Network mechanisms of intentional learning

Publication date: 15 February 2016
Source:NeuroImage, Volume 127
Author(s): Adam Hampshire, Peter J. Hellyer, Beth Parkin, Nole Hiebert, Penny MacDonald, Adrian M. Owen, Robert Leech, James Rowe
The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.



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Temperature dependence of water diffusion pools in brain white matter

Publication date: 15 February 2016
Source:NeuroImage, Volume 127
Author(s): Bibek Dhital, Christian Labadie, Frank Stallmach, Harald E. Möller, Robert Turner
Water diffusion in brain tissue can now be easily investigated using magnetic resonance (MR) techniques, providing unique insights into cellular level microstructure such as axonal orientation. The diffusive motion in white matter is known to be non-Gaussian, with increasing evidence for more than one water-containing tissue compartment. In this study, freshly excised porcine brain white matter was measured using a 125-MHz MR spectrometer (3T) equipped with gradient coils providing magnetic field gradients of up to 35,000mT/m. The sample temperature was varied between −14 and +19°C. The hypothesis tested was that white matter contains two slowly exchanging pools of water molecules with different diffusion properties. A Stejskal–Tanner diffusion sequence with very short gradient pulses and b-factors up to 18.8ms/μm² was used. The dependence on b-factor of the attenuation due to diffusion was robustly fitted by a biexponential function, with comparable volume fractions for each component. The diffusion coefficient of each component follows Arrhenius behavior, with significantly different activation energies. The measured volume fractions are consistent with the existence of three water-containing compartments, the first comprising relatively free cytoplasmic and extracellular water molecules, the second of water molecules in glial processes, and the third comprising water molecules closely associated with membranes, as for example, in the myelin sheaths and elsewhere. The activation energy of the slow diffusion pool suggests proton hopping at the surface of membranes by a Grotthuss mechanism, mediated by hydrating water molecules.



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Decisions about the past are guided by reinstatement of specific memories in the hippocampus and perirhinal cortex

Publication date: 15 February 2016
Source:NeuroImage, Volume 127
Author(s): Michael L. Mack, Alison R. Preston
When faced with a new challenge, we often reflect on related past experiences to guide our behavior. The ability to retrieve memories that overlap with current experience, a process known as pattern completion, is theorized as a critical function of the hippocampus. Although this view has influenced research for decades, there is little empirical support for hippocampal pattern completion to individual memory elements and its influence on behavior. We used pattern analysis of brain activity measured with functional magnetic resonance imaging to demonstrate that specific elements of past experiences are reinstated in the hippocampus, as well as perirhinal cortex (PRC), when making decisions about those experiences. Linking neural measures of specific memory reinstatement in the hippocampus and PRC to behavior with computational modeling revealed that reinstatement predicts the speed of memory-based decisions. Moreover, hippocampal activation during retrieval was selectively coupled to regions of occipito-temporal cortex that showed content-specific item reinstatement. These results provide evidence for hippocampal pattern completion and its role in the mechanisms of decision making.

Graphical abstract

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Kernel regression estimation of fiber orientation mixtures in diffusion MRI

Publication date: 15 February 2016
Source:NeuroImage, Volume 127
Author(s): Ryan P. Cabeen, Mark E. Bastin, David H. Laidlaw
We present and evaluate a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework's data-adaptive extensions are potentially useful for general multi-compartment image processing. We experimentally evaluate our approach with both synthetic data from computational phantoms and in vivo clinical data from human subjects. With synthetic data experiments, we evaluate performance based on errors in fiber orientation, volume fraction, compartment count, and tractography-based connectivity. With in vivo data experiments, we first show improved scan–rescan reproducibility and reliability of quantitative fiber bundle metrics, including mean length, volume, streamline count, and mean volume fraction. We then demonstrate the creation of a multi-fiber tractography atlas from a population of 80 human subjects. In comparison to single tensor atlasing, our multi-fiber atlas shows more complete features of known fiber bundles and includes reconstructions of the lateral projections of the corpus callosum and complex fronto-parietal connections of the superior longitudinal fasciculus I, II, and III.



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Visual motion transforms visual space representations similarly throughout the human visual hierarchy

Publication date: 15 February 2016
Source:NeuroImage, Volume 127
Author(s): Ben M. Harvey, Serge O. Dumoulin
Several studies demonstrate that visual stimulus motion affects neural receptive fields and fMRI response amplitudes. Here we unite results of these two approaches and extend them by examining the effects of visual motion on neural position preferences throughout the hierarchy of human visual field maps. We measured population receptive field (pRF) properties using high-field fMRI (7T), characterizing position preferences simultaneously over large regions of the visual cortex. We measured pRFs properties using sine wave gratings in stationary apertures, moving at various speeds in either the direction of pRF measurement or the orthogonal direction. We find direction- and speed-dependent changes in pRF preferred position and size in all visual field maps examined, including V1, V3A, and the MT+ map TO1. These effects on pRF properties increase up the hierarchy of visual field maps. However, both within and between visual field maps the extent of pRF changes was approximately proportional to pRF size. This suggests that visual motion transforms the representation of visual space similarly throughout the visual hierarchy. Visual motion can also produce an illusory displacement of perceived stimulus position. We demonstrate perceptual displacements using the same stimulus configuration. In contrast to effects on pRF properties, perceptual displacements show only weak effects of motion speed, with far larger speed-independent effects. We describe a model where low-level mechanisms could underlie the observed effects on neural position preferences. We conclude that visual motion induces similar transformations of visuo-spatial representations throughout the visual hierarchy, which may arise through low-level mechanisms.



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MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection

Publication date: 15 February 2016
Source:NeuroImage, Volume 127
Author(s): Jimit Doshi, Guray Erus, Yangming Ou, Susan M. Resnick, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite, Susan Furth, Christos Davatzikos
Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (http://ift.tt/1Rl5t12), a web based platform for remote processing of medical images.



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Functional connectivity MRI tracks memory networks after maze learning in rodents

Publication date: 15 February 2016
Source:NeuroImage, Volume 127
Author(s): Fatima A. Nasrallah, Xuan Vinh To, Der-Yow Chen, Aryeh Routtenberg, Kai-Hsiang Chuang
Learning and memory employs a series of cognitive processes which require the coordination of multiple areas across the brain. However in vivo imaging of cognitive function has been challenging in rodents. Since these processes involve synchronous firing among different brain loci we explored functional connectivity imaging with resting-state fMRI. After 5-day training on a hidden platform watermaze task, notable signal correlations were seen between the hippocampal CA3 and other structures, including thalamus, septum and cingulate cortex, compared to swim control or naïve animals. The connectivity sustained 7days after training and was reorganized toward the cortex, consistent with views of memory trace distribution leading to memory consolidation. These data demonstrates that, after a cognitive task, altered functional connectivity can be detected in the subsequently sedated rodent using in vivo imaging. This approach paves the way to understand dynamics of area-dependent distribution processes in animal models of cognition.



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