Fronto-striatal dynamic connectivity is linked to dopaminergic motor response in Parkinson's disease

INTRODUCTION

Differences in dopaminergic motor response in Parkinson's disease (PD) patients can be related to PD subtypes, and previous fMRI studies associated dopaminergic motor response with corticostriatal functional connectivity. While traditional fMRI analyses have assessed the mean connectivity between regions of interest, an important aspect driving dopaminergic response might lie in the temporal dynamics in corticostriatal connections.

METHODS

This study aims to determine if altered resting-state dynamic functional network connectivity (DFC) is associated with dopaminergic motor response. To test this, static and DFC were assessed in 32 PD patients and 18 healthy controls (HC). Patients were grouped as low and high responders using a median split of their dopaminergic motor response.

RESULTS

Patients featuring a high dopaminergic motor response were observed to spend more time in a regionally integrated state compared to HC. Furthermore, DFC between the anterior midcingulate cortex/dorsal anterior cingulate cortex (aMCC/dACC) and putamen was lower in low responders during a more segregated state and correlated with dopaminergic motor response.

CONCLUSION

The findings of this study revealed that temporal dynamics of fronto-striatal connectivity are associated with clinically relevant information, which may be considered when assessing functional connectivity between regions involved in motor initiation.

Modulating arousal to overcome gait impairments in Parkinson's disease: how the noradrenergic system may act as a double-edged sword

In stressful or anxiety-provoking situations, most people with Parkinson's disease (PD) experience a general worsening of motor symptoms, including their gait impairments. However, a proportion of patients actually report benefits from experiencing-or even purposely inducing-stressful or high-arousal situations. Using data from a large-scale international survey study among 4324 people with PD and gait impairments within the online Fox Insight (USA) and ParkinsonNEXT (NL) cohorts, we demonstrate that individuals with PD deploy an array of mental state alteration strategies to cope with their gait impairment. Crucially, these strategies differ along an axis of arousal-some act to heighten, whereas others diminish, overall sympathetic tone. Together, our observations suggest that arousal may act as a double-edged sword for gait control in PD. We propose a theoretical, neurobiological framework to explain why heightened arousal can have detrimental effects on the occurrence and severity of gait impairments in some individuals, while alleviating them in others. Specifically, we postulate that this seemingly contradictory phenomenon is explained by the inherent features of the ascending arousal system: namely, that arousal is related to task performance by an inverted u-shaped curve (the so-called Yerkes and Dodson relationship). We propose that the noradrenergic locus coeruleus plays an important role in modulating PD symptom severity and expression, by regulating arousal and by mediating network-level functional integration across the brain. The ability of the locus coeruleus to facilitate dynamic 'cross-talk' between distinct, otherwise largely segregated brain regions may facilitate the necessary cerebral compensation for gait impairments in PD. In the presence of suboptimal arousal, compensatory networks may be too segregated to allow for adequate compensation. Conversely, with supraoptimal arousal, increased cross-talk between competing inputs of these complementary networks may emerge and become dysfunctional. Because the locus coeruleus degenerates with disease progression, finetuning of this delicate balance becomes increasingly difficult, heightening the need for mental strategies to self-modulate arousal and facilitate shifting from a sub- or supraoptimal state of arousal to improve gait performance. Recognition of this underlying mechanism emphasises the importance of PD-specific rehabilitation strategies to alleviate gait disability.

Narrow doorways alter brain connectivity and step patterns in isolated REM sleep behaviour disorder

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iRBD had slower and more variable stepping compared to controls in this VR task.

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iRBD showed exaggerated responses when passing narrow compared to wide doorways iRBD had altered task-related brain connectivity which was correlated to motor deficits.

Background

Motor impairments in those with isolated REM sleep behaviour disorder (iRBD) significantly increases the likelihood of developing Lewy body disease (e.g. Parkinson’s disease and Dementia with Lewy Bodies).

Objective

This study sought to explore the prodromal process of neurodegeneration by examining the neural signature underlying motor deficits in iRBD patients.

Methods

A virtual reality (VR) gait paradigm (which has previously been shown to elicit adaptive changes in gait performance whilst navigating doorways in Parkinson’s Disease - PD) was paired with fMRI to investigate whether iRBD patients demonstrated worsened motor performance and altered connectivity across frontoparietal, motor and basal ganglia networks compared to healthy controls. Forty participants (23 iRBD and 17 healthy controls) completed the virtual reality gait task whilst in the MRI scanner, and an additional cohort of 19 Early PD patients completed the behavioural virtual reality gait task.

Results

As predicted, iRBD patients demonstrated slower and more variable stepping compared to healthy control participants and demonstrated an exaggerated response when navigating narrow compared to wide doorways, a phenomenon characteristically seen in PD. The iRBD patients also demonstrated less BOLD signal change in the left posterior putamen and right mesencephalic locomotor region, as well as reduced functional connectivity between the frontoparietal network and the motor network, when navigating narrow versus wide doorways compared to healthy control participants.

Conclusions

Taken together, this study demonstrates that iRBD patients have altered task-related brain connectivity, which may represent the neural underpinnings of early motor impairments that are evident in iRBD.

Identifying a whole-brain connectome-based model in drug-naïve Parkinson's disease for predicting motor impairment

Identifying a whole‐brain connectome‐based predictive model in drug‐naïve patients with Parkinson's disease and verifying its predictions on drug‐managed patients would be useful in determining the intrinsic functional underpinnings of motor impairment and establishing general brain–behavior associations. In this study, we constructed a predictive model from the resting‐state functional data of 47 drug‐naïve patients by using a connectome‐based approach. This model was subsequently validated in 115 drug‐managed patients. The severity of motor impairment was assessed by calculating Unified Parkinson's Disease Rating Scale Part III scores. The predictive performance of model was evaluated using the correlation coefficient (r_true) between predicted and observed scores. As a result, a connectome‐based model for predicting individual motor impairment in drug‐naïve patients was identified with significant performance (r_true = .845, p < .001, p_permu = .002). Two patterns of connection were identified according to correlations between connection strength and the severity of motor impairment. The negative motor‐impairment‐related network contained more within‐network connections in the motor, visual‐related, and default mode networks, whereas the positive motor‐impairment‐related network was constructed mostly with between‐network connections coupling the motor‐visual, motor‐limbic, and motor‐basal ganglia networks. Finally, this predictive model constructed around drug‐naïve patients was confirmed with significant predictive efficacy on drug‐managed patients (r = .209, p = .025), suggesting a generalizability in Parkinson's disease patients under long‐term drug influence. In conclusion, this study identified a whole‐brain connectome‐based model that could predict the severity of motor impairment in Parkinson's patients and furthers our understanding of the functional underpinnings of the disease.

Intrinsic Network Brain Dysfunction Correlates With Temporal Complexity in Generalized Anxiety Disorder and Panic Disorder

Background: Generalized anxiety disorder (GAD) and panic disorder (PD) are the two severe subtypes of anxiety disorders (ADs), which are similar in clinical manifestation, pathogenesis, and treatment. Earlier studies have taken a whole-brain perspective on GAD and PD in the assumption that intrinsic fluctuations are static throughout the entire scan. However, it has recently been suggested that the dynamic alternations in functional connectivity (FC) may reflect the changes in macroscopic neural activity patterns underlying the critical aspects of cognition and behavior, and thus may act as biomarkers of disease. Methods: In this study, the resting-state functional MRI (fMRI) data were collected from 26 patients with GAD, 22 patients with PD, and 26 healthy controls (HCs). We investigated dynamic functional connectivity (DFC) by using the group spatial independent component analysis, a sliding window approach, and the k-means clustering methods. For group comparisons, the temporal properties of DFC states were analyzed statistically. Results: The dynamic analysis demonstrated two discrete connectivity "States" across the entire group, namely, a more segregated State I and a strongly integrated State II. Compared with HCs, patients with both GAD and PD spent more time in the weakly within-network State I, while performing fewer transitions and dwelling shorter in the integrated State II. Additionally, the analysis of DFC strength showed that connections associated with ADs were identified including the regions that belonged to default mode (DM), executive control (EC), and salience (SA) networks, especially the connections between SA and DM networks. However, no significant difference was found between the GAD and PD groups in temporal features and connection strength. Conclusions: More common but less specific alterations were detected in the GAD and PD groups, which implied that they might have similar state-dependent neurophysiological mechanisms and, in addition, could hopefully help us better understand their abnormal affective and cognitive performances in the clinic.

Cognitive load amplifies Parkinson’s tremor through excitatory network influences onto the thalamus

Parkinson’s tremor is related to cerebral activity in both the basal ganglia and a cerebello-thalamo-cortical circuit. It is a common clinical observation that tremor markedly increases during cognitive load (such as mental arithmetic), leading to serious disability. Previous research has shown that this tremor amplification is associated with reduced efficacy of dopaminergic treatment. Understanding the mechanisms of tremor amplification and its relation to catecholamines might help to better control this symptom with a targeted therapy. We reasoned that, during cognitive load, tremor amplification might result from modulatory influences onto the cerebello-thalamo-cortical circuit controlling tremor amplitude, from the ascending arousal system (bottom-up), a cognitive control network (top-down), or their combination. We have tested these hypotheses by measuring concurrent EMG and functional MRI in 33 patients with tremulous Parkinson’s disease, OFF medication, during alternating periods of rest and cognitive load (mental arithmetic). Simultaneous heart rate and pupil diameter recordings indexed activity of the arousal system (which includes noradrenergic afferences). As expected, tremor amplitude correlated with activity in a cerebello-thalamo-cortical circuit; and cognitive load increased tremor amplitude, pupil diameter, heart rate, and cerebral activity in a cognitive control network distributed over fronto-parietal cortex, insula, thalamus and anterior cingulate cortex. The novel finding, obtained through network analyses, indicates that cognitive load influences tremor by increasing activity in the cerebello-thalamo-cortical circuit in two different ways: by stimulating thalamic activity, likely through the ascending arousal system (given that this modulation correlated with changes in pupil diameter), and by strengthening connectivity between the cognitive control network and the cerebello-thalamo-cortical circuit. We conclude that both the bottom-up arousal system and a top-down cognitive control network amplify tremor when a Parkinson’s patient experiences cognitive load. Interventions aimed at attenuating noradrenergic activity or cognitive demands may help to reduce Parkinson’s tremor.

The functional network signature of heterogeneity in freezing of gait

Freezing of gait is a complex, heterogeneous, and highly variable phenomenon whose pathophysiology and neural signature remains enigmatic. Evidence suggests that freezing is associated with impairments across cognitive, motor and affective domains; however, most research to date has focused on investigating one axis of freezing of gait in isolation. This has led to inconsistent findings and a range of different pathophysiological models of freezing of gait, due in large part to the tendency for studies to investigate freezing of gait as a homogeneous entity. To investigate the neural mechanisms of this heterogeneity, we used an established virtual reality paradigm to elicit freezing behaviour in 41 Parkinson's disease patients with freezing of gait and examined individual differences in the component processes (i.e. cognitive, motor and affective function) that underlie freezing of gait in conjunction with task-based functional MRI. First, we combined three unique components of the freezing phenotype: impaired set-shifting ability, step time variability, and self-reported anxiety and depression in a principal components analysis to estimate the severity of freezing behaviour with a multivariate approach. By combining these measures, we were then able to interrogate the pattern of task-based functional connectivity associated with freezing (compared to normal foot tapping) in a sub-cohort of 20 participants who experienced sufficient amounts of freezing during task functional MRI. Specifically, we used the first principal component from our behavioural analysis to classify patterns of functional connectivity into those that were associated with: (i) increased severity; (ii) increased compensation; or (iii) those that were independent of freezing severity. Coupling between the cognitive and limbic networks was associated with 'worse freezing severity', whereas anti-coupling between the putamen and the cognitive and limbic networks was related to 'increased compensation'. Additionally, anti-coupling between cognitive cortical regions and the caudate nucleus were 'independent of freezing severity' and thus may represent common neural underpinnings of freezing that are unaffected by heterogenous factors. Finally, we related these connectivity patterns to each of the individual components (cognitive, motor, affective) in turn, thus exposing latent heterogeneity in the freezing phenotype, while also identifying critical functional network signatures that may represent potential targets for novel therapeutic intervention. In conclusion, our findings provide confirmatory evidence for systems-level impairments in the pathophysiology of freezing of gait and further advance our understanding of the whole-brain deficits that mediate symptom expression in Parkinson's disease.

Towards understanding neural network signatures of motor skill learning in Parkinson's disease and healthy aging

In the past decade, neurorehabilitation has been shown to be an effective therapeutic supplement for patients with Parkinson's disease (PD). However, patients still experience severe problems with the consolidation of learned motor skills. Knowledge on the neural correlates underlying this process is thus essential to optimize rehabilitation for PD. This review investigates the existing studies on neural network connectivity changes in relation to motor learning in healthy aging and PD and critically evaluates the imaging methods used from a methodological point of view. The results indicate that despite neurodegeneration there is still potential to modify connectivity within and between motor and cognitive networks in response to motor training, although these alterations largely bypass the most affected regions in PD. However, so far training-related changes are inferred and possible relationships are not substantiated by brain-behavior correlations. Furthermore, the studies included suffer from many methodological drawbacks. This review also highlights the potential for using neural network measures as predictors for the response to rehabilitation, mainly based on work in young healthy adults. We speculate that future approaches, including graph theory and multimodal neuroimaging, may be more sensitive than brain activation patterns and model-based connectivity maps to capture the effects of motor learning. Overall, this review suggests that methodological developments in neuroimaging will eventually provide more detailed knowledge on how neural networks are modified by training, thereby paving the way for optimized neurorehabilitation for patients.