaluate diagnostic performance. RESULTS: The patients with high-proliferation rectal carcinoma exhibited significantly higher f1 and MK values and significantly lower ADC, MD, f2, and f3 values than those with low-proliferation rectal carcinoma (P < 0.05). LR analysis showed that MD, MK, and f2 were independent predictors for Ki-67 status in rectal carcinoma. Moreover, the combination of these three parameters achieved an optimal diagnostic efficacy (AUC = 0.877, sensitivity = 80.85%, specificity = 84.85%) that was significantly better than that obtained using ADC (AUC = 0.783, Z = 2.347, P = 0.019), f2 (AUC = 0.732, Z = 2.762, P = 0.006), and f3 (AUC = 0.700, Z = 3.071, P = 0.002). The combined diagnosis also showed good performance (AUC = 0.859) in the internal validation analysis based on 1000 bootstrap samples, while the calibration curve demonstrated that the combined diagnosis provided good stability. CONCLUSION: RSI, DKI, and DWI can effectively differentiate between patients with high- and low-proliferation rectal carcinoma. Furthermore, the MD, MK, and f2 imaging parameters may be a novel and promising combination biomarker for examining Ki-67 status in rectal carcinoma.

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BACKGROUND: High b-value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). This study qualitatively and quantitatively compares synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa. METHODS: One hundred fifty-one consecutive patients who underwent prostate MRI and biopsy were included in the study. Axial DWI with b = 0, 500, 1000, and 2000 s/mm2 using a 3T clinical scanner using a 32-channel phased-array body coil were acquired. We retrospectively synthesized DWI for b = 2000 s/mm2 via extrapolation based on mono-exponential decay, using b = 0 and b = 500 s/mm2 (sDWI500) and b = 0, b = 500 s/mm2, and b = 1000 s/mm2 (sDWI1000). Differences in signal intensity between sDWI and aDWI were evaluated within different regions of interest (prostate alone, prostate plus 5 mm, 30 mm and 70 mm margin and full field of view). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC). RESULTS: Within the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46 ± 35% for sDWI1000 and -67 ± 24% for sDWI500. AUC for aDWI, sDWI500, sDWI1000, and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively. CONCLUSION: sDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.

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BACKGROUND: To investigate the association between white matter changes and ventricular expansion in idiopathic normal pressure hydrocephalus (iNPH) based on diffusion spectrum imaging (DSI). METHODS: We included 32 patients with iNPH who underwent DSI using a 3T MRI scanner. The lateral ventricles were manually segmented, and ventricular volumes were measured. Two methods were utilised in the study: manual region-of-interest (ROI) delineation and tract diffusion profile analysis. General fractional anisotropy (GFA) and fractional anisotropy (FA) were extracted in different white matter regions, including the bilateral internal capsule (anterior and posterior limbs) and corpus callosum (body, genu, and splenium) with manual ROI delineation. The 18 main tracts in the brain of each patient were extracted; the diffusion metrics of 100 equidistant nodes on each fibre were calculated, and Spearman's correlation coefficient was used to determine the correlation between diffusion measures and ventricular volume of iNPH patients. RESULTS: The GFA and FA of all ROI showed no significant correlation with lateral ventricular volume. However, in the tract diffusion profile analysis, lateral ventricular volume was positively correlated with part of the cingulum bundle, left corticospinal tract, and bilateral thalamic radiation posterior, whereas it was negatively correlated with the bilateral cingulum parahippocampal (all p < 0.05). CONCLUSIONS: The effect of ventricular enlargement in iNPH on some white matter fibre tracts around the ventricles was limited and polarizing, and most white matter fibre tract integrity changes were not associated with ventricular enlargement; this reflects that multiple pathological mechanisms may have been combined to cause white matter alterations in iNPH.

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Introduction: Ambient air pollution is a neurotoxicant with hypothesized immune-related mechanisms. Adolescent brain structural and functional connectivity may be especially vulnerable to ambient pollution due to the refinement of large-scale brain networks during this period, which vary by sex and have important implications for cognitive, behavioral, and emotional functioning. In the current study we explored associations between air pollutants, immune markers, and structural and functional connectivity in early adolescence by leveraging cross-sectional sex-stratified data from the Adolescent Brain Cognitive Development℠ Study®. Methods: Pollutant concentrations of fine particulate matter, nitrogen dioxide, and ozone were assigned to each child's primary residential address during the prenatal period and childhood (9-10 years-old) using an ensemble-based modeling approach. Data collected at 11-13 years-old included resting-state functional connectivity of the default mode, frontoparietal, and salience networks and limbic regions of interest, intracellular directional and isotropic diffusion of available white matter tracts, and markers of cellular immune activation. Using partial least squares correlation, a multivariate data-driven method that identifies important variables within latent dimensions, we investigated associations between 1) pollutants and structural and functional connectivity, 2) pollutants and immune markers, and 3) immune markers and structural and functional connectivity, in each sex separately. Results: Air pollution exposure was related to white matter intracellular directional and isotropic diffusion at ages 11-13 years, but the direction of associations varied by sex. There were no associations between pollutants and resting-state functional connectivity at ages 11-13 years. Childhood exposure to nitrogen dioxide was negatively correlated with white blood cell count in males. Immune biomarkers were positively correlated with white matter intracellular directional diffusion in females and both white matter intracellular directional and isotropic diffusion in males. Lastly, there was a reliable negative correlation between lymphocyte-to-monocyte ratio and default mode network resting-state functional connectivity in females, as well as a compromised immune marker profile associated with lower resting-state functional connectivity between the salience network and the left hippocampus in males. In post-hoc exploratory analyses, we found that the PLSC-identified white matter tracts and resting-state networks related to processing speed and cognitive control performance from the NIH Toolbox. Conclusions: We identified novel links between childhood nitrogen dioxide and cellular immune activation in males, and brain network connectivity and immune markers in both sexes. Future research should explore the potentially mediating role of immune activity in how pollutants affect neurological outcomes as well as the potential consequences of immune-related patterns of brain connectivity in service of improved brain health for all.

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Exposure to outdoor particulate matter (PM2.5) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

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BACKGROUND: Stroke often damages the basal ganglia, leading to atypical and transient aphasia, indicating that post-stroke basal ganglia aphasia (PSBGA) may be related to different anatomical structural damage and functional remodeling rehabilitation mechanisms. The basal ganglia contain dense white matter tracts (WMTs). Hence, damage to the functional tract may be an essential anatomical structural basis for the development of PSBGA. METHODS: We first analyzed the clinical characteristics of PSBGA in 28 patients and 15 healthy controls (HCs) using the Western Aphasia Battery and neuropsychological test batteries. Moreover, we investigated white matter injury during the acute stage using diffusion magnetic resonance imaging scans for differential tractography. Finally, we used multiple regression models in correlation tractography to analyze the relationship between various language functions and quantitative anisotropy (QA) of WMTs. RESULTS: Compared with HCs, patients with PSBGA showed lower scores for fluency, comprehension (auditory word recognition and sequential commands), naming (object naming and word fluency), reading comprehension of sentences, Mini-Mental State Examination, and Montreal Cognitive Assessment, along with increased scores in Hamilton Anxiety Scale-17 and Hamilton Depression Scale-17 within 7 days after stroke onset (P < 0.05). Differential tractography revealed that patients with PSBGA had damaged fibers, including in the body fibers of the corpus callosum, left cingulum bundles, left parietal aslant tracts, bilateral superior longitudinal fasciculus II, bilateral thalamic radiation tracts, left fornix, corpus callosum tapetum, and forceps major, compared with HCs (FDR < 0.02). Correlation tractography highlighted that better comprehension was correlated with a higher QA of the left inferior fronto-occipital fasciculus (IFOF), corpus callosum forceps minor, and left extreme capsule (FDR < 0.0083). Naming was positively associated with the QA of the left IFOF, forceps minor, left arcuate fasciculus, and uncinate fasciculus (UF) (FDR < 0.0083). Word fluency of naming was also positively associated with the QA of the forceps minor, left IFOF, and thalamic radiation tracts (FDR < 0.0083). Furthermore, reading was positively correlated with the QA of the forceps minor, left IFOF, and UF (FDR < 0.0083). CONCLUSION: PSBGA is primarily characterized by significantly impaired word fluency of naming and preserved repetition abilities, as well as emotional and cognitive dysfunction. Damaged limbic pathways, dorsally located tracts in the left hemisphere, and left basal ganglia pathways are involved in PSBGA pathogenesis. The results of connectometry analysis further refine the current functional localization model of higher-order neural networks associated with language functions.

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BACKGROUND: Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions. OBJECTIVE: To explore how well the tri-compartmental RSI performs in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy. STUDY TYPE: Prospective. POPULATION: 137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years). FIELD STRENGTH/SEQUENCE: T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system. ASSESSMENT: The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusion f 1 $$ {f}_1 $$ , hindered diffusion f 2 $$ {f}_2 $$ , and free diffusion f 3 $$ {f}_3 $$ ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination. STATISTICAL TESTS: Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant. RESULTS: The f 1 $$ {f}_1 $$ , SUVmax were significantly higher, and f 3 $$ {f}_3 $$ , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC ( f 3 $$ {f}_3 $$ ) > AUC ( f 1 $$ {f}_1 $$ ) > AUC (ADC) > AUC ( f 2 $$ {f}_2 $$ ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging. CONCLUSION: RSI-derived parameters ( f 1 $$ {f}_1 $$ , f 3 $$ {f}_3 $$ ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility of f 2 $$ {f}_2 $$ was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy. TECHNICAL EFFICACY: Stage 2.

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OBJECTIVE: To assess renal interstitial fibrosis (IF) using diffusion MRI approaches, and explore whether corticomedullary difference (CMD) of diffusion parameters, combination among MRI parameters, or combination with estimated glomerular filtration rate (eGFR) benefit IF evaluation. METHODS: Forty-two patients with chronic kidney disease were included, undergoing MRI examinations. MRI parameters from apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion-relaxation correlated spectrum imaging (DR-CSI) were obtained both for renal cortex and medulla. CMD of these parameters was calculated. Pathological IF scores (1-3) were obtained by biopsy. Patients were divided into mild (IF = 1, n = 23) and moderate-severe fibrosis (IF = 2-3, n = 19) groups. Group comparisons for MRI parameters were performed. Diagnostic performances were assessed by the receiver operator's curve analysis for discriminating mild from moderate-severe IF patients. RESULTS: Significant inter-group differences existed for cortical ADC, IVIM-D, IVIM-f, DKI-MD, DR-CSI VB, and DR-CSI VC. Significant inter-group differences existed in ΔADC, ΔMD, ΔVB, ΔVC, ΔQB, and ΔQC. Among the cortical MRI parameters, VB displayed the highest AUC = 0.849, while ADC, f, and MD also showed AUC > 0.8. After combining cortical value and CMD, the diagnostic performances of the MRI parameters were slightly improved except for IVIM-D. Combining VB with f brings the best performance (AUC = 0.903) among MRI bi-variant models. A combination of cortical VB, ΔADC, and eGFR brought obvious improvement in diagnostic performance (AUC 0.963 vs 0.879, specificity 0.826 vs 0.896, and sensitivity 1.000 vs 0.842) than eGFR alone. CONCLUSION: Our study shows promising results for the assessment of renal IF using diffusion MRI approaches. CRITICAL RELEVANCE STATEMENT: Our study explores the non-invasive assessment of renal IF, an independent and effective predictor of renal outcomes, by comparing and combining diffusion MRI approaches including compartmental, non-compartmental, and model-free approaches. KEY POINTS: Significant difference exists for diffusion parameters between mild and moderate-severe IF. Generally, cortical parameters show better performance than corresponding CMD. Bi-variant model lifts the diagnostic performance for assessing IF.

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BACKGROUND: We aimed to identify important features of white matter microstructures collectively distinguishing individuals with attention-deficit/hyperactivity disorder (ADHD) from those without ADHD using a machine-learning approach. METHODS: Fifty-one ADHD patients and 60 typically developing controls (TDC) underwent diffusion spectrum imaging at two time points. We evaluated three models to classify ADHD and TDC using various machine-learning algorithms. Model 1 employed baseline white matter features of 45 white matter tracts at Time 1; Model 2 incorporated features from both time points; and Model 3 (main analysis) further included the relative rate of change per year of white matter tracts. RESULTS: The random forest algorithm demonstrated the best performance for classification. Model 1 achieved an area-under-the-curve (AUC) of 0.67. Model 3, incorporating Time 2 variables and relative rate of change per year, improved the performance (AUC = 0.73). In addition to identifying several white matter features at two time points, we found that the relative rate of change per year in the superior longitudinal fasciculus, frontal aslant tract, stria terminalis, inferior fronto-occipital fasciculus, thalamic and striatal tracts, and other tracts involving sensorimotor regions are important features of ADHD. A higher relative change rate in certain tracts was associated with greater improvement in visual attention, spatial short-term memory, and spatial working memory. CONCLUSIONS: Our findings support the significant diagnostic value of white matter microstructure and the developmental change rates of specific tracts, reflecting deviations from typical development trajectories, in identifying ADHD.

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OBJECTIVE: To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging. METHODS: We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants' brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification. RESULTS: The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance. CONCLUSION: The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.

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BACKGROUND: To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue. METHODS: This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model. RESULTS: Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively). CONCLUSIONS: Quantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions.

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Head motion correction is particularly challenging in diffusion-weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE-based SHORELine method was introduced that does not require shell-based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP-PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. PRACTITIONER POINTS: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data. Denoising with MP-PCA can improve head motion correction performance when Eddy is used. SHORELine effectively corrects motion in non-shelled diffusion spectrum imaging data.

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BACKGROUND AND OBJECTIVES: Diffusion basis spectrum imaging (DBSI) extracts multiple anisotropic and isotropic diffusion tensors, providing greater histopathologic specificity than diffusion tensor imaging (DTI). Persistent black holes (PBH) represent areas of severe tissue damage in multiple sclerosis (MS), and a high PBH burden is associated with worse MS disability. This study evaluated the ability of DBSI and DTI to predict which acute contrast-enhancing lesions (CELs) would persist as T1 hypointensities (i.e. PBHs) 12 months later. We expected that a higher radial diffusivity (RD), representing demyelination, and higher DBSI-derived isotropic non-restricted fraction, representing edema and increased extracellular space, of the acute CEL would increase the likelihood of future PBH development. METHODS: In this prospective cohort study, relapsing MS patients with ≥1 CEL(s) underwent monthly MRI scans for 4 to 6 months until gadolinium resolution. DBSI and DTI metrics were quantified when the CEL was most conspicuous during the monthly scans. To determine whether the CEL became a PBH, a follow-up MRI was performed at least 12 months after the final monthly scan. RESULTS: The cohort included 20 MS participants (median age 33 years; 13 women) with 164 CELs. Of these, 59 (36 %) CELs evolved into PBHs. At Gd-max, DTI RD and AD of all CELs increased, and both metrics were significantly elevated for CELs which became PBHs, as compared to non-black holes (NBHs). DTI RD above 0.74 conferred an odds ratio (OR) of 7.76 (CI 3.77-15.98) for a CEL becoming a PBH (AUC 0.80, CI 0.73-0.87); DTI axial diffusivity (AD) above 1.22 conferred an OR of 7.32 (CI 3.38-15.86) for becoming a PBH (AUC 0.75, CI 0.66-0.83). DBSI RD and AD did not predict PBH development in a multivariable model. At Gd-max, DBSI restricted fraction decreased and DBSI non-restricted fraction increased in all CELs, and both metrics were significantly different for CELs which became PBHs, as compared to NBHs. A CEL with a DBSI non-restricted fraction above 0.45 had an OR of 4.77 (CI 2.35-9.66) for becoming a PBH (AUC 0.74, CI 0.66-0.81); a CEL with a DBSI restricted fraction below 0.07 had an OR of 9.58 (CI 4.59-20.02) for becoming a PBH (AUC 0.80, 0.72-0.87). CONCLUSION: Our findings suggest that greater degree of edema/extracellular space in a CEL is a predictor of tissue destruction, as evidenced by PBH evolution.

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COVID-19 remains a significant international public health concern. Yet, the mechanisms through which symptomatology emerges remain poorly understood. While SARS-CoV-2 infection may induce prolonged inflammation within the central nervous system, the evidence primarily stems from limited small-scale case investigations. To address this gap, our study capitalized on longitudinal UK Biobank neuroimaging data acquired prior to and following COVID-19 testing (N = 416 including n = 224 COVID-19 cases; Mage = 58.6). Putative neuroinflammation was assessed in gray matter structures and white matter tracts using non-invasive Diffusion Basis Spectrum Imaging (DBSI), which estimates inflammation-related cellularity (DBSI-restricted fraction; DBSI-RF) and vasogenic edema (DBSI-hindered fraction; DBSI-HF). We hypothesized that COVID-19 case status would be associated with increases in DBSI markers after accounting for potential confound (age, sex, race, body mass index, smoking frequency, and data acquisition interval) and multiple testing. COVID-19 case status was not significantly associated with DBSI-RF (|ß|'s < 0.28, pFDR >0.05), but with greater DBSI-HF in left pre- and post-central gyri and right middle frontal gyrus (ß's > 0.3, all pFDR = 0.03). Intriguingly, the brain areas exhibiting increased putative vasogenic edema had previously been linked to COVID-19-related functional and structural alterations, whereas brain regions displaying subtle differences in cellularity between COVID-19 cases and controls included regions within or functionally connected to the olfactory network, which has been implicated in COVID-19 psychopathology. Nevertheless, our study might not have captured acute and transitory neuroinflammatory effects linked to SARS-CoV-2 infection, possibly due to symptom resolution before the imaging scan. Future research is warranted to explore the potential time- and symptom-dependent neuroinflammatory relationship with COVID-19.

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BACKGROUND: Some children who experience concussions, particularly females, develop long-lasting emotional and behavioral problems. Establishing the potential contribution of preexisting behavioral problems and disrupted white matter maturation has been challenging due to a lack of preinjury data. METHODS: From the Adolescent Brain Cognitive Development cohort, 239 (90 female) children age 12.1 ± 0.6 years who experienced a concussion after study entry at 10.0 ± 0.6 years were compared to 6438 (3245 female) children without head injuries who were age 9.9 ± 0.6 years at baseline and 12.0 ± 0.6 years at follow-up. The Child Behavior Checklist was used to assess internalizing and externalizing behavior at study entry and follow-up. In the children with magnetic resonance imaging data available (concussion n = 134, comparison n = 3520), deep and superficial white matter was characterized by neurite density from restriction spectrum image modeling of diffusion magnetic resonance imaging. Longitudinal ComBat harmonization removed scanner effects. Linear regressions modeled 1) behavior problems at follow-up controlling for baseline behavior, 2) impact of concussion on white matter maturation, and 3) contribution of deviations in white matter maturation to postconcussion behavior problems. RESULTS: Only female children with concussion had higher internalizing behavior problem scores. The youngest children with concussion showed less change in superficial white matter neurite density over 2 years than children with no concussion. In females with concussion, less change in superficial white matter neurite density was correlated with increased internalizing behavior problem scores. CONCLUSIONS: Concussions in female children are associated with emotional problems beyond preinjury levels. Injury to superficial white matter may contribute to persistent internalizing behavior problems in females.

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Auditory hallucinations (AH) are a debilitating symptom in psychosis, impacting cognition and real world functioning. Recent thought conceptualizes AH as a consequence of long-range brain communication dysfunction, or circuitopathy, within the auditory sensory/perceptual, language, and cognitive control systems. Recently we showed in first-episode psychosis (FEP) that, despite overall intact white matter integrity in the cortical-cortical and cortical-subcortical language tracts and the callosal tracts connecting auditory cortices, the severity of AH correlated inversely with white matter integrity. However, that hypothesis-driven isolation of specific tracts likely missed important white matter concomitants of AH. In this report, we used a whole-brain data-driven dimensional approach using correlational tractography to associate AH severity with white matter integrity in a sample of 175 individuals. Diffusion Spectrum Imaging (DSI) was used to image diffusion distribution. Quantitative Anisotropy (QA) in three tracts was greater with increased AH severity (FDR < 0.001) and QA in three tracts was lower with increased AH severity (FDR < 0.01). White matter tracts showing associations between QA and AH were generally associated with frontal-parietal-temporal connectivity (tracts with known relevance for cognitive control and the language system), in the cingulum bundle, and in prefrontal inter-hemispheric connectivity. The results of this whole brain data-driven analysis suggest that subtle white matter alterations connecting frontal, parietal, and temporal lobes in the service of sensory-perceptual, language/semantic, and cognitive control processes impact the expression of auditory hallucination in FEP. Disentangling the distributed neural circuits involved in AH should help to develop novel interventions, such as non-invasive brain stimulation.

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OBJECTIVE: To assess the efficacy and safety of stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation (RFTC), using diffusion spectrum imaging (DSI) tractography to preoperatively delineate the optic radiation (OR) and reduce the risk of visual field defects (VFDs) where the epileptogenic zones (EZs) are located in or close to the eloquent visual areas. METHODS: We prospectively followed up twenty-four consecutive patients (12 males and 12 females) who underwent SEEG-guided RFTC in or near the OR pathway. A distance of ≥ 3.5 mm away from the OR on the targeted electrodes contacts that exhibited relevant ictal onset patterns, IEDs and EES during SEEG recordings, was required as our selection criterion prior to performing RFTC, enough to theoretically prevent VFDs. Using default tracking parameters, the optic radiation was tracked semi-automatically in DSI-studio. RESULTS: There were 12 male and 12 female patients ranging in age from 6 to 57 years, with follow-up period ranging from 6 to 37 months. Nineteen patients responded to RFTC (R+, 79.16 %), and 5 patients did not benefit from RFTC (R-, 20.83 %). The preoperative application of DSI semi-automatic based OR tractography was successful in the protection of the OR in all 24 patients. Three patients experienced a neurologic deficit following RFTC, and five patients had a partial quadrant visual field deficit prior to surgery that did not worsen, and none of the remaining nineteen patients had a quadrant visual field deficit. CONCLUSION: Our study validates the safety and efficacy of SEEG-RFTC as a viable therapeutic approach for epileptic foci situated in or adjacent to the visual eloquent regions. We demonstrate that DSI-based tractography offers superior precision in delineating the OR compared to DTI. We establish that implementing a criterion of a minimum distance of ≥ 3.5 mm in radius from the OR on the targeted electrode contacts prior to conducting RFTC can effectively mitigate the risk of VFDs.

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Photon Counting Detector(PCD)is a device used to detect X-ray photons,which can directly convert the energy of photons into electrical signals to achieve photon counting and measurement.PCD-based energy spectrum computed tomography(PCD-CT)technology can provide additional energy spectral imaging information,and improve imaging quality while reducing radiation dose.Compared with energy integrating detectors(EID),PCD has advantages of high energy conversion efficiency,good imaging quality,exquisite structural design,and wide application range.It has broad application prospects in ultra-low-dose CT,specific disease diagnosis,and industrial inspection.The application of PCD-CT in spectral CT imaging was reviewed to provide a useful reference for its application in clinical medical diagnosis and industrial applications.

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Introduction: Traumatic optic neuropathy (TON) is the optic nerve injury secondary to brain trauma leading to visual impairment and vision loss. Current clinical visual function assessments often fail to detect TON due to slow disease progression and clinically silent lesions resulting in potentially delayed or missed treatment in patients with traumatic brain injury (TBI). Methods: Diffusion basis spectrum imaging (DBSI) is a novel imaging modality that can potentially fill this diagnostic gap. Twenty-two, 16-week-old, male mice were equally divided into a sham or TBI (induced by moderate Closed-Head Impact Model of Engineered Rotational Acceleration device) group. Briefly, mice were anesthetized with isoflurane (5% for 2.5 min followed by 2.5% maintenance during injury induction), had a helmet placed over the head, and were placed in a holder prior to a 2.1-joule impact. Serial visual acuity (VA) assessments, using the Virtual Optometry System, and DBSI scans were performed in both groups of mice. Immunohistochemistry (IHC) and histological analysis of optic nerves was also performed after in vivo MRI. Results: VA of the TBI mice showed unilateral or bilateral impairment. DBSI of the optic nerves exhibited bilateral involvement. IHC results of the optic nerves revealed axonal loss, myelin injury, axonal injury, and increased cellularity in the optic nerves of the TBI mice. Increased DBSI axon volume, decreased DBSI λ||, and elevated DBSI restricted fraction correlated with decreased SMI-312, decreased SMI-31, and increased DAPI density, respectively, suggesting that DBSI can detect coexisting pathologies in the optic nerves of TBI mice. Conclusion: DBSI provides an imaging modality capable of detecting subclinical changes of indirect TON in TBI mice.

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Introduction: This study aimed to examine the white matter characteristics of visual artists (VAs) in terms of visual creativity and the structural connectivity within the cortical visual system. Methods: Diffusion spectrum imaging was utilized to examine the changes in white matter within the cortical visual system of a group of VAs (n = 25) in comparison to a group of healthy controls matched for age and education (n = 24). To assess the integrity of white matter and its relationship with visual creativity, we conducted a comprehensive analysis using region-based and track-specific tractographic examinations. Results: Our study uncovered that VAs demonstrated increased normalized quantitative anisotropy in specific brain regions, including the right inferior temporal gyrus and right lateral occipital gyrus, along with the corresponding white matter fiber tracts connecting these regions. These enhancements within the cortical visual system were also found to be correlated with measures of visual creativity obtained through psychological assessments. Discussion: The noted enhancement in the white matter within the cortical visual system of VAs, along with its association with visual creativity, is consistent with earlier research demonstrating heightened functional connectivity in the same system among VAs. Our study's findings suggest a link between the visual creativity of VAs and structural alterations within the brain's visual system.