Dopaminergic modulation of motor network compensatory mechanisms in Parkinson's disease

The dopaminergic system has a unique gating function in the initiation and execution of movements. When the interhemispheric imbalance of dopamine inherent to the healthy brain is disrupted, as in Parkinson's disease (PD), compensatory mechanisms act to stave off behavioral changes. It has been proposed that two such compensatory mechanisms may be (a) a decrease in motor lateralization, observed in drug-naïve PD patients and (b) reduced inhibition - increased facilitation. Seeking to investigate the differential effect of dopamine depletion and subsequent substitution on compensatory mechanisms in non-drug-naïve PD, we studied 10 PD patients and 16 healthy controls, with patients undergoing two test sessions — “ON” and “OFF” medication. Using a simple visually-cued motor response task and fMRI, we investigated cortical motor activation — in terms of laterality, contra- and ipsilateral percent BOLD signal change and effective connectivity in the parametric empirical Bayes framework. We found that decreased motor lateralization persists in non-drug-naïve PD and is concurrent with decreased contralateral activation in the cortical motor network. Normal lateralization is not reinstated by dopamine substitution. In terms of effective connectivity, disease-related changes primarily affect ipsilaterally-lateralized homotopic cortical motor connections, while medication-related changes affect contralaterally-lateralized homotopic connections. Our findings suggest that, in non-drug-naïve PD, decreased lateralization is no longer an adaptive cortical mechanism, but rather the result of maladaptive changes, related to disease progression and long-term dopamine replacement. These findings highlight the need for the development of noninvasive therapies, which would promote the adaptive mechanisms of the PD brain.

Dopaminergic modulation of cortical motor network lateralization

Bogdan Draganski, Michael Herzog, Maya Anna Jastrzebowska, Renaud Marquis, Lester Melie Garcia

Introduction Unilateral movements are primarily processed in contralateral cortical and subcortical areas and additionally in ipsilateral cerebellum, leading to an asymmetric pattern of neural activation. Decrease of lateralization is characteristic of aging (Naccarato et al., 2006; Wu et al., 2005), and disease, for example, in unilateral brain lesions or stroke (Carr et al., 1993; Rehme et al., 2011) and Parkinson's disease (PD; Wu et al., 2015). The explanation for imbalanced lateralization in drug-naive PD is an adaptive compensation, compatible with the finding that PD-associated deficient input from cortico-subcortical circuits is compensated by reduced cortical inhibition and increased cortical facilitation (Blesa et al., 2017). Here, we investigated the effect of dopamine depletion and substitution on cortical motor lateralization, with the hypothesis that lateralization decreases in advanced PD and that administration of levodopa, at least to a certain extent, reinstates lateralization. Methods We used fMRI to study motor activation in advanced PD patients and in healthy controls (HC) during unilateral upper and lower limb movements. Ten right-handed, left side symptom-dominant PD patients were tested in pseudo-randomized order after intake of their usual dopaminergic medication – 'ON' state – and after withdrawal of medication – 'OFF' state. Eighteen right-handed age-matched HC participated in a single session. We quantified activation lateralization using the average laterality index (AveLI; Matsuo et al., 2012) in three cortical motor regions of interest (ROIs): primary motor cortex (M1), supplementary motor area (SMA) and premotor cortex (PMC), during the four movement conditions. We compared AveLI between group pairs (PD OFF vs. HC, PD ON vs. HC, PD OFF vs. PD ON) within each ROI and movement condition. We estimated the effective connectivity between ROIs using bilinear dynamic causal modeling (DCM; Friston et al., 2003) and developed a measure to quantify the lateralization of the resulting connectivity networks to compare between groups. By constructing a group level parametric empirical Bayes (PEB) model (Friston et al., 2016) over all the subjects and conducting a search over nested models, we compared DCM parameter estimates between groups, thus providing the potential link between changes in motor lateralization and connectivity. Results In line with our predictions, motor activation lateralization as estimated with the AveLI showed a trend towards decrease in the PD OFF group compared to HC, in all three ROIs during left hand movement and in M1 during left foot movement (Fig. 1). Between-group differences were observed solely in conditions corresponding to movement of the more affected body side. Contrary to our hypothesis, dopamine substitution did not reinstate lateralization – in fact, AveLI in the PD ON group closely resembled that of the PD OFF group. Connectivity lateralization of input-specific modulation (DCM.B) networks was significantly lower in all conditions in the PD group as compared to HC. While on the body side more affected by PD, differences were found for both PD OFF and PD ON, input-specific modulation related to the less affected side was more altered in PD ON. PEB analysis revealed qualitatively more between-group differences in input-specific modulation on the more affected PD side and included many interhemispheric connections (Fig. 2). Conclusions Decreased lateralization is not only present in drug-naïve PD patients (Wu et al., 2015) but also in dopa-treated patients. Acute dopamine modulation does not alter lateralization. Decreased lateralization is evident in both fMRI activation amplitudes (as estimated with AveLI) and effective connectivity (as demonstrated through the DCM analysis).

2018

Bayesian modeling of brain function and behavior

Maya Anna Jastrzebowska

The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed generative problems, in which case the incorporation of prior knowledge is optimal and practical. If one wants to take full advantage of joint imaging and behavioral data, the need for comprehensive generative models is evident. Here, I exploited the versatility afforded by Bayesian modeling approaches to investigate a series of questions from clinical, systems, and cognitive neuroscience. First, I investigated the lateralization of the cortical motor network in Parkinson's disease patients on and off dopaminergic medication with fMRI and dynamic causal modeling (DCM). Group-level model comparison revealed that disease and medication effects differentially involve homotopic cortical motor connections, but medication does not appear to have a restorative effect at the systems level. Our findings suggest the presence of maladaptive mechanisms, resulting from disease progression and long-term dopamine replacement. In a second project, I considered the way in which humans handle uncertainty in the parameters of the generative model of a task at hand. Using a novel psychophysics paradigm, participants' responses to fitting a parabola to a set of noisy points were compared to the predictions of a set of regression models, including Bayesian regression and several sub-optimal models. Only Bayesian regression could explain participants' response distributions across various levels of sensory uncertainty, suggesting that humans process parameter uncertainty in accordance with Bayesian regression. This finding deepens our understanding of the way in which humans learn and generalize from sparse, ambiguous data. The topic investigated in the greatest depth in this thesis was visual crowding - the deterioration of target discrimination due to the presence of flanking objects. Traditionally, vision is modeled as a feedforward, hierarchical network. Thus, crowding is explained as a pooling of neighboring elements' features, leading to a "bottleneck" at the earliest stages of vision. However, additional flankers can paradoxically improve performance (uncrowding). First, I used DCM and fMRI to show that recurrent processing is crucial for crowding and un-crowding alike. Higher visual areas modulate the lower areas, suggesting that explicit object representation plays a key role. I then used population receptive field (pRF) mapping to investigate the effects of context on pRF size. In accordance with pooling model predictions, the pRF size in crowding is larger than in the case of no crowding. However, in uncrowding, the pRF size is smaller than in crowding and no crowding, providing further evidence against purely feedforward models. Overall, my findings provide strong empirical evidence of context-dependent feedback modulation in vision. I propose a possible implementation which integrates the observed findings into a generative model of crowding: context-agnostic feedforward mechanisms transmit the information about the target and flankers retinotopically to higher visual areas; a subsequent context-dependent feedback pRF determines whether the target will be enhanced - by decreasing the pRF size - or suppressed - by enlarging the pRF size to encompass flankers.

EPFL2020

Exploring the pathological interaction between LRRK2 and alpha-synuclein

Alessandra Musso

Parkinson’s disease (PD) is the most common age-related neurodegenerative movement disorder that affects 1-2% of the general population over the age of 65 and rising to 4-5% over 80 years of age. It is estimated that 6.3 million people worldwide have Parkinson’s disease and 1.2 million in the European Union (EU) alone. As the disease progresses, patients frequently develop a mask-like facial expression, festinating gait and cognitive impairment and the final clinical symptoms are muscle rigidity, bradykinesia, resting tremor and postural instability in addition to non-motor problems that result in a marked reduction in quality of life. The motor symptoms of PD arise from the progressive degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNpc) leading to a deficiency of the neurotransmitter dopamine (DA) in the striatum. While dopamine replacement therapy is initially effective in treating the motor symptoms of PD, there is no known cure or neuroprotective therapy for this debilitating disease. Although the pathology underlying the disease is well defined, it is still unclear why DAergic neurons degenerate and die. PD is generally considered as a sporadic disease of unknown etiology, however in the last two decades it has become clear that heritability plays an important role in the pathogenesis of PD. Of the different genes associated with familial disease, mutations in two genes encoding for α-synuclein and leucine-rich repeat kinase 2 (LRRK2) proteins cause autosomal dominant PD. One of the neuropathological hallmarks of idiopathic and some familial forms of PD are Lewy bodies, which are intracytoplasmic inclusions that are enriched with fibrillar α- synuclein protein. Notably, LRRK2 mutation carriers with PD predominantly develop Lewy body pathology suggesting that LRRK2 may lie upstream of α-synuclein aggregation. These observations and other in vivo studies have suggested a common pathogenic pathway with LRRK2 potentially regulating the aggregation and neuronal toxicity of α-synuclein. However, the relationship between LRRK2 and α-synuclein in the mammalian brain is presently unclear and requires further evaluation, especially within midbrain dopaminergic neurons that degenerate in PD. The aim of this thesis work was to delineate a potential pathogenic interplay between these two PD-related gene products in mediating neurodegeneration in vivo. Understanding how and where the functional pathways of LRRK2 and α-synuclein converge will provide important insight into the common mechanisms underlying neurodegeneration in PD.[...]

EPFL2014