Diversity in levels of intracellular total creatine and triglycerides in human skeletal muscles observed by 1H-MRS

We used1H-magnetic resonance spectroscopy to noninvasively determine total creatine (TCr), choline-containing compounds (Cho), and intracellular (IT) and extracellular (between-muscle fibers) triglycerides (ET) in three human skeletal muscles. Subjects' (n = 15 men) TCr concentrations in soleus [Sol; 100.2 ± 8.3 (SE) mmol/kg dry wt] were lower (P < 0.05) than those in gastrocnemius (Gast; 125.3 ± 9.2 mmol/kg dry wt) and tibialis anterior (TA; 123.7 ± 8.8 mmol/kg dry wt). The Cho levels in Sol (35.8 ± 3.6 mmol/kg dry wt) and Gast (28.5 ± 3.5 mmol/kg dry wt) were higher (P < 0.001 andP < 0.01, respectively) compared with TA (13.6 ± 2.4 mmol/kg dry wt). The IT values were found to be 44.8 ± 4.6 and 36.5 ± 4.2 mmol/kg dry wt in Sol and Gast, respectively. The IT values of TA (24.5 ± 4.5 mmol/kg dry wt) were lower than those of Sol (P < 0.01) and Gast (P < 0.05). There were no differences in ET [116.0 ± 11.2 (Sol), 119.1 ± 18.5 (Gast), and 91.4 ± 19.2 mmol/kg dry wt (TA)]. It is proposed that the differences in metabolite levels may be due to the differences in fiber-type composition and deposition of metabolites due to the adaptation of different muscles during locomotion.

Brief response to Ashton and colleagues regarding Fractionating Human Intelligence

Ashton and colleagues concede in their response (Ashton, Lee, & Visser, in this issue), that neuroimaging methods provide a relatively unambiguous measure of the levels to which cognitive tasks co-recruit dif- ferent functional brain networks (task mixing). It is also evident from their response that they now accept that task mixing differs from the blended models of the classic literature. However, they still have not grasped how the neuroimaging data can help to constrain models of the neural basis of higher order ‘g’. Specifically, they claim that our analyses are invalid as we assume that functional networks have uncorrelated capacities. They use the simple analogy of a set of exercises that recruit multiple muscle groups to varying extents and highlight the fact that individual differences in strength may correlate across muscle groups. Contrary to their claim, we did not assume in the original article (Hampshire, High- field, Parkin, & Owen, 2012) that functional networks had uncorrelated capacities; instead, the analyses were specifically designed to estimate the scale of those correlations, which we referred to as spatially ‘diffuse’ factors