Mind over chatter: plastic up-regulation of the fMRI alertness network by EEG neurofeedback

EEG neurofeedback (NFB) is a brain-computer interface (BCI) approach used to shape brain oscillations by means of real-time feedback from the electroencephalogram (EEG), which is known to reflect neural activity across cortical networks. Although NFB is being evaluated as a novel tool for treating brain disorders, evidence is scarce on the mechanism of its impact on brain function. In this study with 34 healthy participants, we examined whether, during the performance of an attentional auditory oddball task, the functional connectivity strength of distinct fMRI networks would be plastically altered after a 30-min NFB session of alpha-band reduction (n=17) versus a sham-feedback condition (n=17). Our results reveal that compared to sham, NFB induced a specific increase of functional connectivity within the alertness/salience network (dorsal anterior and mid cingulate), which was detectable 30 minutes after termination of training. Crucially, these effects were significantly correlated with reduced mind-wandering 'on-task' and were coupled to NFB-mediated resting state reductions in the alpha-band (8-12 Hz). No such relationships were evident for the sham condition. Although group default-mode network (DMN) connectivity was not significantly altered following NFB, we observed a positive association between modulations of resting alpha amplitude and precuneal connectivity, both correlating positively with frequency of mind-wandering. Our findings demonstrate a temporally direct, plastic impact of NFB on large-scale brain functional networks, and provide promising neurobehavioral evidence supporting its use as a noninvasive tool to modulate brain function in health and disease

What event-related potentials (ERPs) bring to social neuroscience?

Social cognitive neuroscience is a recent interdisciplinary field that studies the neural basis of the social mind. Event-related potentials (ERPs) provide precise information about the time dynamics of the brain. In this study, we assess the role of ERPs in cognitive neuroscience, particularly in the emerging area of social neuroscience. First, we briefly introduce the technique of ERPs. Subsequently, we describe several ERP components (P1, N1, N170, vertex positive potential, early posterior negativity, N2, P2, P3, N400, N400-like, late positive complex, late positive potential, P600, error-related negativity, feedback error-related negativity, contingent negative variation, readiness potential, lateralized readiness potential, motor potential, re-afferent potential) that assess perceptual, cognitive, and motor processing. Then, we introduce ERP studies in social neuroscience on contextual effects on speech, emotional processing, empathy, and decision making. We provide an outline of ERPs' relevance and applications in the field of social cognitive neuroscience. We also introduce important methodological issues that extend classical ERP research, such as intracranial recordings (iERP) and source location in dense arrays and simultaneous functional magnetic resonance imaging recordings. Further, this review discusses possible caveats of the ERP question assessment on neuroanatomical areas, biophysical origin, and methodological problems, and their relevance to explanatory pluralism and multilevel, contextual, and situated approaches to social neuroscience.Fil: Ibañez, Agustin Mariano. Universidad Diego Portales; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Melloni, Margherita. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Huepe, David. Universidad Diego Portales; ChileFil: Helgiu, Elena. Harvard University; Estados UnidosFil: Rivera Rei, Alvaro. Universidad Diego Portales; ChileFil: Canales Johnson, Andrés. Universidad Diego Portales; ChileFil: Baker, Phil. Universidad Favaloro; ArgentinaFil: Moya, Alvaro. Universidad Favaloro; Argentin

Prefrontal Cortex Based Sex Differences in Tinnitus Perception: Same Tinnitus Intensity, Same Tinnitus Distress, Different Mood

BACKGROUND: Tinnitus refers to auditory phantom sensation. It is estimated that for 2% of the population this auditory phantom percept severely affects the quality of life, due to tinnitus related distress. Although the overall distress levels do not differ between sexes in tinnitus, females are more influenced by distress than males. Typically, pain, sleep, and depression are perceived as significantly more severe by female tinnitus patients. Studies on gender differences in emotional regulation indicate that females with high depressive symptoms show greater attention to emotion, and use less anti-rumination emotional repair strategies than males. METHODOLOGY: The objective of this study was to verify whether the activity and connectivity of the resting brain is different for male and female tinnitus patients using resting-state EEG. CONCLUSIONS: Females had a higher mean score than male tinnitus patients on the BDI-II. Female tinnitus patients differ from male tinnitus patients in the orbitofrontal cortex (OFC) extending to the frontopolar cortex in beta1 and beta2. The OFC is important for emotional processing of sounds. Increased functional alpha connectivity is found between the OFC, insula, subgenual anterior cingulate (sgACC), parahippocampal (PHC) areas and the auditory cortex in females. Our data suggest increased functional connectivity that binds tinnitus-related auditory cortex activity to auditory emotion-related areas via the PHC-sgACC connections resulting in a more depressive state even though the tinnitus intensity and tinnitus-related distress are not different from men. Comparing male tinnitus patients to a control group of males significant differences could be found for beta3 in the posterior cingulate cortex (PCC). The PCC might be related to cognitive and memory-related aspects of the tinnitus percept. Our results propose that sex influences in tinnitus research cannot be ignored and should be taken into account in functional imaging studies related to tinnitus

Effective connectivity during visual processing is affected by emotional state

The limitations of our cognitive resources necessitate the selection of relevant information from the incoming visual stream. This selection and prioritizing of stimuli allows the organism to adapt to the current conditions. However, the characteristics of this process vary with time and depend on numerous external and internal factors. The present study was aimed at determining how the emotional state affects effective connectivity between visual, attentional and control brain areas during the perception of affective visual stimuli. The Directed Transfer Function was applied on a 32-electrode EEG recording to quantify the direction and intensity of the information flow during two sessions: positive and negative. These data were correlated with a self-report of the emotional state. We demonstrated that the current mood, as measured by self-report, is a factor which affects the patterns of effective cortical connectivity. An increase in prefrontal top-down control over the visual and attentional areas was revealed in a state of tension. It was accompanied by increased outflow within and from the areas recognized as the ventral attentional network. By contrast, a positive emotional state was associated with heightened flow from the parietal to the occipital area. The functional significance of the revealed effects is discussed

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems