Functional connectivity of spoken language processing in early-stage Parkinson's disease: An MEG study

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder, well-known for its motor symptoms; however, it also adversely affects cognitive functions, including language, a highly important human ability. PD pathology is associated, even in the early stage of the disease, with alterations in the functional connectivity within cortico-subcortical circuitry of the basal ganglia as well as within cortical networks. Here, we investigated functional cortical connectivity related to spoken language processing in early-stage PD patients. We employed a patient-friendly passive attention-free paradigm to probe neurophysiological correlates of language processing in PD patients without confounds related to active attention and overt motor responses. MEG data were recorded from a group of newly diagnosed PD patients and age-matched healthy controls who were passively presented with spoken word stimuli (action and abstract verbs, as well as grammatically correct and incorrect inflectional forms) while focussing on watching a silent movie. For each of the examined linguistic aspects, a logistic regression classifier was used to classify participants as either PD patients or healthy controls based on functional connectivity within the temporo-fronto-parietal cortical language networks. Classification was successful for action verbs (accuracy = 0.781, p-value = 0.003) and, with lower accuracy, for abstract verbs (accuracy = 0.688, p-value = 0.041) and incorrectly inflected forms (accuracy = 0.648, p-value = 0.021), but not for correctly inflected forms (accuracy = 0.523, p-value = 0.384). Our findings point to quantifiable differences in functional connectivity within the cortical systems underpinning language processing in newly diagnosed PD patients compared to healthy controls, which arise early, in the absence of clinical evidence of deficits in cognitive or general language functions. The techniques presented here may aid future work on establishing neurolinguistic markers to objectively and noninvasively identify functional changes in the brain's language networks even before clinical symptoms emerge.

Keywords: Action verb; Classification; Functional connectivity; Magnetoencephalography (MEG); Morphosyntax; Parkinson’s disease (PD).

Fig. 1

An illustration of the experimental paradigm. A: Action and abstract verbs and matching pseudowords were presented equiprobably with 100 repetitions for each stimulus item in a pseudo-randomised fashion (randomisation was done on subsequent trains of 40 stimuli such that two identical tokens were never presented immediately after each other). B: Correct and incorrect morphosyntactic forms were presented in the same fashion (equiprobably and pseudo-randomly with 100 repetitions for each stimulus item). The stimulus-onset-asynchrony (SOA) was jittered between 1050 and 1150 ms with a mean of 1100 ms. The total duration of each sequence was ∼15 min.

Fig. 2

A priori defined ROIs: primary auditory cortex and auditory belt (BA 41 + BA 42), superior temporal (BA 22), anterior temporal (BA 38), middle temporal (BA 21) and, inferior frontal (pars opercularis BA 44, pars triangularis BA 45 and pars orbitalis BA 47) areas, angular gyrus (BA 39), supramarginal gyrus (BA 40), motor cortex (lateral segment of BA 4) and premotor cortex (lateral segment of BA 6). ROIs were defined bilaterally in both hemispheres on the basis of the PALS-B12 Brodmann Atlas parcellation of the cortical surface, as implemented in Freesurfer.

Fig. 3

Illustration of coefficients from the fitted logistic regression model for the action verb condition, depicted as colour-coded lines connecting respective areas. The values (coded as colour intensity) indicate the contribution to the overall class prediction: higher coefficient values in the red connections contribute more to classifying a participant as a healthy control (HC) and higher coefficient values in the blue connections contribute more to classifying a participant as a PD patient (PD). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Illustration of coefficients from the fitted logistic regression model for the abstract verb condition depicted as colour-coded lines connecting respective areas. The values (coded as colour intensity) indicate the contribution to the overall class prediction: higher coefficient values in the red connections contribute more to classifying a participant as a healthy control (HC) and higher coefficient values in the blue connections contribute more to classifying a participant as a PD patient (PD). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Illustration of coefficients from the fitted logistic regression model for the incorrect morphosyntactic form condition depicted as colour-coded lines connecting respective areas. The values (coded as colour intensity) indicate the contribution to the overall class prediction: higher coefficient values in the red connections contribute more to classifying a participant as a healthy control (HC) and higher coefficient values in the blue connections contribute more to classifying a participant as a PD patient (PD). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

Common features found across the three significant word-type conditions. PDs < HCs: lower correlation values for PD patients relative to controls. PDs > HCs: higher correlation values for PD patients relative to controls.