Clinical connectome fingerprints of cognitive decline

Sorrentino, Pierpaolo; Rucco, Rosaria; Lardone, Anna; Liparoti, Marianna; Lopez, Emahnuel Troisi; Cavaliere, Carlo; Soricelli, Andrea; Jirsa, Viktor K.; Sorrentino, Giuseppe; Amico, Enrico

doi:10.1016/j.neuroimage.2021.118253

Cited by 42 publications

(91 citation statements)

References 52 publications

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“…Specifically, we found that the patients' I-self and I-others were lower than those of the control, while there was no statistically significant difference between the I-diff of the two groups. As previously suggested by Sorrentino et al, the loss of identifiability might be ascribed to the large-scale network reorganisation in ALS which make the functional connectomes of the patients less similar to themselves (Sorrentino et al, 2021b). This could be largely due to the alteration of synchrony patterns in the diseased brain (Proudfoot et al, 2019).…”

Section: Discussionmentioning

confidence: 85%

“…In this study we used an extension of the identifiability approach (Sorrentino et al, 2021b) by crossing the ALS patients and the control group test and re-test FCs to obtain the I-clinical score which can assess how much each patient was similar to healthy controls. We built two hybrid matrices: the first one by correlating the controls' tests with the patients' re-tests.…”

Section: Fingerprint Analysismentioning

confidence: 99%

“…The second was obtained from a correlation between the controls' re-tests and the patients' test. From the average of the values achieved from each hybrid matrix, we found the I-clinical value of each subject (for more detail, see Sorrentino et al (Sorrentino et al, 2021b).…”

Section: Fingerprint Analysismentioning

confidence: 99%

“…Fingerprinting analysis, deriving from functional connectomes (FC), is a methodological approach able to define the subject-specific characteristics of each individual (Amico and Goñi, 2018;Finn et al, 2015;Sareen et al, 2021). It has been tested in both clinical and healthy populations, revealing that individuals affected by neurodegenerative diseases showed a faded identifiability with respect to healthy individuals (Sorrentino et al, 2021b;Svaldi et al, 2021). Interestingly, the reduced identifiability was able to predict the subject-specific clinical features of patients, leading to the concept of Clinical Connectome Fingerprint (CCF) (Sorrentino et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

“…It has been tested in both clinical and healthy populations, revealing that individuals affected by neurodegenerative diseases showed a faded identifiability with respect to healthy individuals (Sorrentino et al, 2021b;Svaldi et al, 2021). Interestingly, the reduced identifiability was able to predict the subject-specific clinical features of patients, leading to the concept of Clinical Connectome Fingerprint (CCF) (Sorrentino et al, 2021b). In particular, investigating the MEG-based FCs of subjects affected by mild cognitive impairment (MCI) and healthy controls, Sorrentino et al (Sorrentino et al, 2021b) built an identification score based on the similarity between FCs of patients and healthy subjects (I-clinical), which predicted the patients' individual cognitive decline.…”

Section: Introductionmentioning

confidence: 99%

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

Romano¹,

Lopez²,

Liparoti

et al. 2022

Preprint

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Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by functional connectivity alterations in both motor and extra-motor brain regions. In network analysis, fingerprint represent a valid approach able to assess the subject-specific connectivity features of a given population. Here, we applied the Clinical Connectome Fingerprint (CCF) analysis to source-reconstructed magnetoencephalography (MEG) signals in a cohort of seventy-eight subjects: thirty-nine ALS patients and thirty-nine healthy controls. We set out to develop an identifiability matrix to assess the extent to which each subject was recognisable based on his/her connectome. The analysis was performed in all five canonical frequency bands. Then we built a multilinear regression model to test the ability of clinical fingerprint to predict the clinical evolution of disease assessed by the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-r), the King s disease staging system and the Milano-Torino Staging (MiToS) disease staging system. We found a drop in patients identifiability in the alpha band compared to the healthy controls. Furthermore, the clinical fingerprint was predictive of ALSFRS-r (p=0.0397; beta;=32.8), King s (p=0.0001; beta;= -7.40) and MiToS (p=0.0025; beta;= -4.9) scores and negatively correlated with King s and MiToS scales according to Spearman s correlation. Our results demonstrated the ability of the CCF approach in assessing the individual motor condition and its relationship with ALS disease. Thanks to the subject-specific characteristic of this approach, we hope that further exploration related to its clinical application may help improve the management of disease.

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Section: Discussionmentioning

confidence: 85%

Section: Fingerprint Analysismentioning

confidence: 99%

Section: Fingerprint Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Romano¹,

Lopez²,

Liparoti

et al. 2022

Preprint

Self Cite

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

Lopez

Minino

Liparoti

et al. 2022

Human Brain Mapping

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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.

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High‐accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding

Hannum

Lopez

Blanco

et al. 2023

Human Brain Mapping

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The human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state. A prerequisite for realizing this aim, however, is that brain networks also serve as reliable markers of an individual. Here, using Human Connectome Project data, we build upon recent studies examining brain‐based fingerprints of individual subjects and cognitive states based on cognitively demanding tasks that assess, for example, working memory, theory of mind, and motor function. Our approach achieves accuracy of up to 99% for both identification of the subject of an fMRI scan, and for classification of the cognitive state of a previously unseen subject in a scan. More broadly, we explore the accuracy and reliability of five different machine learning techniques on subject fingerprinting and cognitive state decoding objectives, using functional connectivity data from fMRI scans of a high number of subjects (865) across a number of cognitive states (8). These results represent an advance on existing techniques for functional connectivity‐based brain fingerprinting and state decoding. Additionally, 16 different functional connectome (FC) matrix construction pipelines are compared in order to characterize the effects of different aspects of the production of FCs on the accuracy of subject and task classification, and to identify possible confounds.

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Clinical connectome fingerprints of cognitive decline

Cited by 42 publications

References 52 publications

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

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

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

High‐accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding

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