Brain Network Fingerprints Predict Impairment in Amyotrophic Lateral Sclerosis Brain Network Fingerprints Predict Impairment in Amyotrophic Lateral Sclerosis

The study covered in this summary was published in medRxiv.org as a preprint and has not yet been peer reviewed.

Key Takeaways

  • The application of Clinical Connectome Fingerprint (CCF) analysis to source-reconstructed magnetoencephalography (MEG) demonstrated the ability to assess the individual motor condition and its relationship with amyotrophic lateral sclerosis (ALS).

  • Due to the subject-specific characteristic of this technique, the authors hope that additional investigation pertinent to its clinical application may help make strides in diagnostic and therapeutic strategies of disease management.

Why This Matters

  • ALS is a neurodegenerative disease marked by functional connectivity changes in both motor and extramotor brain regions.

  • In network analysis, the “clinical fingerprint” serves as a legitimate approach capable of evaluating the subject-specific connectivity aspects of a particular population. Its ability to predict the individual rate of disease progression may represent a promising finding in ALS clinical management.

Study Design

  • Researchers applied the CCF analysis to source-reconstructed MEG signals in a cohort of 78 subjects, including 39 ALS patients (29 males and 10 females) and 39 healthy matched controls (28 males and 11 females).

  • They began by establishing an identifiability matrix to determine the degree to which each subject was recognizable according to his or her connectome.

  • The analysis was conducted in all five canonical frequency bands.

  • The researchers created a multilinear regression model to test the ability of “clinical fingerprint” in predicting the clinical evolution of disease measured by the Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised (ALSFRS-r), the King’s disease staging system, and the Milano-Torino Staging (MiToS) disease staging system.

Key Results

  • An impairment of brain dynamics suggests large-scale communication changes within the brain, reflecting in a loss of identifiability.

  • Researchers noted a decline in patients’ identifiability in the alpha band, or neural oscillations in the frequency of 8–12 Hz, compared to the healthy controls.

  • Furthermore, in comparison with healthy controls, the reliability of the connectivity between different brain regions was decreased in ALS patients.

  • Researchers hypothesized that this outcome could be attributed to the broad functional alterations, which were partly responsible for determining a loss of a stable subject-specific connectivity in ALS patients.

  • In addition, the “clinical fingerprint” was predictive of ALSFRS-r (P = .0397; β = 32.8), King’s (P = .0001; β = -7.40), and MiToS (P = .0025; β = -4.9) scores and negatively corresponded to King’s and MiToS scales according to Spearman’s correlation.

  • Connectomes of patients with greater preservation of their motor functions exhibited more similarity to the connectomes of the healthy individuals.

Limitations

Study Disclosures

This is a summary of a preprint research study, “The Progressive Loss of Brain Network Fingerprints in Amyotrophic Lateral Sclerosis Predicts Clinical Impairment,” by Antonella Romano from University of Naples “Parthenope,” Naples, Italy, and colleagues, provided to you by Medscape. This study has not yet been peer reviewed. The full text of the study can be found on medRxiv.org.

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