Brain Network and Abnormal Hemispheric Asymmetry Analyses to Explore the Marginal Differences in Glucose Metabolic Distributions Among Alzheimer's Disease, Parkinson's Disease Dementia, and Lewy Body Dementia

Facilitating accurate diagnosis and ensuring appropriate treatment of dementia subtypes, including Alzheimer's disease (AD), Parkinson's disease dementia (PDD), and Lewy body dementia (DLB), is clinically important. However, the differences in glucose metabolic distribution among these three dementia subtypes are minor, which can result in difficulties in diagnosis by visual assessment or traditional quantification methods. Here, we explored this issue using novel approaches, including brain network and abnormal hemispheric asymmetry analyses. We generated 18F-labeled fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) images from patients with AD, PDD, and DLB, and healthy control (HC) subjects (n = 22, 18, 22, and 22, respectively) from Huashan hospital, Shanghai, China. Brain network properties were measured and between-group differences evaluated using graph theory. We also calculated and explored asymmetry indices for the cerebral hemispheres in the four groups, to explore whether differences between the two hemispheres were characteristic of each group. Our study revealed significant differences in the network properties of the HC and AD groups (small-world coefficient, 1.36 vs. 1.28; clustering coefficient, 1.48 vs. 1.59; characteristic path length, 1.57 vs. 1.64). In addition, differing hub regions were identified in the different dementias. We also identified rightward asymmetry in the hemispheric brain networks of patients with AD and DLB, and leftward asymmetry in the hemispheric brain networks of patients with PDD, which were attributable to aberrant topological properties in the corresponding hemispheres

Unlocking the enigma: unraveling multiple cognitive dysfunction linked to glymphatic impairment in early Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is one of the world’s well-known neurodegenerative diseases, which is related to the balance mechanism of production and clearance of two proteins (amyloid-β and tau) regulated by the glymphatic system. Latest studies have found that AD patients exhibit impairments to their glymphatic system. However, the alterations in the AD disease continuum, especially in the early stages, remain unclear. Moreover, the relationship between the glymphatic system and cognitive dysfunction is still worth exploring.MethodsA novel diffusion tensor image analysis method was applied to evaluate the activity of the glymphatic system by an index for diffusivity along the perivascular space (ALPS-index). Based on this method, the activity of the glymphatic system was noninvasively evaluated in 300 subjects, including 111 normal controls (NC), 120 subjects with mild cognitive impairment (MCI), and 69 subjects with AD. Partial correlation analysis was applied to explore the association between glymphatic system and cognitive impairment based on three domain-general scales and several domain-specific cognitive scales. Receiver operating characteristic curve analysis was used to evaluate the classification performance of ALPS-index along the AD continuum.ResultsALPS-index was significantly different among NC, MCI and AD groups, and ALPS-index decreased with cognitive decline. In addition, ALPS-index was significantly correlated with the scores of the clinical scales (p<0.05, FDR corrected), especially in left hemisphere. Furthermore, combination of ALPS and fractional anisotropy (FA) values achieved better classification results (NC vs. MCI: AUC = 0.6610, NC vs. AD: AUC = 0.8214).ConclusionHere, we show that the glymphatic system is closely associated with multiple cognitive dysfunctions, and ALPS-index can be used as a biomarker for alterations along the AD continuum. This may provide new targets and strategies for the treatment of AD, and has the potential to assist clinical diagnosis

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

IntroductionIn the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain.MethodsTo address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest.Results and discussionExperimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models

Parametric Estimation of Reference Signal Intensity for Semi-Quantification of Tau Deposition: A Flortaucipir and [18F]-APN-1607 Study

BackgroundTau positron emission tomography (PET) imaging can reveal the pathophysiology and neurodegeneration that occurs in Alzheimer’s disease (AD) in vivo. The standardized uptake value ratio (SUVR) is widely used for semi-quantification of tau deposition but is susceptible to disturbance from the reference region and the partial volume effect (PVE). To overcome this problem, we applied the parametric estimation of reference signal intensity (PERSI) method—which was previously evaluated for flortaucipir imaging—to two tau tracers, flortaucipir and [18F]-APN-1607.MethodsTwo cohorts underwent tau PET scanning. Flortaucipir PET imaging data for cohort I (65 healthy controls [HCs], 60 patients with mild cognitive impairment [MCI], and 12 AD patients) were from the AD Neuroimaging Initiative database. [18F]-APN-1607 ([18F]-PM-PBB3) PET imaging data were for Cohort II, which included 21 patients with a clinical diagnosis of amyloid PET-positive AD and 15 HCs recruited at Huashan Hospital. We used white matter (WM) postprocessed by PERSI (PERSI-WM) as the reference region and compared this with the traditional semi-quantification method that uses the whole cerebellum as the reference. SUVRs were calculated for regions of interest including the frontal, parietal, temporal, and occipital lobes; anterior and posterior cingulate; precuneus; and Braak I/II (entorhinal cortex and hippocampus). Receiver operating characteristic (ROC) curve analysis and effect sizes were used to compare the two methods in terms of ability to discriminate between different clinical groups.ResultsIn both cohorts, regional SUVR determined using the PERSI-WM method was superior to using the cerebellum as reference region for measuring tau retention in AD patients (e.g., SUVR of the temporal lobe: flortaucipir, 1.08 ± 0.17 and [18F]-APN-1607, 1.57 ± 0.34); and estimates of the effect size and areas under the ROC curve (AUC) indicated that it also increased between-group differences (e.g., AUC of the temporal lobe for HC vs AD: flortaucipir, 0.893 and [18F]-APN-1607: 0.949).ConclusionThe PERSI-WM method significantly improves diagnostic discrimination compared to conventional approach of using the cerebellum as a reference region and can mitigate the PVE; it can thus enhance the efficacy of semi-quantification of multiple tau tracers in PET scanning, making it suitable for large-scale clinical application

The nucleus accumbens functional connectivity in patients with insomnia using resting-state fMRI

BackgroundThe aim of this study was to investigate the functional abnormalities between the nucleus accumbens (NAc) and the whole brain in individuals with Insomnia Disorder (ID) using resting-state functional magnetic resonance imaging (fMRI). Additionally, the study aimed to explore the underlying neural mechanisms of ID.MethodsWe enrolled 18 participants with ID and 16 normal controls (NC). Resting-state functional connectivity (FC) between the NAc and the whole brain voxels was calculated and compared between the two groups to identify differential brain region. Receiver operating characteristic (ROC) curve analysis was employed to assess the ability of differential features to distinguish between groups. Furthermore, Pearson correlation analysis was performed to examine the relationship between neurocognitive scores and differential features.ResultsThe ID group exhibited significantly reduced FC values in several brain regions, including the right supplementary motor area, the bilateral middle frontal gyrus, the bilateral median cingulate and paracingulate gyri and the left precuneus. The area under the curve (AUC) of the classification model based on FC in these brain regions was 83.3%. Additionally, the abnormal functional changes observed in ID patients were positively correlated with the Fatigue Severity Scale (R = 0.650, p = 0.004).ConclusionThese findings suggest that the NAc may play a crucial role in the diagnosis of ID and could serve as a potential imaging biomarker, providing insights into the underlying neural mechanisms of the disorder