continuous, interval scale score was calculated through using principal components as extraction method and varimax rotation with Kaiser normalization rotation [58]. Individual factor scores were calculated for each subject through the relative weight and orientation (eigenvalues) of the parameters loading on the factor. Scores were generated using a Z distribution, where the value 0 corresponds to the mean, and values are expressed in terms of standard deviations. In addition, one of the SEM models performed was addressed to investigate differences due to extremes in anxiety values, which led us to classify animals in either low (LA) or highly (HA) anxious. Thus, a categorical dichotomous score was obtained by classifying subjects into groups of LA and HA, using the extreme lower and higher values of the continuous scores obtained from the factor analysis; subjects with scores within 60.25 standard deviations from the mean were excluded from this classification. Parametric statistics: The effect of BLA lesions on anxiety levels in the EPM were analyzed with a Student’s t-test for independent samples. For the analyses of the impact of BLA lesions and anxiety on fluoxetine effects on indices of neurogenesis and depression-like behavior, there was the constraint that anxiety was affected by BLA lesions, and therefore it cannot be treated as an independent variable. Hence, instead of a three-way ANOVA, sequential two- way ANOVAs were used followed by simple effects analyses where appropriate. Analyses were performed according to a 262 design. Since anxiety levels can be affected by BLA lesions [59], the relative contribution of lesion and anxiety on the behavioral and neurogenic effects of fluoxetine, cannot be addressed with ANOVAs. Therefore, we applied structural equation modeling (SEM) as a more flexible alternative statistical technique to achieve the stated aims of the current study. Structural equation modeling (SEM): As a powerful extension of multiple regression analyses, SEM goes beyond bivariate correla- tions, to allow simultaneous tests of the contribution of multiple variables (numerical and categorical), both direct and indirect, and to establish their individual predictive ability over one or more dependent variables. Data were first examined to determine the suitability for multivariate analyses. The SEM analyses were based on the maximum likelihood estimation (MLE) and the following three indices of fit were used to test the ability of the proposed models to provide a plausible representation of the underlying variance structure of the data. In addition to a nonsignificant chi square (x2), allowing a rejection of the hypothesis (i.e. an unacceptable representation of the data), a good-fitting model was considered to have a comparative fit index (CFI) $0.95 and a root mean square approximation of variance (RMSEA) significantly smaller than 0.05 [60,61]. In the figures for each of the proposed models, standardized beta weights along an arrow path indicate the ability of one variable to predict a dependent variable, ranging in absolute value from zero to one, and each with its statistical significance (p,0.05). The optimal model for each condition, including the path strengths between the tested variables were determined by first testing separately either level of each condition (lesion, drug treatment and anxiety level). Subsequently, a multi-group (‘‘stacking’’) approach was used to statistically compare model fit and path strengths between the groups. Here, an ‘inclusive’ model was tested, thus combining into one model effective paths from both levels of each condition. Next, each path was individually constrained to test for selective differences between the levels of each condition, e.g. [62]. Multi- group comparison statistics for stacked models specify the statistical significance of x2 differences (Dx2) when constrained and unconstrained models are compared. Using this approach, significant results indicate a meaningful difference in the strength Amygdala and Neurogenesis of the path(s) tested between the groups (regardless of individual path statistical significance in either group). Furthermore, the squared multiple correlation (R2 or coefficient of determination), representing a measure of the proportion of the variance of the dependent variable that is explained by the independent variables, is also presented. Results Verification of BLA lesions The extent of the lesions for the rats included in the study is shown in Figure S1. Seventeen rats (out of the initial 43 lesioned rats) presenting an average lesion area smaller than 40% (criteria for exclusion) of the total BLA area were eliminated from the analyses of the study, which rendered an n = 26 in the BLA lesion group. As a consequence, the number of animals included in each experimental group was as follows: Sh/Vh (n = 17), Sh/Flx (n = 21), BLA/Vh (n = 12) and BLA/Flx (n = 14). As indicated in Figure S1, the minimum extent of the lesions always included at least 40% of the BLA while in a small percentage of cases the maximal lesion extension occupied adjacent medial and basal areas. Effects of BLA lesions on anxiety levels A factor analysis applied to the behavioral parameters extracted from the EPM identified two main factors representing ‘anxiety’ and ‘locomotion’, with a value for each subject (Table S1). BLA lesions induced a significant reduction of anxiety levels as compared to sham treatment (p,0.01; Figure S2). Effects of BLA lesions and fluoxetine treatment on cell proliferation and survival and on depression-like behavior Independent two-way ANOVAs (BLA lesion and fluoxetine treatment as factors) were performed on indices of cell prolifer- ation and survival and on the measure of depression-like behavior. A first ANOVA on cell proliferation data as indicated by the number of Ki67-IR cells in the DG, revealed a significant increase in Ki67-IR cells with fluoxetine (p,0.01), but no effect of BLA lesions (n.s.), and no significant interaction between the two factors (n.s.) (see section A in Figure 2). ANOVA on cell survival data as indicated by the number of BrdU-IR cells showed a significant interaction between lesion and drug treatment (p,0.05) (see section B in Figure 2), a simple effects analysis confirmed that fluoxetine treatment increased cell survival only in BLA lesioned rats (p,0.01). ANOVA on the immobility time in the forced-swim test indicated a significant reduction with both lesion (p,0.01) and fluoxetine treatment (p,0.01), but no significant interaction (n.s.) (see section C in Figure 2). Effects of anxiety and fluoxetine treatment on cell proliferation and survival and on depression-like behavior In order to study the impact of anxiety as a dichotomized variable, we classified subjects into groups of low (LA) and high (HA) anxiety by selecting those animals whose punctuation in the anxiety factor was 0.25 standard deviations either above (HA; n = 28; 19 sham y 9 lesion) or below (LA; n = 24; 11 sham and 13 lesion) the mean. Mean comparison confirmed that these two groups differed significantly on the anxiety factor (Student-t test; p,0.01; Figure S3). Then, independent two-way ANOVAs (anxiety levels and fluoxetine treatment as factors) were performed PLoS ONE | www.plosone.org 4 January 2010 | Volume 5 | Issue 1 | e8618