Amyloid PET detects the deposition of brain Aß earlier than CSF fluid biomarkers.

INTRODUCTION: Understanding the relationship between amyloid beta (Aß) positron emission tomography (PET) and Aß cerebrospinal fluid (CSF) biomarkers will define their potential utility in Aß treatment. Few population-based or neuropathologic comparisons have been reported. METHODS: Participants 50+ years with Aß PET and Aß CSF biomarkers (phosphorylated tau [p-tau]181/Aß42, n = 505, and Aß42/40, n = 54) were included from the Mayo Clinic Study on Aging. From these participants, an autopsy subgroup was identified (n = 47). The relationships of Aß PET and Aß CSF biomarkers were assessed cross-sectionally in all participants and longitudinally in autopsy data. RESULTS: Cross-sectionally, more participants were Aß PET+ versus Aß CSF- than Aß PET- versus Aß CSF+ with an incremental effect when using Aß PET regions selected for early Aß deposition. The sensitivity for the first detection of Thal phase ≥ 1 in longitudinal data was higher for Aß PET (89%) than p-tau181/Aß42 (64%). DISCUSSION: Aß PET can detect earlier cortical Aß deposition than Aß CSF biomarkers. Aß PET+ versus Aß CSF- findings are several-fold greater using regional Aß PET analyses and in peri-threshold-standardized uptake value ratio participants. HIGHLIGHTS: Amyloid beta (Aß) positron emission tomography (PET) has greater sensitivity for Aß deposition than Aß cerebrospinal fluid (CSF) in early Aß development. A population-based sample of participants (n = 505) with PET and CSF tests was used. Cortical regions showing early Aß on Aß PET were also used in these analyses. Neuropathology was used to validate detection of Aß by Aß PET and Aß CSF biomarkers.

Low to moderate ethanol exposure reduces astrocyte-induced neuroinflammatory signaling and cognitive decline in presymptomatic APP/PS1 mice.

Alcohol use disorder has been associated with the development of neurodegenerative diseases, including Alzheimer's disease (AD). However, recent studies demonstrate that moderate alcohol consumption may be protective against dementia and cognitive decline. We examined astrocyte function, low-density lipoprotein (LDL) receptor-related protein 1 (LRP1), and the NF-κB p65 and IKK-α/ß signaling pathways in modulating neuroinflammation and amyloid beta (Aß) deposition. We assessed apolipoprotein E (ApoE) in the brain of APP/PS1 mice using IHC and ELISA in response to low to moderate ethanol exposure (MEE). First, to confirm the intracerebral distribution of ApoE, we co-stained with GFAP, a marker for astrocytes that biosynthesize ApoE. We sought to investigate whether the ethanol-induced upregulation of LRP1 could potentially inhibit the activity of IL-1ß and TNF-α induced IKK-α/ß towards NF-κB p65, resulting in a reduction of pro-inflammatory cytokines. To evaluate the actual Aß load in the brains of APP/PS1 mice, we performed with a specific antibody Aß (Thioflavin S) on both air- and ethanol-exposed groups, subsequently analyzing Aß levels. We also measured glucose uptake using 18F- fluorodeoxyglucose (FDG)-positron emission tomography (PET). Finally, we investigated whether MEE induced cognitive and memory changes using the Y maze, noble object recognition test, and Morris water maze. Our findings demonstrate that MEE reduced astrocytic glial fibrillary acidic protein (GFAP) and ApoE levels in the cortex and hippocampus in presymptomatic APP/PS1 mice. Interestingly, increased LRP1 protein expression was accompanied by dampening the IKK-α/ß-NF-κB p65 pathway, resulting in decreased IL-1ß and TNF-α levels in male mice. Notably, female mice show reduced levels of anti-inflammatory cytokines IL-4, and IL-10 without altering IL-1ß and TNF-α concentrations. In both males and females, Aß plaques, a hallmark of AD, were reduced in the cortex and hippocampus of APP/PS1 mice exposed to ethanol starting at pre-symptomatic stage. Consistently, MEE increased FDG-PET-based brain activities and normalized cognitive and memory deficits in the APP/PS1 mice. Our findings suggest that MEE may benefit AD pathology via modulating LRP1 expression, potentially reducing neuroinflammation and attenuating Aß deposition. Our study implies that reduced astrocyte-derived ApoE and LDL cholesterol levels are critical for attenuating AD pathology.

Patterns of Early Neocortical Amyloid-ß Accumulation: A PET Population-Based Study.

The widespread deposition of amyloid-ß (Aß) plaques in late-stage Alzheimer disease is well defined and confirmed by in vivo PET. However, there are discrepancies between which regions contribute to the earliest topographic Aß deposition within the neocortex. Methods: This study investigated Aß signals in the perithreshold SUV ratio range using Pittsburgh compound B (PiB) PET in a population-based study cross-sectionally and longitudinally. PiB PET scans from 1,088 participants determined the early patterns of PiB loading in the neocortex. Results: Early-stage Aß loading is seen first in the temporal, cingulate, and occipital regions. Regional early deposition patterns are similar in both apolipoprotein ε4 carriers and noncarriers. Clustering analysis shows groups with different patterns of early amyloid deposition. Conclusion: These findings of initial Aß deposition patterns may be of significance for diagnostics and understanding the development of Alzheimer disease phenotypes.

Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features.

In modern society, the popularity of wearable devices has highlighted the need for data security. Bio-crypto keys (bio-keys), especially in the context of wearable devices, are gaining attention as a next-generation security method. Despite the theoretical advantages of bio-keys, implementing such systems poses practical challenges due to their need for flexibility and convenience. Electrocardiograms (ECGs) have emerged as a potential solution to these issues but face hurdles due to intra-individual variability. This study aims to evaluate the possibility of a stable, flexible, and convenient-to-use bio-key using ECGs. We propose an approach that minimizes biosignal variability using normalization, clustering-based binarization, and the fuzzy extractor, enabling the generation of personalized seeds and offering ease of use. The proposed method achieved a maximum entropy of 0.99 and an authentication accuracy of 95%. This study evaluated various parameter combinations for generating effective bio-keys for personal authentication and proposed the optimal combination. Our research holds potential for security technologies applicable to wearable devices and healthcare systems.

InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography.

BACKGROUND: This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (PPG) signals for sleep monitoring with wearable devices. Furthermore, our aim was to develop a more efficient sleep monitoring method by considering both the interpretability and uncertainty of the model's prediction results, with the goal of providing support to medical professionals in their decision-making process. METHOD: The developed 4-class sleep staging model based on continuous PPG data incorporates several key components: a local attention module, an InceptionTime module, a time-distributed dense layer, a temporal convolutional network (TCN), and a 1D convolutional network (CNN). This model prioritizes both interpretability and uncertainty estimation in its prediction results. The local attention module is introduced to provide insights into the impact of each epoch within the continuous PPG data. It achieves this by leveraging the TCN structure. To quantify the uncertainty of prediction results and facilitate selective predictions, an energy score estimation is employed. By enhancing both the performance and interpretability of the model and taking into consideration the reliability of its predictions, we developed the InsightSleepNet for accurate sleep staging. RESULT: InsightSleepNet was evaluated using three distinct datasets: MESA, CFS, and CAP. Initially, we assessed the model's classification performance both before and after applying an energy score threshold. We observed a significant improvement in the model's performance with the implementation of the energy score threshold. On the MESA dataset, prior to applying the energy score threshold, the accuracy was 84.2% with a Cohen's kappa of 0.742 and weighted F1 score of 0.842. After implementing the energy score threshold, the accuracy increased to a range of 84.8-86.1%, Cohen's kappa values ranged from 0.75 to 0.78 and weighted F1 scores ranged from 0.848 to 0.861. In the case of the CFS dataset, we also noted enhanced performance. Before the application of the energy score threshold, the accuracy stood at 80.6% with a Cohen's kappa of 0.72 and weighted F1 score of 0.808. After thresholding, the accuracy improved to a range of 81.9-85.6%, Cohen's kappa values ranged from 0.74 to 0.79 and weighted F1 scores ranged from 0.821 to 0.857. Similarly, on the CAP dataset, the initial accuracy was 80.6%, accompanied by a Cohen's kappa of 0.73 and weighted F1 score was 0.805. Following the application of the threshold, the accuracy increased to a range of 81.4-84.3%, Cohen's kappa values ranged from 0.74 to 0.79 and weighted F1 scores ranged from 0.813 to 0.842. Additionally, by interpreting the model's predictions, we obtained results indicating a correlation between the peak of the PPG signal and sleep stage classification. CONCLUSION: InsightSleepNet is a 4-class sleep staging model that utilizes continuous PPG data, serves the purpose of continuous sleep monitoring with wearable devices. Beyond its primary function, it might facilitate in-depth sleep analysis by medical professionals and empower them with interpretability for intervention-based predictions. This capability can also support well-informed clinical decision-making, providing valuable insights and serving as a reliable second opinion in medical settings.

Effects of Game-Related Tasks for the Diagnosis and Classification of Gaming Disorder.

Gaming disorder (GD) is an addictive behavior characterized by an insatiable need to play video games and shares similar symptoms with the failure of self-control due to a decline in cognitive function. Current GD diagnostic and screening tools rely on questionnaires and behavioral observations related to cognitive functions to assess an individual's capacity to maintain self-control in everyday life. However, current GD screening approaches rely on subjective symptoms, and a reliable diagnosis requires long-term clinical follow-up. Recent studies have measured biosignals along with cognitive functional tasks to provide objectivity to GD diagnosis and to acquire immediate results. However, people with GD are hypersensitive to game-related cues, so their responses may vary depending on the type of stimuli, and the difference in response to stimuli might manifest as a difference in the degree of change in the biosignal. Therefore, it is critical to choose the correct stimulus type when performing GD diagnostic tasks. In this study, we investigated the task dependence of cognitive decline in GD by comparing two cognitive functional tasks: a continuous performance task (CPT) and video game play. For this study, 69 young male adults were classified into either the gaming disorder group (GD, n = 39) or a healthy control group (HC, n = 30). CPT score, EEG signal (theta, alpha, and beta), and HRV-HF power were assessed. We observed differences in the left frontal region (LF) of the brain between the GD and HC groups during online video game play. The GD group also showed a significant difference in HF power of HRV between CPT and online video gaming. Furthermore, LF and HRV-HF significantly correlated with Young's Internet Addiction Test (Y-IAT) score, which is positively associated with impulsivity score. The amount of change in theta band activity in LF and HRV-HF-both biomarkers for changes in cognitive function-during online video game play suggests that people with GD express task-dependent cognitive decline compared with HC. Our results demonstrate the feasibility of quantifying individual self-regulation ability for gaming and underscore its importance for GD classification.

Uncovering the distinct macro-scale anatomy of dysexecutive and behavioural degenerative diseases.

There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer's disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer's disease pathology (behavioural Alzheimer's disease) and 28 had amnestic Alzheimer's disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as 'eigenbrains'. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer's disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer's disease, then amnestic Alzheimer's disease. Dysexecutive Alzheimer's disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer's disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer's disease, temporo-parietal areas in amnestic Alzheimer's disease and frontotemporal areas in bvFTD and behavioural Alzheimer's disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer's disease patient for behavioural Alzheimer's disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning.

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

Significance of a positive tau PET scan with a negative amyloid PET scan.

INTRODUCTION: The implications of positive tau positron emission tomography (T) with negative beta amyloid positron emission tomography (A) are not well understood. We investigated cognitive performance in participants who were T+ but A-. METHODS: We evaluated 98 participants from the Mayo Clinic who were T+ and A-. Participants were matched 2:1 to A- and T- cognitively unimpaired (CU) controls. Cognitive test scores were compared between different groups. RESULTS: The A-T+ group demonstrated lower performance than the A-T- group on the Mini-Mental Status Exam (MMSE) (p < 0.001), Wechsler Memory Scale-Revised Logical Memory I (p < 0.001) and Logical Memory II (p < 0.001), Auditory Verbal Learning Test (AVLT) delayed recall (p = 0.004), category fluency (animals p = 0.005; vegetables p = 0.021), Trail Making Test A and B (p < 0.001), and others. There were no significant differences in demographic features or apolipoprotein E (APOE) e4 genotype between CU A-T+ and CI A-T+. DISCUSSION: A-T+ participants show an association with lower cognitive performance.

Moderate ethanol exposure reduces astrocyte-induced neuroinflammatorysignaling and cognitive decline in presymptomatic APP/PS1 mice.

Background: Alcohol use disorder (AUD) has been associated with the development of neurodegenerative diseases, including Alzheimer's disease (AD). However, recent studies demonstrate that moderate alcohol consumption may be protective against dementia and cognitive decline. Methods: We examined astrocyte function, low-density lipoprotein (LDL) receptor-related protein 1 (LRP1), and the NF-κB p65 and IKK-α/ß signaling pathways in modulating neuroinflammation and amyloid beta (Aß) deposition. We assessed apolipoprotein E (ApoE) in the mouse brain using IHC and ELISA in response to moderate ethanol exposure (MEE). First, to confirm the intracerebral distribution of ApoE, we co-stained with GFAP, a marker for astrocytes that biosynthesize ApoE. We sought to investigate whether the ethanol-induced upregulation of LRP1 could potentially inhibit the activity of IL-1ß and TNF-α induced IKK-α/ß towards NF-κB p65, resulting in a reduction of pro-inflammatory cytokines. To evaluate the actual Aß load in the brains of APP/PS1 mice, we performed with a specific antibody Aß (Thioflavin S) on both air- and ethanol-exposed groups, subsequently analyzing Aß levels. We also measured glucose uptake activity using 18F-FDG in APP/PS1 mice. Finally, we investigated whether MEE induced cognitive and memory changes using the Y maze, noble objective recognition (NOR) test, and Morris water maze (MWM). Results: Our findings demonstrate that MEE reduced astrocytic glial fibrillary acidic protein (GFAP) and ApoE levels in the cortex and hippocampus in presymptomatic APP/PS1 mice. Interestingly, increased LRP1 protein expression is accompanied by dampening the IKK-α/ß-NF-κB p65 pathway, resulting in decreased IL-1ß and TNF-α levels in male mice. Notably, female mice show reduced anti-inflammatory cytokines, IL-4, and IL-10 levels without altering IL-1ß and TNF-α concentrations. In both males and females, Aß plaques, a hallmark of AD, were reduced in the cortex and hippocampus of ethanol-exposed presymptomatic APP/PS1 mice. Consistently, MEE increased fluorodeoxyglucose (FDG)-positron emission tomography (PET)-based brain activities and normalized cognitive and memory deficits in the APP/PS1 mice. Conclusions: Our findings suggest that MEE may benefit AD pathology via modulating LRP1 expression, potentially reducing neuroinflammation and attenuating Aß deposition. Our study implies that reduced astrocyte derived ApoE and LDL cholesterol levels are critical for attenuating AD pathology.

Mapping Pharmacologically Evoked Neurovascular Activation and Its Suppression in a Rat Model of Tremor Using Functional Ultrasound: A Feasibility Study.

Functional ultrasound (fUS), an emerging hemodynamic-based functional neuroimaging technique, is especially suited to probe brain activity and primarily used in animal models. Increasing use of pharmacological models for essential tremor extends new research to the utilization of fUS imaging in such models. Harmaline-induced tremor is an easily provoked model for the development of new therapies for essential tremor (ET). Furthermore, harmaline-induced tremor can be suppressed by the same classic medications used for essential tremor, which leads to the utilization of this model for preclinical testing. However, changes in local cerebral activities under the effect of tremorgenic doses of harmaline have not been completely investigated. In this study, we explored the feasibility of fUS imaging for visualization of cerebral activation and deactivation associated with harmaline-induced tremor and tremor-suppressing effects of propranolol. The spatial resolution of fUS using a high frame rate imaging enabled us to visualize time-locked and site-specific changes in cerebral blood flow associated with harmaline-evoked tremor. Intraperitoneal administration of harmaline generated significant neural activity changes in the primary motor cortex and ventrolateral thalamus (VL Thal) regions during tremor and then gradually returned to baseline level as tremor subsided with time. To the best of our knowledge, this is the first functional ultrasound study to show the neurovascular activation of harmaline-induced tremor and the therapeutic suppression in a rat model. Thus, fUS can be considered a noninvasive imaging method for studying neuronal activities involved in the ET model and its treatment.

Astrocyte activities in the external globus pallidus regulate action-selection strategies in reward-seeking behaviors.

An imbalance in goal-directed and habitual behavioral control is a hallmark of decision-making-related disorders, including addiction. Although external globus pallidus (GPe) is critical for action selection, which harbors enriched astrocytes, the role of GPe astrocytes involved in action-selection strategies remained unknown. Using in vivo calcium signaling with fiber photometry, we found substantially attenuated GPe astrocytic activity during habitual learning compared to goal-directed learning. The support vector machine analysis predicted the behavioral outcomes. Chemogenetic activation of the astrocytes or inhibition of GPe pan-neuronal activities facilitates the transition from habit to goal-directed reward-seeking behavior. Next, we found increased astrocyte-specific GABA (γ-aminobutyric acid) transporter type 3 (GAT3) messenger RNA expression during habit learning. Notably, the pharmacological inhibition of GAT3 occluded astrocyte activation-induced transition from habitual to goal-directed behavior. On the other hand, attentional stimuli shifted the habit to goal-directed behaviors. Our findings suggest that the GPe astrocytes regulate the action selection strategy and behavioral flexibility.

Deciphering the clinico-radiological heterogeneity of dysexecutive Alzheimer's disease.

Dysexecutive Alzheimer's disease (dAD) manifests as a progressive dysexecutive syndrome without prominent behavioral features, and previous studies suggest clinico-radiological heterogeneity within this syndrome. We uncovered this heterogeneity using unsupervised machine learning in 52 dAD patients with multimodal imaging and cognitive data. A spectral decomposition of covariance between FDG-PET images yielded six latent factors ("eigenbrains") accounting for 48% of variance in patterns of hypometabolism. These eigenbrains differentially related to age at onset, clinical severity, and cognitive performance. A hierarchical clustering on the eigenvalues of these eigenbrains yielded four dAD subtypes, i.e. "left-dominant," "right-dominant," "bi-parietal-dominant," and "heteromodal-diffuse." Patterns of FDG-PET hypometabolism overlapped with those of tau-PET distribution and MRI neurodegeneration for each subtype, whereas patterns of amyloid deposition were similar across subtypes. Subtypes differed in age at onset and clinical severity where the heteromodal-diffuse exhibited a worse clinical picture, and the bi-parietal had a milder clinical presentation. We propose a conceptual framework of executive components based on the clinico-radiological associations observed in dAD. We demonstrate that patients with dAD, despite sharing core clinical features, are diagnosed with variability in their clinical and neuroimaging profiles. Our findings support the use of data-driven approaches to delineate brain-behavior relationships relevant to clinical practice and disease physiology.

Thalamo-cortical network is associated with harmaline-induced tremor in rodent model.

Essential tremor (ET) is the most frequent form of pathologic tremor and one of the most common adult-onset neurologic impairments. However, underlying mechanisms by which structural alterations within the tremor circuit generate the pathological state and how rhythmic neuronal activities propagate and drive tremor remains unclear. Harmaline (HA)-induced tremor model has been most frequently utilized animal model for ET studies, however, there is still a dearth of knowledge over the degree to whether HA-induced tremor mimics the actual underlying pathophysiology of ET, particularly the involvement of thalamo-cortical region. In this study, we investigated the electrophysiological response of the motor circuit including the ventrolateral thalamus (vlTh) and the primary motor cortex (M1), and the modulatory effect of thalamic deep brain stimulation (DBS) using a rat HA-induced tremor model. We found that the theta and high-frequency oscillation (HFO) band power significantly increased after HA administration in both vlTh and M1, and the activity was modulated by the tremor suppression drug (propranolol) and the thalamic DBS. The theta band phase synchronization between the vlTh and M1 was significantly enhanced during the HA-induced tremor, and the transition of cross-frequency coupling in vlTh was found to be associated with the state of HA-induced tremor. Our findings support that the HA tremor could be useful as a valid preclinical model of ET in the context of thalamo-cortical neural network interaction.

Natural COA water inhibits mitochondrial ROS-mediated apoptosis through Plk3 downregulation under STZ diabetic stress in pancreatic ß-cell lines.

Diabetes from pancreatic ß cell death and insulin resistance is a serious metabolic disease in the world. Although the overproduction of mitochondrial reactive oxygen species (ROS) plays an important role in the pathogenesis of diabetes, its specific molecular mechanism remains unclear. Here, we show that the natural Charisma of Aqua (COA) water plays a role in Streptozotocin (STZ) diabetic stress-induced cell death inhibition. STZ induces mitochondrial ROS by increasing Polo-like kinase 3 (Plk3), a major mitotic regulator, in both Beta TC-6 and Beta TC-tet mouse islet cells and leads to apoptosis. Overexpression of Plk3 regulates an increase in mitochondrial ROS as well as cell death, also these events were inhibited by Plk3 gene knockdown in STZ diabetic stimulated-Beta TC-6 cells. Interestingly, we found that natural COA water blocks mitochondrial ROS generation through the reduction of Plk3 and prevents apoptosis in STZ-treated beta cells. Furthermore, using the 3D organoid (ex vivo) system, we confirmed that the insulin secretion of the supernatant medium under STZ treated pancreatic ß-cells is protected by the natural COA water. These findings demonstrate that the natural water COA has a beneficial role in maintaining ß cell function through the inhibition of mitochondrial ROS-mediated cell death, and it might be introduced as a potential insulin stabilizer.

The Overlap Index as a Means of Evaluating Early Tau PET Signal Reliability.

In tau PET, a reliable method to detect early tau accumulation in the brain is crucial. Noise, artifacts, and off-target uptake impede detection of subtle true-positive ligand binding. We hypothesize that identifying voxels with stable activity over time can enhance detection of true-positive tau. Methods: In total, 339 participants in the clinical spectrum ranging from clinically unimpaired to Alzheimer disease dementia underwent at least 2 serial tau PET scans with flortaucipir. The overlap index (OI) method was proposed to detect spatially identical, voxelwise SUV ratio (SUVR) elevation when seen sequentially in serial tau PET scans. The association of OI with tau accumulation, clinical diagnosis, and cognitive findings was evaluated. Results: OI showed good dynamic range in the low-SUVR window. Only OI was able to identify subgroups with increasing tau PET signal in low-SUVR meta-region-of-interest (ROI) groups. OI showed improved association with early clinical disease progression and cognitive scores versus meta-ROI SUVR measures. Conclusion: OI was more sensitive to tau signal elevation and longitudinal change than standard ROI measures, suggesting it is a more sensitive method for detecting early, subtle deposition of neurofibrillary tangles.

Deep learning-based brain age prediction in normal aging and dementia.

Brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging. Here, we developed a deep learning-based brain age prediction model using a large collection of fluorodeoxyglucose positron emission tomography and structural magnetic resonance imaging and tested how the brain age gap relates to degenerative syndromes including mild cognitive impairment, AD, frontotemporal dementia and Lewy body dementia. Occlusion analysis, performed to facilitate the interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap was highly correlated with cognitive impairment and the AD biomarker. The higher gap also showed a longitudinal predictive nature across clinical categories, including cognitively unimpaired individuals who converted to a clinical stage. However, regions generating brain age gaps were different for each diagnostic group of which the AD continuum showed similar patterns to normal aging.

Imaging the response to deep brain stimulation in rodent using functional ultrasound.

In this study, we explored the feasibility of using functional ultrasound (fUS) imaging to visualize cerebral activation associated with thalamic deep brain stimulation (DBS), in rodents. The ventrolateral (VL) thalamus was stimulated using electrical pulses of low and high frequencies of 10 and 100 Hz, respectively, and multiple voltages (1-7 V) and pulse widths (50-1500 µs). The fUS imaging demonstrated DBS-evoked activation of cerebral cortex based on changes of cerebral blood volume, specifically at the primary motor cortex (PMC). Low frequency stimulation (LFS) demonstrated significantly higher PMC activation compared to higher frequency stimulation (HFS), at intensities (5-7 V). Whereas, at lower intensities (1-3 V), only HFS demonstrated visible PMC activation. Further, LFS-evoked cerebral activation was was primarily located at the PMC. Our data presents the functionality and feasibility of fUS imaging as an investigational tool to identify brain areas associated with DBS. This preliminary study is an important stepping stone towards conducting real-time functional ultrasound imaging of DBS in awake and behaving animal models, which is of significant interest to the community for studying motor-related disorders.