Measuring Electrical Brain Activity Helps Identify 4 ALS Subtypes

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by Steve Bryson, PhD |

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Amyotrophic lateral sclerosis (ALS) can be grouped into four disease subtypes based on patterns of changes in electrical signals in the brain, researchers have discovered.

These findings may be valuable to predict future disability and life expectancy, and may help select the patients more likely to benefit in certain clinical trials.

“Understanding how brain networking is disrupted in [ALS] has been the focus of our research for the past 10 years,” Bahman Nasseroleslami, PhD, lead investigator and professor of neuroelectric signal analysis at the University of Dublin, in Ireland, said in a university press release.

“This work [shows] that we are on the right track, and that the technologies we have developed to capture electrical activity in the brain can identify important differences between different patient groups,” he added.

The study, “Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis,” was published in the journal Brain.

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ALS, a disorder characterized by the degeneration of motor neurons, is widely variable in presentation, outcomes, and underlying biological disease processes. Yet, its variability cannot be fully described by existing clinical tools, such as the revised ALS functional rating scale (ALSFRS-R), which measures motor function, and the Edinburgh cognitive and behavioral ALS Screen (ECAS), for cognitive and behavioral change.

Although validated for clinical trial outcomes, these tools cannot accurately predict disease progression and survival, which has implications for the development of ALS therapies.

Current studies suggest that variability in ALS reflects the disruption of different neural networks in the brain — groups of connected or functionally associated neurons. Thus, tools that measure the electrical activity of these networks may provide insights into the functional changes associated with neurodegenerative diseases like ALS.

Recently, Nasseroleslami’s team showed that resting-state electroencephalography (EEG) — which measures the brain’s electrical activity without tasks or instructions — can capture both motor and cognitive networks affected in ALS.

Now, the team used resting-state EEG to determine if different patterns of neural network disruptions can identify ALS patient subgroups with distinct disease outcomes. If so, this technique can be used to “reveal potentially different responses to therapy,” they wrote.

The study included 95 ALS patients who had been diagnosed in the past 18 months. Of these, 70 had limb onset (meaning that their first symptoms were in their limbs), 21 had bulbar onset (speech or swallowing problems were first symptoms), and four had respiratory onset.

Also, five patients had ALS with frontotemporal dementia (FTD), and 11 had mutations in the C9orf72 gene, which is associated with familial ALS.

Data on disease severity was determined by ALSFRS-R and ECAS scores as well as the Beaumont behavioral inventory. For comparison, 77 healthy controls that were matched for age, but not gender, were also included.

EEG recordings were conducted during a resting state, in which participants were seated in a comfortable chair and asked to relax while looking at the letter X on a piece of paper placed in front of them. Tests were divided into three 2-minute recording sessions, allowing rest between sessions to ensure participants remained awake.

The analysis revealed that ALS patients had four distinct clusters of electrical brain wave patterns, reflecting differences in neural activity and connectivity. Although overall clinical scores did not vary significantly across the clusters, the distinct EEG patterns matched specific neurodegenerative profiles.

More specifically, ALS patients in cluster 1 showed moderate limb and mild verbal impairments, with altered memory and executive function, but no changes in language. Cluster 2 was characterized by mild alterations in limb and verbal fluency, moderate memory and language impairment, but no observed defects in executive function.

Patients in cluster 3 had marked limb, language, and verbal fluency impairments. In contrast, those in cluster 4 were primarily characterized by problems with bulbar function (speech and swallowing), verbal fluency, and memory and executive function.

Across the clusters, there was no impairment in the visual perception of spatial relationships between objects, whereas all but those in cluster 2 showed mild behavioral impairment. In addition, individuals in cluster 4 had the shortest survival (median about three years), whereas cluster 2 had the longest survival (median about six years).

While the clusters were not significantly associated with commonly used clinical parameters, the team found that all patients with an initial ALS-FTD diagnosis were included in clusters 3 and 4, which had patients with worse cognitive scores. Likewise, cluster 4 had the highest number of C9orf72-positive patients.

No significant differences were found between cluster groups in disease duration, age, gender, and the use of the ALS therapy riluzole, which may have affected EEG patterns.

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No differences were also seen in King’s staging — which assesses the disease burden in stages from 1, for early disease, to 4, for late disease involving ventilatory and/or feeding support. A final analysis also demonstrated that clinical data alone did not identify these cluster subgroups, unlike EEG measurements.

“We have shown that clusters based on patterns of disruption in brain networks are associated with reproducible aggregates of clinical attributes and rate of disease progression, confirming the clinical relevance of our findings,” the authors wrote. “This indicates that these neurophysiologic patterns provide additional information to that which is discerned by clinical evaluation alone.”

Orla Hardiman, MD, professor of neurology and an expert in ALS research, said: “This is a very important and exciting body of work. A major barrier to providing the right drug for the right patient in [ALS] is the heterogeneity of the disease. This breakthrough research has shown that it is possible to use patterns of brain network dysfunction to identify subgroups of patients that cannot be distinguished by clinical examination.”

“The implications of this work are enormous, as we will have new and reliable ways [to] segregate patients based on what is really happening within the nervous system in [ALS],” she added.