168 results found
Smallwood J, Bernhardt BC, Leech R, et al., 2021, The default mode network in cognition: a topographical perspective, NATURE REVIEWS NEUROSCIENCE, Vol: 22, Pages: 503-513, ISSN: 1471-003X
Fagerholm ED, Tangwiriyasakul C, Friston KJ, et al., 2020, Neural diffusivity and pre-emptive epileptic seizure intervention, PLOS COMPUTATIONAL BIOLOGY, Vol: 16, ISSN: 1553-734X
<jats:title>A<jats:sc>bstract</jats:sc></jats:title><jats:p>For most neuroimaging questions the huge range of possible analytic choices leads to the possibility that conclusions from any single analytic approach may be misleading. Examples of possible choices include the motion regression approach used and smoothing and threshold factors applied during the processing pipeline. Although it is possible to perform a multiverse analysis that evaluates all possible analytic choices, this can be computationally challenging and repeated sequential analyses on the same data can compromise inferential and predictive power. Here, we establish how active learning on a low-dimensional space that captures the inter-relationships between analysis approaches can be used to efficiently approximate the whole multiverse of analyses. This approach balances the benefits of a multiverse analysis without the accompanying cost to statistical power, computational power and the integrity of inferences. We illustrate this approach with a functional MRI dataset of functional connectivity across adolescence, demonstrating how a multiverse of graph theoretic and simple pre-processing steps can be efficiently navigated using active learning. Our study shows how this approach can identify the subset of analysis techniques (i.e., pipelines) which are best able to predict participants’ ages, as well as allowing the performance of different approaches to be quantified.</jats:p>
Daws RE, Scott G, Soreq E, et al., 2020, Optimisation of functional network resources when learning behavioural strategies for performing complex tasks
<jats:title>Abstract</jats:title><jats:p>We developed two novel self-ordered switching (SOS) fMRI paradigms to investigate how human behaviour and underlying network resources are optimised when learning to perform complex tasks with multiple goals. SOS was performed with detailed feedback and minimal pretraining (study 1) or with minimal feedback and substantial pretraining (study 2). In study 1, multiple-demand (MD) system activation became less responsive to routine trial demands but more responsive to the executive switching events with practice. Default Mode Network (DMN) activation showed the opposite relationship. Concomitantly, reaction time learning curves correlated with increased connectivity between functional brain networks and subcortical regions. This ‘fine-tuning’ of network resources correlated with progressively more routine and lower complexity behavioural structure. Furthermore, overall task performance was superior for people who applied structured behavioural routines with low algorithmic complexity. These behavioural and network signatures of learning were less evident in study 2, where task structure was established prior to entering the scanner. Together, these studies demonstrate how detailed feedback monitoring enables network resources to be progressively redeployed in order to efficiently manage concurrent demands.</jats:p><jats:sec><jats:title>Highlights</jats:title><jats:list list-type="bullet"><jats:list-item><jats:p>We examine the optimisation of behaviour and brain-network resources during a novel “self-ordered switching” (SOS) paradigm.</jats:p></jats:list-item><jats:list-item><jats:p>Task performance depended on generating behavioural routines with low algorithmic complexity (i.e., structured behaviours).</jats:p></jats:list-item><jats:list-item><jats:p>Behaviour became more structured and r
Dafflon J, Pinaya WHL, Turkheimer F, et al., 2020, An automated machine learning approach to predict brain age from cortical anatomical measures, HUMAN BRAIN MAPPING, Vol: 41, Pages: 3555-3566, ISSN: 1065-9471
Fagerholm ED, Foulkes W, Gallero-Salas Y, et al., 2020, Conservation laws by virtue of scale symmetries in neural systems, PLoS Computational Biology, Vol: 16, ISSN: 1553-734X
In contrast to the symmetries of translation in space, rotation in space, and translation in time, the known laws of physics are not universally invariant under transformation of scale. However, a special case exists in which the action is scale invariant if it satisfies the following two constraints: 1) it must depend upon a scale-free Lagrangian, and 2) the Lagrangian must change under scale in the same way as the inverse time, . Our contribution lies in the derivation of a generalised Lagrangian, in the form of a power series expansion, that satisfies these constraints. This generalised Lagrangian furnishes a normal form for dynamic causal models–state space models based upon differential equations–that can be used to distinguish scale symmetry from scale freeness in empirical data. We establish face validity with an analysis of simulated data, in which we show how scale symmetry can be identified and how the associated conserved quantities can be estimated in neuronal time series.
Moran RJ, Fagerholm ED, Cullen M, et al., 2020, Estimating required ‘lockdown’ cycles before immunity to SARS-CoV-2: model-based analyses of susceptible population sizes, ‘S0’, in seven European countries, including the UK and Ireland, Wellcome Open Research, Vol: 5, Pages: 85-85
<ns4:p><ns4:bold>Background: </ns4:bold>Following stringent social distancing measures, some European countries are beginning to report a slowed or negative rate of growth of daily case numbers testing positive for the novel coronavirus. The notion that the first wave of infection is close to its peak begs the question of whether future peaks or ‘second waves’ are likely. We sought to determine the current size of the effective (i.e. susceptible) population for seven European countries—to estimate immunity levels following this first wave.</ns4:p><ns4:p> <ns4:bold>Methods: </ns4:bold>We used Bayesian model inversion to estimate epidemic parameters from the reported case and death rates from seven countries using data from late January 2020 to April 5<ns4:sup>th</ns4:sup> 2020. Two distinct generative model types were employed: first a continuous time dynamical-systems implementation of a Susceptible-Exposed-Infectious-Recovered (SEIR) model, and second a partially observable Markov Decision Process or hidden Markov model (HMM) implementation of an SEIR model. Both models parameterise the size of the initial susceptible population (‘S0’), as well as epidemic parameters.</ns4:p><ns4:p> <ns4:bold>Results: </ns4:bold>Both models recapitulated the dynamics of transmissions and disease as given by case and death rates. Crucially, <ns4:italic>maximum a posteriori</ns4:italic> estimates of S0 for each country indicated effective population sizes of below 20% (of total population size), under both the continuous time and HMM models. Using a Bayesian weighted average across all seven countries and both models, we estimated that 6.4% of the total population would be immune. From the two models, the maximum percentage of the effective population was estimated at 19.6% of the total population for the UK, 16.7% for Ireland, 11.4% for Italy, 12.8% for Spain, 18.8
Lorenz R, Simmons LE, Monti RP, et al., 2019, Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization, BRAIN STIMULATION, Vol: 12, Pages: 1484-1489, ISSN: 1935-861X
<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>
Messina R, Leech R, Zelaya F, et al., 2019, Is there an imaging biomarker to discriminate migraine and cluster headache patients?, 19th International Headache Congress of International-Headache-Society, Publisher: SAGE PUBLICATIONS LTD, Pages: 26-26, ISSN: 0333-1024
Turnbull A, Wang HT, Murphy C, et al., 2019, Left dorsolateral prefrontal cortex supports context-dependent prioritisation of off-task thought, NATURE COMMUNICATIONS, Vol: 10, ISSN: 2041-1723
Messina R, Leech R, Zelaya F, et al., 2019, Migraine and cluster headache classification using a supervised machine learning approach: a multimodal MRI study, 5th Congress of the European-Academy-of-Neurology (EAN), Publisher: WILEY, Pages: 78-78, ISSN: 1351-5101
Underwood J, de francesco D, Cole JH, et al., 2019, Validation of a novel multivariate method of defining HIV-associated cognitive impairment, Open Forum Infectious Diseases, Vol: 6, ISSN: 2328-8957
BackgroundThe optimum method of defining cognitive impairment in virally suppressed people-living-with-HIV is unknown. We evaluated the relationships between cognitive impairment, including using a novel multivariate method (NMM), patient reported outcome measures (PROMs) and neuroimaging markers of brain structure across three cohorts.MethodsDifferences in the prevalence of cognitive impairment, PROMs and neuroimaging data from the COBRA, CHARTER and POPPY cohorts (total n=908) were determined between HIV-positive participants with and without cognitive impairment defined using the HIV-associated neurocognitive disorders (HAND), global deficit score (GDS) and NMM criteria.ResultsThe prevalence of cognitive impairment varied by up to 27% between methods used to define impairment (e.g. 48% for HAND vs. 21% for NMM in the CHARTER study). Associations between objective cognitive impairment and subjective cognitive complaints were generally weak. Physical and mental health summary scores (SF-36) were lowest for NMM-defined impairment (p’s<0.05).There were no differences in brain volumes or cortical thickness between participants with and without cognitive impairment defined using the HAND and GDS measures. In contrast, those identified with cognitive impairment by the NMM had reduced mean cortical thickness in both hemispheres (p’s<0.05), as well as smaller brain volumes (p<0.01). The associations with measures of white matter microstructure and brain-predicted age were generally weaker.ConclusionDifferent methods of defining cognitive impairment identify different people with varying symptomatology and measures of brain injury. Overall, NMM-defined impairment was associated with most neuroimaging abnormalities and poorer self-reported health status. This may be due to the statistical advantage of using a multivariate approach.
Limbrick-Oldfield E, Leech R, Wise R, et al., 2019, Financial gain- and loss-related BOLD signals in the human ventral tegmental area and substantia nigra pars compacta, European Journal of Neuroscience, Vol: 49, Pages: 1196-1209, ISSN: 0953-816X
Neurons in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNC) play central roles in reward‐related behaviours. Nonhuman animal studies suggest that these neurons also process aversive events. However, our understanding of how the human VTA and SNC responds to such events is limited and has been hindered by the technical challenge of using functional magnetic resonance imaging (fMRI) to investigate a small structure where the signal is particularly vulnerable to physiological noise. Here we show, using methods optimized specifically for the midbrain (including high‐resolution imaging, a novel registration protocol, and physiological noise modelling), a BOLD (blood‐oxygen‐level dependent) signal to both financial gain and loss in the VTA and SNC, along with a response to nil outcomes that are better or worse than expected in the VTA. Taken together, these findings suggest that the human VTA and SNC are involved in the processing of both appetitive and aversive financial outcomes in humans.
Messina R, Leech R, Zelaya F, et al., 2019, Migraine and Cluster Headache Classification Using a Supervised Machine Learning Approach: A Multimodal MRI Study, 71st Annual Meeting of the American-Academy-of-Neurology (AAN), Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0028-3878
Turkheimer FE, Hellyer P, Kehagia AA, et al., 2019, Conflicting emergences. Weak vs. strong emergence for the modelling of brain function, NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, Vol: 99, Pages: 3-10, ISSN: 0149-7634
Soreq E, Leech R, Hampshire A, 2019, Dynamic network coding of working-memory domains and working-memory processes, Nature Communications, Vol: 10, 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.
Li L, Ribeiro Violante I, Leech R, et al., 2019, Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation, Human Brain Mapping, Vol: 40, Pages: 904-915, ISSN: 1065-9471
Despite its widespread use in cognitive studies, there is still limited understanding of whether and how transcranial direct current stimulation (tDCS) modulates brain network function. To clarify its physiological effects, we assessed brain network function using functional magnetic resonance imaging (fMRI) simultaneously acquired during tDCS stimulation. Cognitive state was manipulated by having subjects perform a Choice Reaction Task or being at “rest.” A novel factorial design was used to assess the effects of brain state and polarity. Anodal and cathodal tDCS were applied to the right inferior frontal gyrus (rIFG), a region involved in controlling activity large‐scale intrinsic connectivity networks during switches of cognitive state. tDCS produced widespread modulation of brain activity in a polarity and brain state dependent manner. In the absence of task, the main effect of tDCS was to accentuate default mode network (DMN) activation and salience network (SN) deactivation. In contrast, during task performance, tDCS increased SN activation. In the absence of task, the main effect of anodal tDCS was more pronounced, whereas cathodal tDCS had a greater effect during task performance. Cathodal tDCS also accentuated the within‐DMN connectivity associated with task performance. There were minimal main effects of stimulation on network connectivity. These results demonstrate that rIFG tDCS can modulate the activity and functional connectivity of large‐scale brain networks involved in cognitive function, in a brain state and polarity dependent manner. This study provides an important insight into mechanisms by which tDCS may modulate cognitive function, and also has implications for the design of future stimulation studies.
De Francesco D, Wit FW, Burkle A, et al., 2019, Do people living with HIV experience greater age advancement than their HIV-negative counterparts?, AIDS, Vol: 33, Pages: 259-268, ISSN: 0269-9370
Objectives: Despite successful antiretroviral therapy, people living with HIV (PLWH)may show signs of premature/accentuated aging. We compared established biomarkersof aging in PLWH, appropriately chosen HIV-negative individuals, and blood donors,and explored factors associated with biological age advancement.Design: Cross-sectional analysis of 134 PLWH on suppressive antiretroviral therapy, 79lifestyle-comparable HIV-negative controls aged 45 years or older from the Co-morBidity in Relation to AIDS (COBRA) cohort, and 35 age-matched blood donors.Methods: Biological age was estimated using a validated algorithm based on 10biomarkers. Associations between ‘age advancement’ (biological minus chronological age) and HIV status/parameters, lifestyle, cytomegalovirus (CMV), hepatitisB (HBV) and hepatitis C virus (HCV) infections were investigated using linear regression.Results: The average (95% CI) age advancement was greater in both HIV-positive [13.2(11.6–14.9) years] and HIV-negative [5.5 (3.8–7.2) years] COBRA participants comparedwith blood donors [7.0 (4.1 to 9.9) years, both P’s< 0.001)], but also in HIV-positivecompared with HIV-negative participants (P < 0.001). Chronic HBV, higher anti-CMVIgG titer and CD8þ T-cell count were each associated with increased age advancement, independently of HIV-status/group. Among HIV-positive participants, ageadvancement was increased by 3.5 (0.1–6.8) years among those with nadir CD4þT-cell count less than 200 cells/ml and by 0.1 (0.06–0.2) years for each additionalmonth of exposure to saquinavir.
Li L, Ribeiro Violante I, Leech R, et al., 2019, Cognitive enhancement with Salience Network electrical stimulation is influenced by network structural connectivity, NeuroImage, Vol: 185, Pages: 425-433, ISSN: 1053-8119
The Salience Network (SN) and its interactions are important for cognitive control. We have previously shown that structural damage to the SN is associated with abnormal functional connectivity between the SN and Default Mode Network (DMN), abnormal DMN deactivation, and impaired response inhibition, which is an important aspect of cognitive control. This suggests that stimulating the SN might enhance cognitive control. Here, we tested whether non-invasive transcranial direct current stimulation (TDCS) could be used to modulate activity within the SN and enhance cognitive control. TDCS was applied to the right inferior frontal gyrus/anterior insula cortex during performance of the Stop Signal Task (SST) and concurrent functional (f)MRI. Anodal TDCS improved response inhibition. Furthermore, stratification of participants based on SN structural connectivity showed that it was an important influence on both behavioural and physiological responses to anodal TDCS. Participants with high fractional anisotropy within the SN showed improved SST performance and increased activation of the SN with anodal TDCS, whilst those with low fractional anisotropy within the SN did not. Cathodal stimulation of the SN produced activation of the right caudate, an effect which was not modulated by SN structural connectivity. Our results show that stimulation targeted to the SN can improve response inhibition, supporting the causal influence of this network on cognitive control and confirming it as a target to produce cognitive enhancement. Our results also highlight the importance of structural connectivity as a modulator of network to TDCS, which should guide the design and interpretation of future stimulation studies.
Roberts R, Ahmad H, Patel M, et al., 2018, An fMRI study of visuo-vestibular interaction in Vestibular Neuritis, NeuroImage: Clinical, Vol: 20, Pages: 1010-1017, ISSN: 2213-1582
Vestibular neuritis (VN) is characterised by acute vertigo due to a sudden loss of unilateral vestibular function. A considerable proportion of VN patients proceed to develop chronic symptoms of dizziness, including visually induced dizziness, specifically during head turns. Here we investigated whether the development of such poor clinical outcomes following VN, is associated with abnormal visuo-vestibular cortical processing. Accordingly, we applied functional magnetic resonance imaging to assess brain responses of chronic VN patients and compared these to controls during both congruent (co-directional) and incongruent (opposite directions) visuo-vestibular stimulation (i.e. emulating situations that provoke symptoms in patients). We observed a focal significant difference in BOLD signal in the primary visual cortex V1 between patients and controls in the congruent condition (small volume corrected level of p < .05 FWE). Importantly, this reduced BOLD signal in V1 was negatively correlated with functional status measured with validated clinical questionnaires. Our findings suggest that central compensation and in turn clinical outcomes in VN are partly mediated by adaptive mechanisms associated with the early visual cortex.
Messina R, Leech R, Zelaya F, et al., 2018, MIGRAINE AND CLUSTER HEADACHE CLASSIFICATION USING A SUPERVISED MACHINE LEARNING APPROACH: A MULTIMODAL MRI STUDY, 17th Biennial Migraine Trust International Symposium (MTIS), Publisher: SAGE PUBLICATIONS LTD, Pages: 138-138, ISSN: 0333-1024
Underwood J, Cole JH, Leech R, et al., 2018, Multivariate pattern analysis of volumetric neuroimaging data and its relationship with cognitive function in treated HIV-disease, Journal of Acquired Immune Deficiency Syndromes, Vol: 78, Pages: 429-436, ISSN: 1525-4135
BACKGROUND: Accurate prediction of longitudinal changes in cognitive function would potentially allow targeted intervention in those at greatest risk of cognitive decline. We sought to build a multivariate model using volumetric neuroimaging data alone to accurately predict cognitive function. METHODS: Volumetric T1-weighted neuroimaging data from virally suppressed HIV-positive individuals from the CHARTER cohort (n=139) were segmented into grey and white matter and spatially normalised before were entering into machine learning models. Prediction of cognitive function at baseline and longitudinally was determined using leave-one-out cross validation. Additionally, a multivariate model of brain ageing was used to measure the deviation of apparent brain age from chronological age and assess its relationship with cognitive function. RESULTS: Cognitive impairment, defined using the global deficit score, was present in 37.4%. However, it was generally mild and occurred more commonly in those with confounding comorbidities (p<0.001). Although multivariate prediction of cognitive impairment as a dichotomous variable at baseline was poor (AUC 0.59), prediction of the global T-score was better than a comparable linear model (adjusted R=0.08, p<0.01 vs. adjusted R=0.01, p=0.14). Accurate prediction of longitudinal changes in cognitive function was not possible (p=0.82).Brain-predicted age exceeded chronological age by mean (95% confidence interval) 1.17 (-0.14-2.53) years, but was greatest in those with confounding comorbidities (5.87 [1.74-9.99] years) and prior AIDS (3.03 [0.00-6.06] years). CONCLUSION: Accurate prediction of cognitive impairment using multivariate models using only T1-weighted data was not achievable, which may reflect the small sample size, heterogeneity of the data or that impairment was usually mild.
Kamourieh S, Braga R, Leech R, et al., 2018, Speech registration in symptomatic memory impairment, Frontiers in Aging Neuroscience, Vol: 10, ISSN: 1663-4365
Background: An inability to recall recent conversations often indicates impaired episodic memory retrieval. It may also reflect a failure of attentive registration of spoken sentences which leads to unsuccessful memory encoding. The hypothesis was that patients complaining of impaired memory would demonstrate impaired function of “multiple demand” (MD) brain regions, whose activation profile generalizes across cognitive domains, during speech registration in naturalistic listening conditions.Methods: Using functional MRI, brain activity was measured in 22 normal participants and 31 patients complaining of memory impairment, 21 of whom had possible or probable Alzheimer’s disease (AD). Participants heard a target speaker, either speaking alone or in the presence of distracting background speech, followed by a question to determine if the target speech had been registered.Results: Patients performed poorly at registering verbal information, which correlated with their scores on a screening test of cognitive impairment. Speech registration was associated with widely distributed activity in both auditory cortex and in MD cortex. Additional regions were most active when the target speech had to be separated from background speech. Activity in midline and lateral frontal MD cortex was reduced in the patients. A central cholinesterase inhibitor to increase brain acetylcholine levels in half the patients was not observed to alter brain activity or improve task performance at a second fMRI scan performed 6–11 weeks later. However, individual performances spontaneously fluctuated between the two scanning sessions, and these performance differences correlated with activity within a right hemisphere fronto-temporal system previously associated with sustained auditory attention.Conclusions: Midline and lateralized frontal regions that are engaged in task-dependent attention to, and registration of, verbal information are potential targets for transcranial
Cole JH, caan M, Underwood J, et al., 2018, No evidence for accelerated ageing-related brain pathology in treated HIV: longitudinal neuroimaging results from the Comorbidity in Relation to AIDS (COBRA) project, Clinical Infectious Diseases, Vol: 66, Pages: 1899-1909, ISSN: 1058-4838
BackgroundDespite successful antiretroviral therapy people living with HIV (PLWH) experience higher rates of age-related morbidity, including abnormal brain structure, brain function and cognitive impairment. This has raised concerns that PLWH may experience accelerated ageing-related brain pathology.MethodsWe performed a multi-centre longitudinal study of 134 virologically-suppressed PLWH (median age = 56.0 years) and 79 demographically-similar HIV-negative controls (median age = 57.2 years). To measure cognitive performance and brain pathology, we conducted detailed neuropsychological assessments and multi-modality neuroimaging (T1-weighted, T2-weighted, diffusion-MRI, resting-state functional-MRI, spectroscopy, arterial spin labelling) at baseline and after two-year follow-up. Group differences in rates of change were assessed using linear mixed effects models.Results123 PLWH and 78 HIV-negative controls completed longitudinal assessments (median interval = 1.97 years). There were no differences between PLWH and HIV-negative controls in age, sex, years of education, smoking, alcohol use, recreational drug use, blood pressure, body-mass index or cholesterol levels.At baseline, PLWH had poorer global cognitive performance (P<0.01), lower grey matter volume (P=0.04), higher white matter hyperintensity load (P=0.02), abnormal white-matter microstructure (P<0.005) and greater ‘brain-predicted age difference’ (P=0.01). Longitudinally, there were no significant differences in rates of change in any neuroimaging measure between PLWH and HIV-negative controls (P>0.1). Cognitive performance was stable across the study period in both groups.ConclusionsOur finding indicate that when receiving successful treatment, middle-aged PLWH are not at increased risk of accelerated ageing-related brain changes or cognitive decline over two years, when compared to closely-matched HIV-negative controls.
Pasha Y, Taylor-Robinson S, Leech R, et al., 2018, L-ORNITHINE L-ASPARTATE IN MINIMAL HEPATIC ENCEPHALOPATHY: POSSIBLE EFFECTS ON THE BRAIN-MUSCLE AXIS?, Annual General Meeting of the British-Society-of-Gastroenterology, Publisher: BMJ PUBLISHING GROUP, Pages: A117-A118, ISSN: 0017-5749
Fagerholm ED, Dinov M, Knopfel T, et al., 2018, The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks, PLOS ONE, Vol: 13, ISSN: 1932-6203
Underwood J, De Francesco D, Leech R, et al., 2018, Medicalising normality? Using a simulated dataset to assess the performance of different diagnostic criteria of HIV-associated cognitive impairment, PLoS ONE, Vol: 13, ISSN: 1932-6203
ObjectiveThe reported prevalence of cognitive impairment remains similar to that reported in the pre-antiretroviral therapy era. This may be partially artefactual due to the methods used to diagnose impairment. In this study, we evaluated the diagnostic performance of the HIV-associated neurocognitive disorder (Frascati criteria) and global deficit score (GDS) methods in comparison to a new, multivariate method of diagnosis.MethodsUsing a simulated ‘normative’ dataset informed by real-world cognitive data from the observational Pharmacokinetic and Clinical Observations in PeoPle Over fiftY (POPPY) cohort study, we evaluated the apparent prevalence of cognitive impairment using the Frascati and GDS definitions, as well as a novel multivariate method based on the Mahalanobis distance. We then quantified the diagnostic properties (including positive and negative predictive values and accuracy) of each method, using bootstrapping with 10,000 replicates, with a separate ‘test’ dataset to which a pre-defined proportion of ‘impaired’ individuals had been added.ResultsThe simulated normative dataset demonstrated that up to ~26% of a normative control population would be diagnosed with cognitive impairment with the Frascati criteria and ~20% with the GDS. In contrast, the multivariate Mahalanobis distance method identified impairment in ~5%. Using the test dataset, diagnostic accuracy [95% confidence intervals] and positive predictive value (PPV) was best for the multivariate method vs. Frascati and GDS (accuracy: 92.8% [90.3–95.2%] vs. 76.1% [72.1–80.0%] and 80.6% [76.6–84.5%] respectively; PPV: 61.2% [48.3–72.2%] vs. 29.4% [22.2–36.8%] and 33.9% [25.6–42.3%] respectively). Increasing the a priori false positive rate for the multivariate Mahalanobis distance method from 5% to 15% resulted in an increase in sensitivity from 77.4% (64.5–89.4%) to 92.2% (83.3–100%) at a cost of specificity from
Lorenz R, Ribeiro Violante I, Monti R, et al., 2018, 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.
Lancaster J, Lorenz R, Leech R, et al., 2018, Bayesian Optimisation for Neuroimaging Pre-processing in Brain Age Classification and Prediction, Frontiers in Aging Neuroscience, Vol: 10, ISSN: 1663-4365
Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Baye
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.