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1.  Advances in diffusion MRI acquisition and processing in the Human Connectome Project 
NeuroImage  2013;80:125-143.
The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, while enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013.
PMCID: PMC3720790  PMID: 23702418
2.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project 
NeuroImage  2013;80:80-104.
The human connectome project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3 Tesla, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 second for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 Tesla magnetic field are also presented, targeting higher resolution, specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields and power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.
PMCID: PMC3740184  PMID: 23702417
3.  A mechanism for value-guided choice based on the excitation-inhibition balance in prefrontal cortex 
Nature neuroscience  2012;15(7):960-961.
Although the ventromedial prefrontal cortex (vmPFC) has long been implicated in reward-guided decision making, its exact role in this process has remained an unresolved issue. Here, we show that vmPFC levels of GABA and glutamate in human volunteers are predictive of both behavioural performance and the dynamics of a neural value comparison signal in a manner as predicted by models of decision-making. These data provide evidence for a neural competition mechanism in vmPFC supporting value-guided choice.
PMCID: PMC4050076  PMID: 22706268
4.  Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems 
In this article, we highlight an issue that arises when using multiple b-values in a model-based analysis of diffusion MR data for tractography. The non-mono-exponential decay, commonly observed in experimental data, is shown to induce over-fitting in the distribution of fibre orientations when not considered in the model. Extra fibre orientations perpendicular to the main orientation arise to compensate for the slower apparent signal decay at higher b-values. We propose a simple extension to the ball and stick model based on a continuous Gamma distribution of diffusivities, which significantly improves the fitting and reduces the over-fitting. Using in-vivo experimental data, we show that this model outperforms a simpler, noise floor model, especially at the interfaces between brain tissues, suggesting that partial volume effects are a major cause of the observed non-mono-exponential decay. This model may be helpful for future data acquisition strategies that may attempt to combine multiple shells to improve estimates of fibre orientations in white matter and near the cortex.
PMCID: PMC3359399  PMID: 22334356
5.  Neural Mechanisms of Foraging 
Science (New York, N.Y.)  2012;336(6077):95-98.
Behavioural economic studies, involving limited numbers of choices, have provided key insights into neural decision-making mechanisms. By contrast, animals’ foraging choices arise in the context of sequences of encounters with prey/food. On each encounter the animal chooses to engage or whether the environment is sufficiently rich that searching elsewhere is merited. The cost of foraging is also critical. We demonstrate humans can alternate between two modes of choice, comparative decision-making and foraging, dependent on distinct neural mechanisms in ventromedial prefrontal (vmPFC) and anterior cingulate cortex (ACC) employing distinct reference frames; in ACC choice variables are represented in invariant reference to foraging/searching for alternatives. While vmPFC encodes values of specific well-defined options, ACC encodes the average value of the foraging environment and cost of foraging.
PMCID: PMC3440844  PMID: 22491854
6.  Mechanisms underlying cortical activity during value-guided choice 
Nature neuroscience  2012;15(3):470-S3.
When choosing between two options, correlates of their value are represented in neural activity throughout the brain. Whether these representations reflect activity fundamental to the computational process of value comparison, as opposed to other computations covarying with value, is unknown. Here, we investigated activity in a biophysically plausible network model that transforms inputs relating to value into categorical choices. A set of characteristic time-varying signals emerged that reflect value comparison. We tested these model predictions in magnetoencephalography data recorded from human subjects performing value-guided decisions. Parietal and prefrontal signals matched closely with model predictions. These results provide a mechanistic explanation of neural signals recorded during value-guided choice, and a means of distinguishing computational roles of different cortical regions whose activity covaries with value.
PMCID: PMC3378494  PMID: 22231429
7.  Integrity of white matter in the corpus callosum correlates with bimanual co-ordination skills 
NeuroImage  2007;36(Suppl 2):T16-T21.
Variation in brain structure may reflect variation in functional properties of specific brain systems. Structural variation may therefore reflect variation in behavioural performance. Here, we use diffusion-weighted magnetic resonance imaging to show that variation in white matter integrity in a specific region in the body of the corpus callosum is associated with variation in performance of a bimanual co-ordination task. When the callosal region showing this association is used as a seed for probabilistic tractography, inter-hemispheric pathways are generated to the supplementary motor area and caudal cingulate motor area. This provides further evidence for the role of medial wall motor areas in bimanual co-ordination and supports the idea that variation in brain structure reflects inter-individual differences in skilled performance.
PMCID: PMC3119816  PMID: 17499163
8.  Effort-based cost-benefit valuation and the human brain 
In both the wild and the laboratory, animals' preferences for one course of action over another reflect not just reward expectations but also the cost in terms of effort that must be invested in pursuing the course of action. The ventral striatum and dorsal anterior cingulate cortex (ACCd) are implicated in the making of cost-benefit decisions in the rat but there is little information about how effort costs are processed and influence calculations of expected net value in other mammals including the human. We carried out a functional magnetic resonance imaging (fMRI) study to determine whether and where activity in the human brain was available to guide effort-based cost-benefit valuation. Subjects were scanned while they performed a series of effortful actions to obtain secondary reinforcers. At the beginning of each trial, subjects were presented with one of eight different visual cues which they had learned indicated how much effort the course of action would entail and how much reward could be expected at its completion. Cue-locked activity in the ventral striatum and midbrain reflected the net value of the course of action, signaling the expected amount of reward discounted by the amount of effort to be invested. Activity in ACCd also reflected the interaction of both expected reward and effort costs. Posterior orbitofrontal and insular activity, however, only reflected the expected reward magnitude. The ventral striatum and anterior cingulate cortex may be the substrate of effort-based cost-benefit valuation in primates as well as in rats.
PMCID: PMC2954048  PMID: 19357278
anterior cingulate cortex; striatum; decision making; reward; effort; ventral tegmental area
9.  Associative learning of social value 
Nature  2008;456(7219):245-249.
Our decisions are guided by information learnt from our environment. This information may come via personal experiences of reward, but also from the behaviour of social partners1, 2. Social learning is widely held to be distinct from other forms of learning in its mechanism and neural implementation; it is often assumed to compete with simpler mechanisms, such as reward-based associative learning, to drive behaviour3. Recently however, neural signals have been observed during social exchange reminiscent of signals seen in associative paradigms4. Here, we demonstrate that social information may be acquired using the same associative processes assumed to underlie reward-based learning. We find that key computational variables for learning in the social and reward domains are processed in a similar fashion, but in parallel neural processing streams. Two neighbouring divisions of the anterior cingulate cortex were central to learning about social and reward-based information, and for determining the extent to which each source of information guides behaviour. When making a decision, however, the information learnt using these parallel streams was combined within ventromedial prefrontal cortex. These findings suggest that human social valuation can be realised via the same associative processes previously established for learning other, simpler, features of the environment.
PMCID: PMC2605577  PMID: 19005555

Results 1-9 (9)