Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer’s Disease continuum?

Lassi, Michael; Fabbiani, Carlo; Mazzeo, Salvatore; Burali, Rachele; Vergani, Alberto Arturo; Giacomucci, Giulia; Moschini, Valentina; Morinelli, Carmen; Emiliani, Filippo; Scarpino, Maenia; Bagnoli, Silvia; Ingannato, Assunta; Nacmias, Benedetta; Padiglioni, Sonia; Micera, Silvestro; Sorbi, Sandro; Grippo, Antonello; Bessi, Valentina; Mazzoni, Annalisa

doi:10.1016/j.nicl.2023.103407

Cited by 22 publications

(12 citation statements)

References 120 publications

(146 reference statements)

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“…MSLZC is a previously reported measurement for studying microstate transitions [9,15,16]. In this study, we also observed differences in MSLZC between the healthy group and the group with the disease, but the significance of the differences was weaker compared to MSNRI.…”

Section: Discussionsupporting

confidence: 66%

“…In this work, we present a novel method of non-randomness to microstate transition sequences since microstate transitioning is non-linear, non-stationary, and non-Markovian [14]. As anticipated [9], maintaining the balance and stability of brain spatiotemporal pattern transitions plays a crucial role in human cognitive health. We found significant differences in the non-randomness of spatiotemporal patterns of microstates between healthy individuals and patients with cognitive impairment disorders.…”

Section: Discussionmentioning

confidence: 98%

“…The Lempel-Ziv complexity measurement quantifies the number of sub-words that can be found in the entire sequence, assessing the richness of a sequence with a finite number of generating elements. In this study, the calculation of microstate sequence Lempel-Ziv complexity (MSLZC) follows the method proposed by Michael Lassi et al [9] The MSLZC is computed by measuring the length of the encoded dictionary in the microstate sequence using the zlib Python package (version 1.3.1), after excluding the Huffman coding component. To address variations in sequence length, normalization by the total length of the signal is conducted.…”

Section: Microstate Sequence Lempel-ziv Complexitymentioning

confidence: 99%

“…EEG microstates consider the spatiotemporal evolution of cortical EEG signals, describing the dynamic characteristics of the entire brain neural networks. Renowned for its remarkable resolution in both space and time, microstate analysis finds extensive utility across various domains including dementia [9,10], depression [11], cognitive psychology [12], and sleep [13], facilitating deeper understanding and exploration within these fields. Previous studies predominantly focused on temporal statistical characteristics of microstates, such as duration, occurrence rate, coverage rate, and transition probabilities, while overlooking the broader patterns and characteristics of large-scale changes in brain activity.…”

Section: Introductionmentioning

confidence: 99%

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Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia

Wan,

Gu,

Peng

et al. 2024

Brain Sciences

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Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space compression of EEG data using microstate analysis. Following this, we assess the non-randomness of these microstate patterns using information-based similarity analysis. The results suggest that this MSNRI metric is a potential marker for distinguishing between health control (HC) and frontotemporal dementia (FTD) (HC vs. FTD: 6.958 vs. 5.756, p < 0.01), as well as between HC and populations with Alzheimer’s disease (AD) (HC vs. AD: 6.958 vs. 5.462, p < 0.001). Healthy individuals exhibit more complex macroscopic structures and non-random spatiotemporal patterns of microstates, whereas dementia disorders lead to more random spatiotemporal patterns. Additionally, we extend the proposed method by integrating the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to explore spatiotemporal dynamic patterns of microstates at specific frequency scales. Moreover, we assessed the effectiveness of this innovative method in predicting cognitive scores. The results demonstrate that the incorporation of CEEMD-enhanced microstate dynamic indicators significantly improved the prediction accuracy of Mini-Mental State Examination (MMSE) scores (R2 = 0.940). The CEEMD-enhanced MSNRI method not only aids in the exploration of large-scale neural changes in populations with dementia but also offers a robust tool for characterizing the dynamics of EEG microstate transitions and their impact on cognitive function.

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Section: Discussionsupporting

confidence: 66%

Section: Discussionmentioning

confidence: 98%

Section: Microstate Sequence Lempel-ziv Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia

Wan,

Gu,

Peng

et al. 2024

Brain Sciences

View full text Add to dashboard Cite

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“…These topographies are stable for a short time period (∼60-120 ms) and rapidly transition to another quasi-stable topography (Lehmann, 1971; Lehmann et al, 1987). As MS simultaneously considers signals recorded from all cortical regions, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders (Van De Ville et al, 2010; Michel and Koenig, 2018), such as schizophrenia (Khanna et al, 2014; Tomescu et al, 2014; Diaz Hernandez et al, 2016; da Cruz et al, 2020; Kim et al, 2021), dementia (Grieder et al, 2016; Lassi et al, 2023) and obsessive-compulsive disorder (Thirioux et al, 2023). Recently, microstate analysis was also applied to depression and anxiety disorders (Al Zoubi et al, 2019; Murphy et al, 2020; Lei et al, 2022; Li et al, 2022; Chivu et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms

Xue,

Shen,

Zhang

et al. 2024

Preprint

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Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of resting-state brain networks on a millisecond time scale. Changes in microstate parameters have been described in patients with psychiatric disorders, indicating their potential as clinical biomarkers with broadband EEG signals (e.g., 1-30 Hz). Considering the distinct information provided by specific frequency bands, we hypothesized that microstates in decomposed frequency band could provide a more detailed depiction of the underlying psychological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with emotional disorders. We conducted clustering on EEG topographies in decomposed frequency band (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their predictive power for the scores of depression and anxiety symptoms were identified by correlation and regression analysis. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate C' (coverage, occurrence, transitions from B to C') in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < .01). These results suggested the value of frequency-specific microstates in predicting emotional symptoms.

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Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings

Amato,

Vergani,

Lassi

et al. 2024

Alz & Dem Diag Ass & Dis Mo

Self Cite

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INTRODUCTIONEarly identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern.METHODSWe developed a cortical model of AD‐related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings.RESULTSOur model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50).DISCUSSIONPersonalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non‐invasive EEG recordings.Highlights Personalized cortical model estimating structural alterations from EEG recordings. Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%) Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%) Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y

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Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer’s Disease continuum?

Cited by 22 publications

References 120 publications

Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia

Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms

Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings

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