Large-scale structural covariance networks changes relate to executive function deficit in betel quid-dependent chewers.

Previous studies demonstrate deficits in executive function for betel quid-dependent (BQD) patients. Large-scale structural covariance network (SCN) based on gray matter (GM) morphometry may be able to explore the neural mechanism of executive dysfunction in BQD individuals. This study aims to identify spatial covariance patterns of GM volume and to investigate their association with executive dysfunction in BQD individuals. Sixty-four BQD individuals and 48 sex- and age-matched healthy controls (HCs) underwent T1-weighted structural MRI examination and executive function assessments, including the Backward Digit Span (BDS) test and Stroop Color and Word (SCW) test. Seventy SCNs based on GM volume covariance patterns were defined using independent component analysis. An SCN-based classifier was constructed to differentiate between BQD and HC individuals. Receiver operating characteristic (ROC) curves were applied to evaluate the performance of the SCN-based classifier. Linear regression analyses were performed to investigate the association between SCN network indices and executive function indices. Six SCNs had higher classifications for differentiating between BQD and HC individuals. The area under the ROC curve of the SCN-based classifier was 0.812 in the training set and 0.771 in the testing set. Furthermore, linear regression analyses demonstrated that the network indices in the thalamus were associated with BDS scores adjusted for age, sex, and education. Large-scale SCNs could provide potential imaging markers for differentiating BQD and HC groups. The loss of network index in the thalamus was associated with working memory, indicating that SCNs could reveal executive dysfunction in BQD individuals.

Abnormal static and dynamic brain network connectivity associated with chronic tinnitus.

In order to comprehensively understand the changes of brain networks in patients with chronic tinnitus, this study combined static and dynamic analysis methods to explore the abnormalities of brain networks. Thirty-two patients with chronic tinnitus and 30 age-, sex- and education-matched healthy controls (HC) were recruited. Independent component analysis was used to identify resting-state networks (RSNs). Static and dynamic functional network connectivity (FNC) were performed. The temporal properties of brain network including mean dwell time (MDT), fraction time (FT) and numbers of transitions (NT) were calculated. Two-sample t test and Spearman's correlation were used for group compares and correlation analysis. Four RSNs showed abnormal FNC including auditory network (AUN), default mode network (DMN), attention network (AN) and sensorimotor network (SMN). For static analysis, tinnitus patients showed significantly decreased FNC in AUN-DMN, AUN-AN, DMN-AN, and DMN-SMN than HC [p < 0.05, false discovery rate (FDR) corrected]. For dynamic analysis, tinnitus patients showed significantly decreased FNC in DMN-AN in state 3 (p < 0.05, FDR corrected). MDT in state 3 was significantly decreased in tinnitus patients (t = 2.039, P = 0.046). In the tinnitus group, the score of tinnitus functional index (TFI) was negatively correlated with MDT and FT in state 4, and the duration of tinnitus was positively correlated with FT in state 1 and NT. Chronic tinnitus causes abnormal brain network connectivity. These abnormal brain networks help to clarify the mechanism of tinnitus generation and chronicity, and provide a potential basis for the treatment of tinnitus.

Assessment of the sacroiliac joint in patients with axial spondyloarthritis via three-dimensional ultrashort echo time magnetic resonance imaging.

Background: Bone erosion in the sacroiliac joint (SIJ) is highly specific for the diagnosis of axial spondyloarthritis (axSpA) and may indicate early disease progression. The 3D ultrashort echo time (3D-UTE) technique excels in providing clear contrast between the articular cartilage and the bone cortex interface. Additionally, it is emerging as a promising quantitative tool for detecting early cartilage changes. Therefore, this study aimed to evaluate the diagnostic performance of 3D-UTE sequences in identifying bone erosion in the SIJ of patients with axSpA and to clarify the potential of cartilage T2* values as a quantitative biomarker for axSpA. Methods: This prospective study employed convenience and consecutive sampling methods to recruit patients diagnosed with axSpA in Peking University Third Hospital who met the Assessment of Spondyloarthritis International Society (ASAS) criteria and also an equal number of healthy volunteers. After providing informed consent, all participants underwent 3D-UTE sequences and conventional T2* mapping of the SIJs. Two radiologists separately interpreted the bone erosion of each SIJ on 3D-UTE sequences. Erosion detection of SIJs via computed tomography (CT) served as the standard of reference. The T2* values of the cartilage were measured and compared, and the diagnostic efficacy of the T2* value for axSpA diagnosis was evaluated. Results: A total of 32 patients and 32 healthy volunteers were included. The 3D-UTE sequence, as separately assessed by two reviewers in terms of its ability to detect erosions, exhibited a notable level of accuracy. For the two reviewers, the respective diagnostic sensitivities were 94.7% and 92.9%, the specificities were 97.4% and 96.5%, positive predictive values were 96.7% and 95.4%, the negative predictive values were 95.9% and 94.5%, the accuracies were 96.2% and 94.9%, and the areas under the curve (AUCs) were 96.1% and 94.7%. For the detection of erosions, the interreader κ value was 0.949. The T2* values of the SIJ cartilage were significantly higher in patients with axSpA than in healthy volunteers. The intraobserver intraclass correlation coefficients (ICCs) for T2* measurements ranged between 80.5% and 82.2%. Meanwhile, the interobserver ICCs for UTE-T2* and gradient echo T2* measurements were 81.5% and 80.8%, respectively. The AUCs of the UTE-T2* values for discriminating patients with axSpA from the healthy volunteers of the two readers were 73.3% and 71.6%, respectively. Conclusions: 3D-UTE sequences can be used as a reliable morphological imaging technique for detecting bone erosion in the SIJ. Additionally, UTE-T2* values of the cartilage may offer a quantitative method for identifying patients with axSpA.

A matched case-control study of early cervical spondylotic myelopathy based on diffusion magnetic resonance imaging.

BACKGROUND: Early cervical spondylotic myelopathy (CSM) is challenging to diagnose and easily missed. Diffusion MRI (dMRI) has the potential to identify early CSM. METHODS: Using diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI), a 1:1 matched case-control study was conducted to evaluate the potential of dMRI in identifying early CSM and assessing uncompressed segments of CSM patients. CSM patients and volunteers were matched by age and spinal location. The differences in dMRI parameters between groups were assessed by the paired t-test, the multicollinearity of the dMRI parameters was evaluated by the variance inflation factor (VIF), and the value of dMRI parameters in distinguishing controls from CSM patients was determined by logistic regression. The univariate t-test was used to analyse differences between CSM patients and volunteers in adjacent uncompressed areas. RESULTS: In total, 56 CSM patients and 56 control volunteers were included. Paired t-tests revealed significant differences in nine dMRI parameters between groups. Multicollinearity calculated through VIF and combined with logistic regression showed that the orientation division index (ODI) was significantly positively correlated (r = 2.12, p = 0.035), and the anisotropic water fraction (AWF) was significantly negatively correlated (r = -0.98, p = 0.015). The fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), isotropic volume fraction (ISOVF), ODI, and AWF were significantly different in the upper and lower uncompressed areas at all ages. CONCLUSION: dMRI can noninvasively identify early CSM patients and potentially identify the extent of CSM lesions involving the cervical spinal cord. CRITICAL RELEVANCE STATEMENT: Diffusion MRI (dMRI) can identify early cervical spondylotic myelopathy (CSM) and has the potential to help determine the extent of CSM involvement. The application of dMRI can help screen for early CSM and develop clinical surgical and rehabilitation treatment plans. KEY POINTS: • Diffusion MRI can differentiate between normal and early-stage cervical spondylotic myelopathy patients. • Diffusion MRI has the ability to identify the extent of spinal cord involvement in cervical spondylotic myelopathy. • Diffusion MRI enables the early screening of cervical spondylotic myelopathy and helps guide clinical treatment.

Biventricular intraventricular mechanical and electrical dyssynchrony in pulmonary arterial hypertension.

Background: Pulmonary arterial hypertension (PAH) leads to myocardial remodeling, manifesting as mechanical dyssynchrony (M-dys) and electrical dyssynchrony (E-dys), in both right (RV) and left ventricles (LV). However, the impacts of layer-specific intraventricular M-dys on biventricular functions and its association with E-dys in PAH remain unclear. Methods: Seventy-nine newly diagnosed patients with PAH undergoing cardiac magnetic resonance scanning were consecutively recruited between January 2011 and December 2017. The biventricular volumetric and layer-specific intraventricular M-dys were analyzed. The QRS duration z-scores were calculated after adjusting for age and sex. Results: 77.22 % of patients were female (mean age 30.30 ± 9.79 years; median follow-up 5.53 years). Further, 29 (36.71 %) patients succumbed to all-cause mortality by the end of the study. At the baseline, LV layer-specific intraventricular M-dys had apparent transmural gradients compared with RV in the radial and circumferential directions. However, deceased patients lost the transmural gradients. The LV longitudinal strain rate time to late diastolic peak in the myocardial region (LVmyoLSRTTLDPintra) predicted long-term survival. The Kaplan-Meier curve revealed that patients with PAH with LVmyoLSRTTLDPintra <20.01 milliseconds had a worse prognosis. Larger right ventricle (RV) intraventricular M-dys resulted in worse RV ejection fraction. However, larger LV intraventricular M-dys in the late diastolic phase indicated remarkable exercise capacity and higher LV stroke volume index. E-dys and intraventricular M-dys had no direct correlations. Conclusions: The layer-specific intraventricular M-dys had varying impacts on biventricular functions in PAH. PAH patients with LVmyoLSRTTLDPintra <20.01 milliseconds had a worse prognosis. LV intraventricular M-dys in the late diastolic phase needs more attention to precisely evaluate LV function.

Predictive Value of the Diffusion Magnetic Resonance Imaging Technique for the Postoperative Outcome of Cervical Spondylotic Myelopathy.

BACKGROUND: Diffusion magnetic resonsance imaging (dMRI) can potentially predict the postoperative outcome of cervical spondylotic myelopathy (CSM). PURPOSE: To explore preoperative dMRI parameters to predict the postoperative outcome of CSM through multifactor correlation analysis. STUDY TYPE: Prospective. POPULATION: Post-surgery CSM patients; 102 total, 73 male (52.42 ± 10.60 years old) and 29 female (52.0 ± 11.45 years old). FIELD STRENGTH/SEQUENCE: 3.0 T/Turbo spin echo T1/T2-weighted, T2*-weighted multiecho gradient echo and dMRI. ASSESSMENT: Spinal cord function was evaluated using modified Japanese Orthopedic Association (mJOA) scoring at different time points: preoperative and 3, 6, and 12 months postoperative. Single-factor correlation and t test analyses were conducted based on fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction, orientation division index, increased signal intensity, compression ratio, age, sex, symptom duration and operation method, and multicollinearity was calculated. The linear quantile mixed model (LQMM) and the linear mixed-effects regression model (LMER) were used for multifactor correlation analysis using the combinations of the above variables. STATISTICAL TESTS: Distance correlation, Pearson's correlation, multiscale graph correlation and t tests were used for the single-factor correlation analyses. The variance inflation factor (VIF) was used to calculate multicollinearity. LQMM and LMER were used for multifactor correlation analyses. P < 0.05 was considered statistically significant. RESULTS: The single-factor correlation between all variables and the postoperative mJOA score was weak (all r < 0.3). The linear relationship was stronger than the nonlinear relationship, and there was no significant multicollinearity (VIF = 1.10-1.94). FA values in the LQMM and LMER models had a significant positive correlation with the mJOA score (r = 5.27-6.04), which was stronger than the other variables. DATA CONCLUSION: The FA value based on dMRI significantly positively correlated with CSM patient postoperative outcomes, helping to predict the surgical outcome and formulate a treatment plan before surgery. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Does right hemisphere compensate for the left in school-age children with large left middle fossa arachnoid cysts?

BACKGROUND: To assess the cognitive function changes and brain network neuroplasticity in school-age children having large (diameter > 5 cm) left middle fossa arachnoid cyst (MFACs). METHODS: Eleven patients and 22 normal controls (NC) between 6 and 14 years of age were included. The CNS Vital Signs (CNS VS) were administered for cognitive assessment. The differences of cognitive data and functional connectivity (FC) in resting-state functional magnetic resonance imaging (rs-fMRI) were compared between the patient group and the NC group. The correlations between the altered FC and cognitive data in the patient group were assessed. RESULTS: Patient group had significantly poorer attention (including Complex Attention, Sustained Attention, Simple Attention, Cognitive Flexibility, and Executive Function) and memory function (Visual Memory and Working Memory) than the NC group (uncorrected p-value, p-unc < 0.05). Whole-brain local correlation (LCOR) analysis showed an extensively lower LCOR in the patient group (voxel threshold p-unc < 0.001, cluster-size threshold of false discovery rate adjusted p (p-FDR) < 0.001). Functional connectivity (FC) analysis showed that bilateral frontal and temporal lobes connectivity in the patient group was significantly lower than the NC group (p-FDR < 0.05). Seed-based FC analysis indicated that there was altered FC between the right temporal lobe and the left temporal-parietal/temporal-occipital area (p-FDR < 0.05). In the patient group, most of the altered FC had a negative correlation to the cognitive score, while the FC in the right temporal lobe-left temporal-occipital area positively correlated to Verbal/Visual Memory (r = 0.41-0.60, p-FDR < 0.05). In correlation analysis between clinical data and cognitive score, the only significant result was a low correlation between cyst size and Reaction Time (-0.30--0.36, P-FDR < 0.05). CONCLUSIONS: School-aged children with large left MFAC showed significantly lower cognitive performance primarily in attention and memory domains. Distinct from neuroplasticity in a unilateral brain lesion, compensation in the healthy hemisphere in MFAC patients was sparse.

Amide proton transfer-weighted imaging and derived radiomics in the classification of adult-type diffuse gliomas.

OBJECTIVES: To evaluate the utility of amide proton transfer-weighted (APTw) MRI imaging and its derived radiomics in classifying adult-type diffuse glioma. MATERIALS AND METHODS: In this prospective study, APTw imaging was performed on 129 patients with adult-type diffuse gliomas. The mean APTw-related metrics (chemical exchange saturation transfer ratio (CESTR), CESTR normalized with the reference value (CESTRnr), and relaxation-compensated inverse magnetization transfer ratio (MTRRex)) and radiomic features within 3D tumor masks were extracted. APTw-radiomics models were developed using a support vector machine (SVM) classifier. Sensitivity analysis with tumor area of interest, different histogram cutoff values, and other classifiers were conducted. RESULTS: CESTR, CESTRnr, and MTRRex in glioblastomas were all significantly higher (p < 0.0003) than those of oligodendrogliomas and astrocytomas, with no significant difference between oligodendrogliomas and astrocytomas. The APTw-related metrics for IDH-wildtype and high-grade gliomas were significantly higher (p < 0.001) than those for the IDH-mutant and low-grade gliomas, with area under the curve (AUCs) of 0.88 for CESTR. The CESTR-radiomics models demonstrated accuracies of 84% (AUC 0.87), 83% (AUC 0.83), 90% (AUC 0.95), and 84% (AUC 0.86) in predicting the IDH mutation status, differentiating glioblastomas from astrocytomas, distinguishing glioblastomas from oligodendrogliomas, and determining high/low grade prediction, respectively, but showed poor performance in distinguishing oligodendrogliomas from astrocytomas (accuracy 63%, AUC 0.63). The sensitivity analysis affirmed the robustness of the APTw signal and APTw-derived radiomics prediction models. CONCLUSION: APTw imaging, along with its derived radiomics, presents a promising quantitative approach for prediction IDH mutation and grading adult-type diffuse glioma. CLINICAL RELEVANCE STATEMENT: Amide proton transfer-weighted imaging, a quantitative imaging biomarker, coupled with its derived radiomics, offers a promising non-invasive approach for predicting IDH mutation status and grading adult-type diffuse gliomas, thereby informing individualized clinical diagnostics and treatment strategies. KEY POINTS: • This study evaluates the differences of different amide proton transfer-weighted metrics across three molecular subtypes and their efficacy in classifying adult-type diffuse glioma. • Chemical exchange saturation transfer ratio normalized with the reference value and relaxation-compensated inverse magnetization transfer ratio effectively predicts IDH mutation/grading, notably the first one. • Amide proton transfer-weighted imaging and its derived radiomics holds potential to be used as a diagnostic tool in routine clinical characterizing adult-type diffuse glioma.

Abnormal spontaneous brain activity in females with autism spectrum disorders.

Objectives: To date, most studies on autism spectrum disorder (ASD) have focused on sample sets that were primarily or entirely composed of males; brain spontaneous activity changes in females remain unclear. The purpose of this study was to explore changes in the brain spontaneous neural activity in females with ASD. Methods: In this study, resting-state functional magnetic resonance images (rs-fMRI) of 41 females with ASD and 41 typically developing (TD) controls were obtained from the ABDIE database. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) of the two groups were calculated to detect the regional brain activity. A two independent sample t-test was used to analyze differences between the ASD and TD groups and a p-value <0.05 was considered statistically significant after false discovery rate (FDR) correction. Pearson correlation analysis was conducted between social responsiveness scale (SRS) scores and the local activity of significantly different brain regions. Results: Compared with the typically developing (TD) group, the values of ALFF and ReHo were significantly increased in the left superior temporal gyrus (STG), while the values of ReHo were significantly decreased in the left superior frontal gyrus (SFG), left middle occipital gyrus (MOG), bilateral superior parietal lobule (SPL), and bilateral precuneus in the females with ASD group. Correlation analysis showed that the ReHo of the right precuneus was positively correlated to the total SRS, social communication, and autistic mannerisms. Conclusion: Spontaneous activity changes in females with ASD involved multiple brain regions and were related to clinical characteristics. Our results may provide some help for further exploring the neurobiological mechanism of females with ASD.

Exploration of the etiology of single small subcortical infarctions using high-resolution vessel wall MRI.

Objective: We aimed to explore imaging indicators for diagnosing the etiology of single small subcortical infarctions (SSI) using high-resolution vessel wall imaging (HR-VWI). Methods: Patients with acute isolated subcortical cerebral infarction were prospectively enrolled and classified as having large artery atherosclerosis (LAA), stroke of undetermined etiology (SUD), or small artery disease (SAD). The infarct information, the cerebral small vessel disease (CSVD) score, morphological characteristics of the lenticulostriate arteries (LSAs), and plaque characteristics were compared between the three groups. Results: Seventy seven patients were enrolled (30 LAA, 28 SUD, and 19 SAD). The total CSVD score of the LAA (P = 0.001) and SUD groups (P = 0.017) was significantly lower than that of the SAD group. The number and total length of LSA branches in the LAA and SUD groups were shorter than in the SAD group. Moreover, the total length laterality index (LI) of the LSAs in the LAA and SUD groups was greater than in the SAD group. The total CSVD score and LI of total length were independent predictors for the SUD and LAA groups. The remodeling index of the SUD group was significantly higher than that of the LAA group (P = 0.002); positive remodeling was dominant in the SUD group (60.7%), whereas remodeling in the LAA group was primarily non-positive (83.3%). Conclusions: SSI with and without plaques on the carrier artery may have different modes of pathogenesis. Patients with plaques may also have a coexisting mechanism of atherosclerosis.

The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer's disease.

Objective: To investigate the relationship between changes in cerebral blood flow (CBF) and gray matter (GM) microstructure in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Methods: A recruited cohort of 23 AD patients, 40 MCI patients, and 37 normal controls (NCs) underwent diffusional kurtosis imaging (DKI) for microstructure evaluation and pseudo-continuous arterial spin labeling (pCASL) for CBF assessment. We investigated the differences in diffusion- and perfusion-related parameters across the three groups, including CBF, mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA). These quantitative parameters were compared using volume-based analyses for the deep GM and surface-based analyses for the cortical GM. The correlation between CBF, diffusion parameters, and cognitive scores was assessed using Spearman coefficients, respectively. The diagnostic performance of different parameters was investigated with k-nearest neighbor (KNN) analysis, using fivefold cross-validation to generate the mean accuracy (mAcc), mean precision (mPre), and mean area under the curve (mAuc). Results: In the cortical GM, CBF reduction primarily occurred in the parietal and temporal lobes. Microstructural abnormalities were predominantly noted in the parietal, temporal, and frontal lobes. In the deep GM, more regions showed DKI and CBF parametric changes at the MCI stage. MD showed most of the significant abnormalities among all the DKI metrics. The MD, FA, MK, and CBF values of many GM regions were significantly correlated with cognitive scores. In the whole sample, the MD, FA, and MK were associated with CBF in most evaluated regions, with lower CBF values associated with higher MD, lower FA, or lower MK values in the left occipital lobe, left frontal lobe, and right parietal lobe. CBF values performed best (mAuc = 0.876) for distinguishing the MCI from the NC group. Last, MD values performed best (mAuc = 0.939) for distinguishing the AD from the NC group. Conclusion: Gray matter microstructure and CBF are closely related in AD. Increased MD, decreased FA, and MK are accompanied by decreased blood perfusion throughout the AD course. Furthermore, CBF values are valuable for the predictive diagnosis of MCI and AD. GM microstructural changes are promising as novel neuroimaging biomarkers of AD.

Regional Dynamic Neuroimaging Changes of Adults with Autism Spectrum Disorder.

Most neuroimaging studies investigating autism spectrum disorder (ASD) have focused on static brain function, but ignored the dynamic features of spontaneous brain activities in the temporal dimension. Research of dynamic brain regional activities might help to fully investigate the mechanisms of ASD patients. This study aimed to examine potential changes in the dynamic characteristics of regional neural activities in adult ASD patients and to detect whether the changes were associated with Autism Diagnostic Observation Schedule (ADOS) scores. Resting-state functional MRI was obtained on 77 adult ASD patients and 76 healthy controls. The dynamic regional homogeneity (dReHo) and dynamic amplitude of low-frequency fluctuations (dALFF) were compared between the two groups. Correlation analyses were also performed between dReHo and dALFF in areas showing group differences and ADOS scores. In ASD group, significant differences in dReHo were observed in the left middle temporal gyrus (MTG.L). Besides, we found increased dALFF in the left middle occipital gyrus (MOG.L), left superior parietal gyrus (SPG.L), left precuneus (PCUN.L), left inferior temporal gyrus (ITG.L), and right inferior frontal gyrus, orbital part (ORBinf.R). Furthermore, a significant positive correlation was found between dALFF in the PCUN.L and the ADOS_TOTAL scores, ADOS_SOCIAL scores; the dALFF in the ITG.L, SPG.L was positively associated with ADOS_SOCIAL scores. In conclusion, adults with ASD have a wide area of dynamic regional brain function abnormalities. These suggested that dynamic regional indexes might be used as a powerful measure to help us obtain a more comprehensive understanding of neural activity in adult ASD patients.

Intrinsic brain activity alterations in patients with Parkinson's disease.

OBJECTIVE: The objective of this study is to explore the brain activity alterations in Parkinson's disease (PD) from the perspectives of neuronal activity, synchronization of neuronal activity, and coordination of whole-brain activity. METHODS: In this study, we recruited 38 PD patients and 35 matched healthy controls (HCs). We explored intrinsic brain activity alterations in PD by comparing resting-state functional magnetic resonance imaging (rs-fMRI) metrics of the amplitude of low-frequency of fluctuation (ALFF), the fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC). Two-sample t-tests were used to determine the differences between the two groups. Spearman correlation analysis was used to explore the relationships between abnormal ALFF, fALFF, PerAF, ReHo, and DC values and clinical indicators such as the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Hoehn and Yahr (H&Y) stage, and duration of disease. RESULTS: Compared with the HCs, PD had increased ALFF,fALFF, and PerAF in the temporal lobe and cerebellum, and decreased ALFF,fALFF, and PerAF in the occipital-parietal lobe in the neuronal activity. In the synchronization of neuronal activity, PD patients had increased ReHo in the right inferior parietal lobule and decreased ReHo in the caudate. In the coordination of whole-brain activity, PD patients had increased DC in the cerebellum and decreased DC in the occipital lobe. Correlation analysis showed that there is a correlation between abnormal brain regions and clinical indicators in PD. Notably, the changes in occipital lobe brain activity were found in ALFF, fALFF, PerAF, and DC, and were most correlated with the clinical indicators of PD patients. CONCLUSIONS: This study found that intrinsic brain function in several occipital-temporal-parietal and cerebellum regions was altered in PD patients, potentially related to the clinical indicators of PD. These results may enhance our understanding of the underlying neural mechanisms of PD and may contribute to further exploring the selection of therapeutic targets in PD patients.

Co-existing intracranial and extracranial carotid atherosclerosis predicts large-artery atherosclerosis stroke recurrence: a single-center prospective study utilizing combined head-and-neck vessel wall imaging.

OBJECTIVES: Intracranial and extracranial plaque features on high-resolution vessel wall imaging (HR-VWI) are associated with large-artery atherosclerosis (LAA) stroke recurrence. However, most studies have focused on a single vascular bed, and the prognostic value of combined intracranial and extracranial plaque features has yet to be studied. This study aimed to investigate the roles of plaque features, plaque number, and co-existing atherosclerosis in predicting stroke recurrence, utilizing combined head-and-neck HR-VWI. METHODS: From September 2016 to March 2020, participants with acute LAA ischemic strokes were prospectively enrolled and underwent combined head-and-neck HR-VWI. The participants were followed for stroke recurrence for at least 12 months or until a subsequent event occurred. The imaging features at baseline, including conventional and histogram plaque features, plaque number, and co-existing atherosclerosis, were evaluated. Univariable Cox regression analysis and the least absolute shrinkage and selection operator (lasso) method were used for variable screening. Multivariable Cox regression analyses were used to determine the independent risk factors of stroke recurrence. RESULTS: A total of 97 participants (59 ± 12 years, 63 men) were followed for a median of 30.9 months, and 21 participants experienced recurrent strokes. Multivariable Cox analysis identified co-existing intracranial high signal on T1-weighted fat-suppressed images (HST1) and extracranial carotid atherosclerosis (HR, 6.12; 95% CI, 2.52-14.82; p = 0.001) as an independent imaging predictor of stroke recurrence. CONCLUSION: Co-existing intracranial HST1 and extracranial carotid atherosclerosis independently predicted LAA stroke recurrence. Combined head-and-neck HR-VWI is a promising technique for atherosclerosis imaging. CLINICAL RELEVANCE STATEMENT: This prospective study using combined head-and-neck HR-VWI highlighted the necessity of both intracranial culprit plaque evaluation and multi-vascular bed assessment, adding value to the prediction of stroke recurrence. KEY POINTS: • This study highlighted the necessity of both intracranial culprit plaque evaluation and multi-vascular bed assessment, adding value to the prediction of stroke recurrence. • This prospective study using combined head-and-neck HR-VWI found co-existing intracranial HST1 and extracranial carotid atherosclerosis to be independent predictors of stroke recurrence.

Early recovery of cognition and brain plasticity after surgery in children with low-grade frontal lobe tumors.

Background: Low-grade frontal lobe tumors (LGFLT) can be cured through total resection, but surgical trauma could impair higher-order cognitive function. We aim to characterize the short-term natural cognitive recovery and brain plasticity in surgically-treated pediatric patients with LGFLT. Methods: Ten pediatric patients with LGFLT were enrolled. Their cognitive function was assessed before the surgery (S0), in the first month post-surgery (S1), and 3-6 months post-surgery (S2), using the CNS Vital Signs battery. DTI and rs-fMRI were performed during the same time periods. Changes of cognition and image metrics between S1>S0 and S2>S1 were analyzed. Results: The Motor Speed (MotSp) and Reaction Time (RT) scores significantly decreased in S1 and recovered in S2. Rs-fMRI showed decreased functional connectivity (FC) between the bilateral frontal lobes and bilateral caudates, putamina, and pallidi in S1>S0 (voxel threshold p -unc < 0.001 , cluster threshold p -FDR < 0.05 ). In S2>S1, FC recovery was observed in the neighboring frontal cortex areas ( p -unc < 0.001 , p -FDR < 0.05 ). Among them, the FC in the caudates-right inferior frontal gyri was positively correlated to the RT ( p -FDR < 0.05 ). A DTI Tract-based spatial statistics (TBSS) analysis showed decreased fractional anisotropy and axial diffusivity mainly in the corticospinal tracts, cingulum, internal capsule, and external capsule at 0-6 months post-surgery (TFCE- p < 0.05 ). The DTI metrics were not associated with the cognitive data. Conclusion: Processing speed impairment after an LGFLT resection can recover naturally within 3-6 months in school-age children. Rs-fMRI is more sensitive to short-term brain plasticity than DTI TBSS analysis. "Map expansion" plasticity in the frontal-basal ganglia circuit may contribute to the recovery.

Altered static and dynamic functional network connectivity in post-stroke cognitive impairment.

Post-stroke cognitive impairment (PSCI) is a common symptom following brain stroke, yet the mechanisms remain unknown. This study aimed to investigate alterations of static and dynamic functional network connectivity (sFNC and dFNC) in PSCI patients. We prospectively recruited 17 PSCI patients and 24 Healthy controls (HC). Restingstate fMRI (rs-fMRI) and Mini-Mental State Examination (MMSE) were performed. Independent component analysis combined with sliding-window and K-means clustering approach were applied to examine the FNC among 11 resting-state networks: auditory network (AUDN), left executive control network (lECN), language network (LN), precuneus network (PCUN), right executive control network (rECN), salience network (SN), visuospatial network (VN), dorsal default mode network (dDMN), higher visual network (hVIS), primary visual network (pVIS), and ventral mode network (vDMN). The FNC and dynamic indices (fraction time, mean dwell time, transition number) were calculated. Static and dynamic measures were compared between two groups and the correlation between clinical and imaging indicators was analyzed. For sFNC, PSCI group showed decreased interactions in dDMN-vDMN, vDMN-SN, dDMN-hVIS, AUDN-rECN, and AUDN-VN. For dFNC, we derived 3 states of FNC that occurred repeatedly. Significant group differences were found, including decreased interactions in the AUDN-VN, AUDN-pVIS in state 2 and dDMN-VN in state 3. The mean dwell time in PSCI group was longer in state 1, and negatively correlated with MMSE score. These results demonstrated that PSCI patients are characterized with altered sFNC and dFNC, which could help us explore the neural mechanisms of the PSCI from a new perspective.

Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment : Detected by Neurite Orientation Dispersion and Density Imaging (NODDI) Combined with Machine Learning.

PURPOSE: This study investigated brain microstructural changes in patients with amnestic mild cognitive impairment (aMCI) by retrospectively analyzing neurite orientation dispersion and density imaging (NODDI) data with machine learning algorithms. METHODS: A total of 26 aMCI patients and 24 healthy controls (HC) underwent NODDI magnetic resonance imaging (MRI) examinations. The NODDI parameters including neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were estimated. Machine learning algorithms such as K­nearest neighbor (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to evaluate the diagnostic efficacy of NODDI parameters in predicting aMCI. The differences in the NODDI parameter values between the aMCI and HC groups were analyzed using the independent sample t­test, False discovery rate (FDR) correction was used for multiple testing. After adjusting for age, sex, and educational years, partial correlation analysis was used to evaluate the relationship between NODDI parameters and clinical cognitive status of aMCI patients. RESULTS: The NDI, ODI, and Viso values of white matter (WM) and gray matter (GM) structure templates combined with the KNN, LR, RF and SVM machine learning algorithms accomplished the discrimination between aMCI and HC groups. The NDI and ODI values decreased (p value range, < 0.001-0.042) and Viso values increased (p value range, < 0.001-0.043) in the aMCI group compared to the HCs. The NDI, ODI, and Viso values of the WM and GM structure templates with significant differences were significantly correlated with mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scores. CONCLUSION: NODDI combined with machine learning algorithms is a promising strategy for early diagnosis of aMCI. Moreover, NODDI parameters correlated with the clinical cognitive status of aMCI patients.

Ppm-level oxygen detection system based on deep-ultraviolet-absorption spectroscopy.

Oxygen is a gas essential to human life and industrial processes. Here, we developed a highly sensitive oxygen gas sensor based on deep ultraviolet absorption spectroscopy between 180 and 200 nm. The implemented method relies on differential absorption spectra extracted from the obtained high-resolution absorption spectra. The detection capability was greatly improved (six-fold) by eliminating air from the open optical path, achieved by purging the entire system with pure nitrogen. A linear relationship was obtained between the optical parameter and the oxygen concentration with a slope of 0.107 and determination coefficient of 0.999. A detection limit of 24 ppm per meter was determined with a response time of 25 s. Good repeatability (standarddeviation=16ppm) and stability were confirmed. We demonstrated that this system can detect ppm oxygen levels with high sensitivity and low uncertainty.

More than just statics: Static and temporal dynamic changes in intrinsic brain activity in unilateral temporal lobe epilepsy.

Background: Temporal lobe epilepsy (TLE) is the most prevalent refractory focal epilepsy and is more likely accompanied by cognitive impairment. The fully understanding of the neuronal activity underlying TLE is of great significance. Objective: This study aimed to comprehensively explore the potential brain activity abnormalities affected by TLE and detect whether the changes were associated with cognition. Methods: Six static intrinsic brain activity (IBA) indicators [amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree centrality (DC), global signal correlation (GSCorr), and voxel-mirrored homotopic connectivity (VMHC)] and their corresponding dynamic indicators, such as dynamic ALFF (dALFF), dynamic fALFF (dfALFF), dynamic ReHo (dReHo), dynamic DC (dDC), dynamic VMHC (dVMHC), and dynamic GSCorr (dGSCorr), in 57 patients with unilateral TLE and 42 healthy volunteers were compared. Correlation analyses were also performed between these indicators in areas displaying group differences and cognitive function, epilepsy duration, and severity. Results: Marked overlap was present among the abnormal brain regions detected using various static and dynamic indicators, primarily including increased ALFF/dALFF/fALFF in the bilateral medial temporal lobe and thalamus, decreased ALFF/dALFF/fALFF in the frontal lobe contralateral to the epileptogenic side, decreased fALFF, ReHo, dReHo, DC, dDC, GSCorr, dGSCorr, and VMHC in the temporal neocortex ipsilateral to the epileptogenic foci, decreased dReHo, dDC, dGSCorr, and dVMHC in the occipital lobe, and increased ALFF, fALFF, dfALFF, ReHo, and DC in the supplementary motor area ipsilateral to the epileptogenic foci. Furthermore, most IBA indicators in the abnormal brain region significantly correlated with the duration of epilepsy and several cognitive scale scores (P < 0.05). Conclusion: The combined application of static and dynamic IBA indicators could comprehensively reveal more real abnormal neuronal activity and the impairment and compensatory mechanisms of cognitive function in TLE. Moreover, it might help in the lateralization of epileptogenic foci and exploration of the transmission and inhibition pathways of epileptic activity.

A diagnostic index based on pseudo-continuous arterial spin labeling and T1-mapping improves efficacy in discriminating Alzheimer's disease from normal cognition.

Background: Pseudo-continuous arterial spin labeling (pCASL) is widely used to quantify cerebral blood flow (CBF) abnormalities in patients with Alzheimer's disease (AD). T1-mapping techniques assess microstructural characteristics in various pathologic changes, but their application in AD remains in the exploratory stage. We hypothesized that combining quantitative CBF and T1 values would generate diagnostic results with higher accuracy than using either method alone in discriminating AD patients from cognitively normal control (NC) subjects. Materials and methods: A total of 45 patients diagnosed with AD and 33 NC subjects were enrolled, and cognitive assessment was performed for each participant according to the Chinese version of the Mini-Mental State Examination (MMSE). T1-weighted magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) and pCASL sequence were scanned on a 3T MR scanner. A brain morphometric analysis was integrated into prototype sequence, providing tissue classification and morphometric segmentation results. Quantitative CBF and T1 values of each brain region were automatically generated inline after data acquisition. Independent samples t-test was used to compare regional CBF and T1 values controlled by false discovery rate correction (corrected p < 0.01). The model with combined CBF and T1 values was compared with the individual index by performing receiver operating characteristic curves analysis. The associations between the MMSE score and CBF and T1 values of the brain were investigated using partial correlations. Results: Cerebral blood flow of the right caudate nucleus (RCc) and left hippocampus (LHc) was significantly lower in the AD group compared with the NC group, while the T1 values of the right caudate nucleus (RCt) and left hippocampus (LHt) increased in the AD group. Prediction accuracies of 73.1, 77.2, 75.9, and 81.3% were achieved for each of the above parameters, respectively. In distinguishing patients from controls using the corresponding optimized cut-off values, most combinations of parameters were elevated (area under curve = 0.775-0.894). The highest area under curve value was 0.944, by combining RCc, LHc, RCt, and LHt. Conclusion: In this preliminary study, the combined model based on pCASL and T1-mapping improved the diagnostic performance of discriminating AD and NC groups. T1-mapping may become a competitive technique for quantitatively measuring pathologic changes in the brain.