Biology subjects

Geerligs, L.

Publications and source records attributed to Geerligs, L..

15 recordsLinked to original sources

bioRxiv2026

Memory and Hippocampal Responses to Event Boundaries are Modulated by Global Brain States

Our daily experiences unfold as a continuous stream, yet we perceive and remember them as discrete events. Event boundaries, the moments of transition between these events, are known to elicit increases in hippocampal activity believed to reflect memory encoding. However, it remains unknown how this hippocampal response relates to large-scale brain dynamics. Here, using fMRI data from two independent datasets (Sherlock and StudyFor-rest), we applied the Greedy State Boundary Search (GSBS) algorithm to whole-brain activity patterns and identified two recurring global brain states corresponding to the Default Mode Network (DMN) and Task-Positive Network (TPN). We found that event boundaries were associated with an increased probability of being in the TPN state, and that hippocampal activity was generally higher during TPN states. The hippocampal response to event boundaries appeared predominantly during TPN states. When overall state-related differences in baseline hippocampal activity were controlled for, event boundaries elicited a hippocampal response regardless of the concurrent global state. Critically, individual differences in the tendency to shift toward the TPN state at event boundaries; as well as overall time spent at the DMN state predicted subsequent memory for narrative content, whereas univariate hippocampal activity at boundaries did not. These findings demonstrate that hippocampal event boundary responses are modulated by global brain state dynamics, and suggest that the interplay between large-scale network configurations and event segmentation plays a key role in how continuous experience is encoded into memory.

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bioRxiv2025

Multi-Scale Anti-Correlated Neural States Dominate Naturalistic Whole-Brain Activity

The human brains response to naturalistic stimuli is characterized by complex spatiotemporal dynamics. Within these dynamics there is a transitioning structure between sets of anti-correlated neural states that is frequently observed but has not been systematically investigated across scales. In this paper, we use three different naturalistic fMRI datasets to quantify anti-correlation in global and local neural states during naturalistic viewing or listening and investigate their interdependence and their relationship to changes in the stimuli. We demonstrate that continuous naturalistic brain activity shows an anti-correlational structure that spans both global and local spatial scales, with regions in the dorsal attention network showing strong alignment between local and global state transitions. On the global scale, ongoing dynamics are dominated by two antagonistic states that correspond to Default Mode Network and Task Positive Network configurations, with a third transitional state mediating between them. On the local scale, we observe anti-correlated neural states that are associated with periods of relatively high and low brain activity. Across the brain, these are driven by subsets of voxels that are systematically anti-correlated with their areas dominant activity pattern. This antagonism is related to stimulus changes, which tend to trigger a switch to the TPN state globally and to high activity states locally. On the local scale we also see a modality-specific pattern, with visual changes mostly driving transitions in visual cortical regions and auditory changes predominantly affecting auditory and language-related areas. The consistency of these findings across datasets with different stimulus types (audiovisual and purely auditory) indicates that anti-correlated neural states represent a domain-general organizational principle of brain function. We propose that anti-correlated dynamics functionally represent a convergent solution to the fundamental challenge of maintaining coherent internal representations while remaining responsive to meaningful changes in the environment.

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bioRxiv2025

Neural network-based encoding in free-viewing fMRI with precision models

Representations learned by convolutional neural networks (CNNs) exhibit a remarkable resemblance to information processing patterns observed in the primate visual system on large neuroimaging datasets collected under diverse, naturalistic visual stimulation, but with instruction for participants to maintain central fixation. This viewing condition, however, diverges significantly from ecologically valid visual behaviour, suppresses activity in visually active regions, and imposes substantial cognitive load on the viewing task. We present a modification of the encoding model framework, adapting it for use with naturalistic vision datasets acquired under fully natural viewing conditions, without fixation, by incorporating eye-tracking data. Our gaze-aware encoding models were trained on the StudyForrest dataset, which features task-free naturalistic movie viewing. By combining eye-tracking data with the visual content of movie frames, we generate combined subject-wise gaze-stimulus specific feature time series. These time series are constructed by sampling only the locally and temporally relevant elements of the CNN feature map for each fixation. Our results demonstrate that gaze-aware encoding models match the performance of conventional encoding models with 112x fewer model parameters. Gaze-aware encoding models were especially beneficial for participants with more dynamic eye-movement patterns. Therefore, this approach opens the door to more ecologically valid models that can be built in more naturalistic settings, such as playing games or navigating virtual environments.

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bioRxiv2025

Temporal Propagation of Neural State Boundaries in Naturalistic Context

Our senses receive a continuous stream of complex information, which we segment into discrete events. Previous research has related such events to neural states: temporally and regionally specific stable patterns of brain activity. The aim of this paper was to investigate whether there was evidence for top-down or bottom-up propagation of neural state boundaries. To do so, we used intracranial measurements with high temporal resolution while subjects were watching a movie. As this is the first study of neural states in intracranial data in the context of event segmentation, we also investigated whether known properties of neural states could be replicated. The neural state boundaries indeed aligned with stimulus features and between brain areas. Importantly, we found evidence for top-down propagation of neural state boundaries at the onsets and offsets of clauses. Interestingly, we did not observe a consistent top-down or bottom-up propagation in general across all timepoints, suggesting that neural state boundaries could propagate in both a top-down and bottomup manner, with the direction depending on the stimulus input at that moment. Taken together, our findings provide new insights on how neural state boundaries are shared across brain regions and strengthen the foundation of studying neural states in electrophysiology.

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bioRxiv2025

Temporal dedifferentiation of neural states with age during naturalistic viewing

Highlights- Age affects neural activity patterns related to event perception - With age, neural state duration increases in multiple brain regions - This effect is strongest in the visual cortex and ventromedial prefrontal cortex - The overlap between neural states and event boundaries does not decline with age - This suggest that coarse event perception is relatively preserved with age While life is experienced continuously, we tend to perceive it as a series of events. At a neural level, this event segmentation process has been linked to changes in neural states. An open question is whether these neural states differ with age. Participants (N = 577) from the CamCAN cohort viewed an 8-min movie during fMRI. A data-driven state segmentation method was used to identify neural state changes. To study the effects of age, participants were sorted into 34 age groups. We show that neural states become significantly longer with increasing age, particularly in visual and ventromedial prefrontal cortices. Perceived event boundaries overlapped with state changes in superior temporal and dorsomedial prefrontal regions, but there was no effect of age on this relationship. Our results suggest reduced temporal differentiation of successive neural states with increasing age. Nevertheless, preserved alignment between neural states and perceived events suggests that coarse event segmentation remains intact.

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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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bioRxiv2024

Sensory processing sensitivity is associated with neural synchrony and functional connectivity during threatening movies

Sensory processing sensitivity (SPS) is an evolutionarily conserved trait describing a persons sensitivity to subtle stimuli, their depth of processing, emotional reactivity, and susceptibility to being overwhelmed. SPS is considered a fundamental and evolutionarily conserved trait, yet its neural mechanisms remain insufficiently understood. Therefore, we investigated whether SPS relates to processing movies differently in the central executive (CEN), default mode (DMN), and salience (SN) networks. We obtained positive and negative dimension Sensory Processing Sensitivity Questionnaire (short-form) scores and (neutral and threat aural framing) movie-fMRI data from a population-based sample (Healthy Brain Study, N=238, agemean=34years). We performed a priori inter-subject representation similarity, activation, and inter-subject functional connectivity analyses to characterize SPS-dimension-related neural responses during movie-viewing. More similar negative dimension SPS score related to more neural synchrony in the CEN and SN during threat. Higher negative dimension SPS score related to reduced CEN-DMN functional connectivity during threat, an effect shared across between-network regions but most strongly driven by reduced connectivity between right dorsomedial prefrontal cortex and left lateral prefrontal cortex. Our findings suggest that highly sensitive individuals exhibit distinct CEN differences shaping environmental perception, process threat differently, and each SPSQ-SF dimension may involve unique neurological mechanisms.

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bioRxiv2024

The Neural Basis of Event Segmentation:Stable Features in the Environment areReflected by Neural States

Our senses receive a continuous stream of complex information. Parsing this information into meaningful events allows us to extract relevant information, remember it, and act upon it. Previous research has related these events to so-called neural states: temporally and regionally specific stable patterns of brain activity, which tend to coincide with events in the stimulus. Neural states show a temporal cortical hierarchy: short states are present in early sensory areas, while longer states can be found in higher-level areas. Here we investigated what these neural states represent. We hypothesized that states at different levels of the cortical hierarchy are shaped by aspects of the stimulus to which these brain areas are responsive. To test this hypothesis, we analyzed fMRI data of participants watching a movie, using a data-driven method to identify the neural states. We found support for the aforementioned hypothesis: specifically the parahippocampal place area and retrosplenial cortex, known to be sensitive to places, showed an alignment between neural state boundaries and moments in the movie with a change in location, independent of changes in visual features and other covariates. These findings suggest that neural states reflect stable features in the (internal model of) the external environment, and that the cortical temporal hierarchy partly reflects the temporal scales at which representations of the environment evolve.

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bioRxiv2022

Lifespan differences in visual short-term memory load-modulated functional connectivity

Working memory is critical to higher-order executive processes and declines throughout the adult lifespan. However, our understanding of the neural mechanisms underlying this decline is limited. Recent work suggests that functional connectivity between frontal control and posterior visual regions may be critical, but examinations of age differences therein have been limited to a small set of brain regions and extreme group designs (i.e., comparing young and older adults). In this study, we build on previous research by using a lifespan cohort and a whole-brain approach to investigate working memory load-modulated functional connectivity in relation to age and performance. The article reports on analysis of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data. Participants from a population-based lifespan cohort (N=111, age 23-86) performed a visual short-term memory task during functional magnetic resonance imaging. Visual short-term memory was measured with a delayed recall task for visual motion with three different loads. Whole-brain load-modulated connectivity was estimated using psychophysiological interactions in a hundred regions of interest, sorted into seven networks (Schaefer et al., 2018, Yeo et al., 2011). Results showed that load-modulated functional connectivity was strongest within the dorsal attention network followed by the visual network during encoding and maintenance. With increasing age, load-modulated functional connectivity strength decreased throughout the cortex. Within the dorsal attention network, increased load-modulated connectivity strength was related to better task performance in an age-invariant way. Our results demonstrate the widespread negative impact of age on the modulation of functional connectivity by working memory load. Older adults might already be close to ceiling in terms of their resources at the lowest load and therefore less able to further increase connectivity with increasing task demands. HighlightsO_LIWe examine visual short-term memory load-related functional connectivity C_LIO_LIAnd how age and performance affect modulation of connectivity by memory load C_LIO_LIModulation of connectivity is strongest in dorsal attention and visual networks C_LIO_LILoad-modulated connectivity strength decreases with increasing age C_LIO_LILoad-modulated connectivity in the dorsal attention network relates to performance C_LI

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bioRxiv2021

Beta-band desynchronization reflects uncertainty in effector selection during motor planning

While 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 1000-1500 ms before the presentation of the target, whereby the cue was valid in 50% of trials. As evidence for motor planning during the cueing phase, behavioral observations showed that the cue affected later hand choice. Furthermore, reaction times were longer in the choice 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 cueing 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, similar to target uncertainty, selectively modulates beta-band power during motor planning. New & NoteworthyWhile reach-related beta-band power in central cortical areas is known to modulate with the number of potential targets, here we show, using a cueing 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.

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bioRxiv2021

A nested cortical hierarchy of neural states underlies event segmentation in the human brain

A fundamental aspect of human experience is that it is segmented into discrete events. This may be underpinned by transitions between distinct neural states. Using an innovative data-driven state segmentation method, we investigate how neural states are organized across the cortical hierarchy and where in the cortex neural state boundaries and perceived event boundaries overlap. Our results show that neural state boundaries are organized in a temporal cortical hierarchy, with short states in primary sensory regions, and long states in lateral and medial prefrontal cortex. State boundaries are shared within and between groups of brain regions that resemble well-known functional networks. Perceived event boundaries overlap with neural state boundaries across large parts of the cortical hierarchy, particularly when those state boundaries demarcate a strong transition or are shared between brain regions. Taken together, these findings suggest that a partially nested cortical hierarchy of neural states forms the basis of event segmentation.

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bioRxiv2020

Detecting neural state transitions underlying event segmentation

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. HighlightsO_LIBoundaries between meaningful events are related to neural state transitions. C_LIO_LINeural states are temporarily stable regional brain activity patterns. C_LIO_LIWe introduce novel methods for data-driven detection of neural state boundaries. C_LIO_LIThese methods can identify the location and the number of neural state boundaries. C_LIO_LISimulations and empirical data support the reliability and validity of our methods. C_LI

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bioRxiv2020

Improving the sensitivity of cluster-based statistics for fMRI data

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 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 are sensitive to physiologically 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 paper, we report on a set of simulations that demonstrate (1) that the proposed methods control the false-alarm rate, (2) that the sensitivity profiles of cluster-based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (3) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to five-fold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.

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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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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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