Computational modeling of resting-state activity demonstrates markers of normalcy in children with prenatal or perinatal stroke

Recent Progress in Brain Network Models for Medical Applications: A Review

Importance: Pathological perturbations of the brain often spread via connectome to fundamentally alter functional consequences. By integrating multimodal neuroimaging data with mathematical neural mass modeling, brain network models (BNMs) enable to quantitatively characterize aberrant network dynamics underlying multiple neurological and psychiatric disorders. We delved into the advancements of BNM-based medical applications, discussed the prevalent challenges within this field, and provided possible solutions and future directions. Highlights: This paper reviewed the theoretical foundations and current medical applications of computational BNMs. Composed of neural mass models, the BNM framework allows to investigate large-scale brain dynamics behind brain diseases by linking the simulated functional signals to the empirical neurophysiological data, and has shown promise in exploring neuropathological mechanisms, elucidating therapeutic effects, and predicting disease outcome. Despite that several limitations existed, one promising trend of this research field is to precisely guide clinical neuromodulation treatment based on individual BNM simulation. Conclusion: BNM carries the potential to help understand the mechanism underlying how neuropathology affects brain network dynamics, further contributing to decision-making in clinical diagnosis and treatment. Several constraints must be addressed and surmounted to pave the way for its utilization in the clinic.

Structural-and-dynamical similarity predicts compensatory brain areas driving the post-lesion functional recovery mechanism

The focal lesion alters the excitation-inhibition (E-I) balance and healthy functional connectivity patterns, which may recover over time. One possible mechanism for the brain to counter the insult is global reshaping functional connectivity alterations. However, the operational principles by which this can be achieved remain unknown. We propose a novel equivalence principle based on structural and dynamic similarity analysis to predict whether specific compensatory areas initiate lost E-I regulation after lesion. We hypothesize that similar structural areas (SSAs) and dynamically similar areas (DSAs) corresponding to a lesioned site are the crucial dynamical units to restore lost homeostatic balance within the surviving cortical brain regions. SSAs and DSAs are independent measures, one based on structural similarity properties measured by Jaccard Index and the other based on post-lesion recovery time. We unravel the relationship between SSA and DSA by simulating a whole brain mean field model deployed on top of a virtually lesioned structural connectome from human neuroimaging data to characterize global brain dynamics and functional connectivity at the level of individual subjects. Our results suggest that wiring proximity and similarity are the 2 major guiding principles of compensation-related utilization of hemisphere in the post-lesion functional connectivity re-organization process.

Cognitive outcome is related to functional thalamo-cortical connectivity after pediatric stroke

The thalamus has complex connections with the cortex and is involved in various cognitive processes. Despite increasing interest in the thalamus and the underlying thalamo-cortical interaction, little is known about thalamo-cortical connections after pediatric arterial ischemic stroke. Therefore, the aim of this study was to investigate thalamo-cortical connections and their association with cognitive performance after arterial ischemic stroke.

Twenty patients in the chronic phase after pediatric arterial ischemic stroke (≥ 2 years after diagnosis, diagnosed <16 years; aged 5–23 years, mean 15.1 years) and twenty healthy controls matched for age and sex were examined in a cross-sectional study design. Cognitive performance (selective attention, inhibition, working memory, and cognitive flexibility) was evaluated using standardized neuropsychological tests. Resting-state functional magnetic resonance imaging was used to examine functional thalamo-cortical connectivity. Lesion masks were integrated in the preprocessing pipeline to ensure that structurally damaged voxels did not influence functional connectivity analyses.

Cognitive performance (selective attention, inhibition and working memory) was significantly reduced in patients compared to controls. Network analyses revealed significantly lower thalamo-cortical connectivity for the motor, auditory, visual, default mode network, salience, left/right executive and dorsal attention network in patients compared to controls. Interestingly, analyses revealed as well higher thalamo-cortical connectivity in some subdivisions of the thalamus for the default mode network (medial nuclei), motor (lateral nuclei), dorsal attention (anterior nuclei), and the left executive network (posterior nuclei) in patients compared to controls. Increased and decreased thalamo-cortical connectivity strength within the same networks was, however, found in different thalamic sub-divisions. Thus, alterations in thalamo-cortical connectivity strength after pediatric stroke seem to point in both directions, with stronger as well as weaker thalamo-cortical connectivity in patients compared to controls. Multivariate linear regression, with lesion size and age as covariates, revealed significant correlations between cognitive performance (selective attention, inhibition, and working memory) and the strength of thalamo-cortical connectivity in the motor, auditory, visual, default mode network, posterior default mode network, salience, left/right executive, and dorsal attention network after childhood stroke.

Our data suggest that the interaction between different sub-nuclei of the thalamus and several cortical networks relates to post-stroke cognition. The variability in cognitive outcomes after pediatric stroke might partly be explained by functional thalamo-cortical connectivity strength.

Investigating the network consequences of focal brain lesions through comparisons of real and simulated lesions

Given the increased interest in the functional human connectome, a number of computer simulation studies have sought to develop a better quantitative understanding of the effects of focal lesions on the brain’s functional network organization. However, there has been little work evaluating the predictions of this simulation work vis a vis real lesioned connectomes. One of the few relevant studies reported findings from real chronic focal lesions that only partially confirmed simulation predictions. We hypothesize that these discrepancies arose because although the effects of focal lesions likely consist of two components: short-term node subtraction and long-term network re-organization, previous simulation studies have primarily modeled only the short-term consequences of the subtraction of lesioned nodes and their connections. To evaluate this hypothesis, we compared network properties (modularity, participation coefficient, within-module degree) between real functional connectomes obtained from chronic stroke participants and “pseudo-lesioned” functional connectomes generated by subtracting the same sets of lesioned nodes/connections from healthy control connectomes. We found that, as we hypothesized, the network properties of real-lesioned connectomes in chronic stroke differed from those of the pseudo-lesioned connectomes which instantiated only the short-term consequences of node subtraction. Reflecting the long-term consequences of focal lesions, we found re-organization of the neurotopography of global and local hubs in the real but not the pseudo-lesioned connectomes. We conclude that the long-term network re-organization that occurs in response to focal lesions involves changes in functional connectivity within the remaining intact neural tissue that go well beyond the short-term consequences of node subtraction.

Perinatal arterial ischemic stroke

Perinatal arterial ischemic stroke is a relatively common and serious neurologic disorder that can affect the fetus, the preterm, and the term-born infant. It carries significant long-term disabilities. Herein we describe the current understanding of its etiology, pathophysiology and classification, different presentations, and optimal early management. We discuss the role of different brain imaging modalities in defining the extent of lesions and the impact this has on the prediction of outcomes. In recent years there has been progress in treatments, making early diagnosis and the understanding of likely morbidities imperative. An overview is given of the range of possible outcomes and optimal approaches to follow-up and support for the child and their family in the light of present knowledge.

Data-driven brain network models differentiate variability across language tasks

The relationship between brain structure and function has been probed using a variety of approaches, but how the underlying structural connectivity of the human brain drives behavior is far from understood. To investigate the effect of anatomical brain organization on human task performance, we use a data-driven computational modeling approach and explore the functional effects of naturally occurring structural differences in brain networks. We construct personalized brain network models by combining anatomical connectivity estimated from diffusion spectrum imaging of individual subjects with a nonlinear model of brain dynamics. By performing computational experiments in which we measure the excitability of the global brain network and spread of synchronization following a targeted computational stimulation, we quantify how individual variation in the underlying connectivity impacts both local and global brain dynamics. We further relate the computational results to individual variability in the subjects' performance of three language-demanding tasks both before and after transcranial magnetic stimulation to the left-inferior frontal gyrus. Our results show that task performance correlates with either local or global measures of functional activity, depending on the complexity of the task. By emphasizing differences in the underlying structural connectivity, our model serves as a powerful tool to assess individual differences in task performances, to dissociate the effect of targeted stimulation in tasks that differ in cognitive demand, and to pave the way for the development of personalized therapeutics.

Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models

Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

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Modeled local and global dynamics correlate with individual cognition in Alzheimer's.

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Proof of concept of The Virtual Brain to characterize individual dynamics

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Brain-behaviour relations depend on the network modeled (whole brain or limbic).

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Model parameters predict cognition better than metrics of neuroimaging data.

Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson's disease

Deep brain stimulation (DBS) for Parkinson's disease is a highly effective treatment in controlling otherwise debilitating symptoms. Yet the underlying brain mechanisms are currently not well understood. Whole-brain computational modeling was used to disclose the effects of DBS during resting-state functional Magnetic Resonance Imaging in ten patients with Parkinson's disease. Specifically, we explored the local and global impact that DBS has in creating asynchronous, stable or critical oscillatory conditions using a supercritical bifurcation model. We found that DBS shifts global brain dynamics of patients towards a Healthy regime. This effect was more pronounced in very specific brain areas such as the thalamus, globus pallidus and orbitofrontal regions of the right hemisphere (with the left hemisphere not analyzed given artifacts arising from the electrode lead). Global aspects of integration and synchronization were also rebalanced. Empirically, we found higher communicability and coherence brain measures during DBS-ON compared to DBS-OFF. Finally, using our model as a framework, artificial in silico DBS was applied to find potential alternative target areas for stimulation and whole-brain rebalancing. These results offer important insights into the underlying large-scale effects of DBS as well as in finding novel stimulation targets, which may offer a route to more efficacious treatments.

Clinical neuroimaging in the preterm infant: Diagnosis and prognosis

Perinatal care advances emerging over the past twenty years have helped to diminish the mortality and severe neurological morbidity of extremely and very preterm neonates (e.g., cystic Periventricular Leukomalacia [c-PVL] and Germinal Matrix Hemorrhage - Intraventricular Hemorrhage [GMH-IVH grade 3-4/4]; 22 to < 32 weeks of gestational age, GA). However, motor and/or cognitive disabilities associated with mild-to-moderate white and gray matter injury are frequently present in this population (e.g., non-cystic Periventricular Leukomalacia [non-cystic PVL], neuronal-axonal injury and GMH-IVH grade 1-2/4). Brain research studies using magnetic resonance imaging (MRI) report that 50% to 80% of extremely and very preterm neonates have diffuse white matter abnormalities (WMA) which correspond to only the minimum grade of severity. Nevertheless, mild-to-moderate diffuse WMA has also been associated with significant affectations of motor and cognitive activities. Due to increased neonatal survival and the intrinsic characteristics of diffuse WMA, there is a growing need to study the brain of the premature infant using non-invasive neuroimaging techniques sensitive to microscopic and/or diffuse lesions. This emerging need has led the scientific community to try to bridge the gap between concepts or ideas from different methodologies and approaches; for instance, neuropathology, neuroimaging and clinical findings. This is evident from the combination of intense pre-clinical and clinicopathologic research along with neonatal neurology and quantitative neuroimaging research. In the following review, we explore literature relating the most frequently observed neuropathological patterns with the recent neuroimaging findings in preterm newborns and infants with perinatal brain injury. Specifically, we focus our discussions on the use of neuroimaging to aid diagnosis, measure morphometric brain damage, and track long-term neurodevelopmental outcomes.

Decreased integration and information capacity in stroke measured by whole brain models of resting state activity

While several studies have shown that focal lesions affect the communication between structurally normal regions of the brain, and that these changes may correlate with behavioural deficits, their impact on brain's information processing capacity is currently unknown. Here we test the hypothesis that focal lesions decrease the brain's information processing capacity, of which changes in functional connectivity may be a measurable correlate. To measure processing capacity, we turned to whole brain computational modelling to estimate the integration and segregation of information in brain networks. First, we measured functional connectivity between different brain areas with resting state functional magnetic resonance imaging in healthy subjects (n = 26), and subjects who had suffered a cortical stroke (n = 36). We then used a whole-brain network model that coupled average excitatory activities of local regions via anatomical connectivity. Model parameters were optimized in each healthy or stroke participant to maximize correlation between model and empirical functional connectivity, so that the model's effective connectivity was a veridical representation of healthy or lesioned brain networks. Subsequently, we calculated two model-based measures: 'integration', a graph theoretical measure obtained from functional connectivity, which measures the connectedness of brain networks, and 'information capacity', an information theoretical measure that cannot be obtained empirically, representative of the segregative ability of brain networks to encode distinct stimuli. We found that both measures were decreased in stroke patients, as compared to healthy controls, particularly at the level of resting-state networks. Furthermore, we found that these measures, especially information capacity, correlate with measures of behavioural impairment and the segregation of resting-state networks empirically measured. This study shows that focal lesions affect the brain's ability to represent stimuli and task states, and that information capacity measured through whole brain models is a theory-driven measure of processing capacity that could be used as a biomarker of injury for outcome prediction or target for rehabilitation intervention.

Resting-State Functional Connectivity and Cognitive Impairment in Children with Perinatal Stroke

Perinatal stroke is a leading cause of congenital hemiparesis and neurocognitive deficits in children. Dysfunctions in the large-scale resting-state functional networks may underlie cognitive and behavioral disability in these children. We studied resting-state functional connectivity in patients with perinatal stroke collected from the Estonian Pediatric Stroke Database. Neurodevelopment of children was assessed by the Pediatric Stroke Outcome Measurement and the Kaufman Assessment Battery. The study included 36 children (age range 7.6–17.9 years): 10 with periventricular venous infarction (PVI), 7 with arterial ischemic stroke (AIS), and 19 controls. There were no differences in severity of hemiparesis between the PVI and AIS groups. A significant increase in default mode network connectivity (FDR 0.1) and lower cognitive functions (p < 0.05) were found in children with AIS compared to the controls and the PVI group. The children with PVI had no significant differences in the resting-state networks compared to the controls and their cognitive functions were normal. Our findings demonstrate impairment in cognitive functions and neural network profile in hemiparetic children with AIS compared to children with PVI and controls. Changes in the resting-state networks found in children with AIS could possibly serve as the underlying derangements of cognitive brain functions in these children.

Quantitative evaluation of simulated functional brain networks in graph theoretical analysis

There is increasing interest in the potential of whole-brain computational models to provide mechanistic insights into resting-state brain networks. It is therefore important to determine the degree to which computational models reproduce the topological features of empirical functional brain networks. We used empirical connectivity data derived from diffusion spectrum and resting-state functional magnetic resonance imaging data from healthy individuals. Empirical and simulated functional networks, constrained by structural connectivity, were defined based on 66 brain anatomical regions (nodes). Simulated functional data were generated using the Kuramoto model in which each anatomical region acts as a phase oscillator. Network topology was studied using graph theory in the empirical and simulated data. The difference (relative error) between graph theory measures derived from empirical and simulated data was then estimated. We found that simulated data can be used with confidence to model graph measures of global network organization at different dynamic states and highlight the sensitive dependence of the solutions obtained in simulated data on the specified connection densities. This study provides a method for the quantitative evaluation and external validation of graph theory metrics derived from simulated data that can be used to inform future study designs.

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We tested the convergence of simulated and empirical brain network topology.

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Simulated functional data were based on the Kuramoto model.

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Network topology was defined using graph theory metrics.

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Graph theory metrics of global network topology showed greater convergence.

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The solutions obtained in the simulated data depended on connection density.

Does the regulation of local excitation-inhibition balance aid in recovery of functional connectivity? A computational account

Computational modeling of the spontaneous dynamics over the whole brain provides critical insight into the spatiotemporal organization of brain dynamics at multiple resolutions and their alteration to changes in brain structure (e.g. in diseased states, aging, across individuals). Recent experimental evidence further suggests that the adverse effect of lesions is visible on spontaneous dynamics characterized by changes in resting state functional connectivity and its graph theoretical properties (e.g. modularity). These changes originate from altered neural dynamics in individual brain areas that are otherwise poised towards a homeostatic equilibrium to maintain a stable excitatory and inhibitory activity. In this work, we employ a homeostatic inhibitory mechanism, balancing excitation and inhibition in the local brain areas of the entire cortex under neurological impairments like lesions to understand global functional recovery (across brain networks and individuals). Previous computational and empirical studies have demonstrated that the resting state functional connectivity varies primarily due to the location and specific topological characteristics of the lesion. We show that local homeostatic balance provides a functional recovery by re-establishing excitation-inhibition balance in all areas that are affected by lesion. We systematically compare the extent of recovery in the primary hub areas (e.g. default mode network (DMN), medial temporal lobe, medial prefrontal cortex) as well as other sensory areas like primary motor area, supplementary motor area, fronto-parietal and temporo-parietal networks. Our findings suggest that stability and richness similar to the normal brain dynamics at rest are achievable by re-establishment of balance.