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Frontiers in Systems Neuroscience 2024Primary Progressive Aphasia (PPA) is a neurodegenerative disease characterized by linguistic impairment. The two main clinical subtypes are semantic (svPPA) and...
INTRODUCTION
Primary Progressive Aphasia (PPA) is a neurodegenerative disease characterized by linguistic impairment. The two main clinical subtypes are semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants. Diagnosing and classifying PPA patients represents a complex challenge that requires the integration of multimodal information, including clinical, biological, and radiological features. Structural neuroimaging can play a crucial role in aiding the differential diagnosis of PPA and constructing diagnostic support systems.
METHODS
In this study, we conducted a white matter texture analysis on T1-weighted images, including 56 patients with PPA (31 svPPA and 25 nfvPPA), and 53 age- and sex-matched controls. We trained a tree-based algorithm over combined clinical/radiomics measures and used Shapley Additive Explanations (SHAP) model to extract the greater impactful measures in distinguishing svPPA and nfvPPA patients from controls and each other.
RESULTS
Radiomics-integrated classification models demonstrated an accuracy of 95% in distinguishing svPPA patients from controls and of 93.7% in distinguishing svPPA from nfvPPA. An accuracy of 93.7% was observed in differentiating nfvPPA patients from controls. Moreover, Shapley values showed the strong involvement of the white matter near left entorhinal cortex in patients classification models.
DISCUSSION
Our study provides new evidence for the usefulness of radiomics features in classifying patients with svPPA and nfvPPA, demonstrating the effectiveness of an explainable machine learning approach in extracting the most impactful features for assessing PPA.
PubMed: 38562661
DOI: 10.3389/fnsys.2024.1324437 -
Scientific Reports Apr 2024This study aimed to investigate the effects of reproducing an ultrasonic sound above 20 kHz on the subjective impressions of water sounds using psychological and...
This study aimed to investigate the effects of reproducing an ultrasonic sound above 20 kHz on the subjective impressions of water sounds using psychological and physiological information obtained by the semantic differential method and electroencephalography (EEG), respectively. The results indicated that the ultrasonic component affected the subjective impression of the water sounds. In addition, regarding the relationship between psychological and physiological aspects, a moderate correlation was confirmed between the EEG change rate and subjective impressions. However, no differences in characteristics were found between with and without the ultrasound component, suggesting that ultrasound does not directly affect the relationship between subjective impressions and EEG energy at the current stage. Furthermore, the correlations calculated for the left and right channels in the occipital region differed significantly, which suggests functional asymmetry for sound perception between the right and left hemispheres.
Topics: Sound; Hearing; Electroencephalography; Auditory Perception; Acoustic Stimulation
PubMed: 38561365
DOI: 10.1038/s41598-024-57749-w -
PloS One 2024Android malware is becoming more common, and its invasion of smart devices has brought immeasurable losses to people's lives. Most existing Android malware detection...
Android malware is becoming more common, and its invasion of smart devices has brought immeasurable losses to people's lives. Most existing Android malware detection methods extract Android features from the original application files without considering the high-order hidden information behind them, but these hidden information can reflect malicious behaviors. To solve this problem, this paper proposes Z2F, a detection framework based on multidimensional Android feature extraction and graph neural networks for Android applications. Z2F first extracts seven types of Android features from the original Android application and then embeds them into a heterogeneous graph. On this basis, we design 12 kinds of meta-structures to analyze different semantic spaces of heterogeneous graphs, mine high-order hidden semantic information, and adopt a multi-layer graph attention mechanism to iteratively embed and update information. In this paper, a total of 14429 Android applications were detected and 1039726 Android features were extracted, with a detection accuracy of 99.7%.
Topics: Humans; Neural Networks, Computer; Records; Semantic Differential; Semantics
PubMed: 38547074
DOI: 10.1371/journal.pone.0300975 -
Frontiers in Integrative Neuroscience 2024We investigated the factors underlying naturalistic action recognition and understanding, as well as the errors occurring during recognition failures.
INTRODUCTION
We investigated the factors underlying naturalistic action recognition and understanding, as well as the errors occurring during recognition failures.
METHODS
Participants saw full-light stimuli of ten different whole-body actions presented in three different conditions: as normal videos, as videos with the temporal order of the frames scrambled, and as single static representative frames. After each stimulus presentation participants completed one of two tasks-a forced choice task where they were given the ten potential action labels as options, or a free description task, where they could describe the action performed in each stimulus in their own words.
RESULTS
While generally, a combination of form, motion, and temporal information led to the highest action understanding, for some actions form information was sufficient and adding motion and temporal information did not increase recognition accuracy. We also analyzed errors in action recognition and found primarily two different types.
DISCUSSION
One type of error was on the semantic level, while the other consisted of reverting to the kinematic level of body part processing without any attribution of semantics. We elaborate on these results in the context of naturalistic action perception.
PubMed: 38533314
DOI: 10.3389/fnint.2024.1302960 -
Journal of Experimental Zoology. Part... Mar 2024The chin, a distinguishing feature of Homo sapiens, has sparked ongoing debates regarding its evolutionary origins and adaptive significance. We contend that these...
The chin, a distinguishing feature of Homo sapiens, has sparked ongoing debates regarding its evolutionary origins and adaptive significance. We contend that these controversies stem from a fundamental disagreement about what constitutes a well-defined biological trait, a problem that has received insufficient attention despite its recognized importance in biology. In this paper, we leverage paleoanthropological research on the human chin to investigate the general issue of character or trait identification. First, we examine four accounts of the human chin from the existing literature: the mandibular differential growth byproduct, the bony prominence, the inverted T-relief, and the symphyseal angle. We then generalize from these accounts and propose a three-stage framework for the process of character identification: description, detection, and justification. We use this framework to reinterpret the four accounts, elucidating key points of contention surrounding the chin as well as other morphological characters. We show that debates over the chin carry broad and important biological implications that extend beyond this trait and that are not mere semantic issues of definition.
Topics: Humans; Animals; Chin; Mandible; Biological Evolution
PubMed: 38528769
DOI: 10.1002/jez.b.23249 -
Journal of Hospital Medicine Jun 2024Formulating a thoughtful problem representation (PR) is fundamental to sound clinical reasoning and an essential component of medical education. Aside from basic...
BACKGROUND
Formulating a thoughtful problem representation (PR) is fundamental to sound clinical reasoning and an essential component of medical education. Aside from basic structural recommendations, little consensus exists on what characterizes high-quality PRs.
OBJECTIVES
To elucidate characteristics that distinguish PRs created by experts and novices.
METHODS
Early internal medicine residents (novices) and inpatient teaching faculty (experts) from two academic medical centers were given two written clinical vignettes and were instructed to write a PR and three-item differential diagnosis for each. Deductive content analysis described the characteristics comprising PRs. An initial codebook of characteristics was refined iteratively. The primary outcome was differences in characteristic frequencies between groups. The secondary outcome was characteristics correlating with diagnostic accuracy. Mixed-effects regression with random effects modeling compared case-level outcomes by group.
RESULTS
Overall, 167 PRs were analyzed from 30 novices and 54 experts. Experts included 0.8 fewer comorbidities (p < .01) and 0.6 more examination findings (p = .01) than novices on average. Experts were less likely to include irrelevant comorbidities (odds ratio [OR] = 0.4, 95% confidence interval [CI] = 0.2-0.8) or a diagnosis (OR = 0.3, 95% CI = 0.1-0.8) compared with novices. Experts encapsulated clinical data into higher-order terms (e.g., sepsis) than novices (p < .01) while including similar numbers of semantic qualifiers (SQs). Regardless of expertise level, PRs following a three-part structure (e.g., demographics, temporal course, and clinical syndrome) and including temporal SQs were associated with diagnostic accuracy (p < .01).
CONCLUSIONS
Compared with novices, expert PRs include less irrelevant data and synthesize information into higher-order concepts. Future studies should determine whether targeted educational interventions for PRs improve diagnostic accuracy.
Topics: Humans; Internal Medicine; Internship and Residency; Clinical Competence; Female; Clinical Reasoning; Male; Adult; Diagnosis, Differential
PubMed: 38528679
DOI: 10.1002/jhm.13335 -
Ageing Research Reviews Apr 2024Among the central features of Alzheimer's disease (AD) progression are altered levels of the neuropeptide somatostatin (SST), and the colocalisation of SST-positive... (Review)
Review
Among the central features of Alzheimer's disease (AD) progression are altered levels of the neuropeptide somatostatin (SST), and the colocalisation of SST-positive interneurons (SST-INs) with amyloid-β plaques, leading to cell death. In this theoretical review, I propose a molecular model for the pathogenesis of AD based on SST-IN hypofunction and hyperactivity. Namely, hypofunctional and hyperactive SST-INs struggle to control hyperactivity in medial regions in early stages, leading to axonal Aβ production through excessive presynaptic GABAB inhibition, GABAB1a/APP complex downregulation and internalisation. Concomitantly, excessive SST-14 release accumulates near SST-INs in the form of amyloids, which bind to Aβ to form toxic mixed oligomers. This leads to differential SST-IN death through excitotoxicity, further disinhibition, SST deficits, and increased Aβ release, fibrillation and plaque formation. Aβ plaques, hyperactive networks and SST-IN distributions thereby tightly overlap in the brain. Conversely, chronic stimulation of postsynaptic SST2/4 on gulutamatergic neurons by hyperactive SST-INs promotes intense Mitogen-Activated Protein Kinase (MAPK) p38 activity, leading to somatodendritic p-tau staining and apoptosis/neurodegeneration - in agreement with a near complete overlap between p38 and neurofibrillary tangles. This model is suitable to explain some of the principal risk factors and markers of AD progression, including mitochondrial dysfunction, APOE4 genotype, sex-dependent vulnerability, overactive glial cells, dystrophic neurites, synaptic/spine losses, inter alia. Finally, the model can also shed light on qualitative aspects of AD neuropsychology, especially within the domains of spatial and declarative (episodic, semantic) memory, under an overlying pattern of contextual indiscrimination, ensemble instability, interference and generalisation.
Topics: Humans; Alzheimer Disease; Amyloid beta-Peptides; Somatostatin; Neurons; Neurofibrillary Tangles
PubMed: 38484981
DOI: 10.1016/j.arr.2024.102270 -
PloS One 2024Animal-assisted interventions are being increasingly used in studies that support various health effects. This study compared the psychophysiological and emotional...
Animal-assisted interventions are being increasingly used in studies that support various health effects. This study compared the psychophysiological and emotional responses during diverse activities with a dog to understand the impact of activity type. This study included 30 healthy adults (average age: 27.9 ± 8.4 years). Participants performed eight different activities with a dog for 3 minutes each. These activities included meeting, playing, feeding, massaging, grooming, photographing, hugging, and walking. Brain waves in the prefrontal, frontal, parietal, and occipital lobes were measured during the activities. Subjective evaluation of their emotions was recorded after each activity via the Profile of Mood States, Semantic Differential Method, and Stress Numeric Rating Scale. The alpha (relative, relative slow, relative fast) power spectra indicated that the brain's relaxation and resting state significantly increased when playing with and walking a dog. The beta (relative, relative low, and relative mid) power spectra significantly increased during dog massage, grooming, and playing activities, indicating improved concentration without stress. Notably, playing with a dog positively affected both relaxation and concentration. The Profile of Mood States outcome showed that activities such as feeding, massaging, and hugging the dog decreased the total mood disorder score, which indicated a positive effect on participants' moods. The Semantic Differential Method revealed that participants felt comfortable and natural while walking with a dog and relaxed when massaging it. Participants showed significantly lower stress moods in all the activities. This study demonstrated that specific dog activities could activate stronger relaxation, emotional stability, attention, concentration, and creativity by facilitating increased brain activity. In addition, interactions with dogs could decrease stress and induce positive emotional responses. These results provide data that forms the basis for the composition of the AAI program and may be applicable as a reference to determine the most effective activities for specific applications.
Topics: Adult; Humans; Dogs; Animals; Young Adult; Emotions; Brain; Affect; Relaxation; Brain Waves
PubMed: 38478472
DOI: 10.1371/journal.pone.0298384 -
Scientific Reports Feb 2024Accurate labeling of lung nodules in computed tomography (CT) images is crucial in early lung cancer diagnosis and before nodule resection surgery. However, the...
Accurate labeling of lung nodules in computed tomography (CT) images is crucial in early lung cancer diagnosis and before nodule resection surgery. However, the irregular shape of lung nodules in CT images and the complex lung environment make it much more challenging to segment lung nodules accurately. On this basis, we propose an improved V-Net segmentation method based on pixel threshold separation and attention mechanism for lung nodules. This method first offers a data augment strategy to solve the problem of insufficient samples in 3D medical datasets. In addition, we integrate the feature extraction module based on pixel threshold separation into the model to enhance the feature extraction ability under different thresholds on the one hand. On the other hand, the model introduces channel and spatial attention modules to make the model pay more attention to important semantic information and improve its generalization ability and accuracy. Experiments show that the Dice similarity coefficients of the improved model on the public datasets LUNA16 and LNDb are 94.9% and 81.1% respectively, and the sensitivities reach 92.7% and 76.9% respectively. which is superior to most existing UNet architecture models and comparable to the manual level segmentation results by medical technologists.
Topics: Humans; Differential Threshold; Generalization, Psychological; Lung Neoplasms; Medical Laboratory Personnel; Product Labeling; Image Processing, Computer-Assisted
PubMed: 38413699
DOI: 10.1038/s41598-024-55178-3 -
Frontiers in Neuroscience 2024Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical,...
BACKGROUND
Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN).
METHODS
Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach.
RESULTS
The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping.
CONCLUSION
In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.
PubMed: 38384484
DOI: 10.3389/fnins.2024.1331677