Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling.

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.

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.

Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity.

Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes.

Whole-brain dynamical modelling for classification of Parkinson's disease.

Simulated whole-brain connectomes demonstrate enhanced inter-individual variability depending on the data processing and modelling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson's disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioural data, such as subject classes, which we refer to as behavioural model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine learning reported in this study also demonstrated that the performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that the temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improved prediction performance. Based on our findings, we suggest that combining the simulation results with empirical data is effective for inter-individual research and its clinical application.

Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia.

BACKGROUND: Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. METHODS: We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls). RESULTS: We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05). CONCLUSIONS: The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels.

Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion's share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models.

Inter-subject and inter-parcellation variability of resting-state whole-brain dynamical modeling.

Modern approaches to investigate complex brain dynamics suggest to represent the brain as a functional network of brain regions defined by a brain atlas, while edges represent the structural or functional connectivity among them. This approach is also utilized for mathematical modeling of the resting-state brain dynamics, where the applied brain parcellation plays an essential role in deriving the model network and governing the modeling results. There is however no consensus and empirical evidence on how a given brain atlas affects the model outcome, and the choice of parcellation is still rather arbitrary. Accordingly, we explore the impact of brain parcellation on inter-subject and inter-parcellation variability of model fitting to empirical data. Our objective is to provide a comprehensive empirical evidence of potential influences of parcellation choice on resting-state whole-brain dynamical modeling. We show that brain atlases strongly influence the quality of model validation and propose several variables calculated from empirical data to account for the observed variability. A few classes of such data variables can be distinguished depending on their inter-subject and inter-parcellation explanatory power.

Tractography density affects whole-brain structural architecture and resting-state dynamical modeling.

Dynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functional connectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of the whole-brain mathematical models informed by SC. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT) used for extraction of SC, cortical parcellations based on functional and anatomical brain properties and distinct model fitting modalities. The main objective of this study is to explore how the quality of the model validation can vary across the considered simulation conditions. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can be stratified into subgroups with divergent behaviors induced by the varying WBT density such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations.

Dynamic causal modeling of hippocampal activity measured via mesoscopic voltage-sensitive dye imaging.

The aim of this paper is to present a dynamic causal modeling (DCM) framework for hippocampal activity measured via voltage-sensitive dye imaging (VSDI). We propose a DCM model of the hippocampus that summarizes interactions between the hilus, CA3 and CA1 regions. The activity of each region is governed via a neuronal mass model with two inhibitory and one/two excitatory neuronal populations, which can be linked to measurement VSDI by scaling neuronal activity. To optimize the model structure for the hippocampus, we propose two Bayesian schemes: Bayesian hyperparameter optimization to estimate the unknown electrophysiological properties necessary for constructing a mesoscopic hippocampus model; and Bayesian model reduction to determine the parameterization of neural properties, and to test and include potential connections (morphologically inferred without direct evidence yet) in the model by evaluating group-level model evidence. The proposed method was applied to model spatiotemporal patterns of accumulative responses to consecutive stimuli in separate groups of wild-type mice and epileptic aristaless-related homeobox gene (Arx) conditional knock-out mutant mice (Arx-/+;Dlx5/6CRE-IRES-GFP) in order to identify group differences in the effective connectivity within the hippocampus. The causal role of each group-differing connectivity in generating mutant-like responses was further tested. The group-level analysis identified altered intra- and inter-regional effective connectivity, some of which are crucial for explaining mutant-like responses. The modelling results for the hippocampal activity suggest the plausibility of the proposed mesoscopic hippocampus model and the usefulness of utilizing the Bayesian framework for model construction in the mesoscale modeling of neural interactions using DCM.

Fully Automated and Real-Time Volumetric Measurement of Infarct Core and Penumbra in Diffusion- and Perfusion-Weighted MRI of Patients with Hyper-Acute Stroke.

Multimodal magnetic resonance imaging (MRI) has emerged as a promising tool for diagnosing ischemic stroke and for determining treatment strategies in the acute phase. The detection and quantification of the penumbra and the infarct core regions aid the assessment of the potential risks and benefits of thrombolysis by providing information on salvageable tissue or ischemic lesion age. In this study, we proposed a fully automated and real-time algorithm to compute parameter maps of perfusion-weighted images (PWIs) and to identify an infarct core from diffusion-weighted images (DWIs). DWI and PWI were obtained using a 1.5 Tesla MRI scanner for 15 patients with acute ischemic stroke. Parameter maps of PWI were computed using restricted gamma-variate curve fitting and Fourier-based deconvolution. The ischemic penumbra was identified using time-to-maximum (Tmax) > 6 s as the mutual optimal threshold, while the infarct core was segmented using an adaptive thresholding on DWI. When the penumbra on PWI was compared with that generated using commercial software Pearson's linear correlation coefficient between penumbra volumes was 0.601 (p = 0.030), and the Dice coefficient was 0.51 ± 0.15. The infarct core on DWI was compared with the manually segmented gold standard. Dice coefficient between the manually drawn and automated segmented infarct cores was 0.62 ± 0.18. The processing times for PWI and DWI were 222.9 ± 16.4 and 53.4 ± 4.8 s, respectively. In conclusion, we demonstrate a fully automated and real-time algorithm to segment the penumbra and the infarct core regions based on PWI and DWI.

Dynamic causal modeling for calcium imaging: Exploration of differential effective connectivity for sensory processing in a barrel cortical column.

Multi-photon calcium imaging (CaI) is an important tool to assess activities of neural populations within a column in the sensory cortex. However, the complex asymmetrical interactions among neural populations, termed effective connectivity, cannot be directly assessed by measuring the activity of each neuron or neural population using CaI but calls for computational modeling. To estimate effective connectivity among neural populations, we proposed a dynamic causal model (DCM) for CaI by combining a convolution-based dynamic neural state model and a dynamic calcium ion concentration model for CaI signals. After conducting a simulation study to evaluate DCM for CaI, we applied it to an experimental CaI signals measured at the layer 2/3 of a barrel cortical column that differentially responds to hit and error whisking trials in mice. We first identified neural populations and constructed computational models with intrinsic connectivity of neural populations within the layer 2/3 of the barrel cortex and extrinsic connectivity with latent external modes. Bayesian model inversion and comparison shows that interactions with latent inhibitory and excitatory external modes explain the observed CaI signals within the barrel cortical column better than any other tested models, with a single external mode or without any latent modes. The best model also showed differential intrinsic and extrinsic effective connectivity between hit and error trials in the functional hierarchy. Both simulation and experimental results suggest the usefulness of DCM for CaI in terms of exploration of hierarchical interactions among neural populations observed in CaI.

Effective connectivity during working memory and resting states: A DCM study.

Although the relationship between resting-state functional connectivity and task-related activity has been addressed, the relationship between task and resting-state directed or effective connectivity - and its behavioral concomitants - remains elusive. We evaluated effective connectivity under an N-back working memory task in 24 participants using stochastic dynamic causal modelling (DCM) of 7 T fMRI data. We repeated the analysis using resting-state data, from the same subjects, to model connectivity among the same brain regions engaged by the N-back task. This allowed us to: (i) examine the relationship between intrinsic (task-independent) effective connectivity during resting (Arest) and task states (Atask), (ii) cluster phenotypes of task-related changes in effective connectivity (Btask) across participants, (iii) identify edges (Btask) showing high inter-individual effective connectivity differences and (iv) associate reaction times with the similarity between Btask and Arest in these edges. We found a strong correlation between Arest and Atask over subjects but a marked difference between Btask and Arest. We further observed a strong clustering of individuals in terms of Btask, which was not apparent in Arest. The task-related effective connectivity Btask varied highly in the edges from the parietal to the frontal lobes across individuals, so the three groups were clustered mainly by the effective connectivity within these networks. The similarity between Btask and Arest at the edges from the parietal to the frontal lobes was positively correlated with 2-back reaction times. This result implies that a greater change in context-sensitive coupling - from resting-state connectivity - is associated with faster reaction times. In summary, task-dependent connectivity endows resting-state connectivity with a context sensitivity, which predicts the speed of information processing during the N-back task.