Intelligence and eeg measures of information flow: efficiency and homeostatic neuroplasticity.

The purpose of this study was to explore the relationship between the magnitude of EEG information flow and intelligence. The electroencephalogram (EEG) was recorded from 19 scalp locations from 371 subjects ranging in age from 5 years to 17.6 years. The Wechler Intelligence Scale for Children (WISC-R) was administered for individuals between 5 years of age and 16 years and the Weschler Adult Intelligence Scale revised (WAIS-R) was administered to subjects older than 16 years to estimate I.Q. The phase slope index estimated the magnitude of information flow between all electrode combinations for difference frequency bands. Discriminant analyses were performed between high I.Q. (>120) and low I.Q. groups (<90). The magnitude of information flow was inversely related to I.Q. especially in the alpha and beta frequency bands. Long distance inter-electrode distances exhibited greater information flow than short inter-electrode distances. Frontal-parietal correlations were the most significant. It is concluded that higher I.Q. is related to increased efficiency of local information processing and reduced long distance compensatory dynamics that supports a small-world model of intelligence.

Intelligence and EEG phase reset: a two compartmental model of phase shift and lock.

OBJECTIVES: The purpose of this study was to explore the relationship between EEG phase reset and performance on the Wechsler Intelligence test. METHODS: The electroencephalogram (EEG) was recorded from 19 scalp locations from 378 subjects ranging in age from 5 years to 17.6 years. The Wechsler Intelligence test (WISC-R) was administered to the same subjects on the same day but not while the EEG was recorded. Complex demodulation was used to compute instantaneous EEG phase differences between pairs of electrodes and the 1st and 2nd derivatives were used to measure phase reset by phase shift duration and phase lock duration. The dependent variable was full scale I.Q. and the independent variables were phase shift duration (SD) and phase lock duration (LD) with age as a covariate. RESULTS: Phase shift duration (40-90 ms) was positively related to intelligence (P<.00001) and the phase lock duration (100-800 ms) was negatively related to intelligence (P<.00001). Phase reset in short interelectrode distances (6 cm) was more highly correlated to I.Q. (P<.0001) than in long distances (>12 cm). CONCLUSIONS: The duration of unstable phase dynamics and phase locking represent a bounded optimization process, for example, too long a duration of phase locking then less flexibility and too short of a phase shift then reduced neural resources. A two compartmental model of local field coupling and neuron synchrony to a preferred phase was developed to explain the findings.

Spatial-temporal current source correlations and cortical connectivity.

The purpose of this study was to explore spatial-temporal correlations between 3-dimensional current density estimates using Low Resolution Electromagnetic Tomography (LORETA). The electroencephalogram (EEG) was recorded from 19 scalp locations from 97 subjects. LORETA current density was computed for 2,394 gray matter pixels. The gray matter pixels were grouped into 33 left hemisphere and 33 right hemisphere regions of interest (ROIs) based on groupings of Brodmann areas. The average source current density in a given region of interest (ROI) was computed for each 2 second epoch of EEG and then a Pearson product correlation coefficient was computed over the time series of successive 2 second epochs of current density between all pairwise combinations of ROIs during the resting eyes-closed EEG session. Rhythmic changes in source correlation as a function of distance were present in all regions of interest. Also, maximum correlations at certain frequencies were present independent of distance. The occipital regions exhibited the highest short distance correlations and the frontal regions exhibited the highest long distance correlations. In general, the right hemisphere exhibited higher intra-hemispheric source correlations than the left hemisphere especially in the temporal, parietal and occipital cortex. The strongest left vs. right hemisphere differences were in the alpha frequency band (8-12 Hz) and in the gamma frequency band (37-40 Hz). The pattern of spatial frequencies in different cortical lobules is consistent with differences in neural packing density and the operation of 'U' shaped fiber systems. The general conclusions were: 1--the higher the packing density then the greater the intra-cortical connection contribution to LORETA source correlations, 2--spatial frequencies are primarily due to intra-cortical 'U' shaped fiber connections and long distance fiber connections, 3--posterior and temporal cortical intra-hemispheric coupling is generally stronger in the right hemisphere than in the left hemisphere.

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 (< or =90), middle IQ (>90 to <120) and high IQ (> or =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 subject's 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.

EEG and intelligence: relations between EEG coherence, EEG phase delay and power.

OBJECTIVE: There are two inter-related categories of EEG measurement: 1, EEG currents or power and; 2, EEG network properties such as coherence and phase delays. The purpose of this study was to compare the ability of these two different categories of EEG measurement to predict performance on the Weschler Intelligence test (WISC-R). METHODS: Resting eyes closed EEG was recorded from 19 scalp locations with a linked ears reference from 442 subjects aged 5-52 years. The Weschler Intelligence test was administered to the same subjects but not while the EEG was recorded. Subjects were divided into high IQ (> or = 120) and low IQ (< or = 90) groups. EEG variables at P<.05 were entered into a factor analysis and then the single highest loading variable on each factor was entered into a discriminant analysis where groups were high IQ vs. low.Q. RESULTS: Discriminant analysis of high vs. low IQ was 92.81-97.14% accurate. Discriminant scores of intermediate IQ subjects (i.e. 90 < IQ < 120) were intermediate between the high and low IQ groups. Linear regression predictions of IQ significantly correlated with the discriminant scores (r = 0.818-0.825, P < 10(-6)). The ranking of effect size was EEG phase > EEG coherence > EEG amplitude asymmetry > absolute power > relative power and power ratios. The strongest correlations to IQ were short EEG phase delays in the frontal lobes and long phase delays in the posterior cortical regions, reduced coherence and increased absolute power. CONCLUSIONS: The findings are consistent with increased neural efficiency and increased brain complexity as positively related to intelligence, and with frontal lobe synchronization of neural resources as a significant contributing factor to EEG and intelligence correlations. SIGNIFICANCE: Quantitative EEG predictions of intelligence provide medium to strong effect size estimates of cognitive functioning while simultaneously revealing a deeper understanding of the neurophysiological substrates of intelligence.

Evaluation and validity of a LORETA normative EEG database.

To evaluate the reliability and validity of a Z-score normative EEG database for Low Resolution Electromagnetic Tomography (LORETA), EEG digital samples (2 second intervals sampled 128 Hz, 1 to 2 minutes eyes closed) were acquired from 106 normal subjects, and the cross-spectrum was computed and multiplied by the Key Institute's LORETA 2,394 gray matter pixel T Matrix. After a log10 transform or a Box-Cox transform the mean and standard deviation of the *.lor files were computed for each of the 2394 gray matter pixels, from 1 to 30 Hz, for each of the subjects. Tests of Gaussianity were computed in order to best approximate a normal distribution for each frequency and gray matter pixel. The relative sensitivity of a Z-score database was computed by measuring the approximation to a Gaussian distribution. The validity of the LORETA normative database was evaluated by the degree to which confirmed brain pathologies were localized using the LORETA normative database. Log10 and Box-Cox transforms approximated Gaussian distribution in the range of 95.64% to 99.75% accuracy. The percentage of normative Z-score values at 2 standard deviations ranged from 1.21% to 3.54%, and the percentage of Z-scores at 3 standard deviations ranged from 0% to 0.83%. Left temporal lobe epilepsy, right sensory motor hematoma and a right hemisphere stroke exhibited maximum Z-score deviations in the same locations as the pathologies. We conclude: (1) Adequate approximation to a Gaussian distribution can be achieved using LORETA by using a log10 transform or a Box-Cox transform and parametric statistics, (2) a Z-Score normative database is valid with adequate sensitivity when using LORETA, and (3) the Z-score LORETA normative database also consistently localized known pathologies to the expected Brodmann areas as an hypothesis test based on the surface EEG before computing LORETA.

Parametric vs. non-parametric statistics of low resolution electromagnetic tomography (LORETA).

This study compared the relative statistical sensitivity of non-parametric and parametric statistics of 3-dimensional current sources as estimated by the EEG inverse solution Low Resolution Electromagnetic Tomography (LORETA). One would expect approximately 5% false positives (classification of a normal as abnormal) at the P < .025 level of probability (two tailed test) and approximately 1% false positives at the P < .005 level. EEG digital samples (2 second intervals sampled 128 Hz, 1 to 2 minutes eyes closed) from 43 normal adult subjects were imported into the Key Institute's LORETA program. We then used the Key Institute's cross-spectrum and the Key Institute's LORETA output files (*.lor) as the 2,394 gray matter pixel representation of 3-dimensional currents at different frequencies. The mean and standard deviation *.lor files were computed for each of the 2,394 gray matter pixels for each of the 43 subjects. Tests of Gaussianity and different transforms were computed in order to best approximate a normal distribution for each frequency and gray matter pixel. The relative sensitivity of parametric vs. non-parametric statistics were compared using a "leave-one-out" cross validation method in which individual normal subjects were withdrawn and then statistically classified as being either normal or abnormal based on the remaining subjects. Log10 transforms approximated Gaussian distribution in the range of 95% to 99% accuracy. Parametric Z score tests at P < .05 cross-validation demonstrated an average misclassification rate of approximately 4.25%, and range over the 2,394 gray matter pixels was 27.66% to 0.11%. At P < .01 parametric Z score cross-validation false positives were 0.26% and ranged from 6.65% to 0% false positives. The non-parametric Key Institute's t-max statistic at P < .05 had an average misclassification error rate of 7.64% and ranged from 43.37% to 0.04% false positives. The nonparametric t-max at P < .01 had an average misclassification rate of 6.67% and ranged from 41.34% to 0% false positives of the 2,394 gray matter pixels for any cross-validated normal subject. In conclusion, adequate approximation to Gaussian distribution and high cross-validation can be achieved by the Key Institute's LORETA programs by using a log10 transform and parametric statistics, and parametric normative comparisons had lower false positive rates than the non-parametric tests.

Estimation of the EEG power spectrum using MRI T(2) relaxation time in traumatic brain injury.

OBJECTIVES: To study the relationship between magnetic resonance imaging (MRI) T(2) relaxation time and the power spectrum of the electroencephalogram (EEG) in long-term follow up of traumatic brain injury. METHODS: Nineteen channel quantitative electroencephalograms or qEEG, tests of cognitive function and quantitative MRI T(2) relaxation times (qMRI) were measured in 18 mild to severe closed head injured outpatients 2 months to 4.6 years after injury and 11 normal controls. MRI T(2) and the Laplacian of T(2) were then correlated with the power spectrum of the scalp electrical potentials and current source densities of the qEEG. RESULTS: qEEG and qMRI T(2) were related by a frequency tuning with maxima in the alpha (8-12Hz) and the lower EEG frequencies (0.5-5Hz), which varied as a function of spatial location. The Laplacian of T(2) acted like a spatial-temporal "lens" by increasing the spatial-temporal resolution of correlation between 3-dimensional T(2) and the ear referenced alert but resting spontaneous qEEG. CONCLUSIONS: The severity of traumatic brain injury can be modeled by a linear transfer function that relates the molecular qMRI to qEEG resonant frequencies.

An EEG severity index of traumatic brain injury.

EEG spectral analyses were conducted from 19 scalp locations for patients with mild (n=40), moderate (n=25), and severe (n=43) traumatic brain injury (TBI), 15 days to 4 years after injury. Severity of TBI was judged by emergency hospital admission records (Glasgow Coma Score and duration of coma and amnesia). Highest-loading EEG variables on each factor that differed significantly between severe and mild TBI by univariate t-test were entered into a multivariate discriminant analysis, yielding 16 variables. Discriminant analysis between mild and severe TBI groups showed classification accuracy of 96.39%, sensitivity 95.45%, and specificity 97.44%. The EEG discriminant score also measured intermediate severity in moderate TBI patients. Results were cross-validated in 503 VA patients. Significant correlations between EEG discriminant scores, emergency admission measures, and post-trauma neuropsychological test scores validated the discriminant function as an index of severity of injury and a classifier of the extremes of severity.

Biophysical linkage between MRI and EEG coherence in closed head injury.

Using conventional MRI procedures, nuclear magnetic resonance (NMR) of brain water proton (1H) T2 relaxation times and EEG coherence were obtained from two independent groups of closed head injured (CHI) patients and a group of normal control subjects. Statistically significant intercorrelations were observed between 1H T2 relaxation times of the cortical gray and white matter and EEG coherence. The analyses showed that lengthened 1H T2 relaxation times of the cortical gray and white matter were related to: (1) decreased EEG coherence between short interelectrode distances (e.g., 7 cm) and increased EEG coherence between long interelectrode distances (e.g., 28 cm), (2) differences in EEG frequency in which T2 relaxation time was most strongly related to the gray matter in the delta and theta frequencies in CHI patients, and (3) increased T2 relaxation time and decreased short-distance EEG coherence were related to reduced cognitive function. The results were interpreted in terms of reduced integrity of protein/lipid neural membranes and the efficiency and effectiveness of short- and long-distance neural synchronization following traumatic brain injury.

Biophysical linkage between MRI and EEG amplitude in closed head injury.

Nuclear magnetic resonance of brain water proton (1H) T2 relaxation times and measures of absolute amplitude of EEG were obtained from 19 closed head injured patients. The relationship between EEG and T2 relaxation time differed as a function of both EEG frequency and gray matter versus white matter. White matter T2 relaxation time was positively correlated with increased EEG amplitude in the delta frequency band (0.5-3.5 Hz). In contrast, lengthened gray matter T2 relaxation time was inversely correlated with EEG amplitude in the alpha and beta frequency bands (7-22 Hz). These findings are consistent with clinical EEG studies in which white matter lesions are related to increased EEG delta amplitude and gray matter lesions are related to decreased EEG alpha and beta frequency amplitude. Estimates of the severity of injury were obtained by neuropsychological measurements, in which lengthened T2 relaxation times in both the neocortical gray and white matter were correlated with diminished cognitive function. Decreased EEG beta and alpha amplitude and increased EEG delta amplitude were also correlated with diminished cognitive function. The findings imply a biophysical linkage between the state of protein-lipid structures of the brain as measured by the MRI and the scalp-recorded EEG.

Quantitative MRI of the gray-white matter distribution in traumatic brain injury.

Quantitative analyses were performed on magnetic resonance images (MRIs) obtained from the brains of 31 traumatic brain-injured (TBI) patients and 25 normal control subjects. The quantitative analyses involved comparisons of the shapes of proton density gray scale pixel histograms obtained from both 3-mm and 5-mm slice thickness. Image segmentation was accomplished by a multispectral fuzzy C-means and/or k-nearest-neighbor (kNN) algorithms and manual classification was used to label segmented classes into CSF, white matter, and other. Shape descriptors were derived from the pixel intensity histograms of the combined gray matter and white matter classes for each MRI slice. Statistical analyses revealed significant differences in pixel intensity distributions between TBI and control subjects. Normal control subjects tended to exhibit bimodal gray matter-white matter histograms, whereas, TBI patients tended to exhibit unimodal gray matter-white matter histograms. In the control subjects the pixels intermediate in intensity between gray and white matter were located primarily at the border between the gray and white matter, whereas TBI patients exhibited a thickening of the number of intermediate pixels at the border as well as an increase in intermediate pixels in the middle of the gray and white matter. The greater the severity of TBI, then the larger the number of intermediate intensity pixels within and between gray and white matter. Further analyses demonstrated shifts in magnetic resonance relaxation times in gray and white matter in TBI patients, which suggested that the tendency toward unimodality in TBI patients represents a pathological reduction in brain differentiation due to measurable biophysical change.