Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions

Reshaping computational neuropsychiatry beyond synaptopathy

Computational neuropsychiatry is a leading discipline in explaining psychopathology in terms of neuronal message passing, distributed processing and belief propagation in neuronal networks. Active Inference (AI) is a way of representing this dysfunctional signal processing. According to the AI approach, all neuronal processing and action selection can be explained by maximizing Bayesian model evidence or minimizing variational free energy. Following these principles, it has been suggested that dysconnection in neuronal networks results in aberrant belief updating and erroneous inference, leading to psychiatric and neurologic symptoms. However, there is a classic distinction between disorders of inference (or synaptopathy-including the majority of psychiatric disorders) and disorders of brain function (including vascular neurological pathologies and severe forms of tauopathy and synucleinopathies). This distinction is generally based on the idea that synaptopathies impair neuromodulatory precision weighting, leading to rigid inferences or heightened sensitivity to noise, while disorders of brain function are linked to damage in the nervous system (disconnection). This makes it challenging to apply the logic of the free energy principle. We suggest that this distinction will enable future models of neuropsychiatric symptoms to be improved by considering more than neuronal message passing.

Brain structural-functional coupling mechanism in mild subcortical stroke and its relationship with cognition

OBJECTIVES

Stroke can lead to significant restructuring of brain structure and function. However, the precise changes in the coordination between brain structure and function in subcortical stroke patients remain unclear. We investigated alterations in brain structural-functional coupling (SC-FC coupling) and their impact on cognitive function in subcortical basal ganglia infarction patients.

METHODS

The study comprised 40 patients with mild stroke with basal ganglia region infarcts and 29 healthy controls (HC) who underwent multidimensional neuroimaging examination and neuropsychological testing. The subcortical stroke patients were divided into post-stroke cognitive impairment (PSCI) and stroke with no cognitive impairment (NPSCI) groups based on cognitive performance, with 22 individuals undergoing follow-up examination after three months. We investigated differences in brain structural-functional coupling across three groups, and their associations with cognitive functions.

RESULTS

Compared to both HC participants and NPSCI, PSCI exhibited significantly reduced structural-functional coupling strength in specific brain regions. After a three-month period, there was observed an increase in structural-functional coupling strength within the frontal lobe (precentral gyrus and paracentral lobule). The strength of SC-FC coupling within the precentral gyrus, precuneus, and paracentral lobule regions demonstrated a decline correlating with the deterioration of cognitive function (MoCA, memory and visual motor speed functions).

CONCLUSIONS

After subcortical basal ganglia stroke, PSCI patients demonstrated decreased SC-FC coupling in the frontal lobe region, correlating with multidimensional cognitive impairment. Three months later, there was an increase in SC-FC coupling in the frontal lobe, suggesting a compensatory mechanism during the recovery phase of cognitive impairment following stroke.

Impairments of neurovascular coupling after stroke lower glymphatic system function and lead to depressive symptom: A longitudinal cohort study

BACKGROUND

Respective changes in neurovascular coupling (NVC) and glymphatic function have been reported in post-stroke depression (PSD). Recent studies have found a link between NVC and waste clearance by the glymphatic system, which has not been illustrated in PSD.

METHOD

We prospectively recruited ninety-six stroke patients and forty-four healthy controls (HC), with fifty-nine patients undergoing a second MRI scan. NVC metrics were investigated by exploring Pearson correlation coefficients and ratios between cerebral blood flow (CBF) and BOLD-derived quantitative maps (ALFF, fALFF, REHO maps). Diffusion tensor imaging along the perivascular (DTI-ALPS) index was used to reflect glymphatic function. We first analyzed the altered NVC metrics in stroke patients relative to the HC group. Then, we explored the relationship between NVC metrics, ALPS index and depressive symptoms at baseline and during the follow-up period through correlation and mediation analyses.

RESULTS

Stroke patients exhibited significantly lower global CBF-fALFF coupling and ALPS index. At the regional level, abnormal NVC alterations in brain regions involved in cognition, emotion, and sensorimotor function in PSD. Baseline analyses showed that ALPS index exhibited positive associations with both global and local NVC and abnormal regional NVC may contribute to generation of PSD by reducing glymphatic function (β = -0.075, p < 0.05, CI = [-0.169 to -0.012]). Longitudinal analyses similarly showed that ALPS index changes were positively associated with changes in NVC and mediated improvements in depressive symptoms.

CONCLUSION

Our findings suggest that NVC abnormalities leading to impaired glymphatic system function may be a potential neurobiological mechanism of PSD.

Exploring the Relationship between Abnormal Communication Efficiency of Cerebral Cortex and Multiple Cognitive Functions in Mild Subcortical Stroke: A Resting-State fMRI Study

BACKGROUND

The purpose of this study was to explore the specific regions of abnormal cortical communication efficiency in patients with mild subcortical stroke and to investigate the relationship between these communication efficiency abnormalities and multidimensional cognition.

METHODS

The research involved 35 patients with mild strokes affecting the basal ganglia and 29 healthy controls (HC). Comprehensive neuroimaging and neuropsychological assessments were conducted. Stroke patients were categorized into post-stroke cognitive impairment (PSCI) (MoCA ≤ 22) and non-cognitively impaired stroke patients (NPSCI) (MoCA ≥ 23) based on their cognitive performance. Additionally, 22 patients were reassessed three months later.

RESULTS

PSCI patients, compared to HC and NPSCI groups, had significantly higher communication efficiency in specific brain regions. A notable finding was the significant correlation between increased communication efficiency in the medioventral occipital cortex and multidimensional cognitive decline. However, this increased communication efficiency in PSCI patients lessened during the three-month follow-up period.

CONCLUSIONS

the heightened communication efficiency in the medio-ventral occipital cortex may represent a compensatory mechanism for cognitive impairment in PSCI patients, which undergoes adjustment three months after stroke.

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesize that characterizing brain activity in terms of transient events may provide a signature of consciousness. For this, we analyze temporal dynamics of spatiotemporally overlapping functional networks obtained from fMRI transient activity across different anesthetics and levels of anesthesia. We first show a striking homology in spatial organization of networks between monkeys and humans, indicating cross-species similarities in resting-state fMRI structure. We then track how network organization shifts under different anesthesia conditions in macaque monkeys. While the spatial aspect of the networks is preserved, their temporal dynamics are highly affected by anesthesia. Networks express for longer durations and co-activate in an anesthetic-specific configuration. Additionally, hierarchical brain organization is disrupted with a consciousness-level-signature role of the default mode network. In conclusion, large-scale brain network temporal dynamics capture differences in anesthetic-specific consciousness-level, paving the way towards a clinical translation of these cortical signature.

The emergence of multiscale connectomics-based approaches in stroke recovery

Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments in brain readout technologies and progress in complex network modeling have revolutionized current understanding of the effects of stroke on brain networks at a macroscale, reorganization of smaller scale brain networks remains incompletely understood. In this review, we use a conceptual framework of graph theory to define brain networks from nano- to macroscales. Highlighting stroke-related brain connectivity studies at multiple scales, we argue that multiscale connectomics-based approaches may provide new routes to better evaluate brain structural and functional remapping after stroke and during recovery.

Attention Please! Unravelling the Link Between Brain Network Connectivity and Cognitive Attention Following Acquired Brain Injury: A Systematic Review of Structural and Functional Measures

Traumatic brain injury (TBI) and stroke are the most common causes of acquired brain injury (ABI), annually affecting 69 million and 15 million people, respectively. Following ABI, the relationship between brain network disruption and common cognitive issues including attention dysfunction is heterogenous. Using PRISMA guidelines, we systematically reviewed 43 studies published by February 2023 that reported correlations between attention and connectivity. Across all ages and stages of recovery, following TBI, greater attention was associated with greater structural efficiency within/between executive control network (ECN), salience network (SN), and default mode network (DMN) and greater functional connectivity (fc) within/between ECN and DMN, indicating DMN interference. Following stroke, greater attention was associated with greater structural connectivity (sc) within ECN; or greater fc within the dorsal attention network (DAN). In childhood ABI populations, decreases in structural network segregation were associated with greater attention. Longitudinal recovery from TBI was associated with normalization of DMN activity, and in stroke, normalization of DMN and DAN activity. Results improve clinical understanding of attention-related connectivity changes after ABI. Recommendations for future research include increased use of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to measure connectivity at the point of care, standardized attention and connectivity outcome measures and analysis pipelines, detailed reporting of patient symptomatology, and casual analysis of attention-related connectivity using brain stimulation.

Activation changes in patients with post-stroke cognitive impairment receiving intermittent theta burst stimulation: A functional near-infrared spectroscopy study

BACKGROUND

Intermittent theta burst stimulation (iTBS) has demonstrated efficacy in patients with cognitive impairment. However, activation patterns and mechanisms of iTBS for post-stroke cognitive impairment (PSCI) remain insufficiently understood.

OBJECTIVE

To investigate the activation patterns and potential benefits of using iTBS in patients with PSCI.

METHODS

A total of forty-four patients with PSCI were enrolled and divided into an iTBS group (iTBS and cognitive training) or a control group (cognitive training alone). Outcomes were assessed based on the activation in functional near-infrared spectroscopy (fNIRS), as well as Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) and the modified Barthel Index (MBI).

RESULTS

Thirty-eight patients completed the interventions and assessments. Increased cortical activation was observed in the iTBS group after the interventions, including the right superior temporal gyrus (STG), left frontopolar cortex (FPC) and left orbitofrontal cortex (OFC). Both groups showed significant improvements in LOTCA and MBI after the interventions (p < 0.05). Furthermore, the iTBS group augmented superior improvement in the total score of MBI and LOTCA compared to the control group, especially in visuomotor organization and thinking operations (p < 0.05).

CONCLUSION

iTBS altered activation patterns and improved cognitive function in patients with PSCI. The activation induced by iTBS may contribute to the improvement of cognitive function.

Using predictive validity to compare associations between brain damage and behavior

Lesion-behavior mapping (LBM) provides a statistical map of the association between voxel-wise brain damage and individual differences in behavior. To understand whether two behaviors are mediated by damage to distinct regions, researchers often compare LBM weight outputs by either the Overlap method or the Correlation method. However, these methods lack statistical criteria to determine whether two LBM are distinct versus the same and are disconnected from a major goal of LBMs: predicting behavior from brain damage. Without such criteria, researchers may draw conclusions from numeric differences between LBMs that are irrelevant to predicting behavior. We developed and validated a predictive validity comparison method (PVC) that establishes a statistical criterion for comparing two LBMs using predictive accuracy: two LBMs are distinct if and only if they provide unique predictive power for the behaviors being assessed. We applied PVC to two lesion-behavior stroke data sets, demonstrating its utility for determining when behaviors arise from the same versus different lesion patterns. Using region-of-interest-based simulations derived from proportion damage from a large data set (n = 131), PVC accurately detected when behaviors were mediated by different regions (high sensitivity) versus the same region (high specificity). Both the Overlap method and Correlation method performed poorly on the simulated data. By objectively determining whether two behavioral deficits can be explained by single versus distinct patterns of brain damage, PVC provides a critical advance in establishing the brain bases of behavior. We have developed and released a GUI-driven web app to encourage widespread adoption.

Decoding cerebro-spinal signatures of human behavior: application to motor sequence learning

Mapping the neural patterns that drive human behavior is a key challenge in neuroscience. Even the simplest of our everyday actions stem from the dynamic and complex interplay of multiple neural structures across the central nervous system (CNS). Yet, most neuroimaging research has focused on investigating cerebral mechanisms, while the way the spinal cord accompanies the brain in shaping human behavior has been largely overlooked. Although the recent advent of functional magnetic resonance imaging (fMRI) sequences that can simultaneously target the brain and spinal cord has opened up new avenues for studying these mechanisms at multiple levels of the CNS, research to date has been limited to inferential univariate techniques that cannot fully unveil the intricacies of the underlying neural states. To address this, we propose to go beyond traditional analyses and instead use a data-driven multivariate approach leveraging the dynamic content of cerebro-spinal signals using innovation-driven coactivation patterns (iCAPs). We demonstrate the relevance of this approach in a simultaneous brain-spinal cord fMRI dataset acquired during motor sequence learning (MSL), to highlight how large-scale CNS plasticity underpins rapid improvements in early skill acquisition and slower consolidation after extended practice. Specifically, we uncovered cortical, subcortical and spinal functional networks, which were used to decode the different stages of learning with a high accuracy and, thus, delineate meaningful cerebro-spinal signatures of learning progression. Our results provide compelling evidence that the dynamics of neural signals, paired with a data-driven approach, can be used to disentangle the modular organization of the CNS. While we outline the potential of this framework to probe the neural correlates of motor learning, its versatility makes it broadly applicable to explore the functioning of cerebro-spinal networks in other experimental or pathological conditions.

Altered directional functional connectivity underlies post-stroke cognitive recovery

Cortical ischaemic strokes result in cognitive deficits depending on the area of the affected brain. However, we have demonstrated that difficulties with attention and processing speed can occur even with small subcortical infarcts. Symptoms appear independent of lesion location, suggesting they arise from generalized disruption of cognitive networks. Longitudinal studies evaluating directional measures of functional connectivity in this population are lacking. We evaluated six patients with minor stroke exhibiting cognitive impairment 6-8 weeks post-infarct and four age-similar controls. Resting-state magnetoencephalography data were collected. Clinical and imaging evaluations of both groups were repeated 6- and 12 months later. Network Localized Granger Causality was used to determine differences in directional connectivity between groups and across visits, which were correlated with clinical performance. Directional connectivity patterns remained stable across visits for controls. After the stroke, inter-hemispheric connectivity between the frontoparietal cortex and the non-frontoparietal cortex significantly increased between visits 1 and 2, corresponding to uniform improvement in reaction times and cognitive scores. Initially, the majority of functional links originated from non-frontal areas contralateral to the lesion, connecting to ipsilesional brain regions. By visit 2, inter-hemispheric connections, directed from the ipsilesional to the contralesional cortex significantly increased. At visit 3, patients demonstrating continued favourable cognitive recovery showed less reliance on these inter-hemispheric connections. These changes were not observed in those without continued improvement. Our findings provide supporting evidence that the neural basis of early post-stroke cognitive dysfunction occurs at the network level, and continued recovery correlates with the evolution of inter-hemispheric connectivity.

Advanced Diffusion MRI of Stroke Recovery

There is an urgent need for ways to improve our understanding of poststroke recovery to inform the development of novel rehabilitative interventions, and improve the clinical management of stroke patients. Supported by the notion that predictive information on poststroke recovery is embedded not only in the individual brain regions, but also the connections throughout the brain, majority of previous investigations have focused on the relationship between brain functional connections and post-stroke deficit and recovery. However, considering the fact that it is the static anatomical brain connections that constrain and facilitate the dynamic functional brain connections, the microstructures and structural connections of the brain may potentially be better alternatives to the functional MRI-based biomarkers of stroke recovery. This review, therefore, seeks to provide an overview of the basic concept and applications of two recently proposed advanced diffusion MRI techniques, namely lesion network mapping and fixel-based morphometry, that may be useful for the investigation of stroke recovery at the local and global levels of the brain. This review will also highlight the application of some of other emerging advanced diffusion MRI techniques that warrant further investigation in the context of stroke recovery research.

Regional homogeneity alterations in multifrequency bands in patients with basal ganglia stroke: A resting-state functional magnetic resonance imaging study

Objective

The aim of this study was to investigate the spontaneous regional neural activity abnormalities in patients with acute basal ganglia ischemic stroke (BGIS) using a multifrequency bands regional homogeneity (ReHo) method and to explore whether the alteration of ReHo values was associated with clinical characteristics.

Methods

In this study, 34 patients with acute BGIS and 44 healthy controls (HCs) were recruited. All participants were examined by resting-state functional magnetic resonance imaging (rs-fMRI). The ReHo method was used to detect the alterations of spontaneous neural activities in patients with acute BGIS. A two-sample t-test comparison was performed to compare the ReHo value between the two groups, and a Pearson correlation analysis was conducted to assess the relationship between the regional neural activity abnormalities and clinical characteristics.

Results

Compared with the HCs, the patients with acute BGIS showed increased ReHo in the left caudate and subregions such as the right caudate and left putamen in conventional frequency bands. In the slow-5 frequency band, patients with BGIS showed decreased ReHo in the left medial cingulum of BGIS compared to the HCs and other subregions such as bilateral caudate and left putamen. No brain regions with ReHo alterations were found in the slow-4 frequency band. Moreover, we found that the ReHo value of left caudate was positively correlated with the NIHSS score.

Conclusion

Our findings revealed the alterations of ReHo in patients with acute BGIS in a specific frequency band and provided a new insight into the pathogenesis mechanism of BGIS. This study demonstrated the frequency-specific characteristics of ReHo in patients with acute BGIS, which may have a positive effect on the future neuroimaging studies.