Journal of Clinical EEG & Neuroscience, January 2005
Table of Contents
Parametric vs. Non-Parametric Statistics of Low Resolution Electromagnetic Tomography (LORETA)
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statistics were compared using a “leave-one-out” cross validation method in which individual normal subjects were withdrawn and then statistically classified as being either normal or abnormal based on the remaining subjects.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 non-parametric 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.
A negative ERP component, N270, which was considered to reflect conflict processing activity in human brain, was evoked by S2 of the mismatch condition. The patient group showed a delayed and smaller N270 than the control group. The prolongation of its peak latency was significant at P3 and P4 electrodes, and the reduction of its peak amplitude was significant at F3, F4, P3 and P4 electrodes. The amplitude of P300 elicited in the match condition was decreased in the patient group at P3 and P4 electrodes, but its latency did not differ from the control group. The results indicate that MDD patients as a group showed cognitive decline. N270 is a sensitive index in revealing cognitive impairment.
EEG and Seizures in Autistic Children and Adolescents: Further Findings with Therapeutic Implications
EEG signals were recorded from eight healthy volunteers during nightly sleep. We estimated the values of ApEn in EEG signals in each sleep stage. The ApEn values for EEG signals (mean ± SD) were 0.896 ± 0.264 during eyes-closed waking state, 0.738 ± 0.089 during Stage I, 0.615 ± 0.107 during Stage II, 0.487 ± 0.101 during Stage III, 0.397 ± 0.078 during Stage IV and 0.789 ± 0.182 during REM sleep. The ApEn values were found to differ with statistical significance among the six different stages of consciousness (ANOVA, p<0.001). ApEn of EEG was statistically significantly lower during Stage IV and higher during wake and REM sleep.
We conclude that ApEn measurement can be useful to estimate sleep stages and the complexity in brain activity.
Schizophrenic (SZ) and major depressive disorder (MDD) patients showed significantly greater high frequency (HF) power than healthy controls (HC) in all sleep stages (p<0.0001). SZs also exhibited significantly greater HF power than MDD patients in all sleep stages except wakefulness (W) (p<0.05). In all groups, gamma (35-45Hz) power was greater in W, decreased during slow wave sleep (SWS) and decreased further during rapid eye movement (REM). Beta 2 (20-35 Hz) power was greater in W and REM than in SWS. Only positive symptoms exhibited an association with HF power.
These findings provide further evidence that PD patients have a lower degree of inter-hemispheric functional connectivity in the frontal region and intra-hemispheric functional connectivity in the bilateral temporal region, and that chronic condition or frequent panic attacks in PD patients may be related to the pathophysiological CNS changes.
Auditory and Visual P300 Evoked Potentials Do Not Predict Response to Valproate Treatment of Aggression in Patients with Borderline and Antisocial Personality Disorders
Auditory Hallucinations in Psychosis: Dysfunctional Cortical Connectivity in Neural Substrates of Central Auditory Processing and Episodic Verbal Memory
Introduction: Theories of the neurobiological basis of auditory hallucinations (AHs) in psychosis mainly conceptualise these phenomena as “inner speech” which is misattributed to an external agency. However, the majority of neuroimaging studies of AHs report no evidence of activation of Broca’s area, an otherwise reliable concommitant of “inner speech.” However, we have previously reported behavioral evidence of impaired interhemispheric transfer in a hallucinating patient group (McKay et al, American Journal of Psychiatry, 157: 759-766).
Methods: Brain activation associated with self-reported AHs was measured using PET in patients with psychosis (n=8) compared to perception of transient random human speech in non-hallucinating patients (n=7) and normal controls (n=8). Effective connectivity was also compared using both EEG and fMRI measures in two other groups of patients with and without AHs.
Results: Externally generated speech sounds elicited extensive bilateral activation of auditory cortical regions (Brodmann areas 40, 41, 42 and 22). In contrast, hallucinations were associated with a network of activation including bilateral auditory association cortex, left limbic regions and hippocampus, right medial frontal and right prefrontal regions. Connectivity between left and right auditory association cortex appeared to be lower in patients with hallucinations.
Conclusions: The observed pattern of activation is most consistent with models of auditory hallucinations as mis-remembered episodic memory of speech (Copolov et al, Psychiatry Research: Neuroimaging, 122: 139-152). We formulated a new model of AHs in which AHs were related to reduced connectivity in neural substrates of episodic verbal memory and central auditry processing.
POSTER: Structural Changes of the Corpus Callosum in Subjects with Mild Cognitive Impairment
Introduction: Previous studies demonstrate a significant atrophy of the corpus callosum (CC) in patients with Alzheimer’s disease (AD). If structural changes of the CC already occur in mild cognitive impairment (MCI) has not yet been investigated.
Methods: In the present study 21 subjects with mild cognitive impairment (MCI) (mean age 66.2 ± 0.75 years), 21 healthy controls (mean age 66.6 ± 0.6 years) and 10 age-matched patients with AD (mean MMSE 19.2 ± 3.85) were investigated using quantitative MRI. After drawing a horizontal line in the mid-sagittal T1 weighted slice from the most anterior to the most posterior point of the CC, it was divided in 5 parts (CC1-5) by constructing vertical lines of equal distance perpendicular to the horizontal line.
Results: The three groups did not differ significantly in intracranial volume. As expected, the CC was significantly smaller in patients with AD than in healthy controls and subjects with MCI. Atrophy was most severe in the rostral parts (CC1-3).
The subjects with MCI did not show significant difference in total callosal area compared to healthy controls, but significant smaller size in the two rostral segments (CC1 + CC2).
Conclusions: These findings support the hypothesis that MCI may represent a preclinical stage of AD.