Biology subjects

Cam-CAN,

Publications and source records attributed to Cam-CAN,.

At least 19 recordsLinked to original sources

bioRxiv2026

Brain representations of visual short-term memories during healthy human aging: occipital decline and frontoparietal compensation

Visual short-term memory declines steeply with age, but the link to neural representations of memory content remains unknown. We used fMRI decoding to relate remembered motion-direction representations in visual and frontoparietal cortex to age and performance, in 115 adults, aged 25-87, from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) cohort. Decoding was strongest in visual cortex, but also significant in frontoparietal regions, with different profiles. Visual representations weakened with memory load and age, and statistically mediated age-related performance decline, especially during recall. This coupling, however, weakened with age: some older adults retained stronger visual representations than performance implied, suggesting impaired access to a partially preserved memory trace. Frontoparietal representations, instead, were largely insensitive to load and strengthened with age during maintenance, partially offsetting performance decline. Therefore, interventions to boost VSTM in older adults might target compensatory frontoparietal representations during memory maintenance, and readout of untapped visual representations at recall.

neuroscience↗

bioRxiv2026

Inter- and intra-individual variability in structure-function coupling in human brain

Microstructure shapes functional brain dynamics. Yet this notion has remained largely untested for human brain signals measured with electroencephalography and magnetoencephalography (EEG/MEG). In this study, we related functional activity variance to microstructural variance across brain regions and across participants. Across regions, we used multiple independent datasets: EEG (n=209), MEG (n=507), high-resolution MRI (n=10), and the human post-mortem brain. Across participants, we used a multimodal dataset including MEG and high-resolution MRI (n=31). Between regions, alpha power correlated positively with layer IV thickness and with myelin and iron estimates at mid-cortical depth. By contrast, between participants, alpha power correlated negatively with mid-cortical myelin and iron estimates. Using computational modeling, we demonstrated that regional effects are consistent with excitatory changes, whereas inter-individual effects may be driven by inhibitory influence. This dissociation reveals distinct underlying mechanisms and indicates that different factors influence intra-versus inter-individual variations in alpha activity.

neuroscience↗

bioRxiv2024

Multiple neural pathways to successful visual short-term memory across the adult lifespan

Cognitive task performance can be supported through multiple brain activation patterns, a concept referred to as brain degeneracy. We used a novel approach to consider brain degeneracy during a visual short-term memory task (VSTM) across the adult lifespan in the cross-sectional Cam-CAN study (n = 113, 23-87 years old). Our main goal was to identify subgroups of participants whose VSTM performance was characterized by distinct brain activation patterns. First, we identified seven brain modules that responded similarly to the VSTM task across participants, and resembled previously identified functional networks. Subsequently, latent profile analysis revealed four distinct subgroups of participants. Each subgroup was characterized by different recruitment patterns of these brain modules, predominantly in the frontal control module (FCM), visual module (VM), and default mode module (DMM). Subgroups did not differ in demographics or task performance. However, associations between brain activity and performance varied across subgroups, particularly in the FCM, suggesting that individuals may use different cognitive operations to perform the VSTM task. Further analyses revealed group differences in white matter integrity, mostly in the uncinate fasciculus, suggesting that individual differences in structural brain properties may shape the different brain activation patterns. Altogether, our study contributes to our understanding of how multiple brain activation patterns could underlie cognitive performance.

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bioRxiv2023

Behavioral and neuroimaging correlates of attentional biases to angry faces in individuals in remission from depression: a population-derived study

BackgroundDepressed individuals show attentional biases in the processing of emotional stimuli, such as negative face expressions. Some of these biases persist in previously depressed individuals, but their mechanisms remain largely unknown. MethodsA population-derived cohort (n = 134, 68 females; 21 - 92 years) was recruited by Cam-CAN. Functional MRI was acquired during a gender discrimination task, which used angry and neutral faces. Drift diffusion modelling (DDM) was used to investigate the latent components of the decision process, focusing on the effect of emotional valence. DDM parameters were correlated with activity in brain regions. Results14% of participants reported a history of depression in remission. The best fitting DDM specified a different drift rate for angry and neutral faces. A slower drift rate for angry faces predicted depression in remission (OR 0.092, p = 0.048). This effect persisted after accounting for current depression symptoms and drift rate for neutral faces. Participants with a slower drift rate for angry faces demonstrated increased activations in the bilateral insula, bilateral inferior frontal gyrus and bilateral parietal cortex when viewing angry relative to neutral faces. ConclusionsOur results suggest a persistent attentional bias in the processing of angry faces in individuals with depression in remission, over and above their current depressive symptoms. The imaging findings suggest that the slowing is associated with changes in areas involved in emotional regulation and evidence accumulation. Attentional biases in the processing of emotional information may reflect a trait, rather than state, in individuals with depression.

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bioRxiv2023

Cognition's dependence on functional network integrity with age is conditional on structural network integrity

Maintaining good cognitive function is crucial for well-being across the lifespan. We proposed that the degree of cognitive maintenance is determined by the functional interactions within and between large-scale brain networks. Such connectivity can be represented by the white matter architecture of structural brain networks that shape intrinsic neuronal activity into integrated and distributed functional networks. We explored how the function-structure connectivity convergence, and the divergence of functional connectivity from structural connectivity, contribute to the maintenance of cognitive function across the adult lifespan. Multivariate analyses were used to investigate the relationship between function-structure connectivity convergence and divergence with multivariate cognitive profiles, respectively. Cognitive function was increasingly dependent on function-structure connectivity convergence as age increased. The dependency of cognitive function on connectivity was particularly strong for high-order cortical networks and subcortical networks. The results suggest that brain functional network integrity sustains cognitive functions in old age, as a function of the integrity of the brains structural connectivity.

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bioRxiv2022

Neural contributions to reduced fluid intelligence across the adult lifespan

Fluid intelligence - the ability to solve novel, complex problems - declines steeply during healthy human aging. Using functional magnetic resonance imaging (fMRI), fluid intelligence has been repeatedly associated with activation of a frontoparietal brain network, and focal damage to these regions suggests that fluid intelligence depends on their integrity. It is therefore possible that age-related functional differences in frontoparietal activity contribute to the reduction in fluid intelligence. This paper reports on analysis of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data, a large, population-based, healthy, adult lifespan cohort. The data support a model in which age-related differences in fluid intelligence are partially mediated by the responsiveness of frontoparietal regions to novel problem-solving. We first replicate a prior finding of such mediation using an independent sample. We then precisely localise the mediating brain regions, and show that mediation is specifically associated with voxels most activated by cognitive demand, but not with voxels suppressed by cognitive demand. We quantify the robustness of this result to potential unmodelled confounders, and estimate the causal direction of the effects. Finally, exploratory analyses suggest that neural mediation of age-related differences in fluid intelligence is moderated by the variety of regular physical activities, more reliably than by their frequency or duration. An additional moderating role of the variety of non-physical activities emerged when controlling for head motion. A better understanding of the mechanisms that link healthy aging with lower fluid intelligence may suggest strategies for mitigating such decline. Significance StatementGlobal populations are living longer, driving urgency to understand age-related cognitive declines. Fluid intelligence is of prime importance, because it reflects performance across many domains, and declines especially steeply during healthy aging. Despite consensus that fluid intelligence is associated with particular frontoparietal brain regions, little research has investigated suggestions that under-responsiveness of these regions mediates age-related decline. We replicate a recent demonstration of such mediation, showing specific association with brain regions most activated by cognitive demand, and robustness to moderate confounding by unmodelled variables. By showing that this mediation model is moderated by the variety of regular physical activities, more reliably than by their frequency or duration, we identify a potential modifiable lifestyle factor that may help promote successful aging.

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bioRxiv2021

Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy ageing subjects

Brain ageing is a complex process which requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization and vascular dynamics. However, most neuroimaging studies of ageing have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in ageing. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18 to 88 years (main dataset n = 215; secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy ageing.

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bioRxiv2021

A morphometric double dissociation: cortical thickness is more related to aging; surface area is more related to cognition

The thickness and surface area of cortex are genetically distinct aspects of brain structure, and may be affected differently by age. However, their potential to differentially predict age and cognitive abilities has been largely overlooked, likely because they are typically aggregated into the commonly used measure of volume. In a large sample of healthy adults (N=647, aged 18-88), we investigated the brain-age and brain-cognition relationships of thickness, surface area, and volume, plus five additional morphological shape metrics. Cortical thickness was the metric most strongly associated with age cross-sectionally, as well as exhibiting the steepest longitudinal change over time (subsample N=261, aged 25-84). In contrast, surface area was the best single predictor of age-residualized cognitive abilities (fluid intelligence), and changes in surface area were most strongly associated with cognitive change over time. These findings were replicated in an independent dataset (N=1345, aged 18-93). Our results suggest that cortical thickness and surface area make complementary contributions the age-brain-cognition triangle, and highlight the importance of considering these volumetric components separately.

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bioRxiv2021

The "neural shift" of sleep quality and cognitive ageing: A resting-state MEG study of transient neural dynamics

Sleep quality changes dramatically from young to old age, but its effects on brain dynamics and cognitive functions are not yet fully specified. We applied Hidden Markov Models (HMMs) to resting-state MEG data from a large cohort (N=564) of population-based adults (aged 18-88), in order to characterize transient neural networks and to relate their temporal dynamics to sleep quality and to cognitive performance. Using multivariate analyses of brain-sleep profiles and of brain-cognition profiles, we found that an age-related "neural shift", expressed as decreased occurrence of "lower-order" brain networks, coupled with increased occurrence of "higher-order" networks, was associated with both increased sleep dysfunction and decreased fluid intelligence above and beyond age. These results suggest that poor sleep quality, as evident in ageing, may lead to a behavior-related shift in neural dynamics.

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bioRxiv2021

Does Hemispheric Asymmetry Reduction in Older Adults (HAROLD) in motor cortex reflect compensation?

Older adults tend to display greater brain activation in the non-dominant hemisphere during even basic sensorimotor responses. It is debated whether this Hemispheric Asymmetry Reduction in Older Adults (HAROLD) reflects a compensatory mechanism. Across two independent fMRI experiments involving an adult-lifespan human sample (N = 586 and N = 81; approximately half female) who performed right hand finger responses, we distinguished between these hypotheses using behavioural and multivariate Bayes (MVB) decoding approaches. Standard univariate analyses replicated a HAROLD pattern in motor cortex, but in- and out-of-scanner behavioural results both demonstrated evidence against a compensatory relationship, in that reaction time measures of task performance in older adults did not relate to ipsilateral motor activity. Likewise, MVB showed that this increased ipsilateral activity in older adults did not carry additional information, and if anything, combining ipsilateral with contralateral activity patterns reduced action decoding in older adults (at least in Experiment 1). These results contradict the hypothesis that HAROLD is compensatory, and instead suggest that the age-related, ipsilateral hyper-activation is non-specific, in line with alternative hypotheses about age-related reductions in neural efficiency/differentiation or inter-hemispheric inhibition. Significance StatementA key goal in the cognitive neuroscience of ageing is to provide a mechanistic explanation of how brain-behaviour relationships change with age. One interpretation of the common finding that task-based hemispheric activity becomes more symmetrical in older adults, is that this shift reflects a compensatory mechanism, with the non-dominant hemisphere needing to "help out" with computations normally performed by the dominant hemisphere. Contrary to this view, our behavioural and brain data indicate that the additional activity in ipsilateral motor cortex in older adults is not reflective of better task performance nor better neural representations of finger actions.

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bioRxiv2020

Genetic signatures of human brain structure: A comparison between GWAS and relatedness-based regression

Identifying the genetic variations impacting human brain structure and their further effects on cognitive functions, is important for our understanding of the fundamental bases of cognition. In this study, we take two different approaches to this issue: classical genome-wide association analysis (GWAS) and a relatedness-based regression approach (REL) to search for associations between genotype and brain structural measures of gray matter and white matter. Instead of searching genetic variants by testing the association between a phenotype trait and the genotype of each single-nucleotide polymorphism (SNP) as in GWAS, REL takes advantage of multiple SNPs within a genomic window as a single measure, which potentially find associations wherever the functional SNP is in linkage disequilibrium (LD) with SNPs that have been sampled. We also conducted a simulation analysis to systemically compare GWAS and REL with respect to different levels of LD. Both methods succeed in identifying genetic variations associated with regional and global brain structural measures and tend to give complementary results due to the different aspects of genetic properties used. Simulation results suggest that GWAS outperforms REL when the signal is relatively weak. However, the collective effects due to local LD boost the performance of REL with increasing signal strength, resulting in better performance than GWAS. Our study suggests that the optimal approach may vary across the genome and that pre-testing for LD could allow GWAS to be preferred where LD is high and REL to be used where LD is low, or the local pattern of LD is complex.

genetics↗

bioRxiv2020

Transient resting-state network dynamics in cognitive ageing

It is important to maintain cognitive function in old age, yet the neural substrates that support successful cognitive ageing remain unclear. One factor that might be crucial, but has been overlooked due to limitations of previous data and methods, is the ability of brain networks to flexibly reorganise and coordinate over a millisecond time-scale. Magnetoencephalography (MEG) provides such temporal resolution, and can be combined with Hidden Markov Models (HMMs) to characterise transient neural states. We applied HMMs to resting-state MEG data from a large cohort (N=594) of population-based adults (aged 18-88), who also completed a range of cognitive tasks. Using multivariate analysis of neural and cognitive profiles, we found that decreased occurrence of "lower-order" brain networks, coupled with increased occurrence of "higher-order" networks, was associated with both increasing age and impaired fluid intelligence. These results favour theories of age-related reductions in neural efficiency over current theories of age-related functional compensation.

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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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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

Lifestyle activities in mid-life contribute to cognitive reserve in late-life, independent of education, occupation and late-life activities

IntroductionThis study tested the hypothesis that mid-life intellectual, physical and social activities contribute to cognitive reserve (CR).\n\nMethods205 individuals (196 with MRI) aged 66-88 from the Cambridge Centre for Ageing and Neuroscience (www.cam-can.com) were studied, with cognitive ability and structural brain health measured as fluid IQ and total grey matter volume, respectively. Mid-life activities were measured using the Lifetime of Experiences Questionnaire.\n\nResultsMultivariable linear regression found that mid-life activities (MA) made a unique contribution to late-life cognitive ability independent of education, occupation and late-life activities. Crucially, MA moderated the relationship between late-life cognitive ability and brain structure, with the cognitive ability of people with higher MA less dependent on their brain structure, consistent with the concept of CR.\n\nConclusions. Mid-life intellectual, physical and social activities contribute uniquely to CR. The modifiability of these activities has implications for public health initiatives aimed at dementia prevention.

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bioRxiv2018

Cardiovascular risk factors for micro- and macro-structural brain changes in healthy ageing

Cardiovascular health declines with age, increasing the risk of hypertension and elevated heart rate in middle- and old age. Here, we used multivariate techniques to investigate the associations between cardiovascular health (diastolic blood pressure, systolic blood pressure and heart rate) and white matter macrostructure (lesion volume and number), and microstructure (as measured by Diffusion Weighted Imaging) in the cross-sectional, population-based Cam-CAN cohort (N = 667, aged 18 to 88). We found that cardiovascular health and age made approximately similar contributions to white matter health and explained up to 56% of variance. Lower diastolic blood pressure, higher systolic blood pressure and higher heart rate were each strongly, and independently, associated with white matter abnormalities on all indices. Body mass and exercise were associated with white matter health, both directly and indirectly via cardiovascular health. These results highlight the importance of cardiovascular risk factors for white matter health across the adult lifespan and suggest that systolic blood pressure, diastolic blood pressure and heart rate affect white matter via separate mechanisms.

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bioRxiv2017

Sensory attenuation is related to dopamine dose in Parkinson’s disease

Abnormal initiation and control of voluntary movements are among the principal manifestations of Parkinsons disease (PD). However, the processes underlying these abnormalities and their potential remediation by dopamine treatment remain poorly understood. Normally, movements depend on the integration of sensory information with the predicted consequences of action. This integration leads to a suppression in the intensity of predicted sensations, and increases the relative salience of unexpected stimuli to facilitate the control of movements. We examined this integration process and its relation to dopamine in PD, by measuring sensorimotor attenuation - the reduction in the perceived intensity of predicted sensations from self-generated versus external actions. Patients with idiopathic PD (n=18) and population-derived controls (n=175) matched a set of target forces applied to their left index finger by a torque motor. To match the force, participants either pressed with their right index finger ( Direct condition) or used a linear potentiometer that controlled a motor ( Slider condition). We found that despite changes in sensitivity to different forces, overall sensory attenuation did not differ between medicated PD patients and controls. Importantly, the degree of attenuation was negatively related to PD motor severity but positively related to individual patient dopamine dose, as measured by levodopa dose equivalency. The results suggest that dopamine could regulate the integration of sensorimotor prediction with sensory information to facilitate the control of voluntary movements.

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bioRxiv2017

Assessing dynamic functional connectivity in heterogeneous samples

Several methods have been developed to measure dynamic functional connectivity (dFC) in fMRI data. These methods are often based on a sliding-window analysis, which aims to capture how the brains functional organization varies over the course of a scan. The aim of many studies is to compare dFC across groups, such as younger versus older people. However, spurious group differences in measured dFC may be caused by other sources of heterogeneity between people. For example, the shape of the haemodynamic response function (HRF) and levels of measurement noise have been found to vary with age. We use a generic simulation framework for fMRI data to investigate the effect of such heterogeneity on estimates of dFC. Our findings show that, despite no differences in true dFC, individual differences in measured dFC can result from other (non-dynamic) features of the data, such as differences in neural autocorrelation, HRF shape, connectivity strength and measurement noise. We also find that common dFC methods such as k-means and multilayer modularity approaches can detect spurious group differences in dynamic connectivity due to inappropriate setting of their hyperparameters. fMRI studies therefore need to consider alternative sources of heterogeneity across individuals before concluding differences in dFC.

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