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  Subjects -> PSYCHOLOGY (Total: 877 journals)
Showing 1 - 174 of 174 Journals sorted alphabetically
Acción Psicológica     Open Access   (Followers: 2)
Acta Colombiana de Psicología     Open Access   (Followers: 4)
Acta Comportamentalia     Open Access   (Followers: 3)
Acta de Investigación Psicológica     Open Access   (Followers: 2)
Acta Psychologica     Hybrid Journal   (Followers: 23)
Activités     Open Access   (Followers: 1)
Actualidades en Psicologia     Open Access   (Followers: 1)
Ad verba Liberorum : Journal of Linguistics & Pedagogy & Psychology     Open Access   (Followers: 8)
Addictive Behaviors Reports     Open Access   (Followers: 5)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 20)
ADHD Report The     Full-text available via subscription   (Followers: 6)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 40)
Advances in Mental Health     Hybrid Journal   (Followers: 73)
Advances in Physiotherapy     Hybrid Journal   (Followers: 56)
Advances in Psychology     Full-text available via subscription   (Followers: 60)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 30)
African Journal of Cross-Cultural Psychology and Sport Facilitation     Full-text available via subscription   (Followers: 3)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 409)
Aggressive Behavior     Hybrid Journal   (Followers: 15)
Aging, Neuropsychology, and Cognition     Hybrid Journal   (Followers: 33)
Ágora - studies in psychoanalytic theory     Open Access   (Followers: 3)
Aletheia     Open Access   (Followers: 1)
American Behavioral Scientist     Hybrid Journal   (Followers: 16)
American Imago     Full-text available via subscription   (Followers: 3)
American Journal of Applied Psychology     Open Access   (Followers: 35)
American Journal of Community Psychology     Hybrid Journal   (Followers: 23)
American Journal of Health Behavior     Full-text available via subscription   (Followers: 23)
American Journal of Orthopsychiatry     Hybrid Journal   (Followers: 4)
American Journal of Psychoanalysis     Hybrid Journal   (Followers: 21)
American Psychologist     Full-text available via subscription   (Followers: 182)
Anales de Psicología     Open Access   (Followers: 2)
Análise Psicológica     Open Access   (Followers: 1)
Análisis y Modificación de Conducta     Open Access   (Followers: 2)
Analysis     Full-text available via subscription   (Followers: 4)
Annual Review of Clinical Psychology     Full-text available via subscription   (Followers: 68)
Annual Review of Organizational Psychology and Organizational Behavior     Full-text available via subscription   (Followers: 29)
Annual Review of Psychology     Full-text available via subscription   (Followers: 218)
Anuario de Psicología / The UB Journal of Psychology     Open Access   (Followers: 1)
Anuario de Psicología Jurídica     Open Access   (Followers: 1)
Anxiety, Stress & Coping: An International Journal     Hybrid Journal   (Followers: 23)
Applied and Preventive Psychology     Hybrid Journal   (Followers: 13)
Applied Cognitive Psychology     Hybrid Journal   (Followers: 69)
Applied Neuropsychology : Adult     Hybrid Journal   (Followers: 32)
Applied Neuropsychology : Child     Hybrid Journal   (Followers: 18)
Applied Psychological Measurement     Hybrid Journal   (Followers: 17)
Applied Psychology     Hybrid Journal   (Followers: 143)
Applied Psychology: Health and Well-Being     Hybrid Journal   (Followers: 48)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8)
Archive for the Psychology of Religion / Archiv für Religionspychologie     Hybrid Journal   (Followers: 20)
Archives of Clinical Neuropsychology     Hybrid Journal   (Followers: 26)
Archives of Scientific Psychology     Open Access   (Followers: 4)
Arquivos Brasileiros de Psicologia     Open Access   (Followers: 1)
Asia Pacific Journal of Counselling and Psychotherapy     Hybrid Journal   (Followers: 8)
Asia-Pacific Psychiatry     Hybrid Journal   (Followers: 3)
Asian American Journal of Psychology     Full-text available via subscription   (Followers: 5)
Asian Journal of Business Ethics     Hybrid Journal   (Followers: 7)
Assessment     Hybrid Journal   (Followers: 10)
At-Tajdid : Jurnal Ilmu Tarbiyah     Open Access   (Followers: 2)
Attachment: New Directions in Psychotherapy and Relational Psychoanalysis     Full-text available via subscription   (Followers: 16)
Attention, Perception & Psychophysics     Full-text available via subscription   (Followers: 10)
Australian and Aotearoa New Zealand Psychodrama Association Journal     Full-text available via subscription  
Australian Educational and Developmental Psychologist, The     Full-text available via subscription   (Followers: 6)
Australian Journal of Psychology     Hybrid Journal   (Followers: 19)
Australian Psychologist     Hybrid Journal   (Followers: 11)
Autism Research     Hybrid Journal   (Followers: 31)
Autism Research and Treatment     Open Access   (Followers: 29)
Autism's Own     Open Access  
Autism-Open Access     Open Access   (Followers: 5)
Avaliação Psicológica     Open Access  
Avances en Psicologia Latinoamericana     Open Access   (Followers: 1)
Aviation Psychology and Applied Human Factors     Hybrid Journal   (Followers: 19)
Balint Journal     Hybrid Journal   (Followers: 3)
Barbaroi     Open Access  
Basic and Applied Social Psychology     Hybrid Journal   (Followers: 36)
Behavior Analysis in Practice     Full-text available via subscription   (Followers: 6)
Behavior Analysis: Research and Practice     Full-text available via subscription   (Followers: 2)
Behavior Analyst     Hybrid Journal   (Followers: 4)
Behavior Modification     Hybrid Journal   (Followers: 10)
Behavior Research Methods     Hybrid Journal   (Followers: 18)
Behavior Therapy     Hybrid Journal   (Followers: 47)
Behavioral Development Bulletin     Full-text available via subscription  
Behavioral Interventions     Hybrid Journal   (Followers: 9)
Behavioral Neuroscience     Full-text available via subscription   (Followers: 53)
Behavioral Sciences & the Law     Hybrid Journal   (Followers: 23)
Behavioral Sleep Medicine     Hybrid Journal   (Followers: 6)
Behaviormetrika     Hybrid Journal  
Behaviour     Hybrid Journal   (Followers: 12)
Behaviour Research and Therapy     Hybrid Journal   (Followers: 17)
Behavioural and Cognitive Psychotherapy     Hybrid Journal   (Followers: 131)
Behavioural Processes     Hybrid Journal   (Followers: 7)
Biofeedback     Hybrid Journal   (Followers: 4)
BioPsychoSocial Medicine     Open Access   (Followers: 6)
BMC Psychology     Open Access   (Followers: 16)
Body, Movement and Dance in Psychotherapy: An International Journal for Theory, Research and Practice     Hybrid Journal   (Followers: 9)
Boletim Academia Paulista de Psicologia     Open Access  
Boletim de Psicologia     Open Access  
Brain Informatics     Open Access   (Followers: 1)
British Journal of Clinical Psychology     Full-text available via subscription   (Followers: 138)
British Journal of Developmental Psychology     Full-text available via subscription   (Followers: 36)
British Journal of Educational Psychology     Hybrid Journal   (Followers: 32)
British Journal of Health Psychology     Full-text available via subscription   (Followers: 42)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 21)
British Journal of Psychology     Full-text available via subscription   (Followers: 59)
British Journal of Psychotherapy     Hybrid Journal   (Followers: 65)
British Journal of Social Psychology     Full-text available via subscription   (Followers: 33)
Burnout Research     Open Access   (Followers: 7)
Cadernos de psicanálise (Rio de Janeiro)     Open Access  
Cadernos de Psicologia Social do Trabalho     Open Access  
Canadian Art Therapy Association     Hybrid Journal  
Canadian Journal of Behavioural Science     Full-text available via subscription   (Followers: 5)
Canadian Journal of Experimental Psychology     Full-text available via subscription   (Followers: 14)
Canadian Psychology / Psychologie canadienne     Full-text available via subscription   (Followers: 11)
Case Studies in Sport and Exercise Psychology     Hybrid Journal  
Cendekia : Jurnal Kependidikan dan Kemasyarakatan     Open Access  
Child Development Perspectives     Hybrid Journal   (Followers: 27)
Child Development Research     Open Access   (Followers: 15)
Ciencia Cognitiva     Open Access   (Followers: 2)
Ciencia e Interculturalidad     Open Access  
Ciências & Cognição     Open Access  
Ciencias Psicológicas     Open Access  
Clínica y Salud     Open Access  
Clinical Medicine Insights : Psychiatry     Open Access   (Followers: 10)
Clinical Practice in Pediatric Psychology     Full-text available via subscription   (Followers: 10)
Clinical Psychological Science     Hybrid Journal   (Followers: 11)
Clinical Psychologist     Hybrid Journal   (Followers: 16)
Clinical Psychology & Psychotherapy     Hybrid Journal   (Followers: 69)
Clinical Psychology and Special Education     Open Access   (Followers: 1)
Clinical Psychology Review     Hybrid Journal   (Followers: 35)
Clinical Psychology: Science and Practice     Hybrid Journal   (Followers: 20)
Clinical Schizophrenia & Related Psychoses     Full-text available via subscription   (Followers: 8)
Coaching Psykologi - The Danish Journal of Coaching Psychology     Open Access   (Followers: 1)
Cogent Psychology     Open Access  
Cógito     Open Access  
Cognition & Emotion     Hybrid Journal   (Followers: 35)
Cognitive Behaviour Therapy     Hybrid Journal   (Followers: 14)
Cognitive Neuropsychology     Hybrid Journal   (Followers: 28)
Cognitive Psychology     Hybrid Journal   (Followers: 63)
Cognitive Research : Principles and Implications     Open Access   (Followers: 1)
Consciousness and Cognition     Hybrid Journal   (Followers: 27)
Construção Psicopedagógica     Open Access  
Consulting Psychology Journal : Practice and Research     Full-text available via subscription   (Followers: 3)
Contagion : Journal of Violence, Mimesis, and Culture     Full-text available via subscription   (Followers: 7)
Contemporary Educational Psychology     Hybrid Journal   (Followers: 22)
Contemporary School Psychology     Hybrid Journal   (Followers: 4)
Contextos Clínicos     Open Access  
Counseling Outcome Research and Evaluation     Hybrid Journal   (Followers: 10)
Counseling Psychologist     Hybrid Journal   (Followers: 14)
Counseling Psychology and Psychotherapy     Open Access   (Followers: 7)
Counselling and Psychotherapy Research : Linking research with practice     Hybrid Journal   (Followers: 21)
Counselling and Values     Hybrid Journal   (Followers: 2)
Counselling Psychology Quarterly     Hybrid Journal   (Followers: 10)
Couple and Family Psychoanalysis     Full-text available via subscription   (Followers: 1)
Couple and Family Psychology : Research and Practice     Full-text available via subscription   (Followers: 4)
Creativity Research Journal     Hybrid Journal   (Followers: 20)
Creativity. Theories - Research - Applications     Open Access   (Followers: 1)
Criminal Justice Ethics     Hybrid Journal   (Followers: 8)
Cuadernos de Neuropsicología     Open Access   (Followers: 1)
Cuadernos de Psicologia del Deporte     Open Access  
Cuadernos de Psicopedagogía     Open Access  
Cultural Diversity and Ethnic Minority Psychology     Full-text available via subscription   (Followers: 12)
Cultural-Historical Psychology     Open Access  
Culturas Psi     Open Access  
Culture and Brain     Hybrid Journal   (Followers: 4)
Current Addiction Reports     Hybrid Journal   (Followers: 10)
Current Behavioral Neuroscience Reports     Hybrid Journal   (Followers: 2)
Current Directions In Psychological Science     Hybrid Journal   (Followers: 49)
Current Opinion in Behavioral Sciences     Hybrid Journal   (Followers: 1)
Current Opinion in Psychology     Hybrid Journal   (Followers: 4)
Current Psychological Research     Hybrid Journal   (Followers: 13)
Current Psychology     Hybrid Journal   (Followers: 15)
Current psychology letters     Open Access   (Followers: 2)
Current Research in Psychology     Open Access   (Followers: 21)
Cyberpsychology, Behavior, and Social Networking     Hybrid Journal   (Followers: 14)
Decision     Full-text available via subscription   (Followers: 2)
Depression and Anxiety     Hybrid Journal   (Followers: 15)
Depression Research and Treatment     Open Access   (Followers: 13)
Developmental Cognitive Neuroscience     Open Access   (Followers: 17)
Developmental Neuropsychology     Hybrid Journal   (Followers: 15)
Developmental Psychobiology     Hybrid Journal   (Followers: 9)
Developmental Psychology     Full-text available via subscription   (Followers: 45)
Diagnostica     Hybrid Journal   (Followers: 2)
Dialectica     Hybrid Journal   (Followers: 1)
Discourse     Full-text available via subscription   (Followers: 9)
Diversitas: Perspectivas en Psicologia     Open Access  
Drama Therapy Review     Hybrid Journal   (Followers: 1)
Dreaming     Full-text available via subscription   (Followers: 11)
Drogues, santé et société     Full-text available via subscription  
Dynamics of Asymmetric Conflict: Pathways toward terrorism and genocide     Hybrid Journal   (Followers: 13)
Ecopsychology     Hybrid Journal   (Followers: 6)
ECOS - Estudos Contemporâneos da Subjetividade     Open Access  
Educational Psychology Review     Hybrid Journal   (Followers: 27)
Educational Psychology: An International Journal of Experimental Educational Psychology     Hybrid Journal   (Followers: 47)
Educazione sentimentale     Full-text available via subscription  
Electronic Journal of Research in Educational Psychology     Open Access   (Followers: 7)
Elpis - Czasopismo Teologiczne Katedry Teologii Prawosławnej Uniwersytetu w Białymstoku     Open Access  
Emotion     Full-text available via subscription   (Followers: 32)
Emotion Review     Hybrid Journal   (Followers: 18)
En-Claves del pensamiento     Open Access   (Followers: 1)
Enseñanza e Investigacion en Psicologia     Open Access  
Epiphany     Open Access   (Followers: 3)

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Journal Cover Brain Informatics
  [1 followers]  Follow
  This is an Open Access Journal Open Access journal
   ISSN (Print) 2198-4018 - ISSN (Online) 2198-4026
   Published by SpringerOpen Homepage  [224 journals]
  • Optimized statistical parametric mapping procedure for NIRS data
           contaminated by motion artifacts

    • Abstract: Abstract This study investigated the spatial distribution of brain activity on body schema (BS) modification induced by natural body motion using two versions of a hand-tracing task. In Task 1, participants traced Japanese Hiragana characters using the right forefinger, requiring no BS expansion. In Task 2, participants performed the tracing task with a long stick, requiring BS expansion. Spatial distribution was analyzed using general linear model (GLM)-based statistical parametric mapping of near-infrared spectroscopy data contaminated with motion artifacts caused by the hand-tracing task. Three methods were utilized in series to counter the artifacts, and optimal conditions and modifications were investigated: a model-free method (Step 1), a convolution matrix method (Step 2), and a boxcar-function-based Gaussian convolution method (Step 3). The results revealed four methodological findings: (1) Deoxyhemoglobin was suitable for the GLM because both Akaike information criterion and the variance against the averaged hemodynamic response function were smaller than for other signals, (2) a high-pass filter with a cutoff frequency of .014 Hz was effective, (3) the hemodynamic response function computed from a Gaussian kernel function and its first- and second-derivative terms should be included in the GLM model, and (4) correction of non-autocorrelation and use of effective degrees of freedom were critical. Investigating z-maps computed according to these guidelines revealed that contiguous areas of BA7–BA40–BA21 in the right hemisphere became significantly activated ( \(t(15); p<.001\) , \(p<.01\) , and \(p<.001\) , respectively) during BS modification while performing the hand-tracing task.
      PubDate: 2017-07-29
  • Emotion recognition based on EEG features in movie clips with channel

    • Abstract: Abstract Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain–computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.
      PubDate: 2017-07-15
  • Spreading activation in emotional memory networks and the cumulative
           effects of somatic markers

    • Abstract: Abstract The theory of spreading activation proposes that the activation of a semantic memory node may spread along bidirectional associative links to other related nodes. Although this theory was originally proposed to explain semantic memory networks, a similar process may be said to exist with episodic or emotional memory networks. The Somatic Marker hypothesis proposes that remembering an emotional memory activates the somatic sensations associated with the memory. An integration of these two models suggests that as spreading activation in emotional memory networks increases, a greater number of associated somatic markers would become activated. This process would then result in greater changes in physiological functioning. We sought to investigate this possibility by having subjects recall words associated with sad and happy memories, in addition to a neutral condition. The average ages of the memories and the number of word memories recalled were then correlated with measures of heart rate and skin conductance. The results indicated significant positive correlations between the number of happy word memories and heart rate (r = .384, p = .022) and between the average ages of the sad memories and skin conductance (r = .556, p = .001). Unexpectedly, a significant negative relationship was found between the number of happy word memories and skin conductance (r = −.373, p = .025). The results provide partial support for our hypothesis, indicating that increasing spreading activation in emotional memory networks activates an increasing number of somatic markers and this is then reflected in greater physiological activity at the time of recalling the memories.
      PubDate: 2017-06-01
  • Brain connectivity during encoding and retrieval of spatial information:
           individual differences in navigation skills

    • Abstract: Abstract Emerging evidence suggests that the variations in the ability to navigate through any real or virtual environment are accompanied by distinct underlying cortical activations in multiple regions of the brain. These activations may appear due to the use of different frame of reference (FOR) for representing an environment. The present study investigated the brain dynamics in the good and bad navigators using Graph Theoretical analysis applied to low-density electroencephalography (EEG) data. Individual navigation skills were rated according to the performance in a virtual reality (VR)-based navigation task and the effect of navigator's proclivity towards a particular FOR on the navigation performance was explored. Participants were introduced to a novel virtual environment that they learned from a first-person or an aerial perspective and were subsequently assessed on the basis of efficiency with which they learnt and recalled. The graph theoretical parameters, path length (PL), global efficiency (GE), and clustering coefficient (CC) were computed for the functional connectivity network in the theta and alpha frequency bands. During acquisition of the spatial information, good navigators were distinguished by a lower degree of dispersion in the functional connectivity compared to the bad navigators. Within the groups of good and bad navigators, better performers were characterised by the formation of multiple hubs at various sites and the percentage of connectivity or small world index. The proclivity towards a specific FOR during exploration of a new environment was not found to have any bearing on the spatial learning. These findings may have wider implications for how the functional connectivity in the good and bad navigators differs during spatial information acquisition and retrieval in the domains of rescue operations and defence systems.
      PubDate: 2017-05-16
  • The effect of anger expression style on cardiovascular responses to
           lateralized cognitive stressors

    • Abstract: Abstract To determine the effects of self-reported anger expression style on cerebrally lateralized physiological responses to neuropsychological stressors, changes in systolic blood pressure and heart rate were examined in response to a verbal fluency task and a figural fluency task among individuals reporting either “anger in” or “anger out” expression styles. Significant group by trial interaction effects was found for systolic blood pressure following administration of verbal fluency [F(1,54) = 5.86, p < 0.05] and nonverbal fluency stressors [F(1,54) = 13.68, p < .001]. Similar interactions were seen for systolic heart rate following administration of verbal fluency [F(1,54) = 5.86, p < .005] and nonverbal fluency stressors [F(1,54) = 13.68, p < .001]. The corresponding results are discussed in terms of functional cerebral systems and potential implications for physiological models of anger. Given the association between anger and negative physical health outcomes, there is a clear need to better understand the physiological components of anger. The results of this experiment indicate that a repressive “anger in” expression style is associated with deregulation of the right frontal region. This same region has been shown to be intimately involved in cardiovascular recovery, glucose metabolism, and blood pressure regulation.
      PubDate: 2017-05-15
  • Multiscale modeling in the clinic: diseases of the brain and nervous

    • Abstract: Abstract Computational neuroscience is a field that traces its origins to the efforts of Hodgkin and Huxley, who pioneered quantitative analysis of electrical activity in the nervous system. While also continuing as an independent field, computational neuroscience has combined with computational systems biology, and neural multiscale modeling arose as one offshoot. This consolidation has added electrical, graphical, dynamical system, learning theory, artificial intelligence and neural network viewpoints with the microscale of cellular biology (neuronal and glial), mesoscales of vascular, immunological and neuronal networks, on up to macroscales of cognition and behavior. The complexity of linkages that produces pathophysiology in neurological, neurosurgical and psychiatric disease will require multiscale modeling to provide understanding that exceeds what is possible with statistical analysis or highly simplified models: how to bring together pharmacotherapeutics with neurostimulation, how to personalize therapies, how to combine novel therapies with neurorehabilitation, how to interlace periodic diagnostic updates with frequent reevaluation of therapy, how to understand a physical disease that manifests as a disease of the mind. Multiscale modeling will also help to extend the usefulness of animal models of human diseases in neuroscience, where the disconnects between clinical and animal phenomenology are particularly pronounced. Here we cover areas of particular interest for clinical application of these new modeling neurotechnologies, including epilepsy, traumatic brain injury, ischemic disease, neurorehabilitation, drug addiction, schizophrenia and neurostimulation.
      PubDate: 2017-05-09
  • Preoperative prediction of language function by diffusion tensor imaging

    • Abstract: Abstract For surgery of eloquent tumors in language areas, the accepted gold standard is functional mapping through direct cortical stimulation (DCS) in awake patients. Ever since, neuroscientists are searching for reliable noninvasive detection of function in the human brain, with variable success. The potential of diffusion tensor imaging (DTI) in combination with computational cortical parcellation to predict functional areas in language eloquent tumors has not been assessed so far. We present a proof-of-concept report involving awake surgery for a temporodorsal tumor. Postoperatively, the imaging was extensively studied and a predictive value of multimodal MR imaging for the possible extent of resection was analyzed. After resection using DCS, the extent of resection and functional outcome were correlated with the processed imaging. Preoperative imaging of our patient was taken to compute the lesion volume as a seed for tractography (DTI) and combined with a tractography of the entire hemisphere. For better spatial resolution, an elastic image fusion was performed to correct the distortion of DTI data. After subtotal resection and imaging analysis, the status of the superior part of the lesion could be identified and predicted as functional cortex. There was a strong correlation between the tumor remnant during surgery and the imaging parameters of DTI connectivity of the eloquent tissue. A combination of complex DTI processing may be able to predict function in a patient suffering eloquent brain tumors and thus allow estimation of extent of resection.
      PubDate: 2017-05-04
  • Machine learning–XGBoost analysis of language networks to classify
           patients with epilepsy

    • Abstract: Abstract Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing ‘atypical’ (compared to ‘typical’ in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures. The neurosurgeon should only remove the zone generating seizures and must preserve cognitive functions to avoid deficits. To preserve functions, one should know how they are represented in the patient’s brain, which is in general different from that of healthy subjects. For this purpose, in the pre-surgical stage, robust and efficient methods are required to identify atypical from typical representations. Given the frequent location of regions generating seizures in the vicinity of language networks, one important function to be considered is language. The risk of language impairment after surgery is determined pre-surgically by mapping language networks. In clinical settings, cognitive mapping is classically performed with fMRI. The fMRI analyses allowing the identification of atypical patterns of language networks in patients are not sufficiently robust and require additional statistic approaches. In this study, we report the use of a statistical nonlinear machine learning classification, the Extreme Gradient Boosting (XGBoost) algorithm, to identify atypical patterns and classify 55 participants as healthy subjects or patients with epilepsy. XGBoost analyses were based on neurophysiological features in five language regions (three frontal and two temporal) in both hemispheres and activated with fMRI for a phonological (PHONO) and a semantic (SEM) language task. These features were combined into 135 cognitively plausible subsets and further submitted to selection and binary classification. Classification performance was scored with the Area Under the receiver operating characteristic curve (AUC). Our results showed that the subset SEM_LH BA_47-21 (left fronto-temporal activation induced by the SEM task) provided the best discrimination between the two groups (AUC of 91 ± 5%). The results are discussed in the framework of the current debates of language reorganization in focal epilepsy.
      PubDate: 2017-04-22
  • Fast assembling of neuron fragments in serial 3D sections

    • Abstract: Abstract Reconstructing neurons from 3D image-stacks of serial sections of thick brain tissue is very time-consuming and often becomes a bottleneck in high-throughput brain mapping projects. We developed NeuronStitcher, a software suite for stitching non-overlapping neuron fragments reconstructed in serial 3D image sections. With its efficient algorithm and user-friendly interface, NeuronStitcher has been used successfully to reconstruct very large and complex human and mouse neurons.
      PubDate: 2017-04-01
  • An ontology-based search engine for digital reconstructions of neuronal

    • Abstract: Abstract Neuronal morphology is extremely diverse across and within animal species, developmental stages, brain regions, and cell types. This diversity is functionally important because neuronal structure strongly affects synaptic integration, spiking dynamics, and network connectivity. Digital reconstructions of axonal and dendritic arbors are thus essential to quantify and model information processing in the nervous system. NeuroMorpho.Org is an established repository containing tens of thousands of digitally reconstructed neurons shared by several hundred laboratories worldwide. Each neuron is annotated with specific metadata based on the published references and additional details provided by data owners. The number of represented metadata concepts has grown over the years in parallel with the increase of available data. Until now, however, the lack of standardized terminologies and of an adequately structured metadata schema limited the effectiveness of user searches. Here we present a new organization of NeuroMorpho.Org metadata grounded on a set of interconnected hierarchies focusing on the main dimensions of animal species, anatomical regions, and cell types. We have comprehensively mapped each metadata term in NeuroMorpho.Org to this formal ontology, explicitly resolving all ambiguities caused by synonymy and homonymy. Leveraging this consistent framework, we introduce OntoSearch, a powerful functionality that seamlessly enables retrieval of morphological data based on expert knowledge and logical inferences through an intuitive string-based user interface with auto-complete capability. In addition to returning the data directly matching the search criteria, OntoSearch also identifies a pool of possible hits by taking into consideration incomplete metadata annotation.
      PubDate: 2017-03-23
  • It’s not what you expect: feedback negativity is independent of reward
           expectation and affective responsivity in a non-probabilistic task

    • Abstract: Abstract ERP studies commonly utilize gambling-based reinforcement tasks to elicit feedback negativity (FN) responses. This study used a pattern learning task in order to limit gambling-related fallacious reasoning and possible affective responses to gambling, while investigating relationships between the FN components between high and low reward expectation conditions. Eighteen undergraduates completed measures of reinforcement sensitivity, trait and state affect, and psychophysiological recording. The pattern learning task elicited a FN component for both high and low win expectancy conditions, which was found to be independent of reward expectation and showed little relationship with task and personality variables. We also observed a P3 component, which showed sensitivity to outcome expectancy variation and relationships to measures of anxiety, appetitive motivation, and cortical asymmetry, although these varied by electrode location and expectancy condition. Findings suggest that the FN reflected a binary reward-related signal, with little relationship to reward expectation found in previous studies, in the absence of positive affective responses.
      PubDate: 2017-03-01
  • Name-calling in the hippocampus (and beyond): coming to terms with neuron
           types and properties

    • Abstract: Abstract Widely spread naming inconsistencies in neuroscience pose a vexing obstacle to effective communication within and across areas of expertise. This problem is particularly acute when identifying neuron types and their properties. is a web-accessible neuroinformatics resource that organizes existing data about essential properties of all known neuron types in the rodent hippocampal formation. links evidence supporting the assignment of a property to a type with direct pointers to quotes and figures. Mining this knowledge from peer-reviewed reports reveals the troubling extent of terminological ambiguity and undefined terms. Examples span simple cases of using multiple synonyms and acronyms for the same molecular biomarkers (or other property) to more complex cases of neuronal naming. New publications often use different terms without mapping them to previous terms. As a result, neurons of the same type are assigned disparate names, while neurons of different types are bestowed the same name. Furthermore, non-unique properties are frequently used as names, and several neuron types are not named at all. In order to alleviate this nomenclature confusion regarding hippocampal neuron types and properties, we introduce a new functionality of a fully searchable, curated catalog of human and machine-readable definitions, each linked to the corresponding neuron and property terms. Furthermore, we extend our robust approach to providing each neuron type with an informative name and unique identifier by mapping all encountered synonyms and homonyms.
      PubDate: 2017-03-01
  • Familiarity effects in EEG-based emotion recognition

    • Abstract: Abstract Although emotion detection using electroencephalogram (EEG) data has become a highly active area of research over the last decades, little attention has been paid to stimulus familiarity, a crucial subjectivity issue. Using both our experimental data and a sophisticated database (DEAP dataset), we investigated the effects of familiarity on brain activity based on EEG signals. Focusing on familiarity studies, we allowed subjects to select the same number of familiar and unfamiliar songs; both resulting datasets demonstrated the importance of reporting self-emotion based on the assumption that the emotional state when experiencing music is subjective. We found evidence that music familiarity influences both the power spectra of brainwaves and the brain functional connectivity to a certain level. We conducted an additional experiment using music familiarity in an attempt to recognize emotional states; our empirical results suggested that the use of only songs with low familiarity levels can enhance the performance of EEG-based emotion classification systems that adopt fractal dimension or power spectral density features and support vector machine, multilayer perceptron or C4.5 classifier. This suggests that unfamiliar songs are most appropriate for the construction of an emotion recognition system.
      PubDate: 2017-03-01
  • Two-dimensional enrichment analysis for mining high-level imaging genetic

    • Abstract: Abstract Enrichment analysis has been widely applied in the genome-wide association studies, where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS–BC pair is enriched in a list of gene–QT findings. Using gene expression data from Allen Human Brain Atlas and imaging genetics data from Alzheimer’s Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 25 significant high-level two-dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiological pathways or neurodegenerative diseases, showing the promise of the proposal framework for providing insight into the mechanism of complex diseases.
      PubDate: 2017-03-01
  • Pattern recognition of spectral entropy features for detection of
           alcoholic and control visual ERP’s in multichannel EEGs

    • Abstract: Abstract This paper presents a novel ranking method to select spectral entropy (SE) features that discriminate alcoholic and control visual event-related potentials (ERP’S) in gamma sub-band (30–55 Hz) derived from a 64-channel electroencephalogram (EEG) recording. The ranking is based on a t test statistic that rejects the null hypothesis that the group means of SE values in alcoholics and controls are identical. The SE features with high ranks are indicative of maximal separation between their group means. Various sizes of top ranked feature subsets are evaluated by applying principal component analysis (PCA) and k-nearest neighbor (k-NN) classification. Even though ranking does not influence the performance of classifier significantly with the selection of all 61 active channels, the classification efficiency is directly proportional to the number of principal components (pc). The effect of ranking and PCA on classification is predominantly observed with reduced feature subsets of (N = 25, 15) top ranked features. Results indicate that for N = 25, proposed ranking method improves the k-NN classification accuracy from 91 to 93.87% as the number of pcs increases from 5 to 25. With same number of pcs, the k-NN classifier responds with accuracies of 84.42–91.54% with non-ranked features. Similarly for N = 15 and number of pcs varying from 5 to 15, ranking enhances k-NN detection accuracies from 88.9 to 93.08% as compared to 86.75–91.96% without ranking. This shows that the detection accuracy is increased by 6.5 and 2.8%, respectively, for N = 25, whereas it enhances by 2.2 and 1%, respectively, for N = 15 in comparison with non-ranked features. In the proposed t test ranking method for feature selection, the pcs of only top ranked feature candidates take part in classification process and hence provide better generalization.
      PubDate: 2017-01-21
  • Optshrink LR + S: accelerated fMRI reconstruction using non-convex
           optimal singular value shrinkage

    • Abstract: Abstract This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l 1 minimization. The performance of the proposed method is compared with the existing state-of-the-art algorithms on real fMRI dataset. The proposed OptShrink LR + S method yields good qualitative and quantitative results.
      PubDate: 2017-01-10
  • Test–retest reliability of brain morphology estimates

    • Abstract: Abstract Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. One dataset used a sequence previously optimized for brain morphology analyses and had particularly high reliability. Examining the reliability of morphological measures is critical before the measures can be validly used to investigate inter-individual differences.
      PubDate: 2017-01-05
  • Improved diagonal queue medical image steganography using Chaos theory,
           LFSR, and Rabin cryptosystem

    • Abstract: Abstract In this article, we have proposed an improved diagonal queue medical image steganography for patient secret medical data transmission using chaotic standard map, linear feedback shift register, and Rabin cryptosystem, for improvement of previous technique (Jain and Lenka in Springer Brain Inform 3:39–51, 2016). The proposed algorithm comprises four stages, generation of pseudo-random sequences (pseudo-random sequences are generated by linear feedback shift register and standard chaotic map), permutation and XORing using pseudo-random sequences, encryption using Rabin cryptosystem, and steganography using the improved diagonal queues. Security analysis has been carried out. Performance analysis is observed using MSE, PSNR, maximum embedding capacity, as well as by histogram analysis between various Brain disease stego and cover images.
      PubDate: 2016-09-09
  • Fuzzy clustering-based feature extraction method for mental task

    • Abstract: Abstract A brain computer interface (BCI) is a communication system by which a person can send messages or requests for basic necessities without using peripheral nerves and muscles. Response to mental task-based BCI is one of the privileged areas of investigation. Electroencephalography (EEG) signals are used to represent the brain activities in the BCI domain. For any mental task classification model, the performance of the learning model depends on the extraction of features from EEG signal. In literature, wavelet transform and empirical mode decomposition are two popular feature extraction methods used to analyze a signal having non-linear and non-stationary property. By adopting the virtue of both techniques, a theoretical adaptive filter-based method to decompose non-linear and non-stationary signal has been proposed known as empirical wavelet transform (EWT) in recent past. EWT does not work well for the signals having overlapped in frequency and time domain and failed to provide good features for further classification. In this work, Fuzzy c-means algorithm is utilized along with EWT to handle this problem. It has been observed from the experimental results that EWT along with fuzzy clustering outperforms in comparison to EWT for the EEG-based response to mental task problem. Further, in case of mental task classification, the ratio of samples to features is very small. To handle the problem of small ratio of samples to features, in this paper, we have also utilized three well-known multivariate feature selection methods viz. Bhattacharyya distance (BD), ratio of scatter matrices (SR), and linear regression (LR). The results of experiment demonstrate that the performance of mental task classification has improved considerably by aforesaid methods. Ranking method and Friedman’s statistical test are also performed to rank and compare different combinations of feature extraction methods and feature selection methods which endorse the efficacy of the proposed approach.
      PubDate: 2016-09-03
  • Workload regulation by Sudarshan Kriya: an EEG and ECG perspective

    • Abstract: Abstract Sudarshan Kriya Yoga (SKY) is a type of rhythmic breathing activity, trivially a form of Pranayama that stimulates physical, mental, emotional, and social well-being. The objective of the present work is to verify the effect of meditation in optimizing task efficiency and regulating stress. It builds on to quantitatively answer if SKY will increase workload tolerance for divided attention tasks in the people sank in it. EEG and ECG recordings were taken from a total of twenty-five subjects who had volunteered for the experiment. Subjects were randomly assigned to two groups of ‘control’ and ‘experimental.’ Their objective scores were collected from the experiment based on NASA’s multi-attribute task battery II and was utilized for workload assessment. Both the groups had no prior experience of SKY. The experimental group was provided with an intervention of SKY for a duration of 30 min everyday. Pre- and post-meditation data were acquired from both groups over a period of 30 and 90 days. It was observed that subjective score of workload (WL) was significantly reduced in the experimental group and performance of the subject increased in terms of task performance. Another astute observation included a considerable increase and decrease in the alpha and beta energies and root mean square of the EEG signal for the experimental group and control group, respectively. In addition to this sympathovagal balance index also decreased in experimental group which indicated reduction in stress. SKY had an effect on stress regulation which in turn enhanced their WL tolerance capacity for a particular multitask activity.
      PubDate: 2016-07-18
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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