This additional stimulation caused several of these cells to reve

This additional stimulation caused several of these cells to reverse (Figures S2E and S2F), indicating that stable cells can become reversed cells. Second, we compared the tuning properties prior to adaptation of the cells that reversed and those that remained stable, and we found that the stable cells tended to be more sharply tuned (the DSI values for stable cells were 0.78 ± 0.19 and for reversed cells were 0.63 ± 0.23, mean ± SD; p < 0.02, Mann-Whitney test; the vector sum magnitude values for stable cells were 0.53 ± 0.17 and for reversed cells were 0.38 ± 0.17, p < 0.01, Mann-Whitney test; Figures S2G, S3A, and

S3B). This suggests that cells are more difficult to reverse when their original tuning is sharp. Third, both stable and reversed cells responded to adaptation by significantly reducing their

firing rates to the original PD (from Veliparib order 9.95 ± 5.42 Hz to 2.73 ± 2.68 Hz for reversed cells, p < 0.01 and from 10.38 ± 8.53 Hz to 5.85 ± 5.31 Hz for stable cells, p < 0.02, Mann-Whitney test; Figures S2G and S3C, examples in Figures S2A and S2B). In addition, there was no correlation between a cell’s ability to reverse and the age or genotype of the mouse (Figures S3D and S3E). Altogether, these data suggest that DSGCs that remain stable and those that reverse are not inherently different but rather their likelihood to reverse depends on their initial tuning. Combining the data across all stimulation protocols and categorizing the results from their final DS tests, we found

that most cells significantly altered their directional tuning after exposure to an adaptation protocol (30/74 DSGCs reversed, 15/74 became ambiguous, and 29/74 remained stable). Interestingly, regardless of the adaptation protocols, none of the cells acquired a preference for the direction orthogonal to the original P-N axis. Instead, the PD after adaptation was either close to the original PD (for stable cells) or very towards the original ND (for reversed cells, Figure 2J). To investigate the stability of the reversal, we used a subset of cells for which we maintained recordings and continued to perform DS tests after the reversal. All cells in these experiments maintained their reversed directional preference for the extent of the recording (ranging from 2–23 min, n = 9 cells). Thus, the reversal induced by visual stimulation is apparently robust and long lasting. Direction selectivity is dependent on GABA-A receptor-mediated inhibition (Ariel and Daw, 1982; Caldwell et al., 1978; Kittila and Massey, 1997; Massey et al., 1997; Wei et al., 2011). To determine whether this inhibition also mediates the newly acquired PD, we bath applied a GABA-A blocker (gabazine, 5 μM) after the directional preference of GFP+ DSGCs was reversed.

Although Igf2 availability decreased in adult CSF (Figures 3C and

Although Igf2 availability decreased in adult CSF (Figures 3C and S3B), Igf2 continued to be expressed in adult choroid plexus (data not shown) and maintained adult neurospheres ( Figure 4I), suggesting that low levels of CSF Igf2 contribute to the maintenance of adult neural stem cells. The aberrant increase in Igf2 in advanced GBM patients reinforces the hypothesis that Igf signaling has an influence on proliferation of cortical precursors. Our identification of Igf2 regulation

of neurogenesis and brain size complements a literature in which Igf signaling is well known to influence body and brain size ( Baker et al., 1993, DeChiara et al., 1991 and Purves, 1988), raising Selleckchem VX-770 the intriguing possibility that Igf2 represents a secreted factor that may scale brain size to body size. The activity of growth promoting factors in the CSF and their action on progenitors across the apical surface may be a model for other epithelia including lung, gut, and vascular endothelia that develop in relation to extracellular fluids (Bendall et al., 2007 and Scadden, 2006). Extracellular

fluid apparently regulates the microenvironment of hematopoietic stem cells, where Igf signaling regulates progenitor proliferation (Orkin and Zon, 2008 and Zhang and Lodish, 2004). The differential capacity of Igf signaling to confer a proliferative advantage to stem cells may be regulated in part by Igf’s interactions with binding proteins Androgen Receptor Antagonist cell line or other secreted factors in the environment (Clemmons, 1997). Our experiments focused on the age-associated effects of CSF on survival and proliferation across the cortical ventricular zone. However, STK38 the distribution of CSF resident proteins, as well as the flow of the CSF, may also influence ciliary orientation and maturing ependymal cell polarity (Mirzadeh et al., 2010), which create activity gradients as has been shown for Slit (Sawamoto et al., 2006). If a major component of the stem cell niche reflects secreted factors

acting at long distances from their sources, modulation of the proteomic composition of extracellular fluids may also provide unexpected ways to regulate stem cell behavior in health and disease. For example, while Igf2 activity peaked in embryonic CSF, some CSF-borne Igf persisted in adulthood (Figures 3, S3B, and data not shown). Igf2 and Igf1 in adult CSF may contribute to the retention of neural stem cell properties in the adult SVZ (Doetsch et al., 1999). Importantly, the regulation of CSF growth factors may also extend to pathologic states. Igf2 and other diffusible growth factors that drive neural progenitor proliferation during development are upregulated in some GBM patients (Louis, 2006 and Soroceanu et al., 2007), and GBM patients have elevated Igf2 levels in their CSF.

Additionally, the pattern of sequence-read coverage is inconsiste

Additionally, the pattern of sequence-read coverage is inconsistent with these sources of contamination, as we found no significant enrichment in intergenic reads, nor did we find any systematic pattern of intron presence or absence across all genes. We also analyzed each retained intronic locus by using base composition properties

and public annotations and found that the majority had no evidence for unannotated alternate exons or overlapping genes (see Supplemental Text). Based on the retained intronic loci detected in the initial screen, we selected several candidates to visualize by using in situ hybridization to confirm retention and localization patterns. We assayed intronic probes designed to target microarrayed sequences from RNAs showing varying degrees of intronic sequence retention. Antisense riboprobes were generated and ISRIB purchase used for in situ hybridization to E18 rat neurons in primary cell culture. Cells were costained for

MAP2 protein to indicate dendrito-somatic regions of neurons (Figure 1, insets). All sequences tested showed dendritic in situ hybridization signals consistent with microarray results (Figure 1A). In situ hybridization of exonic probes confirmed the dendritic localization patterns find protocol of the intron-containing transcripts (Figure S2). Further, oligo probes to intron-exon junctions with sequencing support successfully confirmed that each region was within the dendritic compartment by in situ hybridization (Figure 1B). Interestingly, GRIK1 shows a higher dendritic signal for intron 16 joined with an alternate exon than with the canonical exon 17, suggesting an interaction between intronic sequence and the isoforms of the transcript in localization (Bell et al., 2010). Given the widespread occurrence of CIRTs, we hypothesized

that sequence elements important for mRNA regulation may be embedded in the retained intronic sequences and searched for putative regulatory sequences. We found Linifanib (ABT-869) several sets of sequences shared among different introns, including a large number of BC1 RNA-like ID elements. While ID elements are not unique to retained introns, many are found in the dendritic introns detected by microarray and sequencing. Among these intronic ID elements, we found that many retain motifs previously identified as BC1 localization signals that confer targeting to microinjected mRNA (Muslimov et al., 2006) as evidenced by their predicted secondary structures (Figure 2A). A total of 308 blocks of ID-derived sequence were found. Of these, 70 elements appearing in 46 introns across 23 genes were determined to possess mRNA targeting potential: these occurred in the sense orientation and forming a hairpin structure with a basal-medial unbranched helix, a uracil at position 22, and at least 90% sequence identity to the BC1 5′ domain (Table S3). Sequencing data provided evidence that many of these ID-containing loci were present in the dendritic RNA pools.

One critical difference between current injection and a laser pul

One critical difference between current injection and a laser pulse is the number of neurons GDC-0941 order activated: the laser beam will synchronously activate a population of ChIs and/or their axons owing to the extensive overlapping arborization of ChI axons and dendrites (Contant et al., 1996). These data therefore suggest that ChI-driven DA release occurs during synchronization of activity in ChIs. The requirement for synchronization was confirmed by showing that laser stimuli that minimize synchrony in ChIs did not evoke DA release. To achieve this, we recruited activity gradually in a population of ChIs by slowly ramping laser

intensity during continuous exposure until threshold for spiking was reached in a given recorded ChI. Using this protocol, outcome on activity in each ChI was variable (e.g., threshold intensity, see variation in spike frequency in Figure 2C, n = 6) and this protocol did not evoke DA release (Figure 2C, n = 6). Multiple spikes in a given ChI per se did not preclude DA release

since longer duration laser pulses above threshold that evoked burst firing in ChIs were accompanied by DA release (Figure 2D, n = 11). These data show that synchronous check details recruitment of activity in a population of ChIs and/or axons evokes DA release. We also noted that multiple action potentials in a given ChI induced by long laser pulses did not evoke more DA release than a single action potential (compare Figures 2D and 2B), suggesting that ChI-driven DA release does not convey frequency information from individual ChIs. This weak relationship between frequency and DA release is also seen with striatal electrical stimulation when DA axons and ChIs are simultaneously depolarized (Rice and Cragg, 2004 and Zhang and Sulzer, 2004), but not with stimulation of medial forebrain bundle when DA axons are activated (Chergui et al., 1994). These observations suggest that ChI-driven DA release does not report frequency and see more moreover that it may limit how frequency information in ascending

DA axons is transduced into DA release. We therefore explored the relationships between frequency of activation and DA transmission during activation of ChIs only, DA axons only, or both in combination. Trains of four laser pulses at a range of frequencies in ChR2-expressing ChAT-Cre striatum reliably evoked four action potentials in ChIs at corresponding frequencies (Figure 3A), but the consequent DA release was invariant, reaching only DA levels seen with a single light pulse (and single action potentials) (Figures 3B and 3D, n = 8). This refractoriness (or depression) of rerelease after release by single synchronized spikes in ChIs was therefore not due to spike attenuation in ChIs (and was also not due to activation of mAChRs or D2 receptors causing ACh terminal inhibition, data not shown). These data show that ChI-driven DA release is not a direct readout of the frequency of activity in a given ChI.

, 2001) Slits are the principal ligands for the Robo receptors (

, 2001). Slits are the principal ligands for the Robo receptors ( Kidd et al., 1999), to which they bind in association with heparan sulfate proteoglycans ( Hu, 2001). There are three Slit genes in mammals, and all of them are expressed in developing CNS ( Marillat et al., 2001). Slits bind promiscuously to Robo receptors in vitro ( Brose et al., 1999; Li et al., 1999), which suggests that these proteins may cooperate in vivo in those locations in which their expression patterns overlap ( Bagri et al., 2002; Plump et al., 2002). The functions of Robo receptors have been classically studied in postmitotic

cells, most typically in neurons. However, Robo receptors also seem Selleck Osimertinib to be expressed in progenitor cells, at least in some regions of the developing brain (Marillat et al., 2001). A few studies have even hinted at a possible role for Robo receptors in neurogenesis (Andrews et al., 2008; Mehta and Bhat, 2001), but the precise mechanisms through which Slit signaling may control this process are unknown. In Drosophila, slit seems to modulate EGFR activity neurogenesis by promoting asymmetric terminal divisions in particular neural lineages ( Mehta and Bhat, 2001). Considering the highly conserved roles of Slits and their Robo receptors in evolution ( Brose and Tessier-Lavigne, 2000), it is conceivable that Slit/Robo signaling may play a similar role in the vertebrate

brain. Here we have tested the hypothesis that Slit/Robo signaling may contribute to regulate neurogenesis in the mammalian CNS. We focused most of our analysis in the developing cerebral cortex, for which the cellular mechanisms of neurogenesis are beginning to be elucidated Carnitine palmitoyltransferase II (Fietz and Huttner, 2011; Noctor

et al., 2007; Pontious et al., 2008). During early phases of neurogenesis, cortical progenitor cells residing in the ventricular zone (VZ) divide symmetrically to increase the pool of dividing cells. As neurogenesis progresses, VZ progenitors begin to divide asymmetrically to self-renew and produce new neurons or, more frequently, to generate IPCs. These progenitors, which localize to the subventricular zone (SVZ), will generate additional neurons after one or more rounds of divisions. This two-step process of neurogenesis is highly reminiscent to that observed during the development of the CNS in Drosophila ( Skeath and Thor, 2003), but the mechanisms controlling these dynamics remain poorly characterized. We found that progenitor cells throughout the entire mouse brain and spinal cord transiently express Robo1 and Robo2, in particular during early stages of neurogenesis. Analysis of Robo1 and Robo2 double (Robo1/2) mutants revealed that these receptors are required to maintain the proper balance between primary and intermediate progenitors, because loss of Robo signaling leads to a decrease in VZ progenitors and a concomitant increase in the number of IPCs.

Estimates of 50% high-cutoff values for spatial and temporal freq

Estimates of 50% high-cutoff values for spatial and temporal frequency ( Figures 3C and 3D) were also

obtained from the model HIF inhibitor fit (from cross-sections at R(sf, tf0) and R(sf0, tf), respectively). For estimation of the optimal linear classifier of frequency preferences, (sf0, tf0), between AL and PM, we performed linear discriminant analysis and found that the optimal classifier line described was given by log2(sf0) = −5.39 + 0.997∗log2(tf0), which corresponds approximately to an iso-speed line given by speed = tf / sf = 41.9°/s (yellow line, Figure 3B). For the spatial frequency × direction protocol, we first found the preferred orientation (averaged across spatial frequencies), and estimated the peak spatial frequency (at the neuron’s preferred orientation). We then computed orientation and direction selectivity indices as (Rpeak − Rnull) / (Rpeak + Rnull) at the neuron’s preferred spatial frequency (for direction estimates, Rpeak = preferred direction, Rnull = response at 180° from preferred; for orientation estimates, Rpeak = preferred orientation, Rnull = response at 90° from preferred; Kerlin et al., 2010 and Niell and Stryker, 2008). For analyses of influences of locomotion on spatial and temporal frequency responses (Figures 6, S2, and S6),

we divided trials for each stimulus type into C59 wnt molecular weight those in which any wheel motion was observed in the 5 s of stimulus presentation (“moving” trials) and those that lacked any movement (“still” trials).

In a subset of experiments (Figure S2), we analyzed eye position using custom Matlab implementation of a previously described algorithm for pupil tracking (Zoccolan et al., 2010). We thank Glenn Goldey for surgical contributions, Anthony Moffa and Paul Serrano for behavioral training, and Sergey Yurgenson for technical contributions and eye-tracking code. Aleksandr Vagodny, Adrienne Caiado, and Derrick Brittain provided valuable technical assistance. We also thank John Maunsell, Bevil Conway, Jonathan Nassi, Christopher Moore, Rick Born, and members of the Reid Lab—especially Vincent Bonin—for advice, suggestions, and discussion. This work was supported Megestrol Acetate by NIH (R01 EY018742) and by fellowships from the Helen Hay Whitney Foundation (M.L.A. and L.L.G.), the Ludcke Foundation and Pierce Charitable Trust (M.L.A.), and the Sackler Scholar Programme in Psychobiology (A.M.K.). “
“Specialized neural circuits process visual information in parallel hierarchical streams, leading to complex visual perception and behavior. Distinct channels of visual information begin in the retina and synapse through the lateral geniculate nucleus to primary visual cortex (V1), forming the building blocks for visual perception (Nassi and Callaway, 2009).

Prior to stimulation,

Prior to stimulation, OSI-744 clinical trial the majority of surface-labeled FD1R immunoreactivity was concentrated at the cell periphery, whereas endogenous ACV was detected both peripherally and associated with internal structures (Figure 8A, top). Surface-labeled D1 receptors moved to endocytic membrane structures within 2 min after agonist addition and a number of these colocalized with ACV (Figure 8A, bottom). The fraction of D1

receptor immunoreactive structures that also contained ACV is quantified in Figure 8B. We used the same approach to look at the subcellular localization D1 receptors in relationship to Gαs/olf proteins. Prior to stimulation, Gαs/olf immunoreactivity localized both peripherally and in association with internal structures, whereas D1 receptors showed a peripheral distribution consistent with plasma membrane localization (Figure 8C, top). Following

acute receptor activation, D1 receptors redistributed to endocytic vesicles and Gαs/olf immunoreactivity colocalized with a significant fraction these structures (Figures 8C, bottom, and 8D). Examination of this distribution at higher magnification MK-1775 supplier suggested that both downstream transduction proteins localize to subdomains of D1 receptor-containing early endocytic membranes (insets in Figures 8A and 8C). To our knowledge, the present results provide the first analysis of the relationship between D1 receptor trafficking and signaling in neurons, and on a time scale approaching that of physiological dopaminergic neurotransmission. Our results demonstrate that D1 receptors enter the endocytic pathway within ∼1 min after activation by either DA or synthetic agonist and that receptor-mediated accumulation of cellular cAMP occurs with overlapping kinetics. They also establish a causal relationship whereby D1 receptor endocytosis augments acute dopaminergic signaling. heptaminol We demonstrate that recycling

is not required for this response and provide evidence that the endocytosis-dependent signal is generated from an early endosomal membrane, thus distinguishing the present results from endocytosis-dependent resensitization observed for several other GPCRs. Further, our results show that the endocytosis-dependent component of the D1 receptor-mediated signal is functionally relevant as it is required to increase AP firing in a native brain slice preparation. Previous studies of D1 receptor-mediated signaling effects, measured over longer time intervals (≥30 min), have suggested that endocytosis either inhibits (Jackson et al., 2002 and Zhang et al., 2007) or has no effect on dopaminergic signaling (Gardner et al., 2001).

, 1984 and Pickles et al , 1989) Mechano-electrical transduction

, 1984 and Pickles et al., 1989). Mechano-electrical transduction (MET) adaptation presents as a decrease in current during a constant stimulus, where further stimulation recovers Olaparib in vitro the current

(Crawford et al., 1989 and Eatock et al., 1987). Adaptation is implicated in setting the hair bundle’s dynamic range, providing mechanical tuning, setting the hair cell’s resting potential, providing amplification to an incoming mechanical signal, and providing protection from overstimulation (Eatock et al., 1987, Farris et al., 2006, Fettiplace and Ricci, 2003, Hudspeth, 2008, Johnson et al., 2011, Ricci and Fettiplace, 1997 and Ricci et al., 2005). Fundamental hypotheses regarding hair cell adaptation originated from work in low-frequency hair cells contained in the frog saccule, turtle auditory papilla, and mammalian utricle (Assad et al., 1989, Corey and Hudspeth, 1983a, Crawford et al., 1989, Crawford et al., 1991, Eatock et al., 1987, Hacohen et al., 1989 and Howard and Hudspeth, 1987). Two components of adaptation, termed fast and slow (motor),

are distinct in their operating range, kinetics, and underlying mechanisms (Wu et al., 1999); however, Ca2+ entry via the MET channel drives both processes. To generate fast adaptation, Ca2+ is postulated to interact directly with the channel or through an accessory protein (Cheung and Corey, 2006, Choe et al., 1998, Crawford et al., 1989, Crawford et al., 1991 and Gillespie Phosphatidylinositol diacylglycerol-lyase and Müller, 2009); however,

Akt inhibitor myosin motors Ic, VIIa, and XVa have also been implicated in regulating fast adaptation (Kros et al., 2002, Stauffer et al., 2005 and Stepanyan and Frolenkov, 2009). A long-standing slow adaptation model posits that movement of myosin isozymes up and down the stereocilia controls the tension sensed by the MET channels in a Ca2+-dependent manner (Assad and Corey, 1992, Assad et al., 1989, Holt et al., 2002 and Howard and Hudspeth, 1987). Recent data questions whether motor adaptation is relevant to mammalian auditory hair cells. Myosin Ic, the presumptive adaptation motor, does not specifically localize to the upper tip link insertion site in mammalian auditory hair cells, and its expression during development does not match the onset of slow adaptation (Schneider et al., 2006 and Waguespack et al., 2007). Furthermore, the kinetics of myosin Ic do not fit the requirements of the model in terms of climbing and slipping rates (Pyrpassopoulos et al., 2012). Additionally, MET channels are localized to the tops of stereocilia (Beurg et al., 2009) and not at the upper insertion site where myosin motors are thought to reside; therefore, it is unlikely that Ca2+entering through MET channels is directly responsible for regulating these motors.

Their sensitivity to high carrier TFs is also consistent with res

Their sensitivity to high carrier TFs is also consistent with results from cat area 18 which we describe in the next section and human psychophysical studies (D’Antona and Shevell, 2009 and Stockman and Plummer, 1998). We also found no significant relationship between

the peak grating TFs (measured using drifting gratings at the peak grating SF) and peak carrier TFs of Y cells. The grating and carrier TF tuning curves of a Y cell along with a population scatter plot of the peak grating TFs and peak carrier TFs are shown in Figures S5C and S5D. If Y cells initiate a pathway that carries a demodulated representation of the visual scene, then there must be downstream cortical processing of this Luminespib solubility dmso nonlinear representation. To explore this, we recorded from area 18 which receives direct input from LGN Y cells (Humphrey et al., 1985 and Stone and Dreher, 1973). Many area 18 neurons respond to interference patterns (Zhou Veliparib and Baker, 1996), but it is debated whether these responses reflect the processing of subcortical Y cell input or cortical area 17 input (Demb et al., 2001b, Mareschal and Baker, 1998a and Rosenberg et al., 2010). We address this question further by examining the selectivity of area 18 neurons for carrier TF and asking whether the tuning properties are better explained

by input from Y cells or area 17. Consistent with our Y cell measurements and data from Levetiracetam a previous study that measured carrier TF tuning in a small sample of area 18 neurons (Zhou and Baker, 1996), we found that area 18 carrier TF tuning curves were diverse in shape and often broadly tuned (Figure 6). The tuning curves were also well-described

by gamma functions (average r = 0.94 ± 0.04 SD, n = 17). Using these fits to estimate tuning properties (Table 1), we found that area 18 carrier TF tuning curves were similar to those of LGN Y cells. The distributions of Y cell peak carrier TFs and area 18 peak carrier TFs were not significantly different (Kolmogorov-Smirnov test, p = 0.40; Figure 7A). The Y cell right half-heights were significantly greater than the area 18 right half-heights (two-sample t test, p = 0.01), but the two distributions were highly overlapping (Figure 7B). The population of area 18 neurons, like the Y cell population, represented the entire range of tested carrier TFs. Area 18 carrier TF tuning curves measured with the carrier drifting in opposite directions were also similar in shape (average r = 0.90 ± 0.10 SD, n = 17) and carrier direction selectivity was low (average DTI = 0.14 ± 0.10 SD, n = 17). The distributions of Y cell carrier DTI values and area 18 carrier DTI values were not significantly different (Kolmogorov-Smirnov test, p = 0.25).

, 2009 and Kilic et al , 2010) However, this amino acid change d

, 2009 and Kilic et al., 2010). However, this amino acid change does not have any detectable functional consequence in the receptor ( Schiffer et al., 2000), although it could convey aberrant gene dosage and/or unequal allele expression ( Schiffer et al., 2000 and Wilson et al., 2006). Indeed, mRNAs for GluK3 and other glutamate receptors are reduced in the frontal cortex of schizophrenic subjects ( Sokolov, 1998;

but see Meador-Woodruff et al., 2001). As for other subunits, GluK3 gene expression is developmentally regulated and aberrant gene dosage during development may impact disease in adulthood ( Wilson et al., 2006). Thus, further experiments learn more using transgenic animals are warranted. Ku-0059436 in vitro A clear example of gene dosage is provided by trisomy of chromosome 21, leading to Down syndrome. Grik1, the gene coding for GluK1 subunits, is located on human chromosome 21q22.1, and genetic mapping places

Grik1 in the vicinity of genes coding for APP and super oxide dismutase (SOD1; Gregor et al., 1994). However, linkage analysis failed to detect any association with familial amyotrophic lateral sclerosis and there are no data indicating any role for GluK1 gene disequilibrium dosage in Down syndrome. Based on multiple regression analyses, it appears that the effects of anxiety and depression treatment are significantly and independently associated with the Grik4 gene ( Paddock et al., 2007). An association was also observed in female patients with markers in Grik1. Together,

these data indicate that reduced expression of Grik1, Grik4, and other genes encoding KAR subunits could be implicated in mood disorders (but see Li et al., 2008). However, the sign of this implication is clearly elusive and these linkages may be circumstantial given that causal mutations have not yet been identified L-NAME HCl through linkage or candidate gene association studies. It is becoming clear that no conclusions can be reached without more precise information of the role of these subunits in general brain physiology. However, recent studies using experimental models have started to assess how the absence of one of these genes affects behavior. The ablation of Grik4 in mice results in marked hyperactivity ( Catches et al., 2012 and Lowry et al., 2013), one of the endophenotypes of patients with bipolar disorders, which has been interpreted as if lack of GluK4 activity has an anxiolytic and antidepressive-like effect ( Catches et al., 2012). Anxiety and depression are concurrent with bipolar disorders, and these data would in principle support the hypothesis that GluK4 hyperactivity could be a hallmark of bipolar phenotypes. However, genetic data from bipolar patients seem to refute this conclusion. In a case control association study, two SNP haplotypes (rs2282586 and rs1944522) exhibited a protective effect against bipolar disorder in a diverse Scottish population ( Pickard et al., 2006).