A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales

The brain's internal echo: Longer timescales, stronger recurrent connections and higher neural excitation in self regions

BACKGROUND

Understanding the brain's intrinsic architecture has long been a central focus of neuroscience, with recent advances shedding light on its topographic organization along uni and transmodal regions. How the brain's global uni-transmodal topography relates to psychological features like our sense of self remains yet unclear, though.

METHOD

We here combine fMRI brain imaging with computational modeling (Wilson Cowan model) to better understand the temporal, spatial and physiological features underlying the distinction of self and non-self regions within the brain's global topography.

RESULTS

fMRI resting state shows lower myelin content, longer timescales (measured by the autocorrelation window/ACW), and lower global functional connectivity/synchronization (measured by global signal correlation/GSCORR) in self regions (based on the three-layer self topography; Qin et al. 2020) compared to non-self regions. Next, we fit the fMRI data with a neural mass model, the Wilson-Cowan model, which is enriched by structural and functional connectivity data from human MRI/fMRI. We first replicate the empirical data with longer ACW and lower GSCORR in self regions. Next, we demonstrate that self and non-self regions can, based on the same measures in the model, not only be distinguished within the brain's global topography but also within the unimodal and transmodal regions themselves, respectively. Finally, the neural mass model shows that such topographic differentiation relates to two physiological features: self regions exhibit higher intra-regional excitatory recurrent connection and higher levels in their basal neural excitation than non-self regions.

CONCLUSION

Our findings demonstrate the intrinsic nature of the distinction of self and non-self regions within the brain's global uni-transmodal topography as well as their underlying physiological differences with higher levels in both recurrent connections and neural excitation in self regions. The increased recurrent connections in self regions, together with their higher levels of neural excitation and the longer autocorrelation window, may be ideally suited to mediate their self-referential processing: this can thus be seen as a form of 'psychological recurrence' where one and the same input/stimulus is processed in a prolonged echo-chamber like way, that is, an internal echo within the self regions themselves.

Comprehensive profiling of anaesthetised brain dynamics across phylogeny

The intrinsic dynamics of neuronal circuits shape information processing and cognitive function. Combining non-invasive neuroimaging with anaesthetic-induced suppression of information processing provides a unique opportunity to understand how local dynamics mediate the link between neurobiology and the organism's functional repertoire. To address this question, we compile a unique dataset of multi-scale neural activity during wakefulness and anesthesia encompassing human, macaque, marmoset, mouse and nematode. We then apply massive feature extraction to comprehensively characterize local neural dynamics across > 6 000 time-series features. Using dynamics as a common space for comparison across species, we identify a phylogenetically conserved dynamical profile of anaesthesia that encompasses multiple features, including reductions in intrinsic timescales. This dynamical signature has an evolutionarily conserved spatial layout, covarying with transcriptional profiles of excitatory and inhibitory neurotransmission across human, macaque and mouse cortex. At the network level, anesthetic-induced changes in local dynamics manifest as reductions in inter-regional synchrony. This relationship between local dynamics and global connectivity can be recapitulated in silico using a connectome-based computational model. Finally, this dynamical regime of anaesthesia is experimentally reversed in vivo by deep-brain stimulation of the centromedian thalamus in the macaque, resulting in restored arousal and behavioural responsiveness. Altogether, comprehensive dynamical phenotyping reveals that spatiotemporal isolation of local neural activity during anesthesia is conserved across species and anesthetics.

Network Mechanisms Underlying the Regional Diversity of Variance and Time Scales of the Brain's Spontaneous Activity Fluctuations

The brain's activity fluctuations have different temporal scales across the brain regions, with associative regions displaying slower timescales than sensory areas. This hierarchy of timescales has been shown to correlate with both structural brain connectivity and intrinsic regional properties. Here, using publicly available human resting-state fMRI and dMRI data, it was found that, while more structurally connected brain regions presented activity fluctuations with longer timescales, their activity fluctuations presented lower variance. The opposite relationships between the structural connectivity and the variance and temporal scales of resting-state fluctuations, respectively, were not trivially explained by simple network propagation principles. To understand these structure-function relationships, two commonly used whole-brain models were studied, namely, the Hopf and Wilson-Cowan models. These models use the brain's connectome to couple local nodes (representing brain regions) displaying noise-driven oscillations. The models show that the variance and temporal scales of activity fluctuations can oppositely relate to connectivity within specific parameter regions, even when all nodes have the same intrinsic dynamics-but also when intrinsic dynamics are constrained by the myelinization-related macroscopic gradient. These results show that, setting aside intrinsic regional differences, connectivity and network state are sufficient to explain the regional differences in fluctuations' scales. State dependence supports the vision that structure-function relationships can serve as biomarkers of altered brain states. Finally, the results indicate that the hierarchies of timescales and variances reflect a balance between stability and responsivity, with greater and faster responsiveness at the network periphery, while the network core ensures overall system robustness.

Connecting brain and mind through temporo-spatial dynamics: Towards a theory of common currency

Despite major progress in our understanding of the brain, the connection of neural and mental features, that is, brain and mind, remains yet elusive. In our 2020 target paper ("Is temporospatial dynamics the 'common currency' of brain and mind? Spatiotemporal Neuroscience") we proposed the "Common currency hypothesis": temporo-spatial dynamics are shared by neural and mental features, providing their connection. The current paper aims to further support and extend the original description of such common currency into a first outline of a "Common currency theory" (CCT) of neuro-mental relationship. First, we extend the range of examples to thoughts, meditation, depression and attention all lending support that temporal characteristics, (i.e. dynamics) are shared by both neural and mental features. Second, we now also show empirical examples of how spatial characteristics, i.e., topography, are shared by neural and mental features; this is illustrated by topographic reorganization of both neural and mental states in depression and meditation. Third, considering the neuro-mental connection in theoretical terms, we specify their relationship by distinct forms of temporospatial correspondences, ranging on a continuum from simple to complex. In conclusion, we extend our initial hypothesis about the key role of temporo-spatial dynamics in neuro-mental relationship into a first outline of an integrated mind-brain theory, the "Common currency theory" (CCT).

Simulating the impact of white matter connectivity on processing time scales using brain network models

The capacity of the brain to process input across temporal scales is exemplified in human narrative, which requires integration of information ranging from words, over sentences to long paragraphs. It has been shown that this processing is distributed in a hierarchy across multiple areas in the brain with areas close to the sensory cortex, processing on a faster time scale than areas in associative cortex. In this study we used reservoir computing with human derived connectivity to investigate the effect of the structural connectivity on time scales across brain regions during a narrative task paradigm. We systematically tested the effect of removal of selected fibre bundles (IFO, ILF, MLF, SLF I/II/III, UF, AF) on the processing time scales across brain regions. We show that long distance pathways such as the IFO provide a form of shortcut whereby input driven activation in the visual cortex can directly impact distant frontal areas. To validate our model we demonstrated significant correlation of our predicted time scale ordering with empirical results from the intact/scrambled narrative fMRI task paradigm. This study emphasizes structural connectivity's role in brain temporal processing hierarchies, providing a framework for future research on structure and neural dynamics across cognitive tasks.

Test-retest reliability of resting-state EEG intrinsic neural timescales

Intrinsic neural timescales, which reflect the duration of neural information storage within local brain regions and capacity for information integration, are typically measured using autocorrelation windows (ACWs). Extraction of intrinsic neural timescales from resting-state brain activity has been extensively applied in psychiatric disease research. Given the potential of intrinsic neural timescales as a neural marker for psychiatric disorders, investigating their reliability is crucial. This study, using an open-source database, aimed to evaluate the test-retest reliability of ACW-0 and ACW-50 under both eyes-open and eyes-closed conditions across three sessions. The intraclass correlation coefficients (ICCs) were employed to quantify the reliability of the intrinsic neural timescales. Our results showed that intrinsic neural timescales exhibited good reliability (ICC > 0.6) at the whole-brain level across different index types and eye states. Spatially, except for the right temporal region in the eyes-open condition, all other regions showed moderate-to-high ICCs. Over 60% of the electrodes demonstrated moderate-to-high intrinsic neural timescale ICCs under both eyes-open and eyes-closed conditions, with ACW-0 being more stable than ACW-50. Moreover, in the new dataset, the above results were consistently reproduced. The present study comprehensively assessed the reliability of intrinsic neural timescale under various conditions, providing robust evidence for their stability in neuroscience and psychiatry.

Developmental Variations in Recurrent Spatiotemporal Brain Propagations from Childhood to Adulthood

The brain undergoes profound structural and functional transformations from childhood to adolescence. Convergent evidence suggests that neurodevelopment proceeds in a hierarchical manner, characterized by heterogeneous maturation patterns across brain regions and networks. However, the maturation of the intrinsic spatiotemporal propagations of brain activity remains largely unexplored. This study aims to bridge this gap by delineating spatiotemporal propagations from childhood to early adulthood. By leveraging a recently developed approach that captures time-lag dynamic propagations, we characterized intrinsic dynamic propagations along three axes: sensory-association (S-A), 'task-positive' to default networks (TP-D), and somatomotor-visual (SM-V) networks, which progress towards adult-like brain dynamics from childhood to early adulthood. Importantly, we demonstrated that as participants mature, there is a prolonged occurrence of the S-A and TP-D propagation states, indicating that they spend more time in these states. Conversely, the prevalence of SM-V propagation states declines during development. Notably, top-down propagations along the S-A axis exhibited an age-dependent increase in occurrence, serving as a superior predictor of cognitive scores compared to bottom-up S-A propagation. These findings were replicated across two independent cohorts (N = 677 in total), emphasizing the robustness and generalizability of these findings. Our results provide new insights into the emergence of adult-like functional dynamics during youth and their role in supporting cognition.

Mapping and modeling age-related changes in intrinsic neural timescales

Intrinsic timescales of brain regions exhibit heterogeneity, escalating with hierarchical levels, and are crucial for the temporal integration of external stimuli. Aging, often associated with cognitive decline, involves progressive neuronal and synaptic loss, reshaping brain structure and dynamics. However, the impact of these structural changes on temporal coding in the aging brain remains unclear. We mapped intrinsic timescales and gray matter volume (GMV) using magnetic resonance imaging (MRI) in young and elderly adults. We found shorter intrinsic timescales across multiple large-scale functional networks in the elderly cohort, and a significant positive association between intrinsic timescales and GMV. Additionally, age-related decline in performance on visual discrimination tasks was linked to a reduction in intrinsic timescales in the cuneus. To explain these age-related shifts, we developed an age-dependent spiking neuron network model. In younger subjects, brain regions were near a critical branching regime, while regions in elderly subjects had fewer neurons and synapses, pushing the dynamics toward a subcritical regime. The model accurately reproduced the empirical results, showing longer intrinsic timescales in young adults due to critical slowing down. Our findings reveal how age-related structural brain changes may drive alterations in brain dynamics, offering testable predictions and informing possible interventions targeting cognitive decline.

Flexibility of intrinsic neural timescales during distinct behavioral states

Recent neuroimaging studies demonstrate a heterogeneity of timescales prevalent in the brain's ongoing spontaneous activity, labeled intrinsic neural timescales (INT). At the same time, neural timescales also reflect stimulus- or task-related activity. The relationship of the INT during the brain's spontaneous activity with their involvement in task states including behavior remains unclear. To address this question, we combined calcium imaging data of spontaneously behaving mice and human electroencephalography (EEG) during rest and task states with computational modeling. We obtained four primary findings: (i) the distinct behavioral states can be accurately predicted from INT, (ii) INT become longer during behavioral states compared to rest, (iii) INT change from rest to task is correlated negatively with the variability of INT during rest, (iv) neural mass modeling shows a key role of recurrent connections in mediating the rest-task change of INT. Extending current findings, our results show the dynamic nature of the brain's INT in reflecting continuous behavior through their flexible rest-task modulation possibly mediated by recurrent connections.

Hierarchical gradients of multiple timescales in the mammalian forebrain

Many anatomical and physiological features of cortical circuits, ranging from the biophysical properties of synapses to the connectivity patterns among different neuron types, exhibit consistent variation along the hierarchical axis from sensory to association areas. Notably, the temporal correlation of neural activity at rest, known as the intrinsic timescale, increases systematically along this hierarchy in both primates and rodents, analogous to the increasing scale and complexity of spatial receptive fields. However, how the timescales for task-related activity vary across brain regions and whether their hierarchical organization appears consistently across different mammalian species remain unexplored. Here, we show that both the intrinsic timescale and those of task-related activity follow a similar hierarchical gradient in the cortices of monkeys, rats, and mice. We also found that these timescales covary similarly in both the cortex and basal ganglia, whereas the timescales of thalamic activity are shorter than cortical timescales and do not conform to the hierarchical order predicted by their cortical projections. These results suggest that the hierarchical gradient of cortical timescales might represent a universal feature of intracortical circuits in the mammalian brain.

Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

Gradients of Brain Organization: Smooth Sailing from Methods Development to User Community

Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends - or gradients - in brain structure and function, offering a framework to unify principles of brain organization across multiple scales. Strong community enthusiasm for these techniques has been instrumental in their widespread adoption and implementation to answer key questions in neuroscience. Following a brief review of current literature on this framework, this perspective paper will highlight how pragmatic steps aiming to make gradient methods more accessible to the community propelled these techniques to the forefront of neuroscientific inquiry. More specifically, we will emphasize how interest for gradient methods was catalyzed by data sharing, open-source software development, as well as the organization of dedicated workshops led by a diverse team of early career researchers. To this end, we argue that the growing excitement for brain gradients is the result of coordinated and consistent efforts to build an inclusive community and can serve as a case in point for future innovations and conceptual advances in neuroinformatics. We close this perspective paper by discussing challenges for the continuous refinement of neuroscientific theory, methodological innovation, and real-world translation to maintain our collective progress towards integrated models of brain organization.

TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis

Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize an atlas on cortical surfaces through the prediction of tfMRI data. TS-AI first synthesizes a battery of task contrast maps for each individual by leveraging tract-wise anatomical connectivity and resting-state networks. These synthesized maps, along with feature maps of tract-wise anatomical connectivity and resting-state networks, are then fed into an end-to-end deep neural network to individualize an atlas. TS-AI enables the synthesized task contrast maps to be used in individual parcellation without the acquisition of actual task fMRI scans. In addition, a novel feature consistency loss is designed to assign vertices with similar features to the same parcel, which increases individual specificity and mitigates overfitting risks caused by the absence of individual parcellation ground truth. The individualized parcellations were validated by assessing test-retest reliability, homogeneity, and cognitive behavior prediction using diverse reference atlases and datasets, demonstrating the superior performance and generalizability of TS-AI. Sensitivity analysis yielded insights into region-specific features influencing individual variation in functional regionalization. Additionally, TS-AI identified accelerated shrinkage in the medial temporal and cingulate parcels during the progression of Alzheimer's disease, suggesting its potential in clinical research and applications.

Dendrites contribute to the gradient of intrinsic timescales encompassing cortical and subcortical brain networks

Introduction

Cytoarchitectonic studies have uncovered a correlation between higher levels of cortical hierarchy and reduced dendritic size. This hierarchical organization extends to the brain's timescales, revealing longer intrinsic timescales at higher hierarchical levels. However, estimating the contribution of single-neuron dendritic morphology to the hierarchy of timescales, which is typically characterized at a macroscopic level, remains challenging.

Method

Here we mapped the intrinsic timescales of six functional networks using functional magnetic resonance imaging (fMRI) data, and characterized the influence of neuronal dendritic size on intrinsic timescales of brain regions, utilizing a multicompartmental neuronal modeling approach based on digitally reconstructed neurons.

Results

The fMRI results revealed a hierarchy of intrinsic timescales encompassing both cortical and subcortical brain regions. The neuronal modeling indicated that neurons with larger dendritic structures exhibit shorter intrinsic timescales. Together these findings highlight the contribution of dendrites at the neuronal level to the hierarchy of intrinsic timescales at the whole-brain level.

Discussion

This study sheds light on the intricate relationship between neuronal structure, cytoarchitectonic maps, and the hierarchy of timescales in the brain.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

BACKGROUND

Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state functional magnetic resonance imaging (rs-fMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains.

METHODS

rs-fMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron emission tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients.

RESULTS

Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta-power. Exploratory analyses revealed a close statistical relationship between LEN and positive symptom severity in patients.

CONCLUSION

Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

Hierarchical gradients of multiple timescales in the mammalian forebrain

Many anatomical and physiological features of cortical circuits, ranging from the biophysical properties of synapses to the connectivity patterns among different neuron types, exhibit consistent variation along the hierarchical axis from sensory to association areas. Notably, the scale of temporal correlation of neural activity at rest, known as the intrinsic timescale, increases systematically along this hierarchy in both primates and rodents, analogous to the growing scale and complexity of spatial receptive fields. However, how the timescales for task-related activity vary across brain regions and whether their hierarchical organization appears consistently across different mammalian species remain unexplored. Here, we show that both the intrinsic timescale and the timescales of task-related activity follow a similar hierarchical gradient in the cortices of monkeys, rats, and mice. We also found that these timescales covary similarly in both the cortex and basal ganglia, whereas the timescales of thalamic activity are shorter than cortical timescales and do not conform to the hierarchical order predicted by their cortical projections. These results suggest that the hierarchical gradient of cortical timescales might be a universal feature of intra-cortical circuits in the mammalian brain.

Atypical intrinsic neural timescale in the left angular gyrus in Alzheimer's disease

Alzheimer's disease is characterized by cognitive impairment and progressive brain atrophy. Recent human neuroimaging studies reported atypical anatomical and functional changes in some regions in the default mode network in patients with Alzheimer's disease, but which brain area of the default mode network is the key region whose atrophy disturbs the entire network activity and consequently contributes to the symptoms of the disease remains unidentified. Here, in this case-control study, we aimed to identify crucial neural regions that mediated the phenotype of Alzheimer's disease, and as such, we examined the intrinsic neural timescales-a functional metric to evaluate the capacity to integrate diverse neural information-and grey matter volume of the regions in the default mode network using resting-state functional MRI images and structural MRI data obtained from individuals with Alzheimer's disease and cognitively typical people. After confirming the atypically short neural timescale of the entire default mode network in Alzheimer's disease and its link with the symptoms of the disease, we found that the shortened neural timescale of the default mode network was associated with the aberrantly short neural timescale of the left angular gyrus. Moreover, we revealed that the shortened neural timescale of the angular gyrus was correlated with the atypically reduced grey matter volume of this parietal region. Furthermore, we identified an association between the neural structure, brain function and symptoms and proposed a model in which the reduced grey matter volume of the left angular gyrus shortened the intrinsic neural time of the region, which then destabilized the entire neural timescale of the default mode network and resultantly contributed to cognitive decline in Alzheimer's disease. These findings highlight the key role of the left angular gyrus in the anatomical and functional aetiology of Alzheimer's disease.

Is depression a global brain disorder with topographic dynamic reorganization?

Major depressive disorder (MDD) is characterized by a multitude of psychopathological symptoms including affective, cognitive, perceptual, sensorimotor, and social. The neuronal mechanisms underlying such co-occurrence of psychopathological symptoms remain yet unclear. Rather than linking and localizing single psychopathological symptoms to specific regions or networks, this perspective proposes a more global and dynamic topographic approach. We first review recent findings on global brain activity changes during both rest and task states in MDD showing topographic reorganization with a shift from unimodal to transmodal regions. Next, we single out two candidate mechanisms that may underlie and mediate such abnormal uni-/transmodal topography, namely dynamic shifts from shorter to longer timescales and abnormalities in the excitation-inhibition balance. Finally, we show how such topographic shift from unimodal to transmodal regions relates to the various psychopathological symptoms in MDD including their co-occurrence. This amounts to what we describe as 'Topographic dynamic reorganization' which extends our earlier 'Resting state hypothesis of depression' and complements other models of MDD.

Reverse engineering the brain input: Network control theory to identify cognitive task-related control nodes

The human brain receives complex inputs when performing cognitive tasks, which range from external inputs via the senses to internal inputs from other brain regions. However, the explicit inputs to the brain during a cognitive task remain unclear. Here, we present an input identification framework for reverse engineering the control nodes and the corresponding inputs to the brain. The framework is verified with synthetic data generated by a predefined linear system, indicating it can robustly reconstruct data and recover the inputs. Then we apply the framework to the real motor-task fMRI data from 200 human subjects. Our results show that the model with sparse inputs can reconstruct neural dynamics in motor tasks (EV =0.779) and the identified 28 control nodes largely overlap with the motor system. Underpinned by network control theory, our framework offers a general tool for understanding brain inputs.

Interacting ventral temporal gradients of timescales and functional connectivity and their relationships to visual behavior

Cortical gradients in endogenous and stimulus-evoked neurodynamic timescales, and long-range cortical interactions, provide organizational constraints to the brain and influence neural populations' roles in cognition. It is unclear how these functional gradients interrelate and which influence behavior. Here, intracranial recordings from 4,090 electrode contacts in 35 individuals map gradients of neural timescales and functional connectivity to assess their interactions along category-selective ventral temporal cortex. Endogenous and stimulus-evoked information processing timescales were not significantly correlated with one another suggesting that local neural timescales are context dependent and may arise through distinct neurophysiological mechanisms. Endogenous neural timescales correlated with functional connectivity even after removing the effects of shared anatomical gradients. Neural timescales and functional connectivity correlated with how strongly a population's activity predicted behavior in a simple visual task. These results suggest both interrelated and distinct neurophysiological processes give rise to different functional connectivity and neural timescale gradients, which together influence behavior.

A measure centrality index for systematic empirical comparison of consciousness theories

Consciousness science is marred by disparate constructs and methodologies, making it challenging to systematically compare theories. This foundational crisis casts doubts on the scientific character of the field itself. Addressing it, we propose a framework for systematically comparing consciousness theories by introducing a novel inter-theory classification interface, the Measure Centrality Index (MCI). Recognizing its gradient distribution, the MCI assesses the degree of importance a specific empirical measure has for a given consciousness theory. We apply the MCI to probe how the empirical measures of the Global Neuronal Workspace Theory (GNW), Integrated Information Theory (IIT), and Temporospatial Theory of Consciousness (TTC) would fare within the context of the other two. We demonstrate that direct comparison of IIT, GNW, and TTC is meaningful and valid for some measures like Lempel-Ziv Complexity (LZC), Autocorrelation Window (ACW), and possibly Mutual Information (MI). In contrast, it is problematic for others like the anatomical and physiological neural correlates of consciousness (NCC) due to their MCI-based differential weightings within the structure of the theories. In sum, we introduce and provide proof-of-principle of a novel systematic method for direct inter-theory empirical comparisons, thereby addressing isolated evolution of theories and confirmatory bias issues in the state-of-the-art neuroscience of consciousness.

The Brain's Topographical Organization Shapes Dynamic Interaction Patterns That Support Flexible Behavior Based on Rules and Long-Term Knowledge

Adaptive behavior relies both on specific rules that vary across situations and stable long-term knowledge gained from experience. The frontoparietal control network (FPCN) is implicated in the brain's ability to balance these different influences on action. Here, we investigate how the topographical organization of the cortex supports behavioral flexibility within the FPCN. Functional properties of this network might reflect its juxtaposition between the dorsal attention network (DAN) and the default mode network (DMN), two large-scale systems implicated in top-down attention and memory-guided cognition, respectively. Our study tests whether subnetworks of FPCN are topographically proximal to the DAN and the DMN, respectively, and how these topographical differences relate to functional differences: the proximity of each subnetwork is anticipated to play a pivotal role in generating distinct cognitive modes relevant to working memory and long-term memory. We show that FPCN subsystems share multiple anatomical and functional similarities with their neighboring systems (DAN and DMN) and that this topographical architecture supports distinct interaction patterns that give rise to different patterns of functional behavior. The FPCN acts as a unified system when long-term knowledge supports behavior but becomes segregated into discrete subsystems with different patterns of interaction when long-term memory is less relevant. In this way, our study suggests that the topographical organization of the FPCN and the connections it forms with distant regions of cortex are important influences on how this system supports flexible behavior.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

Background

Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state fMRI (rfMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains.

Methods

rfMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron-Emission Tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients.

Results

Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta power. Exploratory analyses revealed a close statistical relationship between LEN and positive PSD symptoms.

Conclusion

Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

CRediT Authorship Contribution Statement

Fabian Hirsch: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization; Ângelo Bumanglag: Methodology, Software, Formal analysis, Writing - Review & Editing; Yifei Zhang: Methodology, Software, Formal analysis, Writing - Review & Editing; Afra Wohlschlaeger: Methodology, Writing - Review & Editing, Supervision, Project administration.

Species -shared and -unique gyral peaks on human and macaque brains

Cortical folding is an important feature of primate brains that plays a crucial role in various cognitive and behavioral processes. Extensive research has revealed both similarities and differences in folding morphology and brain function among primates including macaque and human. The folding morphology is the basis of brain function, making cross-species studies on folding morphology important for understanding brain function and species evolution. However, prior studies on cross-species folding morphology mainly focused on partial regions of the cortex instead of the entire brain. Previously, our research defined a whole-brain landmark based on folding morphology: the gyral peak. It was found to exist stably across individuals and ages in both human and macaque brains. Shared and unique gyral peaks in human and macaque are identified in this study, and their similarities and differences in spatial distribution, anatomical morphology, and functional connectivity were also dicussed.

Co-representation of Functional Brain Networks Is Shaped by Cortical Myeloarchitecture and Reveals Individual Behavioral Ability

Large-scale functional networks are spatially distributed in the human brain. Despite recent progress in differentiating their functional roles, how the brain navigates the spatial coordination among them and the biological relevance of this coordination is still not fully understood. Capitalizing on canonical individualized networks derived from functional MRI data, we proposed a new concept, that is, co-representation of functional brain networks, to delineate the spatial coordination among them. To further quantify the co-representation pattern, we defined two indexes, that is, the co-representation specificity (CoRS) and intensity (CoRI), for separately measuring the extent of specific and average expression of functional networks at each brain location by using the data from both sexes. We found that the identified pattern of co-representation was anchored by cortical regions with three types of cytoarchitectural classes along a sensory-fugal axis, including, at the first end, primary (idiotypic) regions showing high CoRS, at the second end, heteromodal regions showing low CoRS and high CoRI, at the third end, paralimbic regions showing low CoRI. Importantly, we demonstrated the critical role of myeloarchitecture in sculpting the spatial distribution of co-representation by assessing the association with the myelin-related neuroanatomical and transcriptomic profiles. Furthermore, the significance of manifesting the co-representation was revealed in its prediction of individual behavioral ability. Our findings indicated that the spatial coordination among functional networks was built upon an anatomically configured blueprint to facilitate neural information processing, while advancing our understanding of the topographical organization of the brain by emphasizing the assembly of functional networks.

Neural timescales reflect behavioral demands in freely moving rhesus macaques

Previous work demonstrated a highly reproducible cortical hierarchy of neural timescales at rest, with sensory areas displaying fast, and higher-order association areas displaying slower timescales. The question arises how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this lack of variability in the hierarchical organization of neural timescales could reflect the structure of the laboratory contexts. We posit that unconstrained paradigms are ideal to test whether the dynamics of neural timescales reflect behavioral demands. Here we measured timescales of local field potential activity while male rhesus macaques foraged in an open space. We found a hierarchy of neural timescales that differs from previous work. Importantly, although the magnitude of neural timescales expanded with task engagement, the brain areas' relative position in the hierarchy was stable. Next, we demonstrated that the change in neural timescales is dynamic and contains functionally-relevant information, differentiating between similar events in terms of motor demands and associated reward. Finally, we demonstrated that brain areas are differentially affected by these behavioral demands. These results demonstrate that while the space of neural timescales is anatomically constrained, the observed hierarchical organization and magnitude is dependent on behavioral demands.

The significance of structural rich club hubs for the processing of hierarchical stimuli

The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.

Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Intrinsic neural timescales relate to the dynamics of infraslow neural waves

The human brain is a highly dynamic organ that operates across a variety of timescales, the intrinsic neural timescales (INT). In addition to the INT, the neural waves featured by its phase-related processes including their cycles with peak/trough and rise/fall play a key role in shaping the brain's neural activity. However, the relationship between the brain's ongoing wave dynamics and INT remains yet unclear. In this study, we utilized functional magnetic resonance imaging (fMRI) rest and task data from the Human Connectome Project (HCP) to investigate the relationship of infraslow wave dynamics [as measured in terms of speed by changes in its peak frequency (PF)] with INT. Our findings reveal that: (i) the speed of phase dynamics (PF) is associated with distinct parts of the ongoing phase cycles, namely higher PF in peak/trough and lower PF in rise/fall; (ii) there exists a negative correlation between phase dynamics (PF) and INT such that slower PF relates to longer INT; (iii) exposure to a movie alters both PF and INT across the different phase cycles, yet their negative correlation remains intact. Collectively, our results demonstrate that INT relates to infraslow phase dynamics during both rest and task states.

Auditory inputs modulate intrinsic neuronal timescales during sleep

Functional magnetic resonance imaging (fMRI) studies have demonstrated that intrinsic neuronal timescales (INT) undergo modulation by external stimulation during consciousness. It remains unclear if INT keep the ability for significant stimulus-induced modulation during primary unconscious states, such as sleep. This fMRI analysis addresses this question via a dataset that comprises an awake resting-state plus rest and stimulus states during sleep. We analyzed INT measured via temporal autocorrelation supported by median frequency (MF) in the frequency-domain. Our results were replicated using a biophysical model. There were two main findings: (1) INT prolonged while MF decreased from the awake resting-state to the N2 resting-state, and (2) INT shortened while MF increased during the auditory stimulus in sleep. The biophysical model supported these results by demonstrating prolonged INT in slowed neuronal populations that simulate the sleep resting-state compared to an awake state. Conversely, under sine wave input simulating the stimulus state during sleep, the model's regions yielded shortened INT that returned to the awake resting-state level. Our results highlight that INT preserve reactivity to stimuli in states of unconsciousness like sleep, enhancing our understanding of unconscious brain dynamics and their reactivity to stimuli.

The spatial extent of anatomical connections within the thalamus varies across the cortical hierarchy in humans and macaques

Each cortical area has a distinct pattern of anatomical connections within the thalamus, a central subcortical structure composed of functionally and structurally distinct nuclei. Previous studies have suggested that certain cortical areas may have more extensive anatomical connections that target multiple thalamic nuclei, which potentially allows them to modulate distributed information flow. However, there is a lack of quantitative investigations into anatomical connectivity patterns within the thalamus. Consequently, it remains unknown if cortical areas exhibit systematic differences in the extent of their anatomical connections within the thalamus. To address this knowledge gap, we used diffusion magnetic resonance imaging (dMRI) to perform brain-wide probabilistic tractography for 828 healthy adults from the Human Connectome Project. We then developed a framework to quantify the spatial extent of each cortical area's anatomical connections within the thalamus. Additionally, we leveraged resting-state functional MRI, cortical myelin, and human neural gene expression data to test if the extent of anatomical connections within the thalamus varied along the cortical hierarchy. Our results revealed two distinct corticothalamic tractography motifs: 1) a sensorimotor cortical motif characterized by focal thalamic connections targeting posterolateral thalamus, associated with fast, feed-forward information flow; and 2) an associative cortical motif characterized by diffuse thalamic connections targeting anteromedial thalamus, associated with slow, feed-back information flow. These findings were consistent across human subjects and were also observed in macaques, indicating cross-species generalizability. Overall, our study demonstrates that sensorimotor and association cortical areas exhibit differences in the spatial extent of their anatomical connections within the thalamus, which may support functionally-distinct cortico-thalamic information flow.

Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states

Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations

Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists' preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.

Brain-wide changes in excitation-inhibition balance of major depressive disorder: a systematic review of topographic patterns of GABA- and glutamatergic alterations

The excitation-inhibition (E/I) imbalance is an important molecular pathological feature of major depressive disorder (MDD) as altered GABA and glutamate levels have been found in multiple brain regions in patients. Healthy subjects show topographic organization of the E/I balance (EIB) across various brain regions. We here raise the question of whether such EIB topography is altered in MDD. Therefore, we systematically review the gene and protein expressions of inhibitory GABAergic and excitatory glutamatergic signaling-related molecules in postmortem MDD brain studies as proxies for EIB topography. Searches were conducted through PubMed and 45 research articles were finally included. We found: i) brain-wide GABA- and glutamatergic alterations; ii) attenuated GABAergic with enhanced glutamatergic signaling in the cortical-subcortical limbic system; iii) that GABAergic signaling is decreased in regions comprising the default mode network (DMN) while it is increased in lateral prefrontal cortex (LPFC). These together demonstrate abnormal GABA- and glutamatergic signaling-based EIB topographies in MDD. This enhances our pathophysiological understanding of MDD and carries important therapeutic implications for stimulation treatment.

Scale-free dynamics in the core-periphery topography and task alignment decline from conscious to unconscious states

Scale-free physiological processes are ubiquitous in the human organism. Resting-state functional MRI studies observed the loss of scale-free dynamics under anesthesia. In contrast, the modulation of scale-free dynamics during task-related activity remains an open question. We investigate scale-free dynamics in the cerebral cortex's unimodal periphery and transmodal core topography in rest and task states during three conscious levels (awake, sedation, and anesthesia) complemented by computational modelling (Stuart-Landau model). The empirical findings demonstrate that the loss of the brain's intrinsic scale-free dynamics in the core-periphery topography during anesthesia, where pink noise transforms into white noise, disrupts the brain's neuronal alignment with the task's temporal structure. The computational model shows that the stimuli's scale-free dynamics, namely pink noise distinguishes from brown and white noise, also modulate task-related activity. Together, we provide evidence for two mechanisms of consciousness, temporo-spatial nestedness and alignment, suggested by the Temporo-Spatial Theory of Consciousness (TTC).

The development of intrinsic timescales: A comparison between the neonate and adult brain

In human adults and other mammals, different brain regions have distinct intrinsic timescales over which they integrate information, from shorter in unimodal sensory-motor regions to longer in transmodal higher-order regions. These have been related to cognitive performance and clinical symptoms, but it remains unclear how they develop. We asked if there are regional differences in timescales at birth that could shape learning by acting as an inductive bias, or if they develop later as the temporal statistics of the environment are learned. We used resting-state fMRI to characterise timescales in human neonates and adults. They were highly consistent across two independent neonatal groups, but in both sensory-motor and higher order areas, timescales were longer in infants compared to adults, as might be expected from their less developed myelination, and recent evidence of longer neural segments in infants watching naturalistic stimuli. In adults, we replicated the finding that transmodal areas have longer timescales than sensory-motor areas, but in infants the opposite pattern was found, driven by long infant timescales in the somotomotor network. Across regions within single brain networks, both positive (limbic) and negative (visual) correlations were found between neonates and adults. In conclusion, neonatal timescales were found to be highly structured, but distinct from adults, suggesting they act as an inductive bias that favours learning on longer timescales, particularly in unimodal regions and then develop with experience or maturation. This "take it slow" initial approach might help human infants to create more regularised, holistic representations of the input less bound to fleeting details, which would favour the development of abstract and contextual representations.

Functional brain networks reflect spatial and temporal autocorrelation

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.

Neural timescales reflect behavioral demands in freely moving rhesus macaques

Previous work has demonstrated remarkably reproducible and consistent hierarchies of neural timescales across cortical areas at rest. The question arises how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this previously found lack of variability in the hierarchical organization of neural timescales could be a reflection of the structure of the laboratory contexts in which they were measured. Indeed, computational work demonstrates the existence of multiple temporal hierarchies within the same anatomical network when the input structure is altered. We posit that unconstrained behavioral environments where relatively little temporal demands are imposed from the experimenter are an ideal test bed to address the question of whether the hierarchical organization and the magnitude of neural timescales reflect ongoing behavioral demands. To tackle this question, we measured timescales of local field potential activity while rhesus macaques were foraging freely in a large open space. We find a hierarchy of neural timescales that is unique to this foraging environment. Importantly, although the magnitude of neural timescales generally expanded with task engagement, the brain areas' relative position in the hierarchy was stable across the recording sessions. Notably, the magnitude of neural timescales monotonically expanded with task engagement across a relatively long temporal scale spanning the duration of the recording session. Over shorter temporal scales, the magnitude of neural timescales changed dynamically around foraging events. Moreover, the change in the magnitude of neural timescales contained functionally relevant information, differentiating between seemingly similar events in terms of motor demands and associated reward. That is, the patterns of change were associated with the cognitive and behavioral meaning of these events. Finally, we demonstrated that brain areas were differentially affected by these behavioral demands - i.e., the expansion of neural timescales was not the same across all areas. Together, these results demonstrate that the observed hierarchy of neural timescales is context-dependent and that changes in the magnitude of neural timescales are closely related to overall task engagement and behavioral demands.

Topographic-dynamic reorganisation model of dreams (TRoD) - A spatiotemporal approach

Dreams are one of the most bizarre and least understood states of consciousness. Bridging the gap between brain and phenomenology of (un)conscious experience, we propose the Topographic-dynamic Re-organization model of Dreams (TRoD). Topographically, dreams are characterized by a shift towards increased activity and connectivity in the default-mode network (DMN) while they are reduced in the central executive network, including the dorsolateral prefrontal cortex (except in lucid dreaming). This topographic re-organization is accompanied by dynamic changes; a shift towards slower frequencies and longer timescales. This puts dreams dynamically in an intermediate position between awake state and NREM 2/SWS sleep. TRoD proposes that the shift towards DMN and slower frequencies leads to an abnormal spatiotemporal framing of input processing including both internally- and externally-generated inputs (from body and environment). In dreams, a shift away from temporal segregation to temporal integration of inputs results in the often bizarre and highly self-centric mental contents as well as hallucinatory-like states. We conclude that topography and temporal dynamics are core features of the TroD, which may provide the connection of neural and mental activity, e.g., brain and experience during dreams as their "common currency".

Intrinsic neural timescales mediate the cognitive bias of self - temporal integration as key mechanism

Our perceptions and decisions are not always objectively correct as they are featured by a bias related to our self. What are the behavioral, neural, and computational mechanisms of such cognitive bias? Addressing this yet unresolved question, we here investigate whether the cognitive bias is related to temporal integration and segregation as mediated by the brain's Intrinsic neural timescales (INT). Using Signal Detection Theory (SDT), we operationalize the cognitive bias by the Criterion C as distinguished from the sensitivity index d'. This was probed in a self-task based on morphed self- and other faces. Behavioral data demonstrate clear cognitive bias, i.e., Criterion C. That was related to the EEG-based INT as measured by the autocorrelation window (ACW) in especially the transmodal regions dorsolateral prefrontal cortex (dlPFC) and default-mode network (DMN) as distinct from unimodal visual cortex. Finally, simulation of the same paradigm in a large-scale network model shows high degrees of temporal integration of temporally distinct inputs in CMS/DMN and dlPFC while temporal segregation predominates in visual cortex. Together, we demonstrate a key role of INT-based temporal integration in CMS/DMN and dlPFC including its relation to the brain's uni-transmodal topographical organization in mediating the cognitive bias of our self.

Multi-view manifold learning of human brain-state trajectories

The complexity of the human brain gives the illusion that brain activity is intrinsically high-dimensional. Nonlinear dimensionality-reduction methods such as uniform manifold approximation and t-distributed stochastic neighbor embedding have been used for high-throughput biomedical data. However, they have not been used extensively for brain activity data such as those from functional magnetic resonance imaging (fMRI), primarily due to their inability to maintain dynamic structure. Here we introduce a nonlinear manifold learning method for time-series data-including those from fMRI-called temporal potential of heat-diffusion for affinity-based transition embedding (T-PHATE). In addition to recovering a low-dimensional intrinsic manifold geometry from time-series data, T-PHATE exploits the data's autocorrelative structure to faithfully denoise and unveil dynamic trajectories. We empirically validate T-PHATE on three fMRI datasets, showing that it greatly improves data visualization, classification, and segmentation of the data relative to several other state-of-the-art dimensionality-reduction benchmarks. These improvements suggest many potential applications of T-PHATE to other high-dimensional datasets of temporally diffuse processes.

Atypical intrinsic neural timescales in temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy (TLE) is the most common pharmacoresistant epilepsy in adults. Here we profiled local neural function in TLE in vivo, building on prior evidence that has identified widespread structural alterations. Using resting-state functional magnetic resonance imaging (rs-fMRI), we mapped the whole-brain intrinsic neural timescales (INT), which reflect temporal hierarchies of neural processing. Parallel analysis of structural and diffusion MRI data examined associations with TLE-related structural compromise. Finally, we evaluated the clinical utility of INT.

METHODS

We studied 46 patients with TLE and 44 healthy controls from two independent sites, and mapped INT changes in patients relative to controls across hippocampal, subcortical, and neocortical regions. We examined region-specific associations to structural alterations and explored the effects of age and epilepsy duration. Supervised machine learning assessed the utility of INT for identifying patients with TLE vs controls and left- vs right-sided seizure onset.

RESULTS

Relative to controls, TLE showed marked INT reductions across multiple regions bilaterally, indexing faster changing resting activity, with strongest effects in the ipsilateral medial and lateral temporal regions, and bilateral sensorimotor cortices as well as thalamus and hippocampus. Findings were similar, albeit with reduced effect sizes, when correcting for structural alterations. INT reductions in TLE increased with advancing disease duration, yet findings differed from the aging effects seen in controls. INT-derived classifiers discriminated patients vs controls (balanced accuracy, 5-fold: 76% ± 2.65%; cross-site, 72%-83%) and lateralized the focus in TLE (balanced accuracy, 5-fold: 96% ± 2.10%; cross-site, 95%-97%), with high accuracy and cross-site generalizability. Findings were consistent across both acquisition sites and robust when controlling for motion and several methodological confounds.

SIGNIFICANCE

Our findings demonstrate atypical macroscale function in TLE in a topography that extends beyond mesiotemporal epicenters. INT measurements can assist in TLE diagnosis, seizure focus lateralization, and monitoring of disease progression, which emphasizes promising clinical utility.

Activity flow mapping over probabilistic functional connectivity

Emerging evidence indicates that activity flow over resting-state network topology allows the prediction of task activations. However, previous studies have mainly adopted static, linear functional connectivity (FC) estimates as activity flow routes. It is unclear whether an intrinsic network topology that captures the dynamic nature of FC can be a better representation of activity flow routes. Moreover, the effects of between- versus within-network connections and tight versus loose (using rest baseline) task contrasts on the prediction of task-evoked activity across brain systems remain largely unknown. In this study, we first propose a probabilistic FC estimation derived from a dynamic framework as a new activity flow route. Subsequently, activity flow mapping was tested using between- and within-network connections separately for each region as well as using a set of tight task contrasts. Our results showed that probabilistic FC routes substantially improved individual-level activity flow prediction. Although it provided better group-level prediction, the multiple regression approach was more dependent on the length of data points at the individual-level prediction. Regardless of FC type, we consistently observed that between-network connections showed a relatively higher prediction performance in higher-order cognitive control than in primary sensorimotor systems. Furthermore, cognitive control systems exhibit a remarkable increase in prediction accuracy with tight task contrasts and a decrease in sensorimotor systems. This work demonstrates that probabilistic FC estimates are promising routes for activity flow mapping and also uncovers divergent influences of connectional topology and task contrasts on activity flow prediction across brain systems with different functional hierarchies.

Multitask representations in the human cortex transform along a sensory-to-motor hierarchy

Human cognition recruits distributed neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multitask representations across the human cortex using functional magnetic resonance imaging during 26 cognitive tasks in the same individuals. We measured the representational similarity across tasks within a region and the alignment of representations between regions. Representational alignment varied in a graded manner along the sensory-association-motor axis. Multitask dimensionality exhibited compression then expansion along this gradient. To investigate computational principles of multitask representations, we trained multilayer neural network models to transform empirical visual-to-motor representations. Compression-then-expansion organization in models emerged exclusively in a rich training regime, which is associated with learning optimized representations that are robust to noise. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multitask representations across the human cortex to support multitask cognition.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6 800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present

Mindful attention is characterized by acknowledging the present experience as a transient mental event. Early stages of mindfulness practice may require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs-operationalizing effort-distribute changes in neural activity evoked during mindful attention across the white matter network. We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present. We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to one's own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.

Scale-free dynamics of core-periphery topography

The human brain's cerebral cortex exhibits a topographic division into higher-order transmodal core and lower-order unimodal periphery regions. While timescales between the core and periphery region diverge, features of their power spectra, especially scale-free dynamics during resting-state and their mdulation in task states, remain unclear. To answer this question, we investigated the ~1/f-like pink noise manifestation of scale-free dynamics in the core-periphery topography during rest and task states applying infra-slow inter-trial intervals up to 1 min falling inside the BOLD's infra-slow frequency band. The results demonstrate (1) higher resting-state power-law exponent (PLE) in the core compared to the periphery region; (2) significant PLE increases in task across the core and periphery regions; and (3) task-related PLE increases likely followed the task's atypically low event rates, namely the task's periodicity (inter-trial interval = 52-60 s; 0.016-0.019 Hz). A computational model and a replication dataset that used similar infra-slow inter-trial intervals provide further support for our main findings. Altogether, the results show that scale-free dynamics differentiate core and periphery regions in the resting-state and mediate task-related effects.

Spatiotemporal Psychopathology - A Novel Approach to Brain and Symptoms

How can we characterize psychopathological symptoms and connect them to the brain? Current psychopathological symptoms only focus on either the symptoms themselves or predominantly on the brain. This leaves open their intimate connection. A novel approach, Spatiotemporal Psychopathology, proposes that the brain inner spatiotemporal organisation of its neural activity provides the spatiotemporal organization of the psychopathological symptoms. Specifically, the brains' neuronal topography and dynamic is manifest in a more or less analogous spatiotemporal organisation on the mental level, i.e., mental topography and dynamic. This is strongly supported by various examples including major depressive disorder, bipolar disorder, schizophrenia, and autism. We therefore conclude that Spatiotemporal Psychopathology provides a promising approach to intimately connect brain and symptoms.

Default mode network mediates low-frequency fluctuations in brain activity and behavior during sustained attention

The low-frequency (<0.1 Hz) fluctuation in sustained attention attracts enormous interest in cognitive neuroscience and clinical research since it always leads to cognitive and behavioral lapses. What is the source of the spontaneous fluctuation in sustained attention in neural activity, and how does the neural fluctuation relate to behavioral fluctuation? Here, we address these questions by collecting and analyzing two independent fMRI and behavior datasets. We show that the neural (fMRI) fluctuation in a key brain network, the default-mode network (DMN), mediate behavioral (reaction time) fluctuation during sustained attention. DMN shows the increased amplitude of fluctuation, which correlates with the behavioral fluctuation in a similar frequency range (0.01-0.1 Hz) but not in the lower (<0.01 Hz) or higher (>0.1 Hz) frequency range. This was observed during both auditory and visual sustained attention and was replicable across independent datasets. These results provide a novel insight into the neural source of attention-fluctuation and extend the former concept that DMN was deactivated in cognitive tasks. More generally, our findings highlight the temporal dynamic of the brain-behavior relationship.