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

Damage to the shortest structural paths between brain regions is associated with disruptions of resting-state functional connectivity after stroke

Focal brain lesions disrupt resting-state functional connectivity, but the underlying structural mechanisms are unclear. Here, we examined the direct and indirect effects of structural disconnections on resting-state functional connectivity in a large sample of sub-acute stroke patients with heterogeneous brain lesions. We estimated the impact of each patient's lesion on the structural connectome by embedding the lesion in a diffusion MRI streamline tractography atlas constructed using data from healthy individuals. We defined direct disconnections as the loss of direct structural connections between two regions, and indirect disconnections as increases in the shortest structural path length between two regions that lack direct structural connections. We then tested the hypothesis that functional connectivity disruptions would be more severe for disconnected regions than for regions with spared connections. On average, nearly 20% of all region pairs were estimated to be either directly or indirectly disconnected by the lesions in our sample, and extensive disconnections were associated primarily with damage to deep white matter locations. Importantly, both directly and indirectly disconnected region pairs showed more severe functional connectivity disruptions than region pairs with spared direct and indirect connections, respectively, although functional connectivity disruptions tended to be most severe between region pairs that sustained direct structural disconnections. Together, these results emphasize the widespread impacts of focal brain lesions on the structural connectome and show that these impacts are reflected by disruptions of the functional connectome. Further, they indicate that in addition to direct structural disconnections, lesion-induced increases in the structural shortest path lengths between indirectly structurally connected region pairs provide information about the remote functional disruptions caused by focal brain lesions.

Linking Entropy at Rest with the Underlying Structural Connectivity in the Healthy and Lesioned Brain

The brain is a network that mediates information processing through a wide range of states. The extent of state diversity is a reflection of the entropy of the network. Here we measured the entropy of brain regions (nodes) in empirical and modeled functional networks reconstructed from resting state fMRI to address the connection of entropy at rest with the underlying structure measured through diffusion spectrum imaging. Using 18 empirical and 18 modeled stroke networks, we also investigated the effect that focal lesions have on node entropy and information diffusion. Overall, positive correlations between node entropy and structure were observed, especially between node entropy and node strength in both empirical and modeled data. Although lesions were restricted to one hemisphere in all stroke patients, entropy reduction was not only present in nodes from the damaged hemisphere, but also in nodes from the contralesioned hemisphere, an effect replicated in modeled stroke networks. Globally, information diffusion was also affected in empirical and modeled strokes compared with healthy controls. This is the first study showing that artificial lesions affect local and global network aspects in very similar ways compared with empirical strokes, shedding new light into the functional nature of stroke.

Altered dynamics of brain segregation and integration in poststroke aphasia

Poststroke aphasia (PSA) results from direct effect of focal lesions and dysfunction of distributed language networks. However, how flexible the activity at specific nodes control global dynamics is currently unknown. In this study, we demonstrate that alterations in the regional activity may cause imbalances between segregation and integration in temporo-spatial pattern, and the transient dynamics are disrupted in PSA patients. Specifically, we applied dynamic framework to eyes-closed resting-state functional MRI data from PSA patients (n = 17), and age-, gender-, and education-matched healthy controls (HCs, n = 20). Subsequently, we calculated two basis brain organizational principles: "dynamic segregation," obtained from dynamic amplitude of low-frequency fluctuations (dALFF), which represent the specialized processing within interconnected brain regions; and "dynamic integration," obtained from dynamic functional connectivity, which measures the efficient communication between interconnected brain regions. We found that both measures were decreased in the PSA patients within the left frontal and temporal subregions compared to the HCs. PSA patients displayed increased flexibility of interaction between left temporo-frontal subregions and right temporo-parieto-frontal subnetworks. Furthermore, we found that dALFF in the pars triangularis of left inferior frontal gyrus was associated with aphasia quotient. These findings suggest that the reduced temporal flexibility of regional activity in language-relevant cortical regions in PSA is related to the disrupted organization of intrahemispheric networks, leading to a loss of the corresponding functions. By using dynamic framework, our results offer valuable information about the alterations in segregation and integration of spatiotemporal information across networks and illuminate how dysfunction in flexible activity may underlie language deficits in PSA.

Toward neuroimaging-based network biomarkers for transient ischemic attack

Stroke is associated with topological disruptions of large-scale functional brain networks. However, whether these disruptions occur in transient ischemic attack (TIA), an important risk factor for stroke, remains largely unknown. Combining multimodal MRI techniques, we systematically examined TIA-related topological alterations of functional brain networks, and tested their reproducibility, structural, and metabolic substrates, associations with clinical risk factors and abilities as diagnostic and prognostic biomarkers. We found that functional networks in patients with TIA exhibited decreased whole-brain network efficiency, reduced nodal centralities in the bilateral insula and basal ganglia, and impaired connectivity of inter-hemispheric communication. These alterations remained largely unchanged when using different brain parcellation schemes or correcting for micro head motion or for regional gray matter volume, cerebral blood flow or hemodynamic lag of BOLD signals in the patients. Moreover, some alterations correlated with the levels of high-density lipoprotein cholesterol (an index related to ischemic attacks via modulation of atherosclerosis) in the patients, distinguished the patients from healthy individuals, and predicted future ischemic attacks in the patients. Collectively, these findings highlight the emergence of characteristic network dysfunctions in TIA, which may aid in elucidating pathological mechanisms and establishing diagnostic and prognostic biomarkers for the disease.

Brain networks and their relevance for stroke rehabilitation

Stroke has long been regarded as focal disease with circumscribed damage leading to neurological deficits. However, advances in methods for assessing the human brain and in statistics have enabled new tools for the examination of the consequences of stroke on brain structure and function. Thereby, it has become evident that stroke has impact on the entire brain and its network properties and can therefore be considered as a network disease. The present review first gives an overview of current methodological opportunities and pitfalls for assessing stroke-induced changes and reorganization in the human brain. We then summarize principles of plasticity after stroke that have emerged from the assessment of networks. Thereby, it is shown that neurological deficits do not only arise from focal tissue damage but also from local and remote changes in white-matter tracts and in neural interactions among wide-spread networks. Similarly, plasticity and clinical improvements are associated with specific compensatory structural and functional patterns of neural network interactions. Innovative treatment approaches have started to target such network patterns to enhance recovery. Network assessments to predict treatment response and to individualize rehabilitation is a promising way to enhance specific treatment effects and overall outcome after stroke.

Phase fMRI informs whole-brain function connectivity balance across lifespan with connection-specific aging effects during the resting state

A functional magnetic resonance imaging (fMRI) experiment produces complex-valued images consisting of pairwise magnitude and phase images. As different perspective on the same magnetic source, fMRI magnitude and phase data are complementary for brain function analysis. We collected 600-subject fMRI data during rest, decomposed via group-level independent component analysis (ICA) (mICA and pICA for magnitude and phase respectively), and calculated brain functional network connectivity matrices (mFC and pFC). The pFC matrix shows a fewer of significant connections balanced across positive and negative relationships. In comparison, the mFC matrix contains a positively-biased pattern with more significant connections. Our experiment data analyses also show that human brain maintains a whole-brain connection balance in resting state across an age span from 10 to 76 years, however, phase and magnitude data analyses reveal different connection-specific age effects on significant positive and negative subnetwork couplings.

Resting state functional connectivity and neural correlates of face-name encoding in patients with ischemic vascular lesions with and without the involvement of the left inferior frontal gyrus

Face-name association is a relevant ability for social interactions and involves the ventral and dorsolateral prefrontal cortices, particularly in the left hemisphere, bilateral hippocampal, fusiform gyrus and occipital regions. Previous studies demonstrated the primary role of the hippocampus for this ability in healthy subjects. However, no study has examined the participation of the left inferior frontal area, specially the left inferior frontal gyrus (LIFG) in patients with ischemic vascular lesions. In the present study we addressed this issue and investigated the neural correlates and resting state functional connectivity of face-name memory encoding in ischemic patients with LIFG or without lesions in the left IFG (nLIFG) and healthy controls (HC) using fMRI. The main results showed that the nLIFG group demonstrated efficient compensation related to encoding and performance on face-name learning and recognition memory task, in addition to similar brain areas activated during task performance compared to healthy controls. Some of these areas were more activated in nLIFG group, indicating a compensation mechanism. In contrast, the LIFG group showed worse behavior performance, and no signs of an efficient compensation mechanism. Functional connectivity analysis suggested that the left IFG region seems to be important for maintaining the connectivity of the right fusiform gyrus or, perhaps, lesion in this area is associated to maladaptive reorganization. Our findings highlight the relevant role of the left IFG in face-name learning and encoding, possibly as a primary region in addition to the bilateral hippocampal formation and fusiform gyrus.

Homeostatic plasticity and emergence of functional networks in a whole-brain model at criticality

Understanding the relationship between large-scale structural and functional brain networks remains a crucial issue in modern neuroscience. Recently, there has been growing interest in investigating the role of homeostatic plasticity mechanisms, across different spatiotemporal scales, in regulating network activity and brain functioning against a wide range of environmental conditions and brain states (e.g., during learning, development, ageing, neurological diseases). In the present study, we investigate how the inclusion of homeostatic plasticity in a stochastic whole-brain model, implemented as a normalization of the incoming node's excitatory input, affects the macroscopic activity during rest and the formation of functional networks. Importantly, we address the structure-function relationship both at the group and individual-based levels. In this work, we show that normalization of the node's excitatory input improves the correspondence between simulated neural patterns of the model and various brain functional data. Indeed, we find that the best match is achieved when the model control parameter is in its critical value and that normalization minimizes both the variability of the critical points and neuronal activity patterns among subjects. Therefore, our results suggest that the inclusion of homeostatic principles lead to more realistic brain activity consistent with the hallmarks of criticality. Our theoretical framework open new perspectives in personalized brain modeling with potential applications to investigate the deviation from criticality due to structural lesions (e.g. stroke) or brain disorders.

On the low dimensionality of behavioral deficits and alterations of brain network connectivity after focal injury

Traditional neuropsychological approaches emphasize the specificity of behavioral deficits and the modular organization of the brain. At the population level, however, there is emerging evidence that deficits are correlated resulting in a low dimensional structure of post-stroke neurological impairments. Here we consider the implications of low dimensionality for the three-way mapping between structural damage, altered physiology, and behavioral deficits. Understanding this mapping will be aided by large-sample studies that apply multivariate models and focus on explained percentage of variance, as opposed to univariate lesion-symptom techniques that report statistical significance. The low dimensionality of behavioral deficits following stroke is paralleled by widespread, yet relatively consistent, changes in functional connectivity (FC), including a reduction in modularity. Both are related to the structural damage to white matter and subcortical grey commonly produced by stroke. We suggest that large-scale physiological abnormalities following a stroke reduce the variety of neural states visited during task processing and at rest, resulting in a limited repertoire of behavioral states.

Removal of artifacts from resting-state fMRI data in stroke

We examined the effect of lesion on the resting-state functional connectivity in chronic post-stroke patients. We found many instances of strong correlations in BOLD signal measured at different locations within the lesion, making it hard to distinguish from the connectivity between intact and strongly connected regions. Regression of the mean cerebro-spinal fluid signal did not alleviate this problem. The connectomes computed by exclusion of lesioned voxels were not good predictors of the behavioral measures. We came up with a novel method that utilizes Independent Component Analysis (as implemented in FSL MELODIC) to identify the sources of variance in the resting-state fMRI data that are driven by the lesion, and to remove this variance. The resulting functional connectomes show better correlations with the behavioral measures of speech and language, and improve the out-of-sample prediction accuracy of multivariate analysis. We therefore advocate this preprocessing method for studies of post-stroke functional connectivity, particularly in samples with large lesions.

Acute single channel EEG predictors of cognitive function after stroke

BACKGROUND

Early and accurate identification of factors that predict post-stroke cognitive outcome is important to set realistic targets for rehabilitation and to guide patients and their families accordingly. However, behavioral measures of cognition are difficult to obtain in the acute phase of recovery due to clinical factors (e.g. fatigue) and functional barriers (e.g. language deficits). The aim of the current study was to test whether single channel wireless EEG data obtained acutely following stroke could predict longer-term cognitive function.

METHODS

Resting state Relative Power (RP) of delta, theta, alpha, beta, delta/alpha ratio (DAR), and delta/theta ratio (DTR) were obtained from a single electrode over FP1 in 24 participants within 72 hours of a first-ever stroke. The Montreal Cognitive Assessment (MoCA) was administered at 90-days post-stroke. Correlation and regression analyses were completed to identify relationships between 90-day cognitive function and electrophysiological data, neurological status, and demographic characteristics at admission.

RESULTS

Four acute qEEG indices demonstrated moderate to high correlations with 90-day MoCA scores: DTR (r = -0.57, p = 0.01), RP theta (r = 0.50, p = 0.01), RP delta (r = -0.47, p = 0.02), and DAR (r = -0.45, p = 0.03). Acute DTR (b = -0.36, p < 0.05) and stroke severity on admission (b = -0.63, p < 0.01) were the best linear combination of predictors of MoCA scores 90-days post-stroke, accounting for 75% of variance.

CONCLUSIONS

Data generated by a single pre-frontal electrode support the prognostic value of acute DAR, and identify DTR as a potential marker of post-stroke cognitive outcome. Use of single channel recording in an acute clinical setting may provide an efficient and valid predictor of cognitive function after stroke.

[The importance of neuronal networks for motor rehabilitation after a stroke]

Every year in Europe 1.5 million patients suffer a new stroke. Despite the further developments in acute therapy with nationwide stroke units, thrombolysis, thrombectomy and post-acute neurorehabilitation, only a small proportion of patients recover to a satisfactory degree allowing them to return to their normal social and professional life. This makes stroke the main cause of long-term disability with a corresponding impact on patient lives, socioeconomics and the healthcare system. Thus, the concepts of neurorehabilitation have to be extended to enhance the effects of rehabilitative treatment strategies. To achieve this, an understanding of the prediction of the course of recovery, the mechanisms underlying functional recovery and factors influencing recovery have to be enhanced for the development towards patient-tailored precision medicine approaches. A central point towards this is the understanding of stroke as a disease, which not only influences the damaged area but also the associated network. This is crucial for the understanding of the stroke-induced deficits, for prediction of recovery and options for interventional treatment strategies, which can target different areas in this network (e.g. primary motor cortex and secondary motor regions) based on individual factors of the patient. The present article discusses the importance of network alterations for motor neurorehabilitation after a stroke and which novel options, concepts and consequences could arise from this for neurorehabilitation.