, 1985, 1999) and may also develop in association with a variety of focal brain lesions (Martin-Rodriguez and Leon-Carrion, 2010). Deficits of ToM in neurodegenerative disease have attracted much recent attention EPZ5676 ic50 and on clinical and neuroanatomical grounds may be particularly relevant

to bvFTD (Schroeter, 2012; Poletti et al., 2012). Patients with bvFTD frequently have difficulty with aspects of social cognition that are likely to be relevant to ToM, including emotion recognition (Rosen et al., 2005; Kipps et al., 2009b; Omar et al., 2011), empathic concern and perspective taking (Lough et al., 2006; Rankin et al., 2006; Eslinger et al., 2011), and perception of humour and sarcasm (Snowden et al., 2003; Kosmidis et al., 2008; Kipps et al., 2009b). A specific mentalising deficit may be an early feature of bvFTD (Gregory et al., 2002; Adenzato et al., 2010) and neuroanatomical substrates for this deficit have been proposed. The distributed neural network that is damaged in bvFTD (Seeley et al., 2007, Zhou et al., 2010, Zhou et al., 2012 and Raj et al., 2012) overlaps brain areas previously implicated in ToM (Gallagher and Frith, 2003; Carrington and Bailey, 2009). Impaired ability to experience social emotions Entinostat cost has been linked to frontopolar damage in bvFTD (Moll et al., 2011). In addition, bvFTD is often associated with damage involving anterior temporal lobe regions

that represent social concepts underpinning normal mentalising (Zahn et al., 2009): these anterior temporal areas interact with medial PFC during moral

reasoning (Fumagalli and Priori, 2012), from while anterior temporal lobe damage has been implicated in the pathogenesis of cognitive and affective ToM deficits in another FTLD syndrome, semantic dementia (Duval et al., 2012). Relations between mentalising, ToM and music processing have not been widely studied; however, music is likely a priori to engage brain processes relevant to ToM and it is an attractive candidate stimulus for probing such processes in bvFTD. Music typically entails decoding of an emotional ‘message’ and music-making generally has a strong social context across human societies (Mithen, 2005; Levitin, 2007). Music has been shown to modulate semantic information in other cognitive systems, such as language (Koelsch et al., 2004). Deficits in processing emotion information in music have been demonstrated in various disease states, notably the frontotemporal dementias, and are dissociable from the processing of other kinds of musical perceptual information (Stewart et al., 2006; Omar et al., 2010, 2011; Johnson et al., 2011; Hsieh et al., 2012). The brain mechanisms of music processing in health and disease and the brain substrates for processing emotional information in music have received considerable attention (Blood et al., 1999; Blood and Zatorre, 2001; Griffiths et al., 2004; Gosselin et al., 2006; Koelsch et al.

In the Ross Sea the dominant feature was the relatively high concentration of VHOC found in Ross Sea bottom water (or High Salinity Shelf Water, HSSW; (Orsi and Wiederwohl, 2009), a very dense water mass generated by the formation of sea ice and brine rejection. For halocarbons produced in the surface water or sea ice, this process may explain the elevated concentrations in the bottom waters. The environmental half-lives of halocarbons

in sea water at low temperatures are relatively long (i.e., CHBr3 and CH2Br2 half-lives are 686 and 183 years, respectively; (Jeffers et al., 1989 and Vogel et al., 1987). Therefore, this water may keep its halocarbon signature for extended Selleck Caspase inhibitor periods of time. Few investigations of halocarbon distributions have been made in waters in the Southern Ocean (Abrahamsson et al., 2004a, Butler et al., 2007, Carpenter et al., 2007, Hughes et al., 2009 and Reifenhauser and Heumann, 1992). In the Weddell Sea within 40 km of the continental Sea ice (depth, ca. 500 m), CHBr3 has been found to reach mean values of 57 pmol L− 1 in the surface

mixed layer (Carpenter et al., 2007), which is approximately 8–10 times higher than the concentrations we found (Table 2). For the iodinated compounds CH2I2 and CH2BrI, they found concentrations approximately 10–20 times higher than ours. In contrast, the concentrations of CH2ClI were similar. They Thiazovivin nmr suggest that the elevated surface concentrations (78 pmol L− 1 compared to underlying waters of ~ 50 pmol L− 1) originated from production of sea ice algae in the water column, even though they cannot rule out a possible production inside the sea ice followed by a transport out in the water column. Hughes et al. (2009) also found higher levels of CHBr3 and CH2Br2, with concentrations of 280 and 30 pmol L− 1, respectively. Their measurements were also conducted close to land (4 km) with a bottom depth of ca. 500 m. They suggested that these high concentrations were related to a phytoplankton bloom

based on coincidence of high chlorophyll values. However, both these studies (Carpenter et al., 2007 and Hughes et al., 2009), are coastal measurements and are likely to contain a high background crotamiton of halocarbons from macro algal productions. A more comparable dataset was presented by Butler et al. (2007), where surface water and air measurements were performed during the Blast III expedition Feb.–April 1996. They measured average concentrations (~ 8 pmol L− 1) of CHBr3 that were comparable to ours, and concluded that some parts of the surface waters in the Southern Ocean could act as both a source and a sink with respect to CHBr3. Biogenic halocarbon formation is strongly related to photosynthesis and respiration (Abrahamsson et al., 2004b, Ekdahl et al., 1998 and Manley, 2002), and the magnitude of this production is species specific (Ekdahl, 1997, Hughes et al., 2006 and Scarratt and Moore, 1996).

Recent systematic reviews Alectinib mw and meta-analyses reveal a complex relationship between obesity and risk of dementias (Gorospe and Dave,

2007, Beydoun et al., 2008 and Anstey et al., 2011). The majority of studies have found that higher BMI or waist-to-hip ratio in mid-life are associated with an increased risk of developing AD and VaD later in life (Kivipelto et al., 2005, Gustafson, 2006, Whitmer et al., 2007, Whitmer et al., 2008 and Fitzpatrick et al., 2009). A similar association between BMI and VaD risk is found in younger individuals (20–40 years) (Chen et al., 2010), whereas it remains to be determined whether obesity during childhood and adolescence influences dementia risk. In the elderly, however, studies exploring the relationship between obesity and dementia are conflicting. Some studies show that the obesity–dementia relationship persists into late life (Gustafson et al., 2003), whereas others suggest it plateaus and/or reverses (Stewart et al., 2005, Gustafson, 2006, Gustafson et al., 2009, Gustafson et al., 2012, Dahl et

al., 2008 and Fitzpatrick et al., 2009). Generally, risk factors for VaD are the same as for traditional stroke (e.g. type 2 diabetes, hypertension, and dyslipidemia) (Gorelick et al., 2011). Moreover, emerging evidence indicates these vascular risk factors may also be risk markers for AD (Gorelick et al., 2011). Given obesity Veliparib mouse is a common denominator for many of these vascular risk factors; a potential association between obesity and dementia is therefore hardly surprising. However, as outlined in a recent meta-analysis, some evidence suggests that obesity plays an independent role in the aetiology of AD and in some cases of VaD, after controlling for various cardiovascular risk factors (Beydoun et al., 2008). The mechanisms by which obesity influences risk of dementia remain to be fully understood. As discussed above, there is ample evidence of poor cognitive function and brain atrophy

in various age groups of non-demented obese individuals. It is well known that cognitive performance and markers of brain atrophy such as total Etofibrate brain and hippocampal volumes are powerful predictors of cognitive decline and dementia in the general population (Elias et al., 2000, Amieva et al., 2005 and Jack et al., 2005). Moreover, brain atrophy can occur progressively with normal aging (Raz et al., 2005). Thus, obesity-associated atrophy may amplify the risk for dementia and/or cognitive decline by synergistically interacting with the aging process. Consistent with this concept, higher BMI is correlated with brain atrophy in patients diagnosed with AD (Abiles et al., 2010). Furthermore, there is evidence that mid-life obesity is associated with an increased rate of total and hippocampal brain atrophy and cognitive decline a decade later (Debette et al., 2011).