Carl J. Biver

Development of cortical connections as measured by EEG coherence and phase delays.

The purpose of this study was to explore human development of EEG coherence and phase differences over the period from infancy to 16 years of age. The electroencephalogram (EEG) was recorded from 19 scalp locations from 458 subjects ranging in age from 2 months to 16.67 years. EEG coherence and EEG phase differences were computed for the left and right hemispheres in the posterior‐to‐anterior direction (O1/2‐P3/4, O1/2‐C3/4, O1/2‐F3/4, and O1/2‐Fp1/2) and the anterior‐to‐ posterior direction (Fp1/2‐F3/4, Fp1/2‐C3/4, Fp1/2‐P3/4, and Fp1/2‐O1/2) in the beta frequency band (13–25 Hz). Sliding averages of EEG coherence and phase were computed using 1 year averages and 9 month overlapping that produced 64 means from 0.44 years of age to 16.22 years of age. Rhythmic oscillations in coherence and phase were noted in all electrode combinations. Different developmental trajectories were present for coherence and phase differences and for anterior‐to‐posterior and posterior‐to‐anterior directions and inter‐electrode distance. Large changes in EEG coherence and phase were present from ∼ 6 months to 4 years of age followed by a significant linear trend to higher coherence in short distance inter‐electrode distances and longer phase delays in long inter‐electrode distances. The results are consistent with a genetic model of rhythmic long term connection formation that occurs in cycles along a curvilinear trajectory toward adulthood. Competition for dendritic space, development of complexity, and nonlinear dynamic oscillations are discussed. Hum Brain Mapp, 2008.

Self‐organized criticality and the development of EEG phase reset

Objectives: The purpose of this study was to explore human development of self‐organized criticality as measured by EEG phase reset from infancy to 16 years of age. Methods: The electroencephalogram (EEG) was recorded from 19 scalp locations from 458 subjects ranging in age from 2 months to 16.67 years. Complex demodulation was used to compute instantaneous phase differences between pairs of electrodes and the 1st and 2nd derivatives were used to detect the sudden onset and offset times of a phase shift followed by an extended period of phase locking. Mean phase shift duration and phase locking intervals were computed for two symmetrical electrode arrays in the posterior‐to‐anterior locations and the anterior‐to‐posterior directions in the α frequency band (8–13 Hz). Results: Log–log spectral plots demonstrated 1/f α distributions (α ≈ 1) with longer slopes during periods of phase shifting than during periods of phase locking. The mean duration of phase locking (150–450 msec) and phase shift (45–67 msec) generally increased as a function of age. The mean duration of phase shift declined over age in the local frontal regions but increased in distant electrode pairs. Oscillations and growth spurts from mean age 0.4–16 years were consistently present. Conclusions: The development of increased phase stability in local systems is paralleled by lengthened periods of unstable phase in distant connections. Development of the number and/or density of synaptic connections is a likely order parameter to explain oscillations and growth spurts in self‐organized criticality during human brain maturation. Hum Brain Mapp, 2009.

Intelligence and EEG current density using low‐resolution electromagnetic tomography (LORETA)

The purpose of this study was to compare EEG current source densities in high IQ subjects vs. low IQ subjects. Resting eyes closed EEG was recorded from 19 scalp locations with a linked ears reference from 442 subjects ages 5 to 52 years. The Wechsler Intelligence Test was administered and subjects were divided into low IQ (≤90), middle IQ (>90 to <120) and high IQ (≥120) groups. Low‐resolution electromagnetic tomographic current densities (LORETA) from 2,394 cortical gray matter voxels were computed from 1–30 Hz based on each subjects EEG. Differences in current densities using t tests, multivariate analyses of covariance, and regression analyses were used to evaluate the relationships between IQ and current density in Brodmann area groupings of cortical gray matter voxels. Frontal, temporal, parietal, and occipital regions of interest (ROIs) consistently exhibited a direct relationship between LORETA current density and IQ. Maximal t test differences were present at 4 Hz, 9 Hz, 13 Hz, 18 Hz, and 30 Hz with different anatomical regions showing different maxima. Linear regression fits from low to high IQ groups were statistically significant (P < 0.0001). Intelligence is directly related to a general level of arousal and to the synchrony of neural populations driven by thalamo‐cortical resonances. A traveling frame model of sequential microstates is hypothesized to explain the results. Hum Brain Mapp, 2007.

DIFFUSION SPECTRAL IMAGING 'MODULES' CORRELATE WITH EEG LORETA NEUROIMAGING 'MODULES'

Objectives: The purpose of this study was to test the hypothesis that the highest temporal correlations between 3‐dimensional EEG current source density corresponds to anatomical Modules of high synaptic connectivity. Methods: Eyes closed and eyes open EEG was recorded from 19 scalp locations with a linked ears reference from 71 subjects age 13–42 years. LORETA was computed from 1 to 30 Hz in 2,394 cortical gray matter voxels that were grouped into six anatomical Modules corresponding to the ROIs in the Hagmann et al.s [ 2008 ] diffusion spectral imaging (DSI) study. All possible cross‐correlations between voxels within a DSI Module were compared with the correlations between Modules. Results: The Hagmann et al. [ 2008 ] Module correlation structure was replicated in the correlation structure of EEG three‐dimensional current source density. Conclusions: EEG Temporal correlation between brain regions is related to synaptic density as measured by diffusion spectral imaging. Hum Brain Mapp, 2011.

LORETA EEG phase reset of the default mode network

Objectives: The purpose of this study was to explore phase reset of 3-dimensional current sources in Brodmann areas located in the human default mode network (DMN) using Low Resolution Electromagnetic Tomography (LORETA) of the human electroencephalogram (EEG). Methods: The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodmann areas comprising the DMN in the delta frequency band. The Hilbert transform of the LORETA time series was used to compute the instantaneous phase differences between all pairs of Brodmann areas. Phase shift and lock durations were calculated based on the 1st and 2nd derivatives of the time series of phase differences. Results: Phase shift duration exhibited three discrete modes at approximately: (1) 25 ms, (2) 50 ms, and (3) 65 ms. Phase lock duration present primarily at: (1) 300–350 ms and (2) 350–450 ms. Phase shift and lock durations were inversely related and exhibited an exponential change with distance between Brodmann areas. Conclusions: The results are explained by local neural packing density of network hubs and an exponential decrease in connections with distance from a hub. The results are consistent with a discrete temporal model of brain function where anatomical hubs behave like a “shutter” that opens and closes at specific durations as nodes of a network giving rise to temporarily phase locked clusters of neurons for specific durations.

Network Connectivity and LORETA Z Score Biofeedback

This chapter summarizes recent developments in neuroimaging and the neuroscience of functional networks in the brain. The linkage of low-resolution electromagnetic tomography (LORETA) source localization to structural connections in the brain as measured by diffusion tensor imaging (DTI) is presented. Methods of linking symptoms to dysregulation in nodes and connections between nodes in various brain networks are discussed with special emphasis on LORETA and sLORETA real-time neurofeedback.

History and Technical Foundations of Z Score EEG Biofeedback

The history and technical foundations of Z score electroencephalogram (EEG) biofeedback, including LORETA Z score biofeedback, is reviewed. The statistical standards are discussed and the step-by-step conceptual foundations of Z score biofeedback are explained. The central concept is linking symptoms to dysregulated nodes and connections between nodes in networks in the brain. The goal is to reinforce increased stability and efficiency in neural networks by reinforcing toward the center of a normal reference population. The use of Z scores for real-time or “live” biofeedback unifies different EEG metrics (e.g., power, amplitude, coherence, phase) to a single metric, i.e., the metric of the Z score with a mean=0 and a standard deviation=1 in the ideal case. Z score biofeedback also simplifies the EEG biofeedback process by providing clinicians with a “guide” or reference to determine threshold setting for biofeedback. For example, with raw score biofeedback, clinicians must guess at a threshold setting to trigger biofeedback. With Z score biofeedback, the guess work is removed since all metrics are treated the same in which the direction of biofeedback is toward Z=0.

Brainsurfer and Brain Computer Interface Z-Score Biofeedback

A new era of electroencephalogram (EEG) biofeedback is described by contrasting operant conditioning technologies referred to as brain–computer interface (BCI) and EEG biofeedback is also called EEG neurofeedback (NFB). The science of synapse changes as described by Kandel (2006) is the common binding core of these different operant conditioning or instrumental learning methodologies. BCI differs from NFB mostly with the former emphasizing willful consciousness and cognitive strategies using instantaneous feedback and the latter emphasizing subconscious learning and discrete feedback. Both methods are clinically efficacious and can be applied to different patients depending on the best match of clinical symptoms and the method that works best for a given patient. The comparative clinical efficacy as measured by the number of trials or sessions is similar. The application of BCI has historically been used for paralyzed patients, while NFB has a history of a much wider clinical application. There is no study to indicate that BCI methodology cannot also be applied to treat a broader range of clinical symptomology similar to NFB.