Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease

Abstract

Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.

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

“…Fingerprinting analysis based on the functional connectomes (FC) is a methodological approach utilised 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 reduced identifiability with respect to healthy individuals ( Sorrentino et al, 2021b , Svaldi et al, 2021 ). Interestingly, the reduced identifiability could predict individual clinical features of patients, leading to the concept of the Clinical Connectome Fingerprint (CCF) ( Sorrentino et al, 2021b ).…”

Fingerprints of brain disease: Connectome identifiability in cognitive decline and Alzheimer’s disease

“…The next natural step is to explore whether this property of the human brain is maintained during disease. Despite promising findings towards this direction 18,19 , it is to date unclear to what extent FC-fingerprints could be used for mapping disease from human brain data.…”

“…Despite promising findings towards this direction 18,19 , it is to date unclear to what extent FCfingerprints could be used for mapping disease from human brain data.…”

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

“…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).…”