This is consistent with previous studies that have reported that ∼91% of relay neurons receive driving inputs from more than one RGC (Cleland et al., 1971a). The model also predicts that a relatively large fraction of RGC input to superficial dLGN is direction selective (>25%), which is similar to the total fraction of RGCs that are On-Off DS (20%–36%, based on anatomical estimates from Huberman et al., 2009), consistent with the notion that potentially all anterior and posterior DSRGC input to dLGN projects superficially and

that other directions project deeper, maintaining the overall fraction of DS input to dLGN across depths. The random wiring model demonstrates that integration can result by chance from convergence of relatively common direction-selective inputs and give rise to the representation PFT�� research buy of motion that we observed. This suggests a developmental mechanism for establishing local concentrations (i.e., lamination) of incoming fibers of specific direction preference but does not require Selleck Galunisertib selective targeting on a single cell basis to generate ASLGNs and maintain direction selectivity in dLGN. If the conditions of the model are not met physiologically, selective wiring between DSRGCs and ASLGNs may be necessary to generate ASLGNs in the absence of direct axis-selective input.

Regardless of the mechanism, the juxtaposition of horizontal axis and anterior-posterior direction selectivity within the same region suggests a computational role for the superficial dLGN. By both sharpening and integrating direction information within a functional organization, the dLGN

appears to not merely relay direction information from the retina to cortex but instead to organize and to manipulate that information before projecting it downstream. Future studies examining direct functional connectivity analyzed from the retina to thalamus to cortex, as well as of local interneuron circuits within dLGN, may shed light on the mechanisms underlying these computations. For below example, whether sharpening of direction tuning in dLGN results from nonlinear postsynaptic summation (Carandini et al., 2007) or precisely targeted feedforward inhibition (Wang et al., 2011) remains unknown. The methods developed and demonstrated here in combination with other methods are likely to aid these studies. Furthermore, the influence of these computations and the functional-anatomical organization of direction and motion axis information in the dLGN on visual cortical processing, development, and behavior remain intriguing, open questions. All experiments involving living animals were approved by the Salk Institute’s Institutional Animal Care and Use Committee. C57Bl/6 mice were anesthetized with isoflurane (1%–1.5%). A custom metal frame was mounted to the skull (Figure 1).

, 2005). Whether cilia orient in migrating neurons or glia, or otherwise contribute to the guidance of neural cells, is unexplored. Shh is a BVD523 chemoattractant for migrating neurons and axons (Angot et al., 2008, Bourikas et al., 2005 and Charron et al., 2003). This activity of Shh requires the putative Hh coreceptor Boc (Okada et al., 2006) and does not appear to utilize the canonical Shh pathway, instead activating Src family kinases to regulate growth cone turning (Yam et al., 2009). Despite these unusual features, Shh chemoattractant signaling is Smo dependant (Charron et al., 2003 and Yam et al.,

2009), suggesting a link with the cilium. PDGF-AA directs migration of oligodendrocyte precursor cells (Dubois-Dalcq and Murray, 2000, Kessaris et al., 2006, Kiernan and Ffrench-Constant, 1993 and Woodruff et al., 2001), which could be mediated by primary cilia. Favoring this possibility, oligodendrocytes have primary cilia (A. Peters, personal mTOR inhibitor communication; Cenacchi et al., 1996), and the neuroepithelial cells that generate oligodendrocyte precursors are presumed to be ciliated, given that they respond to Shh (Richardson et al., 1997). Based on current knowledge, the primary cilium is unlikely to provide motive force to a migrating cell

but could potentially sense and amplify a distant guidance signal. The relationship between the primary cilium and Wnt signal transduction is an important problem that, despite considerable study, is unresolved. Canonical, β-catenin-dependent Wnt signaling regulates cell fate and proliferation in the nervous system (Angers and Moon, 2009). The planar cell polarity (PCP) Wnt pathway orients sheets of cells, for example, regulating the convergent extension Levetiracetam cell movements that lead to neural tube closure. The PCP pathway is increasingly implicated in neuronal migration and axon guidance, in particular in the orderly development of large axon tracts (Tissir and Goffinet, 2010). This last

observation is intriguing because diffusion tensor imaging in Joubert Syndrome patients reveals that both the corticospinal tract and superior cerebellar peduncle make major mistakes in their trajectories (Poretti et al., 2007). Reports on Wnt signaling and cilia diverge from the model of Shh signaling by indicating that primary cilia suppress, rather than mediate, the canonical Wnt pathway. Mice deficient in Kif3a or Ift88 have shown upregulated signaling ( Corbit et al., 2008). Similarly, reduction of certain BBS proteins (named for their association with Bardet-Biedl Syndrome, BBS, Table 2) stabilizes β-catenin in zebrafish and mammalian cells, leading to upregulated expression of canonical Wnt pathway target genes ( Gerdes et al., 2007).

In humans, four major genes encode for a family of proteins termed neuroligins. These single-pass transmembrane proteins are found at postsynaptic sites, where they support the formation and maintenance of synapses through both intracellular, as well as trans-synaptic interactions ( Washbourne et al., 2004). A cursory look at the neuroligins reveals high sequence and structural

homology and a shared major binding partner in presynaptic neurexin ( Ichtchenko et al., 1996). Indeed, this similarity is borne Anti-cancer Compound Library in vitro out functionally, as all of the neuroligins promote the formation and maintenance of synapses ( Chih et al., 2005; Levinson et al., 2005). However, some notable differences have begun to emerge between the neuroligins, suggesting divergent roles for the individual members of this

family. Most dramatically, differences exist between neuroligin subtypes http://www.selleckchem.com/products/Adriamycin.html with regard to expression patterns at excitatory and inhibitory synapses, with neuroligin 1 (NLGN1) and neuroligin 3 (NLGN3) found at excitatory synapses and neuroligin 2 (NLGN2) and NLGN3 found at inhibitory synapses (Budreck and Scheiffele, 2007; Song et al., 1999; Varoqueaux et al., 2004). However, beyond the broad excitatory/inhibitory divide, subtle differences exist specifically between second the two major neuroligin subtypes found endogenously at excitatory synapses, NLGN1 and NLGN3. Notably, NLGN1 knockout animals have been shown to have deficits in memory (Blundell et al., 2010; Kim et al., 2008), while NLGN3 has been more strongly linked to autism and impairments in social behavior (Radyushkin

et al., 2009). Yet, little has been done to directly compare the physiological roles of these two proteins. In the present study, we explored for possible functional differences between NLGN1 and NLGN3. Using a variety of in vivo and in vitro techniques combining both knockdown and molecular replacement of the subtypes, we present differences in the physiological roles of these two proteins, most strikingly with respect to plasticity. Specifically, we find that NLGN1 has a clear role in the support of LTP in the hippocampus—in young CA1, but extending into adulthood in the dentate gyrus—a role that is not shared by NLGN3. We provide the first molecular dissection of the physiological differences between these neuroligin subtypes at excitatory synapses and find that the unique functions of NLGN1, both the potency of its synaptogenic phenotype and its role in LTP, depend on the inclusion of the B splice insertion site in its extracellular domain.

In orb2ΔQGFP mutant brains, although Orb2 protein was expressed at the same level as in the wild-type orb2+GFP animals, only an Orb2 monomer was observed ( Figure 6A), implying an acute role for the Q domain in Orb2 oligomerization. This result parallels

a complete lack of long-term memory in orb2ΔQ mutant flies. To investigate if Orb2A regulates oligomerization of Orb2B, we fed animals lacking the Orb2A isoform with tyramine. As above, we did not detect Orb2B oligomers, suggesting that Orb2A is crucial for oligomerization (Figure 6A). Finally, to test the role of Orb2A’s Q domain in Orb2 oligomer formation, we analyzed transheterozygous animals in which the Q domain present only in Orb2A and RBD only in Orb2B, able to form

long-term memory (3, orb2ΔQΔAGFP/orb2RBD∗ΔBGFP, LI = 20.68; 1, orb2+GFP, click here LI = 32.39) ( Table S5B). As predicted, in brain extracts from these animals, Orb2 multimers were detected as in the wild-type brains. In contrast, in brain extracts of animals in which the Q domain was lacking specifically in Orb2A and present only in Orb2B, which are unable to form long-term memory, Orb2 oligomers were not detected (2, orb2ΔQΔBGFP/orb2ΔAGFP, LI = 2.86) ( Table S4; Figure 6C). We conclude that Orb2 oligomers are induced Cell Cycle inhibitor by neuronal activity in Orb2A-dependent manner. The Q domain of Orb2A is both essential and sufficient, whereas that of Orb2B is dispensable and insufficient, for Orb2 oligomers formation. These results suggest that Orb2 complexes heptaminol are essential for memory persistence. Local translation of mRNAs in both pre- and postsynaptic compartments is thought to be important for the synaptic modifications that underlie long-lasting memories (Frey and Morris, 1997; Kang and Schuman, 1996; Martin et al., 1997). The CPEB family of proteins regulate local translation (Alarcon et al., 2004; Huang et al., 2006; Si et al., 2003a; Wells et al., 2001; Wu et al., 1998; Zearfoss et al., 2008), and the Drosophila CPEB protein Orb2 is acutely required for long-term memory ( Keleman et al., 2007; Majumdar et al., 2012). However, the detailed molecular mechanism of CPEB function in synaptic

plasticity and memory formation remains elusive. We have shown here that the two Orb2 isoforms, Orb2A and Orb2B, both contribute to long-term memory formation, albeit by distinct mechanisms. The two isoforms share the same RNA-binding and Q domains, yet each uniquely requires only one of these domains for its function in long-term memory formation. Specifically, the Q domain is essential in Orb2A but not Orb2B, whereas the RNA-binding domain is required in Orb2B but not Orb2A. Moreover, we found that Orb2A lacking its RNA-binding domain is able to fully complement Orb2B lacking its Q domain. Such interallelic complementation often reflects the formation of the heteromeric complexes between the encoded proteins (Garen and Garen, 1963; Zhang et al.

First we examined the shape of the mEPSC as an indicator of postsynaptic receptor subunit changes and found no difference in the amplitude to charge ratio of mEPSCs from VGLUT1-, VGLUT2-, or VGLUT3-expressing neurons (Figure 3D). Finally, we examined the rate of refilling of the RRP by depleting the RRP with one 500 mM sucrose application, followed by a second application 5 s later. There were no differences in the percentage of the RRP refilled after 5 s between any of the groups (Figure 3E). Finally we considered that a differential protein-protein interaction might see more underlie the

differences between VGLUT isoforms. The most striking difference between VGLUT1 and VGLUT2/3 that has been reported so far is the existence of the polyproline motif on the C terminus of VGLUT1 that mediates an interaction with endophilins (De Gois et al., 2006, Vinatier

et al., 2006 and Voglmaier et al., 2006), a protein family primarily known for its role in synaptic vesicle endocytosis (Farsad et al., 2001, Guichet et al., 2002, Hill et al., 2001, Ringstad et al., 1999, Schmidt selleck compound et al., 1999, Schuske et al., 2003 and Simpson et al., 1999). Both VGLUT2 and VGLUT3 lack this motif. We therefore expressed VGLUT1 containing a point mutation shown to disrupt endophilin binding (P554A) (Vinatier et al., 2006) in VGLUT1−/− hippocampal cells and repeated the measurements of Pvr, paired-pulse ratio, and 10 Hz stimulation with VGLUT1- and VGLUT2-rescued cells. We found that VGLUT1 P554A unless induced paired-pulse

depression comparable to that observed in VGLUT2-expressing neurons, while VGLUT1 neurons showed facilitation (Figures 4A and 4B). The release probability analysis revealed a 40% increase in Pvr in both VGLUT1 P554A- and VGLUT2-expressing neurons compared to VGLUT1 (Figure 4C). VGLUT1 P554A also increased the amount of steady-state depression in response to 10 Hz stimulation from VGLUT1 levels to VGLUT2 levels (Figure 4D). These changes were accompanied by an increase in the charge contained in the EPSC of VGLUT1 P554A-expressing neurons, while the size of the RRP was not different between the three groups (Figures 4E and 4F). mEPSC amplitudes and frequency of VGLUT1 P554A-expressing neurons were not significantly different from VGLUT1- or VGLUT2-expressing neurons (Figures 4G and 4H). Endophilin is a protein that has been implicated in endocytosis and vesicle cycling, so how the interaction of VGLUT1 with endophilin could lower the probability that a vesicle is released in response to an action potential is unclear. We considered two possible mechanisms. First, VGLUT and endophilin may work together to promote a lower Pvr for synaptic vesicles on which the complex is present.

We obtained mice expressing the channelrhodopsin channel only in Pv-INs by crossing mice expressing Cre-recombinase under the Pv promoter with mice bearing a floxed-Channelrhodopsin construct

(Madisen et al., 2012). We thus had an optogenetic tag to identify Pv-INs by combining extracellular recordings and 3-Methyladenine purchase blue laser activation. We confirmed that laser stimulation selectively activated Pv-INs by verifying three criteria: (1) laser photostimulation activated a cell at short latencies (Figures 7A and 7B); (2) the cell exerted inhibitory influences on other simultaneously recorded cells, as shown by spike cross-correlograms (see Supplemental Experimental Procedures; Figure S5B); (3) they had on average higher AP rates than putative pyramids (see Figure S5A). Next, we performed whole-cell recordings in layer 2/3 pyramids to verify that Pv-IN photostimulation was able to reduce sensory-driven synaptic responses in a graded manner by varying laser power (Figure 7C, top). We set the power so to reduce the unisensory PSPs by approximately one-third (−34.8% ± 4.8%), and, when presented alone, Pv-IN photostimulation reliably induced IPSPs in pyramids (Figure 7C, bottom). This same photostimulation level significantly increased AP rates of Pv-INs within physiological values (Figure 7B; n = 34 cells from 5 mice; medians: from 1.4 Hz to 3.3 Hz, Wilcoxon rank-sum test, p < 0.001; see also Atallah et al., 2012).

We next compared the relative effect of Pv-IN stimulation on unisensory and multisensory synaptic responses of pyramidal cells. selleck chemical Figure 7D shows unisensory and multisensory PSPs without (black) and with (blue) laser activation during unisensory and multisensory stimulation. Pv-IN photostimulation consistently affected

M responses more than either T or V unimodal responses (Figure 7E; n = 13 from 7 mice: T responses: 6.1 ± 0.9 mV versus 4.2 ± 0.8 mV, p < 0.01; V responses: 8.6 ± 1.1 mV versus 5.8 ± 0.9 mV, p < 0.01; preferred unisensory responses: 9.3 ± 1.0 mV versus Thymidine kinase 6.4 ± 0.9 mV, p < 0.001; M responses: 12.2 ± 1.0 mV versus 5.8 ± 0.6 mV, p < 0.001, paired t tests). Importantly, the relative (percent) decrease in PSPs was significantly smaller for unisensory responses than for multisensory responses (Figure 7F; −35.3% ± 4.3% versus −51.9% ± 3.8%; paired t test, p < 0.05). As a consequence, ME of pyramidal cells was dramatically but selectively reduced by Pv-IN photostimulation (Figure 7G; median ME indexes: 0.4 versus 0.1 without and with laser activation, respectively; paired Wilcoxon rank-sum test, p < 0.05). To better understand a possible mechanism by which optogenetic activation of Pv-INs selectively disrupts ME in pyramids, we compared the activity of Pv-INs and putative pyramids during sensory stimulation with and without simultaneous laser activation. We therefore performed extracellular multi-unit activity recordings on putative Pv-INs and pyramids, identified following the three criteria described above.

, 2002). Expression in the Escherichia coli BL21 strain and purification was followed according to Ferreira et al. (2002). Salivary antigen was obtained according to da Silva Vaz Jr et al. (1994). Briefly, partially engorged females from the Porto Alegre strain were dissected in PBS and salivary glands

were separated from other organs and frozen at −70 °C. Salivary glands were macerated Ulixertinib ic50 and sonicated (Ultrasonicator Cole Parmer, 4710, 500 W, 4 and 20% duty cycle) in a solution containing Tris/HCl 10 mM pH 8.2, 1% deoxicolate, leupeptin (8 mg/ml), pepstatin A (1 mg/ml) and TPCK (0.1 mM) and centrifuged at 32,000 × g for 40 min at 4 °C. The soluble fraction (supernatant) was then collected and stored at −70 °C. Sera from six Bos taurus (Hereford) and eight B. indicus (Nelore) bovines from a farm in Pelotas (Brazil), within a region naturally infested with R. microplus, as well as the sera from non-infested

B. indicus animals (negative controls) Fulvestrant mw were kindly provided by the Departamento de Veterinária Preventiva, at the Universidade Federal de Pelotas (Brazil). Additionally, the sera from three bovines (indicated as bovines 1, 2 and 3) submitted to twelve successive experimental infestations were the same described previously by Cruz et al. (2008). Briefly, the infestation regime consisted of six initial heavy infestations with 18,000 larvae (Bagé strain) followed by six light infestations with 800 larvae. All infestations were performed once a month and along the PD184352 (CI-1040) back. rBmPRM was submitted to SDS–PAGE 10% (58 μg/cm) and transferred to the nitrocellulose membrane at 70 V for 1 h at 4 °C (Dunn, 1986). Nitrocellulose strips of 4 mm were blocked for 1 h at room temperature with blocking buffer (cow non-fat dry milk 5%–PBS). Prior to the overnight incubation at 4 °C with the membrane strips, all sera were diluted 1:50 in an E. coli

BL21 strain lysate expressing the pGEX-4T3 vector and incubated for 2 h at room temperature for removal of contaminating anti-vector and E. coli proteins reactive antibodies. As positive control an anti-rRmPRM hyperimmune serum (1:400) raised in bovine was used. Preparation of the E. coli BL21 strain lysate was performed according to Rott et al. (2000). After 3 washes with blocking buffer, the strips were incubated for 1 h with anti-bovine IgG peroxidase conjugate (Sigma), diluted 1:6000 in blocking buffer. The strips were then washed three times with PBS, and the development buffer (5 mg 3,3-diaminobenzidine in 30 ml PBS plus 150 μl H2O2 30% and 100 μl CoCl2 1%) was added. Microtitration plates were incubated overnight at 4 °C with 0.5 μg of rBmPRM or 1 μg salivary gland protein extract diluted in 50 mM carbonate/bicarbonate buffer pH 9.6 per well. Plates were washed three times with blocking buffer, blocked for 1 h with blocking buffer at 37 °C, and then incubated with bovine sera diluted 1:50 in blocking buffer for 1 h at 37 °C.

A more effective way to eliminate unwanted fears would be to erase the fear memory itself. It has long been appreciated that new memories undergo a period of

consolidation in which they are labile and sensitive to disruption (McGaugh, 2000). Long-term synaptic plasticity in the brain requires de novo protein synthesis (Deadwyler et al., 1987, Krug et al., 1984 and Stanton and Sarvey, 1984), and administration of protein synthesis inhibitors soon after learning produces memory impairments (Agranoff et al., 1965, Agranoff and Klinger, 1964 and Davis and Squire, 1984). Therefore, one strategy for reducing pathological fear would be to prevent the consolidation of long-term fear memories soon after a traumatic experience. Consistent with this aim, several VX-770 order CCI-779 cost investigators have now shown that fear memory is inhibited by systemic posttraining protein synthesis inhibition (Bourtchouladze et al., 1998 and Lattal and Abel, 2001). Moreover, infusion of the protein synthesis inhibitor anisomycin into the BLA within hours of fear conditioning disrupts the consolidation of long-term fear memories and reduces conditional fear responses (Maren et al., 2003, Parsons et al., 2006, Schafe and LeDoux, 2000 and Schafe et al., 1999). In addition to protein synthesis inhibitors, administering behavioral interventions soon after fear conditioning might also disrupt long-term

fear memory by interfering with consolidation processes. For example, it has been reported that administering low-frequency stimulation soon after fear conditioning until eliminates conditioning-related changes in MAPK phosphorylation in the BLA, a biochemical correlate of long-term synaptic plasticity and fear memory, as well as fear

memory (Lin et al., 2003a). Based on this evidence, Davis and colleagues explored whether administering extinction trials soon after fear conditioning would yield a permanent loss of fear, rather than the temporary inhibition of fear typically observed with delayed extinction training (Myers et al., 2006). To test this, they administered extinction trials shortly (i.e., 10 min) after fear-potentiated startle conditioning in rats and examined whether fear suppression was more durable than that produced by extinction 24 hr after conditioning. They found that this immediate extinction procedure resulted in a loss of fear that was quite durable and exhibited little spontaneous recovery, reinstatement, or renewal. The implication of these findings is that early extinction training resulted in a permanent fear loss, which is not typical when extinction training is conducted 1 day after conditioning. The lack of fear recovery in this report suggested that immediate extinction might disrupt the consolidation of fear memory, yielding a relatively permanent loss of fear. Although promising, several laboratories have now found that immediate extinction does not always reduce the recovery of fear (Archbold et al., 2010 and Huff et al.

In Alois Alzheimer’s time (1900s), dementia was thought to be caused predominantly by “hardening of the arteries” (arteriosclerotic dementia) (Bowler, 2007 and Jellinger, 2006). Vascular factors were considered a major player in dementia well into the 20th century, until, in the 1980s, the Aβ peptide was identified as the

main component of parenchymal (amyloid plaque) and vascular (amyloid angiopathy) amyloid deposits, pathological hallmarks of AD (Glenner and Wong, 1984 and Kang et al., 1987). Shortly after, mutations in the amyloid precursor protein (APP) gene were identified in familial forms of AD (Bertram and Tanzi, 2008). Since then, the emphasis shifted from vascular dementia to AD, a process defined as the “Alzheimerization of dementia” (Figure 1) (Bowler, MG-132 supplier 2007). However, an increasing appreciation of the impact of cerebrovascular lesions on AD brought to the forefront the importance of cerebrovascular health in cognitive function (Esiri et al., 1999, Gold et al., 2007 and Snowdon et al., 1997).

Furthermore, community-based clinical-pathological studies revealed that the largest proportion of dementia cases have mixed pathology, comprising features of AD (amyloid plaques and neurofibrillary tangles) as well as ischemic lesions (Launer et al., 2008 and Schneider et al., 2009). These developments have promoted an interest to gain a better understanding of how vascular brain lesions affect cognition and how vascular pathology and neurodegeneration interact to amplify their respective pathogenic contribution. The concept of dementia caused by cerebrovascular PF-01367338 research buy pathology has evolved considerably over the years (Figure 2). For many decades vascular dementia was attributed to sclerosis of cerebral arteries leading to diffuse ischemic injury and brain atrophy (Jellinger, 2006). The first significant departure from this concept, inspired by the work of Tomlinson and colleagues (Tomlinson et al., 1970), was proposed by Hachinski Florfenicol and colleagues (Hachinski et al., 1974), who suggested that dementia on vascular bases was caused by multiple

and discrete ischemic lesions in patients with vascular risk factors, such as hypertension (multi-infarct dementia) (Figures 2 and 3). The construct of multi-infarct dementia, by attributing cognitive impairment to multiple strokes, raised the possibility that preventing cerebrovascular diseases could also prevent dementia (Hachinski et al., 1974). The introduction of brain imaging led to the realization that diffuse white matter lesions, termed leukoaraiosis (Hachinski et al., 1987), were a frequent correlate of cognitive impairment, much more common than multiple infarcts, which turned out to be a rare cause of dementia (Hulette et al., 1997) (Figures 2 and 3). Genetic causes of white matter lesions were discovered, the prototypical one being the Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) (Chabriat et al., 2009).

61, F[1, 25] = 14.81, p = 0.0007, Figure 2A) and production (r = 0.73, F[1, 25] = 28.00, p < 0.0001, Figure 2B). In contrast, buy Fulvestrant FA in the left ECFS did not correlate with either comprehension (r = 0.00, F[1, 25] < 1, p = 0.99, Figure 2C) or production (r < 0, Figure 2D) of syntax, nor did FA in the UF correlate with either comprehension (r < 0, Figure 2E) or production (r < 0, Figure 2F) measures. These findings suggest that syntactic processing relies primarily on dorsal, and not ventral, tracts. PPA is typically characterized by degeneration of the left hemisphere, but the right hemisphere is often affected

to a lesser extent. In our sample, FA values in the left and right SLF/Arcuate were correlated (r = 0.57, F[1, 25] = 12.17, p = 0.0018). To assess whether the right SLF/Arcuate might also click here be predictive of syntactic deficits, we included both the left and right SLF/Arcuate as independent variables. Only the left SLF/Arcuate predicted comprehension (partial r = 0.50, F[1, 24] = 7.92, p = 0.0096) and production (partial r = 0.60, F[1, 24] = 13.81, p = 0.0011). The right SLF/Arcuate did not predict either syntactic comprehension (partial r = 0.10, F[1, 24] = 0.27, p = 0.61) or production (partial r = 0.23, F[1, 24] = 1.28, p = 0.27). This suggests that syntactic processing depends on the left but not the right SLF/Arcuate. Therefore,

we considered only left hemisphere tracts in the remainder of our analyses. The 27 patients varied in several important respects. First, PPA patients can be sub-classified into nonfluent, semantic and logopenic variants based on clinical and speech-language features (Gorno-Tempini et al., 2011), and all three variants were represented in our sample. Second, patients varied in terms of severity, which we quantified with Mini Mental Status Examination (MMSE) score. Third, some PPA patients had executive impairments (which

we quantified Resminostat with a modified Trail-Making Test and a test of Design Fluency), and many nonfluent variant PPA patients had concomitant motor speech deficits (which we quantified with an apraxia of speech rating) in addition to agrammatism. Deficits such as these may contribute to syntactic processing deficits. Indeed, all of these measures were significantly associated with syntactic comprehension and/or production scores, and several, such as the apraxia of speech rating, were correlated with FA in the left SLF/Arcuate (see Supplemental Text available online). To ensure that the relationship between left SLF/Arcuate integrity and syntax was not secondary to any of these factors, we included all of these factors as covariates separately (see Supplemental Text) and simultaneously. In the full models with all potential mediating factors included, FA in the SLF/Arcuate continued to predict syntactic comprehension (partial r = 0.63, F[1, 17] = 10.29, p = 0.0052) and production (partial r = 0.54, F[1, 17] = 8.52, p = 0.