PURPOSE: Rapid eye movement (REM) behavior disorder (RBD) is a common non-motor symptom in Parkinson’s disease (PD) patients. However, its pathogenesis remains unclear. Our study aimed to investigate the topological properties of the functional brain network in PD patients with probable REM sleep behavior disorder (PD-pRBD). METHOD: Using the RBD screening questionnaire, 32 PD patients without RBD (PD-nRBD) and 17 PD-pRBD patients were included. All participants underwent resting-state functional Magnetic Resonance Imaging (rs-fMRI) scan and clinical assessment. We compared the differences between groups in global and nodal topological properties of the brain functional network based on graph theory analysis. Partial correlation analysis was used to estimate the relationship between the significant metrics and clinical scales. RESULTS: The PD-pRBD group showed no significant differences in global topological properties compared to the PD-nRBD group. However, the PD-pRBD group had significantly higher nodal degree centrality in the left cuneus, nodal betweenness centrality in the left putamen and Pallidum, and nodal local efficiency in the left amygdala. They also showed lower nodal local efficiency in the right frontal operculum, inferior frontal gyrus, and left anterior cingulate cortex. Moreover, in the PD-pRBD group, nodal local efficiency in the left anterior cingulate cortex demonstrated a negative correlation with HAMD-24 scores. CONCLUSION: Both PD-pRBD and PD-nRBD patients displayed small-world properties, but PD-pRBD showed more extensive changes of nodal properties than PD-nRBD within the limbic system, basal ganglion, and visual associated regions. These region-specific changes offer crucial insights into the pathophysiological mechanisms of PD comorbid with RBD in brain network analysis.

BackgroundHuman Epidermal Growth Factor Receptor 2 (HER2), a component of the epidermal growth factor receptor family, is thought to be related to advanced prostate cancer (PCa) when overexpressed. Currently, most research on HER2 is limited to molecular pathology, with relatively few studies focused on imaging aspects.ObjectivesTo develop a predictive model by extracting high-throughput radiomics features from magnetic resonance imaging and combining them with clinical characteristics for predicting HER2 overexpression.Materials and methodsA total of 201 patients who underwent radical prostatectomy and HER2 immunohistochemistry were retrospectively enrolled. These patients were randomly divided into a training set (n=160) and a test set (n=41). Multimodal radiomics features extracted from T2-weighted imaging (T2WI) and apparent diffusion coefficient maps (ADC) were selected using Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) with ten-fold cross-validation. Predictive models were developed and evaluated based on discrimination and clinical utility.ResultsThe combined model integrating ISUP Grade, PSA and Radscore achieved an area under the curve (AUC) of 0.841 (95% CI: 0.697-0.955) in the test set, significantly outperforming the clinical model (AUC = 0.580; p = 0.02, DeLong test) and demonstrating a modest improvement over the Radscore model (AUC = 0.838 (0.693-0.951). Evaluation results showed consistent discriminatory power: 0.78 accuracy, 0.77 sensitivity, and 0.79 specificity, indicating well-balanced performance between positive and negative classes. Decision curve analysis and Waterfall plot demonstrated strong clinical applicability.ConclusionThe combined model effectively predicts HER2 overexpression in prostate cancer, with potential to inform more personalized treatment strategies for HER2-overexpressing PCa patients.

BACKGROUND:Iron is a redox-active biological trace element with concentration-dependent effects on human health and disease. This study evaluated multiple methods to quantify iron oxide nanoparticles for potential theragnostic applications in brain cancer. METHODS:Iron quantification protocols were developed for magnetic resonance imaging (MRI), x-ray fluorescence spectroscopy (XRF), UV-visible spectroscopy (UV-Vis), and inductively coupled plasma mass spectrometry (ICP-MS). The techniques were evaluated in cell culture models and murine brain tumors treated with the iron oxide nanoparticle ferumoxytol. All methods were assessed for correlation between administered ferumoxytol dose and iron measurement as well as agreement with the ICP-MS gold standard. RESULTS:In vivo MRI T2* and XRF iron measurements correlated with administered ferumoxytol dose (T2*: ρ = -0.64, p = 0.0034; XRF: ρ = 0.97, p = 2 x 10-15) and ex vivo ICP-MS results (T2*: ρ = -0.69, p = 0.012; XRF: ρ = 0.91, p < 2.2 x 10-16) with stronger, more significant correlations emerging for XRF than MRI T2*. However, unlike MRI, XRF cannot identify the spatial distribution of iron deposits. The developed UV-Vis protocol quantified iron in cell culture samples but failed in ex vivo brain tumor tissue. CONCLUSION:The successful in vivo and ex vivo quantification of tumor iron accumulation indicates that these methods are relevant in preclinical and clinical settings involving diagnosis and treatment of various human diseases. This work establishes a clear framework for choosing the ideal method based on iron concentration, equipment, budget, and sample identity.

Background Renal fibrosis (RF) is a key pathological hallmark and prognostic indicator of chronic kidney disease (CKD). Accurate evaluation of RF is critical for risk stratification and therapeutic decision-making, yet the current assessment relies mainly on renal biopsy, which has several limitations. This study aimed to develop a two-stage artificial intelligence framework that integrates multimodal MRI (native T-1 mapping, ADC, and T-2* mapping) and clinical indicators for the noninvasive assessment of RF in patients with CKD. Methods This prospective study included 152 patients with biopsy-proven CKD (RF 1: no RF, 34 patients; RF 2: mild RF, 69 patients; and RF 3: moderate to severe RF, 49 patients). The dataset was randomly partitioned into training and test cohorts at a 2:1 ratio. A two-stage model combining MobileNetV2-SE-based deep learning features with clinical indicators was developed for RF classification. Two binary tasks were performed: RF presence (RF 1 vs. RF 2 and RF 3) and severity (RF 2 vs. RF 3). Nested cross-validation was applied for model development and hyperparameter tuning, and bootstrapping was used to assess performance robustness. Model performance was evaluated with the area under the curve (AUC), calibration curves, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP) visualization. Results Compared with single-modality models, the multimodal deep learning model (DL-combine), which is based exclusively on native T-1 mapping, ADC, and T-2* mapping, demonstrated favourable and stable performance (test AUC: 0.930). Among the 14 classifiers, XGBoost performed numerically better in terms of RF presence (mean AUCs: 0.986, 0.887; accuracy: 0.947, 0.829), whereas ExtraTree performed better in terms of RF severity assessment (mean AUCs: 0.935, 0.883; accuracy: 0.886, 0.848). Calibration curves and DCA confirmed robust predictive reliability and clinical utility. SHAP analysis highlighted the relative contributions of the DL-sign and eGFR. Conclusion This two-stage multimodal MRI-based framework provides accurate and interpretable assessment of RF across different stages of CKD, supporting noninvasive risk stratification and complementary clinical decision-making.

The artificial intelligence-assisted ASPECTS (AI-ASPECTS) system has become an increasingly common tool in clinical practice for assessing acute ischemic stroke (AIS). However, current AI-ASPECTS implementations still rely on the conventional expert-evaluation framework, which uses a simplified two-slice atlas and arbitrarily selected lesion-load thresholds. Our study aimed to develop a refined AI-assisted ASPECTS (Ref-AI-ASPECTS) framework featuring a seamless whole middle cerebral artery (MCA) territory atlas and region-specific, optimally determined lesion-load thresholds, and comprehensively evaluate the performance of this framework across various clinical scenarios for AIS. We enrolled a cohort of 7,655 AIS patients from eleven centers. Modified atlas was created by expanding conventional atlas based on full MCA territory. Ref-AI-ASPECTS with modified atlas and specific lesion-load thresholds was established using a genetic algorithm. The clinical utility of Ref-AI-ASPECTS was assessed by comparing it to the conventional framework (Con-AI-ASPECTS) in terms of correlation with NIHSS scores on admission, dichotomized prediction of mRS at 3 months, and consistency with expert scoring across the training DWI data, external DWI data, expanded CT data, and real-world prospective DWI data. The Ref-AI-ASPECTS frameworks with modified atlas and specific lesion-load thresholds (2

Background:Diffusion tensor image analysis along the perivascular space (DTI-ALPS) has been used for diagnosing Alzheimer's disease (AD); however, few studies have examined the relationship between the DTI-ALPS index and cortical metrics, and the differentiation between AD severity levels remains unclear. This study aimed to explore the differences in DTI-ALPS index and cortex among AD patients with varying severities and to analyze the interactions between DTI-ALPS index, cortical metrics, and cognitive function. Methods:A total of 19 individuals with mild cognitive impairment (MCI), 17 individuals exhibiting mild AD, 25 individuals with moderate AD, and 28 healthy controls (HC) who were matched for age, sex, and education level were recruited. All the participants underwent diffusion tensor imaging (DTI) magnetic resonance imaging (MRI), followed by the calculation of the DTI-ALPS index to assess lymphatic system function. FreeSurfer (v7.4.1) was used to calculate thickness, volume, local gyre index, and area. One-way analysis of variance (ANOVA) was performed to compare the differences among HC, MCI, mild AD, and moderate AD groups. Pearson correlation analysis was employed to investigate the connection between the DTI-ALPS index and cognitive function, along with cortical metrics. Results:The HC, MCI, mild AD, and moderate AD groups exhibited significant differences in the DTI-ALPS index of the left hemisphere (P=0.008), whereas 13 cortical metrics revealed a statistical significance between groups (P<0.05). In the left hemisphere, the DTI-ALPS index showed a positive trend with the Montreal Cognitive Assessment (MoCA) score (r=0.397, P<0.001). Higher DTI-ALPS was also associated with an increase in 10 cortical metrics after controlling for age, sex, and education. Conclusions:There is a significant relationship between the DTI-ALPS index, cortical metrics, and cognitive function. This result may suggest that lymphatic dysfunction indicated by the DTI-ALPS index could mirror cortical structural degeneration and cognitive decline within the pathological process of AD. DTI-ALPS can be used as an indicator of structural degeneration and decline in cognitive function in AD.

The development of simple, rapid, sensitive and noninvasive theranostic agents for acute gastritis is crucial. Herein, an engineering catalase-conjugated bismuth nanoparticle was fabricated for near-infrared photoacoustic imaging and computed tomography imaging of acute alcoholic gastritis. This nanoparticle could quickly respond to H2O2 and H+ overexpressed in the microenvironment of acute gastritis in mice, emitting strong signals for precise localization. Additionally, it adhered to the damaged gastric mucosa for an extended period, acting as a long-acting mucosal protector by inhibiting related inflammatory reactions and promoting mucosal repair. The use of this catalase-assembled nanoparticle could extend its residence time in the stomach, thereby reducing the drug dose and treatment duration. These findings of our study underscored the potential of this multifunctional nanoplatform for integrated diagnosis and treatment of gastrointestinal inflammatory diseases.

Background: Prostate cancer (PCa) with low levels of prostate-specific antigen (PSA) (0-4 ng/mL) includes PCa detected through biopsy and incidental PCa (IPC) in patients with previous prostate surgeries. The study was conducted to compare these two groups of patients undergoing radical prostatectomy (RP), aiming to assess pathological characteristics and suggest strategies for predicting and managing low PSA PCa. Methods: A retrospective analysis was performed on two categories of low PSA PCa patients. Baseline for RP, preoperative and postoperative pathological data, and biochemical recurrence (BCR) were evaluated. Results: Fifty patients were analyzed. There were 80% of tumors being clinically significant and in earlystage, indicating a favorable prognosis for most low PSA PCa patients, and the use of preoperative androgen deprivation therapy (ADT) treatment may be beneficial for a small subset of patients with advanced tumors. Patients with low PSA and IPC history had lower PSA levels, PSAD, and prostate volume, however, BCR rates did not significantly differ between low PSA patients with and without IPC history. mpMRI and PSAD demonstrated potential in predicting PCa in low PSA cases. Conclusions: Predicting low PSA PCa remains challenging, but mpMRI and PSAD could be valuable predictors. Both low PSA groups showed a likelihood of clinical significance, with favorable pathological features. Early diagnosis and treatment are crucial, especially for aggressive IPC PCa tumors. Reevaluating PSA thresholds is vital to avoid missed or misdiagnosed low PSA cases.

Background:Chronic kidney disease (CKD) is a major global health challenge, while renal fibrosis (RF) is the key pathological process and represents irreversible kidney damage. There is an urgent need for non-invasive techniques for assessment of RF. This study aimed to assess the diagnostic value of integrating native T1 mapping, readout segmentation of long variable echo-trains-diffusion-weighted imaging (RESOLVE-DWI), and T2* mapping imaging with clinical indicators in detecting RF caused by CKD. Methods:A prospective analysis was conducted on 117 patients with a clinical diagnosis of CKD who were scheduled for renal biopsy and underwent multiparametric magnetic resonance imaging (MRI) (native T1 mapping, RESOLVE-DWI, and T2* mapping) examinations from September 2021 to December 2023. Patients were divided into RF 1 (no fibrosis; n=23), RF 2 (mild RF, ≤25% fibrosis; n=54), and RF 3 (moderate to severe RF, >25% fibrosis; n=40). Univariate and multivariate logistic regression analyses were used to identify independent predictors for the presence of RF (RF 1 vs. RF 2 + RF 3) and the severity of RF (RF 2 vs. RF 3). Then, combined models were constructed. Receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic performance of the models. Areas under the curves (AUCs) were compared using DeLong's test. Results:The independent predictors for the presence of RF were the estimated glomerular filtration rate (eGFR), mean corticomedullary T1 ratio (T1%), and mean corticomedullary apparent diffusion coefficient (ADC) ratio (ADC%). The independent predictors for the severity of RF were the eGFR and mean corticomedullary T1 difference (ΔT1). The AUC of the combined model-1 (eGFR + T1% + ADC%) was 0.919, which was significantly greater than that of the eGFR (AUC =0.828, P=0.008) and ADC% (AUC =0.801, P=0.009), but not significantly different from that of T1% (AUC =0.879, P=0.087). The diagnostic sensitivity of the combined model-1 for identifying RF increased to 90.4% and the specificity was 87.0%. The AUC of the combined model-2 (eGFR + ΔT1) was 0.887, which was significantly greater compared with the eGFR (AUC =0.808, P=0.019) and ΔT1 (AUC =0.834, P=0.032) models. When the eGFR was combined with ΔT1, the sensitivity of the combined model-2 to discriminate mild RF from moderate to severe RF increased to 92.5%, with a specificity of 77.8%. Conclusions:Native T1 mapping and RESOLVE-DWI in combination with the eGFR can improve the diagnostic sensitivity of CKD-related RF, thus contributing to the early detection of RF and clinical decision-making.

Background Few studies have investigated the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a free-breathing golden-angle radial stack-of-stars volume-interpolated breath-hold examination (FB radial VIBE) sequence in the lung. Purpose To investigate whether DCE-MRI using the FB radial VIBE sequence can assess morphological and kinetic parameters in patients with pulmonary lesions, with computed tomography (CT) as the reference. Material and Methods In total, 43 patients (30 men; mean age = 64 years) with one lesion each were prospectively enrolled. Morphological and kinetic features on MRI were calculated. The diagnostic performance of morphological MR features was evaluated using a receiver operating characteristic (ROC) curve. Kinetic features were compared among subgroups based on histopathological subtype, lesion size, and lymph node metastasis. Results The maximum diameter was not significantly different between CT and MRI (3.66 ± 1.62 cm vs. 3.64 ± 1.72 cm; P = 0.663). Spiculation, lobulation, cavitation or bubble-like areas of low attenuation, and lymph node enlargement had an area under the ROC curve (AUC) >0.9, while pleural indentation yielded an AUC of 0.788. The lung cancer group had significantly lower K trans , V e , and initial AUC values than the other cause inflammation group (0.203, 0.158, and 0.589 vs. 0.597, 0.385, and 1.626; P < 0.05) but significantly higher values than the tuberculosis group ( P < 0.05). Conclusion Morphology features derived from FB radial VIBE have high correlations with CT, and kinetic analyses show significant differences between benign and malignant lesions. DCE-MRI with FB radial VIBE could serve as a complementary quantification tool to CT for radiation-free assessments of lung lesions.

BACKGROUND AND OBJECTIVE:Parkinson's disease (PD), a prevalent neurodegenerative disorder, assumes a more adverse prognosis when accompanied by rapid eye movement sleep disorder (RBD). Non-motor symptoms, particularly sleep and emotional disturbances, significantly impair patients' quality of life. This study aimed to investigate the neuroimaging underpinnings of PD-RBD using structural and functional magnetic resonance imaging (MRI) and to explore the associations between these imaging biomarkers and non-motor symptoms. METHOD:Brain scans were acquired from 33 PD patients without and 21 with probable RBD (PD-pRBD). Comparative analyses were performed to evaluate structural and functional alterations between the two groups. Additionally, the correlations between neuroimaging metrics and clinical assessment scales were assessed. RESULTS:PD-pRBD patients demonstrated more pronounced grey matter atrophy, particularly in the putamen and insula. Functional MRI revealed decreased amplitude of low-frequency fluctuations (ALFF) in the bilateral posterior cingulate cortex and left precuneus of PD-pRBD patients. Furthermore, reduced functional connectivity (FC) was observed in specific regions of the whole brain and within the default mode network (DMN) in PD-pRBD. Notably, a negative correlation was found between mean ALFF values in the left posterior cingulate cortex of PD-pRBD patients and Hamilton Depression Rating Scale scores. CONCLUSION:PD-pRBD is characterized by more severe grey matter loss and functional MRI abnormalities compared to PD alone. Dysfunction of the posterior cingulate cortex is implicated in more pronounced affective impairments, providing novel insights into the complex pathophysiology of PD-RBD.

BackgroundAlzheimer’s disease (AD) is a complex neurodegenerative disorder characterized by progressively worsening cognitive decline and memory loss. Excessive iron accumulation produces severe cognitive impairment. However, there are no uniform conclusions about changes in brain iron content in AD. This study aimed to investigate the iron content of the deep brain nuclei in AD, and its correlation with cognitive function.MethodsThirty-one patients with mild to moderate AD, 17 patients with mild cognitive impairment (MCI), and 20 age-, sex-, and education-matched healthy controls (HC) were collected. The QSM was used to quantify the magnetic susceptibility values of the caudate nucleus, putamen, globus pallidus, substantia nigra, red nucleus, and dentate nucleus, and to analyze the differences that existed between the three groups. As well as the correlation between the magnetic susceptibility values and cognitive function was calculated.ResultsThe magnetic susceptibility values of bilateral globus pallidus, left putamen, and bilateral substantia nigra were significantly higher in AD patients than in HC, and the magnetic susceptibility values of the right globus pallidus were significantly higher in AD patients than in MCI (all p < 0.05). The magnetic susceptibility values of the left dentate nucleus in the AD group were negatively correlated with the writing function of the MMSE subitem (r = −0.42, p = 0.020), and the magnetic susceptibility values of the left caudate nucleus and right dentate nucleus were significantly and negatively correlated with the naming function and language function of the MoCA subitem, respectively (r = −0.43, p = 0.019; r = −0.36, p = 0.048).ConclusionMagnetic susceptibility values based on QSM correlate with cognitive function are valuable in discriminating AD from MCI and AD from HC.

Chronic kidney disease (CKD) raises major concerns for global public health as it is characterized by high prevalence, low awareness, high healthcare costs and poor prognosis. Therefore, our study prospectively established and validated native T1 mapping-based radiomics models for the prediction of renal fibrosis and renal function in patients with CKD. Moreover, the area under the receiver operating characteristic curve (AUC) and diagnostic sensitivity, specificity, accuracy, positive predictive value and negative predictive value were used to evaluate its performance. Thus, our results show that radiomics based on native T1 mapping images can better identify renal function and renal fibrosis in patients with CKD and outperform conventional T1 mapping parameters of ΔT1 and T1%, thus providing more information for CKD management and clinical decision-making.

IntroductionIdiopathic rapid eye movement sleep behavior disorder (iRBD) and Parkinson's disease (PD) have been found to have changes in cerebral perfusion and overlap of some of the lesioned brain areas. However, a consensus regarding the specific location and diagnostic significance of these cerebral blood perfusion alternations remains elusive in both iRBD and PD. The present study evaluated the patterns of cerebral blood flow changes in iRBD and PD.Material and methodsA total of 59 right-handed subjects were enrolled, including 15 patients with iRBD, 20 patients with PD, and 24 healthy controls (HC). They were randomly divided into groups at a ratio of 4 to 1 for training and testing. A PASL sequence was employed to obtain quantitative cerebral blood flow (CBF) maps. The CBF values were calculated from these acquired maps. In addition, AutoGluon was employed to construct a classifier for CBF features selection and classification. An independent t-test was performed for CBF variations, with age and sex as nuisance variables. The performance of the feature was evaluated using receiver operating characteristic (ROC) curves. A significance level of P < 0.05 was considered significant. CBF in several brain regions, including the left median cingulate and paracingulate gyri and the right middle occipital gyrus (MOG), showed significant differences between PD and HC, demonstrating good classification performance. The combined model that integrates all features achieved even higher performance with an AUC of 0.9380. Additionally, CBF values in multiple brain regions, including the right MOG and the left angular gyrus, displayed significant differences between PD and iRBD. Particularly, CBF values in the left angular gyrus exhibited good performance in classifying PD and iRBD. The combined model achieved improved performance, with an AUC of 0.8533. No significant differences were found in brain regions when comparing CBF values between iRBD and HC subjects.ConclusionsASL-based quantitative CBF change features can offer reliable biomarkers to assist in the diagnosis of PD. Regarding the characteristic of CBF in the right MOG, it is anticipated to serve as an imaging biomarker for predicting the progression of iRBD to PD.

Glutathione (GSH)-activatable probes hold great promise for in vivo cancer imaging, but are restricted by their dependence on non-selective intracellular GSH enrichment and uncontrollable background noise. Here, a holographically activatable nanoprobe caging manganese tetraoxide is shown for tumor-selective contrast enhancement in magnetic resonance imaging (MRI) through cooperative GSH/albumin-mediated cascade signal amplification in tumors and rapid elimination in normal tissues. Once targeting tumors, the endocytosed nanoprobe effectively senses the lysosomal microenvironment to undergo instantaneous decomposition into Mn2+ with threshold GSH concentration of ≈ 0.12 mm for brightening MRI signals, thus achieving high contrast tumor imaging and flexible monitoring of GSH-relevant cisplatin resistance during chemotherapy. Upon efficient up-regulation of extracellular GSH in tumor via exogenous injection, the relaxivity-silent interstitial nanoprobe remarkably evolves into Mn2+ that are further captured/retained and re-activated into ultrahigh-relaxivity-capable complex by stromal albumin in the tumor, and simultaneously allows the renal clearance of off-targeted nanoprobe in the form of Mn2+ via lymphatic vessels for suppressing background noise to distinguish tiny liver metastasis. These findings demonstrate the concept of holographic tumor activation via both tumor GSH/albumin-mediated cascade signal amplification and simultaneous background suppression for precise tumor malignancy detection, surveillance, and surgical guidance.

This study aimed to determine the pattern of fractional dimension (FD) in Alzheimer's disease (AD) patients, and investigate the relationship between FD and the locus coeruleus (LC) signal intensity.A total of 27 patients with AD and 25 healthy controls (HC) were collected to estimate the pattern of fractional dimension (FD) and cortical thickness (CT) using the Computational Anatomy Toolbox (CAT12), and statistically analyze between groups on a vertex level using statistical parametric mapping 12. In addition, they were examined by neuromelanin sensitive MRI(NM-MRI) technique to calculate the locus coeruleus signal contrast ratios (LC-CRs). Additionally, correlations between the pattern of FD and LC-CRs were further examined.Compared to HC, AD patients showed widespread lower CT and FD Furthermore, significant positive correlation was found between local fractional dimension (LFD) of the left rostral middle frontal cortex and LC-CRs. Results suggest lower cortical LFD is associated with LCCRs that may reflect a reduction due to broader neurodegenerative processes. This finding may highlight the potential utility for advanced measures of cortical complexity in assessing brain health and early identification of neurodegenerative processes.

Background and Objectives Pathologic progression across the cortex is a key feature of Parkinson disease (PD). Cortical gyrification is a morphologic feature of human cerebral cortex that is tightly linked to the integrity of underlying axonal connectivity. Monitoring cortical gyrification reductions may provide a sensitive marker of progression through structural connectivity, preceding the progressive stages of PD pathology. We aimed to examine the progressive cortical gyrification reductions and their associations with overlying cortical thickness, white matter (WM) integrity, striatum dopamine availability, serum neurofilament light (NfL) chain, and CSF α-synuclein levels in PD. Methods This study included a longitudinal dataset with baseline (T0), 1-year (T1), and 4-year (T4) follow-ups and 2 cross-sectional datasets. Local gyrification index (LGI) was computed from T1-weighted MRI data to measure cortical gyrification. Fractional anisotropy (FA) was computed from diffusion-weighted MRI data to measure WM integrity. Striatal binding ratio (SBR) was measured from 123Ioflupane SPECT scans. Serum NfL and CSF α-synuclein levels were also measured. Results The longitudinal dataset included 113 patients with de novo PD and 55 healthy controls (HCs). The cross-sectional datasets included 116 patients with relatively more advanced PD and 85 HCs. Compared with HCs, patients with de novo PD showed accelerated LGI and FA reductions over 1-year period and a further decline at 4-year follow-up. Across the 3 time points, the LGI paralleled and correlated with FA (p = 0.002 at T0, p = 0.0214 at T1, and p = 0.0037 at T4) and SBR (p = 0.0095 at T0, p = 0.0035 at T1, and p = 0.0096 at T4) but not with overlying cortical thickness in patients with PD. Both LGI and FA correlated with serum NfL level (LGI: p < 0.0001 at T0, p = 0.0043 at T1; FA: p < 0.0001 at T0, p = 0.0001 at T1) but not with CSF α-synuclein level in patients with PD. In the 2 cross-sectional datasets, we revealed similar patterns of LGI and FA reductions and associations between LGI and FA in patients with more advanced PD. Discussion We demonstrated progressive reductions in cortical gyrification that were robustly associated with WM microstructure, striatum dopamine availability, and serum NfL level in PD. Our findings may contribute biomarkers for PD progression and potential pathways for early interventions of PD.

Background : The neural basis of pain in Parkinson’s disease (PD) is poorly understood. This study aimed to explore the alterations of spontaneous neuronal activity and functional connectivity (FC) pattern in PD with chronic pain by amplitude of low-frequency fluctuation (ALFF)and functional connectivity (FC). Methods : A total of 41 PD patients with pain (PDP), 41 PD patients without pain (nPDP), and 29 matched pain-free normal healthy controls (NCs) were enrolled in the study. The non-motor symptoms questionnaire (NMSQ) and the visual analog scale (VAS) were applied to pain screening and pain severity assessment. ALFF and FC were measured by resting-state functional MRI (rs-fMRI). ALFF was applied to investigate regional cerebral activity, and FC was used to evaluate functional integration of the brain network. Results : Compared with nPDP patients, PDP patients showed increased ALFF in the right superior frontal gyrus (SFG), supplementary motor area (SMA) and left paracentral lobule (PCL), precentral gyrus (PrG), while decreased ALFF in the right putamen. Only the ALFF value of the right putamen was negatively correlated with the VAS score in the PDP patients. PDP patients showed diminished FC in the right putamen with the midbrain, anterior cingulate cortex (ACC), orbito-frontal cortex (OFC), middle frontal gyrus (MFG), posterior cerebellar lobe, and middle temporal gyrus (MTG), as compared with nPDP patients. Conclusion : This study does disclose that anomalous regional brain activity within the motor cortex and putamen, as well as aberrant functional integration of the putamen with multiple brain regions are involved in the neural mechanism of pain in PD patients.

Our purpose was to devise a radiomics model using preoperative computed tomography angiography (CTA) images to differentiate new from old emboli of acute lower limb arterial embolism. 57 patients (95 regions of interest; training set: n = 57; internal validation set: n = 38) with femoral popliteal acute lower limb arterial embolism confirmed by pathology and with preoperative CTA images were retrospectively analyzed. We selected the best prediction model according to the model performance tested by area under the curve (AUC) analysis across 1,000 iterations of prediction from three most common machine learning methods: support vector machine, feed-forward neural network (FNN), and random forest, through several steps of feature selection. Then, the selected best model was also validated in an external validation dataset (n = 24). The established radiomics signature had good predictive efficacy. FNN exhibited the best model performance on the training and validation groups: its AUC value was 0.960 (95% CI, 0.899-1). The accuracy of this model was 89.5%, and its sensitivity and specificity were 0.938 and 0.864, respectively. The AUC of external validation dataset was 0.793. Our radiomics model based on preoperative CTA images is valuable. The radiomics approach of preoperative CTA to differentiate new emboli from old is feasible.

Purpose This study aimed to develop machine learning models for the diagnosis of Parkinson’s disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance. Methods Brain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson’s Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson’s correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets. Results After feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong’s test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models. Conclusion The combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.