Search

Contains fulltext : 249538.pdf (Publisher’s version ) (Open Access) Recently, cognitive neuroscience has experienced unprecedented growth in the availability of large-scale datasets. These developments hold great methodological and theoretical promise: they allow increased statistical power, the use of nonparametric and generative models, the examination of individual differences, and more. Nevertheless, unlike most ´traditional´ cognitive neuroscience research, which uses controlled experimental designs, large-scale projects often collect neuroimaging data not directly related to a particular task (e.g., resting state). This creates a gap between small- and large-scale studies that is not solely due to differences in sample size. Measures obtained with large-scale studies might tap into different neurocognitive mechanisms and thus show little overlap with the mechanisms probed by small-scale studies. In this opinion article, we aim to address this gap and its potential implications for the interpretation of research findings in cognitive neuroscience. 10 p.

Environmental factors may play a role in how the personality trait sensory processing sensitivity (SPS) relates to different health-related outcomes. To better understand vulnerability and resilience in different environments, we investigated associations of SPS with stress-related and neurodevelopmental disorder traits, well-being, and somatic health in a population-based sample of 252 adults. We then tested SPS interactions with lifetime or current measures of positive or negative environments. Overall, higher SPS related to more burnout, anxiety, depression, stress, health complaints and nonprescription medication use, but not to neurodevelopmental disorder traits. Associations with burnout, anxiety, and health complaints remained after controlling for neuroticism. In unfavorable environments, higher SPS related to worse health while, in favorable environments, it related to better health, supporting the theory of SPS as a factor for differential susceptibility to environments. Our work demonstrates that SPS may be a risk factor for stress-related and somatic symptoms, independent of neuroticism.

A common finding in the aging literature is that of the brain's decreased within‐ and increased between‐network functional connectivity. However, it remains unclear what is causing this shift in network organization with age. Given the essential role of the ascending arousal system (ARAS) in cortical activation and previous findings of disrupted ARAS functioning with age, it is possible that age differences in ARAS functioning contribute to disrupted cortical connectivity. We test this possibility here using resting state fMRI data from over 500 individuals across the lifespan from the Cambridge Center for Aging and Neuroscience (Cam‐CAN) population‐based cohort. Our results show that ARAS‐cortical connectivity declines with age and, consistent with our expectations, significantly mediates some age‐related differences in connectivity within and between association networks (specifically, within the default mode and between the default mode and salience networks). Additionally, connectivity between the ARAS and association networks predicted cognitive performance across several tasks over and above the effects of age and connectivity within the cortical networks themselves. These findings suggest that age differences in cortical connectivity may be driven, at least in part, by altered arousal signals from the brainstem and that ARAS–cortical connectivity relates to cognitive performance with age., A common finding in the aging literature is that of the brain's decreased within‐ and increased between‐network functional connectivity. However, it remains unclear what is causing this shift in network organization with age. Our findings suggest that age differences in cortical connectivity may be driven, at least in part, by altered arousal signals from the brainstem and that ARAS–cortical connectivity relates to cognitive performance with age.

Although beta-band activity during motor planning is known to be modulated by uncertainty about where to act, less is known about its modulations to uncertainty about how to act. To investigate this issue, we recorded oscillatory brain activity with EEG while human participants (n = 17) performed a hand choice reaching task. The reaching hand was either predetermined or of participants' choice, and the target was close to one of the two hands or at about equal distance from both. To measure neural activity in a motion artifact-free time window, the location of the upcoming target was cued 1,000-1,500 ms before the presentation of the target, whereby the cue was valid in 50% of trials. As evidence for motor planning during the cuing phase, behavioral observations showed that the cue affected later hand choice. Furthermore, reaction times were longer in the choice trials than in the predetermined trials, supporting the notion of a competitive process for hand selection. Modulations of beta-band power over central cortical regions, but not alpha-band or theta-band power, were in line with these observations. During the cuing period, reaches in predetermined trials were preceded by larger decreases in beta-band power than reaches in choice trials. Cue direction did not affect reaction times or beta-band power, which may be due to the cue being invalid in 50% of trials, retaining effector uncertainty during motor planning. Our findings suggest that effector uncertainty modulates beta-band power during motor planning.NEW & NOTEWORTHY Although reach-related beta-band power in central cortical areas is known to modulate with the number of potential targets, here we show, using a cuing paradigm, that the power in this frequency band, but not in the alpha or theta band, is also modulated by the uncertainty of which hand to use. This finding supports the notion that multiple possible effector-specific actions can be specified in parallel up to the level of motor preparation.

Working memory and episodic memory are two different processes, although the nature of their interrelationship is debated. As these processes are predominantly studied in isolation, it is unclear whether they crucially rely on different neural substrates. To obtain more insight in this, 81 adults with sub-acute ischemic stroke and 29 elderly controls were assessed on a visual working memory task, followed by a surprise subsequent memory test for the same stimuli. Multivariate, atlas- and track-based lesion-symptom mapping (LSM) analyses were performed to identify anatomical correlates of visual memory. Behavioral results gave moderate evidence for independence between discriminability in working memory and subsequent memory, and strong evidence for a correlation in response bias on the two tasks in stroke patients. LSM analyses suggested there might be independent regions associated with working memory and episodic memory. Lesions in the right arcuate fasciculus were more strongly associated with discriminability in working memory than in subsequent memory, while lesions in the frontal operculum in the right hemisphere were more strongly associated with criterion setting in subsequent memory. These findings support the view that some processes involved in working memory and episodic memory rely on separate mechanisms, while acknowledging that there might also be shared processes. Supplementary Information The online version contains supplementary material available at 10.1007/s00429-021-02281-0.

Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substantial improvements of the sensitivity of cluster‐based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this article, we report on a set of simulations and analyses of real task fMRI data that demonstrate (a) that the proposed methods control the false‐alarm rate, (b) that the sensitivity profiles of cluster‐based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (c) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to fivefold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference., We present a combination of two approaches for functional magnetic resonance imaging data analysis that together result in substantial improvements of the sensitivity of cluster‐based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is the min(p) method, which is a novel way of combining test statistics with different sensitivity profiles.

Visual representations can be generated via feedforward or feedback processes. The extent to which these processes result in overlapping representations remains unclear. Previous work has shown that imagined stimuli elicit similar representations as perceived stimuli throughout the visual cortex. However, while representations during imagery are indeed only caused by feedback processing, neural processing during perception is an interplay of both feedforward and feedback processing. This means that any overlap could be due to overlap in feedback processes. In the current study we aimed to investigate this issue by characterizing the overlap between feedforward- and feedback-initiated category-representations during imagery, conscious perception and unconscious processing using fMRI. While all three conditions elicited stimulus representations in left lateral occipital cortex (LOC), significant similarities were only observed between imagery and conscious perception in this area. Furthermore, PPI-analyses revealed stronger connectivity between frontal areas and left LOC during conscious perception and imagery compared to unconscious processing. Together, these findings can be explained by the idea that long-range feedback modifies visual representations, thereby reducing neural overlap between purely feedforward and feedback-initiated stimulus representations measured by fMRI. Neural representations caused by feedback, either stimulus-driven (perception) or internally-driven (imagery), are however relatively similar.

Naturalistic stimuli (e.g., movies) provide the opportunity to study lifelike experiences in the lab. While young adults respond to these stimuli in a highly synchronized manner [as indexed by intersubject correlations (ISC) in their neural activity], older adults respond more idiosyncratically. Here, we examine whether eye-movement synchrony (eye-ISC) also declines with age during movie-watching and whether it relates to memory for the movie. Our results show no age-related decline in eye-ISC, suggesting that age differences in neural ISC are not caused by differences in viewing patterns. Both age groups recalled the same number of episodic details from the movie, but older adults recalled proportionally fewer episodic details due to their greater output of semantic and false information. In both age groups, higher eye-ISC related to a higher proportion of internal details and a lower proportion of false information being recalled. Finally, both older and younger adults showed better cued recall for cues taken from within the same event than those spanning an event boundary, further confirming that events are stored in long-term memory as discrete units with stronger associations within than across event boundaries. Taken together, these findings suggest that naturalistic stimuli drive perception in a similar way in younger and older adults, but age differences in neural synchrony further up the information processing stream may contribute to subtle differences in event memory. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Contains fulltext : 226126.pdf (Publisher’s version ) (Open Access) 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 an 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 brain’s functional connectivity structure across a group of young individuals and a group of old individuals. 12 p.

Contains fulltext : 230769.pdf (Publisher’s version ) (Open Access) Visual memory for objects involves the integration, or binding, of individual features into a coherent representation. We used a novel approach to assess feature binding, using a delayed-reproduction task in combination with computational modeling and lesion analysis. We assessed stroke patients and neurotypical controls on a visual working memory task in which spatial arrays of colored disks were presented. After a brief delay, participants either had to report the color of one disk cued by its location or the location of one disk cued by its color. Our results demonstrate that, in the controls, report imprecision and swap errors (non-target reports) can be explained by a single source of variability. Stroke patients showed an overall decrease in memory precision for both color and location, with only limited evidence for deviations from the predicted relationship between report precision and swap errors. These deviations were primarily deficits in reporting items rather than selecting items based on the cue. Atlas-based lesion-symptom mapping showed that selection and reporting deficits, precision in reporting color, and precision in reporting location were associated with different lesion profiles. Deficits in binding are associated with lesions in the left somatosensory cortex, deficits in the precision of reporting color with bilateral fronto-parietal regions, and no anatomical substrates were identified for precision in reporting location. Our results converge with previous reports that working memory representations are widely distributed in the brain and can be found across sensory, parietal, temporal, and prefrontal cortices. Stroke patients demonstrate mostly subtle impairments in visual working memory, perhaps because representations from different areas in the brain can partly compensate for impaired encoding in lesioned areas. These findings contribute to understanding of the relation between memorizing features and their bound representations. 12 p.

Contains fulltext : 237528.pdf (Publisher’s version ) (Open Access) Visual representations can be generated via feedforward or feedback processes. The extent to which these processes result in overlapping representations remains unclear. Previous work has shown that imagined stimuli elicit similar representations as perceived stimuli throughout the visual cortex. However, while representations during imagery are indeed only caused by feedback processing, neural processing during perception is an interplay of both feedforward and feedback processing. This means that any representational overlap could be due to overlap in feedback processes. In the current study we aimed to investigate this issue by characterizing the overlap between feedforward- and feedback-initiated category-representations during imagery, conscious perception and unconscious processing using fMRI in humans of either sex. While all three conditions elicited stimulus representations in left lateral occipital cortex (LOC), significant similarities were only observed between imagery and conscious perception in this area. Furthermore, connectivity analyses revealed stronger connectivity between frontal areas and left LOC during conscious perception and imagery compared to unconscious processing. Together, these findings can be explained by the idea that long-range feedback modifies visual representations, thereby reducing representational overlap between purely feedforward and feedback-initiated stimulus representations measured by fMRI. Neural representations influenced by feedback, either stimulus-driven (perception) or purely internally-driven (imagery), are however relatively similar.Significance statementPrevious research has shown substantial neural overlap between imagery and perception, suggesting overlap between bottom-up and top-down processes. However, because conscious perception also involves top-down processing, this overlap could instead reflect similarity in feedback processes. In this study, we showed that the overlap between perception and imagery disappears when stimuli are rendered unconscious via backward masking, suggesting reduced overlap between purely bottom-up and top-down generated representations. 13 p.

Segmenting perceptual experience into meaningful events is a key cognitive process that helps us make sense of what is happening around us in the moment, as well as helping us recall past events. Nevertheless, little is known about the underlying neural mechanisms of the event segmentation process. Recent work has suggested that event segmentation can be linked to regional changes in neural activity patterns. Accurate methods for identifying such activity changes are important to allow further investigation of the neural basis of event segmentation and its link to the temporal processing hierarchy of the brain. In this study, we introduce a new set of elegant and simple methods to study these mechanisms. We introduce a method for identifying the boundaries between neural states in a brain area and a complementary one for identifying the number of neural states. Furthermore, we present the results of a comprehensive set of simulations and analyses of empirical fMRI data to provide guidelines for reliable estimation of neural states and show that our proposed methods outperform the current state-of-the-art in the literature. This methodological innovation will allow researchers to make headway in investigating the neural basis of event segmentation and information processing during naturalistic stimulation.HighlightsBoundaries between meaningful events are related to neural state transitions.Neural states are temporarily stable regional brain activity patterns.We introduce novel methods for data-driven detection of neural state boundaries.These methods can identify the location and the number of neural state boundaries.Simulations and empirical data support the reliability and validity of our methods.

Naturalistic stimuli (e.g., movies) provide the opportunity to study lifelike experiences in the lab. While young adults respond to these stimuli in a highly synchronized manner (as indexed by intersubject correlations [ISC] in their neural activity), older adults respond more idiosyncratically. Here, we examine whether eye movement synchrony (eye-ISC) also declines with age during movie-watching and whether it relates to memory for the movie. Our results show no age-related decline in eye-ISC, suggesting that age differences in neural ISC are not caused by differences in viewing patterns. Both age groups recalled the same number of episodic details from the movie, however, older adults recalled more semantic and false information. In both age groups, more recall of false information related to lower eye-ISC. Finally, older adults showed better cued-recall than younger adults across event boundaries, suggesting that older adults may form broader associations across events when encoding everyday experiences.

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 18–88). We found that cardiovascular health and age made approximately similar contributions to white matter health and explained up to 56% of variance therein. 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 health via separate mechanisms., Highlights • Cardiovascular health is related to white matter lesion burden and diffusivity. • Low diastolic pressure, high systolic pressure, and higher heart rate contribute independently. • Cardiovascular health and age explain up to 56% of variance in white matter health. • The uncinate fasciculus, inferior fronto-occipital fasciculus, and forceps minor show most sensitivity. • Lower BMI and more exercise may have protective effects.

Contains fulltext : 195401.pdf (Author’s version postprint ) (Open Access) We know how age affects the brain during lab-based tasks, but what about situations truer to everyday life, such as watching movies? We measured fMRI activity while participants (N=577) from the Cambridge Centre for Ageing and Neuroscience (www.cam-can.com) watched a movie. Watching the same movie induces significant inter-subject synchronization of brain activity across participants. These cross-subject correlations suggest that viewers are processing incoming information in a similar (or shared) way. We show that with advancing age, synchrony is preserved in some areas, including the language network, but decreased in others, including the medial prefrontal cortex, medial temporal lobe and fronto-parietal network. Synchrony declines were driven by more idiosyncratic responding in older adults and were associated with regionally-distinct temporal profiles and functional connectivity patterns, as well as declines in white matter integrity. These findings suggest that areas involved in language processing remain intact with age, while regions involved in attentional control and memory may show age-related declines, even in situations similar to daily life. 15 p.

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 brain's 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., Graphical abstract fx1, Highlights • Estimates of dFC may be affected by various sources of between-people heterogeneity. • We simulated fMRI data to investigate the effect of such heterogeneity on dFC. • Differences in estimated dFC resulted from non-dynamic features of the data. • Choice of parameters in common methods produced spurious group differences in dFC.

Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre-processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (www.cam-can.com). This dataset contained two sessions of resting-state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between-participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high-pass filtering, instead of band-pass filtering, produced stronger and more reliable age-effects. Head motion was correlated with gray-matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substantial improvements of the sensitivity of cluster-based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this article, we report on a set of simulations and analyses of real task fMRI data that demonstrate (a) that the proposed methods control the false-alarm rate, (b) that the sensitivity profiles of cluster-based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (c) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to fivefold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.

The adaption of movement to changes in the environment varies across life span. Recent evidence has linked motor adaptation and its reduction with age to differences in “explicit” learning processes. We examine differences in brain structure and cognition underlying motor adaptation in a population-based cohort (n = 322, aged 18–89 years) using a visuomotor learning task and structural magnetic resonance imaging. Reduced motor adaptation with age was associated with reduced volume in striatum, prefrontal, and sensorimotor cortical regions, but not cerebellum. Medial temporal lobe volume, including the hippocampus, became a stronger determinant of motor adaptation with age. Consistent with the role of the medial temporal lobes, declarative long-term memory showed a similar interaction, whereby memory was more positively correlated with motor adaptation with increasing age. By contrast, visual short-term memory was related to motor adaptation, independently of age. These results support the hypothesis that cerebellar learning is largely unaffected in old age, and the reduction in motor adaptation with age is driven by a decline in explicit memory systems., Highlights • Reduced motor adaptation with age relates to less graey matter in motor regions. • These regions are striatum, premotor and prefrontal cortex, but not the cerebellum. • With age, motor adaptation is more positively related to medial temporal lobe areas. • Motor adaptation relates to short-term memory measures regardless of age. • With age, motor adaptation is more positively related to long-term memory scores.

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 brain's functional connectivity structure across a group of young individuals and a group of old individuals.

A large body of research shows that aging is accompanied by localized changes in brain structure and function. However, over the past decade the neuroimaging community has begun to recognize the importance of investigating the brain as a network. Brain regions don’t function independently, rather they form an expansive network that allows for communication between distant areas and enables complex cognitive functioning. Hence, age-related changes in the network structure might explain changes in cognitive functioning.Characterizing this network by investigating the brain’s functional connectivity has enabled new insights into brain organization. In this chapter, we will outline how the brain’s functional connectivity is affected by aging and how changes in functional connectivity relate to changes in cognitive functioning. We will address how neurodegenerative pathology influences functional connectivity and how, based on these measurements, biomarkers for clinical outcome might be developed in the future.

Item does not contain fulltext This is a commentary on Campbell and Schacter (2016), "Ageing and the resting state: Is cognition obsolete?" (Journal Language, Cognition and Neuroscience, Advance online publication. http://doi.org/10.1080/23273798.2016.1227858). Campbell and Schacter argue that resting state data have a limited ability to contribute to the study of neurocognitive ageing and that the field should focus more on results from carefully controlled experimental designs. In this commentary, we argue for a different perspective on future research directions in neurocognitive ageing. Specifically for the need to use a more integrative approach; combining rest and task data as well as information from different modalities to obtain a better understanding of the neural mechanisms that underlie healthy cognitive ageing. Potential benefits of this integrative approach are illustrated with a number of examples. In addition, we discuss some of the advantages of using resting state data as part of this integrative approach. 8 p.

Neuroticism and genetic variation in the serotonin-transporter (SLC6A4) and catechol-O-methyltransferase (COMT) gene are risk factors for psychopathology. Alterations in the functional integration and segregation of neural circuits have recently been found in individuals scoring higher on neuroticism. The aim of the current study was to investigate how genetic risk factors impact functional network organization and whether genetic risk factors moderate the association between neuroticism and functional network organization. We applied graph theory analysis on resting-state fMRI data in a sample of 120 women selected based on their neuroticism score, and genotyped two polymorphisms: 5-HTTLPR (S-carriers and L-homozygotes) and COMT (rs4680-rs165599; COMT risk group and COMT non-risk group). For the 5-HTTLPR polymorphism, we found that subnetworks related to cognitive control show less connections with other subnetworks in S-carriers compared to L-homozygotes. The COMT polymorphism moderated the association between neuroticism and functional network organization. We found that neuroticism was associated with lower efficiency coefficients in visual and somatosensory-motor subnetworks in the COMT risk group compared to the COMT non-risk group. The findings of altered topology of specific subnetworks point to different cognitive-emotional processes that may be affected in relation to the genetic risk factors, concerning emotion regulation in S-carriers (5-HTTLPR) and emotional salience processing in COMT risk carriers. Electronic supplementary material The online version of this article (doi:10.1007/s11682-016-9626-2) contains supplementary material, which is available to authorized users.

Focal lesions can affect connectivity between distal brain regions (connectional diaschisis) and impact the graph-theoretic properties of major brain networks (connectomic diaschisis). Given its unique anatomy and diverse range of functions, the hippocampus has been claimed to be a critical "hub" in brain networks. We investigated the effects of hippocampal lesions on structural and functional connectivity in six patients with amnesia, using a range of magnetic resonance imaging (MRI) analyses. Neuropsychological assessment revealed marked episodic memory impairment and generally intact performance across other cognitive domains. The hippocampus was the only brain structure exhibiting reduced grey-matter volume that was consistent across patients, and the fornix was the only major white-matter tract to show altered structural connectivity according to both diffusion metrics. Nonetheless, functional MRI revealed both increases and decreases in functional connectivity. Analysis at the level of regions within the default-mode network revealed reduced functional connectivity, including between nonhippocampal regions (connectional diaschisis). Analysis at the level of functional networks revealed reduced connectivity between thalamic and precuneus networks, but increased connectivity between the default-mode network and frontal executive network. The overall functional connectome showed evidence of increased functional segregation in patients (connectomic diaschisis). Together, these results point to dynamic reorganization following hippocampal lesions, with both decreased and increased functional connectivity involving limbic-diencephalic structures and larger-scale networks. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc.

The personality trait neuroticism is a potent risk marker for psychopathology. Although the neurobiological basis remains unclear, studies have suggested that alterations in connectivity may underlie it. Therefore, the aim of the current study was to shed more light on the functional network organization in neuroticism. To this end, we applied graph theory on resting-state functional magnetic resonance imaging (fMRI) data in 120 women selected based on their neuroticism score. Binary and weighted brain-wide graphs were constructed to examine changes in the functional network structure and functional connectivity strength. Furthermore, graphs were partitioned into modules to specifically investigate connectivity within and between functional subnetworks related to emotion processing and cognitive control. Subsequently, complex network measures (ie, efficiency and modularity) were calculated on the brain-wide graphs and modules, and correlated with neuroticism scores. Compared with low neurotic individuals, high neurotic individuals exhibited a whole-brain network structure resembling more that of a random network and had overall weaker functional connections. Furthermore, in these high neurotic individuals, functional subnetworks could be delineated less clearly and the majority of these subnetworks showed lower efficiency, while the affective subnetwork showed higher efficiency. In addition, the cingulo-operculum subnetwork demonstrated more ties with other functional subnetworks in association with neuroticism. In conclusion, the 'neurotic brain' has a less than optimal functional network organization and shows signs of functional disconnectivity. Moreover, in high compared with low neurotic individuals, emotion and salience subnetworks have a more prominent role in the information exchange, while sensory(-motor) and cognitive control subnetworks have a less prominent role.

Slowing is a common feature of ageing, yet a direct relationship between neural slowing and brain atrophy is yet to be established in healthy humans. We combine magnetoencephalographic (MEG) measures of neural processing speed with magnetic resonance imaging (MRI) measures of white and grey matter in a large population-derived cohort to investigate the relationship between age-related structural differences and visual evoked field (VEF) and auditory evoked field (AEF) delay across two different tasks. Here we use a novel technique to show that VEFs exhibit a constant delay, whereas AEFs exhibit delay that accumulates over time. White-matter (WM) microstructure in the optic radiation partially mediates visual delay, suggesting increased transmission time, whereas grey matter (GM) in auditory cortex partially mediates auditory delay, suggesting less efficient local processing. Our results demonstrate that age has dissociable effects on neural processing speed, and that these effects relate to different types of brain atrophy., Neural processing speed slows with age, but the relationship between this slowing and brain atrophy is unknown. Here, authors show that age-related functional brain differences in auditory and visual processing are partly due to structural differences in the distinct brain regions underlying these processes.

Aging affects functional connectivity between brain areas, however, a complete picture of how aging affects integration of information within and between functional networks is missing. We used complex network measures, derived from a brain-wide graph, to provide a comprehensive overview of age-related changes in functional connectivity. Functional connectivity in young and older participants was assessed during resting-state fMRI. The results show that aging has a large impact, not only on connectivity within functional networks but also on connectivity between the different functional networks in the brain. Brain networks in the elderly showed decreased modularity (less distinct functional networks) and decreased local efficiency. Connectivity decreased with age within networks supporting higher level cognitive functions, that is, within the default mode, cingulo-opercular and fronto-parietal control networks. Conversely, no changes in connectivity within the somatomotor and visual networks, networks implicated in primary information processing, were observed. Connectivity between these networks even increased with age. A brain-wide analysis approach of functional connectivity in the aging brain thus seems fundamental in understanding how age affects integration of information.

Item does not contain fulltext Remitted patients with major depressive disorder (rMDD) often report more fluctuations in mood as residual symptomatology. It is unclear how this affective instability is associated with information processing related to the default mode (DMS), salience/reward (SRS) and fronto-parietal (FPS) subnetworks in rMDD patients at high risk of recurrence (rrMDD). Sixty-two unipolar, drug-free rrMDD patients ([ges]2 MDD-episodes) and 41 HC (HC) were recruited. We used Experience Sampling Methodology (ESM) to monitor mood/cognitions (10 times a day for 6 days) and calculated affective instability using the mean adjusted absolute successive difference. Subsequently, we collected resting-state functional Magnetic Resonance Imaging data and performed graph theory to obtain network metrics of integration within (local efficiency) the DMS, SRS and FPS, and between (participation coefficient) these subnetworks and others. In rrMDD patients compared to HC, we found that affective instability was increased in most negative mood/cognition variables and that the DMS had less connections with other subnetworks. Furthermore, we found that rrMDD patients, who showed more instability in feeling down and irritated, had less connections between the SRS and other subnetworks and higher local efficiency coefficients in the FPS, respectively. In conclusion, rrMDD patients, compared to HC, are less stable in their negative mood and these dynamics are related to differences in information processing within and between specific functional subnetworks. These results are a first step to gain a better understanding of how mood fluctuations in real-life are represented in the brain and provide insights in the vulnerability profile of MDD. 10 p.

Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre‐processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (www.cam-can.com). This dataset contained two sessions of resting‐state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between‐participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high‐pass filtering, instead of band‐pass filtering, produced stronger and more reliable age‐effects. Head motion was correlated with gray‐matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125–4156, 2017. © 2017 Wiley Periodicals, Inc.