An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.

Spatial learning ability of rats following differing levels of exposure to alcohol during early postnatal life

Rats exposed to a relatively high dose (7.5 g/kg body weight) of alcohol on either the fifth or tenth postnatal day of age have been reported to have long-lasting deficits in spatial learning ability as tested on the Morris water maze task. The question arises concerning the level of alcohol required to achieve this effect. Wistar rats were exposed to either 2, 4 or 6 g/kg body weight of ethanol administered as a 10% solution. This ethanol was given over an 8-h period on the fifth postnatal day of age by means of an intragastric cannula. Gastrostomy controls received a 5% sucrose solution substituted isocalorically for the ethanol. Another set of pups raised by their mother were used as suckle controls. All surgical procedures were carried out under halothane vapour anaesthesia. After the artificial feeding regimes all pups were returned to lactating dams and weaned at 21 days of age. The spatial learning ability of these rats was tested in the Morris water maze when they were between 61-64 days of age. This task requires the rats to swim in a pool containing water made opaque and locate and climb onto a submerged platform. The time taken to accomplish this is known as the escape latency. Each rat was subjected to 24 trials over 3 days of the test period. Statistical analysis of the escape latency data revealed that the rats given 6 g/kg body weight of ethanol had significant deficits in their spatial learning ability compared with their control groups. However, there was no significant difference in spatial learning ability for the rats given either 2 or 4 g/kg body weight of ethanol compared with their respective gastrostomy or suckle control animals. We concluded that ethanol exposure greater than 4 g/kg over an 8-h period to 5-day-old rats is required for them to develop long-term deficits in spatial learning behaviour. (C) 1998 Elsevier Science Inc.

Retinal neurons: cell types and coupled networks

Retinal neurons with distinct dendritic morphologies are likely to comprise different cell types, subject to three important caveats. First, it is necessary to avoid creating “artificial” cell types based on arbitrary criteria—for example, the presence of two or three primary dendrites. Second, it is essential to take into account changes in morphology with retinal eccentricity and cell density. Third, the retina contains imperfections like any natural system and a significant number of retinal neurons display aberrant morphologies or make aberrant connections that are not typical of the population as a whole. Many types of retinal ganglion cells show diverse patterns of tracer coupling, with the simplest pattern represented by the homologous coupling shown by On-Off direction-selective (DS) ganglion cells in the rabbit retina. Neighboring DS ganglion cells with a common preferred direction have regularly spaced somata and territorial dendritic fields, whereas DS ganglion cells with different preferred directions may have closely spaced somata and overlapping dendritic fields.