Neuro-ophthalmological and electrophysiological characterization of mitochondrial diseases.

Aims and methods: 1) characterization of patients with Dominant Optic Atrophy (DOA) associated with mutations in AFG3L2 and ACO2 genes in comparison with classical OPA1-DOA; 2) characterization of patients with mtDNA mutations causing MELAS and MERRF syndromes and correlation with heteroplasmy; 3) longitudinal evaluation of subacute m.11778G>A/MTND4 Leber’s Hereditary Optic Neuropathy (LHON) patients co-treated with rAAV2/2-ND4 gene therapy and idebenone. We performed a comprehensive neuro-ophthalmological assessment coupled with electrophysiological examination. Results: 1) We described and compared 23 ACO2 and 13 AFG3L2 patients with 72 OPA1 patients. All patients presented temporally predominant optic atrophy, with ACO2 showing higher RNFL and GCL thicknesses at OCT, while AFG3L2 was virtually-indistinguishable from OPA1. 2) Retinopathy was the most common manifestation in 17/33 MELAS patients, conversely, optic atrophy was the most common finding in 7/8 MERRF patients. Correlation of heteroplasmy with neuro-ophthalmological parameters failed to disclose any significance in MELAS, while it negatively correlated with OCT parameters in MERRF. 3) We compared modifications in visual acuity, OCT and electrophysiological parameters at 3 timepoints in 9 LHON patients. We observed significant decrease of RNFL thickness and reduction of PhNR amplitude. Visual acuity improved of about -0.37 LogMAR, correlating significantly with time from onset and from injection, but not with idebenone therapy duration. Discussion: 1) ACO2 seems associated to better preservation of retinal ganglion cells, depending on a different pathogenic mechanism involving mtDNA maintenance, as opposed to AFG3L2 which is involved in OPA1 processing. 2) MELAS and MERRF patients presented with a clearly distinct ocular phenotype, possibly reflecting a selective susceptibility of different retinal cell types to global energy defect or oxidative stress. 3) Follow up of LHON patients treated with gene therapy confirmed the deterioration in OCT and electrophysiological parameters, while the amount of visual improvement was similar to the one observed in recent clinical trials.

Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena.