The Cortical Neurophysiological Signature of Amyotrophic Lateral Sclerosis

Neurology Biophysics Medical Imaging Life Sciences Immunology Medicine
Amyotrophic Lateral Sclerosis Motor Neuron Disease GABAergic Interneuronal Circuits Frequency Band Analysis Electromagnetic Neuroimaging

Analysis of Cortical Neurophysiological Characteristics of ALS and Its Potential as a Biomarker

Background

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that affects adults, characterized by a gradual loss of the integrity of the brain, spinal cord, and peripheral motor system. Although clinical and genetic studies have revealed overlaps with frontotemporal dementia and identified multiple upstream biological pathways, there are currently no effective drug therapies to slow disease progression. The current trials rely on outcomes such as extended survival periods, which are not sensitive. There is an urgent need for biomarkers that better reflect individual disease activity to rapidly test drug effects.

Source

The study was authored by Michael Trubshaw, Chetan Gohil, and Katie Yoganathan from the University of Oxford, UK. The paper was published on May 13, 2024, in the journal Brain Communications.

Research Methods and Workflow

This observational, cross-sectional, case-control study used task-free magnetoencephalography (MEG). The study involved 36 ALS patients and 51 healthy controls. Each participant underwent 8-10 minutes of task-free (eyes-open) MEG scanning followed by a structural MRI scan for registration. Using the MEG data processing toolkit (OHBA Software Library, OSL), the extracted MEG metrics were analyzed for band power, 1/f index (complexity), and amplitude envelope correlation (connectivity).

Specific steps included:

  1. MEG Data Collection: MEG scanning recorded the power spectral density and oscillation power in specific frequency bands.
  2. MRI Registration: Data was calibrated using structural MRI scans and three reference points.
  3. Data Processing and Analysis: Data was purified and denoised using the OHBA software library and MaxFilter software, followed by band-pass filtering and beamforming for modeling and analysis.

The statistical analysis of the experimental design was performed using a generalized linear model based on non-parametric permutations, correcting for multiple comparisons and confounding factors. To test whether the extracted metrics could predict disease severity, a random forest regression model was trained and evaluated.

Results

In the power analysis, it was found that the ALS group showed reduced β-wave power and increased γ-wave power in the sensorimotor area. The reduction in β-wave power reflected a decline in inhibitory neuron functionality, corresponding with the increase in γ-wave power. For the 1/f index, a significant reduction in the right fronto-parietal region of the ALS group indicated higher cortical activity complexity, possibly related to higher cortical excitability.

For connectivity analysis, ALS patients exhibited significantly increased global connectivity in the θ and γ frequency bands, particularly positively correlated with higher functional impairment scores (ALSFRS-R). Furthermore, the increase in θ-band connectivity was mainly observed in the frontal and occipital regions, while γ-band connectivity was more prominent in the temporal region.

In the prediction model, random forest regression could significantly predict changes in ALS functional scores (R²=0.242, p=0.002), indicating the potential of MEG metrics in disease prediction.

Conclusion

This study successfully extracted cortical neurophysiological characteristics of ALS using MEG, finding that changes in power, complexity, and connectivity correlate with disease severity. The study suggests the potential of MEG metrics as predictive biomarkers for ALS, providing sensitive indicators for future experimental drug efficacy evaluations.

Highlights

  1. New Findings: Discovered reductions in β-wave power and increases in γ-wave power in specific cortical regions of ALS, potentially reflecting the loss of inhibitory GABAergic circuits.
  2. Complexity Study: Changes in the 1/f index reflect high complexity of cortical activity, indicating higher cortical excitability.
  3. Power and Connectivity Relationship: Clarified how changes in power and connectivity in different frequency bands relate to motor function impairment and brain network compensatory mechanisms.
  4. Predictive Function: MEG metrics are promising as predictive biomarkers for ALS, enhancing the accuracy of future experimental drug studies.

This study expands the understanding of cortical neurophysiological changes in ALS, providing a new direction for developing more precise therapeutic pathways. It also highlights the potential of MEG in high temporal resolution non-invasive brain function monitoring, possibly offering broad prospects for the diagnosis and treatment of ALS and other neurodegenerative diseases in the future.