CAMR: cross-aligned multimodal representation learning for cancer survival prediction.

MOTIVATION: Accurately predicting cancer survival is crucial for helping clinicians to plan appropriate treatments, which largely improves the life quality of cancer patients and spares the related medical costs. Recent advances in survival prediction methods suggest that integrating complementary information from different modalities, e.g. histopathological images and genomic data, plays a key role in enhancing predictive performance. Despite promising results obtained by existing multimodal methods, the disparate and heterogeneous characteristics of multimodal data cause the so-called modality gap problem, which brings in dramatically diverse modality representations in feature space. Consequently, detrimental modality gaps make it difficult for comprehensive integration of multimodal information via representation learning and therefore pose a great challenge to further improvements of cancer survival prediction. RESULTS: To solve the above problems, we propose a novel method called cross-aligned multimodal representation learning (CAMR), which generates both modality-invariant and -specific representations for more accurate cancer survival prediction. Specifically, a cross-modality representation alignment learning network is introduced to reduce modality gaps by effectively learning modality-invariant representations in a common subspace, which is achieved by aligning the distributions of different modality representations through adversarial training. Besides, we adopt a cross-modality fusion module to fuse modality-invariant representations into a unified cross-modality representation for each patient. Meanwhile, CAMR learns modality-specific representations which complement modality-invariant representations and therefore provides a holistic view of the multimodal data for cancer survival prediction. Comprehensive experiment results demonstrate that CAMR can successfully narrow modality gaps and consistently yields better performance than other survival prediction methods using multimodal data. AVAILABILITY AND IMPLEMENTATION: CAMR is freely available at https://github.com/wxq-ustc/CAMR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage.

BACKGROUND: Spontaneous intracerebral hemorrhage (sICH) is associated with significant mortality and morbidity. Predicting the prognosis of patients with sICH remains an important issue, which significantly affects treatment decisions. Utilizing readily available clinical parameters to anticipate the unfavorable prognosis of sICH patients holds notable clinical significance. This study employs five machine learning algorithms to establish a practical platform for the prediction of short-term prognostic outcomes in individuals afflicted with sICH. METHODS: Within the framework of this retrospective analysis, the model underwent training utilizing data gleaned from 413 cases from the training center, with subsequent validation employing data from external validation center. Comprehensive clinical information, laboratory analysis results, and imaging features pertaining to sICH patients were harnessed as training features for machine learning. We developed and validated the model efficacy using all the selected features of the patients using five models: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), XGboost and LightGBM, respectively. The process of Recursive Feature Elimination (RFE) was executed for optimal feature screening. An internal five-fold cross-validation was employed to pinpoint the most suitable hyperparameters for the model, while an external five-fold cross-validation was implemented to discern the machine learning model demonstrating the superior average performance. Finally, the machine learning model with the best average performance is selected as our final model while using it for external validation. Evaluation of the machine learning model's performance was comprehensively conducted through the utilization of the ROC curve, accuracy, and other relevant indicators. The SHAP diagram was utilized to elucidate the variable importance within the model, culminating in the amalgamation of the above metrics to discern the most succinct features and establish a practical prognostic prediction platform. RESULTS: A total of 413 patients with sICH patients were collected in the training center, of which 180 were patients with poor prognosis. A total of 74 patients with sICH were collected in the external validation center, of which 26 were patients with poor prognosis. Within the training set, the test set AUC values for SVM, LR, RF, XGBoost, and LightGBM models were recorded as 0.87, 0.896, 0.916, 0.885, and 0.912, respectively. The best average performance of the machine learning models in the training set was the RF model (average AUC: 0.906 ± 0.029, P < 0.01). The model still maintains a good performance in the external validation center, with an AUC of 0.817 (95% CI 0.705-0.928). Pertaining to feature importance for short-term prognostic attributes of sICH patients, the NIHSS score reigned supreme, succeeded by AST, Age, white blood cell, and hematoma volume, among others. In culmination, guided by the RF model's variable importance weight and the model's ROC curve insights, the NIHSS score, AST, Age, white blood cell, and hematoma volume were integrated to forge a short-term prognostic prediction platform tailored for sICH patients. CONCLUSION: We constructed a prediction model based on the results of the RF model incorporating five clinically accessible predictors with reliable predictive efficacy for the short-term prognosis of sICH patients. Meanwhile, the performance of the external validation set was also more stable, which can be used for accurate prediction of short-term prognosis of sICH patients.

HFBSurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction.

MOTIVATION: Cancer survival prediction can greatly assist clinicians in planning patient treatments and improving their life quality. Recent evidence suggests the fusion of multimodal data, such as genomic data and pathological images, is crucial for understanding cancer heterogeneity and enhancing survival prediction. As a powerful multimodal fusion technique, Kronecker product has shown its superiority in predicting survival. However, this technique introduces a large number of parameters that may lead to high computational cost and a risk of overfitting, thus limiting its applicability and improvement in performance. Another limitation of existing approaches using Kronecker product is that they only mine relations for one single time to learn multimodal representation and therefore face significant challenges in deeply mining rich information from multimodal data for accurate survival prediction. RESULTS: To address the above limitations, we present a novel hierarchical multimodal fusion approach named HFBSurv by employing factorized bilinear model to fuse genomic and image features step by step. Specifically, with a multiple fusion strategy HFBSurv decomposes the fusion problem into different levels and each of them integrates and passes information progressively from the low level to the high level, thus leading to the more specialized fusion procedure and expressive multimodal representation. In this hierarchical framework, both modality-specific and cross-modality attentional factorized bilinear modules are designed to not only capture and quantify complex relations from multimodal data, but also dramatically reduce computational complexity. Extensive experiments demonstrate that our method performs an effective hierarchical fusion of multimodal data and achieves consistently better performance than other methods for survival prediction. AVAILABILITY AND IMPLEMENTATION: HFBSurv is freely available at https://github.com/Liruiqing-ustc/HFBSurv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Baseline free water within the visual processing system predicts future psychosis in Parkinson disease.

BACKGROUND AND PURPOSE: As psychosis is associated with decreased quality of life, increased institutionalization, and mortality in Parkinson disease (PD), it is essential to identify individuals at risk for future psychosis. This longitudinal study aimed to investigate whether diffusion tensor imaging (DTI) metrics of white matter hold independent utility for predicting future psychosis in PD, and whether they could be combined with clinical predictors to improve the prognostication of PD psychosis. METHODS: This study included 123 newly diagnosed PD patients collected in the Parkinson's Progression Markers Initiative. Tract-based spatial statistics were used to compare baseline DTI metrics between PD patients who developed psychosis and those who did not during follow-up. Binary logistic regression analyses were performed to identify the clinical and white matter markers predictive of psychosis. RESULTS: Among DTI measures, both higher baseline whole brain (odds ratio [OR] = 1.711, p = 0.016) free water (FW) and visual processing system (OR = 1.680, p < 0.001) FW were associated with an increased risk of future psychosis. Baseline FW remained a significant indicator of future psychosis in PD after controlling for clinical predictors. Moreover, the accuracy of prediction of psychosis using clinical predictors alone (area under the curve [AUC] = 0.742, 95% confidence interval [CI] = 0.655-0.816) was significantly improved by the addition of the visual processing system FW (AUC = 0.856, 95% CI = 0.781-0.912; Delong method, p = 0.022). CONCLUSIONS: Baseline FW of the visual processing system incurs an independent risk of future psychosis in PD, thus providing an opportunity for multiple-modality marker models to include a white matter marker.

Education reduces cognitive dysfunction in Alzheimer's disease by changing regional cerebral perfusion: An in-vivo arterial spin labeling study.

OBJECTIVE: Formal education and other cognitive challenges influence brain structure and improve function. It is believed that cognitive activities create a cognitive reserve (CR) that can slow the decline due to aging and neurodegenerative diseases. This study investigated alterations of regional cerebral blood flow (rCBF) associated with high and low CR in different stages of Alzheimer's disease (AD) and examined whether rCBF alteration mediates the relationship between education and cognitive performance. METHODS: Patients with AD or amnestic mild cognitive impairment (aMCI) and healthy controls were divided into low cognitive reserve (LCR) and high cognitive reserve (HCR) subgroups according to median of education years (≤ 9 vs. > 9 years). The final study population included 89 AD patients (67 LCR, 22 HCR), 74 aMCI patients (44 LCR, 30 HCR), and 66 healthy controls (29 LCR, 37 HCR). All subjects were examined by arterial spin labeling magnetic resonance imaging and a neurocognitive test battery. rCBF was compared among groups by two-way analysis of variance. Mediation analyses were used to explore the relationships among education, rCBF, and cognitive test scores. RESULTS: There were significant interaction effects of disease state (AD, aMCI, HC) and education level (LCR, HCR) on CBF in right hippocampus, posterior cingulate cortex, and right inferior parietal cortex (R_IPC). Education regulated episodic memory score by influencing right hippocampal CBF in HC_HCR and aMCI_HCR subgroups. CONCLUSION: Our results indicate that the protective effect of education against cognitive dysfunction in early-stage AD is mediated at least partially by altered CBF in right hippocampus.

Consistent Extended Kalman Filter-Based Cooperative Localization of Multiple Autonomous Underwater Vehicles.

In order to solve the problem of inconsistent state estimation when multiple autonomous underwater vehicles (AUVs) are co-located, this paper proposes a method of multi-AUV co-location based on the consistent extended Kalman filter (EKF). Firstly, the dynamic model of cooperative positioning system follower AUV under two leaders alternately transmitting navigation information is established. Secondly, the observability of the standard linearization estimator based on the lead-follower multi-AUV cooperative positioning system is analyzed by comparing the subspace of the observable matrix of state estimation with that of an ideal observable matrix, it can be concluded that the estimation of state by standard EKF is inconsistent. Finally, aiming at the problem of inconsistent state estimation, a consistent EKF multi-AUV cooperative localization algorithm is designed. The algorithm corrects the linearized measurement values in the Jacobian matrix for cooperative positioning, ensuring that the linearized estimator can obtain accurate measurement values. The positioning results of the follower AUV under dead reckoning, standard EKF, and consistent EKF algorithms are simulated, analyzed, and compared with the real trajectory of the following AUV. The simulation results show that the follower AUV with a consistent EKF algorithm can keep synchronization with the leader AUV more stably.

Better modulation for risk decision-making after optimized magnetic stimulation.

Traditional repetitive transcranial magnetic stimulation can only produce a significant but weak effect on the cortex while theta burst stimulation (TBS), a patterned accelerated form of stimulation, can produce a stronger poststimulation effect, which may improve decision-making abilities. We designed a comparative assessment of the effect of intermittent TBS (iTBS), 20 Hz, in two risk decision-making tasks on healthy controls. Participants were randomized and assigned to the iTBS (n = 29), 20 Hz (n = 29), or sham (n = 29) groups. The effects of the different methods of left dorsolateral prefrontal cortex stimulation on risk decision-making functions were compared based on subjects' performance in the Game of Dice Task (GDT) and Risky Gains Task (RGT). The main indicators were positive and negative feedback utilization rates of GDT and RGT. Both iTBS and 20 Hz stimulation resulted in significant improvements upon negative feedback in the GDT, with increases in safe options and reductions in risky options; iTBS stimulation increased subjects' use of positive feedback in the GDT and RGT (all p < 0.05). Furthermore, the iTBS group had a stronger feedback risk reduction effect than the 20 Hz or sham group following RGT negative feedback (p < 0.05). Individuals would integrate positive and negative information more efficiently, leading to them making rational choices after excitatory transcranial magnetic stimulation. Moreover, iTBS has a stronger risk reduction effect following negative feedback than the 20Hz stimulation did. In summary, iTBS might have clinical value in decision promotion.

The protective impact of education on brain structure and function in Alzheimer's disease.

BACKGROUND: The Cognitive Reserve (CR) theory posits that brains with higher reserve can cope with more cerebral damage to minimize clinical manifestations. The aim of this study was to examine the effect of education (CR proxy) on brain structure and function in Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) patients and in cognitively healthy elderly (HC) individuals. METHODS: Fifty-seven AD patients, 57 aMCI patients and 48 HCs were included to investigate the relationships between education years and gray matter volume (GMV), regional homogeneity (ReHo) and functional connectivity (FC) in brain regions to show associations with both structure and function. Taking the severity of the disease into account, we further assessed the relationships in AD stratified analyses. RESULTS: In AD group, the GMV of the dorsal anterior cingulate cortex (dACC) and ReHo in the left inferior temporal cortex (ITC) were inversely associated with education years, after adjustment for age, sex, Mini-Mental State Examination (MMSE), and total intracranial volume or head motion parameters. Seed-based FC analyses revealed that education years were negatively correlated with the FC between the left anterior ITC and left mid frontal cortex as well as right superior frontal cortex and right angular gyrus. Stratified analyses results indicated that this negative relation between education and GMV, ReHo, FC was mainly present in mild AD, which was attenuated in moderate AD and aMCI groups. CONCLUSIONS: Our results support the CR theory, and suggest that CR may be protective against AD related brain pathology at the early stage of clinical dementia. These findings could provide the locus of CR-related functional brain mechanisms and a specific time-window for therapeutic interventions to help AD patients to cope better with the brain pathological damage by increasing CR.

The vessel density of the superficial retinal capillary plexus as a new biomarker in cerebral small vessel disease: an optical coherence tomography angiography study.

BACKGROUND: Optical coherence tomography angiography (OCTA) is a novel and noninvasive technique for the quantitative assessment of retinal microvascular perfusion. Since the retinal and cerebral small vessels share similar embryological origins, anatomical features, and physiological properties, altered retinal microvasculature might provide a new perspective on the mechanisms of cerebral small vessel disease (CSVD). OBJECTIVE: We aimed to evaluate retinal vessel density (VD) in patients with CSVD using OCTA and identify associations with cerebral magnetic resonance imaging (MRI) markers and cognitive function. METHODS: We prospectively recruited 47 CSVD patients and 30 healthy controls (HCs) to participate in the study. All participants underwent OCTA to evaluate retinal microvascular perfusion. The VDs of the macular region in the superficial retinal capillary plexus (SRCP), deep retinal capillary plexus (DRCP), and foveal avascular zone (FAZ) were determined, along with the VD of the optic nerve head (ONH) in the radial peripapillary capillary (RPC) network. Additionally, cerebral MRI and cognitive function tests were performed. RESULTS: In the macula area, the VD of the CSVD patients was significantly lower than HCs in the temporal quadrant of SRCP. In the ONH area, CSVD patients had lower VD than HCs in the peripapillary RPC network. According to multiple linear regression analysis, decreased VD of the macular SRCP was associated with white matter hyperintensity scores after adjustment for age, hypertension, diabetes, and hyperlipidemia. Furthermore, the VD of the macular SRCP was significantly correlated with CSVD patients' cognitive function, especially global cognition, memory function, attention function, information processing, and executive function. CONCLUSION: OCTA revealed a significant decrease in retinal microvascular perfusion in CSVD patients, and retinal hypoperfusion was related to MRI markers and cognitive function, suggesting that these parameters could have potential utility as early disease biomarkers.

Development and Validation of Paradigms Based on the Global-First Topological Approach for Alzheimer's Disease Severity Staging.

Introduction: Conventional methods like patient history, neuropsychological testing, cerebrospinal fluid examination, and magnetic resonance imaging are widely used to diagnose cases in the current clinical setting but are limited in classifying Alzheimer's disease (AD) stages. Patients with AD exhibit visual perception deficits, which may be a potential target to assess the severity of the disease according to visual paradigms. However, owing to the inconsistent forms of perceived objects, the defects of current visual processing paradigms often lead to inconsistent results and a lack of sensitivity and specificity. Methods: We develop two paradigms based on global-first topological approach of visual perception, which avoids inconsistent results and lack of sensitivity and specificity owing to the inconsistent forms of perceived objects in traditional paradigms, delineate a unique detection strategy from perception organization (Experiment 1) and visual working memory (VWM) (Experiment 2). Results: Except for the significant differences of the reaction times (RTs) between groups, significant differences were found when AD subjects recognize small figures due to the consistency of global and local figures in similarity test. The difference of RTs between recognizing global and local figures can be recognized in AD and mild cognitive impairment (MCI) group compared to healthy elderly (HE) in similarity test (Experiment 1). The memory capacity of AD patients was significantly lower than MCI group. Topological interference effect was observed in MCI and HE group, whereas MCI patients may have a greater difference trend in non-topological and topological changes than HE (Experiment 2). Conclusion: Our paradigms provide a new strategy, which can assist clinical severity staging and linking topological approach of visual perception with pathophysiological processes in AD.

Patients with episodic migraine without aura have an increased rate of delayed discounting.

OBJECTIVE: This study aimed to explore decision-making impulsivity and its neural mechanisms in patients with episodic migraine without aura (EMoA). BACKGROUND: Previous evidence indicates increased impulsivity and altered reward processing in patients with chronic migraine and medication overuse; however, whether the same holds true for those with EMoA is unclear. METHODS: Patients newly diagnosed with EMoA (n = 51) and healthy controls (HC, n = 45) were recruited. All participants completed delay discounting task, cognitive assessments, a questionnaire for headache profile, and resting-state function magnetic resonance imaging scans. Resting-state functional connectivity (RSFC) between the regions of interest and the entire brain was explored. RESULTS: Patients with EMoA showed a steeper subjective discount rate than HCs (F = 4.74, p = .032), which was positively related to a history of migraines (r = .742, p < .001). RSFC among the ventral striatum (vSTR), ventromedial prefrontal cortex, and occipital cortex was lower in patients with EMoA than in control groups, which was correlated with history (r' = .294, p = .036) and subjective discount rate (r' = .380, p = .006). Additionally, discounting rates and RSFC between the vSTR and occipital regions were significantly abnormal in the triptan group than the non-triptan group. Mediating effect analysis indicated a significant mediating effect in the change in RSFC between the vSTR and occipital status, history of triptan use, and subjective discount rate. CONCLUSION: This study further elucidated that an increase in delayed discounting rate exists in patients with EMoA and is related to the abnormality of the value processing network.

Automatic peak detection algorithm based on continuous wavelet transform for complex chromatograms from multi-detector micro-scale gas chromatographs.

Peak detection for chromatograms, including the detection of peak retention times, peak start locations, and peak end locations, is an important processing step for extracting peak information that is used for chemical recognition. Compared to benchtop gas chromatographs, the chromatograms generated by microscale gas chromatographs (µGCs) often contain higher noise levels, peak overlap, peak asymmetry, and both positive and negative chromatographic peaks, increasing the challenges for peak detection. This paper reports an automatic peak detection algorithm based on continuous wavelet transform (CWT) for chromatograms generated by multi-detector µGCs. The relationship between chemical retention time and peak width is leveraged to differentiate chromatographic peaks from noise and baseline drift. Special features in the CWT coefficients are leveraged to detect peak overlap and asymmetry. For certain detectors that may generate positive and negative chromatographic peaks, the peaks cannot be independently detected reliably, but the peak information can be well extracted using peak information generated by other in-line single-polarity detectors. The implemented algorithm provided a true positive rate of 97.2 % and false discovery rate of 7.8 % for chromatograms generated by a µGC with three integrated detectors, two capacitive and one photoionization. The chromatograms included complex scenarios with positive and negative chromatographic peaks, up to five consecutive overlapping peaks, peak asymmetry factor up to 24, and signal-to-noise ratios spanning 9-2800.

Brain functional specialization and cooperation in Alzheimer's disease.

BACKGROUND: Cerebral specialization and interhemispheric cooperation are two vital features of the human brain. Their dysfunction may be associated with disease progression in patients with Alzheimer's disease (AD), which is featured as progressive cognitive degeneration and asymmetric neuropathology. OBJECTIVE: This study aimed to examine and define two inherent properties of hemispheric function in patients with AD by utilizing resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: Sixty-four clinically diagnosed AD patients and 52 age- and sex-matched cognitively normal subjects were recruited and underwent MRI and clinical evaluation. We calculated and compared brain specialization (autonomy index, AI) and interhemispheric cooperation (connectivity between functionally homotopic voxels, CFH). RESULTS: In comparison to healthy controls, patients with AD exhibited enhanced AI in the left middle occipital gyrus. This increase in specialization can be attributed to reduced functional connectivity in the contralateral region, such as the right temporal lobe. The CFH of the bilateral precuneus and prefrontal areas was significantly decreased in AD patients compared to controls. Imaging-cognitive correlation analysis indicated that the CFH of the right prefrontal cortex was marginally positively related to the Montreal Cognitive Assessment score in patients and the Auditory Verbal Learning Test score. Moreover, taking abnormal AI and CFH values as features, support vector machine-based classification achieved good accuracy, sensitivity, specificity, and area under the curve by leave-one-out cross-validation. CONCLUSION: This study suggests that individuals with AD have abnormal cerebral specialization and interhemispheric cooperation. This provides new insights for further elucidation of the pathological mechanisms of AD.

A Study on the Effect of Executive Control Network Functional Connection on the Therapeutic Efficacy of Repetitive Transcranial Magnetic Stimulation in Alzheimer's Disease.

Background: Alzheimer's disease (AD) is a neurodegenerative disease characterized by brain network dysfunction. Few studies have investigated whether the functional connections between executive control networks (ECN) and other brain regions can predict the therapeutic effect of repetitive transcranial magnetic stimulation (rTMS). Objective: The purpose of this study is to examine the relationship between the functional connectivity (FC) within ECN networks and the efficacy of rTMS. Methods: We recruited AD patients for rTMS treatment. We established an ECN using baseline period fMRI data and conducted an analysis of the ECN's FC throughout the brain. Concurrently, the support vector regression (SVR) method was employed to project post-rTMS cognitive scores, utilizing the connectional attributes of the ECN as predictive markers. Results: The average age of the patients was 66.86±8.44 years, with 8 males and 13 females. Significant improvement on most cognitive measures. We use ECN connectivity and brain region functions in baseline patients as features for SVR model training and fitting. The SVR model could demonstrate significant predictability for changes in Montreal Cognitive Assessment scores among AD patients after rTMS treatment. The brain regions that contributed most to the prediction of the model (the top 10% of weights) were located in the medial temporal lobe, middle temporal gyrus, frontal lobe, parietal lobe and occipital lobe. Conclusions: The stronger the antagonism between ECN and parieto-occipital lobe function, the better the prediction of cognitive improvement; the stronger the synergy between ECN and fronto-temporal lobe function, the better the prediction of cognitive improvement.

Abnormal EEG microstates in Alzheimer's disease: predictors of ß-amyloid deposition degree and disease classification.

Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aß 1-42 levels, while duration of microstate C was correlated with CSF Aß 1-40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aß deposition, and may be useful predictors for disease classification.

Effects of a periodic intermittent theta burst stimulation in Alzheimer's disease.

Background: Previous studies have demonstrated that excitatory repetitive transcranial magnetic stimulation (rTMS) can improve the cognitive function of patients with Alzheimer's disease (AD). Intermittent theta burst stimulation (iTBS) is a novel excitatory rTMS protocol for brain activity stimulation with the ability to induce long-term potentiation-like plasticity and represents a promising treatment for AD. However, the long-term effects of iTBS on cognitive decline and brain structure in patients with AD are unknown. Aims: We aimed to explore whether repeating accelerated iTBS every three months could slow down the cognitive decline in patients with AD. Methods: In this randomised, assessor-blinded, controlled trial, iTBS was administered to the left dorsolateral prefrontal cortex (DLPFC) of 42 patients with AD for 14 days every 13 weeks. Measurements included the Montreal Cognitive Assessment (MoCA), a comprehensive neuropsychological battery, and the grey matter volume (GMV) of the hippocampus. Patients were evaluated at baseline and after follow-up. The longitudinal pipeline of the Computational Anatomy Toolbox for SPM was used to detect significant treatment-related changes over time. Results: The iTBS group maintained MoCA scores relative to the control group (t=3.26, p=0.013) and reduced hippocampal atrophy, which was significantly correlated with global degeneration scale changes. The baseline Mini-Mental State Examination (MMSE) score, apolipoprotein E genotype and Clinical Dementia Rating were indicative of MoCA scores at follow-up. Moreover, the GMV of the left (t=0.08, p=0.996) and right (t=0.19, p=0.977) hippocampus were maintained in the active group but significantly declined in the control group (left: t=4.13, p<0.001; right: t=5.31, p<0.001). GMV change in the left (r=0.35, p=0.023) and right (r=0.36, p=0.021) hippocampus across the intervention positively correlated with MoCA changes; left hippocampal GMV change was negatively correlated with global degeneration scale (r=-0.32, p=0.041) changes. Conclusions: DLPFC-iTBS may be a feasible and easy-to-implement non-pharmacological intervention to slow down the progressive decline of overall cognition and quality of life in patients with AD, providing a new AD treatment option. Trial registration number: NCT04754152.

[Effect of Biomass Burning on Carbonaceous Aerosol Composition and Light Absorption in Guangxi Regional Background Site].

Carbonaceous aerosols are an important component of fine particulate matter (PM2.5) in the atmosphere, having great impacts on air quality, human health, and the climate. In this study, PM2.5 samples were collected from November 2017 to October 2018 in a background site of Guangxi Province to investigate the potential impacts of biomass burning, an essential source of carbonaceous aerosols, on carbonaceous aerosols. Further, the composition of carbonaceous aerosols, sugar compounds, and the light absorption coefficient (babs) of water-soluble brown carbon (BrC) were also conducted. Considering the effect of the degradation of atmospheric levoglucosan (LG), the concentration of the corrected LG was quantified using the aging of air masses (AAM) index. Then, the contribution of biomass burning (BB) to organic carbon (OC) [BB-OC] was quantified using the corrected LG-derived molecular tracer method combined with the Bayesian mixing model. Here, we further explored the potential sources of water-soluble BrC using correlation analysis. In this research, the mean AAM index was 0.40±0.28 during the study period, indicating that the atmospheric LG had undergone a photochemical degradation process. The characteristic ratio combined with the Bayesian mixing model indicated that the crop straw (i.e., corn, rice, and sugarcane straw) was the dominant biomass fuel type in the Guangxi Region, contributing 22%, 23%, and 18% of OC without the correction of LG and 16%, 21%, and 17% with the corrected LG concentration, respectively. The neglection of LG degradation led to the underestimation of BB-OC, in which the BB-OC values with and without correction were 49.0% and 21.1%, respectively. Here, the annual mean babs of water-soluble BrC was (8.7±10.7) Mm-1, and its main sources were BB, fossil fuel combustion, and vegetation emission.

The correlation of retinal neurodegeneration and brain degeneration in patients with Alzheimer's disease using optical coherence tomography angiography and MRI.

Introduction: Pathological changes in Alzheimer's disease can cause retina and optic nerve degeneration. The retinal changes are correlated with cognitive function. This study aimed to explore the relationship of retinal differences with neuroimaging in patients with Alzheimer's disease, analyze the association of cognitive function with retinal structure and vascular density, and identify potential additional biomarkers for early diagnosis of Alzheimer's disease. Method: We performed magnetic resonance imaging (MRI) scans and neuropsychological assessments in 28 patients with mild Alzheimer's disease and 28 healthy controls. Retinal structure and vascular density were evaluated by optical coherence tomography angiography (OCTA). Furthermore, we analyzed the correlation between neuroimaging and OCTA parameters in patients with mild Alzheimer's disease with adjustment for age, gender, years of education, and hypertension. Results: In patients with mild Alzheimer's disease, OCTA-detected retinal parameters were not significantly correlated with MRI-detected neuroimaging parameters after Bonferroni correction for multiple testing. Under multivariable analysis controlled for age, gender, years of education, and hypertension, the S-Hemi (0-3) sector of macular thickness was significantly associated with Mini-cog (ß = 0.583, P = 0.002) with Bonferroni-corrected threshold at P < 0.003. Conclusion: Our findings suggested decreased macular thickness might be associated with cognitive function in mild AD patients. However, the differences in retinal parameters didn't correspond to MRI-detected parameters in this study. Whether OCTA can be used as a new detection method mirroring MRI for evaluating the effect of neuronal degeneration in patients with mild Alzheimer's disease still needs to be investigated by more rigorous and larger studies in the future.

White Matter Changes as an Independent Predictor of Alzheimer's Disease.

BACKGROUND: Abnormalities in white matter (WM) may be a crucial physiologic feature of Alzheimer's disease (AD). However, neuroimaging's ability to visualize the underlying functional degradation of the WM region in AD is unclear. OBJECTIVE: This study aimed to explore the differences in amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) in the WM region of patients with AD and healthy controls (HC) and to investigate further whether these values can provide supplementary information for diagnosing AD. METHODS: Forty-eight patients with AD and 46 age-matched HC were enrolled and underwent resting-state functional magnetic resonance imaging and a neuropsychological battery assessment. We analyzed the differences in WM activity between the two groups and further explored the correlation between WM activity in the different regions and cognitive function in the AD group. Finally, a machine learning algorithm was adopted to construct a classifier in detecting the clinical classification ability of the values of ALFF/ALFF in the WM. RESULTS: Compared with HCs, patients with AD had lower WM activity in the right anterior thalamic radiation, left frontal aslant tract, and left forceps minor, which are all positively related to global cognitive function, memory, and attention function (all p < 0.05). Based on the combined WM ALFF and fALFF characteristics in the different regions, individuals not previously assessed were classified with moderate accuracy (75%), sensitivity (71%), specificity (79%), and area under the receiver operating characteristic curve (85%). CONCLUSION: Our results suggest that WM activity is reduced in AD and can be used for disease classification.

Intracranial Atherosclerotic Stenosis Is Associated with Cognitive Impairment in Patients with Nondisabling Ischemic Stroke: A pCASL-Based Study.

Background: Intracranial atherosclerotic stenosis (ICAS) is a key risk factor for vascular cognitive impairment. Cerebral blood flow (CBF) and the spatial coefficient of variation (sCoV) of CBF images (based on pseudocontinuous arterial spin labeling) are used to explore abnormal cerebral perfusion. We aimed to probe the mechanisms underlying cognitive impairment in patients with nondisabling anterior circulation macrovascular disease. Methods: This study included 47 patients with ICAS or occlusion and 40 controls. All participants underwent global and individual neuropsychology assessments and magnetic resonance imaging scan. The correlations between cognitive function and abnormal perfusion were explored. Results: The CBF in the ipsilateral middle cerebral artery (MCA) territory of the lesion side decreased significantly, while it increased on the contralateral side. CBF value had a significant correlation with the memory function in the right cerebral artery lesion group. The sCoV in both gray matter (GM) and the ipsilateral MCA territory of the lesion increased significantly. The sCoV value based on the GM territory or MCA territory was significantly correlated with global cognitive function, memory function, and executive function in patients with ICAS. Conclusions: The cognitive function of patients with severe ICAS or occlusion in anterior circulation was significantly impaired. sCoV could be a better indicator of cognitive impairment than CBF. Interventions to relieve vascular stenosis or occlusion and delay cognitive impairment or improve cognitive function should be actively considered.