Biology subjects

Margulies, D. S.

Publications and source records attributed to Margulies, D. S..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Cortical gradients during naturalistic processing are hierarchical and modality-specific

Understanding cortical topographic organization and how it supports complex perceptual and cognitive processes is a fundamental question in neuroscience. Previous work has characterized functional gradients that demonstrate large-scale principles of cortical organization. How these gradients are modulated by rich ecological stimuli remains unknown. Here, we utilize naturalistic stimuli via movie-fMRI to assess macroscale functional organization. We identify principal movie gradients that delineate separate hierarchies anchored in sensorimotor, visual, and auditory/language areas. At the opposite/heteromodal end of these perception-to-cognition axes, we find a more central role for the frontoparietal network along with the default network. Even across different movie stimuli, movie gradients demonstrated good reliability, suggesting that these hierarchies reflect a brain state common across different naturalistic conditions. The relative position of brain areas within movie gradients showed stronger and more numerous correlations with cognitive behavioral scores compared to resting state gradients. Together, these findings provide an ecologically valid representation of the principles underlying cortical organization while the brain is active and engaged in multimodal, dynamic perceptual and cognitive processing. HighlightsO_LIMovie-fMRI reveals novel, more granular principles of hierarchical cortical organization C_LIO_LITop movie gradients delineate three separate perception-to-cognition hierarchies C_LIO_LIA distinctive third gradient in movie-watching is anchored by auditory/language regions C_LIO_LIGradient scores demonstrate good reliability even across different movie stimuli C_LIO_LIMovie gradients yield stronger correlations with behavior relative to resting state gradients C_LI

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bioRxiv2022

Flexing the principal gradient of the cerebral cortex to suit changing semantic task demands

Understanding how thought emerges from the topographical structure of the cerebral cortex is a primary goal of cognitive neuroscience. Recent work has revealed a principal gradient of intrinsic connectivity capturing the separation of sensory-motor cortex from transmodal regions of the default mode network (DMN); this is thought to facilitate memory-guided cognition. However, studies have not explored how this dimension of connectivity changes when conceptual retrieval is controlled to suit the context. We used gradient decomposition of informational connectivity in a semantic association task to establish how the similarity in connectivity across brain regions changes during familiar and more original patterns of retrieval. Multivoxel activation patterns at opposite ends of the principal gradient were more divergent when participants retrieved stronger associations; therefore, when long-term semantic information is sufficient for ongoing cognition, regions supporting heteromodal memory are functionally separated from sensory-motor experience. In contrast, when less related concepts were linked, this dimension of connectivity was reduced in strength as semantic control regions separated from the DMN to generate more flexible and original responses. We also observed fewer dimensions within the neural response towards the apex of the principal gradient when strong associations were retrieved, reflecting less complex or varied neural coding across trials and participants. In this way, the principal gradient explains how semantic cognition is organised in the human cerebral cortex: the separation of DMN from sensory-motor systems is a hallmark of the retrieval of strong conceptual links that are culturally shared. Significance statementA central task in neuroscience is to understand how cognition emerges from the topographical structure of cerebral cortex. We used fMRI during a semantic task to assess informational connectivity as participants retrieved stereotypical or more unusual associations. The principal gradient of variation in informational connectivity captured the separation of heteromodal memory regions from unimodal cortex. This separation was reduced when weaker associations were retrieved; these trials also produced higher-dimensional neural responses in heteromodal regions. We conclude that the separation of DMN from sensory-motor systems is a hallmark of the retrieval of strong conceptual links that are culturally shared, while more complex and diverse cognition can be generated as this separation in connectivity is reduced.

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bioRxiv2021

An open MRI dataset for multiscale neuroscience

AO_SCPLOWBSTRACTC_SCPLOWMultimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54{+/-}5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from i) resting-state functional imaging, ii) diffusion tractography, iii) microstructure covariance analysis, and iv) geodesic cortical distance, gathered across multiple parcellation scales. Alongside, we share large-scale gradients estimated from each modality and parcellation scale. Our dataset will facilitate future research examining the coupling between brain microstructure, connectivity, and macroscale function. MICA-MICs is available on the Canadian Open Neuroscience Platforms data portal (https://portal.conp.ca).

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bioRxiv2021

Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment

The human brain varies across individuals in its morphology, function, and cognitive capacities. Variability is particularly high in phylogenetically modern regions associated with higher order cognitive abilities, but its relationship to the layout and strength of functional networks is poorly understood. In this study we disentangled the variability of two key aspects of functional connectivity: strength and topography. We then compared the genetic and environmental influences on these two features. Genetic contribution is heterogeneously distributed across the cortex and differs for strength and topography. In heteromodal areas genes predominantly affect the topography of networks, while their connectivity strength is shaped primarily by random environmental influence such as learning. We identified peak areas of genetic control of topography overlapping with parts of the processing stream from primary areas to network hubs in the default mode network, suggesting the coordination of spatial configurations across those processing pathways. These findings provide a detailed map of the diverse contribution of heritability and individual experience to the strength and topography of functional brain architecture.

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bioRxiv2021

Functional architecture of the aging brain

The intrinsic functional organization of the brain changes into older adulthood. Age differences are observed at multiple spatial scales, from global reductions in modularity and segregation of distributed brain systems, to network-specific patterns of dedifferentiation. Whether dedifferentiation reflects an inevitable, global shift in brain function with age, circumscribed, experience dependent changes, or both, is uncertain. We employed a multi-method strategy to interrogate dedifferentiation at multiple spatial scales. Multi-echo (ME) resting-state fMRI was collected in younger (n=181) and older (n=120) healthy adults. Cortical parcellation sensitive to individual variation was implemented for precision functional mapping of each participant, while preserving group-level parcel and network labels. ME-fMRI processing and gradient mapping identified global and macroscale network differences. Multivariate functional connectivity methods tested for microscale, edge-level differences. Older adults had lower BOLD signal dimensionality, consistent with global network dedifferentiation. Gradients were largely age-invariant. Edge-level analyses revealed discrete, network-specific dedifferentiation patterns in older adults. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal control network regions showed greater connectivity; and the dorsal attention network was more integrated with heteromodal regions. These findings highlight the importance of multi-scale, multi-method approaches to characterize the architecture of functional brain aging.

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bioRxiv2021

A tale of two gradients: Differences between the left and right hemispheres predict semantic cognition and visual reasoning

Decomposition of whole-brain functional connectivity patterns reveals a principal gradient that captures the separation of sensorimotor cortex from heteromodal regions in the default mode network (DMN); this gradient captures the systematic order of networks on the cortical surface. Functional homotopy is strongest in sensorimotor areas, and weakest in heteromodal cortices, suggesting there may be differences between the left and right hemispheres (LH/RH) in the principal gradient, especially towards its apex. This study characterised hemispheric differences in the position of large-scale cortical networks along the principal gradient, and their functional significance. We collected resting-state fMRI and semantic and non-verbal reasoning task performance in 175+ healthy volunteers. We then extracted the principal gradient of connectivity for each participant and tested which networks showed significant hemispheric differences in gradient value. We investigated the functional associations of these differences by regressing participants behavioural efficiency in tasks outside the scanner against their interhemispheric gradient difference for each network. LH showed a higher overall principal gradient value, consistent with its role in heteromodal semantic cognition. One frontotemporal control subnetwork was linked to individual differences in semantic cognition: when it was nearer heteromodal DMN on the principal gradient in LH, participants showed more efficient semantic retrieval. In contrast, when a dorsal attention subnetwork was closer to the heteromodal end of the principal gradient in RH, participants showed better visual reasoning. Lateralization of function may reflect differences in connectivity between control and heteromodal regions in LH, and attention and visual regions in RH.

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bioRxiv2021

Gradients of receptor expression in the macaque cortex

Dynamics and functions of neural circuits depend on synaptic interactions mediated by receptors. Therefore, a comprehensive map of receptor organization is needed to understand how different functions may emerge across distinct cortical regions. Here we use in-vitro receptor autoradiography to measure the density of 14 neurotransmitter receptor types in 109 areas of macaque cortex. We integrate the receptor data with other anatomical, genetic and functional connectivity data into a common cortical space. We uncovered a principal gradient of increasing receptor expression per neuron aligned with cortical hierarchy from early sensory cortex to higher cognitive areas. A second gradient, primarily driven by 5-HT1A receptors, peaks in the anterior and subcallosal cingulate, suggesting that the macaque may be a promising animal model for major depressive disorder. The receptor gradients may enable rapid, reliable information processing in sensory cortical areas and slow, flexible integration of information in higher cognitive areas.

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bioRxiv2020

Shifting gradients of macroscale cortical organization mark the transition from childhood to adolescence

The transition from childhood to adolescence is marked by pronounced shifts in brain structure and function that coincide with the development of physical, cognitive, and social abilities. Prior work in adult populations has characterized the topographical organization of cortex, revealing macroscale functional gradients that extend from unimodal (somato/motor and visual) regions through the cortical association areas that underpin complex cognition in humans. However, the presence of these core functional gradients across development as well as their maturational course have yet to be established. Here, leveraging 378 resting-state fMRI scans from 190 healthy individuals aged 6-17 years, we demonstrate that the transition from childhood to adolescence is reflected in the gradual maturation of gradient patterns across the cortical sheet. In children, the overarching organizational gradient is anchored within unimodal cortex, between somato/motor and visual territories. Conversely, in adolescence the principal gradient of connectivity transitions into an adult-like spatial framework, with the default network at the opposite end of a spectrum from primary sensory and motor regions. The observed gradient transitions are gradually refined with age, reaching a sharp inflection point in 13- and 14-year-olds. Functional maturation was nonuniformly distributed across cortical networks. Unimodal networks reached their mature positions early in development, while association regions, in particular medial prefrontal cortex, reached a later peak during adolescence. These data reveal age-dependent changes in the macroscale organization of cortex and suggest the scheduled maturation of functional gradient patterns may be critically important for understanding how cognitive and behavioral capabilities are refined across development. SignificanceHuman abilities and behavior change dramatically across development, emerging from a cascade of hierarchical changes in brain circuitry. Here, we describe age-dependent shifts in the macroscale functional organization of cortex in childhood and adolescence. The characterization of functional connectivity patterns in children revealed an overarching organizational framework anchored within unimodal cortex, between somato/motor and visual regions. Conversely, in adolescents we observed a transition into an adult-like gradient that situates the default network at the opposite end of a spectrum from primary sensory and motor regions. This spatial framework emerged gradually with age, reaching a sharp inflection point at the transition from childhood to adolescence. These data reveal the presence of a developmental change from a functional motif first dominated by the distinction between sensory and motor systems, and then balanced through interactions with later-maturing aspects of association cortex that support more abstract cognitive functions.

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bioRxiv2020

Distinct default mode network subsystems show similarities and differences in the effect of task focus across reading and autobiographical memory

While reading, the mind can wander to unrelated autobiographical information, creating a perceptually-decoupled state detrimental to narrative comprehension. To understand how this mind-wandering state emerges, we asked whether retrieving autobiographical content necessitates functional disengagement from visual input. In Experiment 1, brain activity was recorded using functional magnetic resonance imaging (fMRI) in an experimental situation mimicking naturally occurring mind-wandering, allowing us to precisely delineate neural regions involved in memory and reading. Individuals read expository texts and ignored personally relevant autobiographical memories, as well as the opposite situation. Medial regions of the default mode network (DMN) were recruited during memory retrieval. In contrast, left temporal and lateral prefrontal regions of the DMN, as well as ventral visual cortex, were recruited when reading for comprehension. Experiment 2 used functional connectivity at rest to establish that (i) DMN regions linked to memory are more functionally decoupled from regions of ventral visual cortex than regions in the same network engaged when reading, and (ii) individuals reporting more mind-wandering and worse comprehension, while reading in the lab, showed increased functional decoupling between visually-connected DMN sites important for reading and a region of dorsal occipital cortex linked to autobiographical memory in Experiment 1. These data suggest we lose track of the narrative when our mind wanders because the generation of autobiographical mental content relies on cortical regions within the DMN which are functionally decoupled from ventral visual regions engaged during reading. Significance statementWhen the mind wanders during reading, we lose track of information from the narrative. We hypothesised that poor comprehension occurs because retrieving autobiographical memories reduces the perceptual coupling necessary to understand written words. We show that default mode network (DMN) areas involved in reading are functionally more connected to ventral visual regions than DMN regions important for autobiographical memory. Furthermore, individuals who mind-wander more, and comprehend less, have weaker connectivity between visually-coupled DMN regions linked to reading and dorsal occipital areas linked to autobiographical memory. These data suggest that when our minds wander during reading, retrieval of personally-relevant information activates DMN regions that are functionally disconnected from visual input, creating a perceptually decoupled state detrimental to comprehension.

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bioRxiv2020

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Cognitive decline occurs in healthy and pathological aging, and both may be preceded by subtle changes in the brain -- offering a basis for cognitive predictions. Previous work has largely focused on predicting a diagnostic label from structural brain imaging. Our study broadens the scope of applications to cognitive decline in healthy aging by predicting future decline as a continuous trajectory, rather than a diagnostic label. Furthermore, since brain structure as well as function changes in aging, it is reasonable to expect predictive gains when using multiple brain imaging modalities. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Non-brain data (including demographics and clinical and neuropsychological scores) were combined with structural and functional connectivity MRI data from the OASIS-3 project (N = 662; age = 46 - 96y). The combined input data was entered into cross-validated multi-target random forest models to predict future cognitive decline (measured by the Clinical Dementia Rating and the Mini-Mental State Examination), on average 5.8y into the future. The analysis was preregistered and all analysis code is publicly available. We found that combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42) and that cognitive performance, daily functioning, and subcortical volume drove the performance of our model. In contrast, including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective cognitive, pharmacological, and behavioral interventions to be tailored to the individual.

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bioRxiv2020

TOWARD A CONNECTIVITY GRADIENT-BASED FRAMEWORK FOR REPRODUCIBLE BIOMARKER DISCOVERY

AO_SCPLOWBSTRACTC_SCPLOWDespite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent studies applying dimensionality reduction techniques to resting-state fMRI (R-fMRI) have unveiled neurocognitively meaningful connectivity gradients that are present in both human and primate brains, and appear to differ meaningfully among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209x2) and the Midnight scan club (n=9), we tested the following key biomarker traits - reliability, reproducibility and predictive validity - of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (R-fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least [≥]20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment. HO_SCPLOWIGHLIGHTSC_SCPLOW- There is a growing need to identify benchmark parameters in advancing functional connectivity gradients into a reliable biomarker. - Here, we explored multidimensional parameter space in calculating functional gradients to improve their reproducibility, reliability and predictive validity. - We demonstrated that more reproducible and reliable gradient markers tend to have higher predictive power for unseen phenotypic scores across various cognitive domains. - We showed that the low-dimensional connectivity gradient approach could outperform raw edge-based analyses in terms of predicting phenotypic scores. - We highlight the necessity of optimizing parameters for new imaging methods before their widespread deployment.

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bioRxiv2020

Altered Sensorimotor-to-Transmodal Hierarchical Organization in Schizophrenia

For decades, schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state functional magnetic resonance imaging (rsfMRI) to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients vs. 122 controls). Using these techniques, we demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and frontoparietal and ventral attention regions along the classical sensation-to-cognition continuum established in prior neuroanatomical work. The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascaded impairments stemming from the disrupted somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

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bioRxiv2020

Shaping Brain Structure: Genetic and Phylogenetic Axes of Macro Scale Organization of Cortical Thickness

Structural and functional characteristics of the cortex systematically vary along global axes as a function of cytoarchitecture, gene expression, and connectivity. The topology of the cerebral cortex has been proposed to be a prerequisite for the emergence of human cognition and explain both the impact and progression of pathology. However, the neurogenetic origin of these organizational axes in humans remains incompletely understood. To address this gap in the literature our current study assessed macro scale cortical organization through an unsupervised machine learning analysis of cortical thickness covariance patterns and used converging methods to evaluate its genetic basis. In a large-scale sample of twins (n=899) we found structural covariance of thickness to be organized along both an anterior-to-posterior and inferior-to-superior axis. We found that both axes showed a high degree of correspondence in pairs of identical twins, suggesting a strong heritable component in humans. Furthermore, comparing these dimensions in macaques and humans highlighted similar organizational principles in both species demonstrating that these axes of cortical organization are phylogenetically conserved within primate species. Finally, we found that in both humans and macaques the inferior-superior dimension of cortical organization was aligned with the predictions of the dual-origin theory, highlighting the possibility that the macroscale organization of primate brain structure is subject to multiple distinct neurodevelopmental trajectories. Together, our study establishes the genetic basis of natural axes in the cerebral cortex along which structure is organized and so provides important insights into the organization of human cognition that will inform both our understanding of how structure guides function and for the progression of pathology in diseases.

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bioRxiv2020

A gradient from long-term memory to novel cognition: graded transitions through default mode and executive cortex

Human cognition flexibly guides decision-making in familiar and novel situations. Although these decisions are often treated as dichotomous, in reality, situations are neither completely familiar, nor entirely new. Contemporary accounts of brain organization suggest that neural function is organized along a gradient from unimodal regions of sensorimotor cortex, through executive regions to transmodal default network. We examined whether this graded view of neural organization helps to explain how decision-making changes across situations that vary in their alignment with long-term knowledge. Functional magnetic resonance imaging found that as decisions are made in an increasingly familiar context, the BOLD signal follows this neural gradient, with stronger responses in default regions when items are linked in long-term memory. In this way, neural organization is optimized to support decision-making in both highly familiar and less familiar situations.

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bioRxiv2020

Hierarchical organization of the human subcortex unveiled with functional connectivity gradients

Understanding the topographic organization of the human brain remains a major goal in neuroscience. Brain atlases are fundamental to this goal, yet many contemporary human atlases cover only the cerebral cortex, leaving the subcortex a terra incognita. We revealed the complex topographic organization of the human subcortex by disambiguating smooth connectivity gradients from discrete areal boundaries in resting-state fMRI data acquired from more than 1000 healthy adults. This unveiled four scales of subcortical organization, recapitulating well-known anatomical nuclei at the coarsest scale and delineating 27 new bilateral regions at the finest. Ultra-high field strength fMRI corroborated and extended this organizational structure, enabling delineation of finer subdivisions of hippocampus and amygdala, while task-evoked fMRI revealed a subtle reorganization of subcortical topography in response to changing cognitive demands. A new subcortical atlas was delineated, personalized to account for individual connectivity differences and utilized to uncover reproducible relationships between subcortical connectivity and individual variation in human behaviors. Linking cortical networks to subcortical regions recapitulated a task-positive to task-negative organizational axis. The new atlas enables holistic connectome mapping and characterization of cortico-subcortical connectivity.

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bioRxiv2019

Emergence of neural dynamics within a co-ordinate space of large-scale neural hierarchies

When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of negative rumination. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brains response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands.

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bioRxiv2019

Neurocognitive patterns dissociating semantic processing from executive control are linked to more detailed off-task mental time travel

1Features of ongoing experience are common across individuals and cultures. However, certain people express specific patterns to a greater extent than others. The current psychological theory assumes that individual differences in thought patterns occur because different types of experience depend on differences in associated neurocognitive mechanisms. Consequently, individual variation in the underlying neurocognitive architecture is hypothesised to determine the ease with which certain thought patterns are generated or maintained. Our study (N=178) tested this hypothesis using multivariate pattern analysis to infer shared variance among measures of cognitive function and neural organisation and examined whether these hidden structures explain reports of the patterns of on-going thoughts people experienced in the lab. We found that relatively better performance on tasks requiring primarily semantic knowledge, rather than executive control, was linked to a neural functional organisation that was associated, via meta-analysis, with task labels related to semantic associations (sentence processing, reading and verbal semantics). Variability of this functional mode predicted significant individual variation in the types of thoughts that individuals experienced in the laboratory: Neurocognitive patterns linked to better performance at tasks that required guidance from semantic representation, rather than those dependent on executive control, were associated with patterns of thought characterised by greater subjective detail and a focus on time periods other than the here and now. These relationships were consistent across different days and did not vary with task condition, indicating they are relatively stable features of an individuals cognitive profile. Together these data confirm that individual variation in aspects of ongoing experience can be inferred from hidden neurocognitive architecture and demonstrate that performance trade-offs between executive control and long term semantic knowledge are linked to a persons tendency to imagine situations that transcend the here and now.

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bioRxiv2019

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Understanding how higher order cognitive function emerges from the underlying brain structure depends on quantifying how the behaviour of discrete regions are integrated within the broader cortical landscape. Recent work has established that this macroscale brain organization and function can be quantified in a compact manner through the use of multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. More generally, its macroscale perspective on brain organization offers novel possibilities to investigate the complex relationships between brain structure, function, and cognition in a quantified manner. Here, we present a compact workflow and open-access toolbox that allows for (i) the identification of gradients (from structural or functional imaging data), (ii) their alignment (across subjects or modalities), and (iii) their visualization (in embedding or cortical space). Our toolbox also allows for controlled association studies between gradients with other brain-level features, adjusted with respect to several null models that account for spatial autocorrelation. The toolbox is implemented in both Python and Matlab, programming languages widely used by the neuroimaging and network neuroscience communities. Several use-case examples and validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.

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