A developmental perspective of self-construals and sex differences: comment on Cross and Madson (1997)

S. E. Cross and L. Madson (1997) proposed that women and men differ in self-construals, with women as interdependent and men as independent, and that these construals are seen to underlie many sex differences in social behavior. In this article, the authors address the issues of sex differences in self-construals, the stability of self-construals, and the centrality of interdependence-independence to sex differences. They examine the proposal next from a developmental perspective, suggesting that development of the self does not precede children's gender-related behavior. Evidence regarding the socialization processes that may contribute to sex differences in self-construals is largely inconclusive, especially regarding the differential treatment of boys and girls by parents. Their developmental perspective provides insights into ways that Cross and Madson's approach can be elaborated to explain sex differences in social behavior.     

Case report of unusual leukoencephalopathy preceding primary CNS lymphoma

A previously healthy 35 year old woman presented with bilateral uveitis associated with multiple, evolving, non-enhancing white matter lesions consistent with a progressive leukoencephalopathy such as multiple sclerosis. Thirty months after her initial presentation, she was diagnosed with primary CNS lymphoma and died 14 months later. The unusual clinical course preceding the diagnosis suggests that a demyelinating disease may have preceded, and possibly heralded, the development of primary CNS lymphoma. Cases of "sentinel lesions" heralding the diagnosis of primary CNS lymphoma have been reported, and this case further corroborates such instances and raises further issues regarding possible neoplastic transformation occurring in inflammatory diseases such as multiple sclerosis.     

Nonlinear dynamics of epileptic seizures on basis of intracranial EEG recordings

PURPOSE: An understanding of the principles governing the behavior of complex neuronal networks, in particular their capability of generating epileptic seizures implies the characterization of the conditions under which a transition from the interictal to the ictal state takes place. Signal analysis methods derived from the theory of nonlinear dynamics provide new tools to characterize the behavior of such networks, and are particularly relevant for the analysis of epileptiform activity. METHODS: We calculated the correlation dimension, tested for irreversibility, and made recurrence plots of EEG signals recorded intracranially both during interictal and ictal states in temporal lobe epilepsy patients who were surgical candidates. RESULTS: Epileptic seizure activity often, but not always, emerges as a low-dimensional oscillation. In general, the seizure behaves as a nonstationary phenomenon during which both phases of low and high complexity may occur. Nevertheless a low dimension may be found mainly in the zone of ictal onset and nearby structures. Both the zone of ictal onset and the pattern of propagation of seizure activity in the brain could be identified using this type of analysis. Furthermore, the results obtained were in close agreement with visual inspection of the EEG records. CONCLUSIONS: Application of these mathematical tools provides novel insights into the spatio-temporal dynamics of "epileptic brain states". In this way it may be of practical use in the localization of an epileptogenic region in the brain, and thus be of assistance in the presurgical evaluation of patients with localization-related epilepsy.     

Feature-recognizing MRI

We describe a new theory of MR imaging that utilizes prior information in the form of a set of "training" images thought to be similar to the "unknown" objects to be scanned. First, the training images are processed to find an orthonormal series representation of these images that is more convergent than the usual Fourier series. The coefficients in this new series can be calculated from a subset of the phase-encoded signals needed to construct the Fourier image representation. The characteristics of the training images also determine exactly which phase-encoded signals should be measured in order to minimize error in the image reconstruction. The optimal phase-encodings are usually scattered nonuniformly in kappa-space. Good results were obtained when this theory was applied to imaging data from simulated objects and to experimental data from phantom scans. This theory provides the basis for developing efficient scanning and image reconstruction techniques that are "tailored" to each body part or to particular disease states.