Paul Easton

Quantitative electrophysiological characteristics and subtyping of schizophrenia.

Quantitative descriptors of resting electroencephalogram (EEG) (QEEG) and event-related potentials (QERP) to visual and auditory stimuli were obtained from normal subjects and 94 chronic schizophrenic patients on medication, 25 chronic schizophrenics off medication, and 15 schizophrenics with no history of medication. These schizophrenic groups showed a high incidence of neurometric features that were significantly deviant from normative values. Multivariate discriminant analysis using these features successfully separated the schizophrenic patients from normals with high accuracy in independent replication. The data from the medicated group were subjected to cluster analysis. Newly developed algorithms were used for objective selection of the most effective set of variables for clustering and the optimum number of clusters to be sought. Five clusters were obtained, containing roughly equivalent proportions of the sample with markedly different QEEG profiles. The whole sample was then classified into these clusters. Each cluster contained patients both on and off medication, but patients who had never been medicated were classified into only three of these clusters. No significant clinical or demographic differences were found between members of the five clusters; however, clear differences in QERP profiles were seen. These results are described in detail and possible physiological and pharmacological implications are discussed.

Stabilization of Hebbian neural nets by inhibitory learning

In Hebbian neural models synaptic reinforcement occurs when the pre- and post-synaptic neurons are simultaneously active. This causes an instability toward unlimited growth of excitatory synapses. The system can be stabilized by recurrent inhibition via modifiable inhibitory synapses. When this process is included, it is possible to dispense with the non-linear normalization or cut-off conditions which were necessary for stability in previous models. The present formulation is response-linear if synaptic changes are slow. It is self-consistent because the stabilizing effects will tend to keep most neural activity in the middle range, where neural response is approximately linear. The linearized equations are tensor invariant under a class of rotations of the state space. Using this, the response to stimulation may be derived as a set of independent modes of activity distributed over the net, which may be identified with cell assemblies. A continuously infinite set of equivalent solutions exists.

Neurometric Topographic Mapping of EEG and Evoked Potential Features: Application to Clinical Diagnosis and Cognitive Evaluation

There is widespread agreement that topographic maps of EEG measures are difficult to evaluate unless referred to a normative data base. This raises the difficult question of how “normal” should be defined. Because the particular target of our method, which we call “neurometries,” was cognitive dysfunctions and psychiatric disorders, the instruments used to evaluate our “normal” subjects included an extensive psychiatric and neuropsychological test battery, a psychiatric as well as neurological examination, achievement tests, and determination of eye, hand, and foot dominance. Medical and psychosocial histories, pre— and perinatal data, and current and past school or work records were also evaluated. The subset of instruments used varied with age. Subjects with significant abnormal findings or events in their history which placed them at risk were excluded. Additional exclusion criteria included current use of prescription drugs, a history of head injury or loss of consciousness, any previous EEG or neurological examination, and febrile convulsions.

Standardized varimax descriptors of event related potentials: Basic considerations

SummaryThis 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.

Neurometric Functional Imaging: II. Cross-Spectral Coherence at Rest and During Mental Activity

Cross-spectral coherence is one way to quantify the dynamic interrelationships among brain regions, possibly reflecting the neurofunctional organization of the brain. The data reported in this chapter were from a pilot study performed in normal adults for the purpose of exploring whether mental activity would be reflected as different changes in cross-spectral coherence of the EEG (John et al., 1989).