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  Subjects -> PSYCHOLOGY (Total: 983 journals)
Showing 601 - 174 of 174 Journals sorted by number of followers
Nature Human Behaviour     Hybrid Journal   (Followers: 56)
American Journal of Applied Psychology     Open Access   (Followers: 55)
Annual Review of Organizational Psychology and Organizational Behavior     Full-text available via subscription   (Followers: 52)
Frontiers in Behavioral Neuroscience     Open Access   (Followers: 30)
Violence and Gender     Full-text available via subscription   (Followers: 23)
Counseling Psychology and Psychotherapy     Open Access   (Followers: 21)
Behavior Analysis in Practice     Full-text available via subscription   (Followers: 19)
Current Opinion in Psychology     Hybrid Journal   (Followers: 16)
Current Addiction Reports     Hybrid Journal   (Followers: 15)
Sport, Exercise, and Performance Psychology     Full-text available via subscription   (Followers: 15)
Journal of Social and Political Psychology     Open Access   (Followers: 15)
Review Journal of Autism and Developmental Disorders     Hybrid Journal   (Followers: 15)
Journal of Music Therapy     Hybrid Journal   (Followers: 14)
Psychology of Sexual Orientation and Gender Diversity     Full-text available via subscription   (Followers: 14)
Clinical Practice in Pediatric Psychology     Full-text available via subscription   (Followers: 14)
Autism's Own     Open Access   (Followers: 13)
Case Studies in Sport and Exercise Psychology     Hybrid Journal   (Followers: 13)
Journal of Gender-Based Violence     Hybrid Journal   (Followers: 13)
Glossa Psycholinguistics     Open Access   (Followers: 12)
Journal of Experimental Psychology : Animal Learning and Cognition     Full-text available via subscription   (Followers: 11)
Health Psychology Open     Open Access   (Followers: 11)
Clinical Psychology and Special Education     Open Access   (Followers: 10)
Current Opinion in Behavioral Sciences     Hybrid Journal   (Followers: 10)
Couple and Family Psychology : Research and Practice     Full-text available via subscription   (Followers: 10)
Journal of Psychosocial Rehabilitation and Mental Health     Hybrid Journal   (Followers: 10)
Behavior Analysis: Research and Practice     Full-text available via subscription   (Followers: 8)
Music Therapy Perspectives     Hybrid Journal   (Followers: 8)
Addictive Behaviors Reports     Open Access   (Followers: 8)
International Journal of Yoga : Philosophy, Psychology and Parapsychology     Open Access   (Followers: 8)
Psychology of Consciousness : Theory, Research, and Practice     Full-text available via subscription   (Followers: 7)
Evolutionary Behavioral Sciences     Full-text available via subscription   (Followers: 7)
Psychomusicology : Music, Mind, and Brain     Full-text available via subscription   (Followers: 7)
Asian American Journal of Psychology     Full-text available via subscription   (Followers: 7)
Contemporary School Psychology     Hybrid Journal   (Followers: 7)
Qualitative Psychology     Full-text available via subscription   (Followers: 6)
Creativity. Theories ? Research ? Applications     Open Access   (Followers: 6)
Cultural-Historical Psychology     Open Access   (Followers: 6)
Review of Behavioral Economics     Full-text available via subscription   (Followers: 6)
Internet Interventions : The application of information technology in mental and behavioural health     Open Access   (Followers: 6)
Decision     Full-text available via subscription   (Followers: 6)
Behavior Analyst     Hybrid Journal   (Followers: 6)
Neurology, Neuropsychiatry, Psychosomatics     Open Access   (Followers: 6)
Evolutionary Psychological Science     Hybrid Journal   (Followers: 6)
Journal of Behavioral Addictions     Open Access   (Followers: 5)
Journal of Cognitive Historiography     Full-text available via subscription   (Followers: 5)
Journal of Individual Psychology     Full-text available via subscription   (Followers: 5)
Porn Studies     Hybrid Journal   (Followers: 5)
Journal of Social, Behavioral, and Health Sciences     Open Access   (Followers: 5)
OA Autism     Open Access   (Followers: 5)
Social Psychology and Society     Open Access   (Followers: 5)
European Journal of Investigation in Health, Psychology and Education     Open Access   (Followers: 5)
Cognitive Research : Principles and Implications     Open Access   (Followers: 5)
Counselling and Values     Hybrid Journal   (Followers: 5)
Revista Científica Arbitrada de la Fundación MenteClara     Open Access   (Followers: 5)
Archives of Scientific Psychology     Open Access   (Followers: 5)
Drama Therapy Review     Hybrid Journal   (Followers: 4)
Psyke & Logos     Open Access   (Followers: 4)
Current Behavioral Neuroscience Reports     Hybrid Journal   (Followers: 4)
International Journal of Educational and Psychological Researches     Open Access   (Followers: 4)
Policy Insights from the Behavioral and Brain Sciences     Full-text available via subscription   (Followers: 4)
Voices : A World Forum for Music Therapy     Open Access   (Followers: 4)
SUCHT - Zeitschrift für Wissenschaft und Praxis / Journal of Addiction Research and Practice     Hybrid Journal   (Followers: 4)
African Journal of Cross-Cultural Psychology and Sport Facilitation     Full-text available via subscription   (Followers: 4)
Journal of Amateur Sport     Open Access   (Followers: 3)
Psychology and Law     Open Access   (Followers: 3)
Phenomenology and Mind     Open Access   (Followers: 3)
Spirituality in Clinical Practice     Full-text available via subscription   (Followers: 3)
Nigerian Journal of Guidance and Counselling     Full-text available via subscription   (Followers: 2)
Zeitschrift für Neuropsychologie     Hybrid Journal   (Followers: 2)
Lebenswelt : Aesthetics and philosophy of experience     Open Access   (Followers: 2)
Behavioral Development Bulletin     Full-text available via subscription   (Followers: 2)
Research in Psychology and Behavioral Sciences     Open Access   (Followers: 2)
Zeitschrift für Psychiatrie, Psychologie und Psychotherapie     Hybrid Journal   (Followers: 2)
Couple and Family Psychoanalysis     Full-text available via subscription   (Followers: 2)
Multisensory Research     Hybrid Journal   (Followers: 2)
Social Action : The Journal for Social Action in Counseling and Psychology     Free   (Followers: 2)
European Journal of Behavior Analysis     Hybrid Journal   (Followers: 2)
Language and Text     Open Access   (Followers: 2)
Social Inclusion     Open Access   (Followers: 2)
International Review of Social Psychology / Revue Internationale de Psychologie Sociale     Open Access   (Followers: 2)
Journal of Language Aggression and Conflict     Hybrid Journal   (Followers: 2)
Inquiry : Critical Thinking Across the Disciplines     Full-text available via subscription   (Followers: 2)
Journal of Dynamic Decision Making     Open Access   (Followers: 2)
Phenomenology & Practice     Open Access   (Followers: 2)
Sexual Offending : Theory, Research, and Prevention     Open Access   (Followers: 2)
Voices : The Art and Science of Psychotherapy     Full-text available via subscription   (Followers: 1)
Jurnal Psikologi Pendidikan dan Konseling : Jurnal Kajian Psikologi Pendidikan dan Bimbingan Konseling     Open Access   (Followers: 1)
Acta de Investigación Psicológica     Open Access   (Followers: 1)
Psychosomatic Medicine and General Practice     Open Access   (Followers: 1)
Journal of Numerical Cognition     Open Access   (Followers: 1)
Canadian Art Therapy Association     Hybrid Journal   (Followers: 1)
Quantitative Methods for Psychology     Open Access   (Followers: 1)
Wawasan     Open Access   (Followers: 1)
Zeitschrift für Gerontopsychologie und -psychiatrie     Full-text available via subscription   (Followers: 1)
Psicologia e Saber Social     Open Access   (Followers: 1)
Pragmatic Case Studies in Psychotherapy     Open Access   (Followers: 1)
Psychological Science and Education     Open Access   (Followers: 1)
Psychological Science and Education psyedu.ru     Open Access   (Followers: 1)
Activités     Open Access   (Followers: 1)
Journal of Mind and Medical Sciences     Open Access   (Followers: 1)
Journal of Educational, Cultural and Psychological Studies     Open Access   (Followers: 1)
Journal of Addiction & Prevention     Open Access   (Followers: 1)
Russian Psychological Journal     Open Access   (Followers: 1)
Epiphany     Open Access   (Followers: 1)
Neuropsychoanalysis : An Interdisciplinary Journal for Psychoanalysis and the Neurosciences     Hybrid Journal   (Followers: 1)
Zeitschrift für Pädagogische Psychologie     Hybrid Journal   (Followers: 1)
Zeitschrift für Kinder- und Jugendpsychiatrie und Psychotherapie     Hybrid Journal   (Followers: 1)
Canadian Journal of Art Therapy : Research, Practice, and Issues     Hybrid Journal   (Followers: 1)
Tempo Psicanalitico     Open Access   (Followers: 1)
FLEKS : Scandinavian Journal of Intercultural Theory and Practice     Open Access   (Followers: 1)
Undecidable Unconscious : A Journal of Deconstruction and Psychoanalysis     Full-text available via subscription   (Followers: 1)
Brain Informatics     Open Access   (Followers: 1)
Journal of Psychology in Africa     Full-text available via subscription   (Followers: 1)
Revista de Estudios e Investigación en Psicología y Educación     Open Access  
Persona Studies     Open Access  
Indigenous : Jurnal Ilmiah Psikologi     Open Access  
Intuisi : Jurnal Psikologi Ilmiah     Open Access  
Setting     Full-text available via subscription  
Revista de Psicologia     Open Access  
Behaviormetrika     Hybrid Journal  
European Yearbook of the History of Psychology     Full-text available via subscription  
Interacciones. Revista de Avances en Psicología     Open Access  
Psicologia     Open Access  
Journal für Psychoanalyse     Open Access  
Siglo Cero. Revista Española sobre Discapacidad Intelectual     Open Access  
Miscelánea Comillas. Revista de Ciencias Humanas y Sociales     Open Access  
New School Psychology Bulletin     Open Access  
TESTFÓRUM     Open Access  
S : Journal of the Circle for Lacanian Ideology Critique     Open Access  
International Journal of Psychoanalysis and Education     Open Access  
Quaderns de Psicologia     Open Access  
Satir International Journal     Open Access  
Mudanças - Psicologia da Saúde     Open Access  
Journal of Creating Value     Full-text available via subscription  
Tajdida : Jurnal Pemikiran dan Gerakan Muhammadiyah     Open Access  
Estudos Interdisciplinares em Psicologia     Open Access  
Psychologie du Travail et des Organisations     Hybrid Journal  
Cendekia : Jurnal Kependidikan dan Kemasyarakatan     Open Access  
Visnyk of NTUU - Philosophy. Psychology. Pedagogics     Open Access  
Revista Costarricense de Psicología     Open Access  
Informes Psicológicos     Open Access  
Jurnal Psikologi     Open Access  
Zeitschrift für Differentielle und Diagnostische Psychologie     Full-text available via subscription  
Klart språk i Norden     Open Access  
Revista Pequén     Open Access  
Pensando Psicología     Open Access  
Ciencias Psicológicas     Open Access  
Revista de Cultura Teológica     Open Access  
Journal of Modern Foreign Psychology     Open Access  
Experimental Psychology (Russia)     Open Access  
Elpis - Czasopismo Teologiczne Katedry Teologii Prawosławnej Uniwersytetu w Białymstoku     Open Access  
International Journal of Comparative Psychology     Open Access  
Гуманітарний вісник Запорізької державної інженерної академії     Open Access  
Revista Latinoamericana de Psicología     Open Access  
Cogent Psychology     Open Access  
Ajayu Órgano de Difusión Científica del Departamento de Psicología UCBSP     Open Access  
Psicologia     Open Access  
Análise Psicológica     Open Access  
Rivista Internazionale di Filosofia e Psicologia     Open Access  
Facta Universitatis, Series : Philosophy, Sociology, Psychology and History     Open Access  
Universal Journal of Psychology     Open Access  
Revista Internacional de Psicologia     Open Access  
Terapia familiare     Full-text available via subscription  
Studi Junghiani     Full-text available via subscription  
Ruolo Terapeutico (IL)     Full-text available via subscription  
Rivista Sperimentale di Freniatria     Full-text available via subscription  
Rivista di Psicoterapia Relazionale     Full-text available via subscription  
Ricerche di psicologia     Full-text available via subscription  
Ricerca Psicoanalitica : Journal of the Relationship in Psychoanalysis     Open Access  
Quaderni di Gestalt     Full-text available via subscription  
Psicoterapia e Scienze Umane     Full-text available via subscription  
Psicologia di Comunità. Gruppi, ricerca-azione, modelli formativi     Full-text available via subscription  
Psicologia della salute     Full-text available via subscription  
Psicobiettivo     Full-text available via subscription  
Psicoanalisi     Full-text available via subscription  
Ipnosi     Full-text available via subscription  
Interazioni     Full-text available via subscription  
Gruppi     Full-text available via subscription  
Forum : Journal of the International Association of Group Psychoterapy     Full-text available via subscription  
Educazione sentimentale     Full-text available via subscription  
Revista Wímb Lu     Open Access  
International Perspectives in Psychology : Research, Practice, Consultation     Full-text available via subscription  
Lernen und Lernstörungen     Hybrid Journal  
Inkanyiso : Journal of Humanities and Social Sciences     Open Access  
Online Readings in Psychology and Culture     Open Access  
Winnicott e-prints     Open Access  
Trivium : Estudos Interdisciplinares     Open Access  
Temas em Psicologia     Open Access  
Stylus (Rio de Janeiro)     Open Access  
Salud & Sociedad: investigaciones en psicologia de la salud y psicologia social     Open Access  
Revista Psicopedagogia     Open Access  
Revista Psicologia Política     Open Access  
Revista Psicologia e Saúde     Open Access  
Revista Psicologia     Open Access  
Revista Mexicana de Orientación Educativa     Open Access  
Revista do NUFEN     Open Access  
Revista de Etologia     Open Access  
Revista da SPAGESP     Open Access  
Revista da SBPH     Open Access  
Revista da Abordagem Gestáltica     Open Access  

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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  [228 journals]
  • A systematic review of the prediction of consumer preference using EEG
           measures and machine-learning in neuromarketing research

    • Abstract: Introduction The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking. Methods Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review. Results Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour. Conclusions and implications FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.
      PubDate: 2022-11-14
       
  • Assisted neuroscience knowledge extraction via machine learning applied to
           neural reconstruction metadata on NeuroMorpho.Org

    • Abstract: Abstract The amount of unstructured text produced daily in scholarly journals is enormous. Systematically identifying, sorting, and structuring information from such a volume of data is increasingly challenging for researchers even in delimited domains. Named entity recognition is a fundamental natural language processing tool that can be trained to annotate, structure, and extract information from scientific articles. Here, we harness state-of-the-art machine learning techniques and develop a smart neuroscience metadata suggestion system accessible by both humans through a user-friendly graphical interface and machines via Application Programming Interface. We demonstrate a practical application to the public repository of neural reconstructions, NeuroMorpho.Org, thus expanding the existing web-based metadata management system currently in use. Quantitative analysis indicates that the suggestion system reduces personnel labor by at least 50%. Moreover, our results show that larger training datasets with the same software architecture are unlikely to further improve performance without ad-hoc heuristics due to intrinsic ambiguities in neuroscience nomenclature. All components of this project are released open source for community enhancement and extensions to additional applications.
      PubDate: 2022-11-07
       
  • Hemodynamic functional connectivity optimization of frequency EEG
           microstates enables attention LSTM framework to classify distinct temporal
           cortical communications of different cognitive tasks

    • Abstract: Abstract Temporal analysis of global cortical communication of cognitive tasks in coarse EEG information is still challenging due to the underlying complex neural mechanisms. This study proposes an attention-based time-series deep learning framework that processes fMRI functional connectivity optimized quasi-stable frequency microstates for classifying distinct temporal cortical communications of the cognitive task. Seventy volunteers were subjected to visual target detection tasks, and their electroencephalogram (EEG) and functional MRI (fMRI) were acquired simultaneously. At first, the acquired EEG information was preprocessed and bandpass to delta, theta, alpha, beta, and gamma bands and then subjected to quasi-stable frequency-microstate estimation. Subsequently, time-series elicitation of each frequency microstates is optimized with graph theory measures of simultaneously eliciting fMRI functional connectivity between frontal, parietal, and temporal cortices. The distinct neural mechanisms associated with each optimized frequency-microstate were analyzed using microstate-informed fMRI. Finally, these optimized, quasi-stable frequency microstates were employed to train and validate the attention-based Long Short-Term Memory (LSTM) time-series architecture for classifying distinct temporal cortical communications of the target from other cognitive tasks. The temporal, sliding input sampling windows were chosen between 180 to 750 ms/segment based on the stability of transition probabilities of the optimized microstates. The results revealed 12 distinct frequency microstates capable of deciphering target detections' temporal cortical communications from other task engagements. Particularly, fMRI functional connectivity measures of target engagement were observed significantly correlated with the right-diagonal delta (r = 0.31), anterior–posterior theta (r = 0.35), left–right theta (r = − 0.32), alpha (r = − 0.31) microstates. Further, neuro-vascular information of microstate-informed fMRI analysis revealed the association of delta/theta and alpha/beta microstates with cortical communications and local neural processing, respectively. The classification accuracies of the attention-based LSTM were higher than the traditional LSTM architectures, particularly the frameworks that sampled the EEG data with a temporal width of 300 ms/segment. In conclusion, the study demonstrates reliable temporal classifications of global cortical communication of distinct tasks using an attention-based LSTM utilizing fMRI functional connectivity optimized quasi-stable frequency microstates.
      PubDate: 2022-10-11
       
  • Machine learning methods for the study of cybersickness: a systematic
           review

    • Abstract: Abstract This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness.
      PubDate: 2022-10-09
       
  • Early detection of Alzheimer’s disease using neuropsychological tests: a
           predict–diagnose approach using neural networks

    • Abstract: Abstract Alzheimer’s disease (AD) is a slowly progressing disease for which there is no known therapeutic cure at present. Ongoing research around the world is actively engaged in the quest for identifying markers that can help predict the future cognitive state of individuals so that measures can be taken to prevent the onset or arrest the progression of the disease. Researchers are interested in both biological and neuropsychological markers that can serve as good predictors of the future cognitive state of individuals. The goal of this study is to identify non-invasive, inexpensive markers and develop neural network models that learn the relationship between those markers and the future cognitive state. To that end, we use the renowned Alzheimer’s Disease Neuroimaging Initiative (ADNI) data for a handful of neuropsychological tests to train Recurrent Neural Network (RNN) models to predict future neuropsychological test results and Multi-Level Perceptron (MLP) models to diagnose the future cognitive states of trial participants based on those predicted results. The results demonstrate that the predicted cognitive states match the actual cognitive states of ADNI test subjects with a high level of accuracy. Therefore, this novel two-step technique can serve as an effective tool for the prediction of Alzheimer’s disease progression. The reliance of the results on inexpensive, non-invasive tests implies that this technique can be used in countries around the world including those with limited financial resources.
      PubDate: 2022-09-27
       
  • RNeuMark: A Riemannian EEG Analysis Framework for Neuromarketing

    • Abstract: Abstract Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. Electroencephalography (EEG) is the most commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported by various signal descriptors, while the quest for profitable novel ways is still an open research question. Here, we suggest the use of sample covariance matrices as alternative descriptors, that encapsulate the coordinated neural activity from distinct brain areas, and the adoption of Riemannian geometry for their handling. We first establish the suitability of Riemannian approach for neuromarketing-related problems and then suggest a relevant decoding scheme for predicting consumers’ choices (e.g., willing to buy or not a specific product). Since the decision-making process involves the concurrent interaction of various cognitive processes and consequently of distinct brain rhythms, the proposed decoder takes the form of an ensemble classifier that builds upon a multi-view perspective, with each view dedicated to a specific frequency band. Adopting a standard machine learning procedure, and using a set of trials (training data) in conjunction with the associated behavior labels (“buy”/ “not buy”), we train a battery of classifiers accordingly. Each classifier is designed to operate in the space recovered from the inter-trial distances of SCMs and to cast a rhythm-depended decision that is eventually combined with the predictions of the rest ones. The demonstration and evaluation of the proposed approach are performed in 2 neuromarketing-related datasets of different nature. The first is employed to showcase the potential of the suggested descriptor, while the second to showcase the decoder’s superiority against popular alternatives in the field.
      PubDate: 2022-09-16
       
  • Epilepsy seizure prediction with few-shot learning method

    • Abstract: Abstract Epileptic seizures prediction and timely alarms allow the patient to take effective and preventive actions. In this paper, a convolutional neural network (CNN) is proposed to diagnose the preictal period. Our goal is for those epileptic patients in whom seizures occur late and it is very challenging to record the preictal signal for them. In the previous works, generalized methods were inevitably used for this group of patients which were not very accurate. Our approach to solve this problem is to provide a few-shot learning method. This method, having the previous knowledge, is trained with only a small number of samples, learns new tasks and reduces the efforts to collect more data. Evaluation results for three patients from the CHB–MIT database, for a 10-min seizure prediction horizon (SPH) and a 20-min seizure occurrence period (SOP), averaged sensitivity of 95.70% and a false prediction rate (FPR) of 0.057/h and for the 5-min prediction horizon and the 25-min seizure occurrence period averaged sensitivity of 98.52% and a false prediction rate of (FPR) of 0.045/h. The proposed few-shot learning method, based on previous knowledge gained from the generalizable method, is regulated with a few new patient samples for the patient. Our results show that the accuracy obtained in this method is higher than the generalizable methods.
      PubDate: 2022-09-16
       
  • A multi-expert ensemble system for predicting Alzheimer transition using
           clinical features

    • Abstract: Abstract Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.
      PubDate: 2022-09-03
       
  • ABOT: an open-source online benchmarking tool for machine learning-based
           artefact detection and removal methods from neuronal signals

    • Abstract: Abstract Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.
      PubDate: 2022-09-01
       
  • SmaRT2P: a software for generating and processing smart line recording
           trajectories for population two-photon calcium imaging

    • Abstract: Abstract Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.
      PubDate: 2022-08-04
       
  • A robust framework to investigate the reliability and stability of
           explainable artificial intelligence markers of Mild Cognitive Impairment
           and Alzheimer’s Disease

    • Abstract: Abstract In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer’s disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of cognitive decline with remarkable results. However, less attention has been devoted to the explainability of these models. In this work, we present a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models. We demonstrate that the SHAP values can accurately characterize how each index affects a patient’s cognitive status. Furthermore, we show that a longitudinal analysis of SHAP values can provide effective information on Alzheimer’s disease progression.
      PubDate: 2022-07-26
       
  • Machine learning-based ABA treatment recommendation and personalization
           for autism spectrum disorder: an exploratory study

    • Abstract: Abstract Autism spectrum is a brain development condition that impairs an individual’s capacity to communicate socially and manifests through strict routines and obsessive–compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81–84%, with a normalized discounted cumulative gain of 79–81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models’ treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.
      PubDate: 2022-07-25
       
  • Classifying oscillatory brain activity associated with Indian Rasas using
           network metrics

    • Abstract: Abstract Neural signatures for the western classification of emotions have been widely discussed in the literature. The ancient Indian treatise on performing arts known as Natyashastra categorizes emotions into nine classes, known as Rasas. Rasa—as opposed to a pure emotion—is defined as a superposition of certain transitory, dominant, and temperamental emotional states. Although Rasas have been widely discussed in the text, dedicated brain imaging studies have not been conducted in their research. Our study examines the neural oscillations, recorded through electroencephalography (EEG) imaging, that are elicited while experiencing emotional states corresponding to Rasas. We identify differences among them using network-based functional connectivity metrics in five different frequency bands. Further, Random Forest models are trained on the extracted network features, and we present our findings based on classifier predictions. We observe slow (delta) and fast brain waves (beta and gamma) exhibited the maximum discriminating features between Rasas, whereas alpha and theta bands showed fewer distinguishable pairs. Out of nine Rasas, Sringaram (love), Bibhatsam (odious), and Bhayanakam (terror) were distinguishable from other Rasas the most across frequency bands. On the scale of most network metrics, Raudram (rage) and Sringaram are on the extremes, which also resulted in their good classification accuracy of 95%. This is reminiscent of the circumplex model where anger and contentment/happiness are on extremes on the pleasant scale. Interestingly, our results are consistent with the previous studies which highlight the significant role of higher frequency oscillations in the classification of emotions, in contrast to the alpha band that has shows non-significant differences across emotions. This research contributes to one of the first attempts to investigate the neural correlates of Rasas. Therefore, the results of this study can potentially guide the explorations into the entrainment of brain oscillations between performers and viewers, which can further lead to better performances and viewer experience.
      PubDate: 2022-07-15
       
  • Efficient emotion recognition using hyperdimensional computing with
           combinatorial channel encoding and cellular automata

    • Abstract: Abstract In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human–computer interactions; however, the large number of input channels (> 200) and modalities (> 3 ) involved in emotion recognition are significantly expensive from a memory perspective. To address this, methods for memory reduction and optimization are proposed, including a novel approach that takes advantage of the combinatorial nature of the encoding process, and an elementary cellular automaton. HDC with early sensor fusion is implemented alongside the proposed techniques achieving two-class multi-modal classification accuracies of > 76% for valence and > 73% for arousal on the multi-modal AMIGOS and DEAP data sets, almost always better than state of the art. The required vector storage is seamlessly reduced by 98% and the frequency of vector requests by at least 1/5. The results demonstrate the potential of efficient hyperdimensional computing for low-power, multi-channeled emotion recognition tasks.
      PubDate: 2022-06-27
       
  • Stroke recovery phenotyping through network trajectory approaches and
           graph neural networks

    • Abstract: Abstract Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which can present challenges for multivariate regression approaches. This has hindered stroke researchers’ ability to achieve an integrated picture of the complex time-evolving interactions among symptoms. Here, we use tools from network science and machine learning that are particularly well-suited to extracting underlying patterns in such data, and may assist in prediction of recovery patterns. To demonstrate the utility of this approach, we analyzed data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns for 11 different neurological domains at 5 discrete time points. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes, characterized both by distinct clusters of symptoms, as well as differing degrees of symptom severity. We then validated our approach using graph neural networks to determine how well our model performed predictively for stratifying patients into these trajectory profiles at early vs. later time points post-stroke. We demonstrate that trajectory profile clustering is an effective method for identifying clinically relevant recovery subtypes in multidimensional longitudinal datasets, and for early prediction of symptom progression subtypes in individual patients. This paper is the first work introducing network trajectory approaches for stroke recovery phenotyping, and is aimed at enhancing the translation of such novel computational approaches for practical clinical application.
      PubDate: 2022-06-19
       
  • ID-Seg: an infant deep learning-based segmentation framework to improve
           limbic structure estimates

    • Abstract: Abstract Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early neurodevelopment. However, segmenting small regions such as limbic structures is challenging due to their low inter-regional contrast and high curvature. MRI studies of the adult brain have successfully applied deep learning techniques to segment limbic structures, and similar deep learning models are being leveraged for infant studies. However, these deep learning-based infant MRI segmentation models have generally been derived from small datasets, and may suffer from generalization problems. Moreover, the accuracy of segmentations derived from these deep learning models relative to more standard Expectation–Maximization approaches has not been characterized. To address these challenges, we leveraged a large, public infant MRI dataset (n = 473) and the transfer-learning technique to first pre-train a deep convolutional neural network model on two limbic structures: amygdala and hippocampus. Then we used a leave-one-out cross-validation strategy to fine-tune the pre-trained model and evaluated it separately on two independent datasets with manual labels. We term this new approach the Infant Deep learning SEGmentation Framework (ID-Seg). ID-Seg performed well on both datasets with a mean dice similarity score (DSC) of 0.87, a mean intra-class correlation (ICC) of 0.93, and a mean average surface distance (ASD) of 0.31 mm. Compared to the Developmental Human Connectome pipeline (dHCP) pipeline, ID-Seg significantly improved segmentation accuracy. In a third infant MRI dataset (n = 50), we used ID-Seg and dHCP separately to estimate amygdala and hippocampus volumes and shapes. The estimates derived from ID-seg, relative to those from the dHCP, showed stronger associations with behavioral problems assessed in these infants at age 2. In sum, ID-Seg consistently performed well on two different datasets with an 0.87 DSC, however, multi-site testing and extension for brain regions beyond the amygdala and hippocampus are still needed.
      PubDate: 2022-05-28
       
  • Seizure classification with selected frequency bands and EEG montages: a
           Natural Language Processing approach

    • Abstract: Abstract Individualized treatment is crucial for epileptic patients with different types of seizures. The differences among patients impact the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection can ease the manual time-consuming and labor-intensive procedure for diagnose seizure in the clinical setting. In this paper, we present an electroencephalography (EEG) frequency bands (sub-bands) and montages selection (sub-zones) method for classifier training that exploits Natural Language Processing from individual patients’ clinical report. The proposed approach is targeting for individualized treatment. We integrated the prior knowledge from patient’s reports into the classifier-building process, mimicking the authentic thinking process of experienced neurologist’s when diagnosing seizure using EEG. The keywords from clinical documents are mapped to the EEG data in terms of frequency bands and scalp EEG electrodes. The data of experiments are from the Temple University Hospital EEG seizure corpus, and the dataset is divided based on each group of patients with same seizure type and same recording electrode references. The classifier includes Random Forest, Support Vector Machine and Multi-Layer Perceptron. The classification performance indicates that competitive results can be achieve with a small portion of EEG the data. Using the sub-zones selection for Generalized Seizures (GNSZ) on all three electrodes, data are reduced by nearly 50% while the performance metrics remain at the same level with the whole frequency and zones. Moreover, when selecting by sub-zones and sub-bands together for GNSZ with Linked Ears reference, the data range reduced to 0.3% of whole range, and the performance deviates less than 3% from the results with whole range of data. Results show that using proposed approach may lead to more efficient implementations of the seizure classifier to be executed on power-efficient devices for long lasting real-time seizures detection.
      PubDate: 2022-05-27
       
  • Smart imaging to empower brain-wide neuroscience at single-cell levels

    • Abstract: Abstract A deep understanding of the neuronal connectivity and networks with detailed cell typing across brain regions is necessary to unravel the mechanisms behind the emotional and memorial functions as well as to find the treatment of brain impairment. Brain-wide imaging with single-cell resolution provides unique advantages to access morphological features of a neuron and to investigate the connectivity of neuron networks, which has led to exciting discoveries over the past years based on animal models, such as rodents. Nonetheless, high-throughput systems are in urgent demand to support studies of neural morphologies at larger scale and more detailed level, as well as to enable research on non-human primates (NHP) and human brains. The advances in artificial intelligence (AI) and computational resources bring great opportunity to ‘smart’ imaging systems, i.e., to automate, speed up, optimize and upgrade the imaging systems with AI and computational strategies. In this light, we review the important computational techniques that can support smart systems in brain-wide imaging at single-cell resolution.
      PubDate: 2022-05-11
       
  • Individual differences in skill acquisition and transfer assessed by dual
           task training performance and brain activity

    • Abstract: Abstract Assessment of expertise development during training program primarily consists of evaluating interactions between task characteristics, performance, and mental load. Such a traditional assessment framework may lack consideration of individual characteristics when evaluating training on complex tasks, such as driving and piloting, where operators are typically required to execute multiple tasks simultaneously. Studies have already identified individual characteristics arising from intrinsic, context, strategy, personality, and preference as common predictors of performance and mental load. Therefore, this study aims to investigate the effect of individual difference in skill acquisition and transfer using an ecologically valid dual task, behavioral, and brain activity measures. Specifically, we implemented a search and surveillance task (scanning and identifying targets) using a high-fidelity training simulator for the unmanned aircraft sensor operator, acquired behavioral measures (scan, not scan, over scan, and adaptive target find scores) using simulator-based analysis module, and measured brain activity changes (oxyhemoglobin and deoxyhemoglobin) from the prefrontal cortex (PFC) using a portable functional near-infrared spectroscopy (fNIRS) sensor array. The experimental protocol recruited 13 novice participants and had them undergo three easy and two hard sessions to investigate skill acquisition and transfer, respectively. Our results from skill acquisition sessions indicated that performance on both tasks did not change when individual differences were not accounted for. However inclusion of individual differences indicated that some individuals improved only their scan performance (Attention-focused group), while others improved only their target find performance (Accuracy-focused group). Brain activity changes during skill acquisition sessions showed that mental load decreased in the right anterior medial PFC (RAMPFC) in both groups regardless of individual differences. However, mental load increased in the left anterior medial PFC (LAMPFC) of Attention-focused group and decreased in the Accuracy-focused group only when individual differences were included. Transfer results showed no changes in performance regardless of grouping based on individual differences; however, mental load increased in RAMPFC of Attention-focused group and left dorsolateral PFC (LDLPFC) of Accuracy-focused group. Efficiency and involvement results suggest that the Attention-focused group prioritized the scan task, while the Accuracy-focused group prioritized the target find task. In conclusion, training on multitasks results in individual differences. These differences may potentially be due to individual preference. Future studies should incorporate individual differences while assessing skill acquisition and transfer during multitask training.
      PubDate: 2022-04-02
       
  • Hierarchical intrinsically motivated agent planning behavior with dreaming
           in grid environments

    • Abstract: Biologically plausible models of learning may provide a crucial insight for building autonomous intelligent agents capable of performing a wide range of tasks. In this work, we propose a hierarchical model of an agent operating in an unfamiliar environment driven by a reinforcement signal. We use temporal memory to learn sparse distributed representation of state–actions and the basal ganglia model to learn effective action policy on different levels of abstraction. The learned model of the environment is utilized to generate an intrinsic motivation signal, which drives the agent in the absence of the extrinsic signal, and through acting in imagination, which we call dreaming. We demonstrate that the proposed architecture enables an agent to effectively reach goals in grid environments.
      PubDate: 2022-04-02
       
 
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