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•Briefly introduce the biochemical underpinnings of brain iron accumulation in PD.•Overview the advancements in iron-sensitive MRI technologies.•Summarize and discuss the role of QSM as a biomarker in PD.•Specify the value of QSM in the neurosurgery in PD.•Outline the translational concerning and future perspective. Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.

The growing maturity of nanofabrication has ushered massive sophisticated optical structures available on a photonic chip. The integration of subwavelength-structured metasurfaces and metamaterials on the canonical building block of optical waveguides is gradually reshaping the landscape of photonic integrated circuits, giving rise to numerous meta-waveguides with unprecedented strength in controlling guided electromagnetic waves. Here, we review recent advances in meta-structured waveguides that synergize various functional subwavelength photonic architectures with diverse waveguide platforms, such as dielectric or plasmonic waveguides and optical fibers. Foundational results and representative applications are comprehensively summarized. Brief physical models with explicit design tutorials, either physical intuition-based design methods or computer algorithms-based inverse designs, are cataloged as well. We highlight how meta-optics can infuse new degrees of freedom to waveguide-based devices and systems, by enhancing light-matter interaction strength to drastically boost device performance, or offering a versatile designer media for manipulating light in nanoscale to enable novel functionalities. We further discuss current challenges and outline emerging opportunities of this vibrant field for various applications in photonic integrated circuits, biomedical sensing, artificial intelligence and beyond.Recent years have witnessed substantial potential in allying meta-optics with diverse waveguide platforms to enable exotic manipulation of guided light signals. This review cataloged recent advances on meta-waveguides for photonic integration.

•Intracranial arterial pulsation may be a key driver of glymphatic fluid flow.•Associations between arterial pulsatility and glymphatic markers are unknown.•Increased arterial pulsatility index is related to PVS dilation and elevated FW.•The arterial pulsatility index was negatively associated with the ALPS index. Arterial pulsation has been suggested as a key driver of paravascular cerebrospinal fluid flow, which is the foundation of glymphatic clearance. However, whether intracranial arterial pulsatility is associated with glymphatic markers in humans has not yet been studied. Seventy-three community participants were enrolled in the study. 4D phase-contrast magnetic resonance imaging (MRI) was used to quantify the hemodynamic parameters including flow pulsatility index (PIflow) and area pulsatility index (PIarea) from 13 major intracerebral arterial segments. Three presumed neuroimaging markers of the glymphatic system were measured: including dilation of perivascular space (PVS), diffusivity along the perivascular space (ALPS), and volume fraction of free water (FW) in white matter. We explored the relationships between PIarea, PIflow, and the presumed glymphatic markers, controlling for related covariates. PIflow in the internal carotid artery (ICA) C2 segment (OR, 1.05; 95 % CI, 1.01–1.10, per 0.01 increase in PI) and C4 segment (OR, 1.05; 95 % CI, 1.01–1.09) was positively associated with the dilation of basal ganglia PVS, and PIflow in the ICA C4 segment (OR, 1.06, 95 % CI, 1.02–1.10) was correlated with the dilation of PVS in the white matter. ALPS was associated with PIflow in the basilar artery (β, -0.273, p, 0.046) and PIarea in the ICA C2 (β, -0.239, p, 0.041) and C7 segments (β, -0.238, p, 0.037). : Intracranial arterial pulsatility was associated with presumed neuroimaging markers of the glymphatic system, but the results were not consistent across different markers. Further studies are warranted to confirm these findings.

Background Glymphatic dysfunction is a crucial pathway for dementia. Alzheimer’s disease (AD) pathologies co-existing with cerebral small vessel disease (CSVD) is the most common pathogenesis for dementia. We hypothesize that AD pathologies and CSVD could be associated with glymphatic dysfunction, contributing to cognitive impairment. Method Participants completed with amyloid PET, diffusion tensor imaging (DTI), and T2 fluid-attenuated inversion-recovery (FLAIR) sequences were included from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). White matter hyperintensities (WMH), the most common CSVD marker, was evaluated from T2FLAIR images and represented the burden of CSVD. Amyloid PET was used to assess Aβ aggregation in the brain. We used diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, the burden of enlarged perivascular spaces (PVS), and choroid plexus volume to reflect glymphatic function. The relationships between WMH burden/Aβ aggregation and these glymphatic markers as well as the correlations between glymphatic markers and cognitive function were investigated. Furthermore, we conducted mediation analyses to explore the potential mediating effects of glymphatic markers in the relationship between WMH burden/Aβ aggregation and cognition. Results One hundred and thirty-three participants along the AD continuum were included, consisting of 40 CN − , 48 CN + , 26 MCI + , and 19 AD + participants. Our findings revealed that there were negative associations between whole-brain Aβ aggregation ( r  =  − 0.249, p  = 0.022) and WMH burden ( r  =  − 0.458, p  < 0.001) with DTI-ALPS. Additionally, Aβ aggregation ( r  = 0.223, p  = 0.041) and WMH burden ( r  = 0.294, p  = 0.006) were both positively associated with choroid plexus volume. However, we did not observe significant correlations with PVS enlargement severity. DTI-ALPS was positively associated with memory ( r  = 0.470, FDR- p  < 0.001), executive function ( r  = 0.358, FDR- p  = 0.001), visual-spatial ( r  = 0.223, FDR- p  < 0.040), and language ( r  = 0.419, FDR- p  < 0.001). Conversely, choroid plexus volume showed negative correlations with memory ( r  =  − 0.315, FDR- p  = 0.007), executive function ( r  =  − 0.321, FDR- p  = 0.007), visual-spatial ( r  =  − 0.233, FDR- p  = 0.031), and language ( r  =  − 0.261, FDR- p  = 0.021). There were no significant correlations between PVS enlargement severity and cognitive performance. In the mediation analysis, we found that DTI-ALPS acted as a mediator in the relationship between WMH burden/Aβ accumulation and memory and language performances. Conclusion Our study provided evidence that both AD pathology (Aβ) and CSVD were associated with glymphatic dysfunction, which is further related to cognitive impairment. These results may provide a theoretical basis for new targets for treating AD.

Rib fracture detection is time-consuming and demanding work for radiologists. This study aimed to introduce a novel rib fracture detection system based on deep learning which can help radiologists to diagnose rib fractures in chest computer tomography (CT) images conveniently and accurately. A total of 1707 patients were included in this study from a single center. We developed a novel rib fracture detection system on chest CT using a three-step algorithm. According to the examination time, 1507, 100 and 100 patients were allocated to the training set, the validation set and the testing set, respectively. Free Response ROC analysis was performed to evaluate the sensitivity and false positivity of the deep learning algorithm. Precision, recall, F1-score, negative predictive value (NPV) and detection and diagnosis were selected as evaluation metrics to compare the diagnostic efficiency of this system with radiologists. The radiologist-only study was used as a benchmark and the radiologist-model collaboration study was evaluated to assess the model’s clinical applicability. A total of 50,170,399 blocks (fracture blocks, 91,574; normal blocks, 50,078,825) were labelled for training. The F1-score of the Rib Fracture Detection System was 0.890 and the precision, recall and NPV values were 0.869, 0.913 and 0.969, respectively. By interacting with this detection system, the F1-score of the junior and the experienced radiologists had improved from 0.796 to 0.925 and 0.889 to 0.970, respectively; the recall scores had increased from 0.693 to 0.920 and 0.853 to 0.972, respectively. On average, the diagnosis time of radiologist assisted with this detection system was reduced by 65.3 s. The constructed Rib Fracture Detection System has a comparable performance with the experienced radiologist and is readily available to automatically detect rib fracture in the clinical setting with high efficacy, which could reduce diagnosis time and radiologists’ workload in the clinical practice.

•Iron deposition significantly increases with aging in many subcortical nuclei, with uneven distribution within nuclei.•In PD, iron is progressively deposited in the substantia nigra and red nucleus.•In AD, iron is strongly deposited in caudate and putamen.•Regional iron deposition can delineate brain degeneration and predict the severity of neurodegenerative diseases. Brain iron deposition is a promising marker for human brain health, providing insightful information for understanding aging as well as neurodegenerations, e.g., Parkinson's disease (PD) and Alzheimer's disease (AD). To comprehensively evaluate brain iron deposition along with aging, PD-related neurodegeneration, from prodromal PD (pPD) to clinical PD (cPD), and AD-related neurodegeneration, from mild cognitive impairment (MCI) to AD, a total of 726 participants from July 2013 to December 2020, including 100 young adults, 189 old adults, 184 pPD, 171 cPD, 31 MCI and 51 AD patients, were included. Quantitative susceptibility mapping data were acquired and used to quantify regional magnetic susceptibility, and the resulting spatial standard deviations were recorded. A general linear model was applied to perform the inter-group comparison. As a result, relative to young adults, old adults showed significantly higher iron deposition with higher spatial variation in all of the subcortical nuclei (p < 0.01). pPD showed a high spatial variation of iron distribution in the subcortical nuclei except for substantia nigra (SN); and iron deposition in SN and red nucleus (RN) were progressively increased from pPD to cPD (p < 0.01). AD showed significantly higher iron deposition in caudate and putamen with higher spatial variation compared with old adults, pPD and cPD (p < 0.01), and significant iron deposition in SN compared with old adults (p < 0.01). Also, linear regression models had significances in predicting motor score in pPD and cPD (Rmean = 0.443, Ppermutation = 0.001) and cognition score in MCI and AD (Rmean = 0.243, Ppermutation = 0.037). In conclusion, progressive iron deposition in the SN and RN may characterize PD-related neurodegeneration, namely aging to cPD through pPD. On the other hand, extreme iron deposition in the caudate and putamen may characterize AD-related neurodegeneration.

Assessing glymphatic function using in-vivo imaging method is of great value for understanding its contribution to major brain diseases. In the present study, we aim to validate the association between a variety of risk factors and a potential index of glymphatic function—Diffusion Tensor Image Analysis Along the Perivascular Space (ALPS index). We enrolled 142 subjects from communities and performed multi-modality magnetic resonance imaging scans. The ALPS index was calculated from diffusion tensor imaging data, and its associations with demographic factors, vascular factors were investigated using regression analyses. We found that the ALPS index was negatively associated with age (β = −0.284, p < 0.001). Compared to males, females had significantly higher ALPS index (β = −0.243, p = 0.001). Hypertensive subjects had significantly lower ALPS index compared to non-hypertensive subjects (β = −0.189, p = 0.013). Furthermore, venous disruption could decrease ALPS index (β = −0.215, p = 0.003). In general, our results are in consistent with previous conceptions and results from animal studies about the pathophysiology of glymphatic dysfunction. Future studies utilizing this method should consider introducing the above-mentioned factors as important covariates.

Alpha-synucleinopathy is postulated to be central to both idiopathic rapid eye movement sleep behaviour disorder (iRBD) and Parkinson’s disease (PD). Growing evidence suggests an association between the diminished clearance of α-synuclein and glymphatic system dysfunction. However, evidence accumulating primarily based on clinical data to support glymphatic system dysfunction in patients with iRBD and PD is currently insufficient. This study aimed to use diffusion tensor image analysis along the perivascular space (DTI-ALPS) to evaluate glymphatic system activity and its relationship to clinical scores of disease severity in patients with possible iRBD (piRBDs) and those with PD. Further, we validated the correlation between the ALPS index and the prognosis of PD longitudinally. Overall, 168 patients with PD, 119 piRBDs, and 129 healthy controls were enroled. Among them, 50 patients with PD had been longitudinally reexamined. Patients with PD exhibited a lower ALPS index than those with piRBDs ( P  = 0.036), and both patient groups showed a lower ALPS index than healthy controls ( P  < 0.001 and P  = 0.001). The ALPS index and elevated disease severity were negatively correlated in the piRBD and PD subgroups. Moreover, the ALPS index was correlated with cognitive decline in patients with PD in the longitudinal analyses. In conclusion, DTI-ALPS provided neuroimaging evidence of glymphatic system dysfunction in piRBDs and patients with PD; however, the potential of assessing the pathological progress of α-synucleinopathies as an indicator is worth verifying. Further development of imaging methods for glymphatic system function is also warranted.

Background: Mounting evidence has revealed an inverse association between cigarette smoking and the risk of Parkinson’s disease (PD). Meanwhile, cigarette smoking has been found to be associated with cognitive impairment in PD patients. However, the neural mechanisms of the association between cigarette smoking and PD are not fully understood. Objective: The aim of this study is to explore the neural mechanisms of the association between cigarette smoking and PD. Methods: A total of 129 PD patients and 69 controls were recruited from the Parkinson’s Progression Markers Initiative (PPMI) cohort, including 39 PD patients with regular smoking history (PD-S), 90 PD patients without regular smoking history (PD-NS), 26 healthy controls with regular smoking history (HC-S), and 43 healthy controls without regular smoking history (HC-NS). Striatal dopamine transporter (DAT) binding and gray matter (GM) volume of the whole brain were compared among the four groups. Results: PD patients showed significantly reduced striatal DAT binding compared with healthy controls, and HC-S showed significantly reduced striatal DAT binding compared with HC-NS. Moreover, smoking and PD showed a significant interaction effect in the left medial prefrontal cortex (mPFC). PD-S showed reduced GM volume in the left mPFC compared with PD-NS. Conclusion: The degeneration of dopaminergic neurons in PD results in a substantial reduction of the DAT and dopamine levels. Nicotine may act as a stimulant to inhibit the action of striatal DAT, increasing dopamine levels in the synaptic gap. The inverse alteration of dopamine levels between PD and nicotine addiction may be the reason for the inverse association between smoking and the risk of PD. In addition, the mPFC atrophy in PD-S may be associated with cognitive impairment.

In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of early PD by integrating the advantages of various modals. Nineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI) and structural images. For the rsfMRI images, we extracted the characteristics at three different levels: ALFF (amplitude of low-frequency fluctuations), ReHo (regional homogeneity) and RFCS (regional functional connectivity strength). For the structural images, we extracted the volume characteristics from the gray matter (GM), the white matter (WM) and the cerebrospinal fluid (CSF). A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method. Using the proposed methods to classify the data set, good results (accuracy  = 86.96%, sensitivity  = 78.95%, specificity  = 92.59%) were obtained. This method demonstrates a promising diagnosis performance by the integration of information from a variety of imaging modalities, and it shows potential for improving the clinical diagnosis and treatment of PD.