Topographical Map of the 1/f Index in the Whole Brain for Parkinson’s Disease

Authors: Pascal Helson, Daniel Lundqvist, Per Svenningsson, Mikkel C. Vinding, Arvind Kumar

Research Background

Parkinson’s Disease (PD) is a progressive and debilitating brain disorder primarily characterized by motor dysfunction but also affecting perceptual and cognitive processing. Due to the wide range of symptoms and the extensive brain projections of various neuromodulators such as dopamine, many brain regions are simultaneously impacted in PD. To characterize the neural functional changes associated with the disease across the whole brain, this study analyzed resting-state magnetoencephalography (MEG) from PD patients and healthy controls.

Traditional spectral analyses have indicated that PD patients exhibit increased neural activity spectra in low-frequency bands (theta and alpha waves) and decreased activity in high-frequency bands (alpha and gamma waves). Dynamics analyses have also shown correlations between motor and cognitive symptoms. However, studies on changes in frequency peak values are relatively scarce. This study analyzed the MEG power spectra by fitting a power-law function κ/f^λ— where f is the frequency and κ and λ are fitting parameters — to quantify the non-periodic components of neural activity (such as the 1/f index, λ) and investigate their relationships with age and the Unified Parkinson’s Disease Rating Scale (UPDRS).

Source

This paper was authored by Pascal Helson, Daniel Lundqvist, Per Svenningsson, Mikkel C. Vinding, and Arvind Kumar from institutions including KTH Royal Institute of Technology, Karolinska Institute, and Copenhagen University Hospital. The study was published in 2023 in the journal npj Parkinson’s Disease.

Research Process

The study used resting-state MEG data from PD patients and healthy controls, following these steps:

Data Collection and Preprocessing

1. MEG data were collected using 306 sensors and preprocessed into 44 brain source activity signals based on the HCP-MMP1 atlas.
2. Data were categorized into “OFF medication state” and “ON medication state” for PD patients and two measurements for healthy controls.

Spectral Analysis

1. Power spectral density (PSD) was computed using the Welch method.
2. The FOOOF algorithm was used to fit the spectra and extract non-periodic and periodic components.
3. Frequency peak values and power-law index (λ) were estimated using the Fritch condition on the spectra.

Main Results

Slowing of Frequency Peaks

The study found a general slowing of frequency peaks across all bands in PD patients, which medication treatment did not improve and even worsened. Furthermore, there was a reduction in peak values in the alpha and beta bands, with significant peak loss in the gamma band, most notably in sensory and motor regions.

Changes in 1/f Index (λ)

The study demonstrated that λ in sensory and motor regions of PD patients was significantly higher compared to healthy controls. Additionally, λ showed a positive gradient distribution from anterior to posterior areas, which was not observed in healthy controls. Medication treatment did not alter the spatial distribution of λ, indicating that changes in λ in PD are not significantly influenced by dopamine replacement therapy.

Relationship Between Age and λ

λ was positively correlated with age but unrelated to UPDRS-III scores, suggesting that age has a greater impact on brain networks in PD patients than motor symptoms. In measured brain regions, especially sensory areas, older PD patients tended to have higher λ, indicating a greater impact of chronic dopamine alteration on brain networks in older patients.

Temporal Variability Analysis

The temporal variability of λ was different for PD patients, showing less fluctuation, particularly in sensory regions, where it was more stable. Medication did not significantly affect λ variability but seemed to reduce temporal fluctuations in some brain regions, particularly the prefrontal cortex.

Conclusion and Significance

This study is the first to comprehensively describe the topographical map of the power-law index (λ) across the whole brain in PD patients and healthy controls. Key conclusions include: 1. λ in sensory and motor regions of PD patients is significantly higher than in healthy controls, with a marked anterior-posterior gradient. 2. Medication treatment has little impact on the spatial distribution of λ. 3. λ is positively correlated with age but not related to PD motor scores.

By analyzing λ, this study presents new hypotheses regarding cortical network neurocorrelates in PD, suggesting possible alteration in the excitation-inhibition balance in sensory regions. These findings provide new theoretical perspectives for understanding PD and guide future non-invasive techniques and animal model verifications. Moreover, the study proposes that other sensory impairments like visual and auditory functions may be affected by PD, expanding our understanding of PD symptoms and brain dysfunction.

Research Highlights

1. Whole-brain Analysis: Topographical analysis of the power-law index across the entire neocortex, fully revealing the impact of PD on brain function.
2. Combination of Quantitative and Qualitative Analysis: By combining quantitative analysis of spectral peaks with qualitative analysis of λ, specific activity characteristics of different brain regions in PD patients are revealed.
3. New Hypothesis Proposals: Results suggest that PD affects not only motor regions but also significantly impacts neural activity in sensory regions, proposing new research hypotheses.

Innovation in Research Methods

The study employed advanced MEG data analysis techniques, including the FOOOF algorithm and the Welch method, to quantify periodic and non-periodic components of brain activity spectra with high precision. Using these methods, the study not only validated known spectral changes but also revealed previously under-investigated changes in non-periodic components (such as the 1/f index).

Practical Value

The results of this study have significant implications for developing more precise PD diagnosis and treatment plans. Further research into sensory impairments and cognitive functions may lead to new therapeutic strategies. Additionally, the hypotheses proposed can be validated through future animal model experiments, providing a more detailed scientific basis for understanding the neural mechanisms of PD.