Clearly, these are extremely complicated issues. They will most likely require interdisciplinary teams working together to design and carry out large well-designed longitudinal studies using the best tools of developmental

cognitive neuroscience as well as ecologically valid measures of behavior in realistic social contexts. The challenges (and expense) are daunting; however, the stakes for society and the morbidity and mortality of youth are Sorafenib enormous and deserving of the best science that can be used to inform early intervention and prevention strategies in the future. “
“The Notch pathway is well known to regulate neural progenitor maintenance and differentiation in animals (Louvi and Artavanis-Tsakonas, 2006 and Yoon and Gaiano, 2005). In vertebrates, the traditional view has been that Notch receptor activation inhibits neurogenesis to maintain neural stem and/or progenitor cell character, and in some

cases to promote gliogenesis. This view has grown out of many studies that evaluated how Notch pathway manipulation influenced neural cell fate in Xenopus, chick, zebrafish, and mice. Antidiabetic Compound Library cell line However, conclusions drawn from those studies have been oversimplified, most likely because early work on retinal development ( Bao and Cepko, 1997 and Henrique et al., 1997) and cell fate in Xenopus ( Chitnis et al., 1995, Chitnis and Kintner, 1996 and Chitnis, 1995) focused on the generation of neurons as the Adenylyl cyclase primary process, and those studies sought to draw parallels to Notch function during fly neurogenesis. The predominant “textbook” view regarding Notch

in vertebrate neural development is that signaling selects a subset of cells within the germinal zone to become neurons, while the remainder stay undifferentiated for subsequent waves of neurogenesis. Those cells undergoing neuronal differentiation upregulate Notch ligands (see below), and thereby activate Notch receptors on neighboring cells to inhibit their differentiation. This process is routinely referred to as “lateral inhibition.” The basic lateral inhibition model became so conclusively accepted that for some time the field stalled, with additional work expected primarily to fill in the details. While it is true that fundamental elements of how Notch works during vertebrate neural development remain unchallenged, recently, noteworthy progress has been made addressing the following.

e., trials in which the SOL served only as a confirmation of correct spontaneous perception and not as an

event of perceptual insight); REM (“remembered”), trials in which the camouflage image was not spontaneously identified during CAM1 and the solution was subsequently remembered, yielding correct performance on both the multiple choice and the Grid tasks at Test 1 week later (i.e., trials in which the SOL served as a learning event); and NotREM (“not remembered”), trials in which the camouflage was not identified during Study and its solution was not remembered during Test. Each of the three protocol stages (CAM1, SOL, and CAM2) was assigned a separate predictor. Combined with the labels of performance, this resulted in nine predictors (CAM1-REM, CAM1-NotREM, CAM1-SPONT, Roxadustat solubility dmso et cetera). selleck screening library An additional

predictor, blank, was used for all the time frames in which the participants viewed a gray screen. These include 10 s prior to the start of the camouflage run, and the ISIs and ITIs during the run. For each predictor, a boxcar function valued 1 (and 0 for the blank predictor) was convolved with a canonical hemodynamic response function (Boynton et al., 1996). For each comparison of interest, contrasts were created between the appropriate predictors (the main contrast compared activation during REM and NotREM trials; see also the following ROIs subsections and Results), and p values were calculated (t test) for each voxel. For the SOL versus baseline and the object localizer objects versus scrambled-objects contrasts, the p values were adjusted for multiple comparisons using False Discovery Rate controlling procedures (Benjamini Terminal deoxynucleotidyl transferase and Hochberg, 1995, Genovese et al., 2002 and Stanley and Rubin, 2003) before thresholding. Finally, voxels that did not belong to

contiguous clusters of at least five significant functional voxels were eliminated. ROIs were defined in three different ways. First, and based on prior results indicating the occipito-temporal stream as crucial to shape perception and object recognition (Grill-Spector and Malach, 2004), visual cortical ROIs were created from the data obtained in the localizer scan. Data from those runs were modeled using a boxcar predictor for each experimental condition except fixation (objects and scrambled objects). A hemodynamic lag of 4 or 6 s was fitted to the model of each subject by maximizing the extent of the overall visual activations. Statistical maps were created, separately for each observer, by contrasting the objects and scrambled objects predictors, and thresholded at q < 0.005.

6 ± 0.9 to 3.6 ± 0.8 (n = 8 pups, p < 0.05). Consequently, the occurrence of prelimbic NG decreased from 1.1 ± 0.2 bursts/min in ACSF-treated pups to 0.6 ± 0.24 bursts/min (p < 0.05), while cingulate activity and prelimbic SB remained unaffected (Figure 8H). The results of the three experimental approaches indicate that impairment of the Hipp, independently of the induction method, strongly reduces the early network activity of the PL. Oscillatory entrainment of adult prefrontal-hippocampal networks seems to efficiently ensure reciprocal information transfer associated with mnemonic and executive functions. In the present study, we used multielectrode recordings to elucidate when and how

this oscillatory entrainment between the PFC and Hipp develops through life and to get first insights into its mechanisms MEK inhibitor cancer and function. We demonstrate here for

the first time that the rat PFC starts to generate bursts of oscillatory activity with subregion-specific spatial and temporal organization during the first postnatal week. We additionally provide experimental evidence that this discontinuous prefrontal activity Selleckchem GSK2656157 is driven via synaptic projections by hippocampal theta bursts. With ongoing maturation of the PFC and switch from discontinuous bursts to continuous theta-gamma rhythms, reciprocal interactions start to synchronize the prejuvenile prefrontal and hippocampal networks in theta oscillatory rhythms. These data indicate that the prefrontal and hippocampal networks maturate tightly correlated by transient coupling in oscillatory rhythms. During the first two postnatal weeks, the network activity of the rodent PFC undergoes prominent changes, the initially generated discontinuous oscillations being replaced by continuous rhythms. This developmental time window corresponds Histamine H2 receptor to a period of massive cytoarchitectonic and functional reorganization of the PFC and its connectivity. Extending over approximately 3 weeks in rodents and until adolescence in humans (Flechsig, 1901 and Van Eden and Uylings, 1985), the maturation of the PFC is significantly

prolonged when compared with that of the primary sensory cortices. Correspondingly, the emergence of oscillatory bursts in the PFC, first observed at P3, is delayed when compared with the onset around birth of activity patterns in the V1 or S1 (Hanganu et al., 2006 and Yang et al., 2009). These oscillations recorded in urethane-anesthetized rats mirror the physiological activity patterns of the developing PFC, since urethane anesthesia mimics the full spectrum of the natural sleep (Clement et al., 2008) and neonatal rats spend most of their time sleeping (Bolles and Woods, 1964). Therefore, it is not surprising that the incidence and most of the properties of neonatal SB were independent of the presence or level of urethane-anesthesia (see also Yang et al., 2009).

Correct responses were followed by a liquid reward. The correct response for a trial depended on the contingency between KU-55933 mouse the 3D structure of the presented stimulus and the direction of the saccadic response made by the monkey. The 0% signal strength trials were randomly rewarded with a probability of 0.5. Extracellular recordings were made using tungsten microelectrodes (impedance, ∼0.7 MΩ at 1 kHz; FHC). Details

of the physiological recording methods have been described previously (Verhoef et al., 2010). The positions of both eyes were sampled at 1 kHz using an EyeLinkII system. Electrical pulses for microstimulation purposes were delivered using

a pulse generator (DS8000; World Precision Instruments) in series with an optical stimulus isolation unit (DLS100; World Precision Instruments). Stimulation consisted of bipolar current pulse trains of 35 μA delivered at 200 Hz. We used biphasic (cathodal pulse leading) square-wave pulses with a pulse duration of 0.2 ms and 0.1 ms between the cathodal and anodal pulse (total pulse duration = 0.5 ms). Similar parameters have been used in related studies (Afraz et al., 2006 and DeAngelis et al., 1998). We sampled IT along vertical electrode penetrations in find more steps of ∼100–150 μm. For each of these positions, we first selected the optimal (within our stimulus set) 2D-shape outline (e.g., circle, ellipse, square, etc.; size: ∼5 degrees in size) using a passive fixation task. Using this optimal 2D-shape outline, we then tested the 3D-structure selectivity of a site by presenting 100% stereo-coherent many concave and convex 3D structures at one of three different positions in depth (i.e., Near, Fix, Far). We then retracted the electrode to the center of the 3D-structure selective cluster, again verified that the MUA still exhibited the same 3D-structure selectivity, and started the 3D-structure discrimination task. We used

the optimal 2D-shape outline at the cluster center for the discrimination task. We adopted the following criterion for defining a 3D-structure selective cluster: The center-position of a cluster had to be neighbored by MUA-positions having the same 3D-structure selectivity for at least 125 μm in both directions (i.e., up- and downwards). Similar criteria have been used in previous studies (Hanks et al., 2006, Salzman et al., 1990 and Uka and DeAngelis, 2006). If time permitted, we verified the 3D-structure selectivity once again after the microstimulation experiment. The data from four experiments were excluded from our dataset because of changes in 3D-structure selectivity observed after the microstimulation experiment.

, 2002 and Vyazovskiy and Harris, 2013). Once a sensor reports a deviation

from the set point that calls for corrective action, the cv-c-dependent activity switch is thrown. To do so, the homeostat must compare a physiological variable representing the sleep balance of the organism to a reference representing the homeostatic set point. An obvious cellular mechanism for making such a comparison is the generation of an action potential, which classically SCH727965 solubility dmso involves the application of a fixed criterion to variable synaptic input. Unexpectedly, our recordings demonstrate that it is the ability of sleep-promoting neurons to generate action potentials, and not necessarily the magnitude of the synaptic drive they experience, that varies as a

function of sleep need ( Figure 7). Given that the excitability of dorsal FB neurons increases during waking, the barrage of synaptic impulses or the force of endogenous pacemaker currents will, at some point, suffice to push the cells across threshold, marking the onset of sleep and thus closing the regulatory loop. The mechanism we envisage, in which action potential generation by sleep-promoting neurons is a principal variable encoding the sleep balance of the animal, does not exclude that other sleep-related changes take place as well. For example, activity levels in the brain Autophagy Compound Library regions providing a synaptic reference signal to the dorsal FB might determine precisely when the transition to sleep occurs, creating a potential link between the intensity of waking experience and the induction of restorative sleep. Several genetic and pharmacological manipulations that Carnitine dehydrogenase target the mushroom bodies delay the onset of sleep, reduce sleep rebound, and enhance performance after extended waking (Donlea et al., 2012, Seugnet et al., 2008 and Seugnet et al., 2011), highlighting one possible source of relevant input signals to the dorsal FB. Arousal-promoting dopaminergic

projections to the dorsal FB constitute another (Liu et al., 2012 and Ueno et al., 2012). Of course, alternative scenarios are also possible in which sleep need is sensed entirely outside the dorsal FB and communicated to sleep-control neurons via synaptic, metabolic, endocrine, or even glial pathways that feed into cell-intrinsic signaling systems converging on Cv-c. The cell-intrinsic events downstream of Cv-c are already visible in outline. Our measurements of the electrical properties of dorsal FB neurons give a clear indication that sleep history alters the number or conductive properties of ion channels in the plasma membrane of these neurons. When sleep pressure is low, sleep-control neurons have low input resistances and short time constants that are diagnostic of the opening of transmembrane conductances (Figures 5 and 7). These conductances quickly dissipate depolarizing currents, limit the amplitude of membrane potential fluctuations, and oppose spiking.

However, the limited spatial invariance that we observe and the success of local orientation pooling in

predicting responses lead us to suggest that spatial invariance to larger pattern stimuli will be much more restricted than previously suggested, falling within one of our coarse grid locations (about one-third of the RF size). Recent studies at still higher stages of processing such as IT also call into question the spatial extent of invariance in ventral stream representations, suggesting invariance is not intrinsic but is a learned attribute of those representations (Cox and DiCarlo, 2008). It is possible that the 13 neurons excluded from our analyses due to their lack of shape selectivity are purely color selective (see, e.g., Bushnell et al., 2011). The relationship between the

present ISRIB nmr Venetoclax findings and the recent report of segregated orientation and color domains (Tanigawa et al., 2010) remains to be explored. Since cells selective for higher curvature are not strongly tuned for orientation (Figure 3, example neurons II and III), domain segregation might be somewhat reduced if measured using composite shapes (as in our study). We do not see evidence for the response bias toward acute contour curvature as reported in a recent study (Carlson et al., 2011). This could be due to the fact that in our study we explored the fine structure of the entire RF, whereas the stimuli used in the Carlson et al. study were presented at the center of the RF and typically spanned the extent of the RF. The finding that spatial invariance falls off with preference for more curved contours suggests a possible

segregation of function. Spatially invariant neurons selective for orientation may play a role in representing extended regions of uniform texture, where the location of the individual texture elements need not be encoded with great spatial precision. In contrast, neurons Ketanserin selective for curvature are likely activated when an appropriately curved contour falls at a particular location within the RF. This form of spatially selective encoding of curved contours would be useful in localizing contours, particularly at the points of high curvature that often play a critical role in defining shape (Attneave, 1954; Feldman and Singh, 2005). We note that such a code, although parsimonious, would be ambiguous for downstream neurons, which would likely integrate multiple signals to derive an unambiguous interpretation of a complex contour. Although the trade-off between invariance and contour complexity does suggests distinct functions, we also find that V4 responses across this spectrum can be explained using a simple model that pools fine-scale orientation signals. This suggests that differences in invariance and contour complexity depend on differences in the orientation-selective inputs that are pooled to give rise to selectivity in V4.

Consistent with this explanation, an endocytic delay based on a pHluorin assay, in spite of a selective accumulation of CCVs,

but not of CCPs, was previously observed in studies of synaptojanin and auxilin KO synapses (Mani et al., 2007 and Yim et al., 2010). It is also possible that the kinetic delay of endocytosis detected by the pHluorin assay may not be sufficiently robust to reflect an accumulation of ABT-737 clinical trial CCPs. Regardless, EM data demonstrate that the key defect produced by the lack of endophilin is impaired uncoating. Immunofluorescence analysis of the distribution of endocytic proteins in endophilin TKO cultures provided further support to the idea that a large fraction of such proteins is sequestered on assembled coats and that the endophilin KO and synaptojanin KO phenotypes are similar. No difference was observed in the immunoreactivity pattern for the active zone marker Bassoon, indicating no overall difference in the formation, organization, or number of synapses (Figure 6A). However, as reported for dynamin 1 KO and synaptojanin 1 KO neuronal cultures (Ferguson et al., 2007, Hayashi et al., 2008 and Raimondi et al., 2011), the strong accumulation of clathrin-coated CX-5461 solubility dmso structures in nerve terminals (Figure 5) was reflected by a stronger (relative to control) punctate synaptic immunoreactivity for endocytic clathrin-coat components,

namely, clathrin itself (clathrin LC), α-adaptin (a subunit of AP-2), and AP180 (Figures 6A and

6B). Surprisingly, the two synaptic dynamins, dynamin 1 and 3, which are endophilin interactors, were also strongly clustered in both endophilin TKO and synaptojanin 1 KO synapses (Figures 6A and 6B). In contrast, the localization of synaptojanin 1 in endophilin TKO neurons was more diffuse than in the control (Figures 6A and 6B). These findings support the idea that endophilin is more important for the recruitment of synaptojanin than of dynamin to endocytic sites. Amphiphysin 1 and 2, which were also more clustered at endophilin TKO synapses, may participate in this recruitment (Figures 6A and 6B). Rescue experiments to assess the specificity of endocytic protein clustering were performed by transfection of EGFP-clathrin secondly LC (to selectively visualize clathrin in transfected cells) with or without Cherry-tagged endophilin constructs. Robust clustering of the clathrin signal was detected in cultures transfected with EGFP-clathrin LC alone (Figures 6C and S4). In contrast, the distribution of clathrin in cultures cotransfected with full-length endophilin (see E1FL in Figure 6C) was diffuse and similar to the fluorescence observed in WT cultures (Figures 6C, 6D, and S4). Importantly, when EGFP-clathrin LC was coexpressed with the endophilin BAR domain construct, no rescue was observed (Figures 6C, 6D, and S4), demonstrating the importance of the SH3 domain for the rescue.

13 (d, 3H, J = 6.0 Hz, –CH3) 0.89 (s, 9H, 3× –CH3), 0.05 (s, 6H, 2× –CH3). To a solution of 15 (1.7 g, 6.10 mmol) in dry ether, sodium metal pieces (0.56 g, 24.40 mmol) were added and stirred at room temperature for 12 h. The reaction mixture was quenched with few drops of MeOH, evaporated and extracted Vorinostat price with EtOAc (2 × 50 mL). It was washed with water (20 mL), brine (20 mL), dried (Na2SO4) and evaporated. afforded 9 (1.1 g, 73%) as a colorless oil. [α]D −37.4 (c 0.18, CHCl3); 1H NMR (300 MHz, CDCl3): δ 5.89 (m, 1H, olefinic), 5.11 (q, 2H, J = 14.8 Hz, olefinic), 4.02 (m, 1H,

–CH), 3.83 (m, 1H, –CH), 1.60–1.37 (m, 4H, 2× –CH2), 1.06 (d, 3H, J = 5.4 Hz, –CH3), 0.84 (s, 9H, 3× –CH3), 0.01 (s, 6H, 2× –CH3); 13C NMR (75 MHz, CDCl3): δ 141.5, 114.3, 73.1, 68.6, 35.1, 32.9, 26.0, 23.3, 18.0, −4.4, −4.8; IR (KBr): 3386, 2929, Palbociclib purchase 2857, 1465, 1373, 1253, 1134, 1048, 833 cm−1. To a cooled (0 °C) solution of 9 (3.0 g, 12.29 mmol) in dry THF (30 mL), NaH (0.59 g, 24.59 mmol) was added, stirred for 30 min and treated with a solution of PMBBr (2.93 g, 14.74 mmol) in dry THF (15 mL). After 7.5 h stirring at room temperature, the reaction mixture was quenched with sat. NH4Cl solution (10 mL) and extracted with ethyl acetate (2 × 50 mL). The organic layers were washed with water (2 × 10 mL), brine (10 mL) and dried (Na2SO4).

Solvent was evaporated under reduced pressure and purified the residue by column chromatography (60–120 Silica gel, 5% EtOAc in pet. ether) to furnish 16 (3.7 g, 82%) as a yellow liquid. [α]D +26.6 (c 0.7, CHCl3); 1H NMR (300 MHz, CDCl3): δ 7.20 (d, 2H, J = 8.6 Hz, ArH-PMB), 6.83 (d, 2H, J = 8.6 Hz, ArH-PMB), 5.87 (m, 1H, olefinic), 5.19 (q, 2H, J = 4.1, 11.6 Hz, olefinic), 4.54, 4.28 (2d, 2H, J = 11.6 Hz, –OCH2 Ar), 3.78 (m, 1H, –CH), 3.69 (s, 3H, –OCH3), 3.62 (m, 1H, –CH), 1.61–1.32 (m, 4H, 2× –CH2), 1.20 Levetiracetam (d, 3H, J = 6.0 Hz, –CH3), 0.81 (s, 9H, 3× –CH3), 0.03 (s, 6H, 2× –CH3); 13C NMR (75 MHz, CDCl3): δ 149.8, 131.1, 128.5, 128.8, 127.6, 120.9, 72.7, 57.8, 55.3, 35.8, 30.2, 24.9, 23.8, 22.4, −4.3; IR (neat): 3427, 2926, 2863, 1739, 1456, 1268, 1108 cm−1. Ozone was bubbled through a cooled (−78 °C) solution of 16 (5.2 g, 24.19 mmol) in CH2Cl2 (70 mL) until

the pale blue color persisted. Excess ozone was removed with Me2S (2 mL) and stirred for 30 min at 0 °C. The reaction mixture was concentrated under reduced pressure to give aldehyde, which was used for further reaction. To a solution of was dissolved in benzene (50 mL) (methoxycarbonylmethylene)-triphenyl phosphorane (2.5 g, 7.37 mmol) was added at reflux. After 2 h, solvent was evaporated to furnish 17 (2.25 g, 87%) as a yellow liquid. [α]D +45.6 (c 1.4, CHCl3); 1H NMR (CDCl3, 300 MHz): δ 7.20 (d, 2H, J = 8.0 Hz, ArH-PMB), 6.89 (d, 2H, J = 8.0 Hz, ArH-PMB), 6.61 (dd, 1H, J = 6.1, 15.7 Hz, olefinic), 5.76 (d, 1H, J = 15.6 Hz, olefinic), 4.33 (d, 1H, J = 11.7 Hz, benzylic), 4.16 (d, 1H, J = 11.7 Hz, benzylic), 3.81 (m, 1H, –OCH), 3.67 (s, 3H, OCH3), 3.61 (s, 3H, OCH3), 3.

The reason for the temporal difference between the transcriptional rhythms and the desat1-luc reporter in Pdfr5304 flies is not understood. However, given that the desat1-luc transgene contains the Idelalisib molecular weight promotor and the 5′ UTR of the desat1-RE transcript, it is plausible that the expression of the luciferase protein is subject to additional

regulatory influences that are not observable when measuring clock gene and desat1 transcription alone. Mechanisms of posttranscriptional regulation mediated by the 5′ UTR, such as transcript stability and translation, are involved in the circadian regulation of clock-controlled genes in plants and mammals ( Kim et al., 2007, Kim et al., 2011 and Ovadia et al., 2010). Similarly, posttranscriptional regulation via micro-RNAs plays a role in the circadian biology of Drosophila ( Kadener

Selleckchem AZD6244 et al., 2009), and although there are no published examples of such regulation through interactions with the 5′ UTR, such a mechanism is possible. As we have shown here, there are five desat1 isoforms expressed in the oenocytes; each is identical in the protein coding sequence and only distinguishable by the 5′ UTR. The differential regulation of these transcripts by the oenocytes probably occurs at the level of promoter-mediated transcription, but the diversity of 5′ UTRs indicates a posttranscriptional mechanism directing a higher level of regulation of desat1 expression. How PDF signaling events link to the clock and the regulation of clock-controlled genes is not known. As we have discussed, our results indicate that the PDF signaling

pathway may involve complex regulatory interactions occurring at multiple levels during the process no of gene expression. The ability of the oenocytes to maintain a molecular rhythm, albeit shifted, in the absence of a coordinated central clock and behavioral rhythms indicates that the oenocytes, like other peripheral clocks, maintain a high degree of autonomy. As with other peripheral clocks in Drosophila, the oenocytes express the gene encoding for the blue-light photoreceptor CRY (J.J.K.and J.D.L, unpublished data) suggesting that the oenocytes may directly entrain to the light/dark cycle. Therefore, proper phasing between physiological and behavioral rhythms may involve a mechanism whereby semiautonomous, photosensitive peripheral clocks independently tune to the solar day yet remain responsive to temporal input from the CNS. It is conceivable that such a circadian system allows independently entrained oscillators to maintain close phase coherence under varying environmental conditions.

This allows LDS to cover the parameter space more evenly compared to MC and LHS. Each parameter combination, sampled by Sobol’s algorithm, is unique, which means that sampling of N Sobol’s points from a hypercube provides N variants of parameter value on each individual parameter direction. Among the most popular methods of sensitivity analysis are averaged local sensitivities (Balsa-Canto et al., 2010, Kim et al., 2010 and Zi et al., 2008), Sobol’s method (Kim et al., 2010, Rodriguez-Fernandez selleck and Banga, 2010 and Zi et al., 2008), Partial Rank Correlation Coefficient (PRCC)

(Marino et al., 2008 and Zi et al., 2008), and Multi-Parametric Sensitivity Analysis (MPSA) (Yoon and Deisboeck, 2009 and Zi et al., 2008). In general, different SA methods are better suited to specific types of analysis. For example, analysis of a distribution KPT-330 of local sensitivities, can be very useful for the initial scoring of parameters prior to model calibration, especially if sensitivity coefficients can be derived analytically and will not require

numerical differentiation, which significantly increases the computational cost. The choice of the particular SA method significantly depends on the assumed relationship between the input parameters and model output. If a linear trend can be assumed, the methods based on calculation of the Pearson correlation coefficient can be employed. For nonlinear but monotonic dependences, PRCC and standardized rank regression coefficient (SRRC) appear to be the best choice (Marino et al., 2008), as they work with rank transformed values. If no assumption can be made about the relationship between model inputs and outputs, or the dependence is non-monotonic, another group of sensitivity methods can be employed, based on decomposition of the variance of the model output into partial variances, assessing the contribution of each

parameter to the total variance. One of the most powerful variance-based methods is Sobol’s method; however it is also known to be among the most computationally intensive, with the cost growing exponentially with the dimensionality of the parameter space (Rodriguez-Fernandez and Banga, 2010). Another promising method that makes no assumptions aminophylline about the dependence between model parameters and outputs is MPSA (Jia e al., 2007 and Yoon and Deisboeck, 2009). In MPSA all outputs are divided into two groups: “acceptable” and “unacceptable” and parameter distributions in both groups are tested against the null hypothesis that they are taken from the same distribution. The lower is the probability of acceptance of null hypothesis, the higher is the sensitivity of the parameter (Zi et al., 2008). When binary decomposition of model outputs can be naturally introduced the results of MPSA can be very useful (Yoon and Deisboeck, 2009). In our GSA implementation we chose to use PRCC as the preferred method for SA, as one of the most efficient and reliable sampling-based techniques (Marino et al.