The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

Romano, Antonella; Lopez, Emahnuel Troisi; Liparoti, Marianna; Polverino, Arianna; Minino, Roberta; Trojsi, Francesca; Bonavita, Simona; Mandolesi, Laura; Granata, C.; Amico, Enrico; Sorrentino, Giuseppe; Sorrentino, Pierpaolo

doi:10.1016/j.nicl.2022.103095

Cited by 22 publications

(33 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, CCF was able to predict the individual cognitive impairment, assessed through Mini‐Mental State Examination (Sorrentino et al, 2021 ). In a similar study, which involved individuals affected by amyotrophic lateral sclerosis, the same approach was able to find a relationship between the CCF and the disease progression of the patients (Romano et al, 2022 ).…”

Section: Introductionmentioning

confidence: 96%

Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment

Lopez

Minino

Liparoti

et al. 2022

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source‐reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated the identifiability characteristics of each subject in each group. We observed reduced identifiability in patients compared to healthy individuals in the beta band. Furthermore, we found that the reduction in identifiability was proportional to the motor impairment, assessed through the Unified Parkinson's Disease Rating Scale, and, interestingly, able to predict it (at the subject level), through a cross‐validated regression model. Along with previous evidence, this article shows that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.

show abstract

Section: Introductionmentioning

confidence: 96%

Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment

Lopez

Minino

Liparoti

et al. 2022

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

show abstract

“…The clinical utility of CCF was demonstrated by its ability to predict the individual motor impairment in patients affected by ALS (Romano et al, 2022). Another study explored the uniqueness of dynamic functional connectivity patterns across different temporal scales (van de .…”

Section: Discussionmentioning

confidence: 99%

Multiplexity of human brain oscillations as a personal brain signature

Dimitriadis,

Routley,

Linden

et al. 2023

Human Brain Mapping

View full text Add to dashboard Cite

Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.

show abstract

“…In addition, the framework is not restricted by the type of specific association measure, thresholding criterion, or summarizing function used in the summarization step. The framework is applicable to other network-based neurodegenerative disorders, such as Parkinson's disease, progressive supranuclear palsy, and amyotrophic lateral sclerosis, where brain connectivity is disrupted as the diseases progress (Brown et al, 2017 ; Brundin and Melki, 2017 ; Romano et al, 2022 ), although this paper focuses on AD-related brain connectivity. With this flexibility, our framework establishes a milestone for analyzing complex brain connectivity networks to explain or predict neurodegenerative disorders by presenting indicators of the degree to which brain networks collapse or by utilizing them as one of the features for predictive models.…”

Section: Discussionmentioning

confidence: 99%

Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders

et al. 2022

View full text Add to dashboard Cite

BackgroundBrain connectivity is useful for deciphering complex brain dynamics controlling interregional communication. Identifying specific brain phenomena based on brain connectivity and quantifying their levels can help explain or diagnose neurodegenerative disorders.ObjectiveThis study aimed to establish a unified framework to identify brain connectivity-based biomarkers associated with disease progression and summarize them into a single numerical value, with consideration for connectivity-specific structural attributes.MethodsThis study established a framework that unifies the processes of identifying a brain connectivity-based biomarker and mapping its abnormality level into a single numerical value, called a biomarker abnormality summarized from the identified connectivity (BASIC) score. A connectivity-based biomarker was extracted in the form of a connected component associated with disease progression. BASIC scores were constructed to maximize Kendall's rank correlation with the disease, considering the spatial autocorrelation between adjacent edges. Using functional connectivity networks, we validated the BASIC scores in various scenarios.ResultsOur proposed framework was successfully applied to construct connectivity-based biomarker scores associated with disease progression, characterized by two, three, and five stages of Alzheimer's disease, and reflected the continuity of brain alterations as the diseases advanced. The BASIC scores were not only sensitive to disease progression, but also specific to the trajectory of a particular disease. Moreover, this framework can be utilized when disease stages are measured on continuous scales, resulting in a notable prediction performance when applied to the prediction of the disease.ConclusionOur unified framework provides a method to identify brain connectivity-based biomarkers and continuity-reflecting BASIC scores that are sensitive and specific to disease progression.

show abstract

The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

Cited by 22 publications

References 60 publications

Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment

Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment

Multiplexity of human brain oscillations as a personal brain signature

Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders

Contact Info

Product

Resources

About