Neurons were held in current-clamp using a patch-clamp amplifier

Neurons were held in current-clamp using a patch-clamp amplifier (HEKA, EPC10) in the whole-cell configuration. Intracellular solution composition was (in mM): 130 K-methylSO4, 5 KCl, 5 NaCl, 10 HEPES, 2.5 Mg-ATP, 0.3 GTP, and 0.5% neurobiotin. No correction for liquid junction potential was applied. The osmolarity was 265–275 mOsm, pH 7.3. Microelectrodes resistance PD-0332991 mouse was 4–8 MOhms. Uncompensated access resistance was monitored throughout the recordings. Values below 20 MOhms were considered acceptable and the results were discarded if it changed by more than 20%. Whole cell measurements were filtered at 3 kHz

using a patch-clamp amplifier. Recordings were digitized online (10 kHz) with an interface card to a personal check details computer and acquired using Axoscope 7.0 software. Spontaneous EPSPs were detected and analyzed using the MiniAnalysis software. For most stimulation experiments, the movie acquisition time was separated evenly between (1) an ∼3 min resting period during which the cell was held close to Vrest (i.e., zero current injection); (2) an ∼3 min stimulation period during which phasic stimulation protocols were applied; and (3) an ∼3 min recovery period where the cell was brought back to resting membrane potential. Stimulation protocol: suprathreshold current pulses (amplitude: 100–200 pA, duration: 200 ms) repeated at 0.1–0.2 Hz. Vrest was measured

as the membrane potential baseline value obtained in current-clamp mode in the absence of current injection. The action potential threshold (Vthreshold) and amplitude, membrane capacitance (Cm), and resistance (Rm) were tuclazepam measured offline using Clampfit. Vrest and Vthreshold were not corrected for errors due to high input resistance values in developing neurons. Slices were fixed overnight at 4°C in Antigenfix, rinsed

in PBS containing 0.3% Triton X-100 (PBST) and incubated overnight at room temperature in cy3-streptavidin (1/1000 in PBST). Post hoc analysis was performed using a confocal microscope. Stacks of optical sections were collected for computer-assisted neuron reconstructions. Animals were deeply anesthetized with a ketamine (50 mg /ml) and xylazine (7.5 mg /ml) solution at a dose of 2 ml/kg (i.p.) and perfused transcardially with 4% paraformaldehyde in PB (1 ml/g) at a constant flow (2 ml/min). Brains were postfixed overnight in fixative and washed. Horizontal brain sections (40 μm) were routinely processed for multiple immunofluorescence. Briefly, after preincubation in 5% normal donkey serum, sections were incubated in a mixture of primary antibodies from different species, then incubated in appropriate secondary antibodies conjugated with either Al488 (1:500, Molecular Probes; Invitrogen), Cy3, Cy5, DL488, DL549, DL649 (Jackson ImmunoResearch Laboratories) for 2 hr. The dilutions, characteristics, specificity, and sources of primary antibodies are presented in Table S1.

At least 6 weeks after the injection of the virus, we tested the

At least 6 weeks after the injection of the virus, we tested the effect of shining green laser light (532 nm wavelength) onto these SC neurons. In two monkeys (OZ and OM), we presented the laser light while the monkey made visually guided saccades. In the third monkey (RO), we studied changes in neuronal responses during free viewing. The light reached the SC typically via a 200 μm diameter BMS-754807 nmr optic fiber attached to a recording electrode extending 500 μm beyond the flat fiber end (the optrode). We found consistent behavioral effects in monkeys OZ and OM using laser light inactivation. Visually guided saccades showed the same triad of effects as with chemical inactivation: shift in saccadic end

point, reduced peak velocity, and increased latency. Figure 1 shows the effects of laser inactivation at an example site in monkey OZ. We located the optrode

in the SC intermediate layers during each experiment by the center of the movement fields represented by neuronal activity recorded 500 μm below the fiber tip. While the monkey fixated a central bright spot on a dark background, we presented a second spot of light and the monkey was rewarded for making a visually guided saccade to that spot once the fixation spot disappeared. Figure 1A shows the locations in the visual field of the ArchT injection site (hexagon) and the optrode (starburst). Gray points are the endpoints of normal saccades to the visual target. Green points selleckchem are saccade endpoints to the same visual target during SC inactivation. Saccade endpoints shifted on average about 1.02° down in

this example (shown by the arrow), or about 7.3% of the saccade magnitude. These distributions of saccade endpoints were significantly different with and without light (2D Kolmogorov-Smirnov Terminal deoxynucleotidyl transferase [KS] test, p < 0.001). We did not methodically study the effect of laser intensity on behavior. However, we were of course able to eliminate any change in behavior by sufficiently turning down the laser from our default intensity of about 650 mW/mm2. Effects at less than 300 mW/mm2 were negligible if present at all. Also, at several stimulation sites where we tested multiple laser intensities, we could not further increase the magnitude of the saccadic shift by increasing laser illumination, even up to 1600mW/mm2. In addition to changing the endpoints of saccades, photostimulation changed saccade latency and peak velocity. Figure 1B shows the cumulative distribution of saccade latencies without (black) and with (green) laser light. The distribution was shifted to the right with light, an increase of about 7 ms in saccade latency (p = 0.020, Wilcoxon rank-sum test). Figure 1C shows that the light shifted the distribution of peak saccade velocities to the left, a significant reduction in peak velocity of about 79°/s (p < 0.001, t test). Note that subsequent p values without a specified test were obtained from a t test. Figure 1D shows neuronal activity as spike density histograms, aligned to saccade onset.

Measurements of VAMP2 and SYP levels in retinal lysates were perf

Measurements of VAMP2 and SYP levels in retinal lysates were performed by surface plasmon resonance using antibodies

directed against VAMP2 (Cl 69.1) and SYP (Cl 7.2; Synaptic Systems) coupled to a CM3 sensor chip of a Biacore 3000 system. A nonimmune IgG (Jackson Immunoresearch) was used to reduce nonspecific binding as described (Ferracci et al., 2005). Frozen retinae were sonicated (3 × 2 s, 40W) in 300 μl of 10 mM HEPES/NaOH (pH 7.4), 0.32 M sucrose, 5 mM DTT as described (Marconi et al., 2008) and 10,000 × g lysate supernatants were incubated at 37°C during 30 min. Samples were diluted in analysis buffer (50 mM Tris/HCl [pH 7.4], 0.4 M NaCl) and the surface plasmon resonance signal was measured 20 s MDV3100 after the end of each injection (20 μl/min). We used a fluorometric enzyme assay based on the Amplex Red Glutamic Acid kit (Invitrogen) to visualize glutamate release from

acutely isolated Müller cells, which were identified by their unique morphology. Retinae were incubated in papain (0.2 mg/ml; Roche Molecular Biochemicals) for 30 min at 37°C in the dark in Ca2+- and Mg2+-free extracellular solution (140 mM NaCl, 3 mM KCl, 10 mM HEPES, 11 mM glucose [pH 7.4]), which was supplemented with glutamine (0.25 mM), glutamate Selleckchem Alectinib (0.5 mM), methionine sulfoximine (5 mM, Sigma) to block glutamine synthetase, and a photolabile calcium chelator (O-nitrophenyl ethylene glycol tetraacetic acid acetoxymethyl; NP-EGTA, 10μM; Invitrogen). After several washes with extracellular solution, to which MgCl2 (1 mM) and CaCl2 (2 mM) were added, retinae were triturated in extracellular solution containing the components of the Amplex Red Glutamic Acid kit (100 μM Amplex Red reagent, 0.5 U/ml horse radish peroxidase, 0.16 U/ml L-glutamate oxidase,

1.0 U/ml L-glutamate-pyruvate transaminase, 400 μM L-alanine), NP-EGTA (10 μM), methionine sulfoximine (200 μM), and D, L-threo-beta-benzyloxyaspartate (200 μM) to block glial glutamate uptake. The cell suspension was mixed with 1% agarose and from incubated for 15 min in the recording chamber at 37°C. In some experiments, bafilomycin A1 (200 nM, Biozol) was added. Resorufin fluorescence was imaged by confocal laser microscopy (LSM510 Meta, 100×/1.3 Plan-Neofluar oil, Zeiss; 543 nm helium-neon laser, 585 nm long pass filter, pinhole maximally open) above Müller cell endfeet. Calcium transients were induced by four UV pulses (351 nm/364 nm Enterprise UV Laser, 500 ms at maximal intensity) to release calcium from NP-EGTA. Control experiments using fluo-4/AM (488 nm argon laser; 505–550 nm band-pass filter) confirmed that each cell tested (15 out of 15) showed UV-induced calcium transients. Peak amplitudes were calculated as difference between mean fluorescence intensity across four time points acquired before and after the UV pulses.

Therefore, endogenous potentiation of intra-nRT inhibition is poi

Therefore, endogenous potentiation of intra-nRT inhibition is poised to exert an endogenous antioscillatory seizure-suppressing effect, whereas such potentiation in VB could be disadvantageous. It should be noted, however, that VB neurons are BZ-sensitive, as demonstrated here and

in previous studies (Oh et al., 1995; Peden et al., 2008), although systemic treatment with BZs would influence both nRT and VB inhibition such that activity throughout the circuit would be globally suppressed. Indeed, constitutive activation in nRT was ∼60% of maximal, indicating that although there is a substantial degree of endogenous modulation, there is still an extent of enhancement that can be exploited by exogenous BZs, likely explaining the therapeutic efficacy selleck kinase inhibitor of these drugs. Here, we introduce a methodology combining the “sniffer patch” recording configuration (Isaacson et al., 1993; Allen, 1997; Banks and Pearce, 2000) with laser GABA uncaging, which is used to examine nucleus-specific differences

in endogenous BZ site modulation. The main advantage of this method is that GABA exposure to the patches can be normalized independent of patch placement, so that region-specific differences in modulation of GABAergic signaling can be assessed. JQ1 supplier Combined application of GAT antagonists and FLZ was sufficient to completely block the nRT-dependent potentiation. This potentiation is thus mediated in large part by endozepines, and to a lesser

extent by nucleus-specific differences in rates of GABA uptake, with more robust uptake in VB than in nRT. This is consistent with the postulated role for GATs in removing GABA from VB extracellular space in order Dichloromethane dehalogenase to prevent excessive GABABR activation and oscillatory seizure activity (Beenhakker and Huguenard, 2010). The durations of uncaging responses for both nRT and VB patches are longer than those for spontaneous or evoked IPSCs, in accordance with previous studies on patches from thalamic, hippocampal, and cortical neurons (Galarreta and Hestrin, 1997; Jones and Westbrook, 1997; Banks and Pearce, 2000; Schofield and Huguenard, 2007). Therefore, this appears to be an effect of the pulled patch configuration rather than the use of uncaging to apply GABA. Nevertheless, the application of GABA by laser photolysis replicates the nRT versus VB differences in receptor affinity for GABA and kinetics of IPSC decay (Zhang et al., 1997; Mozrzymas et al., 2007; Schofield and Huguenard, 2007). Potentiation of intra-nRT GABAergic transmission by BZs is capable of exerting powerful antioscillatory effects by reducing synchronization in intra-thalamic networks (Huguenard and Prince, 1994a; Huguenard, 1999; Sohal et al., 2003).

The multistability of the global dynamics appears more important

The multistability of the global dynamics appears more important than specific model details and can be achieved in various ways. For instance, dynamic node models can be chosen to be intrinsically unstable (Honey et al., 2007) or to become unstable once individual nodes are linked to each other (Deco et al., 2009). The multistability may then be controlled through parameters describing physical network interactions, such as coupling HKI-272 strength, delays, or noise. Noise, in particular, may provide the means for transitions between different multistable cluster synchronization

states (Ghosh et al., 2008), shaping the occurrence of ICMs. The organization of ICMs has been linked to the concept of criticality (Plenz, 2013). Criticality is associated with the phase transition between ordered and chaotic dynamics and characterized

by long-range correlations and power-law distributions, for instance, of the amplitude of activity fluctuations. As shown by human and animal studies, the dynamics of envelope ICMs exhibits these characteristic features (Linkenkaer-Hansen et al., 2001, He et al., 2010, Palva and Palva, 2011 and Tagliazucchi et al., 2012b). Intuitively, criticality represents a useful operating point between disorder, which provides flexibility but lacks structure, and order, with the opposite features. oxyclozanide In this way, critical dynamics may support the multistable exploration of topological features of brain connectivity and enhance information processing capabilities of neuronal networks selleck kinase inhibitor (Bertschinger and Natschläger, 2004). Indeed, in the critical state, the dynamic range of an excitable network is maximized (Kinouchi and Copelli, 2006) and brain networks optimize their response to inputs as well as their information processing ability (Shew and Plenz, 2013). Computational modeling indicates that envelope ICMs arise in the neural dynamics

right at the critical phase transition (Haimovici et al., 2013) or just below it (Deco and Jirsa, 2012), implying an optimal exploration of the structural connectivity by neural dynamics. Conversely, the typical hierarchical modular organization of brain connectivity appears to facilitate critical dynamics (Kaiser and Hilgetag, 2010 and Wang et al., 2011a). Modeling also suggests that, in the case of envelope ICMs, the structural constraints may allow only a small number of dynamic attractors (Deco and Jirsa, 2012). However, the repertoire of envelope ICMs is substantially expanded by phase ICMs that arise at shorter timescales (Figure 6B) (Honey et al., 2007). That is, different frequency-specific networks defined by ICMs might form and coexist within the constraints imposed by slower network dynamics.

, 2012) has presented with a similar microhemorrhage liability (O

, 2012) has presented with a similar microhemorrhage liability (Ostrowitzki et al., 2012), whereas several other antibodies that bind strongly to soluble Aβ appear to lack the adverse event (Adolfsson et al., HDAC inhibitor 2012; Farlow et al., 2012; La Porte et al., 2011). Our results have significant implications for the clinical development of N-terminal nonplaque-selective

antibodies. The proposed mechanism of action for the N-terminal antibodies was based upon preclinical studies in transgenic mice and, as such, their respective clinical implementation has been modeled upon the phagocytosis mechanism, maximal effector function, and studies performed in patients with extensive deposition. A small neuroimaging study performed with the PET ligand PIB did report a significant amyloid reduction (∼25%) in patients receiving the monoclonal antibody bapineuzumab as compared to placebo controls (Rinne et al., 2010). It is important to highlight that ∼17% of the perceived amyloid reduction Linsitinib was due to an atypical rise in the PIB signature in the small (n = 7) placebo cohort (Ossenkoppele et al., 2012). We demonstrate that N-terminal antibodies that bind both soluble and insoluble Aβ fail to lower existing Aβ deposition in our preclinical PDAPP transgenic model in line with previous reports in the literature. Many studies

have demonstrated that N-terminal Aβ antibodies prevent plaque deposition (Bard et al., 2000; Schenk et al., 1999; Schroeter et al., check 2008) including studies in FcRγ knockout mice (Das et al., 2003). These, along with our current results, suggest that it is unlikely that plaque prevention is due to phagocytosis. A more plausible mechanism for N-terminal Aβ antibodies would be their direct binding to soluble Aβ, either monomer or oligomeric complexes within the CNS, to

facilitate the prevention or elimination of amyloid-promoting Aβ seeds. The anti-seeding and spreading mechanism would not be dependent upon effector function and thus if correct should enable the generation of a safer therapeutic antibody. This strategy is already being clinically implemented with MABT5102A, also known as Roche’s Crenezumab, a humanized N-terminal Aβ antibody engineered as a minimal effector function IgG4 (Adolfsson et al., 2012). Indeed, testing multiple humanized anti-Aβ antibodies that target different mechanisms of action in the clinic (mono or combination therapy) will be important for the field, especially as clinicians begin investigating the presymptomatic populations. In summary, these studies have demonstrated that the development of plaque-specific Aβp3-x antibodies that lack binding to soluble Aβ peptides leads to significant engagement of Aβ deposits (i.e., plaque binding) and to the subsequent removal of existing plaque without a microhemorrhage liability.

, 2011, Millard and Woolf, 1988 and Woodbury et al , 2001) (Figur

, 2011, Millard and Woolf, 1988 and Woodbury et al., 2001) (Figure 1B). Viewed in cross-section, each palisade of the longitudinal lanceolate ending is partially surrounded by processes of a terminal Schwann cell, with the side adjacent to hair shaft keratinocytes often devoid of a glial

covering. The shape and configuration of the palisades and their associated glial cells suggests a mechanism by which Aβ RA-LTMRs are exquisitely sensitive to hair follicle deflection, with putative sites for mechanotransduction located between the nerve fiber and the hair follicle keratinocytes (Halata, 1993 and Takahashi-Iwanaga, DNA Damage inhibitor 2000). With the recent development of mouse genetic tools, anatomical features of LTMRs, such as receptive fields, can now be defined by the number of hair follicles that they associate with. We now appreciate the existence of a variety of anatomical peripheral receptive fields formed by Aβ hair follicle afferents, which can range from single hair follicles to clusters of adjacent hair follicles (Li et al., 2011, Suzuki et al., 2012 and Wu et al., 2012). Aδ-LTMRs. A second major group of hair follicle-associated LTMRs are classified as Aδ-LTMRs according to their intermediate

conduction velocities (Table 1). Hair follicle-specific Aδ-LTMRs were originally described as D-Hair units, meant to reflect their specific response to movements of small sinus and down hairs in the cat and rabbit. Aδ-LTMR-like responses are also found in humans, though not

almost always correlated to hair follicle movement and it remains unclear how, or even if, Aδ-LTMR units influence touch perception (Adriaensen et al., 1983). The unique physiological properties of Aδ-LTMR BMN673 responses have been uncovered through in vivo and in vitro studies of model organisms. Most notably, studies in the cat and mouse reveal that Aδ-LTMR responses exhibit some of the lowest mechanical thresholds and highest dynamic sensitivity of any other LTMR, making Aδ-LTMRs the most sensitive mechanoreceptor in skin (Brown and Iggo, 1967, Burgess and Perl, 1967 and Koltzenburg et al., 1997). Aδ-LTMR physiological profiles are remarkably consistent and uniform within a given animal both in terms of their conduction velocity, which falls within the Aδ range, and their physiological receptive fields, which exhibit little variability from proximal to distal hairy skin. In addition, Aδ-LTMRs are sensitive to rapid cooling, but not warming, of the skin (Adriaensen et al., 1983, Brown and Iggo, 1967 and Li et al., 2011). As with Aβ RA-LTMRs, Aδ-LTMR responses are rapidly adapting and silent in the absence of tactile stimulation (Table 1). During the decades in which Aδ-LTMRs were originally described and subsequently thoroughly characterized, the anatomy of Aδ-LTMRs remained largely unknown, though their sensitivity to down hair movement, in particular air-jet stimulation of hair follicles, led to speculation that they form close associations with hair follicles.

, 2010; López de Silanes et al , 2004) These results reveal the

, 2010; López de Silanes et al., 2004). These results reveal the utility of in vivo HITS-CLIP as a means of clarifying in vitro studies

of RNA-protein interactions, which here initially led to the skewed perception that nElavl proteins Ibrutinib supplier bind only to ARE elements (Table S6). We find that nElavl proteins in fact bind GU-rich elements relative to ARE elements by ∼1.3-fold and that it does so in clusters, analogous to the way in which Nova proteins recognize specific targets by binding clusters of low complexity YCAY elements (Licatalosi et al., 2008; Zhang et al., 2010). Previous studies in Drosophila have indicated that nElavl proteins are able to regulate alternative splicing ( Koushika et al., 2000; Lisbin et al., 2001; Soller and White, 2003, 2005). Prior studies of mammalian nElavl splicing regulation has been less clear, as neither comparisons in genetically modified animals nor direct RNA binding assays have been previously employed. Here, we combined nElavl-RNA direct binding data with bioinformatics and exon junction array data comparing splicing in WT and KO animals to identify a definitive set

of brain transcripts directly regulated by nElavl proteins in vivo. The results demonstrate that nElavl proteins directly bind neuronal pre-mRNA Epigenetic inhibitor to regulate alternative splicing and that the proteins have redundant actions in this regard, as splicing changes were uniformly more pronounced in DKO than Elavl3 or Elavl4 single KO brain. Our nElavl-RNA map is reminiscent of the position-dependence of splicing regulation observed for Nova, Fox2, hnRNP C, hnRNPL, TIA1/2, TDP-43, Mbnl, Ptbp1, and Ptbp2 and generally conforms to the finding that preferential binding to downstream introns leads to exon no inclusion, and to upstream introns exon exclusion (Licatalosi

et al., 2008, 2012; Llorian et al., 2010; Tollervey et al., 2011; Ule and Darnell, 2006; Yeo et al., 2009; Zhang et al., 2008). nElavl-mediated exon exclusion may be more frequently associated with binding to both upstream and downstream introns, a characteristic also noted for TDP-43 associated alternative splicing. As was also seen in the TDP-43 associated alternative splicing RNA-map, nElavl binding was observed in deeper intronic sequences of a small number of cassette exons. Our nElavl-RNA map is also in agreement with several candidate target gene studies examining the role of nElavl proteins in AS. For example, it was recently demonstrated that Elavl3 promotes inclusion of the alternatively spliced exon 6 of the Elavl4 gene by binding to U-rich sequences located in the intron downstream to the alternative exon ( Wang et al., 2010a).

In Figure 5B (bottom), we took advantage of the larger

In Figure 5B (bottom), we took advantage of the larger see more number of traces to smooth the data with a narrower, 15 ms rectangular filter. Research supported by the Howard Hughes Medical Institute, National Institute of Mental Health Grant MH077970, and predoctoral fellowships from the National Science Foundation and the National Institutes of Health (NS655982). We thank Karen MacLeod, Elizabeth Montgomery, Stefanie Tokiyama, Lazslo Bocskai, Darrell

Floyd, Dirk Kleinhesselink, Ken McGary, and Scott Ruffner for technical assistance. Finally, we thank colleagues for helpful comments and discussions. “
“The outcomes expected from various actions vary in multiple dimensions and can often create a conflict. Accordingly, the ability to combine appropriately the information about multiple attributes of

action outcomes is critical for choosing the actions most beneficial to the animal. For example, during intertemporal choice between a small but more immediate reward and a large but more delayed reward, people and animals often choose the smaller reward if the difference in magnitude is too small or if the difference in delay is sufficiently large. This click here indicates that the subjective value of a delayed reward is reduced compared to when the same reward is immediately available. Formally, how steeply the reward value decreases with its delay is given by a temporal discount function. A temporally discounted value for a delayed reward is then given by the magnitude of reward multiplied by its discount function. Humans and many other species of animals tend to choose the reward with the maximum temporally discounted value (Frederick et al., 2002, Green and Myerson, 2004, Kalenscher and Pennartz, 2008 and Hwang et al., 2009). Disruption in this ability to combine appropriately the information about the magnitude and delay of reward characterizes the maladaptive choice behaviors observed in many psychiatric disorders (Madden et al., 1997, Vuchinich and Simpson, 1998,

Mitchell, 1999, Kirby and Petry, 2004 and Reynolds, 2006). Nevertheless, how much temporally discounted values are computed in the brain and used for decision making is not well understood. In particular, previous neuroimaging and lesion studies have highlighted the role of the basal ganglia in decision making involving temporal delays (Cardinal et al., 2001, McClure et al., 2004, McClure et al., 2007, Tanaka et al., 2004, Hariri et al., 2006, Kable and Glimcher, 2007, Wittmann et al., 2007, Weber and Huettel, 2008, Gregorios-Pippas et al., 2009, Pine et al., 2009, Luhmann et al., 2008, Ballard and Knutson, 2009, Bickel et al., 2009 and Xu et al., 2009), but precisely how its different subdivisions contribute to intertemporal choice is not clear. Although previous neurophysiological studies in primates (Apicella et al., 1991, Schultz et al., 1992, Williams et al., 1993, Bowman et al., 1996, Hassani et al.

Our previous

work showed that each of five distinct neuro

Our previous

work showed that each of five distinct neurodegenerative syndromes featured an atrophy pattern that mirrored the healthy functional ICN seeded by the cortical region most atrophied in patients with that syndrome (Seeley et al., 2009). The present study, in contrast, examined every brain region within the five disease-related atrophy find more maps to identify the regions whose connectivity pattern in health most resembled the atrophy map seen in each syndrome (see Figure 2 for a methods schematic). The resulting dataset fully specified the node pair connectivity strengths across all regions atrophied in any of the five diseases; collectively, these regions traversed most cerebral cortical and subcortical structures. With this information in hand, we used graph theoretical analyses to test model-based predictions of how network architecture in health relates to disease-associated tissue loss (Figure 1). Although previously described spatial atrophy patterns (Seeley et al., 2009) specified the brain regions interrogated for the current study, all network connectivity analyses were performed on an independent dataset of 16 healthy subjects aged 57 to 70 (8 females, all right-handed and psychoactive medication-free; see Experimental Procedures). The resulting connectivity patterns and graph metrics were used to relate each region’s healthy connectivity profile to that region’s disease-specific vulnerability,

defined as its atrophy severity in patients. In previous work (Seeley until et al., 2009), we identified VRT752271 nmr regional atrophy maxima for five neurodegenerative syndromes: Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (bvFTD), semantic

dementia (SD), progressive nonfluent aphasia (PNFA), and corticobasal syndrome (CBS). Then, using healthy subjects scanned with task-free fMRI, we used these five atrophy maxima as “seed” regions to derive five ICNs, representing regions whose blood-oxygen level-dependent (BOLD) signal time-series significantly correlated with that of the seed. The atrophy maxima seeded ICNs that resembled the parent atrophy maps, supporting the view that neurodegenerative disease patterns are network based. By studying only one seed region per atrophy pattern, however, this approach could not determine which regions featured maximal connectivity to the other vulnerable regions. We anticipated that each disease-associated pattern would harbor focal “epicenters,” regions whose connectivity patterns—in the healthy brain—most closely mirrored the disease vulnerability pattern. To seek out these epicenters, here we took a more comprehensive, data-driven approach by studying all regions within each of the five atrophy patterns. For example (Figure 2), we created 1,128 4 mm radius spherical regions of interest (ROIs) covering the entire bvFTD atrophy pattern and built 1,128 functional ICN maps, one seeded by each ROI, for each of our 16 healthy subjects.