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Neural dynamics associated with the passage of time and differences between on- and off-task thought are associated with common reductions in task positive regions. Brain activity during on-task thought looks similar to the task-positive end of the task-related gradient, as do regions that decrease over time. This suggests that over time a neural motif emerges that is similar to that seen during off-task thought, and mimics the patterns seen in the absence of a task (left). These same maps can be represented in a three-dimensional space based on each hierarchy (top right). In this plot it can be seen that neural patterns that decline over time (green), and patterns seen during on task states (yellow), are located in the top right quadrant. In contrast, patterns that increase over time (blue) and those associated with off task thought (purple) are located in the bottom right quadrant. Note that the values in the 3D plot are r-values from the group level results (including motion as a covariate) and are therefore related to, but not the same as, the beta-weights representing each subject’s similarity score displayed in the raincloud plots²⁹.
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Cognition is dynamic and involves both the maintenance of and transitions between neurocognitive states. While recent research has identified some of the neural systems involved in sustaining task states, it is less well understood how intrinsic influences on cognition emerge over time. The current study uses fMRI and Multi-Dimensional Experience S...
Citations
... ; https://doi.org/10.1101/2024.04.18.590056 doi: bioRxiv preprint 7 unique functional topography of each individual as this is argued to be important for accurately describing brain organisation 17,18 . At the same time, our state-space approach allows trait-related variation in brain activity, contextual differences in brain activity, and their interaction, to be differentiated along one or more of the dimensions of brain variation focused on in our study (See (20)(21)(22) for prior demonstrations of this approach). One advantage of our state-space method is that it provides a simple low dimensional manifold in which the impact of traits and situations can both be assessed. ...
... . CC-BY-NC-ND 4.0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is (20)(21)(22) in which we calculated the correlation between the whole brain map of an individual's brain activity in a specific task condition (contrasted with the respective baseline) with each of the three dimensions of brain variation generated by the decomposition of brain activity at rest (15). ...
... It has been shown that using broad-scale activations as a measure of brain function offers more statistical power over regional effects, with a relatively moderate decrease in specificity (25). We used functional gradients because they are a convenient tool for organising brain-wide activity (20)(21)(22). However, there are likely better ways to characterise the dimensions of brain variation and organisation in order to perform state-space analyses like the one we report here. ...
Human cognition supports complex behaviour across a range of situations, and traits (such as personality) influence how we react in these different contexts. Although viewing traits as situationally grounded is common in social sciences it is often overlooked in neuroscience. Often studies focus on linking brain activity to trait descriptions of humans examine brain-trait associations in a single task, or, under passive conditions like wakeful rest. These studies, often referred to as brain wide association studies (BWAS) have recently become the subject of controversy because results are often unreliable even with large sample sizes. Although there are important statistical reasons why BWAS yield inconsistent results, we hypothesised that results are inconsistent because the situation in which brain activity is measured will impact the power in detecting a reliable link to a specific trait. To examine this possibility, we performed a state-space analysis in which tasks from the Human Connectome Project (HCP) were organized into a low-dimensional space based on how they activated different large-scale neural systems. We examined how individuals’ observed brain activity across these different contexts related to their personality. Our analysis found that for multiple personality traits (including Agreeableness, Openness to Experience and Conscientiousness) stronger associations with brain activity emerge in some tasks than others. These data establish that for specific personality traits there are situations in which reliable associations with brain activity can be identified with greater accuracy, highlighting the importance of context-bound views of understanding how brain activity links to trait variation in human behaviour.
Significance statement
As a species humans act efficiently in many contexts, however, as individuals our personality makes us more specialised in some situations than others. This “if-then” view of personality is widely accepted in the social sciences but is often overlooked in neuroscience. Here we show adopting a situationally bound view of human traits provides more meaningful descriptions of a brain-trait associations than are possible in traditional brain wide association studies (BWAS) that measure brain activity in a single situation. Our results demonstrate multiple personality traits (including Agreeableness, Openness to Experience and Conscientiousness) show stronger associations with brain activity in some tasks than others, explaining why studies focusing on changes in brain activity at rest can lead to weak or contradictory results.
... The variance explained by gradients four and five was considerably smaller (~5%). Second, the three first gradients have been linked to important features of cognition (16,(28)(29)(30). Third, the lower-dimensional space (3D vs. 5D) facilitates the interpretation of the effect and visualization. ...
... Second, for maximum transparency, we report the full set of predictive models of RS explored in this paper (SI Appendix, Supplementary Notes N2-N5) and utilize formal model comparison to select the winning model (42). Third, we build on prior research investigating brain reconfiguration across conditions along principal gradient axes to analyze the brain data (16,(28)(29)(30). Fourth, we use the ED to measure the metric of the brain state shift as it is widely used to unravel the connectivity patterns underlying brain organization (43)(44)(45). ...
Making healthy dietary choices is essential for keeping weight within a normal range. Yet many people struggle with dietary self-control despite good intentions. What distinguishes neural processing in those who succeed or fail to implement healthy eating goals? Does this vary by weight status? To examine these questions, we utilized an analytical framework of gradients that characterize systematic spatial patterns of large-scale neural activity, which have the advantage of considering the entire suite of processes subserving self-control and potential regulatory tactics at the whole-brain level. Using an established laboratory food task capturing brain responses in natural and regulatory conditions (N = 123), we demonstrate that regulatory changes of dietary brain states in the gradient space predict individual differences in dietary success. Better regulators required smaller shifts in brain states to achieve larger goal-consistent changes in dietary behaviors, pointing toward efficient network organization. This pattern was most pronounced in individuals with lower weight status (low-BMI, body mass index) but absent in high-BMI individuals. Consistent with prior work, regulatory goals increased activity in frontoparietal brain circuits. However, this shift in brain states alone did not predict variance in dietary success. Instead, regulatory success emerged from combined changes along multiple gradients, showcasing the interplay of different large-scale brain networks subserving dietary control and possible regulatory strategies. Our results provide insights into how the brain might solve the problem of dietary control: Dietary success may be easier for people who adopt modes of large-scale brain activation that do not require significant reconfigurations across contexts and goals.
... We used the results of this analysis to produce coordinates for each TR of each movie, which were used as explanatory variables in a linear mixed model in which the location of each mDES probe in the "thought space" described by the PCA dimensions were the dependent variables. We refer to this second analysis as a statespace analysis (see [35][36][37] for prior examples of this approach). Using fMRI data and experience sampling data to map patterns of ongoing thought onto brain activity during movie-watching. ...
... Our final analysis used a "state-space" approach to determine how brain activity at each moment in the film predicted the patterns of thoughts reported at these moments (for prior examples in the domain of tasks, see [35,36], See Methods). In this analysis, the coordinates of the group average of each TR in the "brain-space" are explanatory variables, and the coordinates of each experience sampling moment in the "thought-space" was the dependent variable. ...
Movie watching is a central aspect of our lives and an important paradigm for understanding the brain mechanisms behind cognition as it occurs in daily life. Contemporary views of ongoing thought argue that the ability to make sense of events in the ‘here and now’ depend on the neural processing of incoming sensory information by auditory and visual cortex, which are kept in check by systems in association cortex. However, we currently lack an understanding of how patterns of ongoing thoughts map onto the different brain systems when we watch a film, partly because methods of sampling experience disrupt the dynamics of brain activity and the movie-watching experience. Our study established a novel method for mapping thought patterns onto the brain activity that occurs at different moments of a film, which does not disrupt the time course of brain activity or the movie-watching experience. We found moments when experience sampling highlighted engagement with multi-sensory features of the film or highlighted thoughts with episodic features, regions of sensory cortex were more active and subsequent memory for events in the movie was better—on the other hand, periods of intrusive distraction emerged when activity in regions of association cortex within the frontoparietal system was reduced. These results highlight the critical role sensory systems play in the multi-modal experience of movie-watching and provide evidence for the role of association cortex in reducing distraction when we watch films.
Significance statement
States like movie-watching provide a window into the brain mechanisms behind cognition in daily life. However, we know relatively little about the mapping between brain activity during movies and associated thought patterns because of difficulties in measuring cognition without disrupting how brain activity naturally unfolds. We establish a novel method to link different experiential states to brain activity during movie-watching with minimal interruptions to viewers or disruptions to brain dynamics. We found states of sensory engagement occur in moments of films when activity in visual and auditory cortex are high. In contrast, states of distraction are reduced when activity in frontoparietal regions is high. Our study, therefore, establishes both sensory and association cortex as core features of the movie-watching experience.
... These gradients depict biologically relevant axes that differentiate observed function in major brain systems (for a review, see 13 ) and provide an organizing framework for the relationships between large-scale networks 8,12 . We used these gradients to build a 5-d coordinate system that allows us to organize whole-brain maps in a 'common space' , in which the relative locations within this space provide information regarding the balance of different macroscale systems in a particular context (see also [14][15][16] ). This analytic approach is focused on how different large-scale systems interact together and so provides a biologically relevant macroscale perspective on brain states 17 that complements perspectives that focus on parcels 18 and large-scale networks 19 (see Konu et al. 11 for a region-based analysis of the data used in the current study). ...
... This technique has previously been used to identify covert experiential states, including the deliberate maintenance of task-relevant information, as well as patterns of thought that are less related to the here and now, including thoughts with a social episodic focus, and patterns of thought with different modalities (verbal or visual) (e.g., 10,11,22,23 ). This approach is also sensitive to changes in neural function as indexed by functional magnetic resonance imaging (fMRI) 8,11,14,[24][25][26][27][28][29][30][31][32][33][34] . ...
... Our study shows that experience sampling is sensitive to at least two distinct forms of covert state. Consistent with a wide range of prior studies (e.g., 10,14,22 ), we established a state of off-task social episodic cognition, that was prominent in task situations where individuals are asked to imagine other people, and was absent from demanding tasks like spatial working memory (see Fig. 4). This pattern of thought was related to patterns of whole-brain activity in which the default mode network was more prominent than the fronto-parietal network (see Fig. 5, panel f), and meta-analytic decoding highlighted similarities with prior studies on autobiographical memory (see Fig. 5, panel b). ...
Cognitive neuroscience has gained insight into covert states using experience sampling. Traditionally, this approach has focused on off-task states. However, task-relevant states are also maintained via covert processes. Our study examined whether experience sampling can also provide insights into covert goal-relevant states that support task performance. To address this question, we developed a neural state space, using dimensions of brain function variation, that allows neural correlates of overt and covert states to be examined in a common analytic space. We use this to describe brain activity during task performance, its relation to covert states identified via experience sampling, and links between individual variation in overt and covert states and task performance. Our study established deliberate task focus was linked to faster target detection, and brain states underlying this experience—and target detection—were associated with activity patterns emphasizing the fronto-parietal network. In contrast, brain states underlying off-task experiences—and vigilance periods—were linked to activity patterns emphasizing the default mode network. Our study shows experience sampling can not only describe covert states that are unrelated to the task at hand, but can also be used to highlight the role fronto-parietal regions play in the maintenance of covert task-relevant states.
... Mind wandering, specifically, has frequently been operationalized based on thought contents, although recent empirical investigations have begun to measure subjective, dynamic features such as the extent to which thoughts shift around freely 31,32 . Alternatively, thought dynamics have been characterized by quantifying how experiences change with different durations of time passing 33 . ...
... These data were collected using a 3T imaging Scanner (Siemens Skyra 3 Tesla MRI scanner, TR = 720 ms, TE = 33 ms, flip angle = 52˚, voxel size = 2 mm isotropic, 72 slices, matrix = 104×90, FOV = 208×180 mm, multiband acceleration factor = 8). 100 subjects (ages [22][23][24][25][26][27][28][29][30][31][32][33][34][35] are randomly chosen with 3 subjects excluded due to missing data. ...
... Prior studies have used this approach to understand common changes in neural activity and experience [34], as well as to understand how dynamic states are organized at rest and how they relate to trait variance in experiences and affective processes [35]. The first (principal) gradient tracks a functional hierarchy from primary sensory processing to higher-order functions such as social cognition [20]. ...
... This means that potential parallel processing [52,53] taking place within a single region or network is not accounted for by our model. Importantly, since prior studies have highlighted the utility of states as a tool for identifying individual variation [34], it is possible that our approach could be extended to understand population variation in the utility of higher order components of dynamics. This could be a useful way to describe how brain activity patterns relate to variation in cognitive and affective features of behaviour. ...
The definition of a brain state remains elusive, with varying interpretations across different sub-fields of neuroscience—from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. This lack of consensus presents a significant challenge to the development of accurate models of neural dynamics. However, at the foundation of dynamical systems theory lies a definition of what constitutes the ’state’ of a system—i.e., a specification of the system’s future. Here, we propose to adopt this definition to establish brain states in neuroimaging timeseries by applying Dynamic Causal Modelling (DCM) to low-dimensional embedding of resting and task condition fMRI data. We find that ~90% of subjects in resting conditions are better described by first-order models, whereas ~55% of subjects in task conditions are better described by second-order models. Our work calls into question the status quo of using first-order equations almost exclusively within computational neuroscience and provides a new way of establishing brain states, as well as their associated phase space representations, in neuroimaging datasets.
... In the current work, we studied the difference in asymmetry of functional organization between autistic and NAI. Here functional organization was defined within a gradient framework [30,32,36], differentiating three main axes of organization differentiating: sensory from default networks, linked to a differentiation of perceptual from abstract cognitive functions (G1), sensorimotor from visual networks (G2), and default from multiple demand networks, associated with a differentiation of attention/control functions from more task-negative functions such as autobiographical memory (G3) [32,33,[60][61][62]. Overall we observed that asymmetry effects could be best described by a combination of asymmetry in these axes; yet, we observed axis-specific effects as well. ...
Autism is a neurodevelopmental condition involving atypical sensory-perceptual functions together with language and socio-cognitive deficits. Previous work has reported subtle alterations in the asymmetry of brain structure and reduced laterality of functional activation in individuals with autism relative to non-autistic individuals (NAI). However, whether functional asymmetries show altered intrinsic systematic organization in autism remains unclear. Here, we examined inter- and intra-hemispheric asymmetry of intrinsic functional gradients capturing connectome organization along three axes, stretching between sensory-default, somatomotor-visual, and default-multiple demand networks, to study system-level hemispheric imbalances in autism. We observed decreased leftward functional asymmetry of language network organization in individuals with autism, relative to NAI. Whereas language network asymmetry varied across age groups in NAI, this was not the case in autism, suggesting atypical functional laterality in autism may result from altered developmental trajectories. Finally, we observed that intra- but not inter-hemispheric features were predictive of the severity of autistic traits. Our findings illustrate how regional and patterned functional lateralization is altered in autism at the system level. Such differences may be rooted in atypical developmental trajectories of functional organization asymmetry in autism.
... These gradients are generated using data-driven techniques and depict axes that differentiate observed function in major brain systems (for a review, see 13 ). We used these gradients to build a 3-d coordinate system that allows us to organize brain maps derived in different ways within a 'common space' (see also 14 ). In the current study, we use this common space to examine how patterns of brain activity are related to both 1) covert experiential states that emerge during task processing and that we index via experience sampling and 2) the implementation of different stages of goalrelevant behavior that occur during task completion. ...
... This analysis, therefore, results in a spatial map for each gradient identified in which each parcel contains a 'gradient value'. Prior studies have highlighted that the first three gradients relate to important features of cognition 14,40,52 . We use these three gradients to construct the 3-d neural state-space (see below). ...
Cognitive neuroscience has gained insight into covert states using experience sampling. Traditionally, this approach has focused on off-task states, however, task-relevant states are also maintained via covert processes. Our study examined whether experience sampling can also provide insights into covert goal-relevant states that support task performance. To address this question, we developed a neural state-space, using dimensions of brain function variation, that allows neural correlates of overt and covert states to be examined in a common analytic space. We use this to describe brain activity during task performance, its relation to covert states identified via experience sampling, and links between individual variation in overt and covert states and task performance. Our study established activity patterns within association cortex emphasizing the fronto-parietal network both during target detection and a covert state of deliberate task focus which was associated with better task performance. In contrast, periods of vigilance and a covert off-task state were both linked to activity patterns emphasizing the default mode network. Our study shows experience sampling can not only describe covert states that are unrelated to the task at hand, but can also be used to highlight the role fronto-parietal regions play in the maintenance of covert task-relevant states.
... Finally, interoceptive abilities, attention, and action observation serve as important auxiliary functions of social aptitudes, contributing to self-other distinction and awareness (Tomasello, 1995;Craig, 2009;Kleckner et al., 2017). These capacities combine externally-and internally-oriented cognitive and affective processes and reflect both focused and ongoing thought processes (Chun et al., 2011;Barrett, 2017;Turnbull et al., 2020;Murphy et al., 2019;Sormaz et al., 2018). With increasing progress in task-based functional neuroimaging, we start to have an increasingly precise understanding of brain networks associated with the different processes implicated in social cognition. ...
... Unlike the DMN, the task-positive network, including frontal and parietal regions, engages preferentially in externally-oriented tasks (Buckner et al., 2008;Fox et al., 2005;Duncan, 2010). This axis may differentiate but between DMN-related socio-episodic memory processing from task-focused processing associated with the multiple demand network (Turnbull et al., 2020;Turnbull et al., 2019). Together, the three gradients describe a processing organization, with primary systems and DMN regions showing functional segregation and saliency network functional integration (Park et al., 2021a;Bethlehem et al., 2020;Smallwood et al., 2021). ...
... This could imply a putative role of sensory-motor integration or shift in sensory-association patterning between sensory modalities as a function of mental training (Kerr et al., 2013). Moreover, the secondary gradient has been implicated in guiding task-activation changes linked on-and off-task thought over time (Turnbull et al., 2020). More generally, our findings may be in line with the Global Workspace Theory of cognition, which poses that automated tasks, such as interoception and awareness, can be performed within segregated clusters of regions, whereas those that are challenging, for example perspective-taking, require integration (Dehaene et al., 1998). ...
The human brain supports social cognitive functions, including Theory of Mind, empathy, and compassion, through its intrinsic hierarchical organization. However, it remains unclear how the learning and refinement of social skills shapes brain function and structure. We studied if different types of social mental training induce changes in cortical function and microstructure, investigating 332 healthy adults (197 women, 20–55 years) with repeated multimodal neuroimaging and behavioral testing. Our neuroimaging approach examined longitudinal changes in cortical functional gradients and myelin-sensitive T1 relaxometry, two complementary measures of cortical hierarchical organization. We observed marked changes in intrinsic cortical function and microstructure, which varied as a function of social training content. In particular, cortical function and microstructure changed as a result of attention-mindfulness and socio-cognitive training in regions functionally associated with attention and interoception, including insular and parietal cortices. Conversely, socio-affective and socio-cognitive training resulted in differential microstructural changes in regions classically implicated in interoceptive and emotional processing, including insular and orbitofrontal areas, but did not result in functional reorganization. Notably, longitudinal changes in cortical function and microstructure predicted behavioral change in attention, compassion and perspective-taking. Our work demonstrates functional and microstructural plasticity after the training of social-interoceptive functions, and illustrates the bidirectional relationship between brain organisation and human social skills.
... These gradients, argued to be an 'intrinsic coordinate system' of the human brain (Huntenburg et al., 2018), reflect variations in brain structure (Huntenburg et al., 2017;Paquola et al., 2019;Vázquez-Rodríguez et al., 2019), gene expressions (Burt et al., 2018), and information processing (Huntenburg et al., 2018). We hypothesized that the spatial gradients reported by Margulies et al., 2016 act as a lowdimensional manifold over which large-scale dynamics operate (Bolt et al., 2022;Brown et al., 2021;Karapanagiotidis et al., 2020;Turnbull et al., 2020), such that traversals within this manifold explain large variance in neural dynamics and, consequently, cognition and behavior ( Figure 1C). To test this idea, we situated the mean activity values of the four latent states along the gradients defined by Margulies et al., 2016 (see 'Materials and methods'). ...
Cognition and attention arise from the adaptive coordination of neural systems in response to external and internal demands. The low-dimensional latent subspace that underlies large-scale neural dynamics and the relationships of these dynamics to cognitive and attentional states, however, are unknown. We conducted functional magnetic resonance imaging as human participants performed attention tasks, watched comedy sitcom episodes and an educational documentary, and rested. Whole-brain dynamics traversed a common set of latent states that spanned canonical gradients of functional brain organization, with global desynchronization among functional networks modulating state transitions. Neural state dynamics were synchronized across people during engaging movie watching and aligned to narrative event structures. Neural state dynamics reflected attention fluctuations such that different states indicated engaged attention in task and naturalistic contexts, whereas a common state indicated attention lapses in both contexts. Together, these results demonstrate that traversals along large-scale gradients of human brain organization reflect cognitive and attentional dynamics.
