Catalytic Transfer of Magnetism Using a Neutral Iridium Phenoxide Complex

© 2015 American Chemical Society. A novel neutral iridium carbene complex Ir(κC,O-L1)(COD) (1) [where COD = cyclooctadiene and L1 = 3-(2-methylene-4-nitrophenolate)-1-(2,4,6-trimethylphenyl)imidazolylidene] with a pendant alkoxide ligand has been prepared and characterized. It contains a strong Ir–O bond, and X-ray analysis reveals a distorted square planar structure. NMR spectroscopy reveals dynamic solution-state behavior commensurate with rapid seven-membered ring flipping. In CD2Cl2 solution, under hydrogen at low temperature, this complex dominates, although it exists in equilibrium with a reactive iridium dihydride cyclooctadiene complex. 1 reacts with pyridine and H2 to form neutral Ir(H)2(κC,O-L1)(py)2, which also exists in two conformers that differ according to the orientation of the seven-membered metallocycle, and while its Ir–O bond remains intact, the complex undergoes both pyridine and H2 exchange. As a consequence, when placed under para-hydrogen, efficient polarization transfer catalysis (PTC) is observed via the signal amplification by reversible exchange (SABRE) approach. Due to the neutral character of this catalyst, good hyperpolarization activity is shown in a wide range of solvents for a number of substrates. These observations reflect a dramatic improvement in solvent tolerance of SABRE over that reported for the best PTC precursor IrCl(IMes)(COD). For THF, the associated 1H NMR signal enhancement for the ortho proton signal of pyridine shows an increase of 600-fold at 298 K. The level of signal enhancement can be increased further through warming or varying the magnetic field experienced by the sample at the point of catalytic magnetization transfer

Delineating between-subject heterogeneity in alpha networks with Spatio-Spectral Eigenmodes

Between subject variability in the spatial and spectral structure of oscillatory networks can be highly informative but poses a considerable analytic challenge. Here, we describe a data-driven modal decomposition of a multivariate autoregressive model that simultaneously identifies oscillations by their peak frequency, damping time and network structure. We use this decomposition to define a set of Spatio-Spectral Eigenmodes (SSEs) providing a parsimonious description of oscillatory networks. We show that the multivariate system transfer function can be rewritten in these modal coordinates, and that the full transfer function is a linear superposition of all modes in the decomposition. The modal transfer function is a linear summation and therefore allows for single oscillatory signals to be isolated and analysed in terms of their spectral content, spatial distribution and network structure. We validate the method on simulated data and explore the structure of whole brain oscillatory networks in eyes-open resting state MEG data from the Human Connectome Project. We are able to show a wide between participant variability in peak frequency and network structure of alpha oscillations and show a distinction between occipital ’high-frequency alpha’ and parietal ’low-frequency alpha’. The frequency difference between occipital and parietal alpha components is present within individual participants but is partially masked by larger between subject variability; a 10Hz oscillation may represent the high-frequency occipital component in one participant and the low-frequency parietal component in another. This rich characterisation of individual neural phenotypes has the potential to enhance analyses into the relationship between neural dynamics and a person’s behavioural, cognitive or clinical state

Time scales of representation in the human brain: weighing past information to predict future events

The estimates that humans make of statistical dependencies in the environment and therefore their representation of uncertainty crucially depend on the integration of data over time. As such, the extent to which past events are used to represent uncertainty has been postulated to vary over the cortex. For example, primary visual cortex responds to rapid perturbations in the environment, while frontal cortices involved in executive control encode the longer term contexts within which these perturbations occur. Here we tested whether primary and executive regions can be distinguished by the number of past observations they represent. This was based on a decay-dependent model that weights past observations from a Markov process and Bayesian Model Selection to test the prediction that neuronal responses are characterized by different decay half-lives depending on location in the brain. We show distributions of brain responses for short and long term decay functions in primary and secondary visual and frontal cortices, respectively. We found that visual and parietal responses are released from the burden of the past, enabling an agile response to fluctuations in events as they unfold. In contrast, frontal regions are more concerned with average trends over longer time scales within which local variations are embedded. Specifically, we provide evidence for a temporal gradient for representing context within the prefrontal cortex and possibly beyond to include primary sensory and association areas