Journal Sciences News
The Ocular Surface
15 July 2018
MRI gradient-echo phase contrast of the brain at ultra-short TE with off-resonance saturation
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Hongjiang Wei, Peng Cao, Antje Bischof, Roland G. Henry, Peder E.Z. Larson, Chunlei Liu Larmor-frequency shift or image phase measured by gradient-echo sequences has provided a new source of MRI contrast. This contrast is being used to study both the structure and function of the brain. So far, phase images of the brain have been largely obtained at long echo times as maximum phase signal-to-noise ratio (SNR) is achieved at TE = T2* (
15 July 2018
Correlation of neural activity with behavioral kinematics reveals distinct sensory encoding and evidence accumulation processes during active tactile sensing
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Ioannis Delis, Jacek P. Dmochowski, Paul Sajda, Qi Wang Many real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus to maximize information gain. Though ecologically pervasive, limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object/surface by actively exploring its shape/texture. Here we investigate the neural correlates of active tactile decision-making by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgments. Since sensorimotor behavior underlies decision formation in active sensing tasks, we hypothesized that the neural correlates of decision-related processes would be detectable by relating active sensing to neural activity. Novel brain-behavior correlation analysis revealed that three distinct EEG components, localizing to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively, were coupled with active sensing as their activity significantly correlated with finger kinematics. To probe the functional role of these components, we fit their single-trial-couplings to decision-making performance using a hierarchical-drift-diffusion-model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. Interestingly, the MFG disappeared from components uncovered from control subjects performing active sensing but not required to make perceptual decisions. By uncovering the neural correlates of distinct stimulus encoding and evidence accumulation processes, this study delineated, for the first time, the functional role of cortical areas in active tactile decision-making.
15 July 2018
Simultaneous scalp recorded EEG and local field potentials from monkey ventral premotor cortex during action observation and execution reveals the contribution of mirror and motor neurons to the mu-rhythm
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Marco Bimbi, Fabrizia Festante, Gino Coud
15 July 2018
Bayesian convolutional neural network based MRI brain extraction on nonhuman primates
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Gengyan Zhao, Fang Liu, Jonathan A. Oler, Mary E. Meyerand, Ned H. Kalin, Rasmus M. Birn Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0.985 and a mean average symmetric surface distance of 0.220
15 July 2018
Conflict monitoring mechanism at the single-neuron level in the human ventral anterior cingulate cortex
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Irit Shapira-Lichter, Ido Strauss, Noga Oren, Tomer Gazit, Francesco Sammartino, Peter Giacobbe, Sidney Kennedy, William D. Hutchison, Itzhak Fried, Talma Hendler, Andres M. Lozano Life requires monitoring and adjusting behavior in the face of conflicts. The conflict monitoring theory implicates the anterior cingulate cortex (ACC) in these processes; its ventral aspect (vACC) specializes in emotional conflict. To elucidate the underpinning neural mechanism, we recorded vACC extracellular activity from 12 patients with mood disorders or epilepsy who performed the face-emotional Stroop task. Behaviorally, both conflict detection and adaptation to conflict were evident. The firing rate of neurons in the vACC represented current conflict, i.e., current-congruency. The late onset of the effect is compatible with a role in monitoring. Additionally, early responses of some neurons represented the immediate history of conflicts, i.e., previous-trial-congruency. Finally, in some neurons the response to the current-trial was modulated by previous-trial-congruency, laying the ground for adjusting-to-conflicts. Our results uncover a single neuron level mechanism in the vACC that encodes and integrates past and present emotional conflicts, allowing humans to accommodate their responses accordingly.
15 July 2018
Subcortical sources dominate the neuroelectric auditory frequency-following response to speech
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Gavin M. Bidelman Frequency-following responses (FFRs) are neurophonic potentials that provide a window into the encoding of complex sounds (e.g., speech/music), auditory disorders, and neuroplasticity. While the neural origins of the FFR remain debated, renewed controversy has reemerged after demonstration that FFRs recorded via magnetoencephalography (MEG) are dominated by cortical rather than brainstem structures as previously assumed. Here, we recorded high-density (64 ch) FFRs via EEG and applied state-of-the art source imaging techniques to multichannel data (discrete dipole modeling, distributed imaging, independent component analysis, computational simulations). Our data confirm a mixture of generators localized to bilateral auditory nerve (AN), brainstem inferior colliculus (BS), and bilateral primary auditory cortex (PAC). However, frequency-specific scrutiny of source waveforms showed the relative contribution of these nuclei to the aggregate FFR varied across stimulus frequencies. Whereas AN and BS sources produced robust FFRs up to
15 July 2018
Atypical cortical entrainment to speech in the right hemisphere underpins phonemic deficits in dyslexia
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Giovanni M. Di Liberto, Varghese Peter, Marina Kalashnikova, Usha Goswami, Denis Burnham, Edmund C. Lalor Developmental dyslexia is a multifaceted disorder of learning primarily manifested by difficulties in reading, spelling, and phonological processing. Neural studies suggest that phonological difficulties may reflect impairments in fundamental cortical oscillatory mechanisms. Here we examine cortical mechanisms in children (6–12 years of age) with or without dyslexia (utilising both age- and reading-level-matched controls) using electroencephalography (EEG). EEG data were recorded as participants listened to an audio-story. Novel electrophysiological measures of phonemic processing were derived by quantifying how well the EEG responses tracked phonetic features of speech. Our results provide, for the first time, evidence for impaired low-frequency cortical tracking to phonetic features during natural speech perception in dyslexia. Atypical phonological tracking was focused on the right hemisphere, and correlated with traditional psychometric measures of phonological skills used in diagnostic dyslexia assessments. Accordingly, the novel indices developed here may provide objective metrics to investigate language development and language impairment across languages.
15 July 2018
Intra-hemispheric integration underlies perception of tilt illusion
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Chen Song, Geraint Rees The integration of inputs across the entire visual field into a single conscious experience is fundamental to human visual perception. This integrated nature of visual experience is illustrated by contextual illusions such as the tilt illusion, in which the perceived orientation of a central grating appears tilted away from its physical orientation, due to the modulation by a surrounding grating with a different orientation. Here we investigated the relative contribution of local, intra-hemispheric and global, inter-hemispheric integration mechanisms to perception of the tilt illusion. We used Dynamic Causal Modelling of fMRI signals to estimate effective connectivity in human early visual cortices (V1, V2, V3) during bilateral presentation of a tilt illusion stimulus. Our analysis revealed that neural responses associated with the tilt illusion were modulated by intra- rather than inter-hemispheric connectivity. Crucially, across participants, intra-hemispheric connectivity in V1 correlated with the magnitude of the tilt illusion, while no such correlation was observed for V1 inter-hemispheric connectivity, or V2, V3 connectivity. Moreover, when the illusion stimulus was presented unilaterally rather than bilaterally, the illusion magnitude did not change. Together our findings suggest that perception of the tilt illusion reflects an intra-hemispheric integration mechanism. This is in contrast to the existing literature, which suggests inter-hemispheric modulation of neural activity as early as V1. This discrepancy with our findings may reflect the diversity and complexity of integration mechanisms involved in visual processing and visual perception.
15 July 2018
Lateral prefrontal cortex lesion impairs regulation of internally and externally directed attention
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Julia W.Y. Kam, Anne-Kristin Solbakk, Tor Endestad, Torstein R. Meling, Robert T. Knight Our capacity to flexibly shift between internally and externally directed attention is crucial for successful performance of activities in our daily lives. Neuroimaging studies have implicated the lateral prefrontal cortex (LPFC) in both internally directed processes, including autobiographical memory retrieval and future planning, and externally directed processes, including cognitive control and selective attention. However, the causal involvement of the LPFC in regulating internally directed attention states is unknown. The current study recorded scalp EEG from patients with LPFC lesions and healthy controls as they performed an attention task that instructed them to direct their attention either to the external environment or their internal milieu. We compared frontocentral midline theta and posterior alpha between externally and internally directed attention states. While healthy controls showed increased theta power during externally directed attention and increased alpha power during internally directed attention, LPFC patients revealed no differences between the two attention states in either electrophysiological measure in the analyzed time windows. These findings provide evidence that damage to the LPFC leads to dysregulation of both types of attention, establishing the important role of LPFC in supporting sustained periods of internally and externally directed attention.
15 July 2018
Statistical power comparisons at 3T and 7T with a GO / NOGO task
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Salvatore Torrisi, Gang Chen, Daniel Glen, Peter A. Bandettini, Chris I. Baker, Richard Reynolds, Jeffrey Yen-Ting Liu, Joseph Leshin, Nicholas Balderston, Christian Grillon, Monique Ernst The field of cognitive neuroscience is weighing evidence about whether to move from standard field strength to ultra-high field (UHF). The present study contributes to the evidence by comparing a cognitive neuroscience paradigm at 3
15 July 2018
Distinct phase-amplitude couplings distinguish cognitive processes in human attention
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Ravi V. Chacko, Byungchan Kim, Suh Woo Jung, Amy L. Daitch, Jarod L. Roland, Nicholas V. Metcalf, Maurizio Corbetta, Gordon L. Shulman, Eric C. Leuthardt Spatial attention is the cognitive function that coordinates the selection of visual stimuli with appropriate behavioral responses. Recent studies have reported that phase-amplitude coupling (PAC) of low and high frequencies covaries with spatial attention, but differ on the direction of covariation and the frequency ranges involved. We hypothesized that distinct phase-amplitude frequency pairs have differentiable contributions during tasks that manipulate spatial attention. We investigated this hypothesis with electrocorticography (ECoG) recordings from participants who engaged in a cued spatial attention task. To understand the contribution of PAC to spatial attention we classified cortical sites by their relationship to spatial variables or behavioral performance. Local neural activity in spatial sites was sensitive to spatial variables in the task, while local neural activity in behavioral sites correlated with reaction time. We found two PAC frequency clusters that covaried with different aspects of the task. During a period of cued attention, delta-phase/high-gamma (DH) PAC was sensitive to cue direction in spatial sites. In contrast, theta-alpha-phase/beta-low-gamma-amplitude (TABL) PAC robustly correlated with future reaction times in behavioral sites. Finally, we investigated the origins of TABL PAC and found it corresponded to behaviorally relevant, sharp waveforms, which were also coupled to a low frequency rhythm. We conclude that TABL and DH PAC correspond to distinct mechanisms during spatial attention tasks and that sharp waveforms are elements of a coupled dynamical process.
15 July 2018
Can anomalous diffusion models in magnetic resonance imaging be used to characterise white matter tissue microstructure?
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Qiang Yu, David Reutens, Viktor Vegh Purpose During the time window of diffusion weighted magnetic resonance imaging experiments (DW-MRI), water diffusion in tissue appears to be anomalous as a transient effect, with a mean squared displacement that is not a linear function of time. A number of statistical models have been proposed to describe water diffusion in tissue, and parameters describing anomalous as well as Gaussian diffusion have previously been related to measures of tissue microstructure such as mean axon radius. We analysed the relationship between white matter tissue characteristics and parameters of existing statistical diffusion models. Methods A white matter tissue model (ActiveAx) was used to generate multiple b-value diffusion-weighted magnetic resonance imaging signals. The following models were evaluated to fit the diffusion signal: 1) Gaussian models - 1a) mono-exponential decay and 1b) bi-exponential decay; 2) Anomalous diffusion models - 2a) stretched exponential, 2b) continuous time random walk and 2c) space fractional Bloch-Torrey equation. We identified the best candidate model based on the relationship between the diffusion-derived parameters and mean axon radius and axial diffusivity, and applied it to the in vivo DW-MRI data acquired at 7.0
15 July 2018
Distinctive heritability patterns of subcortical-prefrontal cortex resting state connectivity in childhood: A twin study
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Michelle Achterberg, Marian J. Bakermans-Kranenburg, Marinus H. van Ijzendoorn, Mara van der Meulen, Nim Tottenham, Eveline A. Crone Connectivity between limbic/subcortical and prefrontal-cortical brain regions develops considerably across childhood, but less is known about the heritability of these networks at this age. We tested the heritability of limbic/subcortical-cortical and limbic/subcortical-subcortical functional brain connectivity in 7- to 9-year-old twins (N
15 July 2018
Statistical learning of multisensory regularities is enhanced in musicians: An MEG study
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Evangelos Paraskevopoulos, Nikolas Chalas, Panagiotis Kartsidis, Andreas Wollbrink, Panagiotis Bamidis The present study used magnetoencephalography (MEG) to identify the neural correlates of audiovisual statistical learning, while disentangling the differential contributions of uni- and multi-modal statistical mismatch responses in humans. The applied paradigm was based on a combination of a statistical learning paradigm and a multisensory oddball one, combining an audiovisual, an auditory and a visual stimulation stream, along with the corresponding deviances. Plasticity effects due to musical expertise were investigated by comparing the behavioral and MEG responses of musicians to non-musicians. The behavioral results indicated that the learning was successful for both musicians and non-musicians. The unimodal MEG responses are consistent with previous studies, revealing the contribution of Heschl's gyrus for the identification of auditory statistical mismatches and the contribution of medial temporal and visual association areas for the visual modality. The cortical network underlying audiovisual statistical learning was found to be partly common and partly distinct from the corresponding unimodal networks, comprising right temporal and left inferior frontal sources. Musicians showed enhanced activation in superior temporal and superior frontal gyrus. Connectivity and information processing flow amongst the sources comprising the cortical network of audiovisual statistical learning, as estimated by transfer entropy, was reorganized in musicians, indicating enhanced top-down processing. This neuroplastic effect showed a cross-modal stability between the auditory and audiovisual modalities.
15 July 2018
Disclosing large-scale directed functional connections in MEG with the multivariate phase slope index
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Alessio Basti, Vittorio Pizzella, Federico Chella, Gian Luca Romani, Guido Nolte, Laura Marzetti The phase slope index (PSI) is a method to disclose the direction of frequency-specific neural interactions from magnetoencephalographic (MEG) time series. A fundamental property of PSI is that of vanishing for linear mixing of independent neural sources. This property allows PSI to cope with the artificial instantaneous connectivity among MEG sensors or brain sources induced by the field spread. Nevertheless, PSI is limited by being a bivariate estimator of directionality as opposite to the multidimensional nature of brain activity as revealed by MEG. The purpose of this work is to provide a multivariate generalization of PSI. We termed this measure as the multivariate phase slope index (MPSI). In order to test the ability of MPSI in estimating the directionality, and to compare the MPSI results to those obtained by bivariate PSI approaches based on maximizing imaginary part of coherency and on canonical correlation analysis, we used extensive simulations. We proved that MPSI achieves the highest performance and that in a large number of simulated cases, the bivariate methods, as opposed to MPSI, do not detect a statistically significant directionality. Finally, we applied MPSI to assess seed-based directed functional connectivity in the alpha band from resting state MEG data of 61 subjects from the Human Connectome Project. The obtained results highlight a directed functional coupling in the alpha band between the primary visual cortex and several key regions of well-known resting state networks, e.g. dorsal attention network and fronto-parietal network.
15 July 2018
Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Fiorenzo Artoni, Arnaud Delorme, Scott Makeig Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered ‘dipolar’ ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided.
15 July 2018
A preference for mathematical processing outweighs the selectivity for Arabic numbers in the inferior temporal gyrus
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Mareike Grotheer, Brianna Jeska, Kalanit Grill-Spector A region in the posterior inferior temporal gyrus (ITG), referred to as the number form area (NFA, here ITG-numbers) has been implicated in the visual processing of Arabic numbers. However, it is unknown if this region is specifically involved in the visual encoding of Arabic numbers per se or in mathematical processing more broadly. Using functional magnetic resonance imaging (fMRI) during experiments that systematically vary tasks and stimuli, we find that mathematical processing, not preference to Arabic numbers, consistently drives both mean and distributed responses in the posterior ITG. While we replicated findings of higher responses in ITG-numbers to numbers than other visual stimuli during a 1-back task, this preference to numbers was abolished when participants engaged in mathematical processing. In contrast, an ITG region (ITG-math) that showed higher responses during an adding task vs. other tasks maintained this preference for mathematical processing across a wide range of stimuli including numbers, number/letter morphs, hands, and dice. Analysis of distributed responses across an anatomically-defined posterior ITG expanse further revealed that mathematical task but not Arabic number form can be successfully and consistently decoded from these distributed responses. Together, our findings suggest that the function of neuronal regions in the posterior ITG goes beyond the specific visual processing of Arabic numbers. We hypothesize that they ascribe numerical content to the visual input, irrespective of the format of the stimulus.
15 July 2018
Supervoxel based method for multi-atlas segmentation of brain MR images
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Jie Huo, Jonathan Wu, Jiuwen Cao, Guanghui Wang Multi-atlas segmentation has been widely applied to the analysis of brain MR images. However, the state-of-the-art techniques in multi-atlas segmentation, including both patch-based and learning-based methods, are strongly dependent on the pairwise registration or exhibit huge spatial inconsistency. The paper proposes a new segmentation framework based on supervoxels to solve the existing challenges of previous methods. The supervoxel is an aggregation of voxels with similar attributes, which can be used to replace the voxel grid. By formulating the segmentation as a tissue labeling problem associated with a maximum-a-posteriori inference in Markov random field, the problem is solved via a graphical model with supervoxels being considered as the nodes. In addition, a dense labeling scheme is developed to refine the supervoxel labeling results, and the spatial consistency is incorporated in the proposed method. The proposed approach is robust to the pairwise registration errors and of high computational efficiency. Extensive experimental evaluations on three publically available brain MR datasets demonstrate the effectiveness and superior performance of the proposed approach.

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15 July 2018
Probing the reproducibility of quantitative estimates of structural connectivity derived from global tractography
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Lena V. Schumacher, Marco Reisert, Kai Nitschke, Karl Egger, Horst Urbach, J
15 July 2018
SMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Leo Yu-Feng Liu, Yufeng Liu, Hongtu Zhu With the development of advanced imaging techniques, scientists are interested in identifying imaging biomarkers that are related to different subtypes or transitional stages of various cancers, neuropsychiatric diseases, and neurodegenerative diseases, among many others. In this paper, we propose a novel spatial multi-category angle-based classifier (SMAC) for the efficient identification of such imaging biomarkers. The proposed SMAC not only utilizes the spatial structure of high-dimensional imaging data but also handles both binary and multi-category classification problems. We introduce an efficient algorithm based on an alternative direction method of multipliers to solve the large-scale optimization problem for SMAC. Both our simulation and real data experiments demonstrate the usefulness of SMAC.
15 July 2018
Tracing the interplay between syntactic and lexical features: fMRI evidence from agreement comprehension
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Ileana Qui
15 July 2018
Gaussian process uncertainty in age estimation as a measure of brain abnormality
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Benjamin Gutierrez Becker, Tassilo Klein, Christian Wachinger Multivariate regression models for age estimation are a powerful tool for assessing abnormal brain morphology associated to neuropathology. Age prediction models are built on cohorts of healthy subjects and are built to reflect normal aging patterns. The application of these multivariate models to diseased subjects usually results in high prediction errors, under the hypothesis that neuropathology presents a similar degenerative pattern as that of accelerated aging. In this work, we propose an alternative to the idea that pathology follows a similar trajectory than normal aging. Instead, we propose the use of metrics which measure deviations from the mean aging trajectory. We propose to measure these deviations using two different metrics: uncertainty in a Gaussian process regression model and a newly proposed age weighted uncertainty measure. Consequently, our approach assumes that pathologic brain patterns are different to those of normal aging. We present results for subjects with autism, mild cognitive impairment and Alzheimer’s disease to highlight the versatility of the approach to different diseases and age ranges. We evaluate volume, thickness, and VBM features for quantifying brain morphology. Our evaluations are performed on a large number of images obtained from a variety of publicly available neuroimaging databases. Across all features, our uncertainty based measurements yield a better separation between diseased subjects and healthy individuals than the prediction error. Finally, we illustrate differences in the disease pattern to normal aging, supporting the application of uncertainty as a measure of neuropathology.

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15 July 2018
Bayesian uncertainty quantification in linear models for diffusion MRI
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Jens Sj
15 July 2018
Alignment of alpha-band desynchronization with syntactic structure predicts successful sentence comprehension
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Benedict Vassileiou, Lars Meyer, Caroline Beese, Angela D. Friederici Sentence comprehension requires the encoding of phrases and their relationships into working memory. To date, despite the importance of neural oscillations in language comprehension, the neural-oscillatory dynamics of sentence encoding are only sparsely understood. Although oscillations in a wide range of frequency bands have been reported both for the encoding of unstructured word lists and for working-memory intensive sentences, it is unclear to what extent these frequency bands subserve processes specific to the working-memory component of sentence comprehension or to general verbal working memory. In our auditory electroencephalography study, we isolated the working-memory component of sentence comprehension by adapting a subsequent memory paradigm to sentence comprehension and assessing oscillatory power changes during successful sentence encoding. Time–frequency analyses and source reconstruction revealed alpha-power desynchronization in left-hemispheric language-relevant regions during successful sentence encoding. We further showed that sentence encoding was more successful when source-level alpha-band desynchronization aligned with computational measures of syntactic—compared to lexical-semantic—difficulty. Our results are a preliminary indication of a domain-general mechanism of cortical disinhibition via alpha-band desynchronization superimposed onto the language-relevant cortex, which is beneficial for encoding sentences into working memory.
15 July 2018
The multidimensional representational space of observed socio-affective touch experiences
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Haemy Lee Masson, Stien Van De Plas, Nicky Daniels, Hans Op de Beeck Observed touch interactions provide useful information on how others communicate with the external world. Previous studies revealed shared neural circuits between the direct experience and the passive observation of simple touch, such as being stroked/slapped. Here, we investigate the complexity of the neural representations underlying the understanding of others' socio-affective touch interactions. Importantly, we use a recently developed touch database that contains a larger range of more complex social and non-social touch interactions. Participants judged affective aspects of each touch event and were scanned while watching the same videos. Using correlational multivariate pattern analysis methods, we obtained neural similarity matrices in 18 regions of interest from five different networks: somatosensory, pain, the theory of mind, visual and motor regions. Among them, four networks except motor cortex represent the social nature of the touch, whereas fine-detailed affective information is reflected in more targeted areas such as social brain regions and somatosensory cortex. Lastly, individual social touch preference at the behavioral level was correlated with the involvement of somatosensory areas on representing affective information, suggesting that individuals with higher social touch preference exhibit stronger vicarious emotional responses to others' social touch experiences. Together, these results highlight the overall complexity and the individual modulation of the distributed neural representations underlying the processing of observed socio-affective touch.
15 July 2018
Representation of steady-state visual evoked potentials elicited by luminance flicker in human occipital cortex: An electrocorticography study
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Benjamin Wittevrongel, Elvira Khachatryan, Mansoureh Fahimi Hnazaee, Evelien Carrette, Leen De Taeye, Alfred Meurs, Paul Boon, Dirk Van Roost, Marc M. Van Hulle Despite the widespread use of steady-state visual evoked potentials (SSVEPs) elicited by luminance flicker in clinical and research settings, their spatial and temporal representation in the occipital cortex largely remain elusive. We performed intracranial-EEG recordings in response to targets flickering at frequencies from 11 to 15
15 July 2018
Exploring experimental autoimmune optic neuritis using multimodal imaging
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Praveena Manogaran, Christine Walker-Egger, Marijana Samardzija, Conny Waschkies, Christian Grimm, Markus Rudin, Sven Schippling Background Neuro-axonal injury is a key contributor to non-reversible long-term disability in multiple sclerosis (MS). However, the underlying mechanisms are not yet fully understood. Visual impairment is common among MS patients, in which episodes of optic neuritis (ON) are often followed by structural retinal damage and sustained functional impairment. Alterations in the optic nerve and retina have also been described in experimental autoimmune encephalomyelitis (EAE), a rodent model of MS. Thus, investigating structural anterior visual pathway damage may constitute a unique model for assessing mechanisms and temporal sequence of neurodegeneration in MS. We used a multimodal imaging approach utilizing optical coherence tomography (OCT) and diffusion tensor imaging (DTI) to explore the mechanisms and temporal dynamics of visual pathway damage in the animal model of MS. Methods 7 EAE-MOG35-55 and 5 healthy female C57BL/6J mice were used in this study. Ganglion cell complex (GCC) thickness was derived from an OCT volume scan centred over the optic nerve head, while the structure of the optic nerve and tracts was assessed from DTI and co-registered T2-weighted sequences performed on a 7T MRI scanner. Data was acquired at baseline, disease onset, peak of disease and recovery. Linear mixed effect models were used to account for intra-subject, inter-eye dependencies, group and time point. Correlation analyses assessed the relationship between GCC thickness and DTI parameters. Immunofluorescence staining of retina and optic nerve sections was used to assess distribution of marker proteins for microglia and neurodegeneration (nerve filaments). Results In EAE mice, a significant increase in GCC thickness was observed at disease onset (p
15 July 2018
Directed functional connectivity using dynamic graphical models
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Simon Schwab, Ruth Harbord, Valerio Zerbi, Lloyd Elliott, Soroosh Afyouni, Jim Q. Smith, Mark W. Woolrich, Stephen M. Smith, Thomas E. Nichols There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations and human resting-state fMRI (N
15 July 2018
Human fronto-parietal response scattering subserves vigilance at night
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Giulia Gaggioni, Julien Q.M. Ly, Sarah L. Chellappa, Doroth
1 July 2018
Dynamic reorganization of TMS-evoked activity in subcortical stroke patients
Publication date: 15 July 2018
Source:NeuroImage, Volume 175 Author(s): Maria Concetta Pellicciari, Sonia Bonn
1 July 2018
Decoding the neural signatures of emotions expressed through sound
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Matthew E. Sachs, Assal Habibi, Antonio Damasio, Jonas T. Kaplan Effective social functioning relies in part on the ability to identify emotions from auditory stimuli and respond appropriately. Previous studies have uncovered brain regions engaged by the affective information conveyed by sound. But some of the acoustical properties of sounds that express certain emotions vary remarkably with the instrument used to produce them, for example the human voice or a violin. Do these brain regions respond in the same way to different emotions regardless of the sound source? To address this question, we had participants (N
1 July 2018
Neurophysiological processes and functional neuroanatomical structures underlying proactive effects of emotional conflicts
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Marie Luise Schreiter, Witold Chmielewski, Christian Beste There is a strong inter-relation of cognitive and emotional processes as evidenced by emotional conflict monitoring processes. In the cognitive domain, proactive effects of conflicts have widely been studied; i.e. effects of conflicts in the n-1 trial on trial n. Yet, the neurophysiological processes and associated functional neuroanatomical structures underlying such proactive effects during emotional conflicts have not been investigated. This is done in the current study combining EEG recordings with signal decomposition methods and source localization approaches. We show that an emotional conflict in the n-1 trial differentially influences processing of positive and negative emotions in trial n, but not the processing of conflicts in trial n. The dual competition framework stresses the importance of dissociable 'perceptual' and 'response selection' or cognitive control levels for interactive effects of cognition and emotion. Only once these coding levels were isolated in the neurophysiological data, processes explaining the behavioral effects were detectable. The data show that there is not only a close correspondence between theoretical propositions of the dual competition framework and neurophysiological processes. Rather, processing levels conceptualized in the framework operate in overlapping time windows, but are implemented via distinct functional neuroanatomical structures; the precuneus (BA31) and the insula (BA13). It seems that decoding of information in the precuneus, as well as the integration of information during response selection in the insula is more difficult when confronted with angry facial emotions whenever cognitive control resources have been highly taxed by previous conflicts.
1 July 2018
Functional correlate and delineated connectivity pattern of human motion aftereffect responses substantiate a subjacent visual-vestibular interaction
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Ria Maxine R
1 July 2018
Oxytocin attenuates trust as a subset of more general reinforcement learning, with altered reward circuit functional connectivity in males
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Jaime S. Ide, Sanja Nedic, Kin F. Wong, Shmuel L. Strey, Elizabeth A. Lawson, Bradford C. Dickerson, Lawrence L. Wald, Giancarlo La Camera, Lilianne R. Mujica-Parodi Oxytocin (OT) is an endogenous neuropeptide that, while originally thought to promote trust, has more recently been found to be context-dependent. Here we extend experimental paradigms previously restricted to de novo decision-to-trust, to a more realistic environment in which social relationships evolve in response to iterative feedback over twenty interactions. In a randomized, double blind, placebo-controlled within-subject/crossover experiment of human adult males, we investigated the effects of a single dose of intranasal OT (40 IU) on Bayesian expectation updating and reinforcement learning within a social context, with associated brain circuit dynamics. Subjects participated in a neuroeconomic task (Iterative Trust Game) designed to probe iterative social learning while their brains were scanned using ultra-high field (7T) fMRI. We modeled each subject's behavior using Bayesian updating of belief-states (“willingness to trust”) as well as canonical measures of reinforcement learning (learning rate, inverse temperature). Behavioral trajectories were then used as regressors within fMRI activation and connectivity analyses to identify corresponding brain network functionality affected by OT. Behaviorally, OT reduced feedback learning, without bias with respect to positive versus negative reward. Neurobiologically, reduced learning under OT was associated with muted communication between three key nodes within the reward circuit: the orbitofrontal cortex, amygdala, and lateral (limbic) habenula. Our data suggest that OT, rather than inspiring feelings of generosity, instead attenuates the brain's encoding of prediction error and therefore its ability to modulate pre-existing beliefs. This effect may underlie OT's putative role in promoting what has typically been reported as ‘unjustified trust’ in the face of information that suggests likely betrayal, while also resolving apparent contradictions with regard to OT's context-dependent behavioral effects.
1 July 2018
The bilingual language network: Differential involvement of anterior cingulate, basal ganglia and prefrontal cortex in preparation, monitoring, and execution
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Roy Seo, Andrea Stocco, Chantel S. Prat Research on the neural bases of bilingual language control has largely overlooked the role of preparatory processes, which are central to cognitive control. Additionally, little is known about how the processes involved in global language selection may differ from those involved in the selection of words and morpho-syntactic rules for manipulating them. These processes were examined separately in an fMRI experiment, with an emphasis on understanding how and when general cognitive control regions become activated. Results of region-of-interest analyses on 23 early Spanish-English bilinguals showed that the anterior cingulate cortex (ACC) was primarily engaged during the language preparation phase of the task, whereas the left prefrontal (DLPFC) and pre-supplementary motor areas showed increasing activation from preparation to execution. Activation in the basal ganglia (BG), left middle temporal lobe, and right precentral cortical regions did not significantly differ throughout the task. These results suggest that three core cognitive control regions, the ACC, DLPFC, and BG, which have been previously implicated in bilingual language control, engage in distinct neurocognitive processes. Specifically, the results are consistent with the view that the BG “keep track” of the target language in use throughout various levels of language selection, that the ACC is particularly important for top-down target language preparation, and that the left prefrontal cortex is increasingly involved in selection processes from preparation through task execution.
1 July 2018
Maturation trajectories of cortical resting-state networks depend on the mediating frequency band
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Sheraz Khan, Javeria A. Hashmi, Fahimeh Mamashli, Konstantinos Michmizos, Manfred G. Kitzbichler, Hari Bharadwaj, Yousra Bekhti, Santosh Ganesan, Keri-Lee A. Garel, Susan Whitfield-Gabrieli, Randy L. Gollub, Jian Kong, Lucia M. Vaina, Kunjan D. Rana, Steven M. Stufflebeam, Matti S. H
1 July 2018
Integrated models of neurovascular coupling and BOLD signals: Responses for varying neural activations
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Elshin J. Mathias, Allanah Kenny, Michael J. Plank, Tim David A state-of-the-art integrated model of neurovascular coupling (NVC) (Dormanns et al., 2015b; Dormanns et al., 2016; Kenny et al., 2018) and the BOLD response (Mathias et al., 2017a; Mathias et al., 2017b) is presented with the ability to simulate the fMRI BOLD responses due to continuous neuronal spiking, bursting and cortical spreading depression (CSD) along with the underlying complex vascular coupling. Simulated BOLD responses are compared to experimental BOLD signals observed in the rat barrel cortex and in the hippocampus under seizure conditions showing good agreement. Bursting phenomena provides relatively clear BOLD signals as long as the time between bursts is not too short. For short burst periods the BOLD signal remains constant even though the neuron is in a predominantly bursting mode. Simulation of CSD exhibits large negative BOLD signals. Visco-elastic effects of the capillary bed do not seem to have a large effect on the BOLD signal even for relatively high values of oxygen consumption. While the results of the model suggests that potassium ions released during neural activity could act as the main mediator in NVC, it suggests the possibility of other mechanisms that can coexist and increase blood flow such as the arachidonic acid to epoxyeicosatrienoic acid (EET) pathway. The comparison with experimental cerebral blood flow (CBF) data indicates the possible existence of multiple neural pathways influencing the vascular response. Initial negative BOLD signals occur for all simulations due to the rate at which the metabolic oxygen consumption occurs relative to the dilation of the perfusing cerebro-vasculature. However it is unclear as to whether these are normally seen clinically due to the size of the magnetic field. Experimental comparisons for different animal experiments may very well require variation in the model parameters. The complex integrated model is believed to be the first of its kind to simulate both NVC and the resulting BOLD signal.
1 July 2018
Cortical and subcortical responses to biological motion
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Dorita H.F. Chang, Hiroshi Ban, Yuji Ikegaya, Ichiro Fujita, Nikolaus F. Troje Using fMRI and multivariate analyses we sought to understand the neural representations of articulated body shape and local kinematics in biological motion. We show that in addition to a cortical network that includes areas identified previously for biological motion perception, including the posterior superior temporal sulcus, inferior frontal gyrus, and ventral body areas, the ventral lateral nucleus, a presumably motoric thalamic area is sensitive to both form and kinematic information in biological motion. Our findings suggest that biological motion perception is not achieved as an end-point of segregated cortical form and motion networks as often suggested, but instead involves earlier parts in the visual system including a subcortical network.
1 July 2018
Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Mark Chiew, Nadine N. Graedel, Karla L. Miller Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconstruction fidelity using external constraints, like an experimental design matrix, to partially orient the estimated fMRI temporal subspace. Combining these external constraints with low-rank constraints introduces a new image reconstruction model that is analogous to using a mixture of subspace-decomposition (PCA/ICA) and regression (GLM) models in fMRI analysis. We show that this approach improves fMRI reconstruction quality in simulations and experimental data, focusing on the model problem of detecting subtle 1-s latency shifts between brain regions in a block-design task-fMRI experiment. Successful latency discrimination is shown at acceleration factors up to R
1 July 2018
MIDAS: Regionally linear multivariate discriminative statistical mapping
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Erdem Varol, Aristeidis Sotiras, Christos Davatzikos Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data.

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1 July 2018
Protracted hippocampal development is associated with age-related improvements in memory during early childhood
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Tracy Riggins, Fengji Geng, Morgan Botdorf, Kelsey Canada, Lisa Cox, Gregory R. Hancock The hippocampus is a structure that is critical for memory. Previous studies have shown that age-related differences in specialization along the longitudinal axis of this structure (i.e., subregions) and within its internal circuitry (i.e., subfields) relate to age-related improvements in memory in school-age children and adults. However, the influence of age on hippocampal development and its relations with memory ability earlier in life remains under-investigated. This study examined effects of age and sex on hippocampal subregion (i.e., head, body, tail) and subfield (i.e., subiculum, CA1, CA2-4/DG) volumes, and their relations with memory, using a large sample of 4- to 8-year-old children. Results examining hippocampal subregions suggest influences of both age and sex on the hippocampal head during early childhood. Results examining subfields within hippocampal head suggest these age effects may arise from CA1, whereas sex differences may arise from subiculum and CA2-4/DG. Memory ability was not associated with hippocampal subregion volume but was associated with subfield volume. Specifically, within the hippocampal head, relations between memory and CA1 were moderated by age; in younger children bigger was better, whereas in older children smaller was superior. Within the hippocampal body, smaller CA1 and larger CA2-4/DG contributed to better memory performance across all ages. Together, these results shed light on hippocampal development during early childhood and support claims that the prolonged developmental trajectory of the hippocampus contributes to memory development early in life.
1 July 2018
Low Rank plus Sparse decomposition of ODFs for improved detection of group-level differences and variable correlations in white matter
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Steven H. Baete, Jingyun Chen, Ying-Chia Lin, Xiuyuan Wang, Ricardo Otazo, Fernando E. Boada A novel approach is presented for group statistical analysis of diffusion weighted MRI datasets through voxelwise Orientation Distribution Functions (ODF). Recent advances in MRI acquisition make it possible to use high quality diffusion weighted protocols (multi-shell, large number of gradient directions) for routine in vivo study of white matter architecture. The dimensionality of these data sets is however often reduced to simplify statistical analysis. While these approaches may detect large group differences, they do not fully capitalize on all acquired image volumes. Incorporation of all available diffusion information in the analysis however risks biasing the outcome by outliers. Here we propose a statistical analysis method operating on the ODF, either the diffusion ODF or fiber ODF. To avoid outlier bias and reliably detect voxelwise group differences and correlations with demographic or behavioral variables, we apply the Low-Rank plus Sparse ( L + S ) matrix decomposition on the voxelwise ODFs which separates the sparse individual variability in the sparse matrix S whilst recovering the essential ODF features in the low-rank matrix L. We demonstrate the performance of this ODF L + S approach by replicating the established negative association between global white matter integrity and physical obesity in the Human Connectome dataset. The volume of positive findings p < 0 . 01 , 227 cm 3 , agrees with and expands on the volume found by TBSS (17
1 July 2018
Persistent recruitment of somatosensory cortex during active maintenance of hand images in working memory
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): A. Galvez-Pol, B. Calvo-Merino, A. Capilla, B. Forster Working memory (WM) supports temporary maintenance of task-relevant information. This process is associated with persistent activity in the sensory cortex processing the information (e.g., visual stimuli activate visual cortex). However, we argue here that more multifaceted stimuli moderate this sensory-locked activity and recruit distinctive cortices. Specifically, perception of bodies recruits somatosensory cortex (SCx) beyond early visual areas (suggesting embodiment processes). Here we explore persistent activation in processing areas beyond the sensory cortex initially relevant to the modality of the stimuli. Using visual and somatosensory evoked-potentials in a visual WM task, we isolated different levels of visual and somatosensory involvement during encoding of body and non-body-related images. Persistent activity increased in SCx only when maintaining body images in WM, whereas visual/posterior regions' activity increased significantly when maintaining non-body images. Our results bridge WM and embodiment frameworks, supporting a dynamic WM process where the nature of the information summons specific processing resources.
1 July 2018
Neural and genetic determinants of creativity
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Zhaowen Liu, Jie Zhang, Xiaohua Xie, Edmund T. Rolls, Jiangzhou Sun, Kai Zhang, Zeyu Jiao, Qunlin Chen, Junying Zhang, Jiang Qiu, Jianfeng Feng Creative thinking plays a vital role in almost all aspects of human life. However, little is known about the neural and genetic mechanisms underlying creative thinking. Based on a cross-validation based predictive framework, we searched from the whole-brain connectome (34,716 functional connectivities) and whole genome data (309,996 SNPs) in two datasets (all collected by Southwest University, Chongqing) consisting of altogether 236 subjects, for a better understanding of the brain and genetic underpinning of creativity. Using the Torrance Tests of Creative Thinking score, we found that high figural creativity is mainly related to high functional connectivity between the executive control, attention, and memory retrieval networks (strong top-down effects); and to low functional connectivity between the default mode network, the ventral attention network, and the subcortical and primary sensory networks (weak bottom-up processing) in the first dataset (consisting of 138 subjects). High creativity also correlates significantly with mutations of genes coding for both excitatory and inhibitory neurotransmitters. Combining the brain connectome and the genomic data we can predict individuals' creativity scores with an accuracy of 78.4%, which is significantly better than prediction using single modality data (gene or functional connectivity), indicating the importance of combining multi-modality data. Our neuroimaging prediction model built upon the first dataset was cross-validated by a completely new dataset of 98 subjects (r
1 July 2018
Effects of resveratrol on memory performance, hippocampus connectivity and microstructure in older adults – A randomized controlled trial
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Sebastian Huhn, Frauke Beyer, Rui Zhang, Leonie Lampe, Jana Grothe, J
1 July 2018
A comparison of publicly available linear MRI stereotaxic registration techniques
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Mahsa Dadar, Vladimir S. Fonov, D. Louis Collins Introduction Linear registration to a standard space is one of the major steps in processing and analyzing magnetic resonance images (MRIs) of the brain. Here we present an overview of linear stereotaxic MRI registration and compare the performance of 5 publicly available and extensively used linear registration techniques in medical image analysis. Methods A set of 9693 T1-weighted MR images were obtained for testing from 4 datasets: ADNI, PREVENT-AD, PPMI, and HCP, two of which have multi-center and multi-scanner data and three of which have longitudinal data. Each individual native image was linearly registered to the MNI ICBM152 average template using five versions of MRITOTAL from MINC tools, FLIRT from FSL, two versions of Elastix, spm_affreg from SPM, and ANTs linear registration techniques. Quality control (QC) images were generated from the registered volumes and viewed by an expert rater to assess the quality of the registrations. The QC image contained 60 sub-images (20 of each of axial, sagittal, and coronal views at different levels throughout the brain) overlaid with contours of the ICBM152 template, enabling the expert rater to label the registration as acceptable or unacceptable. The performance of the registration techniques was then compared across different datasets. In addition, the effect of image noise, intensity non-uniformity, age, head size, and atrophy on the performance of the techniques was investigated by comparing differences between age, scaling factor, ventricle volume, brain volume, and white matter hyperintensity (WMH) volumes between passed and failed cases for each method. Results The average registration failure rate among all datasets was 27.41%, 27.14%, 12.74%, 13.03%, 0.44% for the five versions of MRITOTAL techniques, 8.87% for ANTs, 11.11% for FSL, 12.35% for Elastix Affine, 24.40% for Elastix Similarity, and 30.66% for SPM. There were significant effects of signal to noise ratio, image intensity non-uniformity estimates, as well as age, head size, and atrophy related changes between passed and failed registrations. Conclusion Our experiments show that the Revised BestLinReg had the best performance among the evaluated registration techniques while all techniques performed worse for images with higher levels of noise and non-uniformity as well as atrophy related changes.
1 July 2018
Unilateral deep brain stimulation suppresses alpha and beta oscillations in sensorimotor cortices
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Omid Abbasi, Jan Hirschmann, Lena Storzer, Tolga Esat
1 July 2018
Early cross-modal interactions underlie the audiovisual bounce-inducing effect
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Song Zhao, Yajie Wang, Hongyuan Xu, Chengzhi Feng, Wenfeng Feng Two identical visual disks moving towards one another on a two-dimensional display can be perceived as either “streaming through” or “bouncing off” each other after their coincidence/overlapping. A brief sound presented at the moment of the coincidence of the disks could strikingly bias the percept towards bouncing, which was termed the audiovisual bounce-inducing effect (ABE). Although the ABE has been studied intensively since its discovery, the debate about its origin is still unresolved so far. The present study used event-related potential (ERP) recordings to investigate whether or not early neural activities associated with cross-modal interactions play a role on the ABE. The results showed that the fronto-central P2 component
1 July 2018
Bayesian Optimisation of Large-Scale Biophysical Networks
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): J. Hadida, S.N. Sotiropoulos, R.G. Abeysuriya, M.W. Woolrich, S. Jbabdi The relationship between structure and function in the human brain is well established, but not yet well characterised. Large-scale biophysical models allow us to investigate this relationship, by leveraging structural information (e.g. derived from diffusion tractography) in order to couple dynamical models of local neuronal activity into networks of interacting regions distributed across the cortex. In practice however, these models are difficult to parametrise, and their simulation is often delicate and computationally expensive. This undermines the experimental aspect of scientific modelling, and stands in the way of comparing different parametrisations, network architectures, or models in general, with confidence. Here, we advocate the use of Bayesian optimisation for assessing the capabilities of biophysical network models, given a set of desired properties (e.g. band-specific functional connectivity); and in turn the use of this assessment as a principled basis for incremental modelling and model comparison. We adapt an optimisation method designed to cope with costly, high-dimensional, non-convex problems, and demonstrate its use and effectiveness. Using five parameters controlling key aspects of our model, we find that this method is able to converge to regions of high functional similarity with real MEG data, with very few samples given the number of parameters, without getting stuck in local extrema, and while building and exploiting a map of uncertainty defined smoothly across the parameter space. We compare the results obtained using different methods of structural connectivity estimation from diffusion tractography, and find that one method leads to better simulations.

Anticipatory prefrontal cortex activity underlies stress-induced changes in Pavlovian fear conditioning
Publication date: 1 July 2018
Source:NeuroImage, Volume 174 Author(s): Adam M. Goodman, Nathaniel G. Harnett, Muriah D. Wheelock, Danielle R. Hurst, Tyler R. Orem, Ethan W. Gossett, Chelsea A. Dunaway, Sylvie Mrug, David C. Knight Excessive stress exposure often leads to emotional dysfunction, characterized by disruptions in healthy emotional learning, expression, and regulation processes. A prefrontal cortex (PFC)-amygdala circuit appears to underlie these important emotional processes. However, limited human neuroimaging research has investigated whether these brain regions underlie the altered emotional function that develops with stress. Therefore, the present study used functional magnetic resonance imaging (fMRI) to investigate stress-induced changes in PFC-amygdala function during Pavlovian fear conditioning. Participants completed a variant of the Montreal Imaging Stress Task (MIST) followed (25
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