Regression DCM for fMRI

Challenges and Frontiers in Computational Metabolic Psychiatry

One of the primary challenges in metabolic psychiatry is that the disrupted brain functions that underlie psychiatric conditions arise from a complex set of downstream and feedback processes spanning across multiple spatiotemporal scales. Importantly, the same circuit can have multiple points of failure, each of which results in a different type of dysregulation, and thus elicits distinct cascades downstream that produce divergent signs and symptoms. Here, we illustrate this challenge by examining how subtle differences in circuit perturbations can lead to divergent clinical outcomes. We also discuss how computational models can perform the spatially heterogenous integration and bridge in vitro and in vivo paradigms. By leveraging recent methodological advances and tools, computational models can integrate relevant processes across scales (e.g., TCA-cycle, ion channel, neural microassembly, whole-brain macro-circuit) and across physiological systems (e.g., neural, endocrine, immune, vascular), providing a framework that can unite these mechanistic processes in a manner that goes beyond the conceptual and descriptive, to the quantitative and generative. These hold the potential to sharpen our intuitions towards circuit-based models for personalized diagnostics and treatment.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Relationship between central autonomic effective connectivity and heart rate variability: A Resting-state fMRI dynamic causal modeling study

The central autonomic network (CAN) serves as a regulatory hub with top-down regulatory control and integration of bottom-up physiological feedback via the autonomic nervous system. Heart rate variability (HRV)-the time variance of the heart's beat-to-beat intervals-is an index of the CAN's affective and behavioral regulatory capacity. Although neural functional connectivities that are associated with HRV and CAN have been well studied, no published report to date has studied effective (directional) connectivities (EC) that are associated with HRV and CAN. Better understanding of neural EC in the brain has the potential to improve our understanding of how the CAN sub-regions regulate HRV. To begin to address this knowledge gap, we employed resting-state functional magnetic resonance imaging and dynamic causal modeling (DCM) with parametric empirical Bayes analyses in 34 healthy adults (19 females; mean age= 32.68 years [SD= 14.09], age range 18-68 years) to examine the bottom-up and top-down neural circuits associated with HRV. Throughout the whole brain, we identified 12 regions associated with HRV. DCM analyses revealed that the ECs from the right amygdala to the anterior cingulate cortex and to the ventrolateral prefrontal cortex had a negative linear relationship with HRV and a positive linear relationship with heart rate. These findings suggest that ECs from the amygdala to the prefrontal cortex may represent a neural circuit associated with regulation of cardiodynamics.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data

This study challenges the traditional focus on zero-lag statistics in resting-state functional magnetic resonance imaging (rsfMRI) research. Instead, it advocates for considering time-lag interactions to unveil the directionality and asymmetries of the brain hierarchy. Effective connectivity (EC), the state matrix in dynamical causal modeling (DCM), is a commonly used metric for studying dynamical properties and causal interactions within a linear state-space system description. Here, we focused on how time-lag statistics are incorporated within the framework of DCM resulting in an asymmetric EC matrix. Our approach involves decomposing the EC matrix, revealing a steady-state differential cross-covariance matrix that is responsible for modeling information flow and introducing time-irreversibility. Specifically, the system's dynamics, influenced by the off-diagonal part of the differential covariance, exhibit a curl steady-state flow component that breaks detailed balance and diverges the dynamics from equilibrium. Our empirical findings indicate that the EC matrix's outgoing strengths correlate with the flow described by the differential cross covariance, while incoming strengths are primarily driven by zero-lag covariance, emphasizing conditional independence over directionality.

A computational account of the development and evolution of psychotic symptoms

The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing a compensatory relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.

Improved brain effective connectivity modelling by dynamic Bayesian networks

BACKGROUND

If brain effective connectivity network modelling (ECN) could be accurately achieved, early diagnosis of neurodegenerative diseases would be possible. It has been observed in the literature that Dynamic Bayesian Network (DBN) based methods are more successful than others. However, DBNs have not been applied easily and tested much due to computational complexity problems in structure learning.

NEW METHOD

This study introduces an advanced method for modelling brain ECNs using improved discrete DBN (Improved- dDBN) which addresses the computational challenges previously limiting DBN application, offering solutions that enable accurate and fast structure modelling.

RESULTS

The practical data and prior sizes needed for the convergence to the globally correct network structure are proved to be much smaller than the theoretical ones using simulated dDBN data. Besides, Hill Climbing is shown to converge to the true structure at a reasonable iteration step size when the appropriate data and prior sizes are used. Finally, importance of data quantization methods are analysed.

COMPARISON WITH EXISTING METHODS

The Improved-dDBN method performs better and robust, when compared to the existing methods for realistic scenarios such as varying graph complexity, various input conditions, noise cases and non-stationary connections. The data used in these tests is the simulated fMRI BOLD time series proposed in the literature.

CONCLUSIONS

Improved-dDBN is a good candidate to be used on real datasets to accelerate developments in brain ECN modelling and neuroscience. Appropriate data and prior sizes can be identified based on the approach proposed in this study for global and fast convergence.

A ventromedial visual cortical 'Where' stream to the human hippocampus for spatial scenes revealed with magnetoencephalography

The primate including the human hippocampus implicated in episodic memory and navigation represents a spatial view, very different from the place representations in rodents. To understand this system in humans, and the computations performed, the pathway for this spatial view information to reach the hippocampus was analysed in humans. Whole-brain effective connectivity was measured with magnetoencephalography between 30 visual cortical regions and 150 other cortical regions using the HCP-MMP1 atlas in 21 participants while performing a 0-back scene memory task. In a ventromedial visual stream, V1-V4 connect to the ProStriate region where the retrosplenial scene area is located. The ProStriate region has connectivity to ventromedial visual regions VMV1-3 and VVC. These ventromedial regions connect to the medial parahippocampal region PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal regions have effective connectivity to the entorhinal cortex, perirhinal cortex, and hippocampus. In contrast, when viewing faces, the effective connectivity was more through a ventrolateral visual cortical stream via the fusiform face cortex to the inferior temporal visual cortex regions TE2p and TE2a. A ventromedial visual cortical 'Where' stream to the hippocampus for spatial scenes was supported by diffusion topography in 171 HCP participants at 7 T.

A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

Gradient synchronization for multivariate functional data, with application to brain connectivity

Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the static temporal Pearson correlation has been the prevailing measure for functional connectivity. However, recent research has revealed temporally changing patterns of functional connectivity, leading to the study of dynamic functional connectivity. This motivates new similarity measures for pairs of random curves that reflect the dynamic features of functional similarity. Specifically, we introduce gradient synchronization measures in a general setting. These similarity measures are based on the concordance and discordance of the gradients between paired smooth random functions. Asymptotic normality of the proposed estimates is obtained under regularity conditions. We illustrate the proposed synchronization measures via simulations and an application to resting-state fMRI signals from the Alzheimer's Disease Neuroimaging Initiative and they are found to improve discrimination between subjects with different disease status.

Impact of data processing varieties on DCM estimates of effective connectivity from task-fMRI

Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task-evoked EC has hardly ever been addressed. We therefore investigated how task-evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event-related design of the general linear model (GLM) used for the first-level task-evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group-averaged task-evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task-evoked EC between any two data processing conditions via between-group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group-averaged EC depending on the data processing choices. In particular, task-evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event-related GLM design appears to be more sensitive to task-evoked modulations of EC, but provides model parameters with lower certainty than the block-based design, while the latter is more sensitive to the type of activation contrast than is the event-related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task-evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

The connectivity of the human frontal pole cortex, and a theory of its involvement in exploit versus explore

The frontal pole is implicated in humans in whether to exploit resources versus explore alternatives. Effective connectivity, functional connectivity, and tractography were measured between six human frontal pole regions and for comparison 13 dorsolateral and dorsal prefrontal cortex regions, and the 360 cortical regions in the Human Connectome Project Multi-modal-parcellation atlas in 171 HCP participants. The frontal pole regions have effective connectivity with Dorsolateral Prefrontal Cortex regions, the Dorsal Prefrontal Cortex, both implicated in working memory; and with the orbitofrontal and anterior cingulate cortex reward/non-reward system. There is also connectivity with temporal lobe, inferior parietal, and posterior cingulate regions. Given this new connectivity evidence, and evidence from activations and damage, it is proposed that the frontal pole cortex contains autoassociation attractor networks that are normally stable in a short-term memory state, and maintain stability in the other prefrontal networks during stable exploitation of goals and strategies. However, if an input from the orbitofrontal or anterior cingulate cortex that expected reward, non-reward, or punishment is received, this destabilizes the frontal pole and thereby other prefrontal networks to enable exploration of competing alternative goals and strategies. The frontal pole connectivity with reward systems may be key in exploit versus explore.

A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI

Introduction

Computational models play an increasingly important role in describing variation in neural activation in human neuroimaging experiments, including evaluating individual differences in the context of psychiatric neuroimaging. In particular, reinforcement learning (RL) techniques have been widely adopted to examine neural responses to reward prediction errors and stimulus or action values, and how these might vary as a function of clinical status. However, there is a lack of consensus around the importance of the precision of free parameter estimation for these methods, particularly with regard to the learning rate. In the present study, I introduce a novel technique which may be used within a general linear model (GLM) to model the effect of mis-estimation of the learning rate on reward prediction error (RPE)-related neural responses.

Methods

Simulations employed a simple RL algorithm, which was used to generate hypothetical neural activations that would be expected to be observed in functional magnetic resonance imaging (fMRI) studies of RL. Similar RL models were incorporated within a GLM-based analysis method including derivatives, with individual differences in the resulting GLM-derived beta parameters being evaluated with respect to the free parameters of the RL model or being submitted to other validation analyses.

Results

Initial simulations demonstrated that the conventional approach to fitting RL models to RPE responses is more likely to reflect individual differences in a reinforcement efficacy construct (lambda) rather than learning rate (alpha). The proposed method, adding a derivative regressor to the GLM, provides a second regressor which reflects the learning rate. Validation analyses were performed including examining another comparable method which yielded highly similar results, and a demonstration of sensitivity of the method in presence of fMRI-like noise.

Conclusion

Overall, the findings underscore the importance of the lambda parameter for interpreting individual differences in RPE-coupled neural activity, and validate a novel neural metric of the modulation of such activity by individual differences in the learning rate. The method is expected to find application in understanding aberrant reinforcement learning across different psychiatric patient groups including major depression and substance use disorder.

Structural and Physiological Modeling (SAPM) for the Analysis of Functional MRI Data Applied to a Study of Human Nociceptive Processing

A novel method has been developed for analyzing connectivity between regions based on functional magnetic resonance imaging (fMRI) data. This method, termed structural and physiological modeling (SAPM), combines information about blood oxygenation-level dependent (BOLD) responses, anatomy, and physiology to model coordinated signaling across networks of regions, including input and output signaling from each region and whether signaling is predominantly inhibitory or excitatory. The present study builds on a prior proof-of-concept demonstration of the SAPM method by providing evidence for the choice of network model and anatomical sub-regions, demonstrating the reproducibility of the results and identifying statistical thresholds needed to infer significance. The method is further validated by applying it to investigate human nociceptive processing in the brainstem and spinal cord and comparing the results to the known neuroanatomy, including anatomical regions and inhibitory and excitatory signaling. The results of this analysis demonstrate that it is possible to obtain reliable information about input and output signaling from anatomical regions and to identify whether this signaling has predominantly inhibitory or excitatory effects. SAPM provides much more detailed information about neuroanatomy than was previously possible based on fMRI data.

Hierarchical organization of the human ventral visual streams revealed with magnetoencephalography

The hierarchical organization between 25 ventral stream visual cortical regions and 180 cortical regions was measured with magnetoencephalography using the Human Connectome Project Multimodal Parcellation atlas in 83 Human Connectome Project participants performing a visual memory task. The aim was to reveal the hierarchical organization using a whole-brain model based on generative effective connectivity with this fast neuroimaging method. V1-V4 formed a first group of interconnected regions. Especially V4 had connectivity to a ventrolateral visual stream: V8, the fusiform face cortex, and posterior inferior temporal cortex PIT. These regions in turn had effectivity connectivity to inferior temporal cortex visual regions TE2p and TE1p. TE2p and TE1p then have connectivity to anterior temporal lobe regions TE1a, TE1m, TE2a, and TGv, which are multimodal. In a ventromedial visual stream, V1-V4 connect to ventromedial regions VMV1-3 and VVC. VMV1-3 and VVC connect to the medial parahippocampal gyrus PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal PHA1-3 regions have connectivity to the hippocampal system regions the perirhinal cortex, entorhinal cortex, and hippocampus. These effective connectivities of two ventral visual cortical streams measured with magnetoencephalography provide support to the hierarchical organization of brain systems measured with fMRI, and new evidence on directionality.

The arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network

A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, 'arrow of time', in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.

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.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is one of the most common pharmaco-resistant epilepsies in adults. While hippocampal pathology is the hallmark of this condition, emerging evidence indicates that brain alterations extend beyond the mesiotemporal epicenter and affect macroscale brain function and cognition. We studied macroscale functional reorganization in TLE, explored structural substrates, and examined cognitive associations. We investigated a multisite cohort of 95 patients with pharmaco-resistant TLE and 95 healthy controls using state-of-the-art multimodal 3T magnetic resonance imaging (MRI). We quantified macroscale functional topographic organization using connectome dimensionality reduction techniques and estimated directional functional flow using generative models of effective connectivity. We observed atypical functional topographies in patients with TLE relative to controls, manifesting as reduced functional differentiation between sensory/motor networks and transmodal systems such as the default mode network, with peak alterations in bilateral temporal and ventromedial prefrontal cortices. TLE-related topographic changes were consistent in all three included sites and reflected reductions in hierarchical flow patterns between cortical systems. Integration of parallel multimodal MRI data indicated that these findings were independent of TLE-related cortical grey matter atrophy, but mediated by microstructural alterations in the superficial white matter immediately beneath the cortex. The magnitude of functional perturbations was robustly associated with behavioral markers of memory function. Overall, this work provides converging evidence for macroscale functional imbalances, contributing microstructural alterations, and their associations with cognitive dysfunction in TLE.

Auditory cortical connectivity in humans

To understand auditory cortical processing, the effective connectivity between 15 auditory cortical regions and 360 cortical regions was measured in 171 Human Connectome Project participants, and complemented with functional connectivity and diffusion tractography. 1. A hierarchy of auditory cortical processing was identified from Core regions (including A1) to Belt regions LBelt, MBelt, and 52; then to PBelt; and then to HCP A4. 2. A4 has connectivity to anterior temporal lobe TA2, and to HCP A5, which connects to dorsal-bank superior temporal sulcus (STS) regions STGa, STSda, and STSdp. These STS regions also receive visual inputs about moving faces and objects, which are combined with auditory information to help implement multimodal object identification, such as who is speaking, and what is being said. Consistent with this being a "what" ventral auditory stream, these STS regions then have effective connectivity to TPOJ1, STV, PSL, TGv, TGd, and PGi, which are language-related semantic regions connecting to Broca's area, especially BA45. 3. A4 and A5 also have effective connectivity to MT and MST, which connect to superior parietal regions forming a dorsal auditory "where" stream involved in actions in space. Connections of PBelt, A4, and A5 with BA44 may form a language-related dorsal stream.

Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.

The effect of lysergic acid diethylamide (LSD) on whole-brain functional and effective connectivity

Psychedelics have emerged as promising candidate treatments for various psychiatric conditions, and given their clinical potential, there is a need to identify biomarkers that underlie their effects. Here, we investigate the neural mechanisms of lysergic acid diethylamide (LSD) using regression dynamic causal modelling (rDCM), a novel technique that assesses whole-brain effective connectivity (EC) during resting-state functional magnetic resonance imaging (fMRI). We modelled data from two randomised, placebo-controlled, double-blind, cross-over trials, in which 45 participants were administered 100 μg LSD and placebo in two resting-state fMRI sessions. We compared EC against whole-brain functional connectivity (FC) using classical statistics and machine learning methods. Multivariate analyses of EC parameters revealed predominantly stronger interregional connectivity and reduced self-inhibition under LSD compared to placebo, with the notable exception of weakened interregional connectivity and increased self-inhibition in occipital brain regions as well as subcortical regions. Together, these findings suggests that LSD perturbs the Excitation/Inhibition balance of the brain. Notably, whole-brain EC did not only provide additional mechanistic insight into the effects of LSD on the Excitation/Inhibition balance of the brain, but EC also correlated with global subjective effects of LSD and discriminated experimental conditions in a machine learning-based analysis with high accuracy (91.11%), highlighting the potential of using whole-brain EC to decode or predict subjective effects of LSD in the future.

A Survey on Brain Effective Connectivity Network Learning

Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of the pathological mechanism associated with neuropsychiatric diseases and facilitate finding new brain network imaging markers for the early diagnosis and evaluation for the treatment of cerebral diseases. A deeper understanding of brain ECNs also greatly promotes brain-inspired artificial intelligence (AI) research in the context of brain-like neural networks and machine learning. Thus, how to picture and grasp deeper features of brain ECNs from functional magnetic resonance imaging (fMRI) data is currently an important and active research area of the human brain connectome. In this survey, we first show some typical applications and analyze existing challenging problems in learning brain ECNs from fMRI data. Second, we give a taxonomy of ECN learning methods from the perspective of computational science and describe some representative methods in each category. Third, we summarize commonly used evaluation metrics and conduct a performance comparison of several typical algorithms both on simulated and real datasets. Finally, we present the prospects and references for researchers engaged in learning ECNs.

Predicting Future Depressive Episodes from Resting-State fMRI with Generative Embedding

After a first episode of major depressive disorder (MDD), there is substantial risk for a long-term remitting-relapsing course. Prevention and early interventions are thus critically important. Various studies have examined the feasibility of detecting at-risk individuals based on out-of-sample predictions about the future occurrence of depression. However, functional magnetic resonance imaging (MRI) has received very little attention for this purpose so far. Here, we explored the utility of generative models (i.e. different dynamic causal models, DCMs) as well as functional connectivity (FC) for predicting future episodes of depression in never-depressed adults, using a large dataset (N=906) of task-free ("resting state") fMRI data from the UK Biobank. Connectivity analyses were conducted using timeseries from pre-computed spatially independent components of different dimensionalities. Over a three year period, 50% of participants showed indications of at least one depressive episode, while the other 50% did not. Using nested cross-validation for training and a held-out test set (80/20 split), we systematically examined the combination of 8 connectivity feature sets and 17 classifiers. We found that a generative embedding procedure based on combining regression DCM (rDCM) with a support vector machine (SVM) enabled the best predictions, both on the training set (0.63 accuracy, 0.66 area under the curve, AUC) and the test set (0.62 accuracy, 0.64 AUC; p<0.001). However, on the test set, rDCM was only slightly superior to predictions based on FC (0.59 accuracy, 0.61 AUC). Interpreting model predictions based on SHAP (SHapley Additive exPlanations) values suggested that the most predictive connections were widely distributed and not confined to specific networks. Overall, our analyses suggest (i) ways of improving future fMRI-based generative embedding approaches for the early detection of individuals at-risk for depression and that (ii) achieving accuracies of clinical utility may require combination of fMRI with other data modalities.

Computational Modeling of Oddball Sequence Processing Exposes Common and Differential Auditory Network Changes in First-Episode Schizophrenia-Spectrum Disorders and Schizophrenia

BACKGROUND AND HYPOTHESIS

Differences in sound relevance filtering in schizophrenia are proposed to represent a key index of biological changes in brain function in the illness. This study featured a computational modeling approach to test the hypothesis that processing differences might already be evident in first-episode, becoming more pronounced in the established illness.

STUDY DESIGN

Auditory event-related potentials to a typical oddball sequence (rare pitch deviations amongst regular sounds) were recorded from 90 persons with schizophrenia-spectrum disorders (40 first-episode schizophrenia-spectrum, 50 established illness) and age-matched healthy controls. The data were analyzed using dynamic causal modeling to identify the changes in effective connectivity that best explained group differences.

STUDY RESULTS

Group differences were linked to intrinsic (within brain region) connectivity changes. In activity-dependent measures these were restricted to the left auditory cortex in first-episode schizophrenia-spectrum but were more widespread in the established illness. Modeling suggested that both established illness and first-episode schizophrenia-spectrum groups expressed significantly lower inhibition of inhibitory interneuron activity and altered gain on superficial pyramidal cells with the data indicative of differences in both putative N-methyl-d-aspartate glutamate receptor activity-dependent plasticity and classic neuromodulation.

CONCLUSIONS

The study provides further support for the notion that examining the ability to alter responsiveness to structured sound sequences in schizophrenia and first-episode schizophrenia-spectrum could be informative to uncovering the nature and progression of changes in brain function during the illness. Furthermore, modeling suggested that limited differences present at first-episode schizophrenia-spectrum may become more expansive with illness progression.

The human posterior parietal cortex: effective connectome, and its relation to function

The effective connectivity between 21 regions in the human posterior parietal cortex, and 360 cortical regions was measured in 171 Human Connectome Project (HCP) participants using the HCP atlas, and complemented with functional connectivity and diffusion tractography. Intraparietal areas LIP, VIP, MIP, and AIP have connectivity from early cortical visual regions, and to visuomotor regions such as the frontal eye fields, consistent with functions in eye saccades and tracking. Five superior parietal area 7 regions receive from similar areas and from the intraparietal areas, but also receive somatosensory inputs and connect with premotor areas including area 6, consistent with functions in performing actions to reach for, grasp, and manipulate objects. In the anterior inferior parietal cortex, PFop, PFt, and PFcm are mainly somatosensory, and PF in addition receives visuo-motor and visual object information, and is implicated in multimodal shape and body image representations. In the posterior inferior parietal cortex, PFm and PGs combine visuo-motor, visual object, and reward input and connect with the hippocampal system. PGi in addition provides a route to motion-related superior temporal sulcus regions involved in social interactions. PGp has connectivity with intraparietal regions involved in coordinate transforms and may be involved in idiothetic update of hippocampal visual scene representations.

Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators

Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.

The human posterior cingulate, retrosplenial, and medial parietal cortex effective connectome, and implications for memory and navigation

The human posterior cingulate, retrosplenial, and medial parietal cortex are involved in memory and navigation. The functional anatomy underlying these cognitive functions was investigated by measuring the effective connectivity of these Posterior Cingulate Division (PCD) regions in the Human Connectome Project-MMP1 atlas in 171 HCP participants, and complemented with functional connectivity and diffusion tractography. First, the postero-ventral parts of the PCD (31pd, 31pv, 7m, d23ab, and v23ab) have effective connectivity with the temporal pole, inferior temporal visual cortex, cortex in the superior temporal sulcus implicated in auditory and semantic processing, with the reward-related vmPFC and pregenual anterior cingulate cortex, with the inferior parietal cortex, and with the hippocampal system. This connectivity implicates it in hippocampal episodic memory, providing routes for "what," reward and semantic schema-related information to access the hippocampus. Second, the antero-dorsal parts of the PCD (especially 31a and 23d, PCV, and also RSC) have connectivity with early visual cortical areas including those that represent spatial scenes, with the superior parietal cortex, with the pregenual anterior cingulate cortex, and with the hippocampal system. This connectivity implicates it in the "where" component for hippocampal episodic memory and for spatial navigation. The dorsal-transitional-visual (DVT) and ProStriate regions where the retrosplenial scene area is located have connectivity from early visual cortical areas to the parahippocampal scene area, providing a ventromedial route for spatial scene information to reach the hippocampus. These connectivities provide important routes for "what," reward, and "where" scene-related information for human hippocampal episodic memory and navigation. The midcingulate cortex provides a route from the anterior dorsal parts of the PCD and the supracallosal part of the anterior cingulate cortex to premotor regions.

Human amygdala compared to orbitofrontal cortex connectivity, and emotion

The amygdala and orbitofrontal cortex have been implicated in emotion. To understand these regions better in humans, their effective connectivity with 360 cortical regions was measured in 171 humans from the Human Connectome Project, and complemented with functional connectivity and diffusion tractography. The human amygdala has effective connectivity from few cortical regions compared to the orbitofrontal cortex: primarily from auditory cortex A5 and the related superior temporal gyrus and temporal pole regions; the piriform (olfactory) cortex; the lateral orbitofrontal cortex 47m; somatosensory cortex; the hippocampus, entorhinal cortex, perirhinal cortex, and parahippocampal TF; and from the cholinergic nucleus basalis. The amygdala has effective connectivity to the hippocampus, entorhinal and perirhinal cortex; to the temporal pole; and to the lateral orbitofrontal cortex. The orbitofrontal cortex has effective connectivity from gustatory, olfactory, and temporal visual, auditory and pole cortex, and to the pregenual anterior and posterior cingulate cortex, hippocampal system, and prefrontal cortex, and provides for rewards and punishers to be used in reported emotions, and memory and navigation to goals. Given the paucity of amygdalo-neocortical connectivity in humans, it is proposed that the human amygdala is involved primarily in autonomic and conditioned responses via brainstem connectivity, rather than in reported (declarative) emotion.

Dynamic Effective Connectivity using Physiologically informed Dynamic Causal Model with Recurrent Units: A functional Magnetic Resonance Imaging simulation study

Functional MRI (fMRI) is an indirect reflection of neuronal activity. Using generative biophysical model of fMRI data such as Dynamic Causal Model (DCM), the underlying neuronal activities of different brain areas and their causal interactions (i.e., effective connectivity) can be calculated. Most DCM studies typically consider the effective connectivity to be static for a cognitive task within an experimental run. However, changes in experimental conditions during complex tasks such as movie-watching might result in temporal variations in the connectivity strengths. In this fMRI simulation study, we leverage state-of-the-art Physiologically informed DCM (P-DCM) along with a recurrent window approach and discretization of the equations to infer the underlying neuronal dynamics and concurrently the dynamic (time-varying) effective connectivities between various brain regions for task-based fMRI. Results from simulation studies on 3- and 10-region models showed that functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) responses and effective connectivity time-courses can be accurately predicted and distinguished from faulty graphical connectivity models representing cognitive hypotheses. In summary, we propose and validate a novel approach to determine dynamic effective connectivity between brain areas during complex cognitive tasks by combining P-DCM with recurrent units.

The human orbitofrontal cortex, vmPFC, and anterior cingulate cortex effective connectome: emotion, memory, and action

The human orbitofrontal cortex, ventromedial prefrontal cortex (vmPFC), and anterior cingulate cortex are involved in reward processing and thereby in emotion but are also implicated in episodic memory. To understand these regions better, the effective connectivity between 360 cortical regions and 24 subcortical regions was measured in 172 humans from the Human Connectome Project and complemented with functional connectivity and diffusion tractography. The orbitofrontal cortex has effective connectivity from gustatory, olfactory, and temporal visual, auditory, and pole cortical areas. The orbitofrontal cortex has connectivity to the pregenual anterior and posterior cingulate cortex and hippocampal system and provides for rewards to be used in memory and navigation to goals. The orbitofrontal and pregenual anterior cortex have connectivity to the supracallosal anterior cingulate cortex, which projects to midcingulate and other premotor cortical areas and provides for action-outcome learning including limb withdrawal or flight or fight to aversive and nonreward stimuli. The lateral orbitofrontal cortex has outputs to language systems in the inferior frontal gyrus. The medial orbitofrontal cortex connects to the nucleus basalis of Meynert and the pregenual cingulate to the septum, and damage to these cortical regions may contribute to memory impairments by disrupting cholinergic influences on the neocortex and hippocampus.

Causal influences of salience/cerebellar networks on dorsal attention network subserved age-related cognitive slowing

Age-related cognitive slowing is a prominent precursor of cognitive decline. Functional neuroimaging studies found that cognitive processing speed is associated with activation and coupling among frontal, parietal and cerebellar brain networks. However, how the reciprocal influences of inter- and intra-network coupling mediate age-related decline in processing speed remains insufficiently studied. This study examined how inter- and intra-brain network influences mediate age-related slowing. We were interested in the fronto-insular salience network (SN), frontoparietal dorsal attention network (DAN), cerebellar network (CN) and default mode network (DMN). Reaction time (RT) and functional MRI data from 84 participants (aged 18-75) were collected while they were performing the Arrow Task in visual or audial forms. At the subject level, effective connectivities (ECs) were estimated with regression dynamic causal modelling. At the group level, structural equation models (SEMs) were used to model latent speed based on age and the EC mediators. Age was associated with decreased speed and increased inter-network effective connectivity. The CN exerting influence on the DAN (CN → DAN EC) mediated, while the SN → DAN EC suppressed age-related slowing. The DMN and intra-network ECs did not seem to play significant roles in slowing due to ageing. Inter-network connectivity from the CN and SN to the DAN contributes to age-related slowing. The seemingly antagonizing influences of the CN and SN indicate that increased task-related automaticity and decreased effortful control on top-down attention would promote greater speed in older individuals.

Prefrontal and somatosensory-motor cortex effective connectivity in humans

Effective connectivity, functional connectivity, and tractography were measured between 57 cortical frontal and somatosensory regions and the 360 cortical regions in the Human Connectome Project (HCP) multimodal parcellation atlas for 171 HCP participants. A ventral somatosensory stream connects from 3b and 3a via 1 and 2 and then via opercular and frontal opercular regions to the insula, which then connects to inferior parietal PF regions. This stream is implicated in "what"-related somatosensory processing of objects and of the body and in combining with visual inputs in PF. A dorsal "action" somatosensory stream connects from 3b and 3a via 1 and 2 to parietal area 5 and then 7. Inferior prefrontal regions have connectivity with the inferior temporal visual cortex and orbitofrontal cortex, are implicated in working memory for "what" processing streams, and provide connectivity to language systems, including 44, 45, 47l, TPOJ1, and superior temporal visual area. The dorsolateral prefrontal cortex regions that include area 46 have connectivity with parietal area 7 and somatosensory inferior parietal regions and are implicated in working memory for actions and planning. The dorsal prefrontal regions, including 8Ad and 8Av, have connectivity with visual regions of the inferior parietal cortex, including PGs and PGi, and are implicated in visual and auditory top-down attention.

Space-time resolved inference-based neurophysiological process imaging: application to resting-state alpha rhythm

Neural processes are complex and difficult to image. This paper presents a new space-time resolved brain imaging framework, called Neurophysiological Process Imaging (NPI), that identifies neurophysiological processes within cerebral cortex at the macroscopic scale. By fitting uncoupled neural mass models to each electromagnetic source time-series using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately inferred and imaged across the whole cerebral cortex at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural processes in different brain states.

From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis

As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term "mechanistic connectome." The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders.

Unrestricted eye movements strengthen effective connectivity from hippocampal to oculomotor regions during scene construction

Scene construction is a key component of memory recall, navigation, and future imagining, and relies on the medial temporal lobes (MTL). A parallel body of work suggests that eye movements may enable the imagination and construction of scenes, even in the absence of external visual input. There are vast structural and functional connections between regions of the MTL and those of the oculomotor system. However, the directionality of connections between the MTL and oculomotor control regions, and how it relates to scene construction, has not been studied directly in human neuroimaging. In the current study, we used dynamic causal modeling (DCM) to interrogate effective connectivity between the MTL and oculomotor regions using a scene construction task in which participants' eye movements were either restricted (fixed-viewing) or unrestricted (free-viewing). By omitting external visual input, and by contrasting free- versus fixed- viewing, the directionality of neural connectivity during scene construction could be determined. As opposed to when eye movements were restricted, allowing free-viewing during construction of scenes strengthened top-down connections from the MTL to the frontal eye fields, and to lower-level cortical visual processing regions, suppressed bottom-up connections along the visual stream, and enhanced vividness of the constructed scenes. Taken together, these findings provide novel, non-invasive evidence for the underlying, directional, connectivity between the MTL memory system and oculomotor system associated with constructing vivid mental representations of scenes.

A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis

Dynamic causal modeling (DCM) is a tool used for effective connectivity (EC) estimation in neuroimage analysis. But it is a model-driven analysis method, and the structure of the EC network needs to be determined in advance based on a large amount of prior knowledge. This characteristic makes it difficult to apply DCM to the exploratory brain network analysis. The exploratory analysis of DCM can be realized from two perspectives: one is to reduce the computational cost of the model; the other is to reduce the model space. From the perspective of model space reduction, a model space exploration strategy is proposed, including two algorithms. One algorithm, named GreedyEC, starts with reducing EC from full model, and the other, named GreedyROI, start with adding EC from one node model. Then the two algorithms were applied to the task state functional magnetic resonance imaging (fMRI) data of visual object recognition and selected the best DCM model from the perspective of model comparison based on Bayesian model compare method. Results show that combining the results of the two algorithms can further improve the effect of DCM exploratory analysis. For convenience in application, the algorithms were encapsulated into MATLAB function based on SPM to help neuroscience researchers to analyze the brain causal information flow network. The strategy provides a model space exploration tool that may obtain the best model from the perspective of model comparison and lower the threshold of DCM analysis.

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

Multiple cortical visual streams in humans

The effective connectivity between 55 visual cortical regions and 360 cortical regions was measured in 171 HCP participants using the HCP-MMP atlas, and complemented with functional connectivity and diffusion tractography. A Ventrolateral Visual "What" Stream for object and face recognition projects hierarchically to the inferior temporal visual cortex, which projects to the orbitofrontal cortex for reward value and emotion, and to the hippocampal memory system. A Ventromedial Visual "Where" Stream for scene representations connects to the parahippocampal gyrus and hippocampus. An Inferior STS (superior temporal sulcus) cortex Semantic Stream receives from the Ventrolateral Visual Stream, from visual inferior parietal PGi, and from the ventromedial-prefrontal reward system and connects to language systems. A Dorsal Visual Stream connects via V2 and V3A to MT+ Complex regions (including MT and MST), which connect to intraparietal regions (including LIP, VIP and MIP) involved in visual motion and actions in space. It performs coordinate transforms for idiothetic update of Ventromedial Stream scene representations. A Superior STS cortex Semantic Stream receives visual inputs from the Inferior STS Visual Stream, PGi, and STV, and auditory inputs from A5, is activated by face expression, motion and vocalization, and is important in social behaviour, and connects to language systems.

Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement

The development of technologies for brain stimulation provides a means for scientists and clinicians to directly actuate the brain and nervous system. Brain stimulation has shown intriguing potential in terms of modifying particular symptom clusters in patients and behavioral characteristics of subjects. The stage is thus set for optimization of these techniques and the pursuit of more nuanced stimulation objectives, including the modification of complex cognitive functions such as memory and attention. Control theory and engineering will play a key role in the development of these methods, guiding computational and algorithmic strategies for stimulation. In particular, realizing this goal will require new development of frameworks that allow for controlling not only brain activity, but also latent dynamics that underlie neural computation and information processing. In the current opinion, we review recent progress in brain stimulation and outline challenges and potential research pathways associated with exogenous control of cognitive function.

Dynamical differential covariance recovers directional network structure in multiscale neural systems

Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem. Here, we derive dynamical differential covariance (DDC), a method based on dynamical network models that detects directional interactions with low bias and high noise tolerance under nonstationarity conditions. Moreover, DDC scales well with the number of recording sites and the computation required is comparable to that needed for covariance. DDC was validated and compared favorably with other methods on networks with false positive motifs and multiscale neural simulations where the ground-truth connectivity was known. When applied to recordings of resting-state functional magnetic resonance imaging (rs-fMRI), DDC consistently detected regional interactions with strong structural connectivity in over 1,000 individual subjects obtained by diffusion MRI (dMRI). DDC is a promising family of methods for estimating connectivity that can be generalized to a wide range of dynamical models and recording techniques and to other applications where system identification is needed.

The human language effective connectome

To advance understanding of brain networks involved in language, the effective connectivity between 26 cortical regions implicated in language by a community analysis and 360 cortical regions was measured in 171 humans from the Human Connectome Project, and complemented with functional connectivity and diffusion tractography, all using the HCP multimodal parcellation atlas. A (semantic) network (Group 1) involving inferior cortical regions of the superior temporal sulcus cortex (STS) with the adjacent inferior temporal visual cortex TE1a and temporal pole TG, and the connected parietal PGi region, has effective connectivity with inferior temporal visual cortex (TE) regions; with parietal PFm which also has visual connectivity; with posterior cingulate cortex memory-related regions; with the frontal pole, orbitofrontal cortex, and medial prefrontal cortex; with the dorsolateral prefrontal cortex; and with 44 and 45 for output regions. It is proposed that this system can build in its temporal lobe (STS and TG) and parietal parts (PGi and PGs) semantic representations of objects incorporating especially their visual and reward properties. Another (semantic) network (Group 3) involving superior regions of the superior temporal sulcus cortex and more superior temporal lobe regions including STGa, auditory A5, TPOJ1, the STV and the Peri-Sylvian Language area (PSL) has effective connectivity with auditory areas (A1, A4, A5, Pbelt); with relatively early visual areas involved in motion, e.g., MT and MST, and faces/words (FFC); with somatosensory regions (frontal opercular FOP, insula and parietal PF); with other TPOJ regions; and with the inferior frontal gyrus regions (IFJa and IFSp). It is proposed that this system builds semantic representations specialising in auditory and related facial motion information useful in theory of mind and somatosensory / body image information, with outputs directed not only to regions 44 and 45, but also to premotor 55b and midcingulate premotor cortex. Both semantic networks (Groups 1 and 3) have access to the hippocampal episodic memory system via parahippocampal TF. A third largely frontal network (Group 2) (44, 45, 47l; 55b; the Superior Frontal Language region SFL; and including temporal pole TGv) receives effective connectivity from the two semantic systems, and is implicated in syntax and speech output.

Autonomous inference of complex network dynamics from incomplete and noisy data

The availability of empirical data that capture the structure and behaviour of complex networked systems has been greatly increased in recent years; however, a versatile computational toolbox for unveiling a complex system's nodal and interaction dynamics from data remains elusive. Here we develop a two-phase approach for the autonomous inference of complex network dynamics, and its effectiveness is demonstrated by the tests of inferring neuronal, genetic, social and coupled oscillator dynamics on various synthetic and real networks. Importantly, the approach is robust to incompleteness and noises, including low resolution, observational and dynamical noises, missing and spurious links, and dynamical heterogeneity. We apply the two-phase approach to infer the early spreading dynamics of influenza A flu on the worldwide airline network, and the inferred dynamical equation can also capture the spread of severe acute respiratory syndrome and coronavirus disease 2019. These findings together offer an avenue to discover the hidden microscopic mechanisms of a broad array of real networked systems.

Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

Recordings from resting state functional Magnetic Resonance Imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of “ground truth” has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. When we applied dCov to rs-fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose dCov-FCs were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration significantly correlated with behavior.

Enhancing task fMRI preprocessing via individualized model-based filtering of intrinsic activity dynamics

Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work, we describe a method to improve the estimation of task-evoked brain activity by first "filtering-out the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; Singh et al. 2020b) models built from individualized resting-state data to subtract the propagation of spontaneous activity from the task-fMRI signal (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction accuracy of individual differences in behavior across tasks measuring the same construct (cognitive control). Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural effects.

The Effective Connectivity of the Human Hippocampal Memory System

Effective connectivity measurements in the human hippocampal memory system based on the resting-state blood oxygenation-level dependent signal were made in 172 participants in the Human Connectome Project to reveal the directionality and strength of the connectivity. A ventral "what" hippocampal stream involves the temporal lobe cortex, perirhinal and parahippocampal TF cortex, and entorhinal cortex. A dorsal "where" hippocampal stream connects parietal cortex with posterior and retrosplenial cingulate cortex, and with parahippocampal TH cortex, which, in turn, project to the presubiculum, which connects to the hippocampus. A third stream involves the orbitofrontal and ventromedial-prefrontal cortex with effective connectivity with the hippocampal, entorhinal, and perirhinal cortex. There is generally stronger forward connectivity to the hippocampus than backward. Thus separate "what," "where," and "reward" streams can converge in the hippocampus, from which back projections return to the sources. However, unlike the simple dual stream hippocampal model, there is a third stream related to reward value; there is some cross-connectivity between these systems before the hippocampus is reached; and the hippocampus has some effective connectivity with earlier stages of processing than the entorhinal cortex and presubiculum. These findings complement diffusion tractography and provide a foundation for new concepts on the operation of the human hippocampal memory system.

Test-retest reliability of regression dynamic causal modeling

Regression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability—a test-theoretical property of particular importance for clinical applications—together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean intraclass correlation coefficient 0.24–0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates with measures of functional connectivity, rDCM performed favorably—particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications.

Optogenetic activation of striatal D1R and D2R cells differentially engages downstream connected areas beyond the basal ganglia

The basal ganglia (BG) are a group of subcortical nuclei responsible for motor and executive function. Central to BG function are striatal cells expressing D1 (D1R) and D2 (D2R) dopamine receptors. D1R and D2R cells are considered functional antagonists that facilitate voluntary movements and inhibit competing motor patterns, respectively. However, whether they maintain a uniform function across the striatum and what influence they exert outside the BG is unclear. Here, we address these questions by combining optogenetic activation of D1R and D2R cells in the mouse ventrolateral caudoputamen with fMRI. Striatal D1R/D2R stimulation evokes distinct activity within the BG-thalamocortical network and differentially engages cerebellar and prefrontal regions. Computational modeling of effective connectivity confirms that changes in D1R/D2R output drive functional relationships between these regions. Our results suggest a complex functional organization of striatal D1R/D2R cells and hint toward an interconnected fronto-BG-cerebellar network modulated by striatal D1R and D2R cells.

Predicting neuronal response properties from hemodynamic responses in the auditory cortex

Recent functional MRI (fMRI) studies have highlighted differences in responses to natural sounds along the rostral-caudal axis of the human superior temporal gyrus. However, due to the indirect nature of the fMRI signal, it has been challenging to relate these fMRI observations to actual neuronal response properties. To bridge this gap, we present a forward model of the fMRI responses to natural sounds combining a neuronal model of the auditory cortex with physiological modeling of the hemodynamic BOLD response. Neuronal responses are modeled with a dynamic recurrent firing rate model, reflecting the tonotopic, hierarchical processing in the auditory cortex along with the spectro-temporal tradeoff in the rostral-caudal axis of its belt areas. To link modeled neuronal response properties with human fMRI data in the auditory belt regions, we generated a space of neuronal models, which differed parametrically in spectral and temporal specificity of neuronal responses. Then, we obtained predictions of fMRI responses through a biophysical model of the hemodynamic BOLD response (P-DCM). Using Bayesian model comparison, our results showed that the hemodynamic BOLD responses of the caudal belt regions in the human auditory cortex were best explained by modeling faster temporal dynamics and broader spectral tuning of neuronal populations, while rostral belt regions were best explained through fine spectral tuning combined with slower temporal dynamics. These results support the hypotheses of complementary neural information processing along the rostral-caudal axis of the human superior temporal gyrus.