Sensory-motor cortices shape functional connectivity dynamics in the human brain

In-vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here we non-invasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically-plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the GABA-agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 years old) and Asian (7.2 to 7.9 years old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth.

Altered trends of local brain function in classical trigeminal neuralgia patients after a single trigger pain

OBJECTIVE

To investigate the altered trends of regional homogeneity (ReHo) based on time and frequency, and clarify the time-frequency characteristics of ReHo in 48 classical trigeminal neuralgia (CTN) patients after a single pain stimulate.

METHODS

All patients underwent three times resting-state functional MRI (before stimulation (baseline), after stimulation within 5 s (triggering-5 s), and in the 30th min of stimulation (triggering-30 min)). The spontaneous brain activity was investigated by static ReHo (sReHo) in five different frequency bands and dynamic ReHo (dReHo) methods.

RESULTS

In the five frequency bands, the number of brain regions which the sReHo value changed in classical frequency band were most, followed by slow 4 frequency band. The left superior occipital gyrus was only found in slow 2 frequency band and the left superior parietal gyrus was only found in slow 3 frequency band. The dReHo values were changed in midbrain, left thalamus, right putamen, and anterior cingulate cortex, which were all different from the brain regions that the sReHo value altered. There were four altered trends of the sReHo and dReHo, which dominated by decreased at triggering-5 s and increased at triggering-30 min.

CONCLUSIONS

The duration of brain function changed was more than 30 min after a single pain stimulate, although the pain of CTN was transient. The localized functional homogeneity has time-frequency characteristic in CTN patients after a single pain stimulate, and the changed brain regions of the sReHo in five frequency bands and dReHo complemented to each other. Which provided a certain theoretical basis for exploring the pathophysiology of CTN.

A Model of Multi-Finger Coordination in Keystroke Movement

In multi-finger coordinated keystroke actions by professional pianists, movements are precisely regulated by multiple motor neural centers, exhibiting a certain degree of coordination in finger motions. This coordination enhances the flexibility and efficiency of professional pianists' keystrokes. Research on the coordination of keystrokes in professional pianists is of great significance for guiding the movements of piano beginners and the motion planning of exoskeleton robots, among other fields. Currently, research on the coordination of multi-finger piano keystroke actions is still in its infancy. Scholars primarily focus on phenomenological analysis and theoretical description, which lack accurate and practical modeling methods. Considering that the tendon of the ring finger is closely connected to adjacent fingers, resulting in limited flexibility in its movement, this study concentrates on coordinated keystrokes involving the middle and ring fingers. A motion measurement platform is constructed, and Leap Motion is used to collect data from 12 professional pianists. A universal model applicable to multiple individuals for multi-finger coordination in keystroke actions based on the backpropagation (BP) neural network is proposed, which is optimized using a genetic algorithm (GA) and a sparrow search algorithm (SSA). The angular rotation of the ring finger's MCP joint is selected as the model output, while the individual difference information and the angular data of the middle finger's MCP joint serve as inputs. The individual difference information used in this study includes ring finger length, middle finger length, and years of piano training. The results indicate that the proposed SSA-BP neural network-based model demonstrates superior predictive accuracy, with a root mean square error of 4.8328°. Based on this model, the keystroke motion of the ring finger's MCP joint can be accurately predicted from the middle finger's keystroke motion information, offering an evaluative method and scientific guidance for the training of multi-finger coordinated keystrokes in piano learners.

Exploring global and local processes underlying alterations in resting-state functional connectivity and dynamics in schizophrenia

Introduction

We examined changes in large-scale functional connectivity and temporal dynamics and their underlying mechanisms in schizophrenia (ScZ) through measurements of resting-state functional magnetic resonance imaging (rs-fMRI) data and computational modelling.

Methods

The rs-fMRI measurements from patients with chronic ScZ (n=38) and matched healthy controls (n=43), were obtained through the public schizConnect repository. Computational models were constructed based on diffusion-weighted MRI scans and fit to the experimental rs-fMRI data.

Results

We found decreased large-scale functional connectivity across sensory and association areas and for all functional subnetworks for the ScZ group. Additionally global synchrony was reduced in patients while metastability was unaltered. Perturbations of the computational model revealed that decreased global coupling and increased background noise levels both explained the experimentally found deficits better than local changes to the GABAergic or glutamatergic system.

Discussion

The current study suggests that large-scale alterations in ScZ are more likely the result of global rather than local network changes.

Macroscale intrinsic dynamics are associated with microcircuit function in focal and generalized epilepsies

Epilepsies are a group of neurological disorders characterized by abnormal spontaneous brain activity, involving multiscale changes in brain functional organizations. However, it is not clear to what extent the epilepsy-related perturbations of spontaneous brain activity affect macroscale intrinsic dynamics and microcircuit organizations, that supports their pathological relevance. We collect a sample of patients with temporal lobe epilepsy (TLE) and genetic generalized epilepsy with tonic-clonic seizure (GTCS), as well as healthy controls. We extract massive temporal features of fMRI BOLD time-series to characterize macroscale intrinsic dynamics, and simulate microcircuit neuronal dynamics used a large-scale biological model. Here we show whether macroscale intrinsic dynamics and microcircuit dysfunction are differed in epilepsies, and how these changes are linked. Differences in macroscale gradient of time-series features are prominent in the primary network and default mode network in TLE and GTCS. Biophysical simulations indicate reduced recurrent connection within somatomotor microcircuits in both subtypes, and even more reduced in GTCS. We further demonstrate strong spatial correlations between differences in the gradient of macroscale intrinsic dynamics and microcircuit dysfunction in epilepsies. These results emphasize the impact of abnormal neuronal activity on primary network and high-order networks, suggesting a systematic abnormality of brain hierarchical organization.

Manifold alteration between major depressive disorder and healthy control subjects using dynamic mode decomposition in resting-state fMRI data

Background

The World Health Organization has reported that approximately 300 million individuals suffer from the mood disorder known as MDD. Non-invasive measurement techniques have been utilized to reveal the mechanism of MDD, with rsfMRI being the predominant method. The previous functional connectivity and energy landscape studies have shown the difference in the coactivation patterns between MDD and HCs. However, these studies did not consider oscillatory temporal dynamics.

Methods

In this study, the dynamic mode decomposition, a method to compute a set of coherent spatial patterns associated with the oscillation frequency and temporal decay rate, was employed to investigate the alteration of the occurrence of dynamic modes between MDD and HCs. Specifically, The BOLD signals of each subject were transformed into dynamic modes representing coherent spatial patterns and discrete-time eigenvalues to capture temporal variations using dynamic mode decomposition. All the dynamic modes were disentangled into a two-dimensional manifold using t-SNE. Density estimation and density ratio estimation were applied to the two-dimensional manifolds after the two-dimensional manifold was split based on HCs and MDD.

Results

The dynamic modes that uniquely emerged in the MDD were not observed. Instead, we have found some dynamic modes that have shown increased or reduced occurrence in MDD compared with HCs. The reduced dynamic modes were associated with the visual and saliency networks while the increased dynamic modes were associated with the default mode and sensory-motor networks.

Conclusion

To the best of our knowledge, this study showed initial evidence of the alteration of occurrence of the dynamic modes between MDD and HCs. To deepen understanding of how the alteration of the dynamic modes emerges from the structure, it is vital to investigate the relationship between the dynamic modes, cortical thickness, and surface areas.

The neural correlates of arousal: Ventral posterolateral nucleus-global transient co-activation

Arousal and awareness are two components of consciousness whose neural mechanisms remain unclear. Spontaneous peaks of global (brain-wide) blood-oxygenation-level-dependent (BOLD) signal have been found to be sensitive to changes in arousal. By contrasting BOLD signals at different arousal levels, we find decreased activation of the ventral posterolateral nucleus (VPL) during transient peaks in the global signal in low arousal and awareness states (non-rapid eye movement sleep and anesthesia) compared to wakefulness and in eyes-closed compared to eyes-open conditions in healthy awake individuals. Intriguingly, VPL-global co-activation remains high in patients with unresponsive wakefulness syndrome (UWS), who exhibit high arousal without awareness, while it reduces in rapid eye movement sleep, a state characterized by low arousal but high awareness. Furthermore, lower co-activation is found in individuals during N3 sleep compared to patients with UWS. These results demonstrate that co-activation of VPL and global activity is critical to arousal but not to awareness.

Shared and malignancy-specific functional plasticity of dynamic brain properties for patients with left frontal glioma

The time-varying brain activity may parallel the disease progression of cerebral glioma. Assessment of brain dynamics would better characterize the pathological profile of glioma and the relevant functional remodeling. This study aims to investigate the dynamic properties of functional networks based on sliding-window approach for patients with left frontal glioma. The generalized functional plasticity due to glioma was characterized by reduced dynamic amplitude of low-frequency fluctuation of somatosensory networks, reduced dynamic functional connectivity between homotopic regions mainly involving dorsal attention network and subcortical nuclei, and enhanced subcortical dynamic functional connectivity. Malignancy-specific functional remodeling featured a chaotic modification of dynamic amplitude of low-frequency fluctuation and dynamic functional connectivity for low-grade gliomas, and attenuated dynamic functional connectivity of the intrahemispheric cortico-subcortical connections and reduced dynamic amplitude of low-frequency fluctuation of the bilateral caudate for high-grade gliomas. Network dynamic activity was clustered into four distinct configuration states. The occurrence and dwell time of the weakly connected state were reduced in patients' brains. Support vector machine model combined with predictive dynamic features achieved an averaged accuracy of 87.9% in distinguishing low- and high-grade gliomas. In conclusion, dynamic network properties are highly predictive of the malignant grade of gliomas, thus could serve as new biomarkers for disease characterization.

Neurobiological Underpinnings of Hyperarousal in Depression: A Comprehensive Review

Patients with major depressive disorder (MDD) exhibit an abnormal physiological arousal pattern known as hyperarousal, which may contribute to their depressive symptoms. However, the neurobiological mechanisms linking this abnormal arousal to depressive symptoms are not yet fully understood. In this review, we summarize the physiological and neural features of arousal, and review the literature indicating abnormal arousal in depressed patients. Evidence suggests that a hyperarousal state in depression is characterized by abnormalities in sleep behavior, physiological (e.g., heart rate, skin conductance, pupil diameter) and electroencephalography (EEG) features, and altered activity in subcortical (e.g., hypothalamus and locus coeruleus) and cortical regions. While recent studies highlight the importance of subcortical-cortical interactions in arousal, few have explored the relationship between subcortical-cortical interactions and hyperarousal in depressed patients. This gap limits our understanding of the neural mechanism through which hyperarousal affects depressive symptoms, which involves various cognitive processes and the cerebral cortex. Based on the current literature, we propose that the hyperconnectivity in the thalamocortical circuit may contribute to both the hyperarousal pattern and depressive symptoms. Future research should investigate the relationship between thalamocortical connections and abnormal arousal in depression, and explore its implications for non-invasive treatments for depression.

Towards a biologically annotated brain connectome

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

Coordinating upper limbs for octave playing on the piano via neuro-musculoskeletal modeling

Understanding the coordination of multiple biomechanical degrees of freedom in biological organisms is crucial for unraveling the neurophysiological control of sophisticated motor tasks. This study focuses on the cooperative behavior of upper-limb motor movements in the context of octave playing on the piano. While the vertebrate locomotor system has been extensively investigated, the coherence and precision timing of rhythmic movements in the upper-limb system remain incompletely understood. Inspired by the spinal cord neuronal circuits (central pattern generator, CPG), a computational neuro-musculoskeletal model is proposed to explore the coordination of upper-limb motor movements during octave playing across varying tempos and volumes. The proposed model incorporates a CPG-based nervous system, a physiologically-informed mechanical body, and a piano environment to mimic human joint coordination and expressiveness. The model integrates neural rhythm generation, spinal reflex circuits, and biomechanical muscle dynamics while considering piano playing quality and energy expenditure. Based on real-world human subject experiments, the model has been refined to study tempo transitions and volume control during piano playing. This computational approach offers insights into the neurophysiological basis of upper-limb motor coordination in piano playing and its relation to expressive features.

A critical period plasticity framework for the sensorimotor-association axis of cortical neurodevelopment

To understand human brain development it is necessary to describe not only the spatiotemporal patterns of neurodevelopment but also the neurobiological mechanisms that underlie them. Human neuroimaging studies have provided evidence for a hierarchical sensorimotor-to-association (S-A) axis of cortical neurodevelopment. Understanding the biological mechanisms that underlie this program of development using traditional neuroimaging approaches has been challenging. Animal models have been used to identify periods of enhanced experience-dependent plasticity - 'critical periods' - that progress along cortical hierarchies and are governed by a conserved set of neurobiological mechanisms that promote and then restrict plasticity. In this review we hypothesize that the S-A axis of cortical development in humans is partly driven by the cascading maturation of critical period plasticity mechanisms. We then describe how recent advances in in vivo neuroimaging approaches provide a promising path toward testing this hypothesis by linking signals derived from non-invasive imaging to critical period mechanisms.

Dissociating representations of affect and motion in visual cortices

While a delicious dessert being presented to us may elicit strong feelings of happiness and excitement, the same treat falling slowly away can lead to sadness and disappointment. Our emotional response to the item depends on its visual motion direction. Despite this importance, it remains unclear whether (and how) cortical areas devoted to decoding motion direction represents or integrates emotion with perceived motion direction. Motion-selective visual area V5/MT+ sits, both functionally and anatomically, at the nexus of dorsal and ventral visual streams. These pathways, however, differ in how they are modulated by emotional cues. The current study was designed to disentangle how emotion and motion perception interact, as well as use emotion-dependent modulation of visual cortices to understand the relation of V5/MT+ to canonical processing streams. During functional magnetic resonance imaging (fMRI), approaching, receding, or static motion after-effects (MAEs) were induced on stationary positive, negative, and neutral stimuli. An independent localizer scan was conducted to identify the visual-motion area V5/MT+. Through univariate and multivariate analyses, we demonstrated that emotion representations in V5/MT+ share a more similar response profile to that observed in ventral visual than dorsal, visual structures. Specifically, V5/MT+ and ventral structures were sensitive to the emotional content of visual stimuli, whereas dorsal visual structures were not. Overall, this work highlights the critical role of V5/MT+ in the representation and processing of visually acquired emotional content. It further suggests a role for this region in utilizing affectively salient visual information to augment motion perception of biologically relevant stimuli.

Investigating the heterogeneity within the somatosensory-motor network and its relationship with the attention and default systems

The somatosensory-motor network (SMN) not only plays an important role in primary somatosensory and motor processing but is also central to many disorders. However, the SMN heterogeneity related to higher-order systems still remains unclear. Here, we investigated SMN heterogeneity from multiple perspectives. To characterize the SMN substructures in more detail, we used ultra-high-field functional MRI to delineate a finer-grained cortical parcellation containing 430 parcels that is more homogenous than the state-of-the-art parcellation. We personalized the new parcellation to account for individual differences and identified multiscale individual-specific brain structures. We found that the SMN subnetworks showed distinct resting-state functional connectivity (RSFC) patterns. The Hand subnetwork was central within the SMN and exhibited stronger RSFC with the attention systems than the other subnetworks, whereas the Tongue subnetwork exhibited stronger RSFC with the default systems. This two-fold differentiation was observed in the temporal ordering patterns within the SMN. Furthermore, we characterized how the distinct attention and default streams were carried forward into the functions of the SMN using dynamic causal modeling and identified two behavioral domains associated with this SMN fractionation using meta-analytic tools. Overall, our findings provided important insights into the heterogeneous SMN organization at the system level and suggested that the Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.

Multi-modal and multi-model interrogation of large-scale functional brain networks

Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias

Introduction

Neural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. The Virtual Brain (TVB) modeling, by exploiting structural and functional magnetic resonance imaging (MRI), yields mesoscopic parameters of connectivity and synaptic transmission.

Methods

We used TVB to simulate brain networks, which are key for human brain function, in Alzheimer's disease (AD) and frontotemporal dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks.

Results

The pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles.

Discussion

These TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

The impact of regional heterogeneity in whole-brain dynamics in the presence of oscillations

Large variability exists across brain regions in health and disease, considering their cellular and molecular composition, connectivity, and function. Large-scale whole-brain models comprising coupled brain regions provide insights into the underlying dynamics that shape complex patterns of spontaneous brain activity. In particular, biophysically grounded mean-field whole-brain models in the asynchronous regime were used to demonstrate the dynamical consequences of including regional variability. Nevertheless, the role of heterogeneities when brain dynamics are supported by synchronous oscillating state, which is a ubiquitous phenomenon in brain, remains poorly understood. Here, we implemented two models capable of presenting oscillatory behavior with different levels of abstraction: a phenomenological Stuart-Landau model and an exact mean-field model. The fit of these models informed by structural- to functional-weighted MRI signal (T1w/T2w) allowed us to explore the implication of the inclusion of heterogeneities for modeling resting-state fMRI recordings from healthy participants. We found that disease-specific regional functional heterogeneity imposed dynamical consequences within the oscillatory regime in fMRI recordings from neurodegeneration with specific impacts on brain atrophy/structure (Alzheimer's patients). Overall, we found that models with oscillations perform better when structural and functional regional heterogeneities are considered, showing that phenomenological and biophysical models behave similarly at the brink of the Hopf bifurcation.

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).

Noise improves the association between effects of local stimulation and structural degree of brain networks

Stimulation to local areas remarkably affects brain activity patterns, which can be exploited to investigate neural bases of cognitive function and modify pathological brain statuses. There has been growing interest in exploring the fundamental action mechanisms of local stimulation. Nevertheless, how noise amplitude, an essential element in neural dynamics, influences stimulation-induced brain states remains unknown. Here, we systematically examine the effects of local stimulation by using a large-scale biophysical model under different combinations of noise amplitudes and stimulation sites. We demonstrate that noise amplitude nonlinearly and heterogeneously tunes the stimulation effects from both regional and network perspectives. Furthermore, by incorporating the role of the anatomical network, we show that the peak frequencies of unstimulated areas at different stimulation sites averaged across noise amplitudes are highly positively related to structural connectivity. Crucially, the association between the overall changes in functional connectivity as well as the alterations in the constraints imposed by structural connectivity with the structural degree of stimulation sites is nonmonotonically influenced by the noise amplitude, with the association increasing in specific noise amplitude ranges. Moreover, the impacts of local stimulation of cognitive systems depend on the complex interplay between the noise amplitude and average structural degree. Overall, this work provides theoretical insights into how noise amplitude and network structure jointly modulate brain dynamics during stimulation and introduces possibilities for better predicting and controlling stimulation outcomes.

Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average "hard" parcellations (Schaefer et al., 2018), individual-specific "hard" parcellations (Kong et al., 2021a), and an individual-specific "soft" parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average "hard" parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.

Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics

Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. Recently, studies focused on the role of regional variance of model parameters. Such analyses however necessarily depend on the properties of preselected neural mass model. We introduce a method to infer from the functional data both the neural mass model representing the regional dynamics and the region- and subject-specific parameters while respecting the known network structure. We apply the method to human resting-state fMRI. We find that the underlying dynamics can be described as noisy fluctuations around a single fixed point. The method reliably discovers three regional parameters with clear and distinct role in the dynamics, one of which is strongly correlated with the first principal component of the gene expression spatial map. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications for understanding the brain dynamics during aging or neurodegeneration.

Stability of sensorimotor network sculpts the dynamic repertoire of resting state over lifespan

Temporally stable patterns of neural coordination among distributed brain regions are crucial for survival. Recently, many studies highlight association between healthy aging and modifications in organization of functional brain networks, across various time-scales. Nonetheless, quantitative characterization of temporal stability of functional brain networks across healthy aging remains unexplored. This study introduces a data-driven unsupervised approach to capture high-dimensional dynamic functional connectivity (dFC) via low-dimensional patterns and subsequent estimation of temporal stability using quantitative metrics. Healthy aging related changes in temporal stability of dFC were characterized across resting-state, movie-viewing, and sensorimotor tasks (SMT) on a large (n = 645) healthy aging dataset (18-88 years). Prominent results reveal that (1) whole-brain temporal dynamics of dFC movie-watching task is closer to resting-state than to SMT with an overall trend of highest temporal stability observed during SMT followed by movie-watching and resting-state, invariant across lifespan aging, (2) in both tasks conditions stability of neurocognitive networks in young adults is higher than older adults, and (3) temporal stability of whole brain resting-state follows a U-shaped curve along lifespan-a pattern shared by sensorimotor network stability indicating their deeper relationship. Overall, the results can be applied generally for studying cohorts of neurological disorders using neuroimaging tools.

White-matter degradation and dynamical compensation support age-related functional alterations in human brain

Structural connectivity of the brain at different ages is analyzed using diffusion-weighted magnetic resonance imaging (MRI) data. The largest decrease of streamlines is found in frontal regions and for long inter-hemispheric links. The average length of the tracts also decreases, but the clustering is unaffected. From functional MRI we identify age-related changes of dynamic functional connectivity (dFC) and spatial covariation features of functional connectivity (FC) links captured by metaconnectivity. They indicate more stable dFC, but wider range and variance of MC, whereas static features of FC did not show any significant differences with age. We implement individual connectivity in whole-brain models and test several hypotheses for the mechanisms of operation among underlying neural system. We demonstrate that age-related functional fingerprints are only supported if the model accounts for: (i) compensation of the individual brains for the overall loss of structural connectivity and (ii) decrease of propagation velocity due to the loss of myelination. We also show that with these 2 conditions, it is sufficient to decompose the time-delays as bimodal distribution that only distinguishes between intra- and inter-hemispheric delays, and that the same working point also captures the static FC the best, and produces the largest variability at slow time-scales.

Alteration of brain network centrality in CTN patients after a single triggering pain

Objective

The central nervous system may also be involved in the pathogenesis of classical trigeminal neuralgia (CTN). The present study aimed to explore the characteristics of static degree centrality (sDC) and dynamic degree centrality (dDC) at multiple time points after a single triggering pain in CTN patients.

Materials and methods

A total of 43 CTN patients underwent resting-state function magnetic resonance imaging (rs-fMRI) before triggering pain (baseline), within 5 s after triggering pain (triggering-5 s), and 30 min after triggering pain (triggering-30 min). Voxel-based degree centrality (DC) was used to assess the alteration of functional connection at different time points.

Results

The sDC values of the right caudate nucleus, fusiform gyrus, middle temporal gyrus, middle frontal gyrus, and orbital part were decreased in triggering-5 s and increased in triggering-30 min. The sDC value of the bilateral superior frontal gyrus were increased in triggering-5 s and decreased in triggering-30 min. The dDC value of the right lingual gyrus was gradually increased in triggering-5 s and triggering-30 min.

Conclusion

Both the sDC and dDC values were changed after triggering pain, and the brain regions were different between the two parameters, which supplemented each other. The brain regions which the sDC and dDC values were changing reflect the global brain function of CTN patients, and provides a basis for further exploration of the central mechanism of CTN.

Dynamic sensitivity analysis: Defining personalised strategies to drive brain state transitions via whole brain modelling

Traditionally, in neuroimaging, model-free analyses are used to find significant differences between brain states via signal detection theory. Depending on the a priori assumptions about the underlying data, different spatio-temporal features can be analysed. Alternatively, model-based techniques infer features from the data and compare significance from model parameters. However, to assess transitions from one brain state to another remains a challenge in current paradigms. Here, we introduce a "Dynamic Sensitivity Analysis" framework that quantifies transitions between brain states in terms of stimulation ability to rebalance spatio-temporal brain activity towards a target state such as healthy brain dynamics. In practice, it means building a whole-brain model fitted to the spatio-temporal description of brain dynamics, and applying systematic stimulations in-silico to assess the optimal strategy to drive brain dynamics towards a target state. Further, we show how Dynamic Sensitivity Analysis extends to various brain stimulation paradigms, ultimately contributing to improving the efficacy of personalised clinical interventions.

A dynamic gradient architecture generates brain activity states

The human brain exhibits a diverse yet constrained range of activity states. While these states can be faithfully represented in a low-dimensional latent space, our understanding of the constitutive functional anatomy is still evolving. Here we applied dimensionality reduction to task-free and task fMRI data to address whether latent dimensions reflect intrinsic systems and if so, how these systems may interact to generate different activity states. We find that each dimension represents a dynamic activity gradient, including a primary unipolar sensory-association gradient underlying the global signal. The gradients appear stable across individuals and cognitive states, while recapitulating key functional connectivity properties including anticorrelation, modularity, and regional hubness. We then use dynamical systems modeling to show that gradients causally interact via state-specific coupling parameters to create distinct brain activity patterns. Together, these findings indicate that a set of dynamic, intrinsic spatial gradients interact to determine the repertoire of possible brain activity states.

Reliability and subject specificity of personalized whole-brain dynamical models

Dynamical whole-brain models were developed to link structural (SC) and functional connectivity (FC) together into one framework. Nowadays, they are used to investigate the dynamical regimes of the brain and how these relate to behavioral, clinical and demographic traits. However, there is no comprehensive investigation on how reliable and subject specific the modeling results are given the variability of the empirical FC. In this study, we show that the parameters of these models can be fitted with a "poor" to "good" reliability depending on the exact implementation of the modeling paradigm. We find, as a general rule of thumb, that enhanced model personalization leads to increasingly reliable model parameters. In addition, we observe no clear effect of the model complexity evaluated by separately sampling results for linear, phase oscillator and neural mass network models. In fact, the most complex neural mass model often yields modeling results with "poor" reliability comparable to the simple linear model, but demonstrates an enhanced subject specificity of the model similarity maps. Subsequently, we show that the FC simulated by these models can outperform the empirical FC in terms of both reliability and subject specificity. For the structure-function relationship, simulated FC of individual subjects may be identified from the correlations with the empirical SC with an accuracy up to 70%, but not vice versa for non-linear models. We sample all our findings for 8 distinct brain parcellations and 6 modeling conditions and show that the parcellation-induced effect is much more pronounced for the modeling results than for the empirical data. In sum, this study provides an exploratory account on the reliability and subject specificity of dynamical whole-brain models and may be relevant for their further development and application. In particular, our findings suggest that the application of the dynamical whole-brain modeling should be tightly connected with an estimate of the reliability of the results.

Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases

Brain pathologies are characterized by microscopic changes in neurons and synapses that reverberate into large scale networks altering brain dynamics and functional states. An important yet unresolved issue concerns the impact of patients' excitation/inhibition profiles on neurodegenerative diseases including Alzheimer's Disease, Frontotemporal Dementia, and Amyotrophic Lateral Sclerosis. In this work, we used The Virtual Brain (TVB) simulation platform to simulate brain dynamics in healthy and neurodegenerative conditions and to extract information about the excitatory/inhibitory balance in single subjects. The brain structural and functional connectomes were extracted from 3T-MRI (Magnetic Resonance Imaging) scans and TVB nodes were represented by a Wong-Wang neural mass model endowing an explicit representation of the excitatory/inhibitory balance. Simulations were performed including both cerebral and cerebellar nodes and their structural connections to explore cerebellar impact on brain dynamics generation. The potential for clinical translation of TVB derived biophysical parameters was assessed by exploring their association with patients' cognitive performance and testing their discriminative power between clinical conditions. Our results showed that TVB biophysical parameters differed between clinical phenotypes, predicting higher global coupling and inhibition in Alzheimer's Disease and stronger N-methyl-D-aspartate (NMDA) receptor-dependent excitation in Amyotrophic Lateral Sclerosis. These physio-pathological parameters allowed us to perform an advanced analysis of patients' conditions. In backward regressions, TVB-derived parameters significantly contributed to explain the variation of neuropsychological scores and, in discriminant analysis, the combination of TVB parameters and neuropsychological scores significantly improved the discriminative power between clinical conditions. Moreover, cluster analysis provided a unique description of the excitatory/inhibitory balance in individual patients. Importantly, the integration of cerebro-cerebellar loops in simulations improved TVB predictive power, i.e., the correlation between experimental and simulated functional connectivity in all pathological conditions supporting the cerebellar role in brain function disrupted by neurodegeneration. Overall, TVB simulations reveal differences in the excitatory/inhibitory balance of individual patients that, combined with cognitive assessment, can promote the personalized diagnosis and therapy of neurodegenerative diseases.

Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition

The neocortex is a complex neurobiological system with many interacting regions. How these regions work together to subserve flexible behavior and cognition has become increasingly amenable to rigorous research. Here, I review recent experimental and theoretical work on the modus operandi of a multiregional cortex. These studies revealed several general principles for the neocortical interareal connectivity, low-dimensional macroscopic gradients of biological properties across cortical areas, and a hierarchy of timescales for information processing. Theoretical work suggests testable predictions regarding differential excitation and inhibition along feedforward and feedback pathways in the cortical hierarchy. Furthermore, modeling of distributed working memory and simple decision-making has given rise to a novel mathematical concept, dubbed bifurcation in space, that potentially explains how different cortical areas, with a canonical circuit organization but gradients of biological heterogeneities, are able to subserve their respective (e.g., sensory coding versus executive control) functions in a modularly organized brain.

Prediction and control of focal seizure spread: Random walk with restart on heterogeneous brain networks

Whole-brain models offer a promising method of predicting seizure spread, which is critical for successful surgical treatment of focal epilepsy. Existing methods are largely based on structural connectome, which ignores the effects of heterogeneity within the regional excitability of brains. In this study we used a whole-brain model to show that heterogeneity in nodal excitability had a significant impact on seizure propagation in the networks and compromised the prediction accuracy with structural connections. We then addressed this problem with an algorithm based on random walk with restart on graphs. We demonstrated that by establishing a relationship between the restarting probability and the excitability for each node, this algorithm could significantly improve the seizure spread prediction accuracy in heterogeneous networks and was more robust against the extent of heterogeneity. We also strategized surgical seizure control as a process to identify and remove the key nodes (connections) responsible for the early spread of seizures from the focal region. Compared to strategies based on structural connections, virtual surgery with a strategy based on a modified random walk with extended restart generated outcomes with a high success rate while maintaining low damage to the brain by removing fewer anatomical connections. These findings may have potential applications in developing personalized surgery strategies for epilepsy.

Extracting Dynamical Understanding From Neural-Mass Models of Mouse Cortex

New brain atlases with high spatial resolution and whole-brain coverage have rapidly advanced our knowledge of the brain's neural architecture, including the systematic variation of excitatory and inhibitory cell densities across the mammalian cortex. But understanding how the brain's microscale physiology shapes brain dynamics at the macroscale has remained a challenge. While physiologically based mathematical models of brain dynamics are well placed to bridge this explanatory gap, their complexity can form a barrier to providing clear mechanistic interpretation of the dynamics they generate. In this work, we develop a neural-mass model of the mouse cortex and show how bifurcation diagrams, which capture local dynamical responses to inputs and their variation across brain regions, can be used to understand the resulting whole-brain dynamics. We show that strong fits to resting-state functional magnetic resonance imaging (fMRI) data can be found in surprisingly simple dynamical regimes—including where all brain regions are confined to a stable fixed point—in which regions are able to respond strongly to variations in their inputs, consistent with direct structural connections providing a strong constraint on functional connectivity in the anesthetized mouse. We also use bifurcation diagrams to show how perturbations to local excitatory and inhibitory coupling strengths across the cortex, constrained by cell-density data, provide spatially dependent constraints on resulting cortical activity, and support a greater diversity of coincident dynamical regimes. Our work illustrates methods for visualizing and interpreting model performance in terms of underlying dynamical mechanisms, an approach that is crucial for building explanatory and physiologically grounded models of the dynamical principles that underpin large-scale brain activity.

Dynamic primitives of brain network interaction Special Issue "Advances in Mapping the Connectome"

What dynamic processes underly functional brain networks? Functional connectivity (FC) and functional connectivity dynamics (FCD) are used to represent the patterns and dynamics of functional brain networks. FC(D) is related to the synchrony of brain activity: when brain areas oscillate in a coordinated manner this yields a high correlation between their signal time series. To explain the processes underlying FC(D) we review how synchronized oscillations emerge from coupled neural populations in brain network models (BNMs). From detailed spiking networks to more abstract population models, there is strong support for the idea that the brain operates near critical instabilities that give rise to multistable or metastable dynamics that in turn lead to the intermittently synchronized slow oscillations underlying FC(D). We explore further consequences from these fundamental mechanisms and how they fit with reality. We conclude by highlighting the need for integrative brain models that connect separate mechanisms across levels of description and spatiotemporal scales and link them with cognitive function.