General context

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease. While PD is known for its motor symptoms, it can also involve incapacitating cognitive symptoms, some of them potentially being associated with the later development of dementia. However, no effective drug treatment for those cognitive symptoms is available, and identifying different profiles of cognitive deficits remains an unmet clinical challenge.

The objective of the ICON project is to tackle this challenge, by identifying electrophysiological signatures corresponding to distinct profiles of cognitive impairment in PD, as an indispensable first step towards improving prognosis and the conception of innovative treatments targeting specific brain regions / processes.

Such identification of cognitive profiles would be crucial for proposing a personalized therapeutic approach by anticipating the occurrence of cognitive and behavioral adverse effects associated with currently available treatments (such as impulse control disorders or excessive daytime sleepiness) and for the use of novel drugs, potentially targeting specific mechanisms or brain circuits.

To achieve this ambitious objective, we will use an innovative approach combining machine learning techniques on high-resolution electroencephalography (HR-EEG) and neuropsychological assessments of different cognitive domains, with a unique clinical database acquired here in Rennes (STIMPAK+EEGCOG clinical studies).

Today, HR-EEG (256 electrodes used to map electrical activity of the brain) is not used in the standard of care for PD. The reason is twofold: 1) the treatment of motor symptoms, the hallmark of PD, does not currently require EEG; and 2) cognitive symptoms are currently the least well understood aspect of PD, no drug has been identified as effective for those, and neuropsychological tests are currently the only form of evaluation for those symptoms. Those neuropsychological tests are based on the assumption (which has not yet been fully and formally proven) that performance at specific tests is associated with the integrity of specific brain circuits.

With the ICON project, we aim at demonstrating that HR-EEG, providing unprecedented electrical measures of brain activity, can contribute to: 1) improve our understanding of cognitive impairments in PD in terms of brain circuits alterations and 2) pave the way for optimized treatments adapted to specific cognitive deficit profiles identified by hybrid electrophysiological/neuropsychological measures in the future. A secondary but consequential benefit of the ICON project will be to verify the yet-unvalidated assumption that the integrity of specific brain circuits is correlated with the performance at specific neuropsychological tests.

While cognitive impairments (e.g., visuospatial processing) are critical for understanding disease progression in PD, an important and overlooked issue is the limited evidence regarding the relationship between cognitive test scores and corresponding cerebral activity. Clarifying this link between cognitive impairment and changes in cerebral network dynamics is essential, since this supports interpretation of neuropsychological test results, aids in predicting cognitive progression, and guides the personalization of treatment strategies. Here, we hypothesize that advanced HR-EEG neuroimaging can provide valuable complementary insights regarding the complex relationship between profiles of cognitive function decline, neuropsychological scores, and electrophysiological properties of EEG signals, thereby providing novel candidate biomarkers.

In recent years, evidence has emerged that HR-EEG can provide insights into the severity of cognitive deficits in PD and associated alterations in functional connectivity, which has also received some support from the use of MEG. Those promising studies resulted in the identification of connectivity profiles associated with a global decline in cognitive function. However, while overall severity of cognitive deficits is an indicator of brain function; it represents an average across different overlapping cognitive domains. This lack of specificity makes it challenging to directly associate distinct EEG features with individual cognitive domains (e.g., processing of visuo-spatial information such as imagining a 3D-object from different angles), which the authors emphasized as a limitation. Therefore, there is a pressing need to identify the specific domains of cognitions which are altered in PD, and how those alterations induce changes in electrophysiological brain activity measured by EEG, and the associated functional connectivity characteristics that would have clinical relevance.

Furthermore, those previous studies were not correcting for the ubiquitous presence of the so-called “aperiodic activity” (also called “1/f” component) present in EEG signals that overestimates functional connectivity, as we have recently shown5. Therefore, ICON will bridge those gaps and characterize multiple, different domains of cognition, and will use state-of-the-art techniques of HREEG functional connectivity to provide meaningful EEG features, as an effort to establish a dictionary of the cognitive deficit profiles associated with PD and corresponding altered functional circuits. Interestingly, further evidence for the feasibility of the ICON project results from the support that HREEG has a measurable relationship with neuropsychological scores, such as those associated with psychological resilience, or those associated with personality traits. Let us mention that various EEG parameters have already been used to assess characteristics related to cognitive impairments in PD. For instance, spectral analysis of EEG data has shown significant slowing of resting-state oscillatory brain activity in both non-demented and demented PD patients, in comparison to cognitively intact controls.