Levodopa response differs in Parkinson's motor subtypes: A task‐based effective connectivity study

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Excerpt

Motor impairments are the defining symptoms of Parkinson's disease (PD), but these symptoms can be heterogeneous and manifest as different clinical subtypes. Two of the most commonly described motor subtypes are tremor dominant (TD) and postural instability/gait difficulty (PIGD; Jankovic et al., 1990). The TD subtype is characterized by predominant tremor symptoms and has been associated with cerebello‐thalamo‐cortical motor pathway alterations (Zhang, Liu, Chen, & Liu, 2014). In contrast, the PIGD subtype is chiefly associated with bradykinesia and rigidity along with early imbalance and gait impairment. Compared to TD, the PIGD phenotype has been associated with later onset, increased risk of cognitive decline (Alves, Larsen, & Emre, 2006), faster disease progression (Jankovic & Kapadia, 2001), and suboptimal response to dopamine replacement therapy (Vu, Nutt, & Holford, 2012). Clinical manifestations of PIGD are thought to involve cortico‐striato‐thalamo‐cortical (CSTC) circuits (Lewis et al., 2011; Prodoehl et al., 2013). Despite clinical and potential pathophysiological distinctions between these motor subtypes, PD patients with tremor have often been excluded in functional MRI (fMRI) studies in an attempt to avoid motion artifact (Wu et al., 2015), potentially biasing prior functional imaging studies in PD. By investigating the pathophysiological differences between PD motor subtypes, it may be possible to elucidate the mechanisms underlying the distinct phenotypic manifestations and allow for more tailored treatment strategies and improved therapeutic clinical trial designs.
Functional blood oxygen level‐dependent (BOLD) activation differences involving the basal ganglia, supplementary motor area, primary and premotor motor cortices, and parietal lobes have been reported in PD patients compared to healthy controls during simple motor task fMRI paradigms (for review, see Herz, Eickhoff, Løkkegaard, & Siebner, 2014). However, the direction of activation differences in the premotor and primary motor cortices has been notably inconsistent, with reports of hyperactivation (Eckert, Peschel, Heinze, & Rotte, 2006; Haslinger et al., 2001; Lewis et al., 2011; Sabatini et al., 2000; Yu, Sternad, Corcos, & Vaillancourt, 2007), hypoactivation (Buhmann et al., 2003; Burciu et al., 2015; Prodoehl et al., 2013; Tessa, Lucetti, Diciotti, Paoli, & Cecchi, 2012; Tessa et al., 2010), and no activation differences (Cerasa et al., 2006; Elsinger, Rao, & Zimbelman, 2003). Disease stage and medication status at the time of scanning may explain some of these inconsistencies. For instance, hypoactivation of motor areas has been reported in de novo patients (Buhmann et al., 2003; Tessa et al., 2012), while introducing a dopamine agonist or replacement can lead to somewhat mitigating effects in these motor regions (Buhmann et al., 2003; Herz et al., 2014; Lucetti et al., 2014). Another possible explanation for the inconsistencies is the practice of combining motor subtypes as a single clinical sample (e.g., Burciu et al., 2015; Spetsieris, Ma, Dhawan, & Eidelberg, 2009), since the additional heterogeneity may influence outcomes.
Histology, Single‐Photon Emission Computed Tomography (SPECT), Positron Emission Tomography, and conventional BOLD activation fMRI studies have revealed time‐averaged, striatal physiology differences that have implicated CSTC network regions in non‐TD patient populations (Lewis et al., 2011; Prodoehl et al., 2013). However, since PD can be considered a circuit disease (Eckert et al., 2006; Göttlich et al., 2013; Zhang, Wang, Liu, Chen, & Liu, 2015) that can be modulated by dopaminergic stimulation (Lucetti et al., 2014) and external tasks (Wu et al., 2009), investigating the dynamic interaction between regions within the CSTC and dopaminergic medication effects could provide new insight into mechanisms of dysfunction during motor demands. Psychophysiological interaction (PPI) is a linear analysis technique that estimates task‐induced, dynamic coupling between regions, often referred to as effective connectivity (Friston, 2011; O'Reilly, Woolrich, Behrens, Smith, & Heidi, 2012).
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