Unrestricted principal components analysis of brain electrical activity: issues of data dimensionality, artifact, and utility

Longitudinal Evidence for Attenuated Local-Global Deviance Detection as a Precursor of Working Memory Decline

From the perspective of predictive coding, normal aging is accompanied by decreased weighting of sensory inputs and increased reliance on predictions, resulting in the attenuation of prediction errors in older age. Recent electroencephalography (EEG) research further revealed that the age-related shift from sensorium to predictions is hierarchy-selective, as older brains show little reduction in lower-level but significant suppression in higher-level prediction errors. Moreover, the disrupted propagation of prediction errors from the lower-level to the higher-level seems to be linked to deficient maintenance of information in working memory. However, it is unclear whether the hierarchical predictive processing continues to decline with advancing age as working memory. Here, we longitudinally followed a sample of 78 participants from three age groups (including seniors, adults, and adolescents) over three years' time. Seniors exhibited largely preserved local processing [consisting of comparable mismatch negativity (MMN), delayed P3a, and comparable reorienting negativity (RON)] but significantly compromised global processing (consisting of suppressed frontocentral negativity and suppressed P3b) in the auditory local-global paradigm. These electrophysiological responses did not change with the passing of time, unlike working memory which deteriorated with advancing age. Correlation analysis further showed that these electrophysiological responses signaling prediction errors are indicative of concurrent working memory. Moreover, there was a correlation between earlier predictive processing and later working memory but not between earlier working memory and later predictive processing. The temporal asymmetry suggested that the hierarchy-selective attenuation of prediction errors is likely a precursor of working memory decline.

The mismatch negativity to abstract relationship of tone pairs is independent of attention

The mismatch negativity (MMN) implicating a comparison process between the deviant and the memory trace of the standard can be elicited by not only changes in physical features but also violations of abstract patterns. It is considered pre-attentive, yet the use of the passive design makes it difficult to exclude the possibility of attention leak. In contrast to how this issue has been well addressed with the MMN to physical changes, much less research directly investigated the attentional effect on the MMN to abstract relationships. Here we conducted an electroencephalography (EEG) experiment to study whether and how the MMN to abstract relationships is modulated by attention. We adapted the oddball paradigm of Kujala et al. by presenting occasional descending tone pairs among frequent ascending tone pairs, while additionally implementing a novel control of attention. Participants' attention was either directed away from the sounds (with an engaging task of visual target detection, so that the sounds were task-irrelevant) or toward the sounds (with a conventional task of auditory deviant detection, so that the sounds were task-relevant). The MMN to abstract relationships appeared regardless of attention, confirming the pre-attentive assumption. The attention-independence of the frontocentral and supratemporal components of the MMN supported the notion that attention is not required to generate the MMN. At the individual level, a relatively equal number of participants showed attention enhancement and attention suppression. It is unlike the attentional modulation on the P3b, which was robustly elicited in the attended condition only. The concurrent collection of these two neurophysiological markers in both unattended and attended conditions might be potentially suitable for testing clinical populations showing heterogeneous deficits in auditory function independent/dependent of attention.

Decoding Digital Visual Stimulation From Neural Manifold With Fuzzy Leaning on Cortical Oscillatory Dynamics

A crucial point in neuroscience is how to correctly decode cognitive information from brain dynamics for motion control and neural rehabilitation. However, due to the instability and high dimensions of electroencephalogram (EEG) recordings, it is difficult to directly obtain information from original data. Thus, in this work, we design visual experiments and propose a novel decoding method based on the neural manifold of cortical activity to find critical visual information. First, we studied four major frequency bands divided from EEG and found that the responses of the EEG alpha band (8–15 Hz) in the frontal and occipital lobes to visual stimuli occupy a prominent place. Besides, the essential features of EEG data in the alpha band are further mined via two manifold learning methods. We connect temporally consecutive brain states in the t distribution random adjacency embedded (t-SNE) map on the trial-by-trial level and find the brain state dynamics to form a cyclic manifold, with the different tasks forming distinct loops. Meanwhile, it is proved that the latent factors of brain activities estimated by t-SNE can be used for more accurate decoding and the stable neural manifold is found. Taking the latent factors of the manifold as independent inputs, a fuzzy system-based Takagi-Sugeno-Kang model is established and further trained to identify visual EEG signals. The combination of t-SNE and fuzzy learning can highly improve the accuracy of visual cognitive decoding to 81.98%. Moreover, by optimizing the features, it is found that the combination of the frontal lobe, the parietal lobe, and the occipital lobe is the most effective factor for visual decoding with 83.05% accuracy. This work provides a potential tool for decoding visual EEG signals with the help of low-dimensional manifold dynamics, especially contributing to the brain–computer interface (BCI) control, brain function research, and neural rehabilitation.

Attention modulates repetition effects in a context of low periodicity

Stimulus repetition can result in a reduction in neural responses (i.e., repetition suppression) in neuroimaging studies. Predictive coding models of perception postulate that this phenomenon largely reflects the top-down attenuation of prediction errors. Electroencephalography research further demonstrated that repetition effects consist of sequentially ordered attention-independent and attention-dependent components in a context of high periodicity. However, the statistical structure of our auditory environment is richer than that of a fixed pattern. It remains unclear if the attentional modulation of repetition effects can be generalised to a setting which better represents the nature of our auditory environment. Here we used electroencephalography to investigate whether the attention-independent and attention-dependent components of repetition effects previously described in the auditory modality remain in a context of low periodicity where temporary disruption might be absent/present. Participants were presented with repetition trains of various lengths, with/without temporary disruptions. We found attention-independent and attention-dependent repetition effects on, respectively, the P2 and P3a event-related potential components. This pattern of results is in line with previous research, confirming that the attenuation of prediction errors upon stimulus repetition is first registered regardless of attentional state before further attenuation of attended but not unattended prediction errors takes place. However, unlike previous reports, these effects manifested on later components. This divergence from previous studies is discussed in terms of the possible contribution of contextual factors.

Topographic distribution of the EEG ad hoc broad bands during sleep and wakefulness in the spider monkey (Ateles Geoffroyi)

There is evidence that research on sleep among New World monkeys may provide important knowledge related to the evolution of sleep more broadly in the primate order. Digital electroencephalographic (EEG) analyses provide essential knowledge on sleep in the spider monkey. Recently, specific EEG bands related to sleep in these animals have been obtained using principal component analysis, but the exact spatio-temporal distribution of these EEG bands in this species has not yet been analyzed. This study determined the topographic distribution of the EEG spectral power of ad hoc broad bands during rapid eye movement sleep, nonrapid eye movement sleep, and wakefulness. Superficial EEG activity was obtained from the occipital, frontal, and central areas of six young adult male monkeys housed in a laboratory. During wakefulness, occipital areas showed high absolute power in the 1-3, 3-12, and 11-30 Hz ranges, while during nonrapid eye movement 1 sleep the highest absolute power was in the 13-30 Hz range. During nonrapid eye movement 3 sleep, frontal and central areas showed a high absolute power in the 18-19 Hz range. Finally, the right central area showed a high absolute power in the 20-30 Hz range during rapid eye movement sleep. This topographic distribution of EEG bands could represent the brain organization required for arousal and mnemonic processing during sleep in the spider monkey.

A Muscular and Cerebral Physiological Indices Assessment for Stress Measuring during Virtual Wheelchair Guidance

The work presented in this manuscript has the purpose to assess the relationship between human factors and physiological indices. We discuss the relationship between stress as human factor and cerebral and muscular signals as features. Ten male paraplegic, right-handed subjects were volunteers for the experiment (mean age 34 ±6). They drove a virtual wheelchair in an indoor environment. They filled five missions where, in each one, an environmental parameter was changed. Meanwhile, they were equipped with Electromyography (EMG) sensors and Electroencephalography (EEG). Frequency and temporal features were filtered and extracted. Principal component analysis (PCA), Fisher's tests, repeated measure Anova and post hoc Tukey test (α = 0.05) were implemented for statistics. Environmental modifications are subject to induce stress, which impacts muscular and cerebral activities. While the time pressure parameter was the most influent, the transition from static to moving obstacles (avatars), tends to have a significant impact on stress levels. However, adding more moving obstacles did not show any impact. A synchronization factor was noticed between cerebral and muscular features in higher stress levels. Further examination is needed to assess EEG reliability in these situations.

Both contextual regularity and selective attention affect the reduction of precision-weighted prediction errors but in distinct manners

Predictive coding model of perception postulates that the primary objective of the brain is to infer the causes of sensory inputs by reducing prediction errors (i.e., the discrepancy between expected and actual information). Moreover, prediction errors are weighted by their precision (i.e., inverse variance), which quantifies the degree of certainty about the variables. There is accumulating evidence that the reduction of precision-weighted prediction errors can be affected by contextual regularity (as an external factor) and selective attention (as an internal factor). However, it is unclear whether the two factors function together or separately. Here we used electroencephalography (EEG) to examine the putative interaction of contextual regularity and selective attention on this reduction process. Participants were presented with pairs of regular and irregular quartets in attended and unattended conditions. We found that contextual regularity and selective attention independently modulated the N1/MMN where the repetition effect was absent. On the P2, the two factors respectively interacted with the repetition effect without interacting with each other. The results showed that contextual regularity and selective attention likely affect the reduction of precision-weighted prediction errors in distinct manners. While contextual regularity finetunes our efficiency at reducing precision-weighted prediction errors, selective attention seems to modulate the reduction process following the Matthew effect of accumulated advantage.

Principal component analysis of electroencephalographic activity during sleep and wakefulness in the spider monkey (Ateles geoffroyi)

The study of electroencephalographic (EEG) activity during sleep in the spider monkey has provided new insights into primitive arboreal sleep physiology and behavior in anthropoids. Nevertheless, studies conducted to date have maintained the frequency ranges of the EEG bands commonly used with humans. The aim of the present work was to determine the EEG broad bands that characterize sleep and wakefulness in the spider monkey using principal component analysis (PCA). The EEG activity was recorded from the occipital, central, and frontal EEG derivations of six young-adult male spider monkeys housed in a laboratory setting. To determine which frequencies covaried and which were orthogonally independent during sleep and wakefulness, the power EEG spectra and interhemispheric and intrahemispheric EEG correlations from 1 to 30 Hz were subjected to PCA. Findings show that the EEG bands detection differed from those reported previously in both spider monkeys and humans, and that the 1-3 and 2-13 Hz frequency ranges concur with the oscillatory activity elucidated by cellular recordings of subcortical regions. Results show that applying PCA to the EEG spectrum during sleep and wakefulness in the spider monkey led to the identification of frequencies that covaried with, and were orthogonally independent of, other frequencies in each behavioral vigilance state. The new EEG bands differ from those used previously with both spider monkeys and humans. The 1-3 and 2-13 Hz frequency ranges are in accordance with the oscillatory activity elucidated by cellular recordings of subcortical regions in other mammals.

Week-by-week changes in sleep EEG in healthy full-term newborns

Spectral analysis of neonatal sleep is useful for studying brain maturation; however, most studies have analyzed conventional broad bands described for awake adults, so a distinct approach for EEG analysis may disclose new findings.

STUDY OBJECTIVES

To extract independent EEG broad bands using principal component analysis (PCA) and describe week-by-week EEG changes in quiet sleep (QS) and active sleep (AS) during the first 5 weeks of postnatal life in healthy, full-term newborns.

METHODS

Polysomnography of spontaneous sleep was recorded in 60 newborns in 5 groups at 41, 42, 43, 44, and 45 weeks (n = 12 each) postconceptional age (POST-C). QS and AS stages were identified. Absolute power (AP) for 1 Hz bins between 1 and 30 Hz was subjected to PCA to extract independent broad bands.

RESULTS

PCA rendered three independent broad bands distinct from conventional bands. They explained 82.8% of variance: 2-10 Hz, 10-16 Hz, and 17-30 Hz. ANOVAs (group × age × derivations) showed significant higher power at 2-10 Hz with greater age, higher power in QS than AS in all three bands, and significantly higher AP in the left central region, and in the right occipital and temporal areas, in both sleep stages.

CONCLUSION

A different method of analyzing sleep EEG generated new information on brain maturation. The Sigma frequencies identified suggest that sleep spindle maturation begins by at least 41 weeks of POST-C age. Interhemispheric asymmetries during sleep suggest earlier development of the central left region and the right occipital and temporal areas.

A unique pattern of cortical connectivity characterizes patients with attention deficit disorders: a large electroencephalographic coherence study

BACKGROUND

Attentional disorders (ADD) feature decreased attention span, impulsivity, and over-activity interfering with successful lives. Childhood onset ADD frequently persists to adulthood. Etiology may be hereditary or disease associated. Prevalence is 5% but recognition may be 'overshadowed' by comorbidities (brain injury, mood disorder) thereby escaping formal recognition. Blinded diagnosis by MRI has failed. ADD may not itself manifest a single anatomical pattern of brain abnormality but may reflect multiple, unique responses to numerous and diverse etiologies. Alternatively, a stable ADD-specific brain pattern may be better detected by brain physiology. EEG coherence, measuring cortical connectivity, is used to explore this possibility.

METHODS

Participants: Ages 2 to 22 years; 347 ADD and 619 neurotypical controls (CON). Following artifact reduction, principal components analysis (PCA) identifies coherence factors with unique loading patterns. Discriminant function analysis (DFA) determines discrimination success differentiating ADD from CON. Split-half and jackknife analyses estimate prospective diagnostic success. Coherence factor loading constitutes an ADD-specific pattern or 'connectome'.  RESULTS: PCA identified 40 factors explaining 50% of total variance. DFA on CON versus ADD groups utilizing all factors was highly significant (p≤0.0001). ADD subjects were separated into medication and comorbidity subgroups. DFA (stepping allowed) based on CON versus ADD without comorbidities or medication treatment successfully classified the correspondingly held out ADD subjects in every instance. Ten randomly generated split-half replications of the entire population demonstrated high-average classification success for each of the left out test-sets (overall: CON, 83.65%; ADD, 90.07%). Higher success was obtained with more restricted age sub-samples using jackknifing: 2-8 year olds (CON, 90.0%; ADD, 90.6%); 8-14 year olds (CON, 96.8%; ADD 95.9%); and 14-20 year-olds (CON, 100.0%; ADD, 97.1%). The connectome manifested decreased and increased coherence. Patterns were complex and bi-hemispheric; typically reported front-back and left-right loading patterns were not observed. Subtemporal electrodes (seldom utilized) were prominently involved.  CONCLUSIONS: Results demonstrate a stable coherence connectome differentiating ADD from CON subjects including subgroups with and without comorbidities and/or medications. This functional 'connectome', constitutes a diagnostic ADD phenotype. Split-half replications support potential for EEG-based ADD diagnosis, with increased accuracy using limited age ranges. Repeated studies could assist recognition of physiological change from interventions (pharmacological, behavioral).

Distinctive Representation of Mispredicted and Unpredicted Prediction Errors in Human Electroencephalography

The predictive coding model of perception proposes that neuronal responses are modulated by the amount of sensory input that the internal prediction cannot account for (i.e., prediction error). However, there is little consensus on what constitutes nonpredicted stimuli. Conceptually, whereas mispredicted stimuli may induce both prediction error generated by prediction that is not perceived and prediction error generated by sensory input that is not anticipated, unpredicted stimuli involves no top-down, only bottom-up, propagation of information in the system. Here, we examined the possibility that the processing of mispredicted and unpredicted stimuli are dissociable at the neurophysiological level using human electroencephalography. We presented participants with sets of five tones in which the frequency of the fifth tones was predicted, mispredicted, or unpredicted. Participants were required to press a key when they detected a softer fifth tone to maintain their attention. We found that mispredicted and unpredicted stimuli are associated with different amount of cortical activity, probably reflecting differences in prediction error. Moreover, relative to predicted stimuli, the mispredicted prediction error manifested as neuronal enhancement and the unpredicted prediction error manifested as neuronal attenuation on the N1 event-related potential component. These results highlight the importance of differentiating between the two nonpredicted stimuli in theoretical work on predictive coding.

Neurophysiological differences between patients clinically at high risk for schizophrenia and neurotypical controls--first steps in development of a biomarker

BACKGROUND

Schizophrenia is a severe, disabling and prevalent mental disorder without cure and with a variable, incomplete pharmacotherapeutic response. Prior to onset in adolescence or young adulthood a prodromal period of abnormal symptoms lasting weeks to years has been identified and operationalized as clinically high risk (CHR) for schizophrenia. However, only a minority of subjects prospectively identified with CHR convert to schizophrenia, thereby limiting enthusiasm for early intervention(s). This study utilized objective resting electroencephalogram (EEG) quantification to determine whether CHR constitutes a cohesive entity and an evoked potential to assess CHR cortical auditory processing.

METHODS

This study constitutes an EEG-based quantitative neurophysiological comparison between two unmedicated subject groups: 35 neurotypical controls (CON) and 22 CHR patients. After artifact management, principal component analysis (PCA) identified EEG spectral and spectral coherence factors described by associated loading patterns. Discriminant function analysis (DFA) determined factors' discrimination success between subjects in the CON and CHR groups. Loading patterns on DFA-selected factors described CHR-specific spectral and coherence differences when compared to controls. The frequency modulated auditory evoked response (FMAER) explored functional CON-CHR differences within the superior temporal gyri.

RESULTS

Variable reduction by PCA identified 40 coherence-based factors explaining 77.8% of the total variance and 40 spectral factors explaining 95.9% of the variance. DFA demonstrated significant CON-CHR group difference (P <0.00001) and successful jackknifed subject classification (CON, 85.7%; CHR, 86.4% correct). The population distribution plotted along the canonical discriminant variable was clearly bimodal. Coherence factors delineated loading patterns of altered connectivity primarily involving the bilateral posterior temporal electrodes. However, FMAER analysis showed no CON-CHR group differences.

CONCLUSIONS

CHR subjects form a cohesive group, significantly separable from CON subjects by EEG-derived indices. Symptoms of CHR may relate to altered connectivity with the posterior temporal regions but not to primary auditory processing abnormalities within these regions.

Resting state EEG abnormalities in autism spectrum disorders

Autism spectrum disorders (ASD) are a group of complex and heterogeneous developmental disorders involving multiple neural system dysfunctions. In an effort to understand neurophysiological substrates, identify etiopathophysiologically distinct subgroups of patients, and track outcomes of novel treatments with translational biomarkers, EEG (electroencephalography) studies offer a promising research strategy in ASD. Resting-state EEG studies of ASD suggest a U-shaped profile of electrophysiological power alterations, with excessive power in low-frequency and high-frequency bands, abnormal functional connectivity, and enhanced power in the left hemisphere of the brain. In this review, we provide a summary of recent findings, discuss limitations in available research that may contribute to inconsistencies in the literature, and offer suggestions for future research in this area for advancing the understanding of ASD.

The relationship of Asperger's syndrome to autism: a preliminary EEG coherence study

BACKGROUND

It has long been debated whether Asperger's Syndrome (ASP) should be considered part of the Autism Spectrum Disorders (ASD) or whether it constitutes a unique entity. The Diagnostic and Statistical Manual, fourth edition (DSM-IV) differentiated ASP from high functioning autism. However, the new DSM-5 umbrellas ASP within ASD, thus eliminating the ASP diagnosis. To date, no clear biomarkers have reliably distinguished ASP and ASD populations. This study uses EEG coherence, a measure of brain connectivity, to explore possible neurophysiological differences between ASP and ASD.

METHODS

Voluminous coherence data derived from all possible electrode pairs and frequencies were previously reduced by principal components analysis (PCA) to produce a smaller number of unbiased, data-driven coherence factors. In a previous study, these factors significantly and reliably differentiated neurotypical controls from ASD subjects by discriminant function analysis (DFA). These previous DFA rules are now applied to an ASP population to determine if ASP subjects classify as control or ASD subjects. Additionally, a new set of coherence based DFA rules are used to determine whether ASP and ASD subjects can be differentiated from each other.

RESULTS

Using prior EEG coherence based DFA rules that successfully classified subjects as either controls or ASD, 96.2% of ASP subjects are classified as ASD. However, when ASP subjects are directly compared to ASD subjects using new DFA rules, 92.3% ASP subjects are identified as separate from the ASD population. By contrast, five randomly selected subsamples of ASD subjects fail to reach significance when compared to the remaining ASD populations. When represented by the discriminant variable, both the ASD and ASD populations are normally distributed.

CONCLUSIONS

Within a control-ASD dichotomy, an ASP population falls closer to ASD than controls. However, when compared directly with ASD, an ASP population is distinctly separate. The ASP population appears to constitute a neurophysiologically identifiable, normally distributed entity within the higher functioning tail of the ASD population distribution. These results must be replicated with a larger sample given their potentially immense clinical, emotional and financial implications for affected individuals, their families and their caregivers.

Childhood abuse and EEG source localization in crack cocaine dependence

Fourteen subjects with histories of sexual and/or physical abuse in childhood and 13 matched control subjects were selected from a consecutive series of clients in residential treatment for crack cocaine dependence. Standardized low-resolution electromagnetic brain tomography (sLORETA) was used to estimate the source generators of the EEG in a cortical mask with voxel z-scores referenced to normative data at frequency intervals of 039 Hz, with nonparametric permutation to correct by randomization for the number of comparisons and the intercorrelations and variance of distribution of voxel values. Subjects with histories of abuse in childhood had significantly greater EEG power than controls in the theta frequency range (3.51-7.41 Hz), with greatest differences in the 3.90-Hz band distributed mainly in the parahippocampal, fusiform, lingual, posterior cingulate, and insular gyri. The groups did not differ significantly with regard to delta (1.56-3.12 Hz), alpha (7.81-12.48 Hz), beta (12.87-19.89 Hz), and gamma (20.28-35.10 Hz) frequency power. In excess, theta EEG power, a bandwidth of transactions among hippocampus and amygdala and paralimbic and visual association cortex, may be a correlate of childhood exposure to abuse.

School-age effects of the newborn individualized developmental care and assessment program for preterm infants with intrauterine growth restriction: preliminary findings

BACKGROUND

The experience in the newborn intensive care nursery results in premature infants' neurobehavioral and neurophysiological dysfunction and poorer brain structure. Preterms with severe intrauterine growth restriction are doubly jeopardized given their compromised brains. The Newborn Individualized Developmental Care and Assessment Program improved outcome at early school-age for preterms with appropriate intrauterine growth. It also showed effectiveness to nine months for preterms with intrauterine growth restriction. The current study tested effectiveness into school-age for preterms with intrauterine growth restriction regarding executive function (EF), electrophysiology (EEG) and neurostructure (MRI).

METHODS

Twenty-three 9-year-old former growth-restricted preterms, randomized at birth to standard care (14 controls) or to the Newborn Individualized Developmental Care and Assessment Program (9 experimentals) were assessed with standardized measures of cognition, achievement, executive function, electroencephalography, and magnetic resonance imaging. The participating children were comparable to those lost to follow-up, and the controls to the experimentals, in terms of newborn background health and demographics. All outcome measures were corrected for mother's intelligence. Analysis techniques included two-group analysis of variance and stepwise discriminate analysis for the outcome measures, Wilks' lambda and jackknifed classification to ascertain two-group classification success per and across domains; canonical correlation analysis to explore relationships among neuropsychological, electrophysiological and neurostructural domains at school-age, and from the newborn period to school-age.

RESULTS

Controls and experimentals were comparable in age at testing, anthropometric and health parameters, and in cognitive and achievement scores. Experimentals scored better in executive function, spectral coherence, and cerebellar volumes. Furthermore, executive function, spectral coherence and brain structural measures discriminated controls from experimentals. Executive function correlated with coherence and brain structure measures, and with newborn-period neurobehavioral assessment.

CONCLUSION

The intervention in the intensive care nursery improved executive function as well as spectral coherence between occipital and frontal as well as parietal regions. The experimentals' cerebella were significantly larger than the controls'. These results, while preliminary, point to the possibility of long-term brain improvement even of intrauterine growth compromised preterms if individualized intervention begins with admission to the NICU and extends throughout transition home. Larger sample replications are required in order to confirm these results.

CLINICAL TRIAL REGISTRATION

The study is registered as a clinical trial. The trial registration number is NCT00914108.

Narrow band quantitative and multivariate electroencephalogram analysis of peri-adolescent period

Background

The peri-adolescent period is a crucial developmental moment of transition from childhood to emergent adulthood. The present report analyses the differences in Power Spectrum (PS) of the Electroencephalogram (EEG) between late childhood (24 children between 8 and 13 years old) and young adulthood (24 young adults between 18 and 23 years old).

Results

The narrow band analysis of the Electroencephalogram was computed in the frequency range of 0–20 Hz. The analysis of mean and variance suggested that six frequency ranges presented a different rate of maturation at these ages, namely: low delta, delta-theta, low alpha, high alpha, low beta and high beta. For most of these bands the maturation seems to occur later in anterior sites than posterior sites. Correlational analysis showed a lower pattern of correlation between different frequencies in children than in young adults, suggesting a certain asynchrony in the maturation of different rhythms. The topographical analysis revealed similar topographies of the different rhythms in children and young adults. Principal Component Analysis (PCA) demonstrated the same internal structure for the Electroencephalogram of both age groups. Principal Component Analysis allowed to separate four subcomponents in the alpha range. All these subcomponents peaked at a lower frequency in children than in young adults.

Conclusions

The present approaches complement and solve some of the incertitudes when the classical brain broad rhythm analysis is applied. Children have a higher absolute power than young adults for frequency ranges between 0-20 Hz, the correlation of Power Spectrum (PS) with age and the variance age comparison showed that there are six ranges of frequencies that can distinguish the level of EEG maturation in children and adults. The establishment of maturational order of different frequencies and its possible maturational interdependence would require a complete series including all the different ages.

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

BACKGROUND

The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact.

METHODS

Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls.

RESULTS

Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P < 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz).

CONCLUSIONS

Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.

EEG spectral coherence data distinguish chronic fatigue syndrome patients from healthy controls and depressed patients--a case control study

BACKGROUND

Previous studies suggest central nervous system involvement in chronic fatigue syndrome (CFS), yet there are no established diagnostic criteria. CFS may be difficult to differentiate from clinical depression. The study's objective was to determine if spectral coherence, a computational derivative of spectral analysis of the electroencephalogram (EEG), could distinguish patients with CFS from healthy control subjects and not erroneously classify depressed patients as having CFS.

METHODS

This is a study, conducted in an academic medical center electroencephalography laboratory, of 632 subjects: 390 healthy normal controls, 70 patients with carefully defined CFS, 24 with major depression, and 148 with general fatigue. Aside from fatigue, all patients were medically healthy by history and examination. EEGs were obtained and spectral coherences calculated after extensive artifact removal. Principal Components Analysis identified coherence factors and corresponding factor loading patterns. Discriminant analysis determined whether spectral coherence factors could reliably discriminate CFS patients from healthy control subjects without misclassifying depression as CFS.

RESULTS

Analysis of EEG coherence data from a large sample (n = 632) of patients and healthy controls identified 40 factors explaining 55.6% total variance. Factors showed highly significant group differentiation (p < .0004) identifying 89.5% of unmedicated female CFS patients and 92.4% of healthy female controls. Recursive jackknifing showed predictions were stable. A conservative 10-factor discriminant function model was subsequently applied, and also showed highly significant group discrimination (p < .001), accurately classifying 88.9% unmedicated males with CFS, and 82.4% unmedicated male healthy controls. No patient with depression was classified as having CFS. The model was less accurate (73.9%) in identifying CFS patients taking psychoactive medications. Factors involving the temporal lobes were of primary importance.

CONCLUSIONS

EEG spectral coherence analysis identified unmedicated patients with CFS and healthy control subjects without misclassifying depressed patients as CFS, providing evidence that CFS patients demonstrate brain physiology that is not observed in healthy normals or patients with major depression. Studies of new CFS patients and comparison groups are required to determine the possible clinical utility of this test. The results concur with other studies finding neurological abnormalities in CFS, and implicate temporal lobe involvement in CFS pathophysiology.

Identifying robust and sensitive frequency bands for interrogating neural oscillations

Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic ("resting" or "spontaneous") electroencephalogram (EEG) into five bands: delta (1-5Hz), alpha-low (6-9Hz), alpha-high (10-11Hz), beta (12-19Hz), and gamma (>21Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)-based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time-frequency (event-related spectral perturbation, event-related synchronization/desynchronization) domains.

Effects of the Newborn Individualized Developmental Care and Assessment Program (NIDCAP) at age 8 years: preliminary data

The current study reports the effects of NIDCAP (Newborn Individualized Developmental Care and Assessment Program) at 8 years of age for a randomized controlled trial of 38 very early born (< or =29 weeks postmenstrual age), high-risk preterm infants. It was hypothesized that the experimental group at school age in comparison with the control group would perform significantly better neuropsychologically and neuroelectrophysiologically. Twenty-two (11 control, 11 experimental) children of the original 38 (18 control, 20 experimental) participants were studied at school age with a detailed neuropsychological battery and with EEG spectral coherence measures. Results indicated significantly better right hemisphere and frontal lobe function in the experimental group than the control group, both neuropsychologically and neurophysiologically. Neurobehavioral and physiological results in the newborn period successfully predicted the beneficial brain function effects at age 8 years. Results support the conclusion that the NIDCAP intervention has lasting effects into school age.

From synchronous neuronal discharges to subjective awareness?

For practical clinical purposes, as well as because of their deep philosophical implications, it becomes increasingly important to be aware of contemporary studies of the brain mechanisms that generate subjective experiences. Current research has progressed to the point where plausible theoretical proposals can be made about the neurophysiological and neurochemical processes which mediate perception and sustain subjective awareness. An adequate theory of consciousness must describe how information about the environment is encoded by the exogenous system, how memories are stored in the endogenous system and released appropriately for the present circumstances, how the exogenous and endogenous systems interact to produce perception, and explain how consciousness arises from that interaction. Evidence assembled from a variety of neuroscience areas, together with the invariant reversible electrophysiological changes observed with loss and return of consciousness in anesthesia as well as distinctive quantitative electroencephalographic profiles of various psychiatric disorders, provides an empirical foundation for this theory of consciousness. This evidence suggests the need for a paradigm shift to explain how the brain accomplishes the transformation from synchronous and distributed neuronal discharges to seamless global subjective awareness. This chapter undertakes to provide a detailed description and explanation of these complex processes by experimental evidence marshaled from a wide variety of sources.

Long latency evoked potential database for clinical applications: justification and examples

We summarize our experience with the clinical utility of long latency evoked potential (EP) data in clinical QEEG studies. In contrast to common wisdom, such EP data are consistent across appropriately chosen age groups. In a healthy adult population, EP data correlate consistently with independently collected psychological variables. In our pediatric referral population, EP data are of greatest and most unique value in the learning disabilities but also augment detection of abnormality in epilepsy and behavioral abnormality. Selection of subjects for a clinical database on the basis of examined medical, neurological and behavioral health, forms adequately consistent groupings for clinical utility. The use of the Z-SPM is essential for detection of EP abnormality. A minimum of three replications within a clinical study protects against chance/false positives. Also, the true data dimensionality within EP data sets is far less than the total number of variables typically collected.

Optimizing PCA methodology for ERP component identification and measurement: theoretical rationale and empirical evaluation

OBJECTIVE

To determine how specific methodological choices affect "data-driven" simplifications of event-related potentials (ERPs) using principal components analysis (PCA). The usefulness of the extracted component measures can be evaluated by knowledge about the variance distribution of ERPs, which are characterized by the removal of baseline activity. The variance should be small before and at stimulus onset (across and within cases), but large near the end of the recording epoch and at ERP component peaks. These characteristics are preserved with a covariance matrix, but lost with a correlation matrix, which assigns equal weights to each sample point, yielding the possibility that small but systematic variations may form a factor.

METHODS

Varimax-rotated PCAs were performed on simulated and real ERPs, systematically varying extraction criteria (number of factors) and method (correlation/covariance matrix, using unstandardized/standardized loadings before rotation).

RESULTS

Conservative extraction criteria changed the morphology of some components considerably, which had severe implications for inferential statistics. Solutions converged and stabilized with more liberal criteria. Interpretability (more distinctive component waveforms with narrow and unambiguous loading peaks) and statistical conclusions (greater effect stability across extraction criteria) were best for unstandardized covariance-based solutions. In contrast, all standardized covariance- and correlation-based solutions included "high-variance" factors during the baseline, confirming findings for simulated data.

CONCLUSIONS

Unrestricted, unstandardized covariance-based PCA solutions optimize ERP component identification and measurement.

Infant EEG spectral coherence data during quiet sleep: unrestricted principal components analysis--relation of factors to gestational age, medical risk, and neurobehavioral status

EEG spectral coherence data in quiet sleep of 312 infants were evaluated, at 42 weeks post-menstrual age. All were medically healthy and living at home by time of evaluation. The sample consisted of prematurely bom infants with a wide spectrum of underlying risk factors, as well as healthy full-term infants. Initial 3040 coherence variables were reduced by principal components analysis in an unrestricted manner, which avoided the folding of spectral and spatial information into among-subject variance. One hundred fifty factors explained 90% of the total variance; 40 Varimax rotated factors explained 65% of the variance yielding a 50:1 data reduction. Factor loading patterns ranged from multiple spectral bands for a single electrode pair to multiple electrode pairs for a single spectral band and all intermediate possibilities. Simple left-right and anterior-posterior pairings were not observed within the factor loadings. By multiple regression analysis, the 40 factors significantly predicted gestational age at birth. By canonical correlation, significant relationships were demonstrated between the coherence factors and medical risk factors as well as neurobehavioral factors. Using discriminant analysis, the coherence factors successfully discriminated between infants with high and low medical risk status and between those with the best and worst neurobehavioral status. The two factors accounting for the most variance, and chosen across several analyses, indicated increased left central-temporal coherence from 6-24 Hz, and increased frontal-occipital coherence at 10 Hz, for the infants born closest to term with lowest medical risk factors and best neurobehavioral performance.

The neurophysics of consciousness

Consciousness combines information about attributes of the present multimodal sensory environment with relevant elements of the past. Information from each modality is continuously fractionated into distinct features, processed locally by different brain regions relatively specialized for extracting these disparate components and globally by interactions among these regions. Information is represented by levels of synchronization within neuronal populations and of coherence among multiple brain regions that deviate from random fluctuations. Significant deviations constitute local and global negative entropy, or information. Local field potentials reflect the degree of synchronization among the neurons of the local ensembles. Large-scale integration, or 'binding', is proposed to involve oscillations of local field potentials that play an important role in facilitating synchronization and coherence, assessed by neuronal coincidence detectors, and parsed into perceptual frames by cortico-thalamo-cortical loops. The most probable baseline levels of local synchrony, coherent interactions among brain regions, and frame durations have been quantitatively described in large studies of their age-appropriate normative distributions and are considered as an approximation to a conscious 'ground state'. The level of consciousness during anesthesia can be accurately predicted by the magnitude and direction of reversible multivariate deviations from this ground state. An invariant set of changes takes place during anesthesia, independent of the particular anesthetic agent. Evidence from a variety of neuroscience areas supporting these propositions, together with the invariant reversible electrophysiological changes observed with loss and return of consciousness, are used to provide a foundation for this theory of consciousness. This paper illustrates the increasingly recognized need to consider global as well as local processes in the search for better explanations of how the brain accomplishes the transformation from synchronous and distributed neuronal discharges to seamless global subjective awareness.

Auditory evoked response data reduction by PCA: development of variables sensitive to reading disability

Long latency auditory evoked responses (AER) were formed on 232 healthy normal and learning impaired subjects to tone pairs of 50 msec inter-stimulus interval (TALAER) and also to the words "tight" and "tyke" (TTAER). Both evoked potential (EP) type have been used to demonstrate differences between good readers (WIAT Basic Reading score > 115, N = 42) and poor readers (Reading score < 85, N = 42). A largely automated, hands off approach was used to reduce artifact contamination, to develop canonical measures for discriminating good from poor readers, and to predict reading scores across the entire population including intermediate (average) readers. Eye and muscle artifact were diminished by multiple regression. Substantial EP data reduction was enabled by an unrestricted use of Principal Components Analysis (PCA). For each EP type, 40 factors encompassed 70-80% of initial variance, a meaningful data reduction of about 90:1. Factor interpretation was enhanced by mapping of the factor loadings. By discriminant analysis, resulting factors predicted reading group membership with over 80% jackknifed and also split--half replication accuracy. By multiple regression, they produced a canonical variate correlating significantly (p < 0.001) with the Basic Reading score (r = 0.39). The TTAER factors were more useful than the TALAER factors. The relevance of rapid auditory processing and phonemic discrimination measurements to dyslexia is discussed.

On the Reliability of Augmenting/Reducing

Abstract To investigate the impact of several methodological variations in the assessment of augmenting/reducing, auditory evoked potentials to 1000 Hz tones with varying stimulus intensity (59, 71, 79, 88, 92, 96 dB (SPL)) were recorded at 19 EEG sites in 24 participants during two separate recording sessions. The internal consistency analysis revealed only weak correlations for linear regression slopes based on high intensity levels when compared to slopes based on low intensity levels. For base-to-peak and peak-to-peak ERP component measurements, acceptable internal consistency and temporal stability were confirmed for the N1/P2-based slope, and partly for the P2 slope, whereas P1, N1, and P1/N1 slopes were not reliable. After submitting auditory evoked potentials to a covariance-based principal component analysis (PCA), followed by unscaled varimax rotation, temporal stability of slope measures for the corresponding factor scores substantially increased. The findings suggest that at least five to six intensity-levels are required over a relatively broad range to yield a reliable measure of auditory evoked augmenting/reducing. If measured reliably, P2 slopes may reflect stimulus intensity changes more precisely than N1/P2 slopes, and should therefore be evaluated in future studies of individual differences in augmenting/reducing.

Quantitative EEG methods and measures in human psychopharmacological research

The quantitative analysis of electroencephalographic (EEG) signals is an established methodology for objectively describing the central impact of drugs administered to human subjects. This paper outlays the essential objectives and findings of this electrophysiologic measurement model of drug action and addresses the subject, recording, analytical and statistical standards which are required to ensure valid pharmaco-EEG profiling. Copyright 2000 John Wiley & Sons, Ltd.

Auditory evoked responses to single tones and closely spaced tone pairs in children grouped by reading or matrices abilities

Long latency auditory evoked responses (AER) were formed to single tones and rapid tone pairs. Using the t-statistic SPM technique, children with poorer WIAT reading scores demonstrated group difference overlying the left parietal and frontal language regions but just for AER to tone pair stimuli. Variables derived from these regions were not significantly different when the same subjects were grouped by K-BIT Matrices scores. When the same children were regrouped by Matrices scores and compared using the SPM technique, differences were now seen over the right hemisphere, especially in the parietal and frontotemporal regions, for both single and two-tone derived AERs. Variables derived from these regions were not significantly different for children when grouped by reading score. AER data support a specific deficit in two-tone stimulation for poorer reading children over the left hemisphere and also a deficit to both single and two-tone stimulation over the right hemisphere for children with poorer Matrices scores.

[Guidelines for recording/analyzing quantitative EEG and evoked potentials. Part II: Clinical aspects]

Digital EEG (DEEG) and quantitative EEG (QEEG) are recently developed tools present in many clinical situations. Besides showing didactic and research utility, they may also have a clinical role. Although a considerable amount of scientific literature has been published related to QEEG, many controversies still subsist regarding its clinical utilization. Clinical applications are: 1. DEEG is already an established substitute for conventional EEG, representing a clear technical advance. 2. Certain QEEG techniques are an established addition to DEEG for: 2a) screening for epileptic spikes or seizures in long-term recordings; 2b) Operation room and intensive care unit EEG monitoring. 3. Certain QEEG techniques are considered possible useful additions to DEEG: 3a) topographic voltage and dipole analysis in epilepsy evaluations; 3b) frequency analysis in cerebrovascular disease and dementia, mostly when other tests have been inconclusive. 4. QEEG remains investigational for clinical use in postconcussion syndrome, learning disability, attention disorders, schizophrenia, depression, alcoholism and drug abuse. EEG brain mapping and other QEEG techniques should be clinically used only by physicians highly skilled in clinical EEG interpretation and as an adjunct to traditional EEG work.

Growth spurts in brain maturation during middle childhood as indexed by EEG power spectra

Developmental changes in background EEG power spectra were examined in 5-12-year-old children. The results confirmed older and more recent studies that reported continuous maturation and more sudden growth spurts in power spectral amplitude. EEG power in the Delta and Theta frequency bands decreased gradually with age, while power in the Alpha and Beta bands changed very little. Changes in spectral power were relatively increased between 6 and 7 years and between 9, 10 and 11 years. Some methodological problems concerning the assessment of cross-sectional age changes in EEG power spectra were addressed. Peak frequency increased with age; between 5 and 12 years the peak in the power spectrum shifted from fast Theta via slow Alpha to fast Alpha. Transformation of absolute power into relative power produced a high degree of interdependency between the broad bands. This interdependency affected the change with age of relative Alpha. Absolute power Alpha only changed in the eldest children, but because of a substantial decrease in Delta and Theta with increasing age, the proportion of Alpha relative to the other three bands increased. Hence, relative Alpha provided a good indication of the general maturational trend.

Stable quantitative EEG difference in post-LSD visual disorder by split-half analysis: evidence for disinhibition

Hallucinogen persisting perceptual disorder (HPPD) may follow the ingestion of LSD or other hallucinogens in a subset of users. It is characterized by chronic, intermittent or constant visual hallucinations of many sorts persisting beyond the period of acute drug effects. We studied 44 LSD-induced HPPD subjects and 88 matched controls to search for spectral and evoked potential differences using quantitative EEG (qEEG). HPPD subjects demonstrated faster alpha frequency and shorter VER (visual evoked response) latency, consistent with prior animal and human data on response to acute LSD administration which suggest LSD-induced cortical disinhibition. AER (auditory evoked response) latency was prolonged consistent with a differential LSD effect upon visual and auditory systems. The exploratory T-statistic significance probability mapping (T-SPM) technique demonstrated HPPD-control differences mostly involving temporal and left parietal scalp regions, confirmed by a split-half analysis. Significant variables were all derived from the long latency flash VER and click AER. None were derived from spectral analyzed EEG data. Canonical correlation between SPM-derived measures and variables reflecting disease severity was highly significant. A between-group stepwise discriminant analysis based upon a full set of qEEG measures demonstrated 87% prospective classification success by jackknifing and 88% success in a separate split-half analysis.

Substance use disorder exacerbates brain electrophysiological abnormalities in a psychiatrically-ill population

OBJECTIVE

To assess by brain electrical activity mapping whether cocaine and alcohol abuse and dependence would exacerbate electro-physiological abnormalities in a psychiatrically-ill population.

DESIGN, SETTING, AND PARTICIPANTS

Utilizing a brain mapping system, we assessed EEG, Spectral Analysis (Quantitative EEG[QEEG]). Evoked Potentials (Auditory and Visual), and P300 (cognitive evoked potential), in a total of 111 probands divided into three groups: controls (N = 16), psychiatrically-ill without comorbid substance use disorder (N = 34), and psychiatrically-ill with comorbid substance use disorder (cocaine and alcohol abuse and dependence) (N = 61), at an outpatient neuropsychiatric clinic. With regard to demographic data, the group participating in this study did not differ significantly. A comparison was made among the groups to assist in differentiating the effects of substance use disorder compared to psychiatric disease on brain electrical activity.

MAIN OUTCOME MEASURES

An assessment of electrophysiological abnormalities and their brain location in psychiatric and substance use disorder patients was done with a brain electrical activity mapping test.

MAIN RESULTS

Among the non-substance use disorder, psychiatrically-ill (PI) and substance use disorder, psychiatrically-ill (PI/SD) groups, significantly different brain map abnormalities were observed relative to an assessed normal population MANOVA (P = .017). Moreover, with regard to Spectral Analysis, ANOVA was significant at a P = .038, and we found a weighted linear trend of increased abnormal total spectral analysis (P = .0113), whereby substance use was significantly worse than controls. Moreover among the PI and PI/SD groups, significantly greater total evoked potential (EP) brain trap abnormalities were observed when compared with a characterized normal population (P = .0023) with increasing abnormalities as a function of substance use disorder as measured by a weighted linear trend (P = .0022). In order to determine the site of the EPS abnormalities, we evaluated these abnormalities by location. In this regard, we found all temporal abnormalities (AVBITA, see Table 2) among the PI and PI/SD groups to be significantly greater relative to an assessed normal population (P = .0026). Furthermore, we observed a linear trend of increased temporal abnormalities with increasing substance use disorder (P < .0008). In terms of bitemporal abnormalities (AVBIT) among the PI and PI/SD groups, we also found significantly more bitemporal lobe abnormalities in the PI/SD group compared to our control population (P = .009). Additionally, a weighted linear trend of increased abnormal bitemporal lobe abnormalities was observed with increasing substance use disorder (P = .0022). In the frontal lobe similar findings were observed. With AVBIFA the ANOVA was P < .011, with a weighted linear trend of P < .005 and the PI/SD group were significantly more abnormal than PI or CS on a Duncan Range test. It is noteworthy that in a selected group of depressed (Major Depressive Disorder Recurrent, 296.3) patients, we found profound abnormalities in the various brain map parameters tested. MANOVA and Univariate ANOVA's revealed significantly greater abnormalities in the PI and PI/SD groups compared to assessed controls. A MANOVA for total brain abnormalities was significant at P = .043 and univariate ANOVA's for composite measurements of TSA (P = .017), EPS (P = .0002), AVBITA (P = .000015), and AVBIT (P < .00002) are also significant. With regard to EPS and AVBITA a weighted linear trend was observed where there were increasing abnormalities with increasing substance use disorder, P = .0001 and P = .000003, respectively. Most importantly we found that in addition to increased abnormalities with increasing substance use disorder the PI/SD group had significantly more abnormalities compared to the PI group with regard to both the TSA (P < .05) and AVBIT (P < .05) composite parameters as meas

A guide for applying principal-components analysis and confirmatory factor analysis to quantitative electroencephalogram data

Principal-components analysis (PCA) has been used in quantitative electroencephalogram (qEEG) research to statistically reduce the dimensionality of the original qEEG measures to a smaller set of theoretically meaningful component variables. However, PCAs involving qEEG have frequently been performed with small sample sizes, producing solutions that are highly unstable. Moreover, solutions have not been independently confirmed using an independent sample and the more rigorous confirmatory factor analysis (CFA) procedure. This paper was intended to illustrate, by way of example, the process of applying PCA and CFA to qEEG data. Explicit decision rules pertaining to the application of PCA and CFA to qEEG are discussed. In the first of two experiments, PCAs were performed on qEEG measures collected from 102 healthy individuals as they performed an auditory continuous performance task. Component solutions were then validated in an independent sample of 106 healthy individuals using the CFA procedure. The results of this experiment confirmed the validity of an oblique, seven component solution. Measures of internal consistency and test-retest reliability for the seven component solution were high. These results support the use of qEEG data as a stable and valid measure of neurophysiological functioning. As measures of these neurophysiological processes are easily derived, they may prove useful in discriminating between and among clinical (neurological) and control populations. Future research directions are highlighted.

Complexity of focal spikes suggests relative epileptogenicity

The EEGs of 39 children with focal or multifocal spikes were subjected to singular value decomposition (SVD) as provided by a commercial software program. We noted that in children with spikes but no clinical seizures the variance accounted for by the first component averaged 91.9%, whereas in children with seizures it was 68.0% (p < .001). The first component accounted for 85.4% in children with single spike foci, for 71.5% in those with multifocal spikes, and for 61.4% (p < 0.002) in those with both focal spikes and generalized spike-wave complexes. Spikes in the frontal and frontopolar areas were the most complex, suggesting that at least in children they tend to be the partial expression of a generalized seizure tendency rather than a result of strictly local pathology.

Projection onto centroids difference vectors: a new approach to determine between group topographical differences, applied to P3 amplitude in schizophrenia

A common problem in electrophysiological research concerns between group analysis of EEG and ERP topography. This paper proposes a new technique for determining whether or not a significant difference exists between multiple scalp site measurements from two groups. The method requires no a priori assumptions about the data and is thus ideal for exploratory data analysis, and it also requires that only one statistical test need be performed (significantly reducing the possibility of type I errors). The Projection onto Centroids Difference Vectors (PCDV) method involves deriving a measure from each individual of whether their measurements across sites are closer overall to the mean measurements of the rest of their experimental group, or to the other group. These measures from each individual are then compared between groups using a Student's t test, which indicates whether one group's data is significantly spatially different from the other. In this study we describe the method in detail and apply it to both stimulated data and to real auditory P3 data in unmedicated, medicated schizophrenics and matched normal controls. The PCDV method was also compared with statistical probability mapping (SPM). The PCDV method revealed the differences between the normal and patient groups more unambiguously than SPM, and the simulated data revealed that it was not liable to type I errors. PCDV provides an appropriate method for testing any between group EEG and ERP topographical differences.

Spectral coherence in normal adults: unrestricted principal components analysis; relation of factors to age, gender, and neuropsychologic data

This paper demonstrates, by means of Principal Components Analysis (PCA), an objective approach to the reduction of large data sets produced by multichannel spectral coherence analyses. Coherence data, gathered from 371 normal healthy adults using Hjorth/Laplacian referencing during waking eyes-open and eyes-closed states, were analyzed by "unrestricted" PCA where neither spatial nor temporal variance was folded into among subject variance. There was substantial data reduction with our 4416 initial coherence variables for each state reduced to just 150 factors containing approximately 80% of the variance reflecting a 30 fold concentration of information content. Varimax rotation of the first 40 factors, encompassing 50% of the total variance for both states, revealed loading patterns primarily bilateral with no hemispheric bias, relationships primarily between distant single electrode pairs, (although a single electrode to multiple electrode pattern was also observed), and involvement of all spectral bands. Elemental left to right and anterior to posterior coherence patterns, often used on an a priori basis for coherence studies, were not evident among the rotated factor loading patterns. On the basis of high loadings upon extra bipolar artifact channels, 32 factors accounting for approximately 40% of the variance were identified as reflecting artifactual coherence relationships. By multiple regression the 48 non-artifactual factor scores successfully predicted subject age. In general, coherence diminished with age, which may partly explain age-related EEG desynchronization in healthy adults. Coherence factors also predicted 6 of 10 neuropsychologic variables. Gender was successfully predicted by discriminant analysis. No global interpretations about coherence and gender or neuropsychologic function were possible, i.e., almost equal numbers of factors increased as decreased in males as females. PCA derived coherence factor scores are useful for subsequent statistical analyses, but their factor loading plots of cortical coupling may require more experience to fully interpret.

Status of quantitative EEG (QEEG) in clinical practice, 1994

Clinical quantitative EEG (qEEG) is a complex specialty that may include not only standard EEG but also digital ("paperless") EEG, topographic mapping, spectral analysis, spectral coherence, long latency and event related potentials (EP), significance probability mapping (SPM), dipole source localization methodology (DLM), and discriminant function analysis. There are three basic clinical uses: non-specific detection of organicity/encephalopathy, specific categorization of disease or clinical condition, and epileptic source localization. Extreme variations exist in the competency of laboratories practicing clinical qEEG; universally agreed upon standards of practice have not been established but there are a number of efforts to do so. As expected, the clinical value of qEEG to patients varies similarly. Criticisms of qEEG have now been answered: Color displays need not be deceptive. Statistical "capitalization upon chance" can be easily avoided. By training and with newer analytic procedures, artifacts can be recognized and often removed. Data based upon spectral analysis and EP can reliably classify clinical conditions thereby demonstrating a greater sensitivity to EEG/EP data than possible by conventional visual inspection. QEEG is clearly of clinical value when performed in concert with standard EEG and analyzed by clinicians with demonstrated competency in standard EEG followed by specialized training and demonstrated competency in qEEG. QEEG is not a simple substitute for conventional EEG and cannot be seen as a substitute for clinical competence. Although continuing to develop, qEEG technology has matured sufficiently and is now well established. Concerns regarding its clinical use have primarily resulted from its misapplication and misinterpretation stemming, largely, from inadequate personnel training and expertise.

Statistical analysis of topographic maps of short-latency somatosensory evoked potentials in normal and parkinsonian subjects

This work had the following objectives: i) to integrate temporal analysis (N30 peak) with power-spectrum topographic mapping of short-latency somatosensory evoked potentials (SEP's) recorded in parkinsonian and normal control subjects; and ii) to analyze with a new statistical approach the between-group topographical differences in both the time and frequency domains. The principal aim was to better determine the topography of the scalp frontal areas where the amplitude of the N30 wave was previously found to be significantly reduced in parkinsonians. The statistical procedure was based on the combined use of descriptive data analysis (DDA) and multivariate analysis. In the context of DDA, an improved version of significance probability mapping (SPM) was used by which it is possible to evaluate homo- and nonhomoscedastic data with parametric tests. The statistical evaluation of between-group differences was performed with the multivariate Hotelling's T2 test and the associated post hoc test. With this statistical procedure, it was possible to determine that the between-group statistical differences in both the temporal and power spectrum distributions were localized only in midline and contiguous contralateral frontal areas of the scalp.

Standardized varimax descriptors of event related potentials: basic considerations

This paper describes a set of proposed standardized quantitative descriptors of event-related potentials, based upon principal component varimax analysis (PCVA). No claim is made that these mathematical descriptors correspond to discrete neurophysiological processes which generate the ERP. However, adoption and prospective evaluation of such a set of precise, standardized descriptors of the quantitative ERP may eventually result in advances like those which resulted from adoption of equally arbitrary standardized descriptors for QEEG. PCVA was performed on data from normal subjects and from groups of patients with a wide variety of psychiatric disorders ("Abnormals"). This yielded two sets of factor waveshapes, Normal and Abnormal, which were closely similar. Reconstruction of the normal and abnormal ERP data with either set of factors yielded almost identical allocation of variance. These results gave acceptable reassurance that factors derived from normal population could reasonably be used to describe ERP waveshapes from patients. The ERPs at each electrode of the 10/20 System in a "training group" of normal subjects were then reconstructed. The resulting distributions of factor scores were transformed to achieve Gaussianity. Mean values and standard deviations were obtained for the normative distribution of each factor score, the root mean square deviation, the residual and the absolute ERP power at each electrode. Individual ERPs could then be reconstructed with the normal factors, and the resulting factor scores rescaled to "probability of abnormal morphology" by Z-transformation. Statistical probability maps could be generated by using a color scale in standard deviation units. These methods were used to evaluate visual and auditory ERPs from an independent normal "test group" and the patients in the Abnormal sample. High specificity and sensitivity were obtained for many factor Z- scores. Multiple discriminant functions were constructed which separated normal from abnormal patients with high, replicable accuracy. Further development and testing of these descriptors may make them clinically useful.

Brain electrical correlates of psychological measures: strategies and problems

We explore relationships between brain electrical activity and cognitive performance where qEEG data are correlated with psychological variables gathered at a different time. For a population of 202 healthy adults using univariate and multivariate correlation techniques in a split half replication design, we confirm prior findings that subjects with better psychological scores show shorter evoked potential (EP) latency, suggesting that speed of processing is an important factor in cognitive performance. By canonical correlation we demonstrate a consistent, replicable relationship between electrophysiological and behavioral data. We suggest that reliance upon univariate correlation may have fueled early controversies about relationships between electrophysiology and IQ. In addition we correlate psychological factors with the entire qEEG data set (both EP and spectral analyzed EEG) and demonstrate the use a multidimensional image graphics techniques to assist in visual assessment of the resulting correlation matrices.

EEG/EP: new techniques

The topographic analysis of electrical brain activity consists of the extraction of quantitative features which adequately describe the scalp recorded electrical fields of the brain. In the beginning of brain electrical activity mapping most methods centered mainly around the graphical display of multichannel EEG and evoked potential data. Meanwhile quantitative analysis strategies have been developed, and such methods are applied to topographic EEG and evoked potential data enabling the statistical evaluation of the effects of different experimental conditions as well as the comparison of various clinical populations. Major new analysis techniques comprise the computation of global field power and global dissimilarity for determination of components of evoked potential fields, the segmentation of map series by topographical features, time range analysis, FFT approximation for the spatial analysis of EEG frequency bands as well as correlation analysis and spatial principal components analysis (Spatial PCA). Data from experiments dealing with evoked brain activity will illustrate the application of these quantitative methods that also can be used for the analysis of the spontaneous EEG.