Psychopharmacology (2012) 223:281–297 In our case, t is the number of time points and n is the total number of samples for a given time series. Here, we assumed that, if for a given time series Fc=Ft 1:5 (where Fc is the value calculated for this time series and Ft is the theoretical value for the significance level equal to 0.9), it was considered for further analysis. Next, the filtered time series were normalized (Stahlberg et al. 2008). This proce- dure reduces the length of each time series by one, as the value of the expression without any treatment is used as the reference value. The value at each other time point is calcu- lated in accordance with the following formula: AEi ¼ dEi hdEi; SD where dEi is calculated as dEi ¼ log2ð2E0 Ei ; E is the Þ expression at a given time point i; hdEi is the mean; and SD is the standard deviation of the expression at a given time point. Mathematically, SVD means that the n ×m matrix A is represented asA ¼ USV T where U is an n× n matrix; V is an m× m matrix; and S is an m×m diagonal matrix of eigenval- ues of matrix A. Our interest lies in the number of positive eigenvalues, r, on which basis we are able determine the so- called characteristic modes of matrix A, such that 2 X1 3 2 s1v1 3 . . X ¼ 64 .. 75 ¼ 64 .. 7. 5 Xr srvr In the case of a gene expression time series, each row of matrix A represents the expression profile of one gene and each column represents the level of expression of each gene at a given time point. Here, SVD was performed if the number of genes selected in the ANOVA was greater than the number of time points, i.e., 5 (1, 3, 7, 14, and 21 days). The expression profile of any given gene is a linear combi- nation of the obtained modes with coefficients equal to the corresponding elements of the matrix U. The contribution of each mode to the expression profile of each gene can also be calculated (Simek et al. 2004). In situ hybridization studies The brains of animals treated with CIT for 3 days repeatedly or in single dose and from the control group were stored at –80°C. The frozen brains were cut into 12-μm slices using a cryostat (Leica). The following antisense oligonu- cleotide probes complementary to the mRNAs encoding the selected genes were used: c-fos, 5′-GCA GCG GGA GGA TGA CGC CTC GTA GTC CGC GTT GAA ACC CGA GAA CAT-3′ (Curran et al. 1987); Egr1, 5′-CCG TTG CTC AGC AGC ATC ATC TCC TCC AGT TTG GGG TAG TTG TCC-3′ (Milbrandt 1987); Nr4a1, 5′-GGT GGC AGG 285 TGC GGT GGG CGC CGT CTC GGG GCT GGC CAG GTC CAT-3′ (Hazel et al. 1988); and Id1, 5′-GCA CTG CTA CTG GCG ACC TTC ATG ATC CTG AGG ACA GGC GGA GAG G-3′ (designed and checked for specificity in BLAST, NCBI). All probes were synthesized by Gen- omed (Poland) and labeled with [35S]dATP (Hartmann An- alytic) at the 3′ end using terminal transferase (New England Biolabs). Following the preincubation, the brain slices were incubated with hybridization buffer containing the labeled probes (10,000 CPM/μl) for 18 h at 37°C, then were washed in 1× SSC solution, three times for 20 min with 2× SSC containing 50 % formamide (Merck) at 42°C and after that in 1× SSC (15 min), water (5 min) and rinsed in 70 % ethanol. The images were obtained using FujiFilm BAS 5000 Phosphoimager and they were analyzed using Fuji- Film software (Image Gauge, V4.0). The identification of brain structures was defined according to the rat brain atlas (Paxinos and Watson 1998). Statistical analysis was carried out by two-way ANOVA and Student’s t test was used as post hoc test to compare changes in mRNA level in CCX between the 3-day-treated rats and control group. A value of p <0.05 was considered to be significant. Results SVD analyses revealed differences in the global gene expression profiles between treatment types For each drug and brain region, two time series gene ex- pression datasets were compared (from the repeated and single-dose modes of treatment). Time series consisting of six time points (0, 1, 3, 7, 14, and 21 days of treatment) were analyzed by SVD. We show that highly complex sets of gene expression profiles can be represented by a small number of “characteristic modes” that capture most of the expression information in the temporal patterns of gene expression change. Other aspects of the decomposition in- dicate the extent to which a gene exhibits a pattern similar to those provided by the singular vectors. Thus, once a set of interesting patterns has been identified, genes can be ranked by their relationship with given patterns. The overall results of the SVD analysis (SVD modes) are presented in Fig. 2. Distinct modes following repeated and single-dose treatments were detected. The contribution of characteristic mode to the expression profile of each gene was calculated, and genes with the highest contribution to each mode are listed in Table 1. The number of genes whose expression profiles were best described by a characteristic mode were larger for the single-dose treatment. The global temporal change in gene expression following repeated DMI treatment in the PCX was dominated by the top four modes,