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  Subjects -> PSYCHOLOGY (Total: 995 journals)
Showing 1 - 174 of 174 Journals sorted alphabetically
Acción Psicológica     Open Access   (Followers: 4)
Acta Colombiana de Psicología     Open Access   (Followers: 6)
Acta Comportamentalia     Open Access   (Followers: 4)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Psychologica     Hybrid Journal   (Followers: 27)
Activités     Open Access   (Followers: 1)
Actualidades en Psicologia     Open Access   (Followers: 2)
Ad verba Liberorum : Journal of Linguistics & Pedagogy & Psychology     Open Access   (Followers: 9)
Addictive Behaviors Reports     Open Access   (Followers: 9)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 25)
ADHD Report The     Full-text available via subscription   (Followers: 12)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 49)
Advances in Mental Health     Hybrid Journal   (Followers: 84)
Advances in Methods and Practices in Psychological Science     Full-text available via subscription   (Followers: 9)
Advances in Physiotherapy     Hybrid Journal   (Followers: 63)
Advances in Psychology     Full-text available via subscription   (Followers: 66)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 36)
African Journal of Cross-Cultural Psychology and Sport Facilitation     Full-text available via subscription   (Followers: 5)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 469)
Aggressive Behavior     Hybrid Journal   (Followers: 20)
Aging, Neuropsychology, and Cognition     Hybrid Journal   (Followers: 44)
Ágora - studies in psychoanalytic theory     Open Access   (Followers: 3)
Aletheia     Open Access   (Followers: 1)
American Behavioral Scientist     Hybrid Journal   (Followers: 21)
American Imago     Full-text available via subscription   (Followers: 3)
American Journal of Applied Psychology     Open Access   (Followers: 48)
American Journal of Community Psychology     Hybrid Journal   (Followers: 29)
American Journal of Health Behavior     Full-text available via subscription   (Followers: 25)
American Journal of Orthopsychiatry     Hybrid Journal   (Followers: 5)
American Journal of Psychoanalysis     Hybrid Journal   (Followers: 22)
American Journal of Psychology     Full-text available via subscription   (Followers: 37)
American Psychologist     Full-text available via subscription   (Followers: 234)
Anales de Psicología     Open Access   (Followers: 2)
Análise Psicológica     Open Access   (Followers: 1)
Análisis y Modificación de Conducta     Open Access   (Followers: 2)
Analitika : Jurnal Magister Psikologi Uma     Open Access  
Analysis     Full-text available via subscription   (Followers: 3)
Annual Review of Clinical Psychology     Full-text available via subscription   (Followers: 82)
Annual Review of Organizational Psychology and Organizational Behavior     Full-text available via subscription   (Followers: 39)
Annual Review of Psychology     Full-text available via subscription   (Followers: 283)
Anuario de investigaciones (Facultad de Psicología. Universidad de Buenos Aires)     Open Access   (Followers: 1)
Anuario de Investigaciones de la Facultad de Psicología     Open Access  
Anuario de Psicología / The UB Journal of Psychology     Open Access   (Followers: 1)
Anuario de Psicología Jurídica     Open Access   (Followers: 1)
Anuario Pilquen : Sección Divulgación Científica     Open Access  
Anxiety, Stress & Coping: An International Journal     Hybrid Journal   (Followers: 23)
Applied and Preventive Psychology     Hybrid Journal   (Followers: 17)
Applied Cognitive Psychology     Hybrid Journal   (Followers: 75)
Applied Neuropsychology : Adult     Hybrid Journal   (Followers: 42)
Applied Neuropsychology : Child     Hybrid Journal   (Followers: 26)
Applied Psycholinguistics     Hybrid Journal   (Followers: 24)
Applied Psychological Measurement     Hybrid Journal   (Followers: 22)
Applied Psychology     Hybrid Journal   (Followers: 198)
Applied Psychology: Health and Well-Being     Hybrid Journal   (Followers: 55)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8)
Archive for the Psychology of Religion / Archiv für Religionspychologie     Hybrid Journal   (Followers: 26)
Archives of Clinical Neuropsychology     Hybrid Journal   (Followers: 30)
Archives of Scientific Psychology     Open Access   (Followers: 4)
Arquivos Brasileiros de Psicologia     Open Access   (Followers: 1)
Art Therapy Online     Open Access   (Followers: 3)
Asia Pacific Journal of Counselling and Psychotherapy     Hybrid Journal   (Followers: 10)
Asia-Pacific Psychiatry     Hybrid Journal   (Followers: 4)
Asian American Journal of Psychology     Full-text available via subscription   (Followers: 6)
Asian Journal of Behavioural Studies     Open Access   (Followers: 1)
Asian Journal of Business Ethics     Hybrid Journal   (Followers: 11)
Assessment     Hybrid Journal   (Followers: 15)
Attention, Perception & Psychophysics     Full-text available via subscription   (Followers: 13)
Australasian Journal of Organisational Psychology     Hybrid Journal   (Followers: 9)
Australian and Aotearoa New Zealand Psychodrama Association Journal     Full-text available via subscription   (Followers: 1)
Australian Educational and Developmental Psychologist, The     Full-text available via subscription   (Followers: 9)
Australian Journal of Psychology     Hybrid Journal   (Followers: 21)
Australian Journal of Rehabilitation Counseling     Full-text available via subscription   (Followers: 5)
Australian Psychologist     Hybrid Journal   (Followers: 12)
Autism Research     Hybrid Journal   (Followers: 44)
Autism Research and Treatment     Open Access   (Followers: 29)
Autism's Own     Open Access   (Followers: 4)
Autism-Open Access     Open Access   (Followers: 6)
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: 2)
Barbaroi     Open Access  
Basic and Applied Social Psychology     Hybrid Journal   (Followers: 42)
Behavior Analysis in Practice     Full-text available via subscription   (Followers: 13)
Behavior Analysis: Research and Practice     Full-text available via subscription   (Followers: 5)
Behavior Analyst     Hybrid Journal   (Followers: 6)
Behavior Modification     Hybrid Journal   (Followers: 11)
Behavior Research Methods     Hybrid Journal   (Followers: 21)
Behavior Therapy     Hybrid Journal   (Followers: 52)
Behavioral Development Bulletin     Full-text available via subscription  
Behavioral Interventions     Hybrid Journal   (Followers: 15)
Behavioral Neuroscience     Full-text available via subscription   (Followers: 59)
Behavioral Sciences & the Law     Hybrid Journal   (Followers: 28)
Behavioral Sleep Medicine     Hybrid Journal   (Followers: 9)
Behaviormetrika     Hybrid Journal  
Behaviour     Hybrid Journal   (Followers: 13)
Behaviour Change     Full-text available via subscription   (Followers: 13)
Behaviour Research and Therapy     Hybrid Journal   (Followers: 20)
Behavioural and Cognitive Psychotherapy     Hybrid Journal   (Followers: 173)
Behavioural Processes     Hybrid Journal   (Followers: 9)
Biofeedback     Hybrid Journal   (Followers: 4)
BioPsychoSocial Medicine     Open Access   (Followers: 8)
BMC Psychology     Open Access   (Followers: 19)
Body, Movement and Dance in Psychotherapy: An International Journal for Theory, Research and Practice     Hybrid Journal   (Followers: 11)
Boletim Academia Paulista de Psicologia     Open Access  
Boletim de Psicologia     Open Access  
Brain Informatics     Open Access  
British Journal of Clinical Psychology     Full-text available via subscription   (Followers: 176)
British Journal of Developmental Psychology     Full-text available via subscription   (Followers: 40)
British Journal of Educational Psychology     Hybrid Journal   (Followers: 36)
British Journal of Health Psychology     Full-text available via subscription   (Followers: 48)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 20)
British Journal of Psychology     Full-text available via subscription   (Followers: 65)
British Journal of Psychotherapy     Hybrid Journal   (Followers: 68)
British Journal of Social Psychology     Full-text available via subscription   (Followers: 38)
Buletin Psikologi     Open Access  
Burnout Research     Open Access   (Followers: 8)
Cadernos de psicanálise (Rio de Janeiro)     Open Access  
Cadernos de Psicologia Social do Trabalho     Open Access  
Cahiers d’Études sur la Représentation     Open Access  
Canadian Art Therapy Association     Hybrid Journal  
Canadian Journal of Behavioural Science     Full-text available via subscription   (Followers: 6)
Canadian Journal of Experimental Psychology     Full-text available via subscription   (Followers: 19)
Canadian Psychology / Psychologie canadienne     Full-text available via subscription   (Followers: 14)
Case Studies in Sport and Exercise Psychology     Hybrid Journal   (Followers: 4)
Castalia : Revista de Psicología de la Academia     Open Access  
Cendekia : Jurnal Kependidikan dan Kemasyarakatan     Open Access  
Child Development Perspectives     Hybrid Journal   (Followers: 32)
Child Development Research     Open Access   (Followers: 18)
Ciencia Cognitiva     Open Access   (Followers: 2)
Ciencia e Interculturalidad     Open Access   (Followers: 3)
Ciências & Cognição     Open Access  
Ciencias Psicológicas     Open Access  
Clínica y Salud     Open Access  
Clinical Medicine Insights : Psychiatry     Open Access   (Followers: 9)
Clinical Practice & Epidemiology in Mental Health     Open Access  
Clinical Practice in Pediatric Psychology     Full-text available via subscription   (Followers: 11)
Clinical Psychological Science     Hybrid Journal   (Followers: 12)
Clinical Psychologist     Hybrid Journal   (Followers: 19)
Clinical Psychology & Psychotherapy     Hybrid Journal   (Followers: 80)
Clinical Psychology and Special Education     Open Access   (Followers: 4)
Clinical Psychology Review     Hybrid Journal   (Followers: 47)
Clinical Psychology: Science and Practice     Hybrid Journal   (Followers: 24)
Clinical Schizophrenia & Related Psychoses     Full-text available via subscription   (Followers: 10)
Clocks & Sleep     Open Access   (Followers: 1)
Coaching : Theorie & Praxis     Open Access   (Followers: 2)
Coaching Psykologi - The Danish Journal of Coaching Psychology     Open Access   (Followers: 2)
Cogent Psychology     Open Access  
Cógito     Open Access  
Cognition & Emotion     Hybrid Journal   (Followers: 44)
Cognitive Behaviour Therapist     Hybrid Journal   (Followers: 14)
Cognitive Behaviour Therapy     Hybrid Journal   (Followers: 19)
Cognitive Neuropsychology     Hybrid Journal   (Followers: 36)
Cognitive Psychology     Hybrid Journal   (Followers: 73)
Cognitive Research : Principles and Implications     Open Access   (Followers: 3)
Community Psychology in Global Perspective     Open Access  
Consciousness and Cognition     Hybrid Journal   (Followers: 32)
Construção Psicopedagógica     Open Access  
Consulting Psychology Journal : Practice and Research     Full-text available via subscription   (Followers: 4)
Contagion : Journal of Violence, Mimesis, and Culture     Full-text available via subscription   (Followers: 7)
Contemporary Educational Psychology     Hybrid Journal   (Followers: 27)
Contemporary School Psychology     Hybrid Journal   (Followers: 4)
Contextos Clínicos     Open Access  
Counseling et spiritualité / Counselling and Spirituality     Full-text available via subscription   (Followers: 2)
Counseling Outcome Research and Evaluation     Hybrid Journal   (Followers: 12)
Counseling Psychologist     Hybrid Journal   (Followers: 17)
Counseling Psychology and Psychotherapy     Open Access   (Followers: 13)
Counselling and Psychotherapy Research : Linking research with practice     Hybrid Journal   (Followers: 26)
Counselling and Values     Hybrid Journal   (Followers: 5)
Counselling Psychology Quarterly     Hybrid Journal   (Followers: 13)
Couple and Family Psychology : Research and Practice     Full-text available via subscription   (Followers: 7)
Creativity Research Journal     Hybrid Journal   (Followers: 25)
Creativity. Theories ? Research ? Applications     Open Access   (Followers: 5)
Criminal Justice Ethics     Hybrid Journal   (Followers: 11)
Cuadernos de Marte     Open Access  
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: 19)
Cultural-Historical Psychology     Open Access   (Followers: 2)
Culturas Psi     Open Access  
Culture and Brain     Hybrid Journal   (Followers: 4)
Current Addiction Reports     Hybrid Journal   (Followers: 12)
Current Behavioral Neuroscience Reports     Hybrid Journal   (Followers: 2)
Current Directions In Psychological Science     Hybrid Journal   (Followers: 65)
Current Opinion in Behavioral Sciences     Hybrid Journal   (Followers: 9)
Current Opinion in Psychology     Hybrid Journal   (Followers: 15)
Current Psychological Research     Hybrid Journal   (Followers: 15)
Current Psychology     Hybrid Journal   (Followers: 14)
Current psychology letters     Open Access   (Followers: 2)
Current Research in Psychology     Open Access   (Followers: 19)
Cyberpsychology, Behavior, and Social Networking     Hybrid Journal   (Followers: 18)
Decision     Full-text available via subscription   (Followers: 7)
Depression and Anxiety     Hybrid Journal   (Followers: 27)
Depression Research and Treatment     Open Access   (Followers: 15)
Development and Psychopathology     Hybrid Journal   (Followers: 9)
Developmental Cognitive Neuroscience     Open Access   (Followers: 18)
Developmental Neuropsychology     Hybrid Journal   (Followers: 21)
Developmental Psychobiology     Hybrid Journal   (Followers: 9)
Developmental Psychology     Full-text available via subscription   (Followers: 48)

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Similar Journals
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Brain Informatics
Journal Prestige (SJR): 0.124
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2198-4018 - ISSN (Online) 2198-4026
Published by SpringerOpen Homepage  [236 journals]
  • Removal of muscular artifacts in EEG signals: a comparison of linear
           decomposition methods

    • Abstract: The most common approach to reduce muscle artifacts in electroencephalographic signals is to linearly decompose the signals in order to separate artifactual from neural sources, using one of several variants of independent component analysis (ICA). Here we compare three of the most commonly used ICA methods (extended Infomax, FastICA and TDSEP) with two other linear decomposition methods (Fourier-ICA and spatio-spectral decomposition) suitable for the extraction of oscillatory activity. We evaluate the methods’ ability to remove event-locked muscle artifacts while maintaining event-related desynchronization in data from 18 subjects who performed self-paced foot movements. We find that all five analyzed methods drastically reduce the muscle artifacts. For the three ICA methods, adequately high-pass filtering is very important. Compared to the effect of high-pass filtering, differences between the five analyzed methods were small, with extended Infomax performing best.
      PubDate: 2018-01-10
  • Identification and classification of brain tumor MRI images with feature
           extraction using DWT and probabilistic neural network

    • Abstract: The identification, segmentation and detection of infecting area in brain tumor MRI images are a tedious and time-consuming task. The different anatomy structure of human body can be visualized by an image processing concepts. It is very difficult to have vision about the abnormal structures of human brain using simple imaging techniques. Magnetic resonance imaging technique distinguishes and clarifies the neural architecture of human brain. MRI technique contains many imaging modalities that scans and capture the internal structure of human brain. In this study, we have concentrated on noise removal technique, extraction of gray-level co-occurrence matrix (GLCM) features, DWT-based brain tumor region growing segmentation to reduce the complexity and improve the performance. This was followed by morphological filtering which removes the noise that can be formed after segmentation. The probabilistic neural network classifier was used to train and test the performance accuracy in the detection of tumor location in brain MRI images. The experimental results achieved nearly 100% accuracy in identifying normal and abnormal tissues from brain MR images demonstrating the effectiveness of the proposed technique.
      PubDate: 2018-01-08
  • An efficient scheme for mental task classification utilizing reflection
           coefficients obtained from autocorrelation function of EEG signal

    • Abstract: Classification of different mental tasks using electroencephalogram (EEG) signal plays an imperative part in various brain–computer interface (BCI) applications. In the design of BCI systems, features extracted from lower frequency bands of scalp-recorded EEG signals are generally considered to classify mental tasks and higher frequency bands are mostly ignored as noise. However, in this paper, it is demonstrated that high frequency components of EEG signal can provide accommodating data for enhancing the classification performance of the mental task-based BCI. Instead of using autoregressive (AR) parameters considering AR modeling of EEG data, reflection coefficients obtained from EEG signal are proposed as potential features. From a given frame of EEG data, reflection coefficients are directly extracted by using the autocorrelation values in a recursive fashion, which avoids matrix inversion and computation of AR parameters. Use of reflection coefficients not only provides an effective feature vector for EEG signal classification but also offers very low computational burden. Support vector machine classifier is deployed in leave-one-out cross-validation manner to carry out classification process. Extensive simulation is done on an openly accessible dataset containing five different mental tasks. It is found that the proposed scheme can classify mental tasks with a very high level of accuracy as well as low time complexity in contrast with some of the existing strategies.
      PubDate: 2017-12-09
  • Bioplausible multiscale filtering in retino-cortical processing as a
           mechanism in perceptual grouping

    • Abstract: Why does our visual system fail to reconstruct reality, when we look at certain patterns' Where do Geometrical illusions start to emerge in the visual pathway' How far should we take computational models of vision with the same visual ability to detect illusions as we do' This study addresses these questions, by focusing on a specific underlying neural mechanism involved in our visual experiences that affects our final perception. Among many types of visual illusion, ‘Geometrical’ and, in particular, ‘Tilt Illusions’ are rather important, being characterized by misperception of geometric patterns involving lines and tiles in combination with contrasting orientation, size or position. Over the last decade, many new neurophysiological experiments have led to new insights as to how, when and where retinal processing takes place, and the encoding nature of the retinal representation that is sent to the cortex for further processing. Based on these neurobiological discoveries, we provide computer simulation evidence from modelling retinal ganglion cells responses to some complex Tilt Illusions, suggesting that the emergence of tilt in these illusions is partially related to the interaction of multiscale visual processing performed in the retina. The output of our low-level filtering model is presented for several types of Tilt Illusion, predicting that the final tilt percept arises from multiple-scale processing of the Differences of Gaussians and the perceptual interaction of foreground and background elements. The model is a variation of classical receptive field implementation for simple cells in early stages of vision with the scales tuned to the object/texture sizes in the pattern. Our results suggest that this model has a high potential in revealing the underlying mechanism connecting low-level filtering approaches to mid- and high-level explanations such as ‘Anchoring theory’ and ‘Perceptual grouping’.
      PubDate: 2017-09-08
  • Spreading activation in nonverbal memory networks

    • Abstract: Theories of spreading activation primarily involve semantic memory networks. However, the existence of separate verbal and visuospatial memory networks suggests that spreading activation may also occur in visuospatial memory networks. The purpose of the present investigation was to explore this possibility. Specifically, this study sought to create and describe the design frequency corpus and to determine whether this measure of visuospatial spreading activation was related to right hemisphere functioning and spreading activation in verbal memory networks. We used word frequencies taken from the Controlled Oral Word Association Test and design frequencies taken from the Ruff Figural Fluency Test as measures of verbal and visuospatial spreading activation, respectively. Average word and design frequencies were then correlated with measures of left and right cerebral functioning. The results indicated that a significant relationship exists between performance on a test of right posterior functioning (Block Design) and design frequency. A significant negative relationship also exists between spreading activation in semantic memory networks and design frequency. Based on our findings, the hypotheses were supported. Further research will need to be conducted to examine whether spreading activation exists in visuospatial memory networks as well as the parameters that might modulate this spreading activation, such as the influence of neurotransmitters.
      PubDate: 2017-09-01
  • Brain explorer for connectomic analysis

    • Abstract: Visualization plays a vital role in the analysis of multimodal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional, and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. Two types of non-spatial information are represented: (1) time series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image-based phenotypic biomarkers for brain diseases.
      PubDate: 2017-08-23
  • Optimized statistical parametric mapping procedure for NIRS data
           contaminated by motion artifacts

    • 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: 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: 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: 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: 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: 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: 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: 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: 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: 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
  • Pattern recognition of spectral entropy features for detection of
           alcoholic and control visual ERP’s in multichannel EEGs

    • 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
  • Test–retest reliability of brain morphology estimates

    • 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: 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: 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
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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