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  Subjects -> PSYCHOLOGY (Total: 983 journals)
Showing 601 - 174 of 174 Journals sorted by number of followers
Academic Psychiatry and Psychology Journal : APPJ     Open Access   (Followers: 42)
Advanced Journal of Professional Practice     Open Access   (Followers: 30)
Adaptive Human Behavior and Physiology     Hybrid Journal   (Followers: 12)
Advances in Neurodevelopmental Disorders     Hybrid Journal   (Followers: 8)
Aging Psychology     Open Access   (Followers: 8)
Adolescent Research Review     Hybrid Journal   (Followers: 7)
Behavior and Social Issues     Full-text available via subscription   (Followers: 7)
Forensic Science International : Mind and Law     Open Access   (Followers: 7)
Lamella     Open Access   (Followers: 7)
Evolution, Mind and Behaviour     Full-text available via subscription   (Followers: 7)
Current Research in Ecological and Social Psychology     Open Access   (Followers: 7)
Mediation Theory and Practice     Full-text available via subscription   (Followers: 7)
Quality and User Experience     Hybrid Journal   (Followers: 6)
Affective Science     Hybrid Journal   (Followers: 6)
Thérapie familiale     Full-text available via subscription   (Followers: 6)
Behavioural Public Policy     Hybrid Journal   (Followers: 6)
Brain Science Advances     Open Access   (Followers: 6)
International Journal of Applied Positive Psychology     Hybrid Journal   (Followers: 5)
Crime Psychology Review     Hybrid Journal   (Followers: 5)
Consumer Psychology Review     Hybrid Journal   (Followers: 5)
Scandinavian Journal of Sport and Exercise Psychology     Open Access   (Followers: 5)
Journal of Family Trauma, Child Custody & Child Development     Hybrid Journal   (Followers: 5)
Journal of Creativity     Open Access   (Followers: 5)
Revista de Psicodidáctica (English ed.)     Hybrid Journal   (Followers: 5)
Possibility Studies & Society     Hybrid Journal   (Followers: 5)
Clinical Practice & Epidemiology in Mental Health     Open Access   (Followers: 4)
Sleep Medicine : X     Open Access   (Followers: 4)
cultura & psyché : Journal of Cultural Psychology     Hybrid Journal   (Followers: 4)
Beyond Behavior     Hybrid Journal   (Followers: 4)
Journal of Psychosocial Systems     Open Access   (Followers: 4)
Community Psychology in Global Perspective     Open Access   (Followers: 3)
Journal of Play in Adulthood     Open Access   (Followers: 3)
Comprehensive Results in Social Psychology     Hybrid Journal   (Followers: 3)
Behavioural Sciences Undergraduate Journal     Open Access   (Followers: 3)
Journal of Psychosexual Health     Open Access   (Followers: 3)
Journal of Psychology and Theology     Hybrid Journal   (Followers: 3)
Behavioral Disorders     Hybrid Journal   (Followers: 3)
Psychologie Clinique     Full-text available via subscription   (Followers: 3)
Perspectives Psy     Full-text available via subscription   (Followers: 3)
Journal of Behavioral and Cognitive Therapy     Hybrid Journal   (Followers: 3)
Wellbeing, Space & Society     Open Access   (Followers: 3)
Clocks & Sleep     Open Access   (Followers: 2)
Journal of Performance and Mindfulness     Open Access   (Followers: 2)
Human Behavior and Emerging Technologies     Hybrid Journal   (Followers: 2)
International Journal of School & Educational Psychology     Hybrid Journal   (Followers: 2)
Contemporary Psychoanalysis     Hybrid Journal   (Followers: 2)
Psychoanalytic Study of the Child     Hybrid Journal   (Followers: 2)
Personnel Assessment and Decisions     Open Access   (Followers: 2)
Jungian Journal for Scholarly Studies     Open Access   (Followers: 2)
Torture Journal     Open Access   (Followers: 2)
Comprehensive Psychoneuroendocrinology     Open Access   (Followers: 2)
School Psychology Review     Hybrid Journal   (Followers: 2)
Health Sciences Review     Open Access   (Followers: 2)
Gestalt Theory. An International Multidisciplinary Journal     Open Access   (Followers: 1)
KULA : knowldge creation, dissemination, and preservation studies     Open Access   (Followers: 1)
Journal of Threat Assessment and Management     Full-text available via subscription   (Followers: 1)
Scientonomy : Journal for the Science of Science     Open Access   (Followers: 1)
Psych     Open Access   (Followers: 1)
Society and Security Insights     Open Access   (Followers: 1)
Revista Psicológica Herediana     Open Access   (Followers: 1)
Journal of Professional Counseling: Practice, Theory & Research     Hybrid Journal   (Followers: 1)
Journal of Health Service Psychology     Full-text available via subscription   (Followers: 1)
Perspectives on Behavior Science     Hybrid Journal   (Followers: 1)
JCPP Advances     Open Access   (Followers: 1)
SSM - Mental Health     Open Access   (Followers: 1)
Focus on Exceptional Children     Open Access  
Psisula : Prosiding Berkala Psikologi     Open Access  
Know and Share Psychology     Open Access  
Methods in Psychology     Open Access  
Gadjah Mada Journal of Professional Psychology     Open Access  
Revista de Investigacion Psicologica     Open Access  
CES Psicología     Open Access  
Psicoespacios     Open Access  
Katharsis     Open Access  
Journal of Theoretical Social Psychology     Hybrid Journal  
Nordic Psychology     Hybrid Journal  
Scandinavian Psychoanalytic Review     Hybrid Journal  
Human Arenas : An Interdisciplinary Journal of Psychology, Culture, and Meaning     Hybrid Journal  
Journal of Cognitive Enhancement     Hybrid Journal  
Occupational Health Science     Hybrid Journal  
Augmented Human Research     Hybrid Journal  
Spanish Journal of Psychology     Hybrid Journal  
Journal of Graduate Studies in Northern Rajabhat Universities     Open Access  
Journal of Graduate Research     Open Access  
Psicologia e Saúde em Debate     Open Access  
Dhammathas Academic Journal     Open Access  
INSAN Jurnal Psikologi dan Kesehatan Mental     Open Access  
People and Animals : The International Journal of Research and Practice     Open Access  
Heroism Science     Open Access  
Open Psychology Journal     Open Access  
Open Neuroimaging Journal     Open Access  
Studia z Kognitywistyki i Filozofii Umysłu     Open Access  
Studies in Asian Social Science     Open Access  
Psychology     Open Access  
Gogoa     Open Access  
Journal of Global Engagement and Transformation     Open Access  
Cuadernos de Marte     Open Access  
Psocial : Revista de Investigación en Psicología Social     Open Access  
Journal of Cognitive Systems     Open Access  
Jurnal Ilmiah Psikologi Terapan     Open Access  
Revista Laborativa     Open Access  
Jurnal Educatio : Jurnal Pendidikan Indonesia     Open Access  
Journal of Technology in Behavioral Science     Hybrid Journal  
Western Undergraduate Psychology Journal     Open Access  
Zeitschrift für Psychosomatische Medizin und Psychotherapie     Hybrid Journal  
Zeitschrift für Individualpsychologie     Hybrid Journal  
Wege zum Menschen : Zeitschrift für Seelsorge und Beratung, heilendes und soziales Handeln     Hybrid Journal  
Themenzentrierte Interaktion     Hybrid Journal  
Praxis der Kinderpsychologie und Kinderpsychiatrie     Hybrid Journal  
Musiktherapeutische Umschau : Forschung und Praxis der Musiktherapie     Hybrid Journal  

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Brain Informatics
Journal Prestige (SJR): 0.124
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2198-4018 - ISSN (Online) 2198-4026
Published by SpringerOpen Homepage  [229 journals]
  • A deep learning based cognitive model to probe the relation between
           psychophysics and electrophysiology of flicker stimulus

    • Abstract: Abstract The flicker stimulus is a visual stimulus of intermittent illumination. A flicker stimulus can appear flickering or steady to a human subject, depending on the physical parameters associated with the stimulus. When the flickering light appears steady, flicker fusion is said to have occurred. This work aims to bridge the gap between the psychophysics of flicker fusion and the electrophysiology associated with flicker stimulus through a Deep Learning based computational model of flicker perception. Convolutional Recurrent Neural Networks (CRNNs) were trained with psychophysics data of flicker stimulus obtained from a human subject. We claim that many of the reported features of electrophysiology of the flicker stimulus, including the presence of fundamentals and harmonics of the stimulus, can be explained as the result of a temporal convolution operation on the flicker stimulus. We further show that the convolution layer output of a CRNN trained with psychophysics data is more responsive to specific frequencies as in human EEG response to flicker, and the convolution layer of a trained CRNN can give a nearly sinusoidal output for 10 hertz flicker stimulus as reported for some human subjects.
      PubDate: 2024-07-10
       
  • Improving Likert scale big data analysis in psychometric health economics:
           reliability of the new compositional data approach

    • Abstract: Abstract Bipolar psychometric scales data are widely used in psychologic healthcare. Adequate psychological profiling benefits patients and saves time and costs. Grant funding depends on the quality of psychotherapeutic measures. Bipolar Likert scales yield compositional data because any order of magnitude of agreement towards an item assertion implies a complementary order of magnitude of disagreement. Using an isometric log-ratio (ilr) transformation the bivariate information can be transformed towards the real valued interval scale yielding unbiased statistical results increasing the statistical power of the Pearson correlation significance test if the Central Limit Theorem (CLT) of statistics is satisfied. In practice, however, the applicability of the CLT depends on the number of summands (i.e., the number of items) and the variance of the data generating process (DGP) of the ilr transformed data. Via simulation we provide evidence that the ilr approach also works satisfactory if the CLT is violated. That is, the ilr approach is robust towards extremely large or infinite variances of the underlying DGP increasing the statistical power of the correlation test. The study generalizes former results pointing out the universality and reliability of the ilr approach in psychometric big data analysis affecting psychometric health economics, patient welfare, grant funding, economic decision making and profits.
      PubDate: 2024-07-10
       
  • A systematic review on EEG-based neuromarketing: recent trends and
           analyzing techniques

    • Abstract: Abstract Neuromarketing is an emerging research field that aims to understand consumers’ decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.
      PubDate: 2024-06-05
       
  • Multi-view graph-based interview representation to improve depression
           level estimation

    • Abstract: Abstract Depression is a serious mental illness that affects millions worldwide and consequently has attracted considerable research interest in recent years. Within the field of automated depression estimation, most researchers focus on neural network architectures while ignoring other research directions. Within this paper, we explore an alternate approach and study the impact of input representations on the learning ability of the models. In particular, we work with graph-based representations to highlight different aspects of input transcripts, both at the interview and corpus levels. We use sentence similarity graphs and keyword correlation graphs to exemplify the advantages of graphical representations over sequential models for binary classification problems within depression estimation. Additionally, we design multi-view architectures that split interview transcripts into question and answer views in order to take into account dialogue structure. Our experiments show the benefits of multi-view based graphical input encodings over sequential models and provide new state-of-the-art results for binary classification on the gold standard DAIC-WOZ dataset. Further analysis establishes our method as a means for generating meaningful insights and visual summaries of interview transcripts that can be used by medical professionals.
      PubDate: 2024-06-04
       
  • Brain age gap estimation using attention-based ResNet method for
           Alzheimer’s disease detection

    • Abstract: Abstract This study investigates the correlation between brain age and chronological age in healthy individuals using brain MRI images, aiming to identify potential biomarkers for neurodegenerative diseases like Alzheimer's. To achieve this, a novel attention-based ResNet method, 3D-Attention-Resent-SVR, is proposed to accurately estimate brain age and distinguish between Cognitively Normal (CN) and Alzheimer’s disease (AD) individuals by computing the brain age gap (BAG). Unlike conventional methods, which often rely on single datasets, our approach addresses potential biases by employing four datasets for training and testing. The results, based on a combined dataset from four public sources comprising 3844 data points, demonstrate the model's efficacy with a mean absolute error (MAE) of 2.05 for brain age gap estimation. Moreover, the model's generalizability is showcased by training on three datasets and testing on a separate one, yielding a remarkable MAE of 2.4. Furthermore, leveraging BAG as the sole biomarker, our method achieves an accuracy of 92% and an AUC of 0.87 in Alzheimer's disease detection on the ADNI dataset. These findings underscore the potential of our approach in assisting with early detection and disease monitoring, emphasizing the strong correlation between BAG and AD.
      PubDate: 2024-06-04
       
  • Connecto-informatics at the mesoscale: current advances in image
           processing and analysis for mapping the brain connectivity

    • Abstract: Abstract Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers’ approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.
      PubDate: 2024-06-04
       
  • Correction: Semantic representation of neural circuit knowledge in
           Caenorhabditis elegans

    • PubDate: 2024-05-15
       
  • Effective relax acquisition: a novel approach to classify relaxed state in
           alpha band EEG-based transformation

    • Abstract: A relaxed state is essential for effective hypnotherapy, a crucial component of mental health treatments. During hypnotherapy sessions, neurologists rely on the patient’s relaxed state to introduce positive suggestions. While EEG is a widely recognized method for detecting human emotions, analyzing EEG data presents challenges due to its multi-channel, multi-band nature, leading to high-dimensional data. Furthermore, determining the onset of relaxation remains challenging for neurologists. This paper presents the Effective Relax Acquisition (ERA) method designed to identify the beginning of a relaxed state. ERA employs sub-band sampling within the Alpha band for the frequency domain and segments the data into four-period groups for the time domain analysis. Data enhancement strategies include using Window Length (WL) and Overlapping Shifting Windows (OSW) scenarios. Dimensionality reduction is achieved through Principal Component Analysis (PCA) by prioritizing the most significant eigenvector values. Our experimental results indicate that the relaxed state is predominantly observable in the high Alpha sub-band, particularly within the fourth period group. The ERA demonstrates high accuracy with a WL of 3 s and OSW of 0.25 s using the KNN classifier (90.63%). These findings validate the effectiveness of ERA in accurately identifying relaxed states while managing the complexity of EEG data. Graphical abstract
      PubDate: 2024-05-13
       
  • EEG source imaging of hand movement-related areas: an evaluation of the
           reconstruction and classification accuracy with optimized channels

    • Abstract: Abstract The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10–10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.
      PubDate: 2024-05-04
       
  • Interpreting artificial intelligence models: a systematic review on the
           application of LIME and SHAP in Alzheimer’s disease detection

    • Abstract: Abstract Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) and Shaply Additive exPlanation (SHAP) frameworks have grown as popular interpretive tools for ML and DL models. This article provides a systematic review of the application of LIME and SHAP in interpreting the detection of Alzheimer’s disease (AD). Adhering to PRISMA and Kitchenham’s guidelines, we identified 23 relevant articles and investigated these frameworks’ prospective capabilities, benefits, and challenges in depth. The results emphasise XAI’s crucial role in strengthening the trustworthiness of AI-based AD predictions. This review aims to provide fundamental capabilities of LIME and SHAP XAI frameworks in enhancing fidelity within clinical decision support systems for AD prognosis.
      PubDate: 2024-04-05
       
  • Examining the reliability of brain age algorithms under varying degrees of
           participant motion

    • Abstract: Abstract Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland–Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956–0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.
      PubDate: 2024-04-04
       
  • Rejuvenating classical brain electrophysiology source localization methods
           with spatial graph Fourier filters for source extents estimation

    • Abstract: Abstract EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.
      PubDate: 2024-03-12
       
  • Cross subject emotion identification from multichannel EEG sub-bands using
           Tsallis entropy feature and KNN classifier

    • Abstract: Abstract Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.
      PubDate: 2024-03-05
       
  • An automatic method using MFCC features for sleep stage classification

    • Abstract: Abstract Sleep stage classification is a necessary step for diagnosing sleep disorders. Generally, experts use traditional methods based on every 30 seconds (s) of the biological signals, such as electrooculograms (EOGs), electrocardiograms (ECGs), electromyograms (EMGs), and electroencephalograms (EEGs), to classify sleep stages. Recently, various state-of-the-art approaches based on a deep learning model have been demonstrated to have efficient and accurate outcomes in sleep stage classification. In this paper, a novel deep convolutional neural network (CNN) combined with a long short-time memory (LSTM) model is proposed for sleep scoring tasks. A key frequency domain feature named Mel-frequency Cepstral Coefficient (MFCC) is extracted from EEG and EMG signals. The proposed method can learn features from frequency domains on different bio-signal channels. It firstly extracts the MFCC features from multi-channel signals, and then inputs them to several convolutional layers and an LSTM layer. Secondly, the learned representations are fed to a fully connected layer and a softmax classifier for sleep stage classification. The experiments are conducted on two widely used sleep datasets, Sleep Heart Health Study (SHHS) and Vincent’s University Hospital/University College Dublin Sleep Apnoea (UCDDB) to test the effectiveness of the method. The results of this study indicate that the model can perform well in the classification of sleep stages using the features of the 2-dimensional (2D) MFCC feature. The advantage of using the feature is that it can be used to input a two-dimensional data stream, which can be used to retain information about each sleep stage. Using 2D data streams can reduce the time it takes to retrieve the data from the one-dimensional stream. Another advantage of this method is that it eliminates the need for deep layers, which can help improve the performance of the model. For instance, by reducing the number of layers, our seven layers of the model structure takes around 400 s to train and test 100 subjects in the SHHS1 dataset. Its best accuracy and Cohen’s kappa are 82.35% and 0.75 for the SHHS dataset, and 73.07% and 0.63 for the UCDDB dataset, respectively.
      PubDate: 2024-02-10
       
  • 3D convolutional neural networks uncover modality-specific brain-imaging
           predictors for Alzheimer’s disease sub-scores

    • Abstract: Abstract Different aspects of cognitive functions are affected in patients with Alzheimer’s disease. To date, little is known about the associations between features from brain-imaging and individual Alzheimer’s disease (AD)-related cognitive functional changes. In addition, how these associations differ among different imaging modalities is unclear. Here, we trained and investigated 3D convolutional neural network (CNN) models that predicted sub-scores of the 13-item Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS–Cog13) based on MRI and FDG–PET brain-imaging data. Analysis of the trained network showed that each key ADAS–Cog13 sub-score was associated with a specific set of brain features within an imaging modality. Furthermore, different association patterns were observed in MRI and FDG–PET modalities. According to MRI, cognitive sub-scores were typically associated with structural changes of subcortical regions, including amygdala, hippocampus, and putamen. Comparatively, according to FDG–PET, cognitive functions were typically associated with metabolic changes of cortical regions, including the cingulated gyrus, occipital cortex, middle front gyrus, precuneus cortex, and the cerebellum. These findings brought insights into complex AD etiology and emphasized the importance of investigating different brain-imaging modalities.
      PubDate: 2024-02-04
       
  • The onset of motor learning impairments in Parkinson’s disease: a
           computational investigation

    • Abstract: Abstract The basal ganglia (BG) is part of a basic feedback circuit regulating cortical function, such as voluntary movements control, via their influence on thalamocortical projections. BG disorders, namely Parkinson’s disease (PD), characterized by the loss of neurons in the substantia nigra, involve the progressive loss of motor functions. At the present, PD is incurable. Converging evidences suggest the onset of PD-specific pathology prior to the appearance of classical motor signs. This latent phase of neurodegeneration in PD is of particular relevance in developing more effective therapies by intervening at the earliest stages of the disease. Therefore, a key challenge in PD research is to identify and validate markers for the preclinical and prodromal stages of the illness. We propose a mechanistic neurocomputational model of the BG at a mesoscopic scale to investigate the behavior of the simulated neural system after several degrees of lesion of the substantia nigra, with the aim of possibly evaluating which is the smallest lesion compromising motor learning. In other words, we developed a working framework for the analysis of theoretical early-stage PD. While simulations in healthy conditions confirm the key role of dopamine in learning, in pathological conditions the network predicts that there may exist abnormalities of the motor learning process, for physiological alterations in the BG, that do not yet involve the presence of symptoms typical of the clinical diagnosis.
      PubDate: 2024-01-29
       
  • Synergistic integration of Multi-View Brain Networks and advanced machine
           learning techniques for auditory disorders diagnostics

    • Abstract: Abstract In the field of audiology, achieving accurate discrimination of auditory impairments remains a formidable challenge. Conditions such as deafness and tinnitus exert a substantial impact on patients’ overall quality of life, emphasizing the urgent need for precise and efficient classification methods. This study introduces an innovative approach, utilizing Multi-View Brain Network data acquired from three distinct cohorts: 51 deaf patients, 54 with tinnitus, and 42 normal controls. Electroencephalogram (EEG) recording data were meticulously collected, focusing on 70 electrodes attached to an end-to-end key with 10 regions of interest (ROI). This data is synergistically integrated with machine learning algorithms. To tackle the inherently high-dimensional nature of brain connectivity data, principal component analysis (PCA) is employed for feature reduction, enhancing interpretability. The proposed approach undergoes evaluation using ensemble learning techniques, including Random Forest, Extra Trees, Gradient Boosting, and CatBoost. The performance of the proposed models is scrutinized across a comprehensive set of metrics, encompassing cross-validation accuracy (CVA), precision, recall, F1-score, Kappa, and Matthews correlation coefficient (MCC). The proposed models demonstrate statistical significance and effectively diagnose auditory disorders, contributing to early detection and personalized treatment, thereby enhancing patient outcomes and quality of life. Notably, they exhibit reliability and robustness, characterized by high Kappa and MCC values. This research represents a significant advancement in the intersection of audiology, neuroimaging, and machine learning, with transformative implications for clinical practice and care.
      PubDate: 2024-01-14
       
  • Deep learning based joint fusion approach to exploit anatomical and
           functional brain information in autism spectrum disorders

    • Abstract: Background: The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD). Material and methods We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework. Results The AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain. Conclusions Our results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.
      PubDate: 2024-01-09
       
  • Addiction-related brain networks identification via Graph Diffusion
           Reconstruction Network

    • Abstract: Abstract Functional magnetic resonance imaging (fMRI) provides insights into complex patterns of brain functional changes, making it a valuable tool for exploring addiction-related brain connectivity. However, effectively extracting addiction-related brain connectivity from fMRI data remains challenging due to the intricate and non-linear nature of brain connections. Therefore, this paper proposed the Graph Diffusion Reconstruction Network (GDRN), a novel framework designed to capture addiction-related brain connectivity from fMRI data acquired from addicted rats. The proposed GDRN incorporates a diffusion reconstruction module that effectively maintains the unity of data distribution by reconstructing the training samples, thereby enhancing the model’s ability to reconstruct nicotine addiction-related brain networks. Experimental evaluations conducted on a nicotine addiction rat dataset demonstrate that the proposed GDRN effectively explores nicotine addiction-related brain connectivity. The findings suggest that the GDRN holds promise for uncovering and understanding the complex neural mechanisms underlying addiction using fMRI data.
      PubDate: 2024-01-08
       
  • Behavioural relevance of redundant and synergistic stimulus information
           between functionally connected neurons in mouse auditory cortex

    • Abstract: Abstract Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity—that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations.
      PubDate: 2023-12-05
       
 
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  Subjects -> PSYCHOLOGY (Total: 983 journals)
Showing 601 - 174 of 174 Journals sorted by number of followers
Academic Psychiatry and Psychology Journal : APPJ     Open Access   (Followers: 42)
Advanced Journal of Professional Practice     Open Access   (Followers: 30)
Adaptive Human Behavior and Physiology     Hybrid Journal   (Followers: 12)
Advances in Neurodevelopmental Disorders     Hybrid Journal   (Followers: 8)
Aging Psychology     Open Access   (Followers: 8)
Adolescent Research Review     Hybrid Journal   (Followers: 7)
Behavior and Social Issues     Full-text available via subscription   (Followers: 7)
Forensic Science International : Mind and Law     Open Access   (Followers: 7)
Lamella     Open Access   (Followers: 7)
Evolution, Mind and Behaviour     Full-text available via subscription   (Followers: 7)
Current Research in Ecological and Social Psychology     Open Access   (Followers: 7)
Mediation Theory and Practice     Full-text available via subscription   (Followers: 7)
Quality and User Experience     Hybrid Journal   (Followers: 6)
Affective Science     Hybrid Journal   (Followers: 6)
Thérapie familiale     Full-text available via subscription   (Followers: 6)
Behavioural Public Policy     Hybrid Journal   (Followers: 6)
Brain Science Advances     Open Access   (Followers: 6)
International Journal of Applied Positive Psychology     Hybrid Journal   (Followers: 5)
Crime Psychology Review     Hybrid Journal   (Followers: 5)
Consumer Psychology Review     Hybrid Journal   (Followers: 5)
Scandinavian Journal of Sport and Exercise Psychology     Open Access   (Followers: 5)
Journal of Family Trauma, Child Custody & Child Development     Hybrid Journal   (Followers: 5)
Journal of Creativity     Open Access   (Followers: 5)
Revista de Psicodidáctica (English ed.)     Hybrid Journal   (Followers: 5)
Possibility Studies & Society     Hybrid Journal   (Followers: 5)
Clinical Practice & Epidemiology in Mental Health     Open Access   (Followers: 4)
Sleep Medicine : X     Open Access   (Followers: 4)
cultura & psyché : Journal of Cultural Psychology     Hybrid Journal   (Followers: 4)
Beyond Behavior     Hybrid Journal   (Followers: 4)
Journal of Psychosocial Systems     Open Access   (Followers: 4)
Community Psychology in Global Perspective     Open Access   (Followers: 3)
Journal of Play in Adulthood     Open Access   (Followers: 3)
Comprehensive Results in Social Psychology     Hybrid Journal   (Followers: 3)
Behavioural Sciences Undergraduate Journal     Open Access   (Followers: 3)
Journal of Psychosexual Health     Open Access   (Followers: 3)
Journal of Psychology and Theology     Hybrid Journal   (Followers: 3)
Behavioral Disorders     Hybrid Journal   (Followers: 3)
Psychologie Clinique     Full-text available via subscription   (Followers: 3)
Perspectives Psy     Full-text available via subscription   (Followers: 3)
Journal of Behavioral and Cognitive Therapy     Hybrid Journal   (Followers: 3)
Wellbeing, Space & Society     Open Access   (Followers: 3)
Clocks & Sleep     Open Access   (Followers: 2)
Journal of Performance and Mindfulness     Open Access   (Followers: 2)
Human Behavior and Emerging Technologies     Hybrid Journal   (Followers: 2)
International Journal of School & Educational Psychology     Hybrid Journal   (Followers: 2)
Contemporary Psychoanalysis     Hybrid Journal   (Followers: 2)
Psychoanalytic Study of the Child     Hybrid Journal   (Followers: 2)
Personnel Assessment and Decisions     Open Access   (Followers: 2)
Jungian Journal for Scholarly Studies     Open Access   (Followers: 2)
Torture Journal     Open Access   (Followers: 2)
Comprehensive Psychoneuroendocrinology     Open Access   (Followers: 2)
School Psychology Review     Hybrid Journal   (Followers: 2)
Health Sciences Review     Open Access   (Followers: 2)
Gestalt Theory. An International Multidisciplinary Journal     Open Access   (Followers: 1)
KULA : knowldge creation, dissemination, and preservation studies     Open Access   (Followers: 1)
Journal of Threat Assessment and Management     Full-text available via subscription   (Followers: 1)
Scientonomy : Journal for the Science of Science     Open Access   (Followers: 1)
Psych     Open Access   (Followers: 1)
Society and Security Insights     Open Access   (Followers: 1)
Revista Psicológica Herediana     Open Access   (Followers: 1)
Journal of Professional Counseling: Practice, Theory & Research     Hybrid Journal   (Followers: 1)
Journal of Health Service Psychology     Full-text available via subscription   (Followers: 1)
Perspectives on Behavior Science     Hybrid Journal   (Followers: 1)
JCPP Advances     Open Access   (Followers: 1)
SSM - Mental Health     Open Access   (Followers: 1)
Focus on Exceptional Children     Open Access  
Psisula : Prosiding Berkala Psikologi     Open Access  
Know and Share Psychology     Open Access  
Methods in Psychology     Open Access  
Gadjah Mada Journal of Professional Psychology     Open Access  
Revista de Investigacion Psicologica     Open Access  
CES Psicología     Open Access  
Psicoespacios     Open Access  
Katharsis     Open Access  
Journal of Theoretical Social Psychology     Hybrid Journal  
Nordic Psychology     Hybrid Journal  
Scandinavian Psychoanalytic Review     Hybrid Journal  
Human Arenas : An Interdisciplinary Journal of Psychology, Culture, and Meaning     Hybrid Journal  
Journal of Cognitive Enhancement     Hybrid Journal  
Occupational Health Science     Hybrid Journal  
Augmented Human Research     Hybrid Journal  
Spanish Journal of Psychology     Hybrid Journal  
Journal of Graduate Studies in Northern Rajabhat Universities     Open Access  
Journal of Graduate Research     Open Access  
Psicologia e Saúde em Debate     Open Access  
Dhammathas Academic Journal     Open Access  
INSAN Jurnal Psikologi dan Kesehatan Mental     Open Access  
People and Animals : The International Journal of Research and Practice     Open Access  
Heroism Science     Open Access  
Open Psychology Journal     Open Access  
Open Neuroimaging Journal     Open Access  
Studia z Kognitywistyki i Filozofii Umysłu     Open Access  
Studies in Asian Social Science     Open Access  
Psychology     Open Access  
Gogoa     Open Access  
Journal of Global Engagement and Transformation     Open Access  
Cuadernos de Marte     Open Access  
Psocial : Revista de Investigación en Psicología Social     Open Access  
Journal of Cognitive Systems     Open Access  
Jurnal Ilmiah Psikologi Terapan     Open Access  
Revista Laborativa     Open Access  
Jurnal Educatio : Jurnal Pendidikan Indonesia     Open Access  
Journal of Technology in Behavioral Science     Hybrid Journal  
Western Undergraduate Psychology Journal     Open Access  
Zeitschrift für Psychosomatische Medizin und Psychotherapie     Hybrid Journal  
Zeitschrift für Individualpsychologie     Hybrid Journal  
Wege zum Menschen : Zeitschrift für Seelsorge und Beratung, heilendes und soziales Handeln     Hybrid Journal  
Themenzentrierte Interaktion     Hybrid Journal  
Praxis der Kinderpsychologie und Kinderpsychiatrie     Hybrid Journal  
Musiktherapeutische Umschau : Forschung und Praxis der Musiktherapie     Hybrid Journal  

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School of Mathematical and Computer Sciences
Heriot-Watt University
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Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


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