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RISS 인기검색어
- 검색키워드
- 저자 : M. J. Hamilton (검색결과 3 건)
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A Powerful Chiral Counterion Strategy for Asymmetric Transition Metal Catalysis
Hamilton, G. L.,Kang, E. J.,Mba, M.,Toste, F. D. American Association for the Advancement of Scienc 2007 Science Vol.317 No.5837
<P>Traditionally, transition metal-catalyzed enantioselective transformations rely on chiral ligands tightly bound to the metal to induce asymmetric product distributions. Here we report high enantioselectivities conferred by a chiral counterion in a metal-catalyzed reaction. Two different transformations catalyzed by cationic gold(I) complexes generated products in 90 to 99% enantiomeric excess with the use of chiral binaphthol-derived phosphate anions. Furthermore, we show that the chiral counterion can be combined additively with chiral ligands to enable an asymmetric transformation that cannot be achieved by either method alone. This concept of relaying chiral information via an ion pair should be applicable to a vast number of metal-mediated processes.</P>
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Bulgarelli, Chiara,Blasi, Anna,Arridge, Simon,Powell, Samuel,de Klerk, Carina C.J.M.,Southgate, Victoria,Brigadoi, Sabrina,Penny, William,Tak, Sungho,Hamilton, Antonia Elsevier 2018 NeuroImage Vol.175 No.-
<P><B>Abstract</B></P> <P>Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions. DCM has recently been applied to adult fNIRS, but not to infants. The present paper provides a proof-of-principle for the application of this method to infant fNIRS data and a demonstration of the robustness of this method using a simultaneously recorded fMRI-fNIRS single case study, thereby allowing the use of this technique in future infant studies.</P> <P>fMRI and fNIRS were simultaneously recorded from a 6-month-old sleeping infant, who was presented with auditory stimuli in a block design. Both fMRI and fNIRS data were preprocessed using SPM, and analysed using a general linear model approach. The main challenges that adapting DCM for fNIRS infant data posed included: (i) the import of the structural image of the participant for spatial pre-processing, (ii) the spatial registration of the optodes on the structural image of the infant, (iii) calculation of an accurate 3-layer segmentation of the structural image, (iv) creation of a high-density mesh as well as (v) the estimation of the NIRS optical sensitivity functions. To assess our results, we compared the values obtained for variational Free Energy (F), Bayesian Model Selection (BMS) and Bayesian Model Average (BMA) with the same set of possible models applied to both the fMRI and fNIRS datasets. We found high correspondence in F, BMS, and BMA between fMRI and fNIRS data, therefore showing for the first time high reliability of DCM applied to infant fNIRS data. This work opens new avenues for future research on effective connectivity in infancy by contributing a data analysis pipeline and guidance for applying DCM to infant fNIRS data.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Connectivity studies give important insights into infant brain development. </LI> <LI> fNIRS is a valuable method for infancy studies, but can we analyse connectivity? </LI> <LI> On fMRI-fNIRS acquired simultaneously, we estimate effective connectivity with DCM. </LI> <LI> We showed high correspondence of DCM values between fMRI and fNIRS data. </LI> <LI> We validated DCM on fNIRS infant data, providing guidance for future projects. </LI> </UL> </P>
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