BackgroundBipolar disorders (BD) is a common, chronic and disabling psychiatric condition. In addition to being characterized by significant clinical heterogeneity, notable disturbances of sleep and cognitive function are frequently observed in all phases of the disease. Currently, there is no readily available biomarker in current clinical practice to help diagnose or predict the disease course. Thus, identification of biomarkers in BD is today a major challenge. In this context, the study of electrophysiological biomarkers based on electroretinogram (ERG) measurements in BD seems highly promising. The BiMAR study aims to compare electrophysiological data measured with ERG between a group of euthymic patients with BD and a group of healthy control subjects. Secondarily, we will also describe the existing potential relationship between clinical, sleep and neuropsychological phenotypes of patients and electrophysiological data.MethodsThe BiMAR study is a comparative and monocentric study carried out at the Expert Center for BD in Nancy, France. In total, 70 euthymic adult patients with BD and 70 healthy control subjects will be recruited. Electrophysiological recordings with ERG and electroencephalogram (EEG) will be performed with a virtual reality headset after a standardized clinical evaluation to all participants. Then, an actigraphic monitoring of 21 consecutive days will be carried out. At the end of this period a neuropsychological evaluation will be performed during a second visit. The primary outcome will be electrophysiological measurements with ERG flash and pattern. Secondary outcomes will be EEG data, sleep settings, clinical and neuropsychological assessments. For patients only, a complementary ancillary study, carried out at the University Hospital of Nancy, will be proposed to assess the retinal structure and microvascularization using Optical Coherence Tomography. Recruitment started in January 2022 and will continue until the end of July 2023.DiscussionThe BiMAR study will contribute to identifying candidate ERG electrophysiological markers for helping the diagnosis of BD and identify subgroups of patients with different clinical profiles. Eventually, this would allow earlier diagnosis and personalized therapeutic interventions.Clinical trial registrationThe study is registered at Clinicaltrials.gov, NCT05161546, on 17 December 2021 (https://clinicaltrials.gov/ct2/show/NCT05161546).

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Visual electrophysiological deficits have been reported in neurodegenerative disorders as well as in mental disorders. Such alterations have been mentioned in both the retina and the cortex, notably affecting the photoreceptors, retinal ganglion cells (RGCs) and the primary visual cortex. Interestingly, such impairments emphasize the functional role of the visual system. For this purpose, the present study reviews the existing literature with the aim of identifying key alterations in electroretinograms (ERGs) and visual evoked potentials electroencephalograms (VEP-EEGs) of subjects with neurodegenerative and psychiatric disorders. We focused on psychiatric and neurodegenerative diseases due to similarities in their neuropathophysiological mechanisms. Our research focuses on decoupled and coupled ERG/VEP-EEG results obtained with black-and-white checkerboards or low-level visual stimuli. A decoupled approach means recording first the ERG, then the VEP-EEG in the same subject with the same visual stimuli. The second method means recording both ERG and VEP-EEG simultaneously in the same participant with the same visual stimuli. Both coupled and decoupled results were found, indicating deficits mainly in the N95 ERG wave and the P100 VEP-EEG wave in Parkinson’s, Alzheimer’s, and major depressive disorder. Such results reinforce the link between the retina and the visual cortex for the diagnosis of psychiatric and neurodegenerative diseases. With that in mind, medical devices using coupled ERG/VEP-EEG measurements are being developed in order to further investigate the relationship between the retina and the visual cortex. These new techniques outline future challenges in mental health and the use of machine learning for the diagnosis of mental disorders, which would be a crucial step toward precision psychiatry.