MRI index of glymphatic system mediates the influence of locus coeruleus on cognition in Parkinson's disease.

INTRODUCTION: Although locus coeruleus (LC) has been demonstrated to play a critical role in the cognitive function of Parkinson's disease (PD), the underlying mechanism has not been elucidated. The objective was to investigate the relationship among LC degeneration, cognitive performance, and the glymphatic function in PD. METHODS: In this retrospective study, 71 PD subjects (21 with normal cognition; 29 with cognitive impairment (PD-MCI); 21 with dementia (PDD)) and 26 healthy controls were included. All participants underwent neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and diffusion tensor image scanning on a 3.0 T scanner. The brain glymphatic function was measured using diffusion along the perivascular space (ALPS) index, while LC degeneration was estimated using the NM contrast-to-noise ratio of LC (CNRLC). RESULTS: The ALPS index was significantly lower in both the whole PD group (P = 0.04) and the PDD subgroup (P = 0.02) when compared to the controls. Similarly, the CNRLC was lower in the whole PD group (P < 0.001) compared to the controls. In the PD group, a positive correlation was found between the ALPS index and both the Montreal Cognitive Assessment (MoCA) score (r = 0.36; P = 0.002) and CNRLC (r = 0.26; P = 0.03). Mediation analysis demonstrated that the ALPS index acted as a significant mediator between CNRLC and the MoCA score in PD subjects. CONCLUSION: The ALPS index, a neuroimaging marker of glymphatic function, serves as a mediator between LC degeneration and cognitive function in PD.

Gut Microbiota Metabolites Mediate Bax to Reduce Neuronal Apoptosis via cGAS/STING Axis in Epilepsy.

The beneficial effects of gut flora on reducing nerve cell apoptosis and inflammation and improving epilepsy (EP) symptoms have been reported, but the specific mechanism of action is still unclear. A series of in vitro and in vivo experiments revealed the relationship between gut microbiota metabolites and the cGAS/STING axis and their role in EP. These results suggest that antibiotic-induced dysbiosis of gut microbiota exacerbated epileptic symptoms, probiotic supplements reduced epileptic symptoms in mice. Antibiotics and probiotics altered the diversity and composition of gut microbiota. The changes in gut bacteria composition, such as in the abundance of Firmicutes, Bacteroidetes, Lactobacillus and Ruminococcus, were associated with the production of short-chain fatty acids (SCFA) in the gut. The concentrations of propionate, butyrate and isovalerate were changed after feeding antibiotics and probiotics, and the increase in butyrate levels reduced the expression of cGAS/STING in nerve cell further reduced Bax protein expression. The reduction of Bax protein attenuated the hippocampal neuron cell apoptosis in PTZ-induced EP and EP progression. Our findings provide new insights into the roles and mechanisms of action of the gut microbiota in attenuating EP symptoms and progression.

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.

Diagnosing Parkinson's disease by combining neuromelanin and iron imaging features using an automated midbrain template approach.

BACKGROUND AND PURPOSE: Early diagnosis of Parkinson's disease (PD) is still a clinical challenge. Most previous studies using manual or semi-automated methods for segmenting the substantia nigra (SN) are time-consuming and, despite raters being well-trained, individual variation can be significant. In this study, we used a template-based, automatic, SN subregion segmentation pipeline to detect the neuromelanin (NM) and iron features in the SN and SN pars compacta (SNpc) derived from a single 3D magnetization transfer contrast (MTC) gradient echo (GRE) sequence in an attempt to develop a comprehensive imaging biomarker that could be used to diagnose PD. MATERIALS AND METHODS: A total of 100 PD patients and 100 age- and sex-matched healthy controls (HCs) were imaged on a 3T scanner. NM-based SN (SNNM) boundaries and iron-based SN (SNQSM) boundaries and their overlap region (representing the SNpc) were delineated automatically using a template-based SN subregion segmentation approach based on quantitative susceptibility mapping (QSM) and NM images derived from the same MTC-GRE sequence. All PD and HC subjects were evaluated for the nigrosome-1 (N1) sign by two raters independently. Receiver Operating Characteristic (ROC) analyses were performed to evaluate the utility of SNNM volume, SNQSM volume, SNpc volume and iron content with a variety of thresholds as well as the N1 sign in diagnosing PD. Correlation analyses were performed to study the relationship between these imaging measures and the clinical scales in PD. RESULTS: In this study, we verified the value of the fully automatic template based midbrain deep gray matter mapping approach in differentiating PD patients from HCs. The automatic segmentation of the SN in PD patients led to satisfactory DICE similarity coefficients and volume ratio (VR) values of 0.81 and 1.17 for the SNNM, and 0.87 and 1.05 for the SNQSM, respectively. For the HC group, the average DICE similarity coefficients and VR values were 0.85 and 0.94 for the SNNM, and 0.87 and 0.96 for the SNQSM, respectively. The SNQSM volume tended to decrease with age for both the PD and HC groups but was more severe for the PD group. For diagnosing PD, the N1 sign performed reasonably well by itself (Area Under the Curve (AUC) = 0.783). However, combining the N1 sign with the other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an improved diagnosis of PD with an AUC as high as 0.947 (using an SN threshold of 50ppb and an NM threshold of 0.15). Finally, the SNQSM volume showed a negative correlation with the MDS-UPDRS III (R2 = 0.1, p = 0.036) and the Hoehn and Yahr scale (R2 = 0.04, p = 0.013) in PD patients. CONCLUSION: In summary, this fully automatic template based deep gray matter mapping approach performs well in the segmentation of the SN and its subregions for not only HCs but also PD patients with SN degeneration. The combination of the N1 sign with other quantitative measures (SNNM volume, SNQSM volume, SNpc volume and iron content) resulted in an AUC of 0.947 and provided a comprehensive set of imaging biomarkers that, potentially, could be used to diagnose PD clinically.

Automatic detection of neuromelanin and iron in the midbrain nuclei using a magnetic resonance imaging-based brain template.

Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in-vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC-GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC-GRE sequence was used to find the NM-rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45-81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine-tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM-rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template-based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei.

RNA Interference in Moths: Mechanisms, Applications, and Progress.

The vast majority of lepidopterans, about 90%, are moths. Some moths, particularly their caterpillars, are major agricultural and forestry pests in many parts of the world. However, some other members of moths, such as the silkworm Bombyx mori, are famous for their economic value. Fire et al. in 1998 initially found that exogenous double-stranded RNA (dsRNA) can silence the homolog endogenous mRNA in organisms, which is called RNA interference (RNAi). Soon after, the RNAi technique proved to be very promising not only in gene function determination but also in pest control. However, later studies demonstrate that performing RNAi in moths is not as straightforward as shown in other insect taxa. Nevertheless, since 2007, especially after 2010, an increasing number of reports have been published that describe successful RNAi experiments in different moth species either on gene function analysis or on pest management exploration. So far, more than 100 peer-reviewed papers have reported successful RNAi experiments in moths, covering 10 families and 25 species. By using classic and novel dsRNA delivery methods, these studies effectively silence the expression of various target genes and determine their function in larval development, reproduction, immunology, resistance against chemicals, and other biological processes. In addition, a number of laboratory and field trials have demonstrated that RNAi is also a potential strategy for moth pest management. In this review, therefore, we summarize and discuss the mechanisms and applications of the RNAi technique in moths by focusing on recent progresses.