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Neuropsychologia Jun 2024Research has documented changes in autobiographical memory and episodic future thinking in mild cognitive impairment (MCI) and Alzheimer's disease (AD). However,...
Research has documented changes in autobiographical memory and episodic future thinking in mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, cognitive decline occurs gradually and recent findings suggest that subtle alterations in autobiographical cognition may be evident earlier in the trajectory towards dementia, before AD-related symptoms emerge or a clinical diagnosis has been given. The current study used the Autobiographical Interview to examine the episodic and semantic content of autobiographical past and future events generated by older adults (N = 38) of varying cognitive functioning who were grouped into High (N = 20) and Low Cognition (N = 18) groups based on their Montreal Cognitive Assessment (MoCA) scores. Participants described 12 past and 12 future autobiographical events, and transcripts were scored to quantify the numbers of internal (episodic) or external (non-episodic, including semantic) details. Although the Low Cognition group exhibited a differential reduction for internal details comprising both past and future events, they did not show the expected overproduction of external details relative to the High Cognition group. Multilevel modelling demonstrated that on trials lower in episodic content, semantic content was significantly increased in both groups. Although suggestive of a compensatory mechanism, the magnitude of this inverse relationship did not differ across groups or interact with MoCA scores. This finding indicates that external detail production may be underpinned by mechanisms not affected by cognitive decline, such as narrative style and the ability to contextualize one's past and future events in relation to broader autobiographical knowledge.
PubMed: 38908476
DOI: 10.1016/j.neuropsychologia.2024.108943 -
Epilepsy Research Aug 2023Our work aims to investigate the role of physiological arousal in the expression of neuropsychological deficits in frontal lobe epilepsy (FLE) and mesial temporal lobe...
OBJECTIVE
Our work aims to investigate the role of physiological arousal in the expression of neuropsychological deficits in frontal lobe epilepsy (FLE) and mesial temporal lobe epilepsy (mTLE), by drawing on the Lurian theory of brain function.
METHODS
For this study a total of 43 patients with focal onset epilepsy has been taken; twenty-four patients with FLE, 19 patients with mTLE and 26 healthy controls, all matched for age and education. Participants underwent a comprehensive neuropsychological assessment including various cognitive domains, such as attention, episodic memory, speed of information processing, response inhibition and mental flexibility, working memory, verbal fluency (phonological & semantic).
RESULTS
There were no significant differences between FLE and mTLE patients in terms of neuropsychological performance. However, both FLE and mTLE patients showed significantly worse performance in several cognitive domains than HCs. The results seem to support our hypothesis that aberrant physiological arousal, as reflected in patients' worse performance in vigilance and attention, response inhibition, and processing speed, along with other disease-specific variables, may co-determine neuropsychological dysfunction and/or impairment in both FLE and mTLE.
CONCLUSION
Identifying a differential arousal-related neuropsychological affection in FLE and mTLE, among the known deleterious effects of the functional deficit zone and other disease-related variables, may further our understanding of the underlying cognitive-pathophysiological mechanisms in focal epilepsy syndromes.
Topics: Humans; Epilepsy, Temporal Lobe; Epilepsies, Partial; Epilepsy, Frontal Lobe; Cognition; Arousal; Neuropsychological Tests
PubMed: 37421714
DOI: 10.1016/j.eplepsyres.2023.107189 -
PloS One 2023In recent years, with the development of deep learning technology, deep neural networks have been widely used in the field of medical image segmentation. U-shaped...
In recent years, with the development of deep learning technology, deep neural networks have been widely used in the field of medical image segmentation. U-shaped Network(U-Net) is a segmentation network proposed for medical images based on full-convolution and is gradually becoming the most commonly used segmentation architecture in the medical field. The encoder of U-Net is mainly used to capture the context information in the image, which plays an important role in the performance of the semantic segmentation algorithm. However, it is unstable for U-Net with simple skip connection to perform unstably in global multi-scale modelling, and it is prone to semantic gaps in feature fusion. Inspired by this, in this work, we propose a Deep Tensor Low Rank Channel Cross Fusion Neural Network (DTLR-CS) to replace the simple skip connection in U-Net. To avoid space compression and to solve the high rank problem, we designed a tensor low-ranking module to generate a large number of low-rank tensors containing context features. To reduce semantic differences, we introduced a cross-fusion connection module, which consists of a channel cross-fusion sub-module and a feature connection sub-module. Based on the proposed network, experiments have shown that our network has accurate cell segmentation performance.
Topics: Neural Networks, Computer; Algorithms; Data Compression; Reproduction; Semantic Differential; Image Processing, Computer-Assisted
PubMed: 38032913
DOI: 10.1371/journal.pone.0294727 -
Journal of Neurology May 2024Amygdala atrophy has been found in frontotemporal dementia (FTD), yet the specific changes of its subregions across different FTD phenotypes remain unclear. The aim of...
Amygdala atrophy has been found in frontotemporal dementia (FTD), yet the specific changes of its subregions across different FTD phenotypes remain unclear. The aim of this study was to investigate the volumetric alterations of the amygdala subregions in FTD phenotypes and how they evolve with disease progression. Patients clinically diagnosed with behavioral variant FTD (bvFTD) (n = 20), semantic dementia (SD) (n = 20), primary nonfluent aphasia (PNFA) (n = 20), Alzheimer's disease (AD) (n = 20), and 20 matched healthy controls underwent whole brain structural MRI. The patient groups were followed up annually for up to 3.5 years. Amygdala nuclei were segmented using FreeSurfer, corrected by total intracranial volumes, and grouped into the basolateral, superficial, and centromedial subregions. Linear mixed effects models were applied to identify changes in amygdala subregional volumes over time. At baseline, bvFTD, SD, and AD displayed global amygdala volume reduction, whereas amygdala volume appeared to be preserved in PNFA. Asymmetrical amygdala atrophy (left > right) was most pronounced in SD. Longitudinally, SD and PNFA showed greater rates of annual decline in the right basolateral and superficial subregions compared to bvFTD and AD. The findings provide comprehensive insights into the differential impact of FTD pathology on amygdala subregions, revealing distinct atrophy patterns that evolve over disease progression. The characterization of amygdala subregional involvement in FTD and their potential role as biomarkers carry substantial clinical implications.
Topics: Amygdala; Frontotemporal Dementia; Female; Middle Aged; Aged; Organ Size; Time Factors; Longitudinal Studies; Cross-Sectional Studies; Magnetic Resonance Imaging; Disease Progression; Atrophy; Primary Progressive Nonfluent Aphasia; Alzheimer Disease
PubMed: 38265470
DOI: 10.1007/s00415-023-12172-5 -
Health Care For Women International 2023We aim to determine the effect of antenatal education on the attitudes of expectant mothers toward birth, maternal role attainment and self-confidence levels. We carried...
We aim to determine the effect of antenatal education on the attitudes of expectant mothers toward birth, maternal role attainment and self-confidence levels. We carried out this quasi-experimental, non-randomized, prospective study in a hospital located in Istanbul, in the pre- and post-education model. Women in the education group (EG = 60) attended 6 weeks of education. Women in the control group (CG = 60) participated in a periodic follow-up visit. We collected the data using Childbirth Attitudes Questionnaire (CAQ), Pharis Self-Confidence Scale (PSCS), and Semantic Differential Scale-Myself as Mother (MMS). We made three measures in total: in the first visit, after six weeks and in the sixth week postpartum. We found the mean scores of second measurement of CAQ, PSCS, third measurement of MMS statistically significant in favor of EG ( < 0.05). Antenatal educations positively affect childbirth attitude, maternal role attainment and self-confidence levels.
PubMed: 34346299
DOI: 10.1080/07399332.2021.1935959 -
Neurology Feb 2024The 3 clinical presentations of primary progressive aphasia (PPA) reflect heterogenous neuropathology, which is difficult to be recognized in vivo. Resting-state (RS)...
BACKGROUND AND OBJECTIVES
The 3 clinical presentations of primary progressive aphasia (PPA) reflect heterogenous neuropathology, which is difficult to be recognized in vivo. Resting-state (RS) EEG is promising for the investigation of brain electrical substrates in neurodegenerative conditions. In this study, we aim to explore EEG cortical sources in the characterization of the 3 variants of PPA.
METHODS
This is a cross-sectional, single-center, memory center-based cohort study. Patients with PPA and healthy controls were consecutively recruited at the Neurology Unit, IRCCS San Raffaele Scientific Institute (Milan, Italy). Each participant underwent an RS 19-channel EEG. Using standardized low-resolution brain electromagnetic tomography, EEG current source densities were estimated at voxel level and compared among study groups. Using an RS functional MRI-driven model of source reconstruction, linear lagged connectivity (LLC) values within language and extra-language brain networks were obtained and analyzed among groups.
RESULTS
Eighteen patients with logopenic PPA variant (lvPPA; mean age = 72.7 ± 6.6; % female = 52.4), 21 patients with nonfluent/agrammatic PPA variant (nfvPPA; mean age = 71.7 ± 8.1; % female = 66.6), and 9 patients with semantic PPA variant (svPPA; mean age = 65.0 ± 6.9; % female = 44.4) were enrolled in the study, together with 21 matched healthy controls (mean age = 69.2 ± 6.5; % female = 57.1). Patients with lvPPA showed a higher delta density than healthy controls ( < 0.01) and patients with nfvPPA ( < 0.05) and svPPA ( < 0.05). Patients with lvPPA also displayed a greater theta density over the left posterior hemisphere ( < 0.01) and lower alpha2 values ( < 0.05) over the left frontotemporal regions than controls. Patients with nfvPPA showed a diffuse greater theta density than controls ( < 0.05). LLC was altered in all patients relative to controls ( < 0.05); the alteration was greater at slow frequency bands and within language networks than extra-language networks. Patients with lvPPA also showed greater LLC values at theta band than patients with nfvPPA ( < 0.05).
DISCUSSION
EEG findings in patients with PPA suggest that lvPPA-related pathology is associated with a characteristic disruption of the cortical electrical activity, which might help in the differential diagnosis from svPPA and nfvPPA. EEG connectivity was disrupted in all PPA variants, with distinct findings in disease-specific PPA groups.
CLASSIFICATION OF EVIDENCE
This study provides Class IV evidence that EEG analysis can distinguish PPA due to probable Alzheimer disease from PPA due to probable FTD from normal aging.
Topics: Humans; Female; Aged; Middle Aged; Male; Cohort Studies; Cross-Sectional Studies; Academies and Institutes; Aphasia, Primary Progressive; Electroencephalography
PubMed: 38165298
DOI: 10.1212/WNL.0000000000207993 -
Computers in Biology and Medicine Jun 2024Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been...
Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.
PubMed: 38897145
DOI: 10.1016/j.compbiomed.2024.108769 -
Intensive & Critical Care Nursing Jun 2024This study aimed to investigate the voice use of nurses working in intensive care units (ICUs) and their perception of acoustic environments.
OBJECTIVE
This study aimed to investigate the voice use of nurses working in intensive care units (ICUs) and their perception of acoustic environments.
SETTING AND SAMPLE
The research was conducted in four different hospitals in China during the COVID-19 pandemic. A total of 60 ICU nurses were recruited for their voice use monitoring and 100 nurses participated in the survey.
RESEARCH METHODOLOGY
Firstly, voice-related parameters such as voice level (SPL, dB), fundamental frequency (F0, Hz), and voicing time percentage (Dt, %) were measured using a vocal monitor. To collect data, a non-invasive accelerometer was attached to the participants' necks during their working hours. Secondly, the perception of the ICU acoustic environment was assessed using semantic differential.
RESULTS
The results showed that nurses spoke approximately 0.9-4 dB louder to patients and colleagues in ICUs compared to quiet rooms, and their fundamental frequency (F0) significantly increased during work. The voice levels of nurses were influenced by background noise levels, with a significant correlation coefficient of 0.44 (p < 0.01). Furthermore, the background noise levels ranged from 58.1 to 73.9 dBA, exceeding the guideline values set by the World Health Organisation (WHO). The semantic differential analysis identified 'Stress' and 'Irritation' as the two main components, indicating the prevalence of negative experiences within ICUs.
IMPLICATIONS FOR CLINICAL PRACTICE
This study highlights the potential risk of voice disorders among ICU nurses. The findings also underscore the importance of implementing strategies to reduce noise levels in ICUs to reduce voice disorders among nurses.
Topics: Humans; Phonation; COVID-19; Pandemics; Voice Disorders; Intensive Care Units; Nurses
PubMed: 38232571
DOI: 10.1016/j.iccn.2023.103620 -
A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops.Frontiers in Plant Science 2024Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing...
Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing novel high-quality crop varieties. Predicting the associations between genes and traits requires the organization and analysis of multi-dimensional scientific data. The existing methods for establishing the relationships between genomic data and phenotypic data can only elucidate the associations between genes and individual traits. However, there are relatively few methods for detecting elite polyphenotype genes. In this study, a knowledge graph for traits regulating-genes was constructed by collecting data from the PubMed database and eight other databases related to the staple food crops rice, maize, and wheat as well as the model plant . On the basis of the knowledge graph, a model for predicting traits regulating-genes was constructed by combining the data attributes of the gene nodes and the topological relationship attributes of the gene nodes. Additionally, a scoring method for predicting the genes regulating specific traits was developed to screen for elite polyphenotype genes. A total of 125,591 nodes and 547,224 semantic relationships were included in the knowledge graph. The accuracy of the knowledge graph-based model for predicting traits regulating-genes was 0.89, the precision rate was 0.91, the recall rate was 0.96, and the F1 value was 0.94. Moreover, 4,447 polyphenotype genes for 31 trait combinations were identified, among which the rice polyphenotype gene and the polyphenotype gene were verified via a literature search. Furthermore, the wheat gene was revealed as a potential polyphenotype gene that will need to be further characterized. Meanwhile, the result of venn diagram analysis between the polyphenotype gene datasets (consists of genes that are predicted by our model) and the transcriptome gene datasets (consists of genes that were differential expression in response to disease, drought or salt) showed approximately 70% and 54% polyphenotype genes were identified in the transcriptome datasets of Arabidopsis and rice, respectively. The application of the model driven by knowledge graph for predicting traits regulating-genes represents a novel method for detecting elite polyphenotype genes.
PubMed: 38571713
DOI: 10.3389/fpls.2024.1361716 -
Neuropsychologia Apr 2024Neural circuits related to language exhibit a remarkable ability to reorganize and adapt in response to visual deprivation. Particularly, early and late blindness induce...
Neural circuits related to language exhibit a remarkable ability to reorganize and adapt in response to visual deprivation. Particularly, early and late blindness induce distinct neuroplastic changes in the visual cortex, repurposing it for language and semantic processing. Interestingly, these functional changes provoke a unique cognitive advantage - enhanced verbal working memory, particularly in early blindness. Yet, the underlying neuromechanisms and the impact on language and memory-related circuits remain not fully understood. Here, we applied a brain-constrained neural network mimicking the structural and functional features of the frontotemporal-occipital cortices, to model conceptual acquisition in early and late blindness. The results revealed differential expansion of conceptual-related neural circuits into deprived visual areas depending on the timing of visual loss, which is most prominent in early blindness. This neural recruitment is fundamentally governed by the biological principles of neural circuit expansion and the absence of uncorrelated sensory input. Critically, the degree of these changes is constrained by the availability of neural matter previously allocated to visual experiences, as in the case of late blindness. Moreover, we shed light on the implication of visual deprivation on the neural underpinnings of verbal working memory, revealing longer reverberatory neural activity in 'blind models' as compared to the sighted ones. These findings provide a better understanding of the interplay between visual deprivations, neuroplasticity, language processing and verbal working memory.
Topics: Humans; Memory, Short-Term; Language; Blindness; Brain; Occipital Lobe
PubMed: 38331022
DOI: 10.1016/j.neuropsychologia.2024.108816