Visualization of Multichannel EEG Coherence Networks Based on Community Structure

An electroencephalography (EEG) coherence network is a representation of functional brain connectivity. However, typical visualizations of coherence networks do not allow an easy embedding of spatial information or suffer from visual clutter, especially for multichannel EEG coherence networks. In this paper, a new method for data-driven visualization of multichannel EEG coherence networks is proposed to avoid the drawbacks of conventional methods. This method partitions electrodes into dense groups of spatially connected regions. It not only preserves spatial relationships between regions, but also allows an analysis of the functional connectivity within and between brain regions, which could be used to explore the relationship between functional connectivity and underlying brain structures. In addition, we employ an example to illustrate the difference between the proposed method and two other data-driven methods when applied to coherence networks in older and younger adults who perform a cognitive task. The proposed method can serve as an preprocessing step before a more detailed analysis of EEG coherence networks

Defecting or not defecting: how to "read" human behavior during cooperative games by EEG measurements

Understanding the neural mechanisms responsible for human social interactions is difficult, since the brain activities of two or more individuals have to be examined simultaneously and correlated with the observed social patterns. We introduce the concept of hyper-brain network, a connectivity pattern representing at once the information flow among the cortical regions of a single brain as well as the relations among the areas of two distinct brains. Graph analysis of hyper-brain networks constructed from the EEG scanning of 26 couples of individuals playing the Iterated Prisoner's Dilemma reveals the possibility to predict non-cooperative interactions during the decision-making phase. The hyper-brain networks of two-defector couples have significantly less inter-brain links and overall higher modularity - i.e. the tendency to form two separate subgraphs - than couples playing cooperative or tit-for-tat strategies. The decision to defect can be "read" in advance by evaluating the changes of connectivity pattern in the hyper-brain network

Never Resting Brain: Simultaneous Representation of Two Alpha Related Processes in Humans

Brain activity is continuously modulated, even at “rest”. The alpha rhythm (8–12 Hz) has been known as the hallmark of the brain's idle-state. However, it is still debated if the alpha rhythm reflects synchronization in a distributed network or focal generator and whether it occurs spontaneously or is driven by a stimulus. This EEG/fMRI study aimed to explore the source of alpha modulations and their distribution in the resting brain. By serendipity, while computing the individually defined power modulations of the alpha-band, two simultaneously occurring components of these modulations were found. An ‘induced alpha’ that was correlated with the paradigm (eyes open/ eyes closed), and a ‘spontaneous alpha’ that was on-going and unrelated to the paradigm. These alpha components when used as regressors for BOLD activation revealed two segregated activation maps: the ‘induced map’ included left lateral temporal cortical regions and the hippocampus; the ‘spontaneous map’ included prefrontal cortical regions and the thalamus. Our combined fMRI/EEG approach allowed to computationally untangle two parallel patterns of alpha modulations and underpin their anatomical basis in the human brain. These findings suggest that the human alpha rhythm represents at least two simultaneously occurring processes which characterize the ‘resting brain’; one is related to expected change in sensory information, while the other is endogenous and independent of stimulus change

Satisfaction with care after total hip or knee replacement predicts self-perceived health status after surgery

<p>Abstract</p> <p>Background</p> <p>Inpatient satisfaction with care is a standard indicator of the quality of care delivered during hospitalization. Total hip and knee replacement (THR/TKR) for osteoarthritis (OA) are among the most successful orthopaedic interventions having a positive impact on health-related quality of life (HRQoL). The aim was to evaluate the effect of satisfaction shortly after hospital discharge on 1-month, 6-month and 1-year Medical Outcomes Study 36-item Short Form (SF-36) scores for OA patients after THR and TKR, controlling for patient characteristics, clinical presentation and preoperative SF-36 scores.</p> <p>Methods</p> <p>A multicenter prospective cohort study recruited 231 patients with OA scheduled to receive THR or TKR. Satisfaction was assessed by the Patients Judgment of Hospital Quality (PJHQ) questionnaire and HRQoL by the SF-36 questionnaire. Linear models for repeated measures assessed the relation between satisfaction (scores were dichotomized) and postoperative SF-36 scores.</p> <p>Results</p> <p>Of 231 participants, 189 were followed up 12 months after discharge (mean age 69 SD = 8; 42.6% male). The mean length of hospital stay was 13.5 (SD = 4) days. After adjustment for preoperative SF-36 scores, sociodemographic and clinical patient characteristics, satisfied patients (PJHQ score > 70) had higher SF-36 scores 1 year after surgery than did less-satisfied patients. Admission, medical care, and nursing and daily care scores mainly predicted bodily pain, mental health, social functioning, vitality and general health scores of the SF-36.</p> <p>Conclusion</p> <p>Besides being a quality-of-care indicator, immediate postoperative patient satisfaction with care may bring a new insight into clinical practice, as a predictor of self-perceived health status after surgery.</p

Toward a model-based predictive controller design in brain-computer interfaces

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.Grants K25NS061001 (MK) and K02MH01493 (SJS) from the National Institute of Neurological Disorders And Stroke (NINDS) and the National Institute of Mental Health (NIMH), the Portuguese Foundation for Science and Technology (FCT) Grant SFRH/BD/21529/2005 (NSD), the Pennsylvania Department of Community and Economic Development Keystone Innovation Zone Program Fund (SJS), and the Pennsylvania Department of Health using Tobacco Settlement Fund (SJS)

Anesthetic action on the transmission delay between cortex and thalamus explains the beta-buzz observed under propofol anesthesia

In recent years, more and more surgeries under general anesthesia have been performed with the assistance of electroencephalogram (EEG) monitors. An increase in anesthetic concentration leads to characteristic changes in the power spectra of the EEG. Although tracking the anesthetic-induced changes in EEG rhythms can be employed to estimate the depth of anesthesia, their precise underlying mechanisms are still unknown. A prominent feature in the EEG of some patients is the emergence of a strong power peak in the β–frequency band, which moves to the α–frequency band while increasing the anesthetic concentration. This feature is called the beta-buzz. In the present study, we use a thalamo-cortical neural population feedback model to reproduce observed characteristic features in frontal EEG power obtained experimentally during propofol general anesthesia, such as this beta-buzz. First, we find that the spectral power peak in the α– and δ–frequency ranges depend on the decay rate constant of excitatory and inhibitory synapses, but the anesthetic action on synapses does not explain the beta-buzz. Moreover, considering the action of propofol on the transmission delay between cortex and thalamus, the model reveals that the beta-buzz may result from a prolongation of the transmission delay by increasing propofol concentration. A corresponding relationship between transmission delay and anesthetic blood concentration is derived. Finally, an analytical stability study demonstrates that increasing propofol concentration moves the systems resting state towards its stability threshold

Multivariate analysis of oestrogen receptor alpha, pS2, metallothionein and CD24 expression in invasive breast cancers

Determination of oestrogen receptor alpha (ER) represents at present the most important predictive factor in breast cancers. Data of ours and of other authors suggest that promising predictive/prognostic factors may also include pS2, metallothionein (MT) and CD24. Present study aimed at determining prognostic and predictive value of immunohistochemical determination of ER, pS2, MT, and CD24 expression in sections originating from 104 patients with breast cancer. An univariate and multivariate analysis was performed. Both univariate and multivariate analyses demonstrated that cytoplasmic-membranous expression of CD24 (CD24c-m) represents a strong unfavourable prognostic factor in the entire group and in most of the subgroups of patients. In several subgroups of the patients also a prognostic value was demonstrated of elevated expression of pS2 and of membranous expression of CD24. Our studies demonstrated that all patients with good prognostic factors (higher ER and pS2 expressions, lower MT expression, CD24c-m negativity) survived total period of observation (103 months). The study documented that cytoplasmic-membranous expression of CD24 represented an extremely strong unfavourable prognostic factor in breast cancer. Examination of the entire panel of the studied proteins permitted to select a group of patients of an exceptionally good prognosis