Biology subjects

Henson, R. N.

Publications and source records attributed to Henson, R. N..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Brain age relates to early life factors but not to accelerated brain aging

Brain age is a widely used index for quantifying individuals brain health as deviation from a normative brain aging trajectory. Higher than expected brain age is thought partially to reflect above-average rate of brain aging. We explicitly tested this assumption in two large datasets and found no association between cross-sectional brain age and steeper brain decline measured longitudinally. Rather, brain age in adulthood was associated with early-life influences indexed by birth weight and polygenic scores. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

neuroscience↗

bioRxiv2020

Correcting for Superficial Bias in 7T Gradient Echo fMRI

1The arrival of submillimetre ultra high-field fMRI makes it possible to compare activation profiles across cortical layers. However, the Blood Oxygenation Level Dependent (BOLD) signal measured by Gradient-Echo fMRI is biased towards superficial layers of the cortex, which is a serious confound for laminar analysis. Several univariate and multivariate analysis methods have been proposed to correct this bias. We compare these methods using computational simulations and example human 7T fMRI data from Regions-of-Interest (ROIs) during a visual attention paradigm. The simulations show that two methods - the ratio of ROI means across conditions and a novel application of Deming regression - offer the most robust correction for superficial bias. Deming regression has the additional advantage that it does not require that the conditions differ in their mean activation over voxels within an ROI. When applied to the example dataset, these methods suggest that attentional modulation of activation is similar across cortical layers within the ventral visual stream, despite a naive activation-based analysis producing stronger modulation in superficial layers. Our study demonstrates that accurate correction of superficial bias is crucial to avoid drawing erroneous conclusions from laminar analyses of Gradient-Echo fMRI data.

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bioRxiv2020

Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts

Socio-economic status (SES) has been proposed to have facilitating and protective effects on brain and cognition. Here we show that relationships between SES, brain volumes and general cognitive ability differ significantly across European and US cohorts (4-97 years, N {approx} 500,000; 54,000 with brain imaging). Education was positively related to intracranial (ICV) and total brain gray matter (GM) volume. Income was related to ICV, but not GM. Relationships varied significantly across samples, and SES was more strongly related to brain and cognition in US than European cohorts. Differences in neuroanatomical volumes explained part of the SES-cognition relationships. SES was more strongly related to ICV than to GM, implying that SES-cognition relations in adulthood are less likely grounded in neuroprotective effects on GM volume in aging. Rather, a relationship may be established early in life. The findings underscore that SES has no uniform association with, or impact on, brain and cognition.

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bioRxiv2020

Predictive Neural Computations Support Spoken Word Recognition: Evidence from MEG and Competitor Priming

Human listeners achieve quick and effortless speech comprehension through computations of conditional probability using Bayes rule. However, the neural implementation of Bayesian perceptual inference remains unclear. Competitive-selection accounts (e.g. TRACE) propose that word recognition is achieved through direct inhibitory connections between units representing candidate words that share segments (e.g. hygiene and hijack share /haid{ezh}/). Manipulations that increase lexical uncertainty should increase neural responses associated with word recognition when words cannot be uniquely identified. In contrast, predictive-selection accounts (e.g. Predictive-Coding) proposes that spoken word recognition involves comparing heard and predicted speech sounds and using prediction error to update lexical representations. Increased lexical uncertainty in words like hygiene and hijack will increase prediction error and hence neural activity only at later time points when different segments are predicted. We collected MEG data from male and female listeners to test these two Bayesian mechanisms and used a competitor priming manipulation to change the prior probability of specific words. Lexical decision responses showed delayed recognition of target words (hygiene) following presentation of a neighbouring prime word (hijack) several minutes earlier. However, this effect was not observed with pseudoword primes (higent) or targets (hijure). Crucially, MEG responses in the STG showed greater neural responses for word-primed words after the point at which they were uniquely identified (after /haid{ezh}/ in hygiene) but not before while similar changes were again absent for pseudowords. These findings are consistent with accounts of spoken word recognition in which neural computations of prediction error play a central role. Significance StatementEffective speech perception is critical to daily life and involves computations that combine speech signals with prior knowledge of spoken words; that is, Bayesian perceptual inference. This study specifies the neural mechanisms that support spoken word recognition by testing two distinct implementations of Bayes perceptual inference. Most established theories propose direct competition between lexical units such that inhibition of irrelevant candidates leads to selection of critical words. Our results instead support predictive-selection theories (e.g. Predictive-Coding): by comparing heard and predicted speech sounds, neural computations of prediction error can help listeners continuously update lexical probabilities, allowing for more rapid word identification.

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bioRxiv2020

Memory deficits following hippocampal versus parahippocampal damage double-dissociate according to both process and material type

A central debate in the systems neuroscience of memory concerns whether different medial temporal lobe (MTL) structures support different processes or material-types in recognition memory. We tested a rare patient (Patient MH) with a perirhinal lesion that appeared to spare the hippocampus, using two recognition memory paradigms, each run separately with faces, scenes and words. Replicating reports of a previous case, Patient MH showed impaired familiarity and preserved recollection, relative to controls, with no evidence for any effect of material-type. Moreover, when compared with other amnesic patients, who had hippocampal lesions that appeared to spare the perirhinal cortex, Patient MH showed greater impairment on familiarity and less on recollection, forming a double dissociation. However, when replacing this traditional, binary categorization of patients with a parametric analysis that related memory performance to continuous measures of brain damage across all patients, we found a different pattern: while hippocampal damage predicted recollection, it was parahippocampal instead of perirhinal (or entorhinal) cortex volume that predicted familiarity. Furthermore, there was no evidence that these brain-behavior relationships were moderated by material-type, nor by laterality of damage. Thus, while our data provide the most compelling support yet for dual-process models of recognition memory, in which recollection and familiarity depend on different MTL structures, they suggest that familiarity depends more strongly upon the parahippocampal rather than perirhinal cortex. More generally, our study reinforces the need to go beyond single-case and group studies, and instead examine continuous brain-behavior relationships across larger patient groups.

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bioRxiv2019

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and neurovascular factors

Accurate identification of brain function is necessary to understand neurocognitive ageing, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is therefore essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health and cerebrovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and cerebrovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and cerebrovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and cerebrovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age-differences in RSFA, after controlling for cardiovascular health. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and cerebrovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterise neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.

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bioRxiv2019

Improved motion correction of submillimetre 7T fMRI time series with boundary-based registration (BBR)

Ultra-high field functional magnetic resonance imaging (fMRI) has allowed us to acquire images with submillimetre voxels. However, in order to interpret the data clearly, we need to accurately correct head motion and the resultant distortions. Here, we present a novel application of Boundary Based Registration (BBR) to realign functional Magnetic Resonance Imaging (fMRI) data and evaluate its effectiveness on a set of 7T submillimetre data, as well as millimetre 3T data for comparison. BBR utilizes the boundary information from high contrast present in structural data to drive registration of functional data to the structural data. In our application, we realign each functional volume individually to the structural data, effectively realigning them to each other. In addition, this realignment method removes the need for a secondary aligning of functional data to structural data for purposes such as laminar segmentation or registration to data from other scanners. We demonstrate that BBR realignment outperforms standard realignment methods across a variety of data analysis methods. Further analysis shows that this benefit is an inherent property of the BBR cost function and not due to the difference in target volume. Our results show that BBR realignment is able to accurately correct head motion in 7T data and can be utilized in preprocessing pipelines to improve the quality of 7T data.

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bioRxiv2019

Characterising group-level brain connectivity: a framework using Bayesian exponential random graph models

The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of a entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brains functional connectivity structure across a group of young individuals and a group of old individuals.

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bioRxiv2019

Distinct roles for the Anterior Temporal Lobe and Angular Gyrus in the spatio-temporal cortical semantic network

It is now well recognised that human semantic knowledge is supported by a large neural network distributed over multiple brain regions, but the dynamic organisation of this network remains unknown. Some studies have proposed that a central semantic hub coordinates this network. We explored the possibility of different types of semantic hubs; namely "representational hubs", whose neural activity is modulated by semantic variables, and "connectivity hubs", whose connectivity to distributed areas is modulated by semantic variables. We utilised the spatio-temporal resolution of source-estimated Electro-/Magnetoencephalography data in a word-concreteness task (17 participants, 12 female) in order to: (i) find representational hubs at different timepoints based on semantic modulation of evoked brain activity in source space; (ii) identify connectivity hubs among left Anterior Temporal Lobe (ATL), Angular Gyrus (AG), Middle Temporal Gyrus and Inferior Frontal Gyrus based on their functional connectivity to the whole cortex, in particular sensory-motor-limbic systems; and (iii) explicitly compare network models with and without an intermediate hub linking sensory input to other candidate hub regions using Dynamic Causal Modelling (DCM) of evoked responses. ATLs activity was modulated as early as 150ms post-stimulus, while both ATL and AG showed modulations of functional connectivity with sensory-motor-limbic areas from 150-450ms. DCM favoured models with one intermediate hub, namely ATL in an early time window and AG in a later time-window. Our results support ATL as a single representational hub with an early onset, but suggest that both ATL and AG function as connectivity hubs depending on the stage of semantic processing.

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bioRxiv2019

Tau pathology in early Alzheimer’s disease disrupts selective neurophysiological networks dynamics

The role of aggregation of misfolded Tau protein in the pathogenesis of Alzheimers disease is the subject of rapid biomarker development and new therapeutic strategies to slow or prevent dementia. We tested the hypothesis that Tau pathology is associated with functional organization of widespread neurophysiological networks. We used electro-magnetoencephalography (E/MEG) in combination with [18F]AV1451 PET scanning to quantify Tau-dependent network disruption. Using a graph theoretical approach to MEG connectivity, we quantified nodal measures of functional segregation, centrality and efficiency of information transfer. We correlated these metrics against the nodes uptake of [18F]AV1451. There were both regional- and frequency-specific effects of Tau levels on the efficiency of information transfer and network segregation in early AD. Tau correlated with temporal regional participation coefficient (in delta, theta, beta bands); and temporal lobar eigenvector centrality (in theta, alpha, beta bands), but greater eccentricity at higher frequencies (gamma). The results support the translational development of neurophysiological "signatures" as biomarkers of Alzheimers disease, with potential to facilitate experimental medicines studies.

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bioRxiv2018

Differentiation of mild cognitive impairment using an entorhinal cortex-based test of VR navigation

The entorhinal cortex is one of the first regions to exhibit neurodegeneration in Alzheimers disease, and as such identification of entorhinal cortex dysfunction may aid detection of the disease in its earliest stages. Extensive evidence demonstrates that the entorhinal cortex is critically implicated in navigation underpinned by the firing of spatially modulated neurons. This study tested the hypothesis that entorhinal-dependent navigation is impaired in pre-dementia Alzheimers disease.\n\nForty-five patients with mild cognitive impairment (26 with CSF Alzheimers disease biomarker data: 12 biomarker-positive and 14 biomarker-negative) and 41 healthy control participants undertook an immersive virtual reality path integration test, as a measure of entorhinal-dependent navigation. Behavioural performance was correlated with MRI measures of entorhinal cortex volume, and the classification accuracy of the path integration task was compared with a battery of cognitive tests considered sensitive and specific for early Alzheimers Disease.\n\nBiomarker-positive patients exhibited larger errors in the navigation task than biomarker-negative patients, whose performance did not significantly differ from controls participants. Path-integration errors were negatively correlated with the volumes of the total entorhinal cortex and of its posteromedial subdivision. The path integration task demonstrated higher diagnostic sensitivity and specificity for differentiating biomarker positive versus negative patients (area under the curve = 0.90) than was achieved by the best of the cognitive tests (area under the curve = 0.57).\n\nThis study demonstrates that an entorhinal cortex-based virtual reality navigation task can differentiate patients with mild cognitive impairment at low and high risk of developing dementia, with classification accuracy superior to reference cognitive tests considered to be highly sensitive to early Alzheimers disease. This study provides evidence that navigation tasks may aid early diagnosis of Alzheimers disease, and the basis of this in animal cellular and behavioural studies provides the opportunity to answer the unmet need for translatable outcome measures for comparing treatment effect across preclinical and clinical trial phases of future anti-Alzheimers drugs.

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bioRxiv2018

Multimodal integration and vividness in the angular gyrus during episodic encoding and retrieval

Much evidence suggests that the angular gyrus (AnG) is involved in episodic memory, but its precise role is yet to be determined. We examined two possible accounts, within the same experimental paradigm: the CoBRA account (Shimamura, 2011), which suggests that the AnG acts as a convergence zone that binds multimodal episodic features; and the Subjectivity account (Yazar et al., 2012), which implicates AnG involvement in subjective mnemonic experience (such as vividness or confidence). fMRI was employed during both encoding and retrieval of paired-associates. During study, female and male human participants memorised picture-pairs of common objects (in the unimodal task) or of an object-picture and an environmental sound (in the crossmodal task). At test, they performed a cued-recall task, and further indicated the vividness of their memory. During retrieval, BOLD activation in the AnG was greatest for vividly remembered associates, consistent with the Subjectivity account. During encoding, the same effect of vividness was found, but this was further modulated by task: Greater activations were associated with subsequent recall in the crossmodal than the unimodal task. Thus, encoding data suggests an additional role to the AnG in cross-modal integration, consistent with its role at retrieval proposed by CoBRA. These results resolve some of the puzzles in the literature and indicate that the AnG can play different roles during encoding and retrieval, determined by the cognitive demands posed by different mnemonic tasks.\n\nSignificance StatementWe offer new insights into the multiplicity of processes that are associated with angular gyrus (AnG) activation during encoding and retrieval of newly formed memories. We used fMRI while human participants learned and subsequently recalled pairs of objects presented to the same sensory modality or to different modalities. We were able to show that the AnG is involved when vivid memories are created and retrieved, as well as when encoded information is integrated across different sensory modalities. These findings provide novel evidence for the contribution of the AnG to our subjective experience of remembering, alongside its role in integrative processes that promote subsequent memory.

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bioRxiv2018

Motor learning decline with age is related to differences in the explicit memory system

The ability to adapt ones movements to changes in the environment is fundamental in everyday life, but this ability changes across the lifespan. Although often regarded as an implicit process, recent research has also linked motor adaptation with explicit learning processes. To understand how these processes contribute to differences in motor adaptation with age, we combined a visuomotor learning paradigm with cognitive tasks that measure implicit and explicit processes, and structural brain imaging. In a large population-based cohort from the Cambridge Centre for Ageing and Neuroscience (n=322, aged 18-89 years) we first confirmed that the degree of adaptation to an angular perturbation of visual feedback declined with age. There were no associations between adaptation and sensory attenuation, which has been previously hypothesised to contribute to implicit motor learning. However, interactions between age and scores on two independent memory tasks showed that explicit memory performance was a progressively stronger determinant of motor learning with age. Similarly, interactions between age and grey matter volume in the medial temporal lobe, amygdala and hippocampus showed that grey matter volume in these regions became a stronger determinant of adaptation in older adults. The convergent behavioural and structural imaging results suggest that age-related differences in the explicit memory system is a contributor to the decline in motor adaptation in older age. These results may reflect the more general compensatory reliance on cognitive strategies to maintain motor performance with age.\n\nSIGNIFICANCE STATEMENTThe central nervous system has a remarkable capacity to learn new motor skills and adapt to new environmental dynamics. This capacity is impaired with age, and in many brain disorders. We find that explicit memory performance and its associated medial temporal brain regions deteriorate with age, but the association between this brain system and individual differences in motor learning becomes stronger in older adults. We propose that these results reflect an increased reliance on cognition in order to maintain adaptive motor skill performance. This difference in learning strategy has implications for interventions to improve motor skills in older adults.

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bioRxiv2018

Neural evidence for age differences in representational quality and strategic retrieval processes

Mounting behavioral evidence suggests that declines in both representational quality and controlled retrieval processes contribute to episodic memory decline with age. The present study sought neural evidence for age-related change in these factors by measuring neural differentiation during encoding of paired associates, and changes in regional BOLD activity and functional connectivity during retrieval conditions that placed low (intact pairs) and high (recombined pairs) demands on controlled retrieval processes. Pattern similarity analysis revealed age-related declines in the differentiation of stimulus representations at encoding, manifesting as both reduced pattern similarity between closely related events, and increased pattern similarity between distinct events. During retrieval, both groups exhibited increased recruitment of areas within the core recollection network, including the hippocampus and angular gyrus, when endorsing studied pairs, whereas younger adults exhibited increased recruitment of, and hippocampal connectivity with, lateral prefrontal regions during correct rejections of recombined pairs. These results provide evidence for age-related changes in representational quality and in the neural mechanisms supporting memory retrieval under conditions of high, but not low, control demand.

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bioRxiv2018

Physical activity predicts population-level age-related differences in frontal white matter

Physical activity has positive effects on brain health and cognitive function throughout the lifespan. Thus far, few studies have examined the effects of physical activity on white matter (WM) microstructure and psychomotor speed within the same, population-based sample (critical if conclusions are to extend to the wider population). Here, using diffusion tensor imaging and a simple reaction time task within a relatively large population-derived sample (N = 399; 18-87 years) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN), we demonstrate that physical activity mediates the effect of age on white matter integrity, measured with fractional anisotropy. Higher self-reported daily physical activity was associated with greater preservation of WM in several frontal tracts, including the genu of corpus callosum, uncinate fasciculus, external capsule and anterior limb of the internal capsule. We also show that the age-related slowing is mediated by WM integrity in the genu. Our findings contribute to a growing body of work suggesting that a physically active lifestyle may protect against age-related structural disconnection and slowing.

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bioRxiv2018

Prospective Motion correction improves the sensitivity of fMRI pattern decoding

We evaluated the effectiveness of prospective motion correction (PMC) on a simple visual task when no deliberate subject motion was present. The PMC system utilizes an in-bore optical camera to track an external marker attached to the participant via a custom-moulded mouthpiece. The study was conducted at two resolutions (1.5mm vs 3mm) and under three conditions (PMC On and Mouthpiece On vs PMC Off and Mouthpiece On vs PMC Off and Mouthpiece Off). Multiple data analysis methods were conducted, including univariate and multivariate approaches, and we demonstrated that the benefit of PMC is most apparent for multi-voxel pattern decoding at higher resolutions. Additional testing on two participants showed that our inexpensive, commercially available mouthpiece solution produced comparable results to a dentist-moulded mouthpiece. Our results showed that PMC is increasingly important at higher resolutions for analyses that require accurate voxel registration across time.

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bioRxiv2018

The hippocampal film-editor: sensitivity and specificity to event boundaries in continuous experience

The function of the human hippocampus is normally investigated by experimental manipulation of discrete events. Less is unknown about what triggers hippocampal activity during more naturalistic, continuous experience. We hypothesized that the hippocampus would be sensitive to the occurrence of event boundaries, i.e. moments in time identified by observers as a transition between events. To address this, we analysed functional MRI data from two groups: one (N=253, 131 female) who viewed an 8.5min film and another (N=15, 6 female) who viewed a 120min film. We observed a strong hippocampal response at boundaries defined by independent observers, which was modulated by boundary strength (the number of observers that identified each boundary). In the longer film, there were sufficient boundaries to show that this modulation remained after covarying out a large number of perceptual factors. The hippocampus was the only brain region whose response showed a significant monotonic increase with boundary strength in both films, suggesting that modulation by boundary strength is selective to the hippocampus. This hypothesis-driven approach was complemented by a data-driven approach, in which we identified hippocampal-events as moments in time with the strongest hippocampal activity: The correspondence between these hippocampalevents and event boundaries was highly-significant, revealing that the hippocampal response is not only sensitive, but also specific to event boundaries. We conclude that the hippocampus plays an important role in segmenting the continuous experience that is typical of naturalistic settings.\n\nSignificance statementRecent years have seen the field of human neuroscience research transitioning from experiments with simple stimuli to the study of more complex and naturalistic experience. Nonetheless, our understanding of the function of many brain regions, such as the hippocampus, is based primarily on the study of brief, discrete events. As a result, we know little of what triggers hippocampal activity in real-life settings, when we are exposed to a continuous stream of information. When does the hippocampus \"decide\" to respond during the encoding of naturalistic experience? We reveal here that hippocampal activity measured by fMRI during film-watching is both sensitive and specific to event boundaries, identifying a potential mechanism whereby event boundaries shape experience by modulation of hippocampal activity.

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bioRxiv2017

MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography

We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG provides direct measurement of brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS has provided a solution to structure the organization of magnetic resonance imaging (MRI) data, which nature and acquisition parameters are different. Despite the lack of standard data format for MEG, MEG-BIDS is a principled solution to store, organize and share the typically-large data volumes produced. It builds on BIDS for MRI, and therefore readily yields a multimodal data organization by construction. This is particularly valuable for the anatomical and functional registration of MEG source imaging with MRI. With MEG-BIDS and a growing range of software adopting the standard, the MEG community has a solution to minimize curation overheads, reduce data handling errors and optimize usage of computational resources for analytics. The standard also includes well-defined metadata, to facilitate future data harmonization and sharing efforts.

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