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When asked to perform the same task, different individuals exhibit markedly different patterns of brain activity. This variability is often attributed to volatile factors, such as task strategy or compliance. We propose that individual differences in brain responses are, to a large degree, inherent to the brain and can be predicted from task-independent measurements collected at rest. Using a large set of task conditions, spanning several behavioral domains, we train a simple model that relates task-independent measurements to task activity and evaluate the model by predicting task activation maps for unseen subjects using magnetic resonance imaging. Our model can accurately predict individual differences in brain activity and highlights a coupling between brain connectivity and function that can be captured at the level of individual subjects.

Uncertainty about the function of orbitofrontal cortex (OFC) in guiding decision-making may be a result of its medial (mOFC) and lateral (lOFC) divisions having distinct functions. Here we test the hypothesis that the mOFC is more concerned with reward-guided decision making, in contrast with the lOFC's role in reward-guided learning. Macaques performed three-armed bandit tasks and the effects of selective mOFC lesions were contrasted against lOFC lesions. First, we present analyses that make it possible to measure reward-credit assignment—a crucial component of reward-value learning—independently of the decisions animals make. The mOFC lesions do not lead to impairments in reward-credit assignment that are seen after lOFC lesions. Second, we examined how the reward values of choice options were compared. We present three analyses, one of which examines reward-guided decision making independently of reward-value learning. Lesions of the mOFC, but not the lOFC, disrupted reward-guided decision making. Impairments after mOFC lesions were a function of the multiple option contexts in which decisions were made. Contrary to axiomatic assumptions of decision theory, the mOFC-lesioned animals' value comparisons were no longer independent of irrelevant alternatives.

A fundamental issue in neuroscience is the relation between structure and function. However, gross landmarks do not correspond well to microstructural borders and cytoarchitecture cannot be visualized in a living brain used for functional studies. Here, we used diffusion-weighted and functional MRI to test structure-function relations directly. Distinct neocortical regions were defined as volumes having similar connectivity profiles and borders identified where connectivity changed. Without using prior information, we found an abrupt profile change where the border between supplementary motor area (SMA) and pre-SMA is expected. Consistent with this anatomical assignment, putative SMA and pre-SMA connected to motor and prefrontal regions, respectively. Excellent spatial correlations were found between volumes defined by using connectivity alone and volumes activated during tasks designed to involve SMA or pre-SMA selectively. This finding demonstrates a strong relationship between structure and function in medial frontal cortex and offers a strategy for testing such correspondences elsewhere in the brain.

We present a direct extension of probabilistic diffusion tractography to the case of multiple fibre orientations. Using automatic relevance determination, we are able to perform online selection of the number of fibre orientations supported by the data at each voxel, simplifying the problem of tracking in a multi-orientation field. We then apply the identical probabilistic algorithm to tractography in the multi- and single-fibre cases in a number of example systems which have previously been tracked successfully or unsuccessfully with single-fibre tractography. We show that multi-fibre tractography offers significant advantages in sensitivity when tracking non-dominant fibre populations, but does not dramatically change tractography results for the dominant pathways.

Changes in brain structure occur in remote regions following focal damage such as stroke. Such changes could disrupt processing of information across widely distributed brain networks. We used diffusion MRI tractography to assess connectivity between brain regions in 9 chronic stroke patients and 18 age-matched controls. We applied complex network analysis to calculate ‘communicability’, a measure of the ease with which information can travel across a network. Clustering individuals based on communicability separated patient and control groups, not only in the lesioned hemisphere but also in the contralesional hemisphere, despite the absence of gross structural pathology in the latter. In our highly selected patient group, lesions were localised to the left basal ganglia/internal capsule. We found reduced communicability in patients in regions surrounding the lesions in the affected hemisphere. In addition, communicability was reduced in homologous locations in the contralesional hemisphere for a subset of these regions. We interpret this as evidence for secondary degeneration of fibre pathways which occurs in remote regions interconnected, directly or indirectly, with the area of primary damage. We also identified regions with increased communicability in patients that could represent adaptive, plastic changes post-stroke. Network analysis provides new and powerful tools for understanding subtle changes in interactions across widely distributed brain networks following stroke. ►Using diffusion MRI tractography we assess structural connectivity between brain regions in chronic unilateral stroke patients. ►Complex network analysis enabled us to separate patient and control groups, not only in the lesioned hemisphere but also in the contralesional hemisphere. ►We found evidence for secondary degeneration of fibre pathways which occur in remote regions interconnected, directly or indirectly, with the primary area of damage.

Diffusion MRI and magnetic resonance spectroscopic measurements of selectively neuronally localised N-acetylaspartate (NAA) both have been used widely to assess white matter integrity and axonal loss. We have tested directly the relationship between changes in diffusion MRI parameters and NAA concentrations in the corpus callosum (CC) in an in vivo study of patients with MS. Fifteen MS patients (median EDSS 2.5, range 1–4) were studied with T1 anatomical, T2-weighted, and diffusion-sensitised MRI and PRESS single-voxel MRS. A recently described method, tract-based spatial statistics (TBSS) [Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E. et al., 2006. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487–1505] also was used to perform exploratory voxelwise whole-brain analysis of white matter diffusion fractional anisotropy (FA). We found a strong correlation between callosal size and both mean FA (r=0.68, p<0.005) (related specifically to changes in the radial tensor component) and mean inter-hemispheric motor tract connectivity probability (r=0.74, p<0.001). TBSS confirmed that the diffusion anisotropies of white matter voxels specifically within the callosum were correlated with the callosal size. Individual patient global T2 lesion volumes were correlated with both the probability of callosal connectivity (r=−0.69, p<0.005) and fractional anisotropy across the callosum (r=−0.76, p<0.001). However, absolute concentrations of NAA from the voxel showed no correlation with callosal cross-sectional area, mean connectivity or fractional anisotropy within the callosal pathway. We conclude that diffusion MRI shows changes consistent with sensitivity to axonal loss, but that relative NAA changes are not necessarily related directly to this. Axonal metabolic function, independent of structural integrity, may be a major determinant of NAA measures in MS.

We readdress the diffusion tractography problem in a global and probabilistic manner. Instead of tracking through local orientations, we parameterise the connexions between brain regions at a global level, and then infer on global and local parameters simultaneously in a Bayesian framework. This approach offers a number of important benefits. The global nature of the tractography reduces sensitivity to local noise and modelling errors. By constraining tractography to ensure a connexion is found, and then inferring on the exact location of the connexion, we increase the robustness of connectivity-based parcellations, allowing parcellations of connexions that were previously invisible to tractography. The Bayesian framework allows a direct comparison of the evidence for connecting and non-connecting models, to test whether the connexion is supported by the data. Crucially, by explicit parameterisation of the connexion between brain regions, we infer on a parameter that is shared with models of functional connectivity. This model is a first step toward the joint inference on functional and anatomical connectivity.

As diffusion tractography is increasingly used to generate quantitative measures to address clinical questions, it is important to characterise the inter-session reproducibility and inter-subject variability of these measures. Here, we assess the reproducibility and variability of diffusion tractography measures using diffusion data from 8 subjects scanned 3 times. We used probabilistic tractography to define the cingulum bundle, pyramidal tracts, optic radiations and genu of the corpus callosum in each individual data set using three different methods of seed definition. Measures of mean fractional anisotropy (FA) and mean diffusivity (MD) along the tracts were more reproducible than measures of tract volume. Further, tracts defined using a two region of interest (ROI) approach were more reproducible than those defined using manually placed seed masks alone. For mean FA taken from tracts defined using the two ROI approach, inter-session coefficients of variation (CV) were all below 5% and inter-subject CVs were below 10%; for mean MD inter-session, CVs were all below 3% and inter-subject CVs were below 8%. We use the variability measures found here to calculate the sample sizes required to detect changes in FA, MD or tract volume of a given size, either between groups of subjects or within subjects over time. Finally, we compare tractography results using 60 diffusion encoding directions to those found using a subset of 12 directions; the number of diffusion directions did not have a significant effect on reproducibility, but tracts derived using fewer directions were consistently smaller than those derived using 60 direction data. We suggest that 12 direction data are sufficient for reproducibly defining the core of large bundles but may be less sensitive to smaller pathways.

Electronic gaming machines (EGMs) offer significant revenue streams for mercantile gambling. However, limited clinical and experimental evidence suggests that EGMs are associated with heightened risks of clinically problematic patterns of play. Little is known about the neural structures that might mediate the transition from exploratory EGM play to the ‘addictive’ play seen in problem gamblers; neither is it known how personality traits associated with gambling activity (and gambling problems) influence reinforcement processing while playing EGMs. Using functional magnetic resonance imaging in healthy participants, we show that a single episode of slot-machine play is subsequently associated with reduced amplitudes of blood-oxygenation level-dependent signals within reinforcement-related structures, such as the ventral striatum and caudate nucleus, following winning game outcomes; but increased amplitudes of anticipatory signals within the ventral striatum and amygdala while watching the game reels spin. Trait impulsivity enhanced positive signals within the ventral striatum and amygdala following the delivery of winning outcomes but diminished positive signals following the experience of almost-winning (’near-misses’). These results indicate that a single episode of slot-machine play engages the well-characterised reinforcement-learning mechanisms mediated by ascending dopamine mesolimbic and mesostriatal pathways, to shift reward value of EGMs away from game outcomes towards anticipatory states. Impulsivity, itself linked to problem gambling and heightened vulnerability to other addictive disorders, is associated with divergent coding of winning outcomes and almost-winning experiences within the ventral striatum and amygdala, potentially enhancing the reward value of successful slot-machine game outcomes but, at the same time,modulating the aversive motivational consequences of near-miss outcomes.

We propose a hierarchical infinite mixture model approach to address two issues in connectivity-based parcellations: (i) choosing the number of clusters, and (ii) combining data from different subjects. In a Bayesian setting, we model voxel-wise anatomical connectivity profiles as an infinite mixture of multivariate Gaussian distributions, with a Dirichlet process prior on the cluster parameters. This type of prior allows us to conveniently model the number of clusters and estimate its posterior distribution directly from the data. An important benefit of using Bayesian modelling is the extension to multiple subjects clustering via a hierarchical mixture of Dirichlet processes. Data from different subjects are used to infer on class parameters and the number of classes at individual and group level. Such a method accounts for inter-subject variability, while still benefiting from combining different subjects data to yield more robust estimates of the individual clusterings.