Brain Connectivity and Graph Theory Analysis in Alzheimer’s and Parkinson’s Disease: The Contribution of Electrophysiological Techniques

Miraglia, Francesca; Vecchio, Fabrizio; Pappalettera, Chiara; Nucci, Lorenzo; Cotelli, Maria; Judica, Elda; Ferreri, Florinda; Rossini, Paolo Maria

doi:10.3390/brainsci12030402

Cited by 26 publications

(14 citation statements)

References 62 publications

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“…It would be interesting to parallel such computational studies running the proposed MCLRL on experimental data to verify the model predictions. A recent review [32] covered the implication of graph changes in Parkinson's and Alzheimer's diseases, which are both characterized by a progressive weakening of network connectivity. Other works addressed the study of bipolar/unipolar depression [33] and cognitive aging [34].…”

Section: Data Availability Statementmentioning

confidence: 99%

A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials

Nieus

Borgonovo²,

Diwakar³

et al. 2022

Front. Appl. Math. Stat.

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In neuroscience, the structural connectivity matrix of synaptic weights between neurons is one of the critical factors that determine the overall function of a network of neurons. The mechanisms of signal transduction have been intensively studied at different time and spatial scales and both the cellular and molecular levels. While a better understanding and knowledge of some basic processes of information handling by neurons has been achieved, little is known about the organization and function of complex neuronal networks. Experimental methods are now available to simultaneously monitor the electrical activity of a large number of neurons in real time. The analysis of the data related to the activities of individual neurons can become a very valuable tool for the study of the dynamics and architecture of neural networks. In particular, advances in optical imaging techniques allow us to record up to thousands of neurons nowadays. However, most of the efforts have been focused on calcium signals, that lack relevant aspects of cell activity. In recent years, progresses in the field of genetically encoded voltage indicators have shown that imaging signals could be well suited to record spiking and synaptic events from a large population of neurons. Here, we present a methodology to infer the connectivity of a population of neurons from their voltage traces. At first, putative synaptic events were detected. Then, a multi-class logistic regression was used to fit the putative events to the spiking activities and a penalization term was allowed to regulate the sparseness of the inferred network. The proposed Multi-Class Logistic Regression with L1 penalization (MCLRL) was benchmarked against data obtained from in silico network simulations. MCLRL properly inferred the connectivity of all tested networks, as indicated by the Matthew correlation coefficient (MCC). Importantly, MCLRL was accomplished to reconstruct the connectivity among subgroups of neurons sampled from the network. The robustness of MCLRL to noise was also assessed and the performances remained high (MCC>0.95) even in extremely high noise conditions (>95% noisy events). Finally, we devised a procedure to determine the optimal MCLRL regularization term, which allows us to envision its application to experimental data.

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Section: Data Availability Statementmentioning

confidence: 99%

A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials

Nieus

Borgonovo²,

Diwakar³

et al. 2022

Front. Appl. Math. Stat.

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“…Various electrophysiological techniques, such as positron emission tomography, magnetic resonance imaging, and electroencephalography (EEG), have been widely used to measure neuronal activity [ 9 , 10 , 11 , 12 ]. Among them, EEG technology has the advantages of low cost, good portability, and strong portability [ 13 , 14 , 15 , 16 ]. The electrical signals sent by the human brain through the EEG instrument are captured by placing electrodes on the scalp through EEG technology to record brain activity [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

Fang

Sun

et al. 2023

Diagnostics

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To investigate the differences in functional brain network structures between patients with a high level of generalized anxiety disorder (HGAD) and those with a low level of generalized anxiety disorder (LGAD), a resting-state electroencephalogram (EEG) was recorded in 30 LGAD patients and 21 HGAD patients. Functional connectivity between all pairs of brain regions was determined by the Phase Lag Index (PLI) to construct a functional brain network. Then, the characteristic path length, clustering coefficient, and small world were calculated to estimate functional brain network structures. The results showed that the PLI values of HGAD were significantly increased in alpha2, and significantly decreased in the theta and alpha1 rhythms, and the small-world attributes for both HGAD patients and LGAD patients were less than one for all the rhythms. Moreover, the small-world values of HGAD were significantly lower than those of LGAD in the theta and alpha2 rhythms, which indicated that the brain functional network structure would deteriorate with the increase in generalized anxiety disorder (GAD) severity. Our findings may play a role in the development and understanding of LGAD and HGAD to determine whether interventions that target these brain changes may be effective in treating GAD.

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“…There is growing evidence that EEG biomarkers can be used to identify early abnormalities in neuronal function before cortical tissue loss or cognitive decline. Many researchers believe that the disruption of functional connectivity may be a pathological characteristic of neurodegenerative diseases [ 14 ]. Several studies used coherence as a method of functional connectivity and found a significant reduction in α-band coherence in AD patients [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Phase Lag Index of Resting-State EEG for Identification of Mild Cognitive Impairment Patients with Type 2 Diabetes

Kuang

Xia³

et al. 2022

Brain Sciences

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Mild cognitive impairment (MCI) is one of the important comorbidities of type 2 diabetes mellitus (T2DM). It is critical to find appropriate methods for early diagnosis and objective assessment of mild cognitive impairment patients with type 2 diabetes (T2DM-MCI). Our study aimed to investigate potential early alterations in phase lag index (PLI) and determine whether it can distinguish between T2DM-MCI and normal controls with T2DM (T2DM-NC). EEG was recorded in 30 T2DM-MCI patients and 30 T2DM-NC patients. The phase lag index was computed and used in a logistic regression model to discriminate between groups. The correlation between the phase lag index and Montreal Cognitive Assessment (MoCA) score was assessed. The α-band phase lag index was significantly decreased in the T2DM-MCI group compared with the T2DM-NC group and showed a moderate degree of classification accuracy. The MoCA score was positively correlated with the α-band phase lag index (r = 0.4812, moderate association, p = 0.015). This work shows that the functional connectivity analysis of EEG may offer an effective way to track the cortical dysfunction linked to the cognitive deterioration of T2DM patients, and the α-band phase lag index may have a role in guiding the diagnosis of T2DM-MCI.

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Brain Connectivity and Graph Theory Analysis in Alzheimer’s and Parkinson’s Disease: The Contribution of Electrophysiological Techniques

Cited by 26 publications

References 62 publications

A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials

A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials

Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

Phase Lag Index of Resting-State EEG for Identification of Mild Cognitive Impairment Patients with Type 2 Diabetes

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