Biology subjects

Henson, R. N.

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

41 records · Page 3Linked to original sources

bioRxiv2017

From neurons to voxels - repetition suppression is best modelled by local neural scaling

Inferring neural mechanisms from functional magnetic resonance imaging (fMRI) is challenging because the fMRI signal integrates over millions of neurons. One approach is to compare computational models that map neural activity to fMRI responses, to see which best predicts fMRI data. We used this approach to compare four possible neural mechanisms of fMRI adaptation to repeated stimuli (scaling, sharpening, repulsive shifting and attractive shifting), acting across three domains (global, local and remote). Six features of fMRI repetition effects were identified, both univariate and multivariate, from two independent fMRI experiments. After searching over parameter values, only the local scaling model could simultaneously fit all data features from both experiments. Thus fMRI stimulus repetition effects are best captured by down-scaling neuronal tuning curves in proportion to the difference between the stimulus and neuronal preference. These results emphasize the importance of formal modelling for bridging neuronal and fMRI levels of investigation.

neuroscience↗

bioRxiv2017

Increased prefrontal activity with aging reflects nonspecific neural responses rather than compensation

Elevated prefrontal cortex activity is often observed in healthy older adults despite declines in their memory and other cognitive functions. According to one view, this activity reflects a compensatory functional posterior-to-anterior shift, which contributes to maintenance of cognitive performance when posterior cortical function is impaired. Alternatively, the increased prefrontal activity may be less efficient or less specific, owing to structural and neurochemical changes accompanying aging. These accounts are difficult to distinguish on the basis of average activity levels within brain regions. Instead, we used a novel model-based multivariate analysis technique, applied to two independent functional magnetic resonance imaging datasets from an adult-lifespan human sample (N=123 and N=115; approximately half female). Standard analysis replicated the age-related increase in average prefrontal activation, but multivariate tests revealed that this activity did not carry additional information. The results contradict the hypothesis of a compensatory posterior-to-anterior shift. Instead, they suggest that the increased prefrontal activation reflects reduced efficiency or specificity, rather than compensation.\n\nSignificance statementFunctional brain imaging studies have often shown increased activity in prefrontal brain regions in older adults. This has been proposed to reflect a compensatory shift to greater reliance on prefrontal cortex, helping to maintain cognitive function. Alternatively, activity may become less specific as people age. This is a key question in the neuroscience of aging. In this study, we used novel tests of how different brain regions contribute to long- and short-term memory. We found increased activity in prefrontal cortex in older adults, but this activity carried less information about memory outcomes than activity in visual regions. These findings are relevant for understanding why cognitive abilities decline with age, suggesting that optimal function depends on successful brain maintenance rather than compensation.

neuroscience↗

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.

neuroscience↗

bioRxiv2017

Symptoms of Depression in a Large Healthy Population Cohort are related to Subjective Memory Complaints and Memory Performance in Negative Contexts

Decades of research have investigated the impact of clinical depression on memory, which has revealed biases and in some cases impairments. However, little is understood about the effects of sub-clinical symptoms of depression on memory performance in the general population. Here we report the effects of symptoms of depression on memory problems in a large population-derived cohort (N = 2544), 87% of whom reported at least one symptom of depression. Specifically, we investigate the impact of depressive symptoms on subjective memory complaints, objective memory performance on a standard neuropsychological task and, in a subsample (n = 288), objective memory in affective contexts. There was a dissociation between subjective and objective memory performance, with depressive symptoms showing a robust relationship with self-reports of memory complaints, even after adjusting for age, gender, general cognitive ability and symptoms of anxiety, but not with performance on the standardised measure of verbal memory. Contrary to our expectations, hippocampal volume (assessed in a subsample, n = 592) did not account for significant variance in subjective memory, objective memory or depressive symptoms. Nonetheless, depressive symptoms were related to poorer memory for pictures presented in negative contexts, even after adjusting for memory for pictures in neutral contexts. Thus the symptoms of depression, associated with subjective memory complaints, appear better assessed by memory performance in affective contexts, rather than standardised memory measures. We discuss the implications of these findings for understanding the impact of depressive symptoms on memory functioning in the general population.

neuroscience↗

bioRxiv2017

Adaptive Cortical Parcellations for Source Reconstructed EEG/MEG Connectomes

There is growing interest in the rich temporal and spectral properties of the brain's functional connectome that are provided by Electro- and Magnetoencephalography (EEG/MEG). However, the problem of leakage between brain sources that arises when reconstructing brain activity from EEG/MEG recordings outside the head makes it difficult to distinguish true connections from spurious connections, even when connections are based on measures that ignore zero-lag dependencies. In particular, standard anatomical parcellations for potential cortical sources tend to over- or under-sample the real spatial resolution of EEG/MEG. By using information from cross-talk functions (CTFs) that objectively describe leakage for a given sensor configuration and distributed source reconstruction method, we introduce methods for optimising the number of parcels while simultaneously minimising the leakage between them. More specifically, we compare two image segmentation algorithms: 1) a split-and-merge (SaM) algorithm based on standard anatomical parcellations and 2) a region growing (RG) algorithm based on all the brain vertices with no prior parcellation. Interestingly, when applied to minimum-norm reconstructions for EEG/MEG configurations from real data, both algorithms yielded approximately 70 parcels despite their different starting points, suggesting that this reflects the resolution limit of this particular sensor configuration and reconstruction method. Importantly, when compared against standard anatomical parcellations, resolution matrices of adaptive parcellations showed notably higher sensitivity and distinguishability of parcels. Furthermore, extensive simulations of realistic networks under various circumstances revealed significant improvements in network reconstruction accuracies, particularly in reducing false leakage-induced connections. Adaptive parcellations therefore allow a more accurate reconstruction of functional EEG/MEG connectomes.\n\nHighlightsO_LIWe introduce adaptive cortical parcellation algorithms for E/MEG source estimation.\nC_LIO_LIAlgorithms are based on cross-talk functions and image segmentation methods.\nC_LIO_LIThe resulting parcellations yielded ~70 parcels regardless of starting point.\nC_LIO_LISensitivity and distinguishability improved compared to anatomical parcellations.\nC_LIO_LIAccuracy of realistic whole-brain network reconstruction improved significantly.\nC_LI

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