Neurovascular coupling alteration in drug-naïve Parkinson's disease: The underlying molecular mechanisms and levodopa's restoration effects.

BACKGROUND: Parkinson's disease (PD) patients exhibit an imbalance between neuronal activity and perfusion, referred to as abnormal neurovascular coupling (NVC). Nevertheless, the underlying molecular mechanism and how levodopa, the standard treatment in PD, regulates NVC is largely unknown. MATERIAL AND METHODS: A total of 52 drug-naïve PD patients and 49 normal controls (NCs) were enrolled. NVC was characterized in vivo by relating cerebral blood flow (CBF) and amplitude of low-frequency fluctuations (ALFF). Motor assessments and MRI scanning were conducted on drug-naïve patients before and after levodopa therapy (OFF/ON state). Regional NVC differences between patients and NCs were identified, followed by an assessment of the associated receptors/transporters. The influence of levodopa on NVC, CBF, and ALFF within these abnormal regions was analyzed. RESULTS: Compared to NCs, OFF-state patients showed NVC dysfunction in significantly lower NVC in left precentral, postcentral, superior parietal cortex, and precuneus, along with higher NVC in left anterior cingulate cortex, right olfactory cortex, thalamus, caudate, and putamen (P-value <0.0006). The distribution of NVC differences correlated with the density of dopaminergic, serotonin, MU-opioid, and cholinergic receptors/transporters. Additionally, levodopa ameliorated abnormal NVC in most of these regions, where there were primarily ALFF changes with limited CBF modifications. CONCLUSION: Patients exhibited NVC dysfunction primarily in the striato-thalamo-cortical circuit and motor control regions, which could be driven by dopaminergic and nondopaminergic systems, and levodopa therapy mainly restored abnormal NVC by modulating neuronal activity.

Microstructural alterations of the hypothalamus in Parkinson's disease and probable REM sleep behavior disorder.

BACKGROUND: Whether there is hypothalamic degeneration in Parkinson's disease (PD) and its association with clinical symptoms and pathophysiological changes remains controversial. OBJECTIVES: We aimed to quantify microstructural changes in hypothalamus using a novel deep learning-based tool in patients with PD and those with probable rapid-eye-movement sleep behavior disorder (pRBD). We further assessed whether these microstructural changes associated with clinical symptoms and free thyroxine (FT4) levels. METHODS: This study included 186 PD, 67 pRBD, and 179 healthy controls. Multi-shell diffusion MRI were scanned and mean kurtosis (MK) in hypothalamic subunits were calculated. Participants were assessed using Unified Parkinson's Disease Rating Scale (UPDRS), RBD Questionnaire-Hong Kong (RBDQ-HK), Hamilton Depression Rating Scale (HAMD), and Activity of Daily Living (ADL) Scale. Additionally, a subgroup of PD (n = 31) underwent assessment of FT4. RESULTS: PD showed significant decreases of MK in anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tubular (supTub), and inferior tubular hypothalamus when compared with healthy controls. Similarly, pRBD exhibited decreases of MK in a-iHyp and supTub. In PD group, MK in above four subunits were significantly correlated with UPDRS-I, HAMD, and ADL. Moreover, MK in a-iHyp and a-sHyp were significantly correlated with FT4 level. In pRBD group, correlations were observed between MK in a-iHyp and UPDRS-I. CONCLUSIONS: Our study reveals that microstructural changes in the hypothalamus are already significant at the early neurodegenerative stage. These changes are associated with emotional alterations, daily activity levels, and thyroid hormone levels.

Cholinergic basal forebrain system degeneration underlies postural instability/gait difficulty and attention impairment in Parkinson's disease.

BACKGROUND AND PURPOSE: The specific pathophysiological mechanisms underlying postural instability/gait difficulty (PIGD) and cognitive function in Parkinson's disease (PD) remain unclear. Both postural and gait control, as well as cognitive function, are associated with the cholinergic basal forebrain (cBF) system. METHODS: A total of 84 PD patients and 82 normal controls were enrolled. Each participant underwent motor and cognitive assessments. Diffusion tensor imaging was used to detect structural abnormalities in the cBF system. The cBF was segmented using FreeSurfer, and its fiber tract was traced using probabilistic tractography. To provide information on extracellular water accumulation, free-water fraction (FWf) was quantified. FWf in the cBF and its fiber tract, as well as cortical projection density, were extracted for statistical analyses. RESULTS: Patients had significantly higher FWf in the cBF (p < 0.001) and fiber tract (p = 0.021) than normal controls, as well as significantly lower cBF projection in the occipital (p < 0.001), parietal (p < 0.001) and prefrontal cortex (p = 0.005). In patients, a higher FWf in the cBF correlated with worse PIGD score (r = 0.306, p = 0.006) and longer Trail Making Test A time (r = 0.303, p = 0.007). Attentional function (Trail Making Test A) partially mediated the association between FWf in the cBF and PIGD score (indirect effect, a*b = 0.071; total effect, c = 0.256; p = 0.006). CONCLUSIONS: Our findings suggest that degeneration of the cBF system in PD, from the cBF to its fiber tract and cortical projection, plays an important role in cognitive-motor interaction.

Predictability of inter-regional cerebral perfusion similarity on dopamine responsiveness and the moderation role of cognition in PD patients.

BACKGROUND: Large heterogeneity can be found in dopamine responsiveness of patients with Parkinson's disease (PD). Instantly and objectively understanding dopamine responsiveness of patients may help clinical practice. PURPOSE: This PD study explored the predictability of off-state inter-regional cerebral blood flow (CBF) perfusion similarity on patient's dopamine responsiveness and tested whether the predictive power could be moderated by patient's cognitive status. MATERIALS AND METHOD: The PD cohort with 192 patients (containing off state and on state (PD-off and PD-on)) and the normal control (NC) cohort with 92 subjects were included. The intra-individual CBF relative variation networks were constructed and compared between PD-off and PD-on, PD-off and NC to identify the alterations caused by dopamine depletion. Based on that, regression analysis of off-state inter-regional CBF perfusion similarity on patient's dopamine responsiveness was performed. Finally, moderation analysis was conducted to test the moderation role of cognition on the regression model. RESULTS: In the PD-off cohort, a total of 82 edges in the network were identified that affected by dopamine depletion. Off-state inter-regional CBF perfusion similarity was found that had a significant influence on patient's dopamine responsiveness. Cognitive status was validated that positively moderated the relationship between off-state inter-regional CBF perfusion similarity and dopamine responsiveness. CONCLUSION: Dopamine responsiveness of PD patient could be predicted by off-state inter-regional CBF perfusion similarity. Patient's cognitive status might have a positive moderation effect on his/her dopamine responsiveness.

Functional connectome predicting individual gait function and its relationship with molecular architecture in Parkinson's disease.

Gait impairment is a common symptom of Parkinson's disease (PD), but its neural signature remains unclear due to the interindividual variability of gait performance. Identifying a robust gait-brain correlation at the individual level would provide insight into a generalizable neural basis of gait impairment. In this context, this study aimed to detect connectome that can predict individual gait function of PD, and follow-up analyses assess the molecular architecture underlying the connectome by relating it to the neurotransmitter-receptor/transporter density maps. Resting-state functional magnetic resonance imaging was used to detect the functional connectome, and gait function was assessed via a 10 m-walking test. The functional connectome was first detected within drug-naive patients (N = 48) by using connectome-based predictive modeling following cross-validation and then successfully validated within drug-managed patients (N = 30). The results showed that the motor, subcortical, and visual networks played an important role in predicting gait function. The connectome generated from patients failed to predict the gait function of 33 normal controls (NCs) and had distinct connection patterns compared to NCs. The negative connections (connection negatively correlated with 10 m-walking-time) pattern of the PD connectome was associated with the density of the D2 receptor and VAChT transporter. These findings suggested that gait-associated functional alteration induced by PD pathology differed from that induced by aging degeneration. The brain dysfunction related to gait impairment was more commonly found in regions expressing more dopaminergic and cholinergic neurotransmitters, which may aid in developing targeted treatments.

Presence but not the timing of onset of REM sleep behavior disorder distinguishes evolution patterns in Parkinson's disease.

BACKGROUND: Rapid eye movement (REM) sleep behavior disorder (RBD) could develop preceding or come after motor symptoms during Parkinson's disease (PD). It remains unknown that whether PD with different timing of RBD onset relative to motor symptoms suggests different spatiotemporal sequence of neurodegeneration. This study aimed to explore the sequence of disease progression in crucially involved brain regions in PD with different timing of RBD onset. METHOD: We recruited 157 PD, 16 isolated RBD (iRBD), and 78 healthy controls. PD patients were identified as (1) PD with RBD preceding motor symptoms (PD-preRBD, n = 50), (2) PD with RBD posterior to motor symptoms (PD-postRBD, n = 31), (3) PD without RBD (PD-nonRBD, n = 75). The volumes of crucial brain regions, including the basal ganglia and limbic structures in T1-weighted imaging, and the contrast-noise-ratios of locus coeruleus (LC) and substantia nigra (SN) in neuromelanin-sensitive magnetic resonance imaging, were extracted. To simulate the sequence of disease progression for cross-sectional data, an event-based model was introduced to estimate the maximum likelihood sequence of regions' involvement for each group. Then, a statistical parameter, the Bhattacharya coefficient (BC), was used to evaluate the similarity of the sequence. RESULTS: The model predicted that SN occupied the highest likelihood in the maximum likelihood sequence of disease progression in the all PD subgroups, while LC was specifically positioned earlier to SN in iRBD, a prodromal phase of PD. Subsequent early involvement of LC was observed in the both PD-preRBD and PD-postRBD. In contrast, atrophy in the para-hippocampal gyrus but relatively intact LC in the early stage was demonstrated in PD-nonRBD. Then, the similarity comparisons indicated higher BC between PD-postRBD and PD-preRBD (BC = 0.76) but lower BC between PD-postRBD and PD-nonRBD group (BC = 0.41). iRBD had higher BC against PD-preRBD (BC = 0.66) and PD-postRBD (BC = 0.63) but lower BC against PD- nonRBD (BC = 0.48). CONCLUSION: The spatiotemporal sequence of neurodegeneration between PD-pre and PD-post were similar but distinct from PD-nonRBD. The presence of RBD may be the essential factor for differentiating the degeneration patterns of PD, but the timing of RBD onset has currently proved to be not.

Normalization effect of dopamine replacement therapy on brain functional connectome in Parkinson's disease.

Dopamine replacement therapy (DRT) represents the standard treatment for Parkinson's disease (PD), however, instant and long-term medication influence on patients' brain function have not been delineated. Here, a total of 97 drug-naïve patients, 43 patients under long-term DRT, and 94 normal control (NC) were, retrospectively, enrolled. Resting-state functional magnetic resonance imaging data and motor symptom assessments were conducted before and after levodopa challenge test. Whole-brain functional connectivity (FC) matrices were constructed. Network-based statistics were performed to assess FC difference between drug-naïve patients and NC, and these significant FCs were defined as disease-related connectomes, which were used for further statistical analyses. Patients showed better motor performances after both long-term DRT and levodopa challenge test. Two disease-related connectomes were observed with distinct patterns. The FC of the increased connectome, which mainly consisted of the motor, visual, subcortical, and cerebellum networks, was higher in drug-naïve patients than that in NC and was normalized after long-term DRT (p-value <.050). The decreased connectome was mainly composed of the motor, medial frontal, and salience networks and showed significantly lower FC in all patients than NC (p-value <.050). The global FC of both increased and decreased connectome was significantly enhanced after levodopa challenge test (q-value <0.050, false discovery rate-corrected). The global FC of increased connectome in ON-state was negatively associated with levodopa equivalency dose (r = -.496, q-value = 0.007). Higher global FC of the decreased connectome was related to better motor performances (r = -.310, q-value = 0.022). Our findings provided insights into brain functional alterations under dopaminergic medication and its benefit on motor symptoms.

MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain.

OBJECTIVE: To develop a nomogram based on MRI radiomics and clinical features for preoperatively predicting H3K27M mutation in pediatric high-grade gliomas (pHGGs) with a midline location of the brain. METHODS: The institutional database was reviewed to identify patients with pHGGs with a midline location of the brain who underwent tumor biopsy with preoperative MRI scans between June 2016 and June 2021. A total of 107 patients with pHGGs, including 79 patients with H3K27M mutation, were consecutively included and randomly divided into training and test sets. Radiomics features were extracted from fluid-attenuated inversion recovery (FLAIR), diffusion-weighted (DW) and post-contrast T1-weighted images, and apparent diffusion coefficient (ADC) maps. The minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were performed for radiomics signature construction. Clinical and radiological features were analyzed to select clinical predictors. A nomogram was then developed by incorporating the radiomics signature and selected clinical predictors. RESULTS: Nine radiomics features were selected to construct the radiomics signature, which showed a favorable discriminatory ability in training and test sets with an area under the curve (AUC) of 0.95 and 0.92, respectively. Ring enhancement was identified as an independent clinical predictor (p < 0.01). The nomogram, constructed with radiomics signature and ring enhancement, showed good calibration and discrimination in training and testing sets (AUC: 0.95 and 0.90 respectively). CONCLUSIONS: The nomogram which combined radiomics signature and ring enhancement had a satisfactory ability to predict H3K27M mutation in pHGGs with a midline of the brain. KEY POINTS: • Conventional MRI features were not powerful enough to predict H3K27M mutation status in pediatric high-grade gliomas (pHGGs) with a midline location of the brain. • An MRI-based radiomics signature showed satisfactory ability to predict H3K27M mutation status of pHGGs located in the midline of the brain. • Associating the radiomics signature with clinical factors improved predictive performance.

An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions.

OBJECTIVES: To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images. METHODS: This retrospective study collected 965 pure NME lesions (539 benign and 426 malignant) confirmed by histopathology or follow-up in 903 women. The 754 NME lesions acquired by one MR scanner were randomly split into the training set, validation set, and test set A (482/121/151 lesions). The 211 NME lesions acquired by another MR scanner were used as test set B. The AI system was developed using ResNet-50 with the axial and sagittal MIP images. One senior and one junior radiologist reviewed the MIP images of each case independently and rated its Breast Imaging Reporting and Data System category. The performance of the AI system and the radiologists was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: The AI system yielded AUCs of 0.859 and 0.816 in the test sets A and B, respectively. The AI system achieved comparable performance as the senior radiologist (p = 0.558, p = 0.041) and outperformed the junior radiologist (p < 0.001, p = 0.009) in both test sets A and B. After AI assistance, the AUC of the junior radiologist increased from 0.740 to 0.862 in test set A (p < 0.001) and from 0.732 to 0.843 in test set B (p < 0.001). CONCLUSION: Our MIP-based AI system yielded good applicability in classifying NME lesions in breast MRI and can assist the junior radiologist achieve better performance. KEY POINTS: • Our MIP-based AI system yielded good applicability in the dataset both from the same and a different MR scanner in predicting malignant NME lesions. • The AI system achieved comparable diagnostic performance with the senior radiologist and outperformed the junior radiologist. • This AI system can assist the junior radiologist achieve better performance in the classification of NME lesions in MRI.

Radiogenomics of neuroblastoma in pediatric patients: CT-based radiomics signature in predicting MYCN amplification.

OBJECTIVES: To construct a CT-based radiomics signature and assess its performance in predicting MYCN amplification (MNA) in pediatric patients with neuroblastoma. METHODS: Seventy-eight pediatric patients with neuroblastoma were recruited (55 in training cohort and 23 in test cohort). Radiomics features were extracted automatically from the region of interest (ROI) manually delineated on the three-phase computed tomography (CT) images. Selected radiomics features were retained to construct radiomics signature and a radiomics score (rad-score) was calculated by using the radiomics signature-based formula. A clinical model was established with clinical factors, including clinicopathological data, and CT image features. A combined nomogram was developed with the incorporation of a radiomics signature and clinical factors. The predictive performance was assessed by receiver operating characteristics curve (ROC) analysis and decision curve analysis (DCA). RESULTS: The radiomics signature was constructed using 7 selected radiomics features. The clinical radiomics nomogram, which was based on the radiomics signature and two clinical factors, showed superior predictive performance compared with the clinical model alone (area under the curve (AUC) in the training cohort: 0.95 vs. 0.82, the test cohort: 0.91 vs. 0.70). The clinical utility of clinical radiomics nomogram was confirmed by DCA. CONCLUSIONS: This proposed CT-based radiomics signature was able to predict MNA. Combining the radiomics signature with clinical factors outperformed using clinical model alone for MNA prediction. KEY POINTS: • A CT-based radiomics signature has the ability to predict MYCN amplification (MNA) in neuroblastoma. • Both pre- and post-contrast CT images are valuable in predicting MNA. • Associating the radiomics signature with clinical factors improved the predictive performance of MNA, compared with clinical model alone.

A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening.

OBJECTIVES: To investigate the value of full-field digital mammography-based deep learning (DL) in predicting malignancy of Breast Imaging Reporting and Data System (BI-RADS) 4 microcalcifications. METHODS: A total of 384 patients with 414 pathologically confirmed microcalcifications (221 malignant and 193 benign) were randomly allocated into the training, validation, and testing datasets (272/71/71 lesions) in this retrospective study. A combined DL model was developed incorporating mammography and clinical variables. Model performance was evaluated by using areas under the receiver operating characteristic curve (AUC) and compared with the clinical model, stand-alone DL image model, and BI-RADS approach. The predictive performance for malignancy was also compared between the combined model and human readers (2 juniors and 2 seniors). RESULTS: The combined DL model demonstrated favorable AUC, sensitivity, and specificity of 0.910, 85.3%, and 91.9% in predicting BI-RADS 4 malignant microcalcifications in the testing dataset, which outperformed the clinical model, DL image model, and BI-RADS with AUCs of 0.799, 0.841, and 0.804, respectively. The combined model achieved non-inferior performance as senior radiologists (p = 0.860, p = 0.800) and outperformed junior radiologists (p = 0.155, p = 0.029). The diagnostic performance of two junior radiologists was improved after artificial intelligence assistance with AUCs increased to 0.854 and 0.901 from 0.816 (p = 0.556) and 0.773 (p = 0.046), while the interobserver agreement was improved with a kappa value increased to 0.843 from 0.331. CONCLUSIONS: The combined deep learning model can improve the malignancy prediction of BI-RADS 4 microcalcifications in screening mammography and assist junior radiologists to achieve better performance, which can facilitate clinical decision-making. KEY POINTS: • The combined deep learning model demonstrated high diagnostic power, sensitivity, and specificity for predicting malignant BI-RADS 4 mammographic microcalcifications. • The combined model achieved similar performance with senior breast radiologists, while it outperformed junior breast radiologists. • Deep learning could improve the diagnostic performance of junior radiologists and facilitate clinical decision-making.

Clinical-MRI radiomics enables the prediction of preoperative cerebral spinal fluid dissemination in children with medulloblastoma.

BACKGROUND: Medulloblastoma (MB) is the most common pediatric embryonal tumor. Accurate identification of cerebral spinal fluid (CSF) dissemination is important in prognosis prediction. Both MRI of the central nervous system (CNS) and CSF cytology will appear false positive and negative. Our objective was to investigate the added value of preoperative-enhanced T1-weighted image-based radiomic features to clinical characteristics in predicting preoperative CSF dissemination for children with MB. MATERIALS AND METHODS: This retrospective study included 84 children with histopathologically confirmed MB between November 2006 and November 2018 (training cohort, n=60; internal validation cohort, n=24). A set of cases between December 2018 and February 2020 were used for external validation (n=40). The children with normal head and spine magnetic resonance images (MRI) and no subsequent dissemination in 1 year were diagnosed as non-CSF dissemination. The CSF dissemination was manifested as intracranial or intraspinal nodular-enhanced lesions. Clinical features were collected, and conventional MRI features of preoperative head MRI examinations were evaluated. A total of 385 radiomic features were extracted from preoperative-enhanced T1-weighted images. Minimum redundancy, maximum correlation, and least absolute shrinkage and selection operator were performed to select the features with the best performance in predicting preoperative CSF dissemination. A combined clinical-MRI radiomic prediction model was developed using multivariable logistic regression. Receiver operating curve analysis (ROC) was used to validate the predictive performance. Nomogram and decision curve analysis (DCA) were developed to evaluate the clinical utility of the combined model. RESULTS: One clinical and nine radiomic features were selected for predicting preoperative CSF dissemination. The combined model incorporating clinical and radiomic features had the best predictive performance in the training cohort with an AUC of 0.89. This was validated in the internal and external cohorts with AUCs of 0.87 and 0.73. The clinical utility of the model was confirmed by a clinical-MRI radiomic nomogram and DCA. CONCLUSIONS: The combined model incorporating clinical, conventional MRI, and radiomic features could be applied to predict preoperative CSF dissemination for children with MB as a noninvasive biomarker, which could aid in risk evaluation.

The effect of dopamine replacement therapy on cortical structure in Parkinson's disease.

AIMS: To explore the cortical structural reorganization in Parkinson's disease (PD) patients under chronic dopamine replacement therapy (DRT) in cross-sectional and longitudinal data and determine whether these changes were associated with clinical alterations. METHODS: A total of 61 DRT-treated, 60 untreated PD patients, and 61 normal controls (NC) were retrospectively included. Structural MRI scans and neuropsychological tests were conducted. Cortical thickness and volume were extracted based on FreeSurfer and were analyzed using general linear model to find statistically significant differences among three groups. Correlation analyses were performed among significant cortical areas, medication treatment (duration and dosage), and neuropsychological tests. Longitudinal cortical structural changes of patients who initiated DRT were analyzed using linear mixed-effect model. RESULTS: Significant cortical atrophy was primarily observed in the prefrontal cortex in treated patients, including the cortical thickness of right pars opercularis and the volume of bilateral superior frontal cortex (SFC), left rostral anterior cingulate cortex (rACC), right lateral orbital frontal cortex, right pars orbitalis, and right rostral middle frontal cortex. A negative correlation was detected between the left SFC volume and levodopa equivalent dose (LED) (r = -0.316, p = 0.016), as well as the left rACC volume and medication duration (r = -0.329, p = 0.013). In the patient group, the left SFC volume was positively associated with digit span forward score (r = 0.335, p = 0.017). The left SFC volume reduction was longitudinally correlated with increased LED (standardized coefficient = -0.077, p = 0.001). CONCLUSION: This finding provided insights into the influence of DRT on cortical structure and highlighted the importance of drug dose titration in DRT.

Assessing the Role of Locus Coeruleus Degeneration in Essential Tremor and Parkinson's Disease with Sleep Disorders.

Background: Previous studies have demonstrated the importance of the locus coeruleus (LC) in sleep-wake regulation. Both essential tremor (ET) and Parkinson's disease (PD) share common sleep disorders, such as poor quality of sleep (QoS). LC pathology is a feature of both diseases. A question arises regarding the contribution of LC degeneration to the occurrence of poor QoS. Objective: To evaluate the association between LC impairment and sleep disorders in ET and PD patients. Methods: A total of 83 patients with ET, 124 with PD, and 83 healthy individuals were recruited and divided into ET/PD with/without poor QoS (Sle/NorET and Sle/NorPD) subgroups according to individual Pittsburgh Sleep Quality Index (PSQI) score. Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and free-water imaging derived from diffusion MRI were performed. Subsequently, we evaluated the association between contrast-to-noise ratio of LC (CNRLC) and free-water value of LC (FWLC) with PSQI scores in ET and PD groups. Results: CNRLC was significantly lower in ET (p = 0.047) and PD (p = 0.018) than in healthy individuals, whereas no significant difference was found in FWLC among the groups. No significant differences were observed in CNR/FWLC between patients with/without sleep disorders after multiple comparison correction. No correlation was identified between CNR/FWLC and PSQI in ET and PD patients. Conclusions: LC degeneration was observed in both ET and PD patients, implicating its involvement in the pathophysiology of both diseases. Additionally, no significant association was observed between LC integrity and PSQI, suggesting that LC impairment might not directly relate to overall QoS.

Differential influences of rest tremor on brain fiber architecture in essential tremor and Parkinson's disease.

BACKGROUND: Rest tremor is a movement disorder commonly found in diseases like Parkinson's disease (PD) and essential tremor (ET). Rest tremor typically shows slower progression in PD, but more severe progression in ET. However, the underlying white matter organization of rest tremor behind PD and ET remains unclear. METHODS: This study included 57 ET patients (40 without rest tremor (ETWR), 17 with rest tremor (ETRT)), 68 PD patients (34 without rest tremor (PDWR), 34 with rest tremor (PDRT)), and 62 normal controls (NC). Fixel-based analysis was used to evaluate the structural changes of white matter in rest tremor in these different diseases. RESULTS: The fiber-bundle cross-section (FC) of the right non-decussating dentato-rubro-thalamic tract and several fibers outside the dentato-rubro-thalamic pathway in ETWR were significantly higher than that in NC. The fiber density and cross-section of the left nigro-pallidal in PDWR is significantly lower than that in NC, while the FC of bilateral nigro-pallidal in PDRT is significantly lower than that in NC. CONCLUSION: ET patients with pure action tremor showed over-activation of fiber tracts. However, when superimposed with rest tremor, ET patients no longer exhibited over-activation of fiber tracts, but rather showed a trend of fiber tract damage. Except for the nigro-pallidal degeneration in all PD, PDRT will not experience further deterioration in fiber organization. These results provide important insights into the unique effects of rest tremor on brain fiber architecture in ET and PD.

Functional connectomes of akinetic-rigid and tremor within drug-naïve Parkinson's disease.

AIMS: To detect functional connectomes of akinetic-rigid (AR) and tremor and compare their connection pattern. METHODS: Resting-state functional MRI data of 78 drug-naïve PD patients were enrolled to construct connectomes of AR and tremor via connectome-based predictive modeling (CPM). The connectomes were further validated with 17 drug-naïve patients to verify their replication. RESULTS: The connectomes related to AR and tremor were identified via CPM method and successfully validated in the independent set. Additional regional-based CPM demonstrated neither AR nor tremor could be simplified to functional changes within a single brain region. Computational lesion version of CPM revealed that parietal lobe and limbic system were the most important regions among AR-related connectome, and motor strip and cerebellum were the most important regions among tremor-related connectome. Comparing two connectomes found that the patterns of connection between them were largely distinct, with only four overlapped connections identified. CONCLUSION: AR and tremor were found to be associated with functional changes in multiple brain regions. Distinct connection patterns of AR-related and tremor-related connectomes suggest different neural mechanisms underlying the two symptoms.

Aberrant dentato-rubro-thalamic pathway in action tremor but not rest tremor: A multi-modality magnetic resonance imaging study.

AIMS: The purpose of this study was to clarify the dentato-rubro-thalamic (DRT) pathway in action tremor in comparison to normal controls (NC) and disease controls (i.e., rest tremor) by using multi-modality magnetic resonance imaging (MRI). METHODS: This study included 40 essential tremor (ET) patients, 57 Parkinson's disease (PD) patients (29 with rest tremor, 28 without rest tremor), and 41 NC. We used multi-modality MRI to comprehensively assess major nuclei and fiber tracts of the DRT pathway, which included decussating DRT tract (d-DRTT) and non-decussating DRT tract (nd-DRTT), and compared the differences in DRT pathway components between action and rest tremor. RESULTS: Bilateral dentate nucleus (DN) in the ET group had excessive iron deposition compared with the NC group. Compared with the NC group, significantly decreased mean diffusivity and radial diffusivity were observed in the left nd-DRTT in the ET group, which were negatively correlated with tremor severity. No significant difference in each component of the DRT pathway was observed between the PD subgroup or the PD and NC. CONCLUSION: Aberrant changes in the DRT pathway may be specific to action tremor and were indicating that action tremor may be related to pathological overactivation of the DRT pathway.

Regional white matter hyperintensity volume in Parkinson's disease and associations with the motor signs.

OBJECTIVE: To determine whether white matter hyperintensity (WMH) volumes in specific regions are associated with Parkinson's disease (PD) compared to non-PD controls, and to assess their impact on motor signs through cross-sectional and longitudinal analyses. METHODS: A total of 50 PD participants and 47 age- and gender-matched controls were enrolled. All PD participants were followed up for at least 2 years. To detect regions of greater WMH in the PD, the WMH volume of each region was compared with the corresponding region in the control group. Linear regression and linear mixed effects models were respectively used for cross-sectional and longitudinal analyses of the impact of increases in WMH volume on motor signs. RESULTS: The PD group had greater WMH volume in the occipital region compared with the control group. Cross-sectional analyses only detected a significant correlation between occipital WMH volume and motor function in PD. Occipital WMH volume positively correlated with the severity of tremor, and gait and posture impairments, in the PD group. During the follow-up period, the participants' motor signs progressed and the WMH volumes remained stable, no longitudinal association was detected between them. The baseline occipital WMH volume cannot predict the progression of signs after adjustment for baseline disease duration and the presence of vascular risk factors. INTERPRETATION: PD participants in this study were characterized by greater WMH at the occipital region, and greater occipital WMH volume had cross-sectional associations with worse motor signs, while its longitudinal impact on motor signs progression was limited.

Metabolomic Analysis Reveals the Mechanisms of Hepatotoxicity Induced by Aflatoxin M1 and Ochratoxin A.

Aflatoxin M1 (AFM1) is the only toxin with the maximum residue limit in milk, and ochratoxin A (OTA) represents a common toxin in cereals foods. It is common to find the co-occurrence of these two toxins in the environment. However, the interactive effect of these toxins on hepatoxicity and underlying mechanisms is still unclear. The liver and serum metabolomics in mice exposed to individual AFM1 at 3.5 mg/kg b.w., OTA at 3.5 mg/kg b.w., and their combination for 35 days were conducted based on the UPLC-MS method in the present study. Subsequent metabolome on human hepatocellular liver carcinoma (Hep G2) cells was conducted to narrow down the key metabolites. The phenotypic results on liver weight and serum indicators, such as total bilirubin and glutamyltransferase, showed that the combined toxins had more serious adverse effects than an individual one, indicating that the combined AFM1 and OTA displayed synergistic effects on liver damage. Through the metabolic analysis in liver and serum, we found that (i) a synergistic effect was exerted in the combined toxins, because the number of differentially expressed metabolites on combination treatment was higher than the individual toxins, (ii) OTA played a dominant role in the hepatoxicity induced by the combination of AFM1, and OTA and (iii) lysophosphatidylcholines (LysoPCs), more especially, LysoPC (16:1), were identified as the metabolites most affected by AFM1 and OTA. These findings provided a new insight for identifying the potential biomarkers for the hepatoxicity of AFM1 and OTA.

MRI-based texture analysis for differentiate between pediatric posterior fossa ependymoma type A and B.

PURPOSE: The aim of the study was to evaluate the feasibility of texture analysis in differentiating between posterior fossa ependymoma type A (PF-EPN-A) and type B (PF-EPN-B) among children. MATERIALS AND METHODS: Our retrospective study included 43 patients (37 PF-EPN-A and 6 PF-EPN-B) who were pathologically diagnosed with ependymomas in the posterior fossa. The texture features were extracted automatically from the volume of interests (VOIs), which were manually delineated on fluid-attenuated inversion recovery (FLAIR), contrast-enhanced T1-weighted (T1C), and diffusion-weighted imaging (DWI) MRI sequences. A receiver operating characteristic curve (ROC) was built to assess the diagnostic value of the texture parameters, and the prognostic value was evaluated by survival analysis. RESULTS: Texture parameter [Wavelet-LHH (H: High pass filter, L: Low pass. filter)_glcm (gray-level co-occurrence matrix)_Idn (Inverse difference normalized)] provides valuable information in distinguishing subgroups of ependymomas with higher specificity and positive predictive value (PPV). A total of 27 patients were divided into a high-risk group (IDN value＞0.916) and a low-risk group (IDN value＜0.916) with the most optimistic cut-off value (0.916). The Kaplan-Meier analysis of the survival curves showed significantly longer disease-free survival for low-risk groups compared to high-risk groups [hazard ratio (HR): 0.28, 95% confidence interval (CI): 0.11-0.69; p = 0.017]. CONCLUSION: Our results suggested that the texture parameters based on DWI images can be used to differentiate PF-EPN-A from PF-EPN-B. Texture analysis could be used as a noninvasive tool in distinguishing subgroup pediatric posterior fossa ependymomas and provide reliable prognostic information upon the verification of its reproducibility and feasibility by further studies.