EEG connectivity
Research Papers
Connectivity Assessment and Training: A Partial Directed Coherence Approach
Background. The multivariate autoregressive (MVAR) method to generate a linear model of multichannel signal processes has been employed in many fields but not applied to the assessment of quantitative electroencephalographic (QEEG) connectivity neurofeedback. A measure known as Partial Directed Coherence (PDC) derived in the MVAR framework can offer insensitivity to volume conduction and ability to provide information relating to the direction of information flow between electrode locations, as a function of frequency during QEEG assessment and neurofeedback. Method. This article outlines a variety of reasons why PDC and other related metrics could play a more fundamental role in elucidating the causal relationships underlying EEG connectivity than can be provided though a multivariate analysis of coherence alone. Results. Real-time PDC neurofeedback implementation issues are discussed, technical challenges are outlined, and research questions are proposed. Conclusion. MVAR-based methods are an additional means of relating global to local EEG activity as well as helping to bridge QEEG assessment and neurofeedback protocol generation and treatment.
View Full Paper →Connectivity Theory of Autism: Use of Connectivity Measures in Assessing and Treating Autistic Disorders
Background. Autism is a disorder characterized by deficits in communication, social interaction, a limited range of interests, and repetitive stereotypical behavior. Although it is believed that changes in the brain leading to Autism occur early on in prenatal and early postnatal development, there is no definitive test for a diagnosis of Autism. The diagnosis is made on the basis of behavioral signs and symptoms alone and is usually not made until age 2 or later. There have been numerous neuroanatomical abnormalities noted in Autism, some of which can be linked to neuropsychological dysfunction. Recently a new theory has become prominent which suggests the disorder may be due to aberrant neural connectivity patterns. Evidence in support of this theory has come from anatomical studies of white matter as well as functional neuroimaging studies. Methods. Most studies have employed functional magnetic resonance imaging to investigate connectivity, or electroencephalography (EEG) coherence studies. The high temporal resolution of EEG lends itself well to the investigation of cerebral connectivity. Research suggests there may be patterns of both hyper- and hypoconnectivity between various brain regions. Seven different patterns of abnormal connectivity which can be analyzed with EEG are proposed. Results. Patterns of hyperconnectivity may be found in frontotemporal and left hemispheric regions, whereas patterns of hypoconnectivity are often seen in frontal (orbitofrontal), right posterior (occipital/parietal-temporal), frontal-posterior, and left hemispheric regions. In addition to these patterns of hypo- and hyperconnectivity, a mu rhythm complex has been identified. Treatment goals may be based on coherence anomalies identified by quantitative EEG analysis. Increased coherence between brain regions may be downtrained, whereas decreased coherence between brain regions may be uptrained. Clinical examples of each pattern and a discussion of their neurofeedback treatment are provided. Conclusion. A theory of autistic disorders is presented that has at its' core neural connectivity disturbances. Multivariate EEG connectivity indices are utilized to formulate a typology of connectivity anomalies or patterns that have been observed over a series of autistic patients. These represent phenotypic expressions of the underlying pathology that leads to autistic symptoms. Examples demonstrate how these connectivity metrics can be used to understand autistic disturbances and formulate neurofeedback strategies for remedying these difficulties.
View Full Paper →EEG Connectivity Patterns in Childhood Sexual Abuse: A Multivariate Application Considering Curvature of Brain Space
Introduction. A limitation of the bivariate electroencephalogram (EEG) coherence measure is low precision in location specification in anatomical space and functional connectivity. A more powerful use of functional connectivity of distributed brain systems maybe evaluation of patterns of correlations obtained through the functional connectivity matrix of Principal Component Analysis. The eigenimages that result from such analysis represent a descriptive characterization of anatomically distributed changes in the brain. There is little research exploring the relationship between childhood sexual abuse (CSA) and connectivity patterns in the brain. This study explored the connectivity patterns between 24 high-functioning, unmedicated adults with a history of CSA and age, gender, and handedness matched high-functioning adults with no history of CSA. Method. Resting eyes closed quantitative EEG (QEEG) was recorded from 19 scalp locations with a linked ears reference from 60 unmedicated adult research participants. The QEEG was subjected to measures of connectivity for analysis. Results. A robust analysis of QEEG cortical coherence revealed moderate to large effect sizes indicating patterns of both increased and decreased connectivity between brain locations, which differentiated the groups. Conclusion. The EEG coherence information extended previous work in nonclinical, unmedicated adults and suggested CSA impacts cortical function resulting in lateralized differences. Statistical methods for preventing small distribution changes from making large changes in power or probability coverage because of small and nonnormal samples is also discussed.
View Full Paper →Infrared Imaging and Neurofeedback: Initial Reliability and Validity
Introduction. The neurological correlates underlying positive treatment outcomes for neurofeedback have been either unavailable or difficult to demonstrate. Assessment of brain-related changes associated with neurofeedback is needed to further establish its empirical basis. Infrared (IR) imaging is a noninvasive assessment of brain activity with high spatial and temporal resolution. Method. Study 1, a reliability study, assessed the test-retest stability of IR imaging. In Validity Study 2 and 3, IR imaging assessed brain-related changes prior to and following neurofeedback and passive infrared hemoencephalography (pir HEG) training, respectively. Results. In Study 1, high correlations occurred in pre-post comparisons for IR measures unrelated to treatment. Lower correlation between measures of IR imaging indicated changes in brain activation associated with thermoregulation following neurofeedback training. In Study 2, changes in thermal regulation occurred both within and across sessions. The change in metabolic regulation was enduring and associated with a reduction in core Autistic Spectrum Disorder symptomatology and improved cerebral connectivity. In Study 3, a significant percentage of patients with Traumatic Brain Injury increased thermal readings following pir HEG training and the change in thermal readings was associated with EEG connectivity. Conclusion. Findings indicated that IR imaging may be a reliable and valid measure of treatment outcomes with clinical utility and sensitivity.
View Full Paper →Introduction to Advances in EEG Connectivity
This special issue of the Journal of Neurotherapy has been devoted to Advances in EEG Connectivities. These purposes include providing education to our readers and collaboration among the scientists and authors. Multiple connectivity metrics have been defined with an emphasis on coherence and multivariate connectivity measures. The goals of connectivity measurements should include accuracy compared to known neurological networks and utility in assessment and application for intervention (e.g., EEG coherence training). It is hoped that the information contained in this special issue will form the basis for future advancements in EEG connectivity assessment and intervention.
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