, 2008). Thus, the rAI has a strong causal influence enabling the recruitment of contextually relevant brain regions. Second, along with dACC and thalamus, rAI forms a tonic-alertness loop that forms a vital subcortical-limbic system in a hierarchical attention-processing stream (Sadaghiani et al., 2010). In addition, during task performance, the dACC acts in conjunction with the DLPFC to form a cognitive control loop that modulates the behavioral response (Miller and Cohen, GSK1210151A 2001). Converging evidence from structural and functional neuroimaging studies indicate a crucial role for both the rAI (Palaniyappan and Liddle, 2012) and the DLPFC (Callicott et al., 2000 and Weinberger

et al., 1992) in the pathophysiology of schizophrenia. A number of neuropathological and imaging studies have found abnormalities in the DLPFC, with robust evidence implicating a failure of excitatory-inhibitory neuronal balance in this region (Lewis et al., 2005). Several pooled analyses of structural imaging studies have confirmed that the most consistent gray matter abnormalities across the different stages of schizophrenia occur in the nodes of the SN, especially the anterior insula (Ellison-Wright et al., 2008 and Glahn et al., 2008). fMRI studies suggest that an inefficient recruitment of the frontoparietal executive system is often noted alongside SN dysfunction during task performance

(Hasenkamp et al., 2011, Kasparek et al., 2013, Minzenberg Megestrol Acetate et al., 2009 and Nygård et al., 2012). The presence of SN dysfunction Bortezomib solubility dmso in schizophrenia has also been shown in studies seeking instantaneous functional correlations (also known as functional connectivity) in the blood oxygen level-dependent (BOLD) time series between the rAI and several nodes of the SN (Guller et al., 2012, Pu et al., 2012 and Tu

et al., 2012), and this within-network SN dysconnectivity is related to cognitive dysfunction (Tu et al., 2012). Similar findings of reduced connectivity within the SN in schizophrenia also emerge when seeking time-lagged (−5 to +5 s) rather than instantaneous correlations between the BOLD signals from brain regions constituting large-scale networks (White et al., 2010). It is possible that the disintegration of the salience processing system anchored on the rAI has a causal role in the inefficient cerebral recruitment noted in schizophrenia. To our knowledge, no neuroimaging studies have so far investigated whether a failure in the feedforward causal influence from the salience processing system to the executive system is present in schizophrenia. Following the terminology of Friston (1994) in this Article, we employ the term functional connectivity (FC) to denote the instantaneous, zero-time lagged correlation between brain activity occurring at spatially distinct sites.

9% (20.4%) for pairs with similar orientation preferences and 31.0% (21.5%) for pairs with different orientation preferences. To distinguish the different effects of visual stimulation on low- versus high-frequency signals, we computed the cross-correlation after

either high-pass or low-pass filtering Vm (Figures 4B and 4C). The reduction in Figure 4A was clearly confined to the low-frequency components (Figure 4C), whereas at high frequencies, for most pairs (37/44), visual stimulation either increased or had no effect on the SB203580 cost correlation (Figure 4B). As expected, the width of the cross-correlation of the unfiltered Vm decreased in the presence of a visual stimulus (not shown). To illustrate the spectral structure of Vm synchrony, we computed the coherence spectra of spontaneous and visually evoked activity for each pair and plotted the results in color maps (Figures 4D–4F). Each column represents the coherence spectrum of a distinct pair, presented in order of increasing difference in orientation preference between the cells (Figure 4G). The color maps show coherence of spontaneous activity (Figure 4D) and coherence during effective visual stimulation (Figure 4E). BMS-387032 concentration The difference between these two conditions (Figure 4F) was calculated from the Fisher-transformed coherence (Z; see Experimental Procedures). In Figure 4H, the change in coherence

averaged over the low-frequency (0–10 Hz) or high-frequency (20–80 Hz) range is plotted against difference in preferred orientation. In Figure 4I, the average change in coherence for the high-frequency band is plotted against that for the low-frequency band. In agreement with the results from the cross-correlation analysis in Figures 4A–4C, the overall effect of visual stimulation was to decrease 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase the coherence at low frequencies (Figure 4F, cool colors), and increase the coherence at high frequencies (warm colors). A decrease in coherence at low frequencies occurred in most pairs (41/44), independent of orientation (Figure 4H, lower panel). An increase in coherence at high frequencies occurred primarily in

pairs with difference of orientation preference between 0° and 50° (Figure 4H, upper panel). The two effects—on low- and high-frequency coherence—were not significantly correlated with each other across the population (Figure 4I). Note that the effect of visual stimulation occurred on top of the resting coherence in spontaneous activity, which was itself not dependent on the relative orientation preference (Figure 4D). Visual stimulation then either increased the high-frequency coherence, or left it largely unchanged (e.g., Figure S4) for most pairs (41/44). We asked whether (and how) the visually evoked change in Vm synchrony depended on the change in Vm power. We therefore plotted the mean visually evoked change in coherence against the mean change in Vm power for low frequencies (Figure 5A) and for high frequencies (Figure 5B).

Unreliable neural activity may be expected to degrade perception and generate variability in behavior. A common finding in autism is that individuals with autism exhibit enhanced perception of details and degraded perception of holistic/gestalt stimuli (Simmons et al., 2009). It may be difficult to understand how unreliable neural activity might improve perception of some stimuli and degrade perception of other stimuli. R428 purchase However, greater neural response variability in early visual cortex may enhance the perception of local details through stochastic resonance (McDonnell and Abbott,

2009) and, at the same time, degrade perception of gestalt stimuli (Simmons et al., 2009). Alternatively, greater response variability could alter neural plasticity and learning in a way that would favor overclassification of local details at the expense of gestalt perceptual organization (Cohen, 1994). With regards to behavior,

there is evidence that individuals 5-Fluoracil with autism do exhibit greater trial-by-trial motor variability, which is evident in the accuracy of both reaching movements (Glazebrook et al., 2006) and saccadic eye movements (Takarae et al., 2004). Greater trial-by-trial reaction time variability in autism is evident for a variety of tasks (Castellanos et al., 2005; Geurts et al., 2008) as was also the case in our letter repetition-detection task (Figure S8). Determining the relationship between greater neural response variability and the behavioral symptoms of autism will clearly require additional research. It is notable that signal-to-noise ratios of individuals with autism exhibited a trend of positive correlations with this website IQ scores and negative correlations with autism severity scores (Figure 5), provocatively suggesting that cortical response reliability might be related

to the level of behavioral abilities in autism. We speculate that poor response reliability may be directly related to the development of both secondary and core symptoms of autism. With respect to secondary symptoms, unreliable neural networks are susceptible to epileptic seizures (Rubenstein and Merzenich, 2003), which is one of the most prominent comorbidities in autism (Tuchman and Rapin, 2002). Unreliable neural responses in sensory and motor cortices may also explain why the vast majority of individuals with autism exhibit debilitating sensory sensitivities (Marco et al., 2011), motor clumsiness, and balance problems (Whyatt and Craig, 2012).

033 Hz, resulted in an immediate change of the size of the synaptic

response. This effect was temporally dependent in that by varying the time interval and order of the stimulations, the pairing resulted in various Fulvestrant research buy forms of synaptic plasticity of the SC-CA1 synaptic transmission (Figures 1C and 1D). When pairing SO stimulation 100 ms before the SC stimulation, robust LTP of the EPSC amplitude was induced; intervals of 200 and 50 ms were less effective and only produced a short-term potentiation (STP; Figures 1C and 1D). When the interval was shortened to 10 ms, short-term depression (STD) was induced with a less significant effect at a duration of 20 ms. Concurrent stimulation of the SO and SC did not induce any changes in the synaptic response. However, when the SO stimulation was given after the SC stimulation, LTP was induced at the 10 ms time interval,

with a slight potentiation at 20 ms and no effect at 50, 100, or 200 ms intervals (Figures 1C and 1D). Interestingly, whereas only five pairings were almost as effective as ten when using ±10 ms intervals (SO before or after SC), five pairings induced only STP instead of LTP when pairing SO 100 ms before SC (Figures S1E–S1H). The induction of different forms of plasticity stresses the importance of the timing of cholinergic inputs and local synaptic activity in inducing this type of synaptic plasticity. This plasticity depends on both the EX 527 mouse timing of the cholinergic input and the activity of local hippocampal synapses receiving the input. Thus, one cholinergic input may result in different types of plasticity at different synapses, Smoothened depending on the local glutamatergic activity in each spine. Thus, this timing- and context-dependent mechanism provides not only temporal but also spatial precision. To investigate which AChRs might be involved in mediating these forms of plasticity, bath application of cholinergic receptor antagonists was used during the pairing protocol (Figures

2A–2D); MLA and DHβE were used to test for the α7 and non-α7 nAChRs, respectively, and atropine was used to test for the mAChR (Figures 2E–2H). The LTP induced by the preceding SO stimulation (100 ms) was completely blocked by MLA (10 nM), whereas DHβE (1 μM) and atropine (5 μM) were ineffective (Figure 2A). Similarly, the induction of STD by SO 10 ms before SC was also blocked by MLA, with DHβE and atropine also having no effect (Figure 2B). Therefore, induction of either LTP or STD with prior SO stimulation was due to activation of the α7 nAChR. In contrast the LTP induced when the SO stimulation occurred after (10 ms) SC stimulation was insensitive to blockade of nAChRs but was blocked by the mAChR antagonist atropine ( Figure 2C), indicating that mAChRs mediated this form of plasticity.

Mice were housed, bred, and treated according to the guidelines approved by the Home Office under the Animal (Scientific Procedures) Act 1986. Protocols detailing the generation and genotyping of the genetically modified mice used in this article have been described Selleck Obeticholic Acid previously for NexCre mice ( Goebbels et al., 2006) and are described in Figure S1A and Supplemental Experimental Procedures for Ascl1flox/flox mice. PCR genotyping of Ascl1 ( Guillemot et al., 1993) and Neurog2 ( Fode et al., 1998) mutant and wild-type

alleles was described previously. In utero electroporation, cell counting, and statistical analyses were performed as described previously (Nguyen et al., 2006), with minor modifications as explained in the Supplemental Experimental Procedures. Embryonic brains were dissected in 1 × phosphate buffered saline (PBS) and fixed overnight in 4% paraformaldehyde (PFA)/1 × PBS at 4°C. Fixed samples were cryoprotected overnight in 20% sucrose/1 × PBS at 4°C, mounted in OCT Compound (VWR International), and sectioned coronally with a cryostat (Leica). Nonradioactive RNA in situ hybridizations on frozen sections of brains were performed with digoxigenin-labeled riboprobes as described previously (Cau et al., 1997). The full-length coding sequence for mouse Rnd3 was PCR cloned into pBluescript SK to generate an antisense probe for mouse Rnd3. Probes for mouse Rnd2 ( Heng et al., 2008)

and LacZ ( Seo et al., 2007) were prepared as previously described. Immunolabelings were performed with standard protocols by using find more the following primary antibodies: mouse anti-Ascl1 (1/200, gift from D.J. Anderson),

rat anti-BrdU (1/1000, AbD Serotec), rabbit anti-Caspase-3 (1/1000, R&D Systems), goat anti-EEA1 (1/50, Santa Cruz), chicken anti-GFP (1/700, Millipore), mouse anti-Flag (1/250, Sigma), mouse anti-LAMP1 (1/100, Developmental Studies Hybridoma Bank), mouse anti N-cadherin (1/200, BD Biosciences), mouse anti-Nestin (1/100, Millipore), mouse anti-Rab7 (1/500, Abcam), rabbit anti-Rnd2 (1/50, Santa Cruz), mouse anti-Rnd3 (1/500, Abcam), rabbit anti-Rnd3 (1/50, Abcam), science and mouse anti-transferrin receptor (1/100, Zymed). Cells or sections were then incubated with appropriate fluorescent secondary antibodies. Pretreatment with 2 N HCl for 30 min at 37°C prior to preincubation with primary antibody was performed to detect BrdU. For in vivo FRET analysis, cortices were coelectroporated in utero at E14.5 with the FRET probes for RhoA pRaichu-1298x or pRaichu-1293x (0.25 μg/μl) and control shRNA-RFP, Rnd2 shRNA-RFP, or Rnd3 shRNA-RFP (1 μg/μl). RhoA FRET efficiency was analyzed 1 day later in fixed brain sections. For FRET analysis in dissociated cortical cells, cortices were coelectroporated ex vivo at E14.5 with the same constructs and sliced with a vibratome immediately after electroporation and sections were cultured overnight.

, 1997; Kimura et al., 1999), although the muscarinic effect may be dominant in humans (Thiel and Fink, 2008). Cholinergic influence over the interactions between bottom-up and top-down processing are also evident from the effects of

iontophoresing ACh or the muscarinic antagonist scolopamine on boosting or suppressing attentional effects on firing rates of neurons in area V1 of macaques while they perform a visually demanding task (Herrero et al., 2008). Also, stimulating the basal forebrain (where Buparlisib manufacturer one population of ACh neurons lives) reduces the correlation between visual neurons reporting on natural scenes via a muscarinic mechanism (Goard and Dan, 2009). Looking over a range of shorter timescales, cholinergic neuromodulation has also been implicated in aiding signal detection in rodents PF-02341066 research buy in tasks soliciting forms of sustained attention (McGaughy and Sarter, 1995; Parikh et al., 2007). For instance, Parikh et al. (2007) used amperometry to measure changes in the concentration of ACh in medial prefrontal (mPFC) cortex over various timescales in a Pavlovian task. Here, a cue was provided on each trial, predicting

a reward after a delay of around 2 s or 6 s; the mark of attentional engagement was a cue-evoked shift in behavior, which then led to hastened reward acquisition. Cue detection in the task was impaired by removing cholinergic inputs from the mPFC, suggesting that performance was sensitive to ACh. For normal animals, ACh was substantially released over a short timescale on trials on which animals successfully detected the cue (but not when they failed); successful detection was associated with a decreasing PI-1840 rather than an increasing trend in ACh over the 20 s preceding the cue; and higher tonic levels of ACh concentration (measured over minutes) were tied to larger phasic ACh signals associated with the cue, and faster (Pavlovian) actions. The

various interactions with the medium term (20 s) and longer term (minutes) averages of the ACh concentration remind us of complexities surrounding a commonly reported finding for neuromodulators, (T) namely a inverted U-shaped curve of efficacy (Yerkes and Dodson, 1908). An example finding is that drugs that boost a neuromodulator such as dopamine have a beneficial effect for subjects whose baseline levels are low, but a harmful effect for subjects for whom these levels are high (Kimberg et al., 1997; Cools et al., 2011; Floresco and Magyar, 2006). Alternatively, increasing the tonic activity of a neuromodulator might have the same dual effects, as suggested for norepinephrine (Aston-Jones and Cohen, 2005; Berridge, 2008; Arnsten, 2011).

Polyclonal antibodies were DHHC5 (Sigma-Aldrich), ZDHHC8 (Everest Biotech), and rabbit anti-HA (QED Bioscience). Antibody against the C terminus of GS-7340 solubility dmso GRIP1 has been previously described (Dong et al., 1997). An antibody raised against the unique N terminus of GRIP1b (amino acids 5–19; KKNIPICLQAEEEQER) was affinity purified using the antigenic peptide. Alexa

dye-conjugated fluorescent secondary antibodies and Alexa transferrin were from Invitrogen. All mammalian DHHC5 and DHHC8 sequences reported share an identical C-terminal 15 amino acids, terminating in a type II PDZ ligand. A C-terminal 109 amino acid “bait” from human DHHC8 (Ohno et al., 2006) was subcloned into the pPC97 yeast expression vector and used to screen a rat hippocampal cDNA library. Clones that grew on quadruple-deficient plates (Leu-, Trp-, His-, Ade-) were selected, and their plasmids were isolated and sequenced. Positive clones were subcloned into myc-tagged pRK5 mammalian expression vector,

and C termini of both DHHC5 and DHHC8 were subcloned into a mammalian GST fusion vector (Thomas et al., 2005) for binding experiments in mammalian cells. Full-length untagged rat GRIP1a and mouse GRIP1b cDNAs in pBK expression vector have been previously described (Dong et al., 1997 and Yamazaki et al., 2001). GRIP1b C11S was generated by QuikChange Site-Directed Mutagenesis Kit. A myristoylation selleck chemicals consensus sequence (MGQSLTT; Wyszynski et al., 2002) was added to the N terminus of GRIP1b-C11S by PCR to generate Myr-GRIP1b. The myristoylation consensus contains no polybasic sequence that might affect membrane targeting, and Myr-GRIP1b contained a mutated Cys11- > Ser, so that Vorinostat (SAHA, MK0683) only a single lipid modification

occurs, as for GRIP1bwt. For live imaging, full-length Myr-GRIP1b sequence was amplified by PCR and subcloned into eGFP-N1 vector using NheI and NotI sites. HA-tagged mouse DHHC5 and DHHC8 and mycHis-tagged human DHHC8 cDNA have been previously described (Fukata et al., 2004 and Ohno et al., 2006). Catalytically inactive (DHHC – > DHHS) and deltaC (ΔC) mutants (lacking the last five amino acids that constitute the PDZ ligand) of DHHC5 and DHHC8 were generated by QuikChange. The previously reported kinesin-binding domain (KBD; Setou et al., 2002) of GRIP1b was deleted by Splicing by Overlap Extension (SOE)-based PCR using the Myr-GRIP1b-myc cDNA as template to generate Myr-GRIP1b-myc-deltaKBD. shRNAs (in vector pLKO; Mission shRNA library) targeting sequences identical in both rat and mouse DHHC5 (5′-CCTCAGATGATTCCAAGAGAT-3′) or DHHC8 (5′-CTTCAGTATGGCTACCTTCAT-3′) were tested for their ability to reduce expression of HA-tagged DHHC5 and DHHC8 mouse cDNAs in cotransfected HEK293T cells. After confirming that these sequences effectively and specifically suppressed expression of DHHC5 and DHHC8, respectively, each sequence was amplified by PCR, together with its neighboring H1 promoter.

For electrophysiological

recordings, to achieve sufficient spike numbers, the stimulus probe remained in contact with the skin with a constant displacement, thereby achieving a steady-state firing level. The length of the data for each steady-state epoch was 650 ms, and data were collected in sessions of 100–300 trials; these trials were randomly interleaved with single- and dual-site stimulation of the digits. Single selleck products units were isolated online and sorted (Plexon). Spike synchrony was measured by simultaneous recordings of single units isolated on separate electrodes. Three types of area 3b (A3b) and area 1 (A1) unit pairs were collected: A3b-A3b pairs, A3b-A1 same-digit pairs, and A3b-A1 adjacent-digit pairs. All A3b-A3b pairs were from adjacent digits. The temporal resolution of spikes was 1 ms, and response

histograms were constructed with 5 ms time bins. In each session, 100–300 trials (repetitions) were collected. Joint PSTH were generated. The level of synchrony above or below chance was computed by subtracting the shift-predictor correlogram from the raw correlogram (Aertsen et al., 1989; Brody, 1999a, 1999b). CCGs and their 95% confidence intervals were computed using a 500 ms window ± 250 ms around a lag of 0 ms. CCG peaks were counted as significant if two consecutive values exceeded the confidence intervals within a ±50 ms lag (Cohen and Maunsell, 2010). CCGs were normalized Alectinib for differences in firing rate (Brody, 1999a, 1999b) and shuffle corrected (Perkel et al., 1967). Additionally, we further assessed the significance of correlation by synthesizing thousands of artificial spike trains based on recorded spike times (random permutation approach) and calculating deviation from this

baseline distribution. The correlation strength (CS) (Takeuchi et al., 2011) was defined as CS = R + L, where R and L indicate the summed bins on the right and left sides of each CCG within ±50 ms CYTH4 from the center bin (0 ms). An ASI was defined as ASI = (R − L)/(R + L). A peak weighted to the right suggests prevalence of the feedforward interaction, one weighted to the left suggests prevalence of feedback interaction, and one with equal left and right weights suggests common inputs or recurrent connections. For population comparisons, the nonparametric Wilcoxon test (Kruskal-Wallis test for group comparison) was used to determine significant differences (p < 0.05) between the cumulative distributions of peak-correlation coefficients, the CS, and the ASI. Focal injections of tracer were made in digit-tip locations in area 3b and area 1, as determined by optical imaging and electrophysiological recording. We injected through glass micropipettes with tip inner diameter of 15–20 μm a 1:1 mixture of 10% biotinylated dextrans via iontophoresis (3 μA, 7 s on/off cycle, 20 min) at a depth of 400 μm. After 10–20 days survival, animals were given an overdose of Pentobarbitol (100 mg/kg) and perfused transcardially with fixative.

, 2011), we think that this is not likely because fish can learn the stay task well even after ablating the activated area

for the avoidance task (Figure S5H). In mouse motor cortex, the reward-based instrumental learning of two different actions, lick or no lick, induced correlated activity of specific neural ensembles in motor cortex for each action by learning-related circuit plasticity (Komiyama et al., 2010). Importantly, in the current study, there was no increase in the proportion of neurons correlated to each action, suggesting that changes induced by this learning paradigm probably reflect changes in synaptic strength of a local microcircuit but not the recruitment of a novel population of neurons. In contrast, our results indicate that neurons are tuned to activate at the onset of selleck chemicals llc cue presentation, and the learning of a novel behavioral program could recruit an additional population of neurons into a distinct ensemble. Understanding how neural ensembles encode and retrieve behavioral programs at different timescales is a major challenge in neuroscience (Lisman and Grace, 2005). In the current study, we employed wide-field calcium imaging of the whole zebrafish telencephalon to localize neural activity

during the Selleck Ribociclib retrieval of a behavioral program stored in long-term memory, followed by electrophysiological recordings and anatomical tracing to reveal the underlying functional changes and connectivity in neurons in this cortical region. This approach highlights the use of zebrafish as a model organism for studying memory. Preceding studies, such as in the larval zebrafish adaptive motor control, in the insect olfactory learning or zebrafish olfaction, and in the mouse sensorimotor learning, have demonstrated that observation of activities of cellular ensembles at the level of single cells is possible by using two-photon microscopy (Ahrens et al., 2012; Honegger PLEK2 et al., 2011; Blumhagen et al., 2011; Huber et al., 2012). Application of such technology for the study of zebrafish telencephalon would reveal the mechanisms underlying

the complex neuronal process leading to long-term memory consolidation. Recently, other emerging technologies such as optogenetics or pharmacogenetics have very elegantly succeeded in manipulating the activities of the brain regions or the neural ensembles involved in memory (Goshen et al., 2011; Liu et al., 2012; Garner et al., 2012). Combined application of these technologies in zebrafish will enable us to map the complete neural circuit for learning and memory of behavioral programs and examine communication between brain areas in the formation of neural ensembles that are responsible for the storage and retrieval of the memory. Active avoidance learning has been regarded as one form of reinforcement learning, which requires improvement in an avoidance skill by trial-and-error using relief from the pain of an electric shock as a positive reinforcer (Mowrer, 1956; Maia, 2010; Dayan, 2012).

, 2007). These baseline hemodynamic signatures have a significant impact on the interpretation of activated functional networks associated with different sensory, attentive, or cognitive states (Greicius learn more et al., 2009; Honey et al., 2009; Biswal et al., 2010; Deco et al., 2011; Smith et al., 2009). The link between resting-state metrics and anatomical connectivity has largely been supported by modeling of areal correlations with known interareal connection

patterns (Fox et al., 2005, 2006; Honey et al., 2007; Vincent et al., 2007; Luczak et al., 2009; Schölvinck et al., 2010; Deco and Jirsa, 2012); however, this relationship has not been examined directly with studies of anatomical connectivity (Matsui et al., 2011). The neuronal basis of the resting state is also uncertain. Although hemodynamics-based

networks have been associated with widespread low-frequency correlations in local field potentials (Arieli et al., see more 1996; Cohen and Kohn, 2011, Kenet et al., 2003), there is little evidence that resting-state connectivity is related to underlying neuronal connectivity. Moreover, as resting-state studies have focused on broad cortico-cortical networks, little attention has been paid to resting-state connectivity patterns at finer local cortical scales. In this study, we seek to establish the relationship between anatomical connectivity, functional neuronal connectivity, and local resting-state connectivity patterns revealed by fMRI. Our testbed for this study is the connectivity pattern of digit-tip representations Plasmin in the somatosensory cortex (areas 3b and 1) of squirrel monkeys, an area central to manual

behavior in monkeys and amenable to study with fMRI and electrophysiological and anatomical connectivity techniques. This multimodal approach aims to establish an understanding of local (at the millimeter scale) baseline networks revealed by resting-state connectivity and, furthermore, provide evidence to support a local to global hierarchy of resting states within the brain. Resting-state functional connectivity patterns of digit-tip representations in primary somatosensory cortex (SI) were examined in 11 squirrel monkeys (one case is shown in Figures 1A, 1B, and 1D–1F). Under isoflurane anesthesia, blood oxygen level-dependent (BOLD) maps of digit activation (Figure 1B) and the resting-state acquisitions (static probe touching digit-tip skin; Figures 1D–1F) were recorded using a 9.4T Varian MRI scanner. Seed locations (Figures 1A and 1B, open blue squares) were selected based on fMRI and/or electrophysiological maps of areas 3b and 1 (Figure 1A); using surface vasculature as landmarks (arrowheads), these maps were readily coregistered to the maps acquired by MRI (cf. Chen et al., 2007).