Rapid eye movement sleep behaviour disorder has been evaluated using Parkinson's disease-related metabolic network. It is unknown whether this disorder is itself associated with a unique metabolic ...network. 18F-fluorodeoxyglucose positron emission tomography was performed in 21 patients (age 65.0±5.6 years) with idiopathic rapid eye movement sleep behaviour disorder and 21 age/gender-matched healthy control subjects (age 62.5±7.5 years) to identify a disease-related pattern and examine its evolution in 21 hemi-parkinsonian patients (age 62.6±5.0 years) and 16 moderate parkinsonian patients (age 56.9±12.2 years). We identified a rapid eye movement sleep behaviour disorder-related metabolic network characterized by increased activity in pons, thalamus, medial frontal and sensorimotor areas, hippocampus, supramarginal and inferior temporal gyri, and posterior cerebellum, with decreased activity in occipital and superior temporal regions. Compared to the healthy control subjects, network expressions were elevated (P<0.0001) in the patients with this disorder and in the parkinsonian cohorts but decreased with disease progression. Parkinson's disease-related network activity was also elevated (P<0.0001) in the patients with rapid eye movement sleep behaviour disorder but lower than in the hemi-parkinsonian cohort. Abnormal metabolic networks may provide markers of idiopathic rapid eye movement sleep behaviour disorder to identify those at higher risk to develop neurodegenerative parkinsonism.
Environmental problem is the key to the healthy development of China’s eco-economy, and the environmental responsibility of micro-enterprises under the vision of “Dual Carbon” has attracted more ...attention. Under the effect of formal environmental regulation, firms will improve their environmental performance by improving technology and resource utilization. As an informal environmental system, can government environmental information disclosure (GEID) guide firms to actively carry out green innovation, ultimately improve the carbon emission problem of firms, have a positive impact on the carbon performance of enterprises, and provide strong support to protect ecological environment? To address this question, this study used the Pollution Information Transparency Index (PITI) to measure GEID, and empirically tested the impact of GEID on corporate carbon performance using a sample of listed companies involved in China’s mining and manufacturing industries from 2013 to 2018. The study found that the higher the degree of GEID, the better was the corporate carbon performance. However, the improved public participation weakened the effect of GEID on corporate carbon performance. GEID reduced the carbon emission intensity of firms and improved their carbon performance via green innovation. Further research indicated that the enhanced GEID in state-owned enterprises significantly improved carbon performance of firms. This study provides empirical evidence for GEID to improve corporate carbon performance, and also proposes a policy strategy for the government to guide firms to undertake green innovation and promote firms to improve efficient carbon use.
Objective
Recent studies on a rodent model of Parkinson’s disease (PD) have raised the possibility of increased blood–brain barrier (BBB) permeability, demonstrated by histology, autoradiography, and ...positron emission tomography (PET). However, in human PD patients, in vivo evidence of increased BBB permeability is lacking. We examined the hypothesis that levodopa treatment increases BBB permeability in human subjects with PD, particularly in those with levodopa-induced dyskinesia (LID).
Methods
We used rubidium-82 (
82
Rb) and PET to quantify BBB influx in vivo in 19 PD patients, including eight with LID, and 12 age- and sex-matched healthy subjects. All subjects underwent baseline
82
Rb scans. Seventeen chronically levodopa-treated patients were additionally rescanned during intravenous levodopa infusion. Influx rate constant,
K
1
, by compartmental modeling or net influx transport,
K
i
, by graphical approach could not be estimated reliably. However,
V
d
, the “apparent volume of distribution” based on the
82
Rb concentration in brain tissue and blood, was estimated with good stability as a local measure of the volume of distribution.
Results
Rubidium influx into brain tissue was undetectable in PD patients with or without LID, scanned on and off drug. No significant differences in regional
V
d
were observed for PD patients with or without LID relative to healthy subjects, except in left thalamus. Moreover, changes in
V
d
measured off- and on-levodopa infusion were also not significant for dyskinetic and non-dyskinetic subjects.
Conclusion
82
Rb PET did not reveal significant changes in BBB permeability in PD patients.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Significance We present an innovative approach to evaluate default mode network (DMN) activity in individual subjects using metabolic imaging. After characterizing a distinct set of metabolic resting ...state networks (RSNs) in healthy subjects, network activity was tracked over time in patients with neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease. We found that the dominant normal metabolic RSN, which corresponded to the DMN, is preserved in early-stage Parkinson’s disease patients. Although significant DMN reductions developed later, these changes were reversible in part by dopamine treatment. This finding contrasts with Alzheimer’s disease, in which DMN loss is rapid and continuous, beginning before clinical diagnosis. Metabolic imaging can provide a versatile, quantitative means of assessing brain disease at the network level.
The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson’s disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the “DMN-like” dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer’s disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer’s disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Brain stimulation technology has become a viable modality of reversible interventions in the effective treatment of many neurological and psychiatric disorders. It is aimed to restore brain ...dysfunction by the targeted delivery of specific electronic signal within or outside the brain to modulate neural activity on local and circuit levels. Development of therapeutic approaches with brain stimulation goes in tandem with the use of neuroimaging methodology in every step of the way. Indeed, multimodality neuroimaging tools have played important roles in target identification, neurosurgical planning, placement of stimulators and post-operative confirmation. They have also been indispensable in pre-treatment screen to identify potential responders and in post-treatment to assess the modulation of brain circuitry in relation to clinical outcome measures. Studies in patients to date have elucidated novel neurobiological mechanisms underlying the neuropathogenesis, action of stimulations, brain responses and therapeutic efficacy. In this article, we review some applications of deep brain stimulation for the treatment of several diseases in the field of neurology and psychiatry. We highlight how the synergistic combination of brain stimulation and neuroimaging technology is posed to accelerate the development of symptomatic therapies and bring revolutionary advances in the domain of bioelectronic medicine.
Alcohol abuse affects the brain regions responsible for memory, coordination and emotional processing. Binge alcohol drinking has shown reductions in brain activity, but the molecular targets have ...not been completely elucidated. We hypothesized that brain cells respond to excessive alcohol by releasing a novel inflammatory mediator, called cold inducible RNA-binding protein (CIRP), which is critical for the decreased brain metabolic activity and impaired cognition.
Male wild type (WT) mice and mice deficient in CIRP (CIRP
) were studied before and after exposure to binge alcohol level by assessment of relative brain glucose metabolism with fluorodeoxyglucose (
FDG) and positron emission tomography (PET). Mice were also examined for object-place memory (OPM) and open field (OF) tasks.
Statistical Parametric Analysis (SPM) of
FDG-PET uptake revealed marked decreases in relative glucose metabolism in distinct brain regions of WT mice after binge alcohol. Regional analysis (post hoc) revealed that while activity in the temporal (secondary visual) and limbic (entorhinal/perirhinal) cortices was decreased in WT mice, relative glucose metabolic activity was less suppressed in the CIRP
mice. Group and condition interaction analysis revealed differing responses in relative glucose metabolism (decrease in WT mice but increase in CIRP
mice) after alcohol in brain regions including the hippocampus and the cortical amygdala where the percent changes in metabolic activity correlated with changes in object discrimination performance. Behaviorally, alcohol-treated WT mice were impaired in exploring a repositioned object in the OPM task, and were more anxious in the OF task, whereas CIRP
mice were not impaired in these tasks.
CIRP released from brain cells could be responsible for regional brain metabolic hypoactivity leading to cognitive impairment under binge alcohol conditions.
To analyze age-related cerebral blood flow (CBF) using arterial spin labeling (ASL) MRI in healthy subjects with multivariate principal component analysis (PCA).
50 healthy subjects (mean age 45.8 ± ...18.5 years, range 21-85) had 3D structural MRI and pseudo-continuous ASL MRI at resting state. The relationship between CBF and age was examined with voxel-based univariate analysis using multiple regression and two-sample
-test (median age 41.8 years as a cut-off). An age-related CBF pattern was identified using multivariate PCA.
Age correlated negatively with CBF especially anteriorly and in the cerebellum. After adjusting by global value, CBF was relatively decreased with aging in certain regions and relatively increased in others. The age-related CBF pattern showed relative reductions in frontal and parietal areas and cerebellum, and covarying increases in temporal and occipital areas. Subject scores of this pattern correlated negatively with age (
= 0.588;
< 0.001) and discriminated between the older and younger subgroups (
< 0.001).
A distinct age-related CBF pattern can be identified with multivariate PCA using ASL MRI.
Vascular dysfunction, including cerebral hypoperfusion, plays an important role in the pathogenesis and progression of Alzheimer's disease (AD), independent of amyloid and tau pathology. We ...established an AD-related perfusion pattern (ADRP) measured with arterial spin labeling (ASL) MRI using multivariate spatial covariance analysis.
We obtained multimodal MRI including pseudo-continuous ASL and neurocognitive testing in a total of 55 patients with a diagnosis of mild to moderate AD supported by amyloid PET and 46 normal controls (NCs). An ADRP was established from an identification cohort of 32 patients with AD and 32 NCs using a multivariate analysis method based on scaled subprofile model/principal component analysis, and pattern expression in individual subjects was quantified for both the identification cohort and a validation cohort (23 patients with AD and 14 NCs). Subject expression score of the ADRP was then used to assess diagnostic accuracy and cognitive correlations in AD patients and compared with global and regional cerebral blood flow (CBF) in specific areas identified from voxel-based univariate analysis.
The ADRP featured negative loading in the bilateral middle and posterior cingulate and precuneus, inferior parietal lobule, and frontal areas, and positive loading in the right cerebellum and bilateral basal areas. Subject expression score of the ADRP was significantly elevated in AD patients compared with NCs (P < 0.001) and showed good diagnostic accuracy for AD with area under receiver-operator curve of 0.87 95% CI (0.78-0.96) in the identification cohort and 0.85 in the validation cohort. Moreover, there were negative correlations between subject expression score and global cognitive function and performance in various cognitive domains in patients with AD. The characteristics of the ADRP topography and subject expression scores were supported by analogous findings obtained with regional CBF.
We have reported a characteristic perfusion pattern associated with AD using ASL MRI. Subject expression score of this spatial covariance pattern is a promising MRI biomarker for the identification and monitoring of AD.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The scaled subprofile model (SSM)(1-4) is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components ...(Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data(2,5,6). Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors(7,8). Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects(5,6). Cross-validation within the derivation set can be performed using bootstrap resampling techniques(9). Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets(10). Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation(11). These standardized values can in turn be used to assist in differential diagnosis(12,13) and to assess disease progression and treatment effects at the network level(7,14-16). We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
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