Journal Sciences News
The Ocular Surface
1 October 2018
Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project
Publication date: 1 October 2018
Source:NeuroImage, Volume 179 Author(s): Jelena Bozek, Antonios Makropoulos, Andreas Schuh, Sean Fitzgibbon, Robert Wright, Matthew F. Glasser, Timothy S. Coalson, Jonathan O'Muircheartaigh, Jana Hutter, Anthony N. Price, Lucilio Cordero-Grande, Rui Pedro A.G. Teixeira, Emer Hughes, Nora Tusor, Kelly Pegoretti Baruteau, Mary A. Rutherford, A. David Edwards, Joseph V. Hajnal, Stephen M. Smith, Daniel Rueckert, Mark Jenkinson, Emma C. Robinson We propose a method for constructing a spatio-temporal cortical surface atlas of neonatal brains aged between 36 and 44 weeks of post-menstrual age (PMA) at the time of scan. The data were acquired as part of the Developing Human Connectome Project (dHCP), and the constructed surface atlases are publicly available. The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using cortical folding for driving the alignment. Templates have been generated for the anatomical cortical surface and for the cortical feature maps: sulcal depth, curvature, thickness, T1w/T2w myelin maps and cortical regions. To achieve this, cortical surfaces from 270 infants were first projected onto the sphere. Templates were then generated in two stages: first, a reference space was initialised via affine alignment to a group average adult template. Following this, templates were iteratively refined through repeated alignment of individuals to the template space until the variability of the average feature sets converged. Finally, bias towards the adult reference was removed by applying the inverse of the average affine transformations on the template and de-drifting the template. We used temporal adaptive kernel regression to produce age-dependant atlases for 9 weeks (3644 weeks PMA). The generated templates capture expected patterns of cortical development including an increase in gyrification as well as an increase in thickness and T1w/T2w myelination with increasing age.
1 October 2018
Long-range temporal correlations in the brain distinguish conscious wakefulness from induced unconsciousness
Publication date: 1 October 2018
Source:NeuroImage, Volume 179 Author(s): Thomas Thiery, Tarek Lajnef, Etienne Combrisson, Arthur Dehgan, Pierre Rainville, George A. Mashour, Stefanie Blain-Moraes, Karim Jerbi Rhythmic neuronal synchronization across large-scale networks is thought to play a key role in the regulation of conscious states. Changes in neuronal oscillation amplitude across states of consciousness have been widely reported, but little is known about possible changes in the temporal dynamics of these oscillations. The temporal structure of brain oscillations may provide novel insights into the neural mechanisms underlying consciousness. To address this question, we examined long-range temporal correlations (LRTC) of EEG oscillation amplitudes recorded during both wakefulness and anesthetic-induced unconsciousness. Importantly, the time-varying EEG oscillation envelopes were assessed over the course of a sevoflurane sedation protocol during which the participants alternated between states of consciousness and unconsciousness. Both spectral power and LRTC in oscillation amplitude were computed across multiple frequency bands. State-dependent differences in these features were assessed using non-parametric tests and supervised machine learning. We found that periods of unconsciousness were associated with increases in LRTC in beta (1530Hz) amplitude over frontocentral channels and with a suppression of alpha (813Hz) amplitude over occipitoparietal electrodes. Moreover, classifiers trained to predict states of consciousness on single epochs demonstrated that the combination of beta LRTC with alpha amplitude provided the highest classification accuracy (above 80%). These results suggest that loss of consciousness is accompanied by an augmentation of temporal persistence in neuronal oscillation amplitude, which may reflect an increase in regularity and a decrease in network repertoire compared to the brain's activity during resting-state consciousness.
1 October 2018
Multimodal imaging-based therapeutic fingerprints for optimizing personalized interventions: Application to neurodegeneration
Publication date: 1 October 2018
Source:NeuroImage, Volume 179 Author(s): Yasser Iturria-Medina, F
1 October 2018
Estimating the functional dimensionality of neural representations
Publication date: 1 October 2018
Source:NeuroImage, Volume 179 Author(s): Christiane Ahlheim, Bradley C. Love Recent advances in multivariate fMRI analysis stress the importance of information inherent to voxel patterns. Key to interpreting these patterns is estimating the underlying dimensionality of neural representations. Dimensions may correspond to psychological dimensions, such as length and orientation, or involve other coding schemes. Unfortunately, the noise structure of fMRI data inflates dimensionality estimates and thus makes it difficult to assess the true underlying dimensionality of a pattern. To address this challenge, we developed a novel approach to identify brain regions that carry reliable task-modulated signal and to derive an estimate of the signal's functional dimensionality. We combined singular value decomposition with cross-validation to find the best low-dimensional projection of a pattern of voxel-responses at a single-subject level. Goodness of the low-dimensional reconstruction is measured as Pearson correlation with a test set, which allows to test for significance of the low-dimensional reconstruction across participants. Using hierarchical Bayesian modeling, we derive the best estimate and associated uncertainty of underlying dimensionality across participants. We validated our method on simulated data of varying underlying dimensionality, showing that recovered dimensionalities match closely true dimensionalities. We then applied our method to three published fMRI data sets all involving processing of visual stimuli. The results highlight three possible applications of estimating the functional dimensionality of neural data. Firstly, it can aid evaluation of model-based analyses by revealing which areas express reliable, task-modulated signal that could be missed by specific models. Secondly, it can reveal functional differences across brain regions. Thirdly, knowing the functional dimensionality allows assessing task-related differences in the complexity of neural patterns.
1 October 2018
Added value of money on motor performance feedback: Increased left central beta-band power for rewards and fronto-central theta-band power for punishments
Publication date: 1 October 2018
Source:NeuroImage, Volume 179 Author(s): Rapha
1 October 2018
Cortical processing of breathing perceptions in the athletic brain
Publication date: 1 October 2018
Source:NeuroImage, Volume 179 Author(s): Olivia K. Faull, Pete J. Cox, Kyle T.S. Pattinson Athletes regularly endure large increases in ventilation and accompanying perceptions of breathlessness. Whilst breathing perceptions often correlate poorly with objective measures of lung function in both healthy and clinical populations, we have previously demonstrated closer matching between subjective breathlessness and changes in ventilation in endurance athletes, suggesting that athletes may be more accurate during respiratory interoception. To better understand the link between exercise and breathlessness, we sought to identify the mechanisms by which the brain processing of respiratory perception might be optimised in athletes. Twenty endurance athletes and twenty sedentary controls underwent 7
1 October 2018
Spatial frequency supports the emergence of categorical representations in visual cortex during natural scene perception
Publication date: 1 October 2018
Source:NeuroImage, Volume 179 Author(s): Diana C. Dima, Gavin Perry, Krish D. Singh In navigating our environment, we rapidly process and extract meaning from visual cues. However, the relationship between visual features and categorical representations in natural scene perception is still not well understood. Here, we used natural scene stimuli from different categories and filtered at different spatial frequencies to address this question in a passive viewing paradigm. Using representational similarity analysis (RSA) and cross-decoding of magnetoencephalography (MEG) data, we show that categorical representations emerge in human visual cortex at
September 2018
The influence of brain iron and myelin on magnetic susceptibility and effective transverse relaxation - A biochemical and histological validation study
Publication date: 1 October 2018
Source:NeuroImage, Volume 179 Author(s): Simon Hametner, Verena Endmayr, Andreas Deistung, Pilar Palmrich, Max Prihoda, Evelin Haimburger, Christian Menard, Xiang Feng, Thomas Haider, Marianne Leisser, Ulrike K
September 2018
Feasibility of imaging evoked activity throughout the rat brain using electrical impedance tomography
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Mayo Faulkner, Sana Hannan, Kirill Aristovich, James Avery, David Holder Electrical Impedance Tomography (EIT) is an emerging technique which has been used to image evoked activity during whisker displacement in the cortex of an anaesthetised rat with a spatiotemporal resolution of 200
September 2018
Disentangling reward anticipation with simultaneous pupillometry / fMRI
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Max Schneider, Laura Leuchs, Michael Czisch, Philipp G. S
September 2018
How do children fall asleep? A high-density EEG study of slow waves in the transition from wake to sleep
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Mathilde Spiess, Giulio Bernardi, Salome Kurth, Maya Ringli, Flavia M. Wehrle, Oskar G. Jenni, Reto Huber, Francesca Siclari Introduction Slow waves, the hallmarks of non-rapid eye-movement (NREM) sleep, are thought to reflect maturational changes that occur in the cerebral cortex throughout childhood and adolescence. Recent work in adults has revealed evidence for two distinct synchronization processes involved in the generation of slow waves, which sequentially come into play in the transition to sleep. In order to understand how these two processes are affected by developmental changes, we compared slow waves between children and young adults in the falling asleep period. Methods The sleep onset period (starting 30s before end of alpha activity and ending at the first slow wave sequence) was extracted from 72 sleep onset high-density EEG recordings (128 electrodes) of 49 healthy subjects (age 825). Using an automatic slow wave detection algorithm, the number, amplitude and slope of slow waves were analyzed and compared between children (age 811) and young adults (age 2025). Results Slow wave number and amplitude increased linearly in the falling asleep period in children, while in young adults, isolated high-amplitude slow waves (type I) dominated initially and numerous smaller slow waves (type II) with progressively increasing amplitude occurred later. Compared to young adults, children displayed faster increases in slow wave amplitude and number across the falling asleep period in central and posterior brain regions, respectively, and also showed larger slow waves during wakefulness immediately prior to sleep. Conclusions Children do not display the two temporally dissociated slow wave synchronization processes in the falling asleep period observed in adults, suggesting that maturational factors underlie the temporal segregation of these two processes. Our findings provide novel perspectives for studying how sleep-related behaviors and dreaming differ between children and adults.
September 2018
A task-invariant cognitive reserve network
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Yaakov Stern, Yunglin Gazes, Qolomreza Razlighi, Jason Steffener, Christian Habeck The concept of cognitive reserve (CR) can explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or disease-related brain changes. Epidemiologic evidence indicates that CR helps maintain performance in the face of pathology across multiple cognitive domains. We therefore tried to identify a single, task-invariant CR network that is active during the performance of many disparate tasks. In imaging data acquired from 255 individuals age 2080 while performing 12 different cognitive tasks, we used an iterative approach to derive a multivariate network that was expressed during the performance of all tasks, and whose degree of expression correlated with IQ, a proxy for CR. When applied to held out data or forward applied to fMRI data from an entirely different activation task, network expression correlated with IQ. Expression of the CR pattern accounted for additional variance in fluid reasoning performance over and above the influence of cortical thickness, and also moderated between cortical thickness and reasoning performance, consistent with the behavior of a CR network. The identification of a task-invariant CR network supports the idea that life experiences may result in brain processing differences that might provide reserve against age- or disease-related changes across multiple tasks.
September 2018
Anticipatory neural dynamics of spatial-temporal orienting of attention in younger and older adults
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Simone G. Heideman, Gustavo Rohenkohl, Joshua J. Chauvin, Clare E. Palmer, Freek van Ede, Anna C. Nobre Spatial and temporal expectations act synergistically to facilitate visual perception. In the current study, we sought to investigate the anticipatory oscillatory markers of combined spatial-temporal orienting and to test whether these decline with ageing. We examined anticipatory neural dynamics associated with joint spatial-temporal orienting of attention using magnetoencephalography (MEG) in both younger and older adults. Participants performed a cued covert spatial-temporal orienting task requiring the discrimination of a visual target. Cues indicated both where and when targets would appear. In both age groups, valid spatial-temporal cues significantly enhanced perceptual sensitivity and reduced reaction times. In the MEG data, the main effect of spatial orienting was the lateralised anticipatory modulation of posterior alpha and beta oscillations. In contrast to previous reports, this modulation was not attenuated in older adults; instead it was even more pronounced. The main effect of temporal orienting was a bilateral suppression of posterior alpha and beta oscillations. This effect was restricted to younger adults. Our results also revealed a striking interaction between anticipatory spatial and temporal orienting in the gamma-band (6075
September 2018
Population-averaged atlas of the macroscale human structural connectome and its network topology
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Fang-Cheng Yeh, Sandip Panesar, David Fernandes, Antonio Meola, Masanori Yoshino, Juan C. Fernandez-Miranda, Jean M. Vettel, Timothy Verstynen A comprehensive map of the structural connectome in the human brain has been a coveted resource for understanding macroscopic brain networks. Here we report an expert-vetted, population-averaged atlas of the structural connectome derived from diffusion MRI data (N
September 2018
The distribution of pain activity across the human neonatal brain is sex dependent
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Madeleine Verriotis, Laura Jones, Kimberley Whitehead, Maria Laudiano-Dray, Ismini Panayotidis, Hemani Patel, Judith Meek, Lorenzo Fabrizi, Maria Fitzgerald In adults, there are differences between male and female structural and functional brain connectivity, specifically for those regions involved in pain processing. This may partly explain the observed sex differences in pain sensitivity, tolerance, and inhibitory control, and in the development of chronic pain. However, it is not known if these differences exist from birth. Cortical activity in response to a painful stimulus can be observed in the human neonatal brain, but this nociceptive activity continues to develop in the postnatal period and is qualitatively different from that of adults, partly due to the considerable cortical maturation during this time. This research aimed to investigate the effects of sex and prematurity on the magnitude and spatial distribution pattern of the long-latency nociceptive event-related potential (nERP) using electroencephalography (EEG). We measured the cortical response time-locked to a clinically required heel lance in 81 neonates born between 29 and 42 weeks gestational age (median postnatal age 4 days). The results show that heel lance results in a spatially widespread nERP response in the majority of newborns. Importantly, a widespread pattern is significantly more likely to occur in females, irrespective of gestational age at birth. This effect is not observed for the short latency somatosensory waveform in the same infants, indicating that it is selective for the nociceptive component of the response. These results suggest the early onset of a greater anatomical and functional connectivity reported in the adult female brain, and indicate the presence of pain-related sex differences from birth.
September 2018
Heritability estimates of cortical anatomy: The influence and reliability of different estimation strategies
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Sejal Patel, Raihaan Patel, Min Tae M. Park, Mario Masellis, Jo Knight, M. Mallar Chakravarty Twin study designs have been previously used to investigate the heritability of neuroanatomical measures, such as regional cortical volumes. Volume can be fractionated into surface area and cortical thickness, where both measures are considered to have independent genetic and environmental bases. Region of interest (ROI) and vertex-wise approaches have been used to calculate heritability of cortical thickness and surface area in twin studies. In our study, we estimate heritability using the Human Connectome Project magnetic resonance imaging dataset composed of healthy young twin and non-twin siblings (mean age of 29, sample size of 757). Both ROI and vertex-wise methods were used to compare regional heritability of cortical thickness and surface area. Heritability estimates were controlled for age, sex, and total ipsilateral surface area or mean cortical thickness. In both approaches, heritability estimates of cortical thickness and surface area were lower when accounting for average ipsilateral cortical thickness and total surface area respectively. When comparing both approaches at a regional level, the vertex-wise approach showed higher surface area and lower cortical thickness heritability estimates compared to the ROI approach. The calcarine fissure had the highest surface area heritability estimate (ROI: 44%, vertex-wise: 50%) and posterior cingulate gyrus had the highest cortical thickness heritability (ROI: 50%, vertex-wise 40%). We also observed that limitations in image processing and variability in spatial averaging errors based on regional size may make obtaining true estimates of cortical thickness and surface area challenging in smaller regions. It is important to identify which approach is best suited to estimate heritability based on the research hypothesis and the size of the regions being investigated.
September 2018
High-gamma activity in the human hippocampus and parahippocampus during inter-trial rest periods of a virtual navigation task
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Yi Pu, Brian R. Cornwell, Douglas Cheyne, Blake W. Johnson In rodents, hippocampal cell assemblies formed during learning of a navigation task are observed to re-emerge during resting (offline) periods, accompanied by high-frequency oscillations (HFOs). This phenomenon is believed to reflect mechanisms for strengthening newly-formed memory traces. Using magnetoencephalography recordings and a beamforming source location algorithm (synthetic aperture magnetometry), we investigated high-gamma (80140
September 2018
Cortical fibers orientation mapping using in-vivo whole brain 7
September 2018
Quantifying fast optical signal and event-related potential relationships during a visual oddball task
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Nicole Proulx, Ali-Akbar Samadani, Tom Chau Event-related potentials (ERPs) have previously been used to confirm the existence of the fast optical signal (FOS) but validation methods have mainly been limited to exploring the temporal correspondence of FOS peaks to those of ERPs. The purpose of this study was to systematically quantify the relationship between FOS and ERP responses to a visual oddball task in both time and frequency domains. Near-infrared spectroscopy (NIRS) and electroencephalography (EEG) sensors were co-located over the prefrontal cortex while participants performed a visual oddball task. Fifteen participants completed 2 data collection sessions each, where they were instructed to keep a mental count of oddball images. The oddball condition produced a positive ERP at 200
September 2018
Subregional volumes of the hippocampus in relation to cognitive function and risk of dementia
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Tavia E. Evans, Hieab H.H. Adams, Silvan Licher, Frank J. Wolters, Aad van der Lugt, M. Kamran Ikram, Michael J. O'Sullivan, Meike W. Vernooij, M. Arfan Ikram Background Total hippocampal volume has been consistently linked to cognitive function and dementia. Yet, given its complex and parcellated internal structure, the role of subregions of the hippocampus in cognition and risk of dementia remains relatively underexplored. We studied subregions of the hippocampus in a large population-based cohort to further understand their role in cognitive impairment and dementia risk. Methods We studied 5035 dementia- and stroke-free persons from the Rotterdam Study, aged over 45 years. All participants underwent magnetic resonance imaging (1.5
September 2018
Multivoxel pattern similarity suggests the integration of temporal duration in hippocampal event sequence representations
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Sathesan Thavabalasingam, Edward B. O'Neil, Andy C.H. Lee Recent rodent work suggests the hippocampus may provide a temporal representation of event sequences, in which the order of events and the interval durations between them are encoded. There is, however, limited human evidence for the latter, in particular whether the hippocampus processes duration information pertaining to the passage of time rather than qualitative or quantitative changes in event content. We scanned participants while they made match-mismatch judgements on each trial between a study sequence of events and a subsequent test sequence. Participants explicitly remembered event order or interval duration information (Experiment 1), or monitored order only, with duration being manipulated implicitly (Experiment 2). Hippocampal study-test pattern similarity was significantly reduced by changes to order or duration in mismatch trials, even when duration was processed implicitly. Our findings suggest the human hippocampus processes short intervals within sequences and support the idea that duration information is integrated into hippocampal mnemonic representations.
September 2018
Brain network segregation and integration during an epoch-related working memory fMRI experiment
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Peter Fransson, Bj
September 2018
Model-free and model-based reward prediction errors in EEG
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Thomas D. Sambrook, Ben Hardwick, Andy J. Wills, Jeremy Goslin Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain.
September 2018
The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): B.B. Bankson, M.N. Hebart, I.I.A. Groen, C.I. Baker Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categorical or conceptual representations. Here, we aimed to estimate a lower temporal bound for the emergence of conceptual representations by defining two criteria that characterize such representations: 1) conceptual object representations should generalize across different exemplars of the same object, and 2) these representations should reflect high-level behavioral judgments. To test these criteria, we compared magnetoencephalography (MEG) recordings between two groups of participants (n
September 2018
A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Rafael Ceschin, Alexandria Zahner, William Reynolds, Jenna Gaesser, Giulio Zuccoli, Cecilia W. Lo, Vanathi Gopalakrishnan, Ashok Panigrahy Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance. Volumetric information alone is insufficient for diagnosis. In this study, we developed a computational framework for the automated classification of brain dysmaturation from neonatal MRI, by combining a specific deep neural network implementation with neonatal structural brain segmentation as a method for both clinical pattern recognition and data-driven inference into the underlying structural morphology. We implemented three-dimensional convolution neural networks (3D-CNNs) to specifically classify dysplastic cerebelli, a subset of surface-based subcortical brain dysmaturation, in term infants born with congenital heart disease. We obtained a 0.985
September 2018
Individualized tractography-based parcellation of the globus pallidus pars interna using 7T MRI in movement disorder patients prior to DBS surgery
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): R
September 2018
Mutual connectivity analysis of resting-state functional MRI data with local models
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Adora M. DSouza, Anas Z. Abidin, Udaysankar Chockanathan, Giovanni Schifitto, Axel Wism
September 2018
Handedness-dependent functional organizational patterns within the bilateral vestibular cortical network revealed by fMRI connectivity based parcellation
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): V. Kirsch, R. Boegle, D. Keeser, E. Kierig, B. Ertl-Wagner, T. Brandt, M. Dieterich Current evidence points towards a vestibular cortex that involves a multisensory bilateral temporo-parietal-insular network with a handedness-dependent hemispheric lateralization. This study aimed to identify handedness-dependent organizational patterns of (lateralized and non-lateralized) functional subunits within the human vestibular cortex areas. 60 healthy volunteers (30 left-handed and 30 right-handed) were examined on a 3T MR scanner using resting state functional MRI (fMRI). The data was analyzed in four major steps using a functional connectivity based parcellation (fCBP) approach: (1) independent component analysis (ICA) on a whole brain level to identify different resting state networks (RSN); (2) creation of a vestibular informed mask from four whole brain ICs that included reference coordinates of the vestibular network extracted from meta-analyses of vestibular neuroimaging experiments; (3) Re-ICA confined to the vestibular informed mask; (4) cross-correlation of the activated voxels within the vestibular subunits (parcels) to each other (P-to-P) and to the whole-brain RSN (P-to-RSN). This approach disclosed handedness-dependency, inter-hemispheric symmetry, the scale of connectedness to major whole brain RSN and the grade of spatial overlap of voxels within parcels (common/unique) as meaningful discriminatory organizational categories within the vestibular cortex areas. This network consists of multiple inter-hemisphere symmetric (not lateralized), well-connected (many RSN-assignments) multisensory areas (or hubs; e.g., superior temporal gyrus, temporo-parietal intersection) organized around an asymmetric (lateralized, dominant) and functionally more specialized (few RSN-assignments) core region in the parieto-insular cortex. The latter is in the middle, posterior and inferior insula. In conclusion, the bilateral cortical vestibular network contains not only a handedness-dependent lateralized central region concentrated in the right hemisphere in right-handers and left hemisphere in left-handers, but also surrounding inter-hemisphere symmetric multisensory vestibular areas that seem to be functionally influenced by their neighboring sensory systems (e.g., temporo-parietal intersection by the visual system). One may speculate that the development of an asymmetrical organized vestibular subsystem reflects a more recent phylogenetic evolution of various multisensory vestibular functions. The right hemispheric dominance of spatial orientation and its disorders, spatial neglect and pusher syndrome, may serve as examples.
September 2018
Extracting orthogonal subject- and condition-specific signatures from fMRI data using whole-brain effective connectivity
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Vicente Pallar
September 2018
Likelihood estimation of drug occupancy for brain PET studies
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Martin Schain, Francesca Zanderigo, R. Todd Ogden Neuroimaging with PET is unique in its capability to measure in vivo the occupancy of a drug. The occupancy is typically obtained by conducting PET measurements before and after administration of the drug. For radioligands for which no reference region exists, however, the only established procedure to estimate the occupancy from these data is via linear regression analysis, forming the basis for the so-called Lassen plot. There are several reasons why simple linear regression analysis is not ideal for analyzing these data, including regression attenuation and correlated errors. Here, we propose the use of Likelihood Estimation of Occupancy (LEO) in such a situation. Similar to the Lassen plot, LEO uses the total distribution volume estimates at baseline and at block condition as input, but estimates the non-displaceable distribution volume (V ND) and fractional occupancy (
September 2018
Is the encoding of Reward Prediction Error reliable during development?
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Hanna Keren, Gang Chen, Brenda Benson, Monique Ernst, Ellen Leibenluft, Nathan A. Fox, Daniel S. Pine, Argyris Stringaris Reward Prediction Errors (RPEs), defined as the difference between the expected and received outcomes, are integral to reinforcement learning models and play an important role in development and psychopathology. In humans, RPE encoding can be estimated using fMRI recordings, however, a basic measurement property of RPE signals, their test-retest reliability across different time scales, remains an open question. In this paper, we examine the 3-month and 3-year reliability of RPE encoding in youth (mean age at baseline
September 2018
Dorsal and ventral cortices are coupled by cross-frequency interactions during working memory
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Tzvetan Popov, Ole Jensen, Jan-Mathijs Schoffelen Oscillatory activity in the alpha and gamma bands is considered key in shaping functional brain architecture. Power increases in the high-frequency gamma band are typically reported in parallel to decreases in the low-frequency alpha band. However, their functional significance and in particular their interactions are not well understood. The present study shows that, in the context of an N-back working memory task, alpha power decreases in the dorsal visual stream are related to gamma power increases in early visual areas. Granger causality analysis revealed directed interregional interactions from dorsal to ventral stream areas, in accordance with task demands. Present results reveal a robust, behaviorally relevant, and architectonically decisive power-to-power relationship between alpha and gamma activity. This relationship suggests that anatomically distant power fluctuations in oscillatory activity can link cerebral network dynamics on trial-by-trial basis during cognitive operations such as working memory.
September 2018
Concentric radiofrequency arrays to increase the statistical power of resting-state maps in monkeys
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Kyle M. Gilbert, David J. Schaeffer, Peter Zeman, J
September 2018
Investigating common coding of observed and executed actions in the monkey brain using cross-modal multi-variate fMRI classification
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Prosper Agbesi Fiave, Saloni Sharma, Jan Jastorff, Koen Nelissen Mirror neurons are generally described as a neural substrate hosting shared representations of actions, by simulating or mirroring the actions of others onto the observer's own motor system. Since single neuron recordings are rarely feasible in humans, it has been argued that cross-modal multi-variate pattern analysis (MVPA) of non-invasive fMRI data is a suitable technique to investigate common coding of observed and executed actions, allowing researchers to infer the presence of mirror neurons in the human brain. In an effort to close the gap between monkey electrophysiology and human fMRI data with respect to the mirror neuron system, here we tested this proposal for the first time in the monkey. Rhesus monkeys either performed reach-and-grasp or reach-and-touch motor acts with their right hand in the dark or observed videos of human actors performing similar motor acts. Unimodal decoding showed that both executed or observed motor acts could be decoded from numerous brain regions. Specific portions of rostral parietal, premotor and motor cortices, previously shown to house mirror neurons, in addition to somatosensory regions, yielded significant asymmetric action-specific cross-modal decoding. These results validate the use of cross-modal multi-variate fMRI analyses to probe the representations of own and others' actions in the primate brain and support the proposed mapping of others' actions onto the observer's own motor cortices.
September 2018
A comparison of three fiber tract delineation methods and their impact on white matter analysis
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Valerie J. Sydnor, Ana Mar
September 2018
What if? Neural activity underlying semantic and episodic counterfactual thinking
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Natasha Parikh, Luka Ruzic, Gregory W. Stewart, R. Nathan Spreng, Felipe De Brigard Counterfactual thinking (CFT) is the process of mentally simulating alternative versions of known facts. In the past decade, cognitive neuroscientists have begun to uncover the neural underpinnings of CFT, particularly episodic CFT (eCFT), which activates regions in the default network (DN) also activated by episodic memory (eM) recall. However, the engagement of DN regions is different for distinct kinds of eCFT. More plausible counterfactuals and counterfactuals about oneself show stronger activity in DN regions compared to implausible and other- or object-focused counterfactuals. The current study sought to identify a source for this difference in DN activity. Specifically, self-focused counterfactuals may also be more plausible, suggesting that DN core regions are sensitive to the plausibility of a simulation. On the other hand, plausible and self-focused counterfactuals may involve more episodic information than implausible and other-focused counterfactuals, which would imply DN sensitivity to episodic information. In the current study, we compared episodic and semantic counterfactuals generated to be plausible or implausible against episodic and semantic memory reactivation using fMRI. Taking multivariate and univariate approaches, we found that the DN is engaged more during episodic simulations, including eM and all eCFT, than during semantic simulations. Semantic simulations engaged more inferior temporal and lateral occipital regions. The only region that showed strong plausibility effects was the hippocampus, which was significantly engaged for implausible CFT but not for plausible CFT, suggestive of binding more disparate information. Consequences of these findings for the cognitive neuroscience of mental simulation are discussed.
September 2018
Multidimensional co-segmentation of longitudinal brain MRI ensembles in the presence of a neurodegenerative process
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Shiri Gordon, Irit Dolgopyat, Itamar Kahn, Tammy Riklin Raviv MRI Segmentation of a pathological brain poses a significant challenge, as the available anatomical priors that provide top-down information to aid segmentation are inadequate in the presence of abnormalities. This problem is further complicated for longitudinal data capturing impaired brain development or neurodegenerative conditions, since the dynamic of brain atrophies has to be considered as well. For these cases, the absence of compatible annotated training examples renders the commonly used multi-atlas or machine-learning approaches impractical. We present a novel segmentation approach that accounts for the lack of labeled data via multi-region multi-subject co-segmentation (MMCoSeg) of longitudinal MRI sequences. The underlying, unknown anatomy is learned throughout an iterative process, in which the segmentation of a region is supported both by the segmentation of the neighboring regions, which share common boundaries, and by the segmentation of corresponding regions, in the other jointly segmented images. A 4D multi-region atlas that models the spatio-temporal deformations and can be adapted to different subjects undergoing similar degeneration processes is reconstructed concurrently. An inducible mouse model of p25 accumulation (the CK-p25 mouse) that displays key pathological hallmarks of Alzheimer disease (AD) is used as a gold-standard to test the proposed algorithm by providing a conditional control of rapid neurodegeneration. Applying the MMCoSeg to a cohort of CK-p25 mice and littermate controls yields promising segmentation results that demonstrate high compatibility with expertise manual annotations. An extensive comparative analysis with respect to current well-established, atlas-based segmentation methods highlights the advantage of the proposed approach, which provides accurate segmentation of longitudinal brain MRIs in pathological conditions, where only very few annotated examples are available.

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September 2018
Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Giles L. Colclough, Mark W. Woolrich, Samuel J. Harrison, Pedro A. Rojas L
September 2018
Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Yuan Wang, Yao Wang, Yvonne W. Lui Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a DCM study, one needs to represent the experimental stimuli as a compact vector-valued function of time, which is hard in complex tasks such as book reading and natural movie watching. Deep learning provides the state-of-the-art signal representation solution, encoding complex signals into compact dense vectors while preserving the essence of the original signals. There is growing interest in using Recurrent Neural Networks (RNNs), a major family of deep learning techniques, in fMRI modeling. However, the generic RNNs used in existing studies work as black boxes, making the interpretation of results in a neuroscience context difficult and obscure. In this paper, we propose a new biophysically interpretable RNN built on DCM, DCM-RNN. We generalize the vanilla RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. DCM-RNN uses back propagation for parameter estimation. We believe DCM-RNN is a promising tool for neuroscience. It can fit seamlessly into classical DCM studies. We demonstrate face validity of DCM-RNN in two principal applications of DCM: causal brain architecture hypotheses testing and effective connectivity estimation. We also demonstrate construct validity of DCM-RNN in an attention-visual experiment. Moreover, DCM-RNN enables end-to-end training of DCM and representation learning deep neural networks, extending DCM studies to complex tasks.
September 2018
Signal compartments in ultra-high field multi-echo gradient echo MRI reflect underlying tissue microstructure in the brain
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Shrinath Kadamangudi, David Reutens, Surabhi Sood, Viktor Vegh Gradient recalled echo magnetic resonance imaging (GRE-MRI) at ultra-high field holds great promise for new contrast mechanisms and delineation of putative tissue compartments that contribute to the multi-echo GRE-MRI signal may aid structural characterization. Several studies have adopted the three water-pool compartment model to study white matter brain regions, associating individual compartments with myelin, axonal and extracellular water. However, the number and identifiability of GRE-MRI signal compartments has not been fully explored. We undertook this task for human brain imaging data. Multiple echo time GRE-MRI data were acquired in five healthy participants, specific anatomical structures were segmented in each dataset (substantia nigra, caudate, insula, putamen, thalamus, fornix, internal capsule, corpus callosum and cerebrospinal fluid), and the signal fitted with models comprising one to six signal compartments using a complex-valued plane wave formulation. Information criteria and cluster analysis methods were used to ascertain the number of distinct compartments within the signal from each structure and to determine their respective frequency shifts. We identified five principal signal compartments with different relative contributions to each structure's signal. Voxel-based maps of the volume fraction of each of these compartments were generated and demonstrated spatial correlation with brain anatomy.
September 2018
Focused ultrasound induced opening of the blood-brain barrier disrupts inter-hemispheric resting state functional connectivity in the rat brain
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Nick Todd, Yongzhi Zhang, Michael Arcaro, Lino Becerra, David Borsook, Margaret Livingstone, Nathan McDannold Focused ultrasound (FUS) is a technology capable of delivering therapeutic levels of energy through the intact skull to a tightly localized brain region. Combining the FUS pressure wave with intravenously injected microbubbles creates forces on blood vessel walls that open the blood-brain barrier (BBB). This noninvasive and localized opening of the BBB allows for targeted delivery of pharmacological agents into the brain for use in therapeutic development. It is possible to use FUS power levels such that the BBB is opened without damaging local tissues. However, open questions remain related to the effects that FUS-induced BBB opening has on brain function including local physiology and vascular hemodynamics. We evaluated the effects that FUS-induced BBB opening has on resting state functional magnetic resonance imaging (rs-fMRI) metrics. Data from rs-fMRI was acquired in rats that underwent sham FUS BBB vs. FUS BBB opening targeted to the right primary somatosensory cortex hindlimb region (S1HL). FUS BBB opening reduced the functional connectivity between the right S1HL and other sensorimotor regions, including statistically significant reduction of connectivity to the homologous region in the left hemisphere (left S1HL). The effect was observed in all three metrics analyzed: functional connectivity between anatomically defined regions, whole brain voxel-wise correlation maps based on anatomical seeds, and spatial patterns from independent component analysis. Connectivity metrics for other regions where the BBB was not perturbed were not affected. While it is not clear whether the effect is vascular or neuronal in origin, these results suggest that even safe levels of FUS BBB opening have an effect on the physiological processes that drive the signals measured by BOLD fMRI. As such these effects must be accounted for when carrying out studies using fMRI to evaluate the effects of pharmacological agents delivered via FUS-induced BBB opening.
September 2018
Modulation of neuronal oscillatory activity in the beta- and gamma-band is associated with current individual anxiety levels
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Till R. Schneider, Joerg F. Hipp, Claudia Domnick, Christine Carl, Christian B
September 2018
Distinct neural circuits support incentivized inhibition
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Josiah K. Leong, Kelly H. MacNiven, Gregory R. Samanez-Larkin, Brian Knutson The ability to inhibit responses under high stakes, or incentivized inhibition, is critical for adaptive impulse control. While previous research indicates that right ventrolateral prefrontal cortical (VLPFC) activity plays a key role in response inhibition, less research has addressed how incentives might influence this circuit. By combining a novel behavioral task, functional magnetic resonance imaging (FMRI), and diffusion-weighted imaging (DWI), we targeted and characterized specific neural circuits that support incentivized inhibition. Behaviorally, large incentives enhanced responses to obtain money, but also reduced response inhibition. Functionally, activity in both right VLPFC and right anterior insula (AIns) predicted successful inhibition for high incentives. Structurally, characterization of a novel white-matter tract connecting the right AIns and VLPFC revealed an association of tract coherence with incentivized inhibition performance. Finally, individual differences in right VLPFC activity statistically mediated the association of right AIns-VLPFC tract coherence with incentivized inhibition performance. These multimodal findings bridge brain structure, brain function, and behavior to clarify how individuals can inhibit impulses, even in the face of high stakes.
September 2018
Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer's disease classification
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Zhuo Sun, Yuchuan Qiao, Boudewijn P.F. Lelieveldt, Marius Staring In recent years, machine learning approaches have been successfully applied to the field of neuroimaging for classification and regression tasks. However, many approaches do not give an intuitive relation between the raw features and the diagnosis. Therefore, they are difficult for clinicians to interpret. Moreover, most approaches treat the features extracted from the brain (for example, voxelwise gray matter concentration maps from brain MRI) as independent variables and ignore their spatial and anatomical relations. In this paper, we present a new Support Vector Machine (SVM)-based learning method for the classification of Alzheimer's disease (AD), which integrates spatial-anatomical information. In this way, spatial-neighbor features in the same anatomical region are encouraged to have similar weights in the SVM model. Secondly, we introduce a group lasso penalty to induce structure sparsity, which may help clinicians to assess the key regions involved in the disease. For solving this learning problem, we use an accelerated proximal gradient descent approach. We tested our method on the subset of ADNI data selected by Cuingnet etal. (2011) for Alzheimer's disease classification, as well as on an independent larger dataset from ADNI. Good classification performance is obtained for distinguishing cognitive normals (CN) vs. AD, as well as on distinguishing between various sub-types (e.g. CN vs. Mild Cognitive Impairment). The model trained on Cuignet's dataset for AD vs. CN classification was directly used without re-training to the independent larger dataset. Good performance was achieved, demonstrating the generalizability of the proposed methods. For all experiments, the classification results are comparable or better than the state-of-the-art, while the weight map more clearly indicates the key regions related to Alzheimer's disease.

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September 2018
Modeling hyperoxia-induced BOLD signal dynamics to estimate cerebral blood flow, volume and mean transit time
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): M. Ethan MacDonald, Avery J.L. Berman, Erin L. Mazerolle, Rebecca J. Williams, G. Bruce Pike A new method is proposed for obtaining cerebral perfusion measurements whereby blood oxygen level dependent (BOLD) MRI is used to dynamically monitor hyperoxia-induced changes in the concentration of deoxygenated hemoglobin in the cerebral vasculature. The data is processed using kinetic modeling to yield perfusion metrics, namely: cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). Ten healthy human subjects were continuously imaged with BOLD sequence while a hyperoxic (70% O2) state was interspersed with baseline periods of normoxia. The BOLD time courses were fit with exponential uptake and decay curves and a biophysical model of the BOLD signal was used to estimate oxygen concentration functions. The arterial input function was derived from end-tidal oxygen measurements, and a deconvolution operation between the tissue and arterial concentration functions was used to yield CBF. The venous component of the CBV was calculated from the ratio of the integrals of the estimated tissue and arterial concentration functions. Mean gray and white matter measurements were found to be: 61.6
September 2018
Cortical dynamics underpinning the self-other distinction of touch: ATMS-EEG study
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Alberto Pisoni, Leonor Josefina Romero Lauro, Alessandra Vergallito, Ottavia Maddaluno, Nadia Bolognini Touch supports processes crucial to human social behaviour, adding a bodily dimension to the perception and understanding of others' feelings. Mirror cortical activity was proposed to underpin the interpersonal sharing of touch, allowing an automatic and unconscious simulation of others' somatic states. However, recent evidence questioned the existence of a tactile shared representation in the primary somatosensory cortex (S1), and the neural correlates of self-other distinction in the somatosensory system remains unknown. We address these issues by exploring S1 reactivity, and the associated neural network oscillations and connectivity, to self and others' touch. Transcranial Magnetic Stimulation combined with Electroencephalography (TMS-EEG) recordings were performed during tactile perception and observation, looking for differences in cortical activation and connectivity between felt and seen touch. The sight of a touch directed to a human body part, but not to an object, triggered an early activation of S1 as a felt touch did, which, in both conditions, propagated to fronto-parietal regions. Critically, touch perception and observation shared an effective connectivity network generated in the beta band, which is typically associated to unconscious tactile processing. Conversely, alpha band connectivity, a marker of conscious tactile processing, was detected only for real tactile stimulation. Alpha connectivity within a fronto-parietal pathway seems to underpin the ability to distinguish self and others' somatosensory states, controlling and distinguishing shared tactile representations in S1.
September 2018
It takes two to tango: Suppression of task-irrelevant features requires (spatial) competition
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Matthias M. M
September 2018
Brain-to-brain synchrony in parent-child dyads and the relationship with emotion regulation revealed by fNIRS-based hyperscanning
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Vanessa Reindl, Christian Gerloff, Wolfgang Scharke, Kerstin Konrad Parent-child synchrony, the coupling of behavioral and biological signals during social contact, may fine-tune the child's brain circuitries associated with emotional bond formation and the child's development of emotion regulation. Here, we examined the neurobiological underpinnings of these processes by measuring parent's and child's prefrontal neural activity concurrently with functional near-infrared spectroscopy hyperscanning. Each child played both a cooperative and a competitive game with the parent, mostly the mother, as well as an adult stranger. During cooperation, parent's and child's brain activities synchronized in the dorsolateral prefrontal and frontopolar cortex (FPC), which was predictive for their cooperative performance in subsequent trials. No significant brain-to-brain synchrony was observed in the conditions parent-child competition, stranger-child cooperation and stranger-child competition. Furthermore, parent-child compared to stranger-child brain-to-brain synchrony during cooperation in the FPC mediated the association between the parent's and the child's emotion regulation, as assessed by questionnaires. Thus, we conclude that brain-to-brain synchrony may represent an underlying neural mechanism of the emotional connection between parent and child, which is linked to the child's development of adaptive emotion regulation. Future studies may uncover whether brain-to-brain synchrony can serve as a neurobiological marker of the dyad's socio-emotional interaction, which is sensitive to risk conditions, and can be modified by interventions.

Characterizing the neural coding of symbolic quantities
Publication date: September 2018
Source:NeuroImage, Volume 178 Author(s): Ian M. Lyons, Sian L. Beilock How the brain encodes abstract numerical symbols is a fundamental question in philosophy and cognitive neuroscience alike. Here we probe the nature of symbolic number representation in the brain by characterizing the neural similarity space for symbolic quantities in regions sensitive to their semantic content. In parietal and occipital regions, the similarity space of number symbols was positively predicted by the lexical frequency of numerals in parietal and occipital areas, and was unrelated to numerical ratio. These results are more consistent with a categorical, frequency-based account of symbolic quantity encoding. In contrast, the similarity space of analog quantities was positively predicted by ratio in prefrontal, parietal and occipital regions. We thus provide an explanation for why previous work has indicated that symbolic and analog quantities are distinct: number symbols operate primarily like discrete categories sensitive to input frequency, while analog quantities operate more like approximate perceptual magnitudes. In addition, we find substantial evidence for related patterns of activity across formats in prefrontal, parietal and occipital regions. Crucially however, between-format relations were not specific to individual quantities, indicating common processing as opposed to common representation. Moreover, evidence for between-format processing was strongest for quantities that could be represented as exact, discrete values in both systems (quantities in the 'subitizing' range: 14). In sum, converging evidence presented here indicates that symbolic quantities are coded in the brain as discrete categories sensitive to input frequency and largely independent of approximate, analog quantities.
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