130 results found
Soreq E, Hampshire A, Leech R, 2019, Dynamic network coding of working-memory domains and working-memory processes, Nature Communications, ISSN: 2041-1723
The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. Here, we use machinelearning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes(encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivitythat they evoke. This is the case even when focusing on ‘multiple demands’ brain regions, which are active across all WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively tobrain areas or processing streams; however, the mappings from that literature form salient features within the corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis for classification and become fine-tuned as maintenance load increases. These results accord with a network-codingmechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.
<ns4:p>In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field<ns4:bold>-</ns4:bold>of<ns4:bold>-</ns4:bold>view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.</ns4:p>
Lorenz R, Ribeiro Violante I, Monti R, et al., Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization, Nature Communications, Vol: 9, ISSN: 2041-1723
Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because they overlap spatially and are co-activated by diverse tasks. Characterizing these networks therefore involves studying their activation across many different cognitive tasks, which previously was only possible with meta-analyses. Here, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that segregate ventral and dorsal FPN activity. We identify and subsequently refine two cognitive tasks, Deductive Reasoning and Tower of London, which maximally dissociate the dorsal from ventral FPN. We subsequently investigate these two FPNs in the context of a wider range of FPNs and demonstrate the importance of studying the whole activity profile across tasks to uniquely differentiate any FPN. Our findings deviate from previous meta-analyses and hypothesized functional labels for these FPNs. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy.
<jats:p>Reinforcement learning (RL) is a general-purpose powerful machine learning framework within which we can model various deterministic, non-deterministic and complex environments. We applied RL to the problem of tracking and improving human sustained attention during a simple sustained attention to response task (SART) in a proof of concept study with two subjects, using state-of-the-art deep neural network-based RL in the form of Deep Q Networks (DQNs). While others have used RL in EEG settings previously, none have applied it in a neurofeedback (NFB) setting, which seems a natural problem within Brain Computer Interfaces (BCIs) to tackle using end-to-end RL in the form of DQNs, due to both the problem's non-stationarity and the ability of RL to learn in a continuous setting. Furthermore, while many have explored phasic alerting previously, learning optimal alerting in a personalized way in real time is a less explored field, which we believe RL to be a most suitable solution for. First, we used empirically-derived simulated data of EEG and reaction times and subsequent parameter/algorithmic exploration within this simulated model to pick parameters for the DQN that are more likely to be optimal for the experimental setup and to explore the behavior of DQNs in this task setting. We then applied the method on two subjects and show that we get different but plausible results for both subjects, suggesting something about the behavior of DQNs in this setting. For this experimental part, we used parameters suggested to us by the simulation results. This RL-based behavioral- and neuro-feedback BCI method we have developed here is input feature agnostic and allows for complex continuous actions to be learned in other more complex closed-loop behavioral or neuro-feedback approaches.</jats:p>
De Simoni S, Jenkins PO, Bourke N, et al., 2017, Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury, Brain, Vol: 141, Pages: 148-164, ISSN: 1460-2156
Traumatic brain injury often produces executive dysfunction. This characteristic cognitive impairment often causes long-term problems with behaviour and personality. Frontal lobe injuries are associated with executive dysfunction, but it is unclear how these injuries relate to corticostriatal interactions that are known to play an important role in behavioural control. We hypothesized that executive dysfunction after traumatic brain injury would be associated with abnormal corticostriatal interactions, a question that has not previously been investigated. We used structural and functional MRI measures of connectivity to investigate this. Corticostriatal functional connectivity in healthy individuals was initially defined using a data-driven approach. A constrained independent component analysis approach was applied in 100 healthy adult dataset from the Human Connectome Project. Diffusion tractography was also performed to generate white matter tracts. The output of this analysis was used to compare corticostriatal functional connectivity and structural integrity between groups of 42 patients with traumatic brain injury and 21 age-matched controls. Subdivisions of the caudate and putamen had distinct patterns of functional connectivity. Traumatic brain injury patients showed disruption to functional connectivity between the caudate and a distributed set of cortical regions, including the anterior cingulate cortex. Cognitive impairments in the patients were mainly seen in processing speed and executive function, as well as increased levels of apathy and fatigue. Abnormalities of caudate functional connectivity correlated with these cognitive impairments, with reductions in right caudate connectivity associated with increased executive dysfunction, information processing speed and memory impairment. Structural connectivity, measured using diffusion tensor imaging between the caudate and anterior cingulate cortex was impaired and this also correlated with measures of ex
Dinov M, Leech R, 2017, Modeling uncertainties in EEG microstates: analysis of real and imagined motor movements using probabilistic clustering-driven training of probabilistic neural networks, Frontiers in Human Neuroscience, Vol: 11, ISSN: 1662-5161
Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analogue to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which
Carhart-Harris RL, Roseman L, Bolstridge M, et al., 2017, Psilocybin for treatment-resistant depression: fMRI-measured brain mechanisms, Scientific Reports, Vol: 7, ISSN: 2045-2322
Psilocybin with psychological support is showing promise as a treatment model in psychiatry but its therapeutic mechanisms are poorly understood. Here, cerebral blood flow (CBF) and blood oxygen-level dependent (BOLD) resting-state functional connectivity (RSFC) were measured with functional magnetic resonance imaging (fMRI) before and after treatment with psilocybin (serotonin agonist) for treatment-resistant depression (TRD). Quality pre and post treatment fMRI data were collected from 16 of 19 patients. Decreased depressive symptoms were observed in all 19 patients at 1-week post-treatment and 47% met criteria for response at 5 weeks. Whole-brain analyses revealed post-treatment decreases in CBF in the temporal cortex, including the amygdala. Decreased amygdala CBF correlated with reduced depressive symptoms. Focusing on a priori selected circuitry for RSFC analyses, increased RSFC was observed within the default-mode network (DMN) post-treatment. Increased ventromedial prefrontal cortex-bilateral inferior lateral parietal cortex RSFC was predictive of treatment response at 5-weeks, as was decreased parahippocampal-prefrontal cortex RSFC. These data fill an important knowledge gap regarding the post-treatment brain effects of psilocybin, and are the first in depressed patients. The post-treatment brain changes are different to previously observed acute effects of psilocybin and other ‘psychedelics’ yet were related to clinical outcomes. A ‘reset’ therapeutic mechanism is proposed.
Hellyer P, Clopath C, Kehagia A, et al., 2017, From homeostasis to behavior: Balanced activity in an exploration of embodied dynamic environmental-neural interaction, PLoS Computational Biology, Vol: 13, ISSN: 1553-734X
In recent years, there have been many computational simulations of spontaneous neural dynamics. Here, we describe a simple model of spontaneous neural dynamics that controls an agent moving in a simple virtual environment. These dynamics generate interesting brain-environment feedback interactions that rapidly destabilize neural and behavioral dynamics demonstrating the need for homeostatic mechanisms. We investigate roles for homeostatic plasticity both locally (local inhibition adjusting to balance excitatory input) as well as more globally (regional “task negative” activity that compensates for “task positive”, sensory input in another region) balancing neural activity and leading to more stable behavior (trajectories through the environment). Our results suggest complementary functional roles for both local and macroscale mechanisms in maintaining neural and behavioral dynamics and a novel functional role for macroscopic “task-negative” patterns of activity (e.g., the default mode network).
Sliwinska M, Ribeiro Violante I, Wise R, et al., 2017, Stimulating Multiple-Demand Cortex Enhances Vocabulary Learning, Journal of Neuroscience, Vol: 37, Pages: 7606-7618, ISSN: 1529-2401
It is well established that domain general networks (DGNs) in the human brain become active when diverse novel skills and behaviors are being learnt. However, their causal role in learning remains to be established. In the present study, we first performed functional magnetic resonance imaging on healthy participants to confirm that DGNs were most active in the initial stages of learning a novel vocabulary, consisting of pronounceable nonwords (pseudowords), each associated with a picture of a real object. We then examined, in healthy participants, whether repetitive transcranial magnetic stimulation of a frontal midline node of the cingulo-opercular DGN affected learning rates during the initial stages of learning. We report that stimulation of this node, but not a control brain region, substantially improved both accuracy and response times during the earliest stage of learning pseudowords-object associations. This stimulation had no effect on the processing of established vocabulary, tested by the accuracy and response times when participants decided whether a real word was accurately paired with a picture of an object. These results provide evidence that non-invasive stimulation to DGN nodes can enhance learning rates, thereby demonstrating their causal role in the learning process. We propose that this causal role makes DGNs candidate targets for experimental therapeutics; for example, in stroke patients with aphasia attempting to reacquire a vocabulary.
Privitera R, Birch R, Sinisi M, et al., 2017, Capsaicin 8% patch treatment for amputation stump and phantom limb pain: a clinical and functional MRI study, Journal of Pain Research, Vol: 10, Pages: 1623-1634, ISSN: 1178-7090
Purpose: The aim of this study was to measure the efficacy of a single 60 min application of capsaicin 8% patch in reducing chronic amputation stump and phantom limb pain, associated hypersensitivity with quantitative sensory testing, and changes in brain cortical maps using functional MRI (fMRI) scans.Methods: A capsaicin 8% patch (Qutenza) treatment study was conducted on 14 patients with single limb amputation, who reported pain intensity on the Numerical Pain Rating Scale ≥4/10 for chronic stump or phantom limb pain. Pain assessments, quantitative sensory testing, and fMRI (for the lip pursing task) were performed at baseline and 4 weeks after application of capsaicin 8% patch to the amputation stump. The shift into the hand representation area of the cerebral cortex with the lip pursing task has been correlated with phantom limb pain intensity in previous studies, and was the fMRI clinical model for cortical plasticity used in this study.Results: The mean reduction in spontaneous amputation stump pain, phantom limb pain, and evoked stump pain were −1.007 (p=0.028), −1.414 (p=0.018), and −2.029 (p=0.007), respectively. The areas of brush allodynia and pinprick hypersensitivity in the amputation stump showed marked decreases: −165 cm2, −80% (p=0.001) and −132 cm2, −72% (p=0.001), respectively. fMRI analyses provided objective evidence of the restoration of the brain map, that is, reversal of the shift into the hand representation of the cerebral cortex with the lip pursing task (p<0.05).Conclusion: The results show that capsaicin 8% patch treatment leads to significant reduction in chronic pain and, particularly, in the area of stump hypersensitivity, which may enable patients to wear prostheses, thereby improving mobility and rehabilitation. Phantom limb pain (“central” pain) and associated brain plasticity may be modulated by peripheral inputs, as they can be ameliorated by the peripherally restricted
Geranmayeh F, Wing Chau T, Wise RJS, et al., 2017, Domain-general subregions of the medial prefrontal cortex contribute to recovery of language after stroke, Brain, Vol: 140, Pages: 1947-1958, ISSN: 1460-2156
We hypothesized that the recovery of speech production after left hemisphere stroke not only depends on the integrity of language-specialized brain systems, but also on ‘domain-general’ brain systems that have much broader functional roles. The presupplementary motor area/dorsal anterior cingulate forms part of the cingular-opercular network, which has a broad role in cognition and learning. Consequently, we have previously suggested that variability in the recovery of speech production after aphasic stroke may relate in part to differences in patients’ abilities to engage this domain-general brain region. To test our hypothesis, 27 patients (aged 59 ± 11 years) with a left hemisphere stroke performed behavioural assessments and event-related functional magnetic resonance imaging tasks at two time points; first in the early phase (∼2 weeks) and then ∼4 months after the ictus. The functional magnetic resonance imaging tasks were designed to differentiate between activation related to language production (sentential overt speech production—Speech task) and activation related to cognitive processing (non-verbal decision making). Simple rest and counting conditions were also included in the design. Task-evoked regional brain activations during the early and late phases were compared with a longitudinal measure of recovery of language production. In accordance with a role in cognitive processing, substantial activity was observed within the presupplementary motor area/dorsal anterior cingulate during the decision-making task. Critically, the level of activation within this region during speech production correlated positively with the longitudinal recovery of speech production across the two time points (as measured by the in-scanner performance in the Speech task). This relationship was observed for activation in both the early phase (r = 0.363, P = 0.03 one-tailed) and the late phase (r = 0.538, P = 0.004). Furthermore, presupplem
Lorenz R, Simmons LE, Monti RP, et al., 2017, Assessing tACS-induced phosphene perception using closed-loop Bayesian optimization
<jats:p> Transcranial alternating current stimulation (tACS) can evoke illusory flash-like visual percepts known as <jats:italic>phosphenes</jats:italic> . The perception of phosphenes represents a major experimental challenge when studying tACS-induced effects on cognitive performance. Besides growing concerns that retinal phosphenes themselves could potentially have neuromodulatory effects, the perception of phosphenes may also modify the alertness of participants. Past research has shown that stimulation intensity, frequency and electrode montage affect phosphene perception. However, to date, the effect of an additional tACS parameter on phosphene perception has been completely overlooked: the relative phase difference between stimulation electrodes. This is a crucial and timely topic given the confounding nature of phosphene perception and the increasing number of studies reporting changes in cognitive function following tACS phase manipulations. However, studying phosphene perception for different frequencies and phases simultaneously is not tractable using standard approaches, as the physiologically plausible range of parameters results in a combinatorial explosion of experimental conditions, yielding impracticable experiment durations. To overcome this limitation, here we applied a Bayesian optimization approach to efficiently sample an exhaustive tACS parameter space. Moreover, unlike conventional methodology, which involves subjects judging the perceived phosphene intensity on a rating scale, our study leveraged the strength of human perception by having the optimization driven based on a subject's relative judgement. Applying Bayesian optimization for two different montages, we found that phosphene perception was affected by differences in the relative phase between cortical electrodes. The results were replicated in a second study involving new participants and validated using computational modelling. In summar
Lorenz R, Violante IR, Monti RP, et al., 2017, Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization
<jats:p>Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because different FPNs spatially overlap and are co-activated for diverse tasks. In order to characterize these networks involves studying how they activate across many different cognitive tasks, which has only previously been possible with meta-analyses. Here, building upon meta-analyses as a starting point, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that dissociate ventral and dorsal FPN activity from a large pool of tasks. We identify and subsequently refine two cognitive tasks (Deductive Reasoning and Tower of London) that are optimal for dissociating the FPNs. The identified cognitive tasks are not those predicted by meta-analysis, highlighting a different mapping between cognitive tasks and frontoparietal networks than expected. The optimization approach converged on a similar neural dissociation independently for the two different tasks, suggesting a possible common underlying functional mechanism and the need for neurally-derived cognitive taxonomies.</jats:p>
Underwood J, Cole JH, Caan M, et al., 2017, Grey and white matter abnormalities in treated HIV-disease and their relationship to cognitive function., Clinical Infectious Diseases, Vol: 65, Pages: 422-432, ISSN: 1537-6591
Background: Long-term comorbidities such as cognitive impairment remain prevalent in otherwise effectively treated people-living-with-HIV. We investigate the relationship between cognitive impairment and brain structure in successfully treated patients using multi-modal neuroimaging from the Co-morBidity in Relation to AIDS (COBRA) cohort. Methods: Cognitive function, brain tissue volumes and white matter microstructure were assessed in 134 HIV-positive patients and 79 controls. All patients had suppressed plasma HIV RNA at cohort entry. In addition to comprehensive voxelwise analyses of volumetric and diffusion tensor imaging, we used an unsupervised machine learning approach to combine cognitive, diffusion and volumetric data, taking advantage of the complementary information they provide. Results: Compared to the highly comparable control group, cognitive function was impaired in four out of the six cognitive domains tested (median global T-scores: 50.8 vs. 54.2, p<0.001). Patients had lower grey but not white matter volumes, observed principally in regions where structure generally did not correlate with cognitive function. Widespread abnormalities in white matter microstructure were also seen, including reduced fractional anisotropy with increased mean and radial diffusivity. In contrast to the grey matter, these diffusion abnormalities correlated with cognitive function. Multivariate neuroimaging analysis identified a neuroimaging phenotype associated with poorer cognitive function, HIV-infection and systemic immune activation. Conclusions: Cognitive impairment, lower grey matter volume and white matter microstructural abnormalities were evident in HIV-positive individuals despite fully suppressive antiretroviral therapy. White matter abnormalities appear to be a particularly important determinant of cognitive dysfunction seen in well-treated HIV-positive individuals.
Monti RP, Lorenz R, Hellyer P, et al., 2017, Decoding time-varying functional connectivity networks via linear graph embedding methods, Frontiers in Computational Neuroscience, Vol: 11, ISSN: 1662-5188
An exciting avenue of neuroscientific research involves quantifying the time-varying prop-erties of functional connectivity networks. As a result, many methods have been proposed toestimate the dynamic properties of such networks. However, one of the challenges associatedwith such methods involves the interpretation and visualization of high-dimensional, dynamicnetworks. In this work, we employ graph embedding algorithms to provide low-dimensionalvector representations of networks, thus facilitating traditional objectives such as visualization,interpretation and classification. We focus on linear graph embedding methods based on prin-cipal component analysis and regularized linear discriminant analysis. The proposed graphembedding methods are validated through a series of simulations and applied to fMRI datafrom the Human Connectome Project.
Violante IR, Li LM, Carmichael DW, et al., 2017, Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance., Elife, Vol: 6, ISSN: 2050-084X
Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization.
Violante IR, Li LM, Carmichael DW, et al., 2017, Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance, ELIFE, Vol: 6, ISSN: 2050-084X
Cole JH, underwood J, Caan MWA, et al., 2017, Increased brain-predicted ageing in treated HIV disease, Neurology, Vol: 88, Pages: 1349-1357, ISSN: 0028-3878
Objective: To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent “brain age” using neuroimaging and exploring whether these estimates related to HIV-status, age, cognitive performance and HIV-related clinical parameters.Methods: A large sample of virologically-suppressed HIV-positive adults (N = 162, aged 45-82 years) and highly-comparable HIV-negative controls (N = 105) were recruited as part of the COBRA collaboration. Using T1-MRI scans, a machine-learning model of healthy brain ageing was defined in an independent cohort (N = 2001, aged 18-90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age - chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out.Results: HIV-positive individuals had greater brain-PAD score (mean ± SD = 2.15 ± 7.79 years) compared to HIV-negative individuals (-0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV-infection or other HIV-related measures.Conclusions: Increased apparent brain ageing, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain ageing related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV-infection, suggesting that HIV disease may accentuate, rather than accelerate brain ageing.
Roberts RE, Arshad Q, Patel M, et al., 2016, Functional neuroimaging of visuo-vestibular interaction, Brain Structure & Function, Vol: 222, Pages: 2329-2343, ISSN: 1863-2661
The brain combines visual, vestibular and proprioceptive information to distinguish between self-and world-motion. Often these signals are complementary and indicate that the individual is moving or stationary with respect to the surroundings. However, conflicting visual motion and vestibular cues can lead to ambiguous or false sensations of motion. In this study, we used functional magnetic resonance imaging to explore human brain activation when visual and vestibular cues were either complementary or in conflict. We combined a horizontally moving optokinetic stimulus with caloric irrigation of the right ear to produce conditions where the vestibular activation and visual motion indicatedthe same (congruent) or opposite directions of self-motion (incongruent). Visuo-vestibular conflict was associated with increased activation in a network of brain regions including posterior insular and transverse temporal areas, cerebellar tonsil, cingulate and medial frontal gyri. In the congruent condition there was increased activation in primary and secondary visual cortex. These findings suggest that when sensory information regarding self-motion is contradictory, there is preferential activation of multisensoryvestibular areas to resolve this ambiguity. When cues are congruent there is a bias towards visual cortical activation. The data support the view thata network of brain areas including the posterior insular cortex may play animportant role in integrating and disambiguating visual and vestibular cues.
Monti RP, Lorenz R, Braga RM, et al., 2016, Real-time estimation of dynamic functional connectivity networks., Human Brain Mapping, ISSN: 1097-0193
Two novel and exciting avenues of neuroscientific research involve the study of task-driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real-time. While the former is a well-established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real-time fMRI studies have focused on a single brain region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real-time. In this work, we propose a novel methodology with which to accurately track changes in time-varying functional connectivity networks in real-time. The proposed method is shown to perform competitively when compared to state-of-the-art offline algorithms using both synthetic as well as real-time fMRI data. The proposed method is applied to motor task data from the Human Connectome Project as well as to data obtained from a visuospatial attention task. We demonstrate that the algorithm is able to accurately estimate task-related changes in network structure in real-time. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.
Braga RM, Hellyer PJ, Wise, et al., 2016, Auditory and visual connectivity gradients in frontoparietal cortex, Human Brain Mapping, Vol: 38, Pages: 255-270, ISSN: 1097-0193
A frontoparietal network of brain regions is often implicated in both auditory and visual information processing. Although it is possible that the same set of multimodal regions subserves both modalities, there is increasing evidence that there is a differentiation of sensory function within frontoparietal cortex. Magnetic resonance imaging (MRI) in humans was used to investigate whether different frontoparietal regions showed intrinsic biases in connectivity with visual or auditory modalities. Structural connectivity was assessed with diffusion tractography and functional connectivity was tested using functional MRI. A dorsal–ventral gradient of function was observed, where connectivity with visual cortex dominates dorsal frontal and parietal connections, while connectivity with auditory cortex dominates ventral frontal and parietal regions. A gradient was also observed along the posterior–anterior axis, although in opposite directions in prefrontal and parietal cortices. The results suggest that the location of neural activity within frontoparietal cortex may be influenced by these intrinsic biases toward visual and auditory processing. Thus, the location of activity in frontoparietal cortex may be influenced as much by stimulus modality as the cognitive demands of a task. It was concluded that stimulus modality was spatially encoded throughout frontal and parietal cortices, and was speculated that such an arrangement allows for top–down modulation of modality-specific information to occur within higher-order cortex. This could provide a potentially faster and more efficient pathway by which top–down selection between sensory modalities could occur, by constraining modulations to within frontal and parietal regions, rather than long-range connections to sensory cortices.
Fagerholm ED, Scott G, Shew WL, et al., 2016, Cortical Entropy, Mutual Information and Scale-Free Dynamics in Waking Mice, Cerebral Cortex, Vol: 26, Pages: 3945-3952, ISSN: 1460-2199
Some neural circuits operate with simple dynamics characterized by one or a few well-defined spatiotemporal scales (e.g. central pattern generators). In contrast, cortical neuronal networks often exhibit richer activity patterns in which all spatiotemporal scales are represented. Such "scale-free" cortical dynamics manifest as cascades of activity with cascade sizes that are distributed according to a power-law. Theory and in vitro experiments suggest that information transmission among cortical circuits is optimized by scale-free dynamics. In vivo tests of this hypothesis have been limited by experimental techniques with insufficient spatial coverage and resolution, i.e., restricted access to a wide range of scales. We overcame these limitations by using genetically encoded voltage imaging to track neural activity in layer 2/3 pyramidal cells across the cortex in mice. As mice recovered from anesthesia, we observed three changes: (a) cortical information capacity increased, (b) information transmission among cortical regions increased and (c) neural activity became scale-free. Our results demonstrate that both information capacity and information transmission are maximized in the awake state in cortical regions with scale-free network dynamics.
Dinov M, Lorenz R, Scott G, et al., 2016, Novel modeling of task versus rest brain state predictability using a dynamic time warping spectrum: comparisons and contrasts with other standard measures of brain dynamics, Frontiers in Computational Neuroscience, Vol: 10, ISSN: 1662-5188
Dynamic time warping, or DTW, is a powerful and domain-general sequence alignment method for computing a similarity measure. Such dynamic programming-based techniques like DTW are now the backbone and driver of most bioinformatics methods and discoveries. In neuroscience it has had far less use, though this has begun to change. We wanted to explore new ways of applying DTW, not simply as a measure with which to cluster or compare similarity between features but in a conceptually different way. We have used DTW to provide a more interpretable spectral description of the data, compared to standard approaches such as the Fourier and related transforms. The DTW approach and standard discrete Fourier transform (DFT) are assessed against benchmark measures of neural dynamics. These include EEG microstates, EEG avalanches and the sum squared error (SSE) from a multilayer perceptron (MLP) prediction of the EEG timeseries, and simultaneously acquired FMRI BOLD signal. We explored the relationships between these variables of interest in an EEG-FMRI dataset acquired during a standard cognitive task, which allowed us to explore how DTW differentially performs in different task settings. We found that despite strong correlations between DTW and DFT-spectra, DTW was a better predictor for almost every measure of brain dynamics. Using these DTW measures, we show that predictability is almost always higher in task than in rest states, which is consistent to other theoretical and empirical findings, providing additional evidence for the utility of the DTW approach.
Roseman L, Sereno MI, Leech R, et al., 2016, LSD alters eyes-closed functional connectivity within the early visual cortex in a retinotopic fashion, Human Brain Mapping, Vol: 37, Pages: 3031-3040, ISSN: 1097-0193
The question of how spatially organized activity in the visual cortex behaves during eyes-closed, lysergic acid diethylamide (LSD)-induced "psychedelic imagery" (e.g., visions of geometric patterns and more complex phenomena) has never been empirically addressed, although it has been proposed that under psychedelics, with eyes-closed, the brain may function "as if" there is visual input when there is none. In this work, resting-state functional connectivity (RSFC) data was analyzed from 10 healthy subjects under the influence of LSD and, separately, placebo. It was suspected that eyes-closed psychedelic imagery might involve transient local retinotopic activation, of the sort typically associated with visual stimulation. To test this, it was hypothesized that, under LSD, patches of the visual cortex with congruent retinotopic representations would show greater RSFC than incongruent patches. Using a retinotopic localizer performed during a nondrug baseline condition, nonadjacent patches of V1 and V3 that represent the vertical or the horizontal meridians of the visual field were identified. Subsequently, RSFC between V1 and V3 was measured with respect to these a priori identified patches. Consistent with our prior hypothesis, the difference between RSFC of patches with congruent retinotopic specificity (horizontal-horizontal and vertical-vertical) and those with incongruent specificity (horizontal-vertical and vertical-horizontal) increased significantly under LSD relative to placebo, suggesting that activity within the visual cortex becomes more dependent on its intrinsic retinotopic organization in the drug condition. This result may indicate that under LSD, with eyes-closed, the early visual system behaves as if it were seeing spatially localized visual inputs. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.
Carhart-Harris RL, Muthukumaraswamy S, Roseman L, et al., 2016, Neural correlates of the LSD experience revealed by multimodal neuroimaging, Proceedings of the National Academy of Sciences of the United States of America, Vol: 113, Pages: 4853-4858, ISSN: 1091-6490
Lysergic acid diethylamide (LSD) is the prototypical psychedelic drug, but its effects on the human brain have never been studied before with modern neuroimaging. Here, three complementary neuroimaging techniques: arterial spin labeling (ASL), blood oxygen level-dependent (BOLD) measures, and magnetoencephalography (MEG), implemented during resting state conditions, revealed marked changes in brain activity after LSD that correlated strongly with its characteristic psychological effects. Increased visual cortex cerebral blood flow (CBF), decreased visual cortex alpha power, and a greatly expanded primary visual cortex (V1) functional connectivity profile correlated strongly with ratings of visual hallucinations, implying that intrinsic brain activity exerts greater influence on visual processing in the psychedelic state, thereby defining its hallucinatory quality. LSD’s marked effects on the visual cortex did not significantly correlate with the drug’s other characteristic effects on consciousness, however. Rather, decreased connectivity between the parahippocampus and retrosplenial cortex (RSC) correlated strongly with ratings of “ego-dissolution” and “altered meaning,” implying the importance of this particular circuit for the maintenance of “self” or “ego” and its processing of “meaning.” Strong relationships were also found between the different imaging metrics, enabling firmer inferences to be made about their functional significance. This uniquely comprehensive examination of the LSD state represents an important advance in scientific research with psychedelic drugs at a time of growing interest in their scientific and therapeutic value. The present results contribute important new insights into the characteristic hallucinatory and consciousness-altering properties of psychedelics that inform on how they can model certain pathological states and potentially treat others.
Tagliazucchi E, Roseman L, Kaelen M, et al., 2016, Increased Global Functional Connectivity Correlates with LSD-Induced Ego Dissolution, Current Biology, Vol: 26, Pages: 1043-1050, ISSN: 1879-0445
Braga RM, Fu RZ, Seemungal BM, et al., 2016, Eye movements during auditory attention predict individual differences in dorsal attention network activity, Frontiers in Human Neuroscience, Vol: 10, ISSN: 1662-5161
The neural mechanisms supporting auditory attention are not fully understood. A dorsal frontoparietal network of brain regions is thought to mediate the spatial orienting of attention across all sensory modalities. Key parts of the this network, the frontal eye fields (FEF) and the superior parietal lobes (SPL), contain retinotopic maps and elicit saccades when stimulated. This suggests that their recruitment during auditory attention might reflect crossmodal oculomotor processes; however this has not been confirmed experimentally. Here we investigate whether task-evoked eye movements during an auditory task can predict the magnitude of activity within the dorsal frontoparietal network. A spatial and non-spatial listening task was used with on-line eye-tracking and functional magnetic resonance imaging. No visual stimuli or cues were used. The auditory task elicited systematic eye movements, with saccade rate and gaze position predicting attentional engagement and the cued sound location, respectively. Activity associated with these separate aspects of evoked eye-movements dissociated between the SPL and FEF. However these observed eye movements could not account for all the activation in the frontoparietal network. Our results suggest that the recruitment of the SPL and FEF during attentive listening reflects, at least partly, overt crossmodal oculomotor processes during non-visual attention. Further work is needed to establish whether the network’s remaining contribution to auditory attention is through covert crossmodal processes, or is directly involved in the manipulation of auditory information.
Underwood J, Cole JH, Sharp D, et al., 2016, Brain MRI changes associated with poorer cognitive function despite suppressive antiretroviral therapy, 22nd Annual Conference of the British HIV Association (BHIVA), Publisher: Wiley, Pages: 6-6, ISSN: 1464-2662
Geranmayeh F, Leech R, Wise RJS, 2016, Network dysfunction predicts speech production after left hemisphere stroke, Neurology, Vol: 86, Pages: 1296-1305, ISSN: 0028-3878
Objective: To investigate the role of multiple distributed brain networks, including the default mode, fronto-temporo-parietal, and cingulo-opercular networks, which mediate domain-general and task-specific processes during speech production after aphasic stroke.Methods: We conducted an observational functional MRI study to investigate the effects of a previous left hemisphere stroke on functional connectivity within and between distributed networks as patients described pictures. Study design included various baseline tasks, and we compared results to those of age-matched healthy participants performing the same tasks. We used independent component and psychophysiological interaction analyses.Results: Although activity within individual networks was not predictive of speech production, relative activity between networks was a predictor of both within-scanner and out-of-scanner language performance, over and above that predicted from lesion volume, age, sex, and years of education. Specifically, robust functional imaging predictors were the differential activity between the default mode network and both the left and right fronto-temporo-parietal networks, respectively activated and deactivated during speech. We also observed altered between-network functional connectivity of these networks in patients during speech production.Conclusions: Speech production is dependent on complex interactions among widely distributed brain networks, indicating that residual speech production after stroke depends on more than the restoration of local domain-specific functions. Our understanding of the recovery of function following focal lesions is not adequately captured by consideration of ipsilesional or contralesional brain regions taking over lost domain-specific functions, but is perhaps best considered as the interaction between what remains of domain-specific networks and domain-general systems that regulate behavior.
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