45 (for images of cortical slices), 60× Apo TIRF; NA 1.49 (for analyses of spine densities in cultured neurons), 100× H-TIRF; and NA 1.49 (for analyses of spine densities in brain slices). Dendritic spine density was quantified on secondary dendritic branches that were proximal to the cell body, on z projections for cultured neurons and in the depth of the z stack for slices, using FiJi software (ImageJ; NIH) (see Supplemental Information). Cultured cells or brain tissues were lysed in RIPA buffer (1% NP-40, 0.5% AP24534 mw sodium deoxycholate, 0.1% SDS, 150 mM NaCl in 50 mM Tris

buffer [pH 8]) supplemented with benzonase (0.25 U/μl of lysis buffer; Novagen), and cocktails of protease (Roche) and phosphatase (Sigma-Aldrich) inhibitors. Equal amounts of lysates (20–50 μg) were loaded on a Mini-Protean TGX (4%–20%) SDS-PAGE (Bio-Rad). The separated proteins were transferred onto polyvinylidene difluoride membranes (Amersham). For phospho-specific antibodies, the membranes were blocked PLX3397 for 1 hr with blocking buffer containing 5% BSA in Tris-buffered saline solution and Tween 20 (10 mM Tris-HCl [pH 7.4], 150 mM NaCl, 0.05% Tween 20; TBS-T). For other antibodies, membranes were blocked for 1 hr with blocking buffer containing 5% fat-free dry milk in TBS-T. Membranes were then incubated overnight at

4°C with different primary antibodies diluted in the same blocking buffer. Incubations with HRP-conjugated secondary antibodies were performed for 1 hr at room temperature, and visualization was performed by quantitative chemiluminescence using Fluorochem Q imager (ProteinSimple). Signal intensity was quantified using AlphaView software (ProteinSimple).

Antibodies were the following: anti-phospho-T172-AMPKα (40H9, 1:1,000; Cell Signaling); AMPKα1/2 (1:1,000; Cell Signaling); AMPKα1 (1:1,000; Abcam); CAMKK2 (1:1,000; Santa Cruz Biotechnology); phospho-Tau (S262, S356, S396, S404, and S422, 1:1,000; Invitrogen); phospho-PHF-Tau (S202/Thr205, AT8, 1:1,000; Pierce); Tau5 (1:1,000; Invitrogen); mouse monoclonal anti-GFP (1:2,000; Roche); and anti-Myc (1:5,000, 9E10; Cell Signaling). Human APP and Aβ were detected by western blotting using 12% Tris-Glycine and 16.5% Tris-Tricine gels (Bio-Rad), respectively, and the anti-human APP/Aβ 6E10 antibody (1:1,000; Covance). To control for loading, blots were stripped and reprobed Phosphatidylinositol diacylglycerol-lyase with mouse monoclonal anti-actin (1:5,000; Millipore). Statistical analyses were performed with Prism 6 (GraphPad Software). The statistical test applied for data analysis is indicated in the corresponding figure legend. The normality of the distributions of values obtained for each group/experimental treatment was determined using the Kolmogorov-Smirnov test. Experimental groups where all distributions were Gaussian/normal were assessed using the unpaired t test for two-population comparison, or one-way ANOVA with Dunnett’s post hoc test for multiple comparisons.

The average laser power on the sample is ∼20–30 mW. Most experiments were acquired at frame rates of 1 Hz at a resolution of 512 × 512 pixels using a 40× water-immersion objective (Nikon). Image acquisition was performed using Laser Sharp 2000 software and analyzed post hoc using ImageJ

software (NIH). ΔF/F was calculated identical to slice imaging experiments. For detecting calcium signals in layer V apical tuft dendritic spines, a line crossing the dendrite and the middle of the spine head was drawn and fluorescence intensity along the line was measured using ImageJ (NIH). Imaging experiments were performed on 4- to 5-month-old mice. The surgery was performed as described previously (Dombeck et al., 2009). Briefly, the see more mice were anesthetized with Avertin solution Selleck GSK1349572 (20 mg/ml, 0.5 mg/g body weight) and were placed in a stereotactic apparatus with a heating pad underneath to maintain body temperature. A 2 × 2 mm piece of bone was removed above the motor cortex, somatosensory cortex, or olfactory bulb as determined by stereotactic coordinates, and the dura was kept intact and moist with saline. To dampen heartbeat and breathing-induced motion, we filled the cranial window with Kwik-sil (World Precision Instruments) and covered it with an immobilized glass coverslip.

A custom-designed head plate was however cemented on the cranial window with Meta-bond (Parkell) when the Kwik-sil set. For chronic imaging, two coverslips were joined with ultraviolet curable optical glue (NOR-138, Norland). A smaller insert fit into the craniotomy and a larger piece was attached to the bone. Imaging was performed 7 days postsurgery to allow the window to clear. During imaging of neuronal activity in motor cortex, the head-fixed animals were placed in water to induce swimming-like behavior. The animals were kept alert by presenting a pole or by mild air puffs to the whisker field. An infrared charge-coupled device camera

(CCTV) was used for observing the animal’s behavior during imaging sessions. Sensory stimulations, consisting of puffs of compressed air delivered by a Picospritzer unit (Picospritzer II; General Valve), were applied through a 1-mm-diameter glass pipette placed 15–25 mm rostrolateral from the whiskers. Air puffs (500 ms duration) were given ten times with 10 s intervals to prevent adaptation of whisker-evoked responses. Odorants were delivered using a custom-built odor delivery system in which the saturated vapor of an odorant was diluted into a main stream of clean air. The clean air stream was fixed at 0.6–0.8 L/min throughout the experiment and the odor vapor stream was adjusted to give the final concentration to the animal. A tube opening was positioned <1 cm from the animal’s nostrils.

, 1998; van Duuren et al., mTOR inhibitor 2007a). We show

that NMDARs affect discriminatory coding of OFC neurons especially during stimulus presentation and decision making and shape plasticity of discriminatory firing across learning trials. NMDAR blockade leads to hypersynchronous phase locking in the theta, beta, and high-frequency bands and destroys the functional relationship between theta-band phase-locking and discriminative power by virtue of firing rate. Unilateral blockade of NMDA receptors does not affect behavioral performance during task acquisition but hampers changes in reaction time after reversal of task contingencies. We recorded 623 isolated single units from four rats in 20 counterbalanced sessions (number of sessions for drug/control = 10/10) using a modified microdrive that held 12 tetrodes arranged concentrically around a microdialysis probe (Figure 1). Histological verification indicated

that most recordings were from ventral and lateral orbitofrontal (VO/LO) and agranular insular (AI) cortex with some spread into dorsolateral orbitofrontal (DLO) cortex (Figure 1A). For each rat, recordings were obtained under both drug and control conditions. However, within a single session, only one condition was applied. In drug sessions, we used continuous reverse microdialysis to apply a 0.5 mM ATM/ATR inhibitor clinical trial solution of D-2-amino-5-phosphonopentanoate (D-AP5), a competitive NMDAR blocker, dissolved in aCSF. old A separate autoradiography experiment with 3H-D-AP5 confirmed that the concentration of D-AP5 in OFC was sufficient to antagonize NMDARs (Figures 1B and 1C). Infusions were done unilaterally to minimize the chance

of inducing behavioral effects, which could confound the interpretation of electrophysiological results if present. Rats performed a two-odor, go/no-go discrimination task, with novel odor-outcome associations for each session (Figures 2A and 2B; Schoenbaum et al., 1998; van Duuren et al., 2007a; van Wingerden et al., 2010a, 2010b). Task acquisition was manifested by the emergence of “No-go” and “Go” responses to the odors predicting a negative (S− condition; “correct rejections”) and positive outcome (S+ condition; “hits”), respectively. The acquisition phase of the task was terminated when rats reached a behavioral criterion (85% correct trials, i.e., hits + correct rejections, in a moving 20-trial block), after which a reversal phase followed, in which the previously presented stimulus-outcome pairings were switched. We first examined overall task performance, defined as the average number of trials to reach criterion, normalized per rat to the average number of trials to criterion for all drug and control sessions for that rat. Overall performance did not differ between control and drug sessions (mean ± SEM; control: 103% ± 8.6%, drug: 96.7% ± 9.6%, two-sided t test n.s.; Figure 2C). Reaction time (RT), i.e.

(1988). Five hundred microliters of DNA lysis buffer (100 mM Tris [pH 8.0], 200 mM NaCl, 1% SDS, and 5 mM EDTA) and 6 μl Proteinase K (20 mg/ml) were added to the collected nuclei and incubated overnight at 65°C. RNase cocktail (Ambion) was added and incubated at 65°C for 1 hr. Half of the existing volume of 5 M NaCl solution was added and agitated for 15 s. The solution was Nutlin-3 mw spun down at 13,000 rpm for 3 min. The supernatant containing the DNA was transferred to a 12 ml glass vial. Three times the volume of absolute ethanol was added, and the glass vial was inverted several times to precipitate the DNA. The DNA precipitate was washed three times in DNA washing

solution (70% Ethanol [v/v] and 0.5 M NaCl) and transferred to 500 μl DNase/RNAase free water (GIBCO/Invitrogen). The DNA was quantified and DNA purity verified by UV spectroscopy (NanoDrop). 14C accelerator mass spectrometry (AMS) measurements were performed on graphitized samples. DNA in aqueous solution was freeze dried, combusted to CO2, and reduced to graphite according to the procedures described in Liebl et al. (2010). 14C AMS measurements

of graphitized samples were carried out at the Vienna Environmental Research Accelerator (VERA) of the University of Vienna, a 3 MV Pelletron tandem AMS system (Priller et al., 1997, Rom et al., 1998 and Steier et al., 2004). The setup of VERA for heavy isotopes was described earlier (Vockenhuber et al., 2003).

heptaminol 14C measurement results are reported as F14C according to the recommendation of Reimer et al. (2004). Entinostat cell line Age calibration of 14C concentrations was performed using the software CALIbomb (http://calib.qub.ac.uk/CALIBomb) with the following parameters: smoothing in years, 1 year; resolution, 0.2; 14C calibration, two sigma. For details related to accelerator mass spectrometry measurements and correction for FACS impurities, see Supplemental Experimental Procedures and Figure S4. We thank Marcelo Toro, Albert Busch, and Haythem H.M. Ismail for flow cytometry, Marie-Louise Spångberg for histology, Martina Wennberg and Anna Speles for administrative assistance, and Klaus Mair for preparing carbon samples. This study was supported by the Swedish Research Council, Tobias Stiftelsen, Hjärnfonden, SSF, NARSAD, Knut och Alice Wallenbergs Stiftelse, AFA Försäkringar, the ERC, and the regional agreement on medical training and clinical research between Stockholm County Council and the Karolinska Institutet (ALF 20080508). J.L. was supported by a research grant of the University of Vienna and O.B. by Deutsche Forschungsgemeinschaft. “
“Pain hypersensitivity generated by peripheral injury can result from plastic changes in both the peripheral (Campbell and Meyer, 2006 and Finnerup et al., 2007) and central nervous systems (CNSs) (Costigan et al., 2009, Coull et al., 2003 and Ikeda et al., 2003).

Under basal conditions, dSPNs and iSPNs exhibit small but

significant differences in dendritic morphology and membrane properties that translate into greater excitability of iSPNs over dSPNs. Although the cell types do not differ with regards to input resistance and resting membrane potential, the action potential discharge rate of iSPNs is twice that of dSPNs in response to somatic current injection (Gertler et al., selleck screening library 2008; Kreitzer and Malenka, 2007). Morphologically, the dendrites of dSPNs and iSPNs are studded with a similarly high density of spines, but iSPNs possess more primary dendrites compared to dSPNs, resulting in a functionally greater number of excitatory synaptic contacts onto these cells (Day et al., 2006; Gertler et al., 2008; Kreitzer and Malenka, 2007). The dendrites of iSPNs are also more excitable than those of dSPNs (Day DAPT cell line et al., 2008). While some

of the differential effects of DA on SPN excitability admittedly originate from circuit-level interactions between striatal cells, DA directly influences SPN excitability by modulating ion channels, several of which have been defined. Modulation of any of these channels has the potential to significantly alter SPN excitability, although the relative impact of these changes critically depends on membrane potential, as the array of voltage-gated ion channels engaged at different potentials varies considerably. DA does not significantly alter SPN excitability by modulating leak conductances as DA receptor agonists exert little or no influence on SPN resting membrane potential or input resistance. Instead, most of DA’s reported effects on intrinsic excitability and synaptic integration involve PKA-dependent modulation Tryptophan synthase of voltage-gated K+, Na+, and Ca2+ channels. In both dorsal and ventral striatum, studies

of pharmacologically isolated currents have revealed that D1 receptors facilitate inward rectifier K+ channels belonging to the Kir2 family (Pacheco-Cano et al., 1996; Uchimura and North, 1990) and decrease slowly inactivating A-type K+ currents attributed to KV4 channels (Kitai and Surmeier, 1993). These changes are predicted to impede synaptically driven transitions from the hyperpolarized resting potential (so-called down state) to a more depolarized, sustained potential near spike threshold (up state), while enhancing action potential firing during up states (Wickens and Arbuthnott, 2005). In addition, D1 receptor stimulation increases CaV1 currents (Hernández-López et al., 1997; Song and Surmeier, 1996; Surmeier et al., 1995), which potentiate up state transitions, excitatory synaptic potentials, and action potential discharge (Plotkin et al., 2011; Vergara et al., 2003), and suppresses currents carried by CaV2.1 and CaV2.2 (Surmeier et al., 1995; Zhang et al., 2002), which limit repetitive action potential firing by activating small (SK)- and large (BK)- conductance Ca2+-dependent K+ channels (Hopf et al.

Selecting ROIs that were smaller (150 voxels) or larger (300 voxels) yielded equivalent results to those presented in the manuscript, confirming that the results were not limited to a specific ROI size. The two subcortical ROIs (LGN and MGN) and the three motor ROIs were selected manually using the relevant SPM maps of each group (Figures 1 and 7). We used an identical statistical threshold across the two groups, which yielded similar ROI sizes and locations (Table S2). We performed a trial-triggered average analysis across trials containing identical stimuli to determine mean response amplitude selleck screening library and standard deviation across trials for each sensory

ROI in each sensory experiment (see Figure S3). To demonstrate the robustness of this result we also calculated mean response amplitude and standard deviation across trials using a complementary GLM analysis where the GLM contained a separate predictor for each trial (see Figure S5). In the GLM analysis, we estimated the responses only in the second run of each experiment,

which was statistically independent of the first run used to define the ROIs. We used the same trial-triggered average procedure described above (Figure S3) to assess the variability of ongoing activity fluctuations in two different analyses. In the first analysis we sampled the average time courses from each of the three sensory ROIs during a resting-state experiment, which did not contain any stimulus or task. We performed the trial-triggered average analysis crotamiton according to the trial sequence in the check details sensory experiments (e.g., visual trial sequence for assessing the responses in the visual ROI). Since no stimuli were presented, the mean response amplitudes were indistinguishable from zero. The “trial-by-trial” standard deviations, however, were not zero and captured the variability of ongoing activity, which

fluctuated continuously during rest. In the second analysis, we sampled the average time courses from each of 40 ROIs that did not respond to any of the sensory stimuli. We used the sensory trial sequences (timing of stimulus onsets) to calculate the mean response amplitudes and standard deviations across trials in each ROI, separately for each experiment. We then averaged the results across ROIs to yield a single measure across all nonactivated ROIs. The nonactivated ROIs included the superior frontal cortex, medial frontal cortex, medial orbital frontal, anterior cingulate, precuneus, fusiform gyrus, parahippocampal gyrus, superior parietal cortex, pars opercularis, pars triangularis, pars orbitalis, inferior temporal gyrus, middle temporal gyrus, and insula, in each hemisphere (20 ROIs per hemisphere). ROIs were defined anatomically using the Freesurfer automated parcellation procedure and restricted to 200 adjacent functional voxels so as to match the size of the sensory ROIs. Sensory and motor signal-to-noise ratios were computed separately for each subject in each experiment.

, 2008), another study reported that clustered activation of many glomeruli, i.e. a stronger and more widespread stimulus, triggered CBF responses that were attenuated by global, but not local, postsynaptic blockade (Chaigneau et al., 2007). It is possible that the contribution of presynaptic activity may have been underestimated in studies

focusing on postsynaptic activity because Vorinostat concentration of the lack of direct markers of presynaptic release in these systems, and because classical electrophysiological indicators such as the local field potential mainly report postsynaptic activity (Aroniadou-Anderjaska et al., 1997). Moreover, topical application of postsynaptic blockers will not only decrease the activity of principal neurons, but also presynaptic glutamate release from local excitatory neurons, which are normally recruited by recurrent activity. Notably, thalamocortical synapses contribute to

only a small fraction of the total number of excitatory synapses in many sensory cortical areas (Douglas and Martin, 2007, Peters and Payne, 1993 and White, 1989). Therefore, an experimental perturbation PD-0332991 cost of postsynaptic activity will probably also alter presynaptic release, which is usually very difficult to measure concomitantly. Overall, the results available today indicate that postsynaptic neuronal activity may predominate in the control of CBF when stimulation intensity is high or if widespread activation or coactivation of distant areas occur, while presynaptic/astrocytic activity may predominantly regulate CBF during mild or local sensory stimulation. Such a shift may be optimal for matching the CBF response to metabolic needs—for example, a quantitative analysis of glomerular metabolic demands in the olfactory glomerulus (Nawroth et al.,

2007) showed that postsynaptic receptor activation contributes to less than 0.3% of the total energy budget during low activation but increases exponentially to one-third with stronger activation patterns comparable to those used by Chaigneau et al. (2007). In future studies, Phosphatidylinositol diacylglycerol-lyase these computational predictions could be tested experimentally by harnessing optogenetics to express light-activated proteins in neurons, allowing the experimenter to excite neurons more specifically than feasible with physiological stimuli. Such exogenous activation of neurons with spatiotemporal precision could yield answers to questions such as: (1) how much activity is necessary to cause hemodynamic changes, (2) how local (nonlocal) is the hemodynamic change when neuronal activity is focused to a small volume, (3) is postsynaptic activity dispensable for neurovascular coupling—this can be addressed by expressing optical inhibitors (Han and Boyden, 2007 and Zhang et al., 2007) in postsynaptic neurons.

These reversals were highly robust. They were stable, lasting for the duration of the recording (Figure 1; Selleckchem ABT-263 further analyzed below). In addition, they did not depend on the parameters of the grating that were used to assess directional tuning, such as

spatial and temporal frequencies (see Figure S1 available online). Specifically, the reversals occurred when the gratings in the DS test were symmetric (equal black and white phases), asymmetric (black phase of the grating was three times as long as the white phase, Figure 1A; Figure S1), had different speeds (15 or 30 deg/s), or had different spatial frequencies (ranging from 225 μm/cycle to 1,800 μm/cycle). Since we observed cells reversing their directional preference in response to symmetric and asymmetric gratings of different properties, we combined cells subject to different DS tests in our analysis. Since

individual DSGCs had varying responses to the P-N adaptation protocol, we assessed the change in directional preference using two measurements. (1) We classified adapted cells by the change in their PD by calculating the vector sum and the DSI based on the directional tuning that was acquired after the adaptation protocol. We termed the DSI computed using this newly acquired PD DSI∗. If the adapted cell was sharply tuned (i.e., vector GDC-0941 supplier sum magnitude > 0.2 and DSI∗ > 0.3), the newly acquired PD was set to be the direction of the vector sum, and the change in PD was

calculated as the angle difference between this new PD and the original PD. If this difference was less than 90°, the adapted cell was classified as stable (Figures S2A and S2B), and if it was greater than 90°, the adapted cell was classified as reversed (Figures 1 and 2B). If the cell was not sharply tuned after adaptation (i.e., vector sum magnitude < 0.2 or DSI∗ < 0.3), it was classified as ambiguous much (Figure S2C). (2) We quantified the change in response along the original P-N axis. Here the DSI after adaptation was comparing the response to stimulus moving in the original PD and response to stimulus in the original ND (as in Trenholm et al., 2011). This is unlike DSI∗ in which the computation is based on responses to motions in the adapted PD and ND. Thus, reversed cells would exhibit negative DSI values since their response after adaptation to motion in the original PD is lower than their response after adaptation to motion in the original ND. Based on these two measures, we computed the efficacy of the adaptation protocol. The P-N adaptation protocol led to 38% of DSGCs (9 out of 24) showing reversal (Figure 2C, left), 38% (9 out of 24) becoming ambiguous in their directional tuning (i.e., non-DS), and the minority 25% (6 out of 24) remaining stable. Grouping data across all cells, we found that the P-N adaptation protocol led to a significant reduction in the DSI (Figure 2C, right; and Table S1).

There is a link to previous studies that have shown that subjects often have biases toward certain decisions and that activity in some brain regions is associated with taking decisions that do not conform with the default strategy (Venkatraman et al., 2009a and Venkatraman et al., 2009b). Individual differences in risk-taking behavior may, in extreme cases, be associated with pathological

gambling (Clark and Limbrick-Oldfield, VX-809 purchase 2013). While pathological gambling may be linked with a baseline change in risk proneness/aversion, our results raise the possibility of a link with individual differences in how decisions are influenced by context. An approach focusing on changing sensitivity to contextual factors such as risk pressure may elucidate aspects of developmental change in risky behavior (Blakemore and Robbins, 2012 and Paulsen et al., 2012). Assaying response strategies with low likelihoods of success but with the potential for delivering great gains may be imperative at some points in adolescence. VmPFC and dACC might constitute two distinct decision-making systems rather than components of a single serial system for decision

making (Boorman et al., 2013, Kolling et al., 2012 and Rushworth et al., 2012). There was evidence that vmPFC and dACC acted in independent, or even opposite, FRAX597 order ways in the current study. Although there has been particular interest in the role that vmPFC plays in valuation and decision making (Boorman et al., 2009, Camille et al., 2011, De Martino et al., 2013, Fellows, 2011, FitzGerald et al., 2009, Hunt et al., ADAMTS5 2012, Kolling et al., 2012, Lim et al., 2011, Noonan et al., 2010, Philiastides et al., 2010 and Wunderlich et al., 2012), vmPFC did not mediate the influence of the contextual variable of risk pressure on decision making. Instead, vmPFC became less active as risk bonus increased (Figure 3A). Both lesion and neuroimaging evidence suggest that, in addition to its role in valuation and decision making, vmPFC mediates the repetition of a previously

successful choice or the taking of a default choice (Boorman et al., 2013, Noonan et al., 2012 and Noonan et al., 2010), and the pattern of activity recorded in vmPFC suggests that it was similarly concerned with default responses in the present task. This interpretation is suggested by the following observations. On average, subjects were risk averse and defaulted to taking the safer choice. This was most true on trials in which the risk pressure was low (Figures 1 and 2), and it was on just such trials that vmPFC activity was greatest (Figure 3A). Note that, in this task, default choices occur when decision making is less constrained by context. Instead of vmPFC, both dACC and FPl were preeminent in tracking the risk pressure afforded by the evolving decision context (Figures 4, 5, 6, and 7). FPl and dACC have been coactivated in other studies (Boorman et al., 2011 and Daw et al.

Functional neuroimaging has been singularly successful at identifying functional networks in humans. Most of clinical imaging has tried to identify dysfunctions in these networks in patient populations, and come up against the many difficulties discussed in this RAD001 mw review. By comparison, much less effort has been spent trying to utilize the knowledge about these functional networks for the remediation

of cognitive, emotional, or behavioral deficits. For example, a great deal is now known about the neural systems involved in emotion regulation (Ochsner and Gross, 2005 and Phillips et al., 2008), and this information could be used to train patients with mood disorders (Clark and Beck, 2010). Potentially testing this theory is becoming more tractable with the advent of advanced neuroimaging techniques, particularly real-time fMRI. With real-time feedback about their regional brain activation, patients can be trained to regulate activity in specific

areas or networks, a procedure termed “neurofeedback” (deCharms, 2008, Johnston et al., 2010 and Weiskopf HKI-272 price et al., 2003). In principle this provides the opportunity to influence localized brain activation non-invasively in a way that is controlled by the patients themselves and could allow them to regulate dysfunctional networks or activate compensatory pathways. fMRI-neurofeedback, targeting the anterior cingulate cortex, has shown preliminary success in chronic pain in patients with fibromyalgia (deCharms et al., 2005), and patients with Parkinson’s disease Rolziracetam may benefit from self-regulation of the supplementary motor area (Subramanian

et al., 2011). Ultimately, though, any clinical application of neurofeedback and other brain-based therapies in psychiatric disorders will have to be integrated in a comprehensive biopsychosocial intervention program. Neuroimaging plays a critical role in psychiatry as it can potentially be used to identify biomarkers of disease, prognosis, or treatment, elucidate biological pathways, and help redefine diagnostic boundaries and inform and monitor new therapies. Although several imaging and electrophysiological features have been consistently associated with mental disorders, none of them has the required sensitivity and specificity to qualify as a diagnostic marker. Promising results with low error rates for diagnostic or prognostic applications have been obtained through the use of multivariate classifier techniques, but these have rarely been tested across laboratories and not been validated in larger patient samples. Directions in neuropsychiatric imaging that appear promising transcend the constraints of the currently defined diagnostic boundaries.