Adverse and compensatory neurophysiological slowing in Parkinson's disease

Abstract

Patients with Parkinson's disease (PD) exhibit multifaceted changes in neurophysiological brain activity, hypothesized to represent a global cortical slowing effect. Using task-free magnetoencephalography and extensive clinical assessments, we found that neurophysiological slowing in PD is differentially associated with motor and non-motor symptoms along a sagittal gradient over the cortical anatomy. In superior parietal regions, neurophysiological slowing reflects an adverse effect and scales with cognitive and motor impairments, while across the inferior frontal cortex, neurophysiological slowing is compatible with a compensatory role. This adverse-to-compensatory gradient is sensitive to individual clinical profiles, such as drug regimens and laterality of symptoms; it is also aligned with the topography of neurotransmitter and transporter systems relevant to PD. We conclude that neurophysiological slowing in patients with PD signals both deleterious and protective mechanisms of the disease, from posterior to anterior regions across the cortex, respectively, with functional and clinical relevance to motor and cognitive symptoms.

Keywords: Functional gradient; Neurophysiological slowing; Parkinson’s disease; Spectral parameterization.

Figure 1.. Sagittal gradient of neurophysiological slowing in Parkinson’s disease.

(A) Neural slowing computation: Source-imaged magnetoencephalography (MEG) data are frequency-transformed and the resulting vertex-wise power spectral densities (PSD) are parameterized into rhythmic (i.e., periodic) and arrhythmic (i.e., aperiodic) features using specparam. The PSDs are then binned over typical frequency bands (i.e., delta: 2–4 Hz; theta: 5–7 Hz; alpha: 8–12 Hz; beta: 15–29 Hz) and each spectrally- and spatially-resolved power estimate of neurophysiological signal per patient is normalized to the mean and standard deviation of the same measures in the healthy control group. For each patient and at each spatial location, a linear model is then fit to these latter spectral deviations across frequencies, with the resulting slope capturing the relative slowing (i.e., negative slope values) of brain activity relative to healthy levels. This procedure is performed at each cortical vertex, resulting in a spatially resolved map of neurophysiological slowing per patient. (B) Spatial gradient analysis: Cortical surfaces are first smoothed to reduce the impact of gyrification on the estimation of spatial gradient effects. Per each vertex location, neurophysiological slowing values are separately correlated with motor (i.e., UPDRS-III scores) and cognitive (i.e., sign-reversed neuropsychological scores averaged over cognitive domains) impairments, beyond the effects of age. These partial correlation maps are then linearly scaled using the Fisher-transform and summed per vertex across participants, resulting in a single summary cortical map of the nature and strength of the relationships between neurophysiological slowing and clinical motor/cognitive impairments. A linear multiple regression is then fit to these data and the beta weights extracted, with each of the cardinal axes (X: left – right; Y: posterior – anterior; Z: inferior – superior) represented as a predictor. The neurophysiological slowing data are then randomly permuted across patients and the partial correlation and spatial multiple regression steps repeated 1,000 times, with the resulting beta weights extracted and used to derive empirical null distributions per each predictor. To test for the effect of binary clinical factors on these gradients, the same procedure is performed within each binarized patient subgroup, with the difference in beta weights between the two subgroups used as the statistic of interest.

Figure 2.. Neurophysiological slowing in Parkinson’s disease.

(A) Cortical maps indicate clusters of neurophysiological slowing (computed on the non-parameterized data) in patients with Parkinson’s disease (PD) after spatial multiple comparisons correction with Threshold-Free Cluster Enhancement (p_FWE < .05). Power spectra to the bottom left illustrate the underlying data from the cortical vertex exhibiting the strongest effect of neurophysiological slowing. The horizontal colored lines show the frequency bandwidths used for binning the power spectra. The plot to the bottom right shows the individual patient spectral deviations at this same cortical vertex for each frequency band, with the light grey lines-of-best fit indicating individual neurophysiological slowing slopes, and the overlaid black line and blue shaded area representing the overall group effect and 95% confidence intervals, respectively. These individual and mean neurophysiological slowing effects are also represented as single dots in the scatterplot to the top right. (B) Plots similar to those in (A), but with neurophysiological slowing computed using the rhythmic (i.e., periodic) component of the parameterized spectra. (C) Plots similar to those in (A-B), but with neurophysiological slowing computed using the arrhythmic (i.e., aperiodic) component of the parameterized spectra.

Figure 3.. Neurophysiological slowing associated with cognitive impairments in Parkinson’s disease.

Cortical maps showing where neurophysiological slowing was associated with cognitive function in patients with PD, after first-level correction with Threshold-Free Cluster Enhancement (p_FWE < .05)and second-level FDR correction across related tests (p_FDR < .05). The scatterplots of the residuals illustrate the nature and strength of this relationship at the cortical vertex exhibiting the strongest effect, with lines-of-best-fit, 95% confidence intervals, and R² values overlaid. The scatterplots are meant to emphasize the nature of the effect and allow visual inspection for outliers, not to assess the magnitude of the effects reported.

Figure 4.. Arrhythmic neurophysiological slowing associated with motor impairments in Parkinson’s disease.

Cortical maps of neurophysiological slowing associated with motor function in patients with PD, obtained from first-level correction with Threshold-Free Cluster Enhancement (p_FWE < .05). The scatterplot of residuals illustrates the nature and strength of this relationship at the cortical vertex exhibiting the strongest effect, with the line-of-best-fit, 95% confidence interval, and R² value overlaid. The scatterplots are meant to emphasize the nature of the effect and allow visual inspection for outliers, not to assess the magnitude of the effects reported.

Figure 5.. Anatomical gradient of clinical effects of neurophysiological slowing in Parkinson’s disease.

(A) Cortical maps of the nature and strength of the relationship between neurophysiological slowing and clinical impairments (i.e., partial correlations linearly-scaled and summed across motor and cognitive domains) along the cortex of patients with Parkinson’s disease, with lower values indicating a more pathological relationship (i.e., greater slowing predicting worse clinical deficits) and higher values indicating a possible compensatory effect. Grey vectors plotted along the cardinal anatomical axes show the unstandardized beta weights from a multiple regression of the neurophysiological slowing – clinical impairment relationships on the relevant anatomical coordinates (X: left – right; Y: posterior – anterior; Z: inferior – superior), and indicate the magnitude and direction of the significant anatomical gradient effects. Overlaid p-values were obtained from non-parametric permutations and indicate statistical significance per each axis of the gradient effect. The blue vector indicates the magnitude and direction of the overall significant anatomical gradient effect. (B) Cortical maps of the nature and strength of the neurophysiological slowing – clinical impairment relationships across the cortex of patients with Parkinson’s disease. Here neurophysiological slowing was assessed from the rhythmic (left) and arrhythmic (right) components of the parameterized spectra. The anatomical gradient effects observed in the non-parameterized neurophysiological slowing data (panel A) did not differ qualitatively between the rhythmic and arrhythmic models.

Figure 6.. The anatomical gradients of clinical effects of neurophysiological slowing in Parkinson’s disease are clinically meaningful.

Cortical maps of differences in the nature and strength of relationships between neurophysiological slowing and clinical impairments in patients with Parkinson’s disease, as a function of binary clinical features, including (A) subjective cognitive complaints, (B) drug regimen including dopamine agonists, and (C) laterality of initial symptom onset. Purple vectors plotted along the cardinal spatial axes show the differences in unstandardized multiple regression beta weights between the two clinical feature subgroups of the neurophysiological slowing – clinical impairment relationships on the relevant spatial coordinates (X: left – right; Y: posterior – anterior; Z: inferior – superior). Overlaid p-values were derived from non-parametric permutations and indicate statistical significance per each axis of the difference in the gradient effect. The blue and red vectors indicate the magnitude and direction of the overall anatomical gradient effects per each clinical feature subgroup.

Figure 7.. The clinical relationships of neurophysiological slowing are topographically aligned with selective neurotransmitter system densities and sensory-association organization.

(A) The vector heatmap indicates the strength (standardized β) and statistical significance(*P_FDR < .05, **PFDR < .005) of the topographical alignment between neurophysiological slowing relationships to clinical outcomes and neuromap values, and emphasizes the concordance with the dopamine (D1, D2, and DAT), serotonin (5-HT1a, 5-HT1b, 5-HT2a, 5-HT4, 5-HT6, 5-HTT), acetylcholine (α4β2, M1, VAChT), GABA (GABAa), glutamate (NMDA, mGluR5), norepinephrine (NET) cortical systems, and synapse density (glycoprotein). (B) The parcellated cortical maps show the region-wise relationship between neurophysiological slowing and clinical impairments in patients with Parkinson’s disease (i.e., partial correlations linearly-scaled and summed across motor and cognitive domains, z-scored across brain regions; top) and the sensory-association axis reported by Margulies and colleagues⁵⁵ (bottom). The scatterplot on the right indicates the nature and strength of colocalization between these maps and the regions exhibiting the five highest and five lowest residuals labeled (p < .001; 1,000 permutations; the line indicates the best linear fit).