Biology subjects

Mattioli, P.

Publications and source records attributed to Mattioli, P..

3 recordsLinked to original sources

bioRxiv2023

Altered Brain Dynamics in idiopathic REM sleep behavior disorder: Implications for a continuum from prodromal to overt alpha-synucleinopathies

Idiopathic/isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of alpha-synucleinopathies. Cortical and sub-cortical brain modifications begin years before the emergence of overt neurodegenerative symptoms. To better understand the pathophysiological process impacting the brain from the prodromal to the overt stage of alpha-synucleinopathy, it is essential to assess iRBD patients over time. Recent evidence suggests that the human brain operates at an operating point near a critical phase transition between subcritical and supercritical phases in the systems state space to maintain cognitive and physiological performance. In contrast, a deviation from the critical regime leading to altered oscillatory dynamics has been observed in several pathologies. Here, we investigated if the alpha-synucleinopathy produces a deviation of the operating point already evident in the prodromal phase and if this shift correlates with biological and clinical disease severity. We analyzed a dataset of 59 patients with iRBD (age 69.61 {+/-} 6.98, 50 male) undergoing resting-state high-density EEG, presynaptic dopaminergic imaging, and clinical evaluations. Thirty-one patients (age 72.41 {+/-} 7.05, 31 male) also underwent clinical and instrumental follow-up (mean follow-up period 25.85 {+/-} 10.20 months). To localize the individual operating points along the excitation-inhibition (EI) continuum, we assessed both measures of neuronal EI balance and measures of critical brain dynamics such as long-range temporal correlation (LRTCs) and neuronal bistability in spontaneous narrow-band oscillations. Finally, we correlated critical brain dynamics and EI balance metrics with phase synchronization, nigro-striatal dopaminergic functioning, and clinical performances. Compared to 48 healthy subjects (age 70.25 {+/-} 10.15, 23 male), iRBD patients showed higher values of LRTCs and bistability in the 2-7 Hz band at diagnosis. Patients who eventually phenoconverted to overt alpha-synucleinopathy exhibited a more excitation-dominated (fEI > 1) condition than stable iRBD patients in 5-7 Hz. This higher excitation also directly correlated with phase synchronization in 2-7 Hz, further suggesting a shift of the operating point toward a supercritical state with the disease progression. Moreover, excitation-dominated state and low bistability were associated with deterioration of the nigro-striatal dopaminergic function and tended to correlate with stronger clinical symptoms. In conclusion, this study shows for the first time a deviation of the working point from inhibition-to excitation-dominated states along the continuum from prodromal to overt phases of the disease. These cortical brain dynamics modifications are associated with nigro-striatal dopaminergic impairment. These results increase our knowledge of the physiopathological process underlying alpha-synucleinopathies since prodromal stages, possibly providing new clues on disease-modifying strategies.

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bioRxiv2022

Large-scale network metrics improve the classification performance of rapid-eye-movement sleep behavior disorder patients

Clinical decision support systems based on machine-learning algorithms are largely applied in the context of the diagnosis of neurodegenerative diseases (NDDs). While recent models yield robust classifications in supervised two classes-problems accurately separating Parkinsons disease (PD) from healthy control (HC) subjects, few works looked at prodromal stages of NDDs. Idiopathic Rapid-eye Movement (REM) sleep behavior disorder (iRBD) is considered a prodromal stage of PD with a high chance of phenoconversion but with heterogeneous symptoms that hinder accurate disease prediction. Machine learning (ML) based methods can be used to develop personalized trajectory models, but these require large amounts of observational points with homogenous features significantly reducing the possible imaging modalities to non-invasive and cost-effective techniques such as high-density electrophysiology (hdEEG). In this work, we aimed at quantifying the increase in accuracy and robustness of the classification model with the inclusion of network-based metrics compared to the classical Fourier-based power spectral density (PSD). We performed a series of analyses to quantify significance in cohort-wise metrics, the performance of classification tasks, and the effect of feature selection on model accuracy. We report that amplitude correlation spectral profiles show the largest difference between iRBD and HC subjects mainly in delta and theta bands. Moreover, the inclusion of amplitude correlation and phase synchronization improves the classification performance by up to 11% compared to using PSD alone. Our results show that hdEEG features alone can be used as potential biomarkers in classification problems using iRBD data and that large-scale network metrics improve the performance of the model. This evidence suggests that large-scale brain network metrics should be considered important tools for investigating prodromal stages of NDD as they yield more information without harming the patient, allowing for constant and frequent longitudinal evaluation of patients at high risk of phenoconversion. HighlightsO_LINetwork-based features are important tools to investigate prodromal stages of PD C_LIO_LIAmplitude correlation shows the largest difference between two groups in 9/30 bands C_LIO_LIAmplitude correlation improved up to 11% the performance compared to PSD alone C_LIO_LIClassification robustness increases when we use both network-based EEG features C_LIO_LIClassifier performance worsens when PSD is added to network-based EEG features C_LI

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bioRxiv2021

Phase and amplitude correlations change with disease progression in idiopathic Rapid eye-movement sleep behavior disorder patients

Cognitive decline is a common trait of neurodegenerative diseases of central nervous system and one of the major risk factors associated with faster phenoconversion from prodromal stages. In the transition to full-blown clinical syndromes, increased phase synchronization in the theta, alpha or beta EEG rhythms is thought to reflect the activation of compensatory mechanisms that may counterbalance the cognitive decline of patients affected by Mild Cognitive Impairment (MCI). Patients suffering from idiopathic Rapid eye-movement sleep Behavior Disorder (iRBD) have high risk of developing Parkinson Disease (PD) or Dementia with Lewy Bodies (DLB) and cognitive impairment is among the strongest risk factors together with motor symptoms. Here we wanted to investigate whether altered phase synchronization and amplitude couplings of the brain oscillations could be linked to the balancing of cognitive decline in a longitudinal cohort (N=18) of iRBD patients. We measured high-density Electroencephalographic (HD-EEG) activity at baseline and follow-up and quantified power distribution, orthogonalized amplitude correlation and weighted phase lag index. Despite the overt neurodegenerative progression (three patients converted to PD and one to DLB), cognitive decline was not evident from Mini Mental State Examination (MMSE) or neuropsychological tests. On the other hand, alpha phase synchronization and delta amplitude correlations were significantly different at follow-up compared to baseline. In particular, alpha synchrony was enhanced while delta amplitude coupling was reduced. Those differences were more pronounced among central-posterior channels while frontal channels showed a reduced number of significant edges with respect to surrogates. Both large-scale amplitude and phase coupling significantly correlated with cognitive or neuropsychological scores but not with sleep quality indices. Altogether, these results suggest that increased alpha phase-synchronization and reduced delta amplitude correlation may be considered as electrophysiological signs of an active compensatory mechanism of the cognitive impairment in RDB patients. Large-scale functional modifications could thus be used as significant biomarker in the characterization of prodromal stages of PD. Statement of SignificanceCognitive impairment and RBD emerge much earlier than the better-known motor symptoms distinctive of synucleinopathies. An improved investigation of RBD may constitute an important biomarker for an early diagnosis of the actual neurodegenerative diseases. For the first time, this preliminary study aims to quantify the large-scale network couplings as electrophysiological manifestation of the compensatory mechanism to the cognitive impairment in a longitudinal study of idiopathic RBD patients. Unfortunately, the small number of the subjects limits the generalizability of our observations, but this is only preliminary works in a larger project that aims to investigate advanced electrophysiological markers for an early diagnosis of the synucleinopathies.

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