Sheng-Yao Huang

Rice cultivation is an important source of agricultural methane emissions, and country-level monitoring is needed to evaluate how emission pressure evolves together with rice-sector production and market conditions. This study provides an exploratory dynamic monitoring assessment of methane emissions in Indonesia, the Philippines, and Viet Nam over the period 2000–2023. Annual data from FAOSTAT were used to examine methane emissions, methane emission intensity, rice production indicators, producer prices, and Food CPI. Long-term trends were evaluated using the Mann–Kendall test and Sen’s slope estimator. Dynamic associations were assessed using a parsimonious panel vector autoregression, with unit-root and residual-based cointegration diagnostics, first-difference robustness analysis, alternative Cholesky orderings, generalized impulse responses, and residual-bootstrap confidence intervals. The trend results reveal heterogeneous methane-emission trajectories across the three countries. Methane emissions declined significantly in Indonesia, increased significantly in the Philippines, and showed no statistically significant trend in Viet Nam. In contrast, methane emission intensity declined in all three countries, indicating that total emissions and intensity-based performance may move in different directions. The revised PVAR(1) results suggest that methane-emission responses are statistically associated with selected agricultural and price-related innovations, particularly producer-price innovations under the baseline recursive ordering. However, first-difference results show that many responses are concentrated in the short run, and FEVD results are sensitive to identification assumptions. Generalized impulse response-based decomposition indicates that methane-emission variability is primarily own-driven under ordering-invariant assumptions. Overall, the findings support a cautious monitoring interpretation. Rice-sector methane assessment should track both total emissions and emission intensity while considering production scale, harvested area, and dynamic adjustment patterns. The study provides exploratory evidence for three rice-producing countries and highlights the importance of transparent data construction, parsimonious modelling, and robustness checks when using aggregate country-level data to assess methane-emission dynamics.

Background/Objectives: Alzheimer’s disease (AD) is characterized by progressive brain atrophy marked by cognitive decline and memory loss, which significantly affect patients’ quality of life. Transcranial ultrasound stimulation (TUS) is a potential physical treatment for AD patients. However, the specific brain regions stimulated by TUS and its therapeutic effects remain unclear. Methods: In this study, magnetic resonance imaging (MRI) and FreeSurfer segmentation were employed to assess alterations in the brain volume of AD patients after TUS. Results: Our findings revealed significant volume increases in the corpus callosum (CC) and lateral orbitofrontal cortex (lOFC) in the TUS group. Moreover, the volumetric changes in the CC were strongly correlated with improvements in the Mini-Mental State Examination score, which is a widely used measure of cognitive function of AD patients. Conclusions: TUS has the potential to alleviate disease progression and offers a non-invasive therapeutic approach to the improvement of cognitive function in AD patients.

Amyloid plaques, implicated in Alzheimer’s disease, exhibit a spatial propagation pattern through interconnected brain regions, suggesting network-driven dissemination. This study utilizes PET imaging to investigate these brain connections and introduces an innovative method for analyzing the amyloid network. A modified version of a previously established method is applied to explore distinctive patterns of connectivity alterations across cognitive performance domains. PET images illustrate differences in amyloid accumulation, complemented by quantitative network indices. The normal control group shows minimal amyloid accumulation and preserved network connectivity. The MCI group displays intermediate amyloid deposits and partial similarity to normal controls and AD patients, reflecting the evolving nature of cognitive decline. Alzheimer’s disease patients exhibit high amyloid levels and pronounced disruptions in network connectivity, which are reflected in low levels of global efficiency (Eg) and local efficiency (Eloc). It is mostly in the temporal lobe where connectivity alterations are found, particularly in regions related to memory and cognition. Network connectivity alterations, combined with amyloid PET imaging, show potential as discriminative markers for different cognitive states. Dataset-specific variations must be considered when interpreting connectivity patterns. The variability in MCI and AD overlap emphasizes the heterogeneity in cognitive decline progression, suggesting personalized approaches for neurodegenerative disorders. This study contributes to understanding the evolving network characteristics associated with normal cognition, MCI, and AD, offering valuable insights for developing diagnostic and prognostic markers.

Narcolepsy is a central hypersomnia disorder, and differential diagnoses between its subtypes can be difficult. Hence, we applied machine learning to analyze the positron emission tomography (PET) data of patients with type 1 or type 2 narcolepsy, and patients with type 1 narcolepsy and comorbid schizophrenia, to construct predictive models to facilitate the diagnosis. This is a retrospective and prospective case–control study of adolescent and young adult patients with type 1 or type 2 narcolepsy, and type 1 narcolepsy and comorbid schizophrenia. All participants received ¹⁸-F-fluorodeoxy glucose PET, sleep studies, neurocognitive tests, sleep questionnaires, and human leukocyte antigen typing. The collected PET data were analyzed by feature selections and classification methods in machine learning to construct predictive models. A total of 314 participants with narcolepsy were enrolled; 204 had type 1 narcolepsy, 90 had type 2 narcolepsy, and 20 had type 1 narcolepsy and comorbid schizophrenia. We used three filter methods for feature selection followed by a comparative analysis of classification methods. To apply a small number of regions of interest (ROI) and high classification accuracy, the Naïve Bayes classifier with the Term Variance as feature selection achieved the goal with only three ROIs (left basal ganglia, left Heschl, and left striatum) and produced an accuracy of higher than 99%. The accuracy of our predictive model of PET data are promising and can aid clinicians in the diagnosis of narcolepsy subtypes. Future research with a larger sample size could further refine the predictive model of narcolepsy.

Astasia refers to the inability to maintain upright posture during standing, despite having full motor strength. Impairment of the vestibulocerebellar pathway, graviceptive system, and cingulate motor area have been proposed to be related to astasia. However, the responsible neural pathways remain unclear. We hypothesize that there is a common neural network behind astasia. To test the hypothesis, we reviewed all reported cases with astasia, including ours, and focused on the correlation between anatomical destruction and symptom presentation. A total of 26, including ours, non-psychogenic astasia patients were identified in the English literature. Seventy-three percent of them were associated with other neurologic symptoms and sixty-two percent of reported lesions were on the right side. Contralateral lateropulsion was very common, followed by retropulsion, when describing astasia. Infarction (54%) was the most reported cause. The thalamus (65%) was the most reported location. Infarctions were the fastest to recover (mean: 10.6 days), while lesions at the brainstem needed a longer time (mean: 61.6 days). By combining the character of lateropulsion in astasia and the presentation of an interrupted graviceptive system, we concluded that the primary graviceptive system may be the common neural network behind astasia. Future studies on astasia should focus on the pathological changes in the perception of verticality in the visual world and the body.

Introduction: Postoperative cerebellar mutism syndrome (pCMS) consists of three types of symptoms (motoric, linguistic, and neurobehavioral) in patients with posterior fossa pathologies. The evolutional mechanism of this high cognitive syndromic complex from cerebellar origin remains unconfirmed. Previous studies analyzing CMS patients mostly focused on the association between structural abnormalities that occur during CMS, of which proximal efferent cerebellar pathway (pECP) injury appears to be the most common pathogenesis. However, structural imaging may not be sensitive enough to determine the dynamic course of CMS, since the symptomatology is primarily an output of cerebral operation. Method: We took a network approach in a child during her course of development and recovery of the pCMS. On the other hand, a network neuroscience approach using a mathematical model to extract information from functional imaging to generate interregional connectivity provides abundant evidence that the cerebellum is influential in modulating cerebral functions. Result: This study applied a network approach to children with pCMS. An individual cerebrocerebellar functional network analysis using graph theory was then performed to determine the network dynamics during CMS. Cross-validation of clinical neurophysiology and functional neuroscience suggested the critical role of the pECP within CMS from the network analysis. Conclusion: The employed approach was therefore useful in determining the complex clinical symptoms using individual functional network analysis, which bridges the gap between structural neuroimaging and clinical neurophysiology.

Introduction: Metabolic brain network analysis based on graph theory using FDG PET imaging is potentially useful for investigating brain activity alternation due to metabolism changes in different stages of Alzheimer’s disease (AD). Most studies on metabolic network construction have been based on group data. Here a novel approach in building an individual metabolic network was proposed to investigate individual metabolic network abnormalities. Method: First, a weighting matrix was calculated based on the interregional effect size difference of mean uptake between a single subject and average normal controls (NCs). Then the weighting matrix for a single subject was multiplied by a group-based connectivity matrix from an NC cohort. To study the performance of the proposed individual metabolic network, inter- and intra-hemispheric connectivity patterns in the groups of NC, sMCI (stable mild cognitive impairment), pMCI (progressive mild cognitive impairment), and AD using the proposed individual metabolic network were constructed and compared with those from the group-based results. The network parameters of global efficiency and clustering coefficient and the network density score (NDS) in the default-mode network (DMN) of generated individual metabolic networks were estimated and compared among the disease groups in AD. Results: Our results show that the intra- and inter-hemispheric connectivity patterns estimated from our individual metabolic network are similar to those from the group-based method. In particular, the key patterns of occipital-parietal and occipital-temporal inter-regional connectivity deficits detected in the groupwise network study for differentiating different disease groups in AD were also found in the individual network. A reduction trend was observed for network parameters of global efficiency and clustering coefficient, and also for the NDS from NC, sMCI, pMCI, and AD. There was no significant difference between NC and sMCI for all network parameters. Conclusion: We proposed a novel method in constructing the individual metabolic network using a single-subject FDG PET image and a group-based NC connectivity matrix. The result has shown the effectiveness and feasibility of the proposed individual metabolic network in differentiating disease groups in AD. Future studies should include investigation of inter-individual variability and the correlation of individual network features to disease severities and clinical performance.

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using¹⁸F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using¹⁸F-FDG PET are the key feature for differentiating disease groups in AD.

Background: Patients with late‐life depression may be at the preclinical stage of dementia. However, the neurodegenerative processes in late‐life depression are poorly understood. This study aimed to investigate the distribution patterns of amyloid pathology and neurodegeneration in a depressive population without dementia.Methods: The study recruited 63 middle‐aged and elderly patients with major depressive disorder (MDD) and 22 control subjects. The MDD patients were further subdivided into those with mild cognitive impairment (MCI) (n = 24) and non‐MCI (n = 39) patients. We used the global standardized uptake value ratio of ¹⁸F‐florbetapir (AV‐45/Amyvid) positron emission tomography imaging as a biomarker of cerebral amyloidosis and the hippocampal volume as a biomarker for neurodegeneration. Cutoff points of brain amyloid positivity and hippocampal atrophy were determined using independent data obtained from clinically diagnosed Alzheimer's disease (AD) patients in a previous study.Results: Most of the control subjects (81.8%) were biomarker‐negative, in contrast to the MCI MDD patients (37.5%). A relatively high proportion of the MCI MDD patients (12.5%) exhibited both amyloid positivity and hippocampal atrophy as compared to the control subjects (4.5%) and non‐MCI patients (5.1%). However, a considerable proportion of the MCI MDD patients (29.2%) were categorized into the group with hippocampal atrophy alone, and negative amyloid deposition, as compared to the control subjects (0%) and non‐MCI patients (5.1%).Conclusions: This study highlights the expected heterogeneity of the processes of neurodegeneration in MDD patients. The diverse neurodegenerative processes may have important etiologic and therapeutic implications regarding neurodegenerative pathophysiology in late‐life depression.

An increased level of brain amyloid deposition and a decreased level of cerebral spinal fluid (CSF) Aβ42 are currently considered reliable biomarkers of Alzheimer’s disease (AD); however, the usefulness of plasma Aβ levels are not well-established. This study investigated the relationships between plasma Aβ levels and cerebral amyloidosis in 36 non-demented patients with major depressive disorder (MDD). All participants underwent ¹⁸F-florbetapir PET imaging and provided a blood sample at the same time for immunomagnetic reduction assay to measure the plasma levels of Aβ40 and Aβ42. We found inverse associations of the plasma Aβ42 level and the Aβ42/Aβ40 ratio, and a positive association of the plasma Aβ40 level, with cerebral amyloid deposition in the precuneus, parietal and posterior cingulate cortex. Subgroup analyses in subjects with higher ¹⁸F-florbetapir uptake values or MDD with amnestic mild cognitive impairment revealed more pervasive relationships of plasma Aβ measures with ¹⁸F-florbetapir binding across the brain regions examined. The study suggested that regional brain amyloid deposition in terms of ¹⁸F-florbetapir PET uptake had weak-to-moderate associations with plasma Aβ42 and Aβ40 levels, and the Aβ42/Aβ40 ratio. Validation in a larger population of subjects of known cerebral amyloidosis status is needed. Careful interpretation of plasma data is warranted.