129 results found
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 - of - 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.
Lorenz R, Violante IR, Monti RP, et al., 2018, Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization, NATURE COMMUNICATIONS, Vol: 9, ISSN: 2041-1723
De Simoni S, Jenkins PO, Bourke NJ, et al., 2018, Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury, BRAIN, Vol: 141, Pages: 148-164, ISSN: 0006-8950
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.
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
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
Sliwinska MW, Violante IR, Wise RJS, et al., 2017, Stimulating Multiple-Demand Cortex Enhances Vocabulary Learning, JOURNAL OF NEUROSCIENCE, Vol: 37, Pages: 7606-7618, ISSN: 0270-6474
Underwood J, Cole JH, Caan M, et al., 2017, Gray and White Matter Abnormalities in Treated Human Immunodeficiency Virus Disease and Their Relationship to Cognitive Function, CLINICAL INFECTIOUS DISEASES, Vol: 65, Pages: 422-432, ISSN: 1058-4838
Hellyer PJ, Clopath C, Kehagia AA, 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
Roberts RE, Ahmad H, Arshad Q, et al., 2017, Functional neuroimaging of visuo-vestibular interaction, BRAIN STRUCTURE & FUNCTION, Vol: 222, Pages: 2329-2343, ISSN: 1863-2653
Geranmayeh F, 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: 0006-8950
Lorenz R, Simmons L, Monti R, et al., 2017, Assessing tACS-induced phosphene perception using closed-loop Bayesian optimization
Transcranial alternating current stimulation (tACS) can evoke illusory flash-like visual percepts known as phosphenes . 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 summary, our results have important implications for the experimental design and concl
Lorenz R, Violante I, Monti RP, et al., 2017, Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization
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.
Cole JH, Underwood J, Caan MWA, et al., 2017, Increased brain-predicted aging in treated HIV disease, NEUROLOGY, Vol: 88, Pages: 1349-1357, ISSN: 0028-3878
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
Violante IR, Li LM, Carmichael DW, et al., 2017, Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance., Elife, Vol: 6
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
Lorenz R, Hampshire A, Leech R, 2017, Neuroadaptive Bayesian Optimization and Hypothesis Testing, TRENDS IN COGNITIVE SCIENCES, Vol: 21, Pages: 155-167, ISSN: 1364-6613
Monti RP, Lorenz R, Braga RM, et al., 2017, Real-time estimation of dynamic functional connectivity networks, HUMAN BRAIN MAPPING, Vol: 38, Pages: 202-220, ISSN: 1065-9471
Braga RM, Hellyer PJ, Wise RJS, et al., 2017, Auditory and visual connectivity gradients in frontoparietal cortex, HUMAN BRAIN MAPPING, Vol: 38, Pages: 255-270, ISSN: 1065-9471
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
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: 1047-3211
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: 1065-9471
Dinov M, Lorenz R, Scott G, et al., 2016, Novel Modeling of Task vs. 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
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
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: 0027-8424
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: 0960-9822
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
Underwood J, Cole J, Sharp D, et al., 2016, Brain MRI changes associated with poorer cognitive function despite suppressive antiretroviral therapy, Publisher: WILEY-BLACKWELL, Pages: 6-6, ISSN: 1464-2662
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