Automatic quality control in clinical (1) H MRSI of brain cancer.

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1) H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.

REGULATION OF BRAIN NORADRENERGIC-RECEPTOR FUNCTION BY ADRENOCORTICOTROPHIN AND ANTIDEPRESSANT DRUGS (ADRENERGIC RECEPTOR, CYCLIC-AMP, ADENYLATE CYCLASE, IMIPRAMINE, STRESS)

Human behavior appears to be regulated in part by noradrenergic transmission since antidepressant drugs modify the number and function of (beta)-adrenergic receptors in the central nervous system. Affective illness is also known to be associated with the endocrine system, particularly the hypothalamic-pituitary-adrenal axis. The aim of the present study was to determine whether hormones, in particular adrencorticotrophin (ACTH) and corticosterone, may influence behavior by regulating brain noradrenergic receptor function.^ Chronic treatment with ACTH accelerated the increase or decrease in rat brain (beta)-adrenergic receptor number induced by a lesion of the dorsal noradrenergic bundle or treatment with the antidepressant imipramine. Chronic administration of ACTH alone had no effect on (beta)-receptor number although it reduced norepinephrine stimulated cyclic AMP accumulation in brain slices. Treatment with imipramine also reduced the cyclic AMP response to norepinephrine but was accompanied by a decrease in (beta)-adrenergic receptor number. Both the imipramine and ACTH treatments reduced the affinity of (beta)-adrenergic receptors for norepinephrine, but only the antidepressant modified the potency of the neurotransmitter to stimulate second messenger production. Neither ACTH nor imipramine treatment altered Gpp(NH)p- or fluoride-stimulated adenylate cyclase, cyclic AMP, cyclic GMP, or cyclic GMP-stimulated cyclic AMP phosphodiesterase, or the activity of the guanine nucleotide binding protein (Gs). These findings suggested that post-receptor components of the cyclic nucleotide generating system are not influenced by the hormone or antidepressant. This conclusion was verified by the finding that neither treatment altered adenosine-stimulated cyclic AMP accumulation in brain tissue.^ A detailed examination of the (alpha)- and (beta)-adrenergic receptor components of norepinephrine-stimulated cyclic AMP production revealed that ACTH, but not imipramine, administration reduced the contribution of the (alpha)-receptor mediated response. Like ACTH treatment, corticosterone diminished the (alpha)-adrenergic component indicating that adrenal steroids probably mediate the neurochemical responses to ACTH administration. The data indicate that adrenal steroids and antidepressants decrease noradrenergic receptor function by selectively modifying the (alpha)- and (beta)-receptor components. The functional similarity in the action of the steroid and antidepressants suggests that adrenal hormones normally contribute to the maintenance of receptor systems which regulate affective behavior in man. ^

A case-comparison interview study of brain tumors and employment in occupations with presumed electric and magnetic field exposures

Results from epidemiologic studies suggest that persons working in occupations with presumed electric and magnetic field (EMF) exposures are at increased risk of brain cancer. This study utilized data from a completed, population-based, interview case-control study of central nervous system (CNS) tumors and employment in the petrochemical industry to test the hypothesis that employment in EMF-related occupations increases CNS tumor risk. A total of 375 male residents of the Texas-Louisiana Gulf Coast Area, age 20 to 79, with primary neuroglial CNS tumors diagnosed during the period 1980-84 were identified. A population-based comparison group of 450 age, race and geographically matched males was selected. Occupational histories and potential risk factor data were collected via personal interviews with study subjects or their next-of-kin.^ Adjusted odds ratios were less than 1.0 for persons ever employed in an electrical occupation (OR = 0.65; 95% CI = 0.40-1.09) or whose usual occupation was electrical (OR = 0.76; 95% CI = 0.33-1.73). Relative risk estimates did not increase significantly as time since first employment or duration of employment increased. Examination of CNS tumor risk by high (OR = 0.80), medium (OR = 0.88) and low (OR = 0.45) exposure categories for persons whose usual occupation was electrical did not indicate a dose-response pattern. In addition, the mean age of exposed cases was not significantly younger than that for unexposed cases. Analysis of risk by probability of exposure to EMFs showed non-significant elevations in the adjusted odds ratio for definite exposed workers defined by their usual occupation (OR = 1.78; 95% CI = 0.70-4.51) and ever/never employed status (OR = 1.54; 95% CI = 0.17-4.91).^ These findings suggest that employment in occupations with presumed EMF exposures does not increase CNS tumor risk as was suggested by previous investigations. The results of this study also do not support the EMF-tumor promotion hypothesis. ^

Role of TRP Channels in Mediating the Calcium Signaling Response of Brain Endothelial Cells to Mechanical Stretch

Traumatic brain injury (TBI) often results in disruption of the blood brain barrier (BBB), which is an integral component to maintaining the central nervous system homeostasis. Recently cytosolic calcium levels ([Ca2+]i), observed to elevate following TBI, have been shown to influence endothelial barrier integrity. However, the mechanism by which TBI-induced calcium signaling alters the endothelial barrier remains unknown. In the present study, an in vitro BBB model was utilized to address this issue. Exposure of cells to biaxial mechanical stretch, in the range expected for TBI, resulted in a rapid cytosolic calcium increase. Modulation of intracellular and extracellular Ca2+ reservoirs indicated that Ca2+ influx is the major contributor for the [Ca2+]i elevation. Application of pharmacological inhibitors was used to identify the calcium-permeable channels involved in the stretch-induced Ca2+ influx. Antagonist of transient receptor potential (TRP) channel subfamilies, TRPC and TRPP, demonstrated a reduction of the stretch-induced Ca2+ influx. RNA silencing directed at individual TRP channel subtypes revealed that TRPC1 and TRPP2 largely mediate the stretch-induced Ca2+ response. In addition, we found that nitric oxide (NO) levels increased as a result of mechanical stretch, and that inhibition of TRPC1 and TRPP2 abolished the elevated NO synthesis. Further, as myosin light chain (MLC) phosphorylation and actin cytoskeleton rearrangement are correlated with endothelial barrier disruption, we investigated the effect mechanical stretch had on the myosin-actin cytoskeleton. We found that phosphorylated MLC was increased significantly by 10 minutes post-stretch, and that inhibition of TRP channel activity or NO synthesis both abolished this effect. In addition, actin stress fibers formation significantly increased 2 minutes post-stretch, and was abolished by treatment with TRP channel inhibitors. These results suggest that, in brain endothelial cells, TRPC1 and TRPP2 are activated by TBI-mechanical stress and initiate actin-myosin contraction, which may lead to disruption of the BBB.

Neuroanatomic-based detection algorithm for automatic labeling of brain structures in brain injury

The number and grade of injured neuroanatomic structures and the type of injury determine the degree of impairment after a brain injury event and the recovery options of the patient. However, the body of knowledge and clinical intervention guides are basically focused on functional disorder and they still do not take into account the location of injuries. The prognostic value of location information is not known in detail either. This paper proposes a feature-based detection algorithm, named Neuroanatomic-Based Detection Algorithm (NBDA), based on SURF (Speeded Up Robust Feature) to label anatomical brain structures on cortical and sub-cortical areas. Themain goal is to register injured neuroanatomic structures to generate a database containing patient?s structural impairment profile. This kind of information permits to establish a relation with functional disorders and the prognostic evolution during neurorehabilitation procedures.

Dynamics of brain networks in aesthetic appreciation

Neuroimage experiments have been essential for identifying active brain networks. During cognitive tasks as in, e.g., aesthetic appreciation, such networks include regions that belong to the default mode network (DMN). Theoretically, DMN activity should be interrupted during cognitive tasks demanding attention, as is the case for aesthetic appreciation. Analyzing the functional connectivity dynamics along three temporal windows and two conditions, beautiful and not beautiful stimuli, here we report experimental support for the hypothesis that aesthetic appreciation relies on the activation of two different networks, an initial aesthetic network and a delayed aesthetic network, engaged within distinct time frames. Activation of the DMN might correspond mainly to the delayed aesthetic network. We discuss adaptive and evolutionary explanations for the relationships existing between the DMN and aesthetic networks and offer unique inputs to debates on the mind/brain interaction.

Classical segmentation methods on novel MR imaging: a study of brain tissue segmentation of MP2RAGE vs MPRAGE

MP2RAGE has proven to be a bias-free MR acquisition with excellent contrast between grey and white matter. We investigated the ability of three state-of-the-art algorithms to automatically extract white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) from MPRAGE and MP2RAGE images: unified Segmentation (S) in SPM82 , its extension New Segment (NS), and an in-house Expectation-Maximization Markov Random Field tissue classification3 (EM-MRF) with Graph Cut (GC) optimization4 . Our goal is to quantify the differences between MPRAGE and MP2RAGE-based brain tissue probability maps.

Cognitive reserve is associated with the functional organization of brain networks in healthy aging: a MEG study

The proportion of elderly people in the population has increased rapidly in the last century and consequently "healthy aging" is expected to become a critical area of research in neuroscience. Evidence reveals how healthy aging depends on three main behavioral factors: social lifestyle, cognitive activity and physical activity. In this study, we focused on the role of cognitive activity, concentrating specifically on educational and occupational attainment factors, which were considered two of the main pillars of cognitive reserve. 21 subjects with similar rates of social lifestyle, physical and cognitive activity were selected from a sample of 55 healthy adults. These subjects were divided into two groups according to their level of cognitive reserve; one group comprised subjects with high cognitive reserve (9 members) and the other contained those with low cognitive reserve (12 members). To evaluate the cortical brain connectivity network, all participants were recorded by Magnetoencephalography (MEG) while they performed a memory task (modified version of the Sternberg¿s Task). We then applied two algorithms (Phase Locking Value & Phase-Lag Index) to study the dynamics of functional connectivity. In response to the same task, the subjects with lower cognitive reserve presented higher functional connectivity than those with higher cognitive reserve. These results may indicate that participants with low cognitive reserve needed a greater 'effort' than those with high cognitive reserve to achieve the same level of cognitive performance. Therefore, we conclude that cognitive reserve contributes to the modulation of the functional connectivity patterns of the aging brain.

Development of Brain-Computer Interfaces using Evolvable Hardware

Brain-Computer Interfaces are usually tackled from a medical point of view, correlating observed phenomena to physical facts known about the brain. Existing methods of classification lie in the application of deterministic algorithms and depend on certain degree of knowledge about the underlying phenomena so as to process data. In this demo, different architectures for an evolvable hardware classifier implemented on an FPGA are proposed, in line with the objective of generalizing evolutionary algorithms regardless of the application.

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

Patterns of brain angiogenesis after vascular endothelial growth factor administration in vitro and in vivo

Vascular endothelial growth factor (VEGF) is a secreted endothelial cell mitogen that has been shown to induce vasculogenesis and angiogenesis in many organ systems and tumors. Considering the importance of VEGF to embryonic vascularization and survival, the effects of administered VEGF on developing or adult cerebrovasculature are unknown: can VEGF alter brain angiogenesis or mature cerebrovascular patterns? To examine these questions we exposed fetal, newborn, and adult rat cortical slice explants to graduated doses of recombinant VEGF. The effects of another known angiogenic factor, basic fibroblast growth factor (bFGF), were evaluated in a comparable manner. In addition, we infused VEGF via minipump into the adult cortex. Significant angiogenic effects were found in all VEGF experiments in a dose-responsive manner that were abolished by the addition of VEGF neutralizing antibody. Fetal and newborn explants had a highly complex network of branched vessels that immunoexpressed the flt-1 VEGF receptor, and flk-1 VEGF receptor expression was determined by reverse transcription–PCR. Adult explants had enlarged, dilated vessels that appeared to be an expansion of the existing network. All bFGF-treated explants had substantially fewer vascular profiles. VEGF infusions produced both a remarkable localized neovascularization and, unexpectedly, the expression of flt-1 on reactive astrocytes but not on endothelial cells. The preponderance of neovascularization in vitro and in vivo, however, lacked the blood–brain barrier (BBB) phenotype marker, GLUT-1, suggesting that in brain the angiogenic role of VEGF may differ from a potential BBB functional role, i.e., transport and permeability. VEGF may serve an important capacity in neovascularization or BBB alterations after brain injury.

Rapid treadmilling of brain microtubules free of microtubule-associated proteins in vitro and its suppression by tau

We have determined the treadmilling rate of brain microtubules (MTs) free of MT-associated proteins (MAPs) at polymer mass steady state in vitro by using [3H]GTP-exchange. We developed buffer conditions that suppressed dynamic instability behavior by ≈10-fold to minimize the contribution of dynamic instability to total tubulin-GTP exchange. The MTs treadmilled rapidly under the suppressed dynamic instability conditions, at a minimum rate of 0.2 μm/min. Thus, rapid treadmilling is an intrinsic property of MAP-free MTs. Further, we show that tau, an axonal stabilizing MAP involved in Alzheimer’s disease, strongly suppresses the treadmilling rate. These results indicate that tau’s function in axons might involve suppression of axonal MT treadmilling. We describe mathematically how treadmilling and dynamic instability are mechanistically distinct MT behaviors. Finally, we present a model that explains how small changes in the critical tubulin subunit concentration at MT minus ends, caused by intrinsic differences in rate constants or regulatory proteins, could produce large changes in the treadmilling rate.

Invariance between subjects of brain wave representations of language

In three experiments, electric brain waves of 19 subjects were recorded under several different experimental conditions for two purposes. One was to test how well we could recognize which sentence, from a set of 24 or 48 sentences, was being processed in the cortex. The other was to study the invariance of brain waves between subjects. As in our earlier work, the analysis consisted of averaging over trials to create prototypes and test samples, to both of which Fourier transforms were applied, followed by filtering and an inverse transformation to the time domain. A least-squares criterion of fit between prototypes and test samples was used for classification. In all three experiments, averaging over subjects improved the recognition rates. The most significant finding was the following. When brain waves were averaged separately for two nonoverlapping groups of subjects, one for prototypes and the other for test samples, we were able to recognize correctly 90% of the brain waves generated by 48 different sentences about European geography.

Role of brain allopregnanolone in the plasticity of γ-aminobutyric acid type A receptor in rat brain during pregnancy and after delivery

The relation between changes in brain and plasma concentrations of neurosteroids and the function and structure of γ-aminobutyric acid type A (GABAA) receptors in the brain during pregnancy and after delivery was investigated in rats. In contrast with plasma, where all steroids increased in parallel, the kinetics of changes in the cerebrocortical concentrations of progesterone, allopregnanolone (AP), and allotetrahydrodeoxycorticosterone (THDOC) diverged during pregnancy. Progesterone was already maximally increased between days 10 and 15, whereas AP and allotetrahydrodeoxycorticosterone peaked around day 19. The stimulatory effect of muscimol on 36Cl− uptake by cerebrocortical membrane vesicles was decreased on days 15 and 19 of pregnancy and increased 2 days after delivery. Moreover, the expression in cerebral cortex and hippocampus of the mRNA encoding for γ2L GABAA receptor subunit decreased during pregnancy and had returned to control values 2 days after delivery. Also α1,α2, α3, α4, β1, β2, β3, and γ2S mRNAs were measured and failed to change during pregnancy. Subchronic administration of finasteride, a 5α-reductase inhibitor, to pregnant rats reduced the concentrations of AP more in brain than in plasma as well as prevented the decreases in both the stimulatory effect of muscimol on 36Cl− uptake and the decrease of γ2L mRNA observed during pregnancy. These results indicate that the plasticity of GABAA receptors during pregnancy and after delivery is functionally related to fluctuations in endogenous brain concentrations of AP whose rate of synthesis/metabolism appears to differ in the brain, compared with plasma, in pregnant rats.

Invariance of brain-wave representations of simple visual images and their names

In two experiments, electric brain waves of 14 subjects were recorded under several different conditions to study the invariance of brain-wave representations of simple patches of colors and simple visual shapes and their names, the words blue, circle, etc. As in our earlier work, the analysis consisted of averaging over trials to create prototypes and test samples, to both of which Fourier transforms were applied, followed by filtering and an inverse transformation to the time domain. A least-squares criterion of fit between prototypes and test samples was used for classification. The most significant results were these. By averaging over different subjects, as well as trials, we created prototypes from brain waves evoked by simple visual images and test samples from brain waves evoked by auditory or visual words naming the visual images. We correctly recognized from 60% to 75% of the test-sample brain waves. The general conclusion is that simple shapes such as circles and single-color displays generate brain waves surprisingly similar to those generated by their verbal names. These results, taken together with extensive psychological studies of auditory and visual memory, strongly support the solution proposed for visual shapes, by Bishop Berkeley and David Hume in the 18th century, to the long-standing problem of how the mind represents simple abstract ideas.