Introduction to Fatal Familial Insomnia (FFI) 

Fatal Familial Insomnia (FFI) is an inherited rare neurodegenerative disease that is caused by a mutation in the prion genes (PRNP) which ultimately leads to atrophy in thalamic nucleus. “Fata” in this case, meaning that patients diagnosed with this condition have a mean life expectancy of just a mere 18 months.¹ In order to diagnose this condition, patients or family members may notice the following symptoms.:

• Insomnia 
• Short-term memory 
• Attention deficit 
• Motor issues such as imbalance 
• Hypertension 

Similar to Creutzfeldt-Jakob Disease (CJD), Fatal Familial Insomnia is also part of a family of genetic human prion diseases. Specificity of gene mutation belongs to an autosomal dominant mutation at the codon 178 of the PRNP gene. This directly affects the production of prion protein PrPC, which is directly correlated to an elevated level of oxidative and endoplasmic reticulum stress. Given that it is an inherited condition, the mutation occurs for the gene called D178N, associated with the M129 genotype in the pPrPC gene. The condition ultimately arises based on the conversion rate from healthy prions to unhealthy, mutated ones.²

According to Chen C, there have been 131 cases of Fatal Familial Insomnia reported more in people assigned male at birth (AMAB) than those assigned female at birth (AFAB). Out of 129, there were 72 men and 57 women, with an age range between 17 to 76 years.³

Within the human brain, all cases seem to have an effect on the cortex of the brain. The regions most heavily impacted in FFI appear to be the parietal, temporal, and frontal lobes. Interestingly, the deposition of the prion protein, which is the root cause of FFI, tends to initially favor the brainstem and thalamus, with the thalamus being the most severely impacted by the degenerative changes. The reasons behind this distinct pattern of brain involvement in FFI remains poorly understood, but it may help explain the diverse array of symptoms observed in patients with this rare neurodegenerative disorder. The varying degrees of damage across different brain regions contribute to the complex clinical presentation, which include sleep disturbances, cognitive impairments, autonomic dysfunction, and other neurological manifestations.⁴ 

With FFI being one of the most rare conditions, many underlying causes and effects regarding casualties still remain unknown to scientists. In some cases noted by Scheinken, deaths caused by FFI come directly from degradation of supraoptic nuclei, whereas in other cases, patients who passed away due to this condition do not have enough nerve damage to cause their deaths and could be due to many more underlying reasons. In order to understand this better, scientists have attempted to study patients through Electroencephalography (EEG) under Rapid Eye Movement (REM) sleep.⁵

Role of Electroencephalography (EEG) in FFI 

EEG is a technique that measures the electrical activity generated by the brain's neurons. It offers high temporal resolution, and is able to detect brain signals at a rate of less than a millisecond, which is a key advantage over other brain imaging methods. EEG recording is a completely non-invasive procedure that can be used with both adults and children without limitations. Modern EEG systems have improved spatial resolution, with up to 256 electrode sites recording simultaneously. The brain's electrical signals are produced during all mental states, making EEG a versatile tool for studying various neurological and psychological conditions. The EEG signal consists of different frequency components, ranging from 0.5 to 100 microvolts in amplitude and up to 300 Hz in frequency.⁶

These frequency components can be extracted using signal processing techniques like Fourier transforms, revealing distinct brain wave categories. They are as follows:

• Delta waves (0-4 Hz): High-amplitude waves associated with slow-wave sleep and early development
• Theta waves (4-8 Hz): Observed during drowsiness, arousal, and meditative states
• Alpha waves (8-12 Hz): Prominent during relaxed, eyes-closed conditions
• Sensory motor response (12-13 Hz): Generated by the sensory-motor cortex
• Beta waves (12-30 Hz): Associated with active mental processing and attention 
• Gamma waves (>30 Hz): Involved in cognitive and motor functions, declining with cognitive impairment⁷

In the figure below, one can see that the upper column represents the normal pattern of sleep whereas the column below represents the individuals affected by FFI. Evidently, patients with FFI show an absence of EEG signal in light sleep.The deterioration of the mediodorsal thalamus, a key structure in the brain, severely compromises the patient's capacity to properly conduct the necessary signals that would otherwise allow the formation of sleep spindles. This fundamental disruption in the thalamic function makes it impossible for the patient to attain the normal states of Rapid Eye Movement(REM)sleep.

As FFI progresses into its later stages, brain scans using EEG start to show something unusual — a pattern called periodic sharp wave complexes (PSWCs). These are brief, sharp bursts of activity that appear every few seconds. Researchers like Montagna and his team noticed that over time, these signals become weaker and slower. This drop in brain activity has been linked to the mental and physical decline seen in patients, including memory loss and problems with things like body temperature and heart rate. Simply put, as the brain slows down, so does everything else.⁹

Diagnostic utility of EEG in FFI 

According to a study published in the Journal of Neurology, Neurosurgery, and Psychiatry, EEG abnormalities can be detected in over 80% of FFI patients even in the early stages of the condition. Specifically, the researchers found that the presence of periodic sharp wave complexes (PSWCs) in the EEG, a hallmark feature of FFI, had a specificity of 95% for identifying the condition. Moreover, Researchers have found that the characteristic EEG changes, including the progressive reduction in overall brain activity, are closely linked to insomnia, cognitive decline, and autonomic dysfunction experienced by patients with this rare prion disorder. This intimate relationship between the neurophysiological and clinical aspects of FFI highlights the important role that EEG may play in the early identification and monitoring of this condition.¹⁰

Limitations and challenges of EEG in FFI 

A study reviewing the EEG characteristics of Fatal Familial Insomnia (FFI) patients found that periodic sharp wave complexes (PSWCs)—a common EEG feature in other prion diseases like Creutzfeldt-Jakob Disease—are typically absent in FFI. However, in rare cases, especially during the advanced stages of the disease, some patients may exhibit PSWC-like patterns.¹¹ Additionally, the EEG changes observed in FFI, such as the reduction in overall brain activity, can overlap with those seen in other neurodegenerative disorders, including sporadic Creutzfeldt-Jakob disease. This lack of specificity can complicate the use of EEG for the differential diagnosis of FFI, particularly in the early stages of the disease when symptoms may be less distinct.¹⁰

Future directions and research opportunities 

Looking ahead, EEG has a massive potential to crosslink with machine learning and deep learning to analyse data and to create models. This would ultimately enhance the brain-computer interfaces(BCI) performance. EEG also has the potential to be part of a hybrid BCI, a system that combines two different brain-computer interface technologies where it acts as a neuroimaging technique to enhance performance. An example of such hybrid BCIs would be using both EEG and fMRI. According to a study conducted by the Journal of Neurology, Neurosurgery shows that this hybrid BCI is a promising technique that shows a more comprehensive mechanism behind FFI. 

Summary

• Fatal Familial Insomnia (FFI) is a rare, inherited neurodegenerative disease caused by a mutation in the prion genes (PRNP), leading to thalamic atrophy
• The disease has a mean life expectancy of just 18 months and is characterized by symptoms like insomnia, memory issues, attention deficits, and motor problems 
• EEG (Electroencephalography) is a useful tool in understanding and diagnosing FFI, as it can detect specific brain activity patterns associated with the disease 
• In FFI patients, EEG often shows an absence of sleep spindles and the emergence of periodic sharp wave complexes (PSWCs) in later stages, reflecting the deterioration of the mediodorsal thalamus
• EEG abnormalities can be detected in over 80% of FFI patients, and the presence of PSWCs has a 95% specificity for identifying the condition. However, the EEG changes in FFI can overlap with other neurodegenerative disorders, limiting the specificity of EEG in the early stages of the disease
• Future research opportunities include using EEG in combination with machine learning and deep learning techniques, as well as hybrid brain-computer interfaces (BCIs) that use multiple neuroimaging modalities