Phenotyping Superagers Using Resting-State fMRI

Godoy, Laiz Laura de; Neto, Adalberto Studart; Paula, Demetrius Ribeiro de; Green, Nathan; Halder, Arjama; Arantes, P.; Chaim, Khallil Taverna; Moraes, Natália Cristina; Yassuda, Mônica Sanches; Nitrini, Ricardo; Dresler, Martin; Leite, Claudia da Costa; Panovska‐Griffiths, Jasmina; Soddu, Andrea; Bisdas, Sotirios

doi:10.3174/ajnr.a7820

Cited by 2 publications

(2 citation statements)

References 58 publications

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“…Future research may also make use of MRI scanners with higher magnetic field strength (e.g., 7 Tesla) or employ high-precision machine learning methods, both of which have been demonstrated to improve sensitivity 90 , 91 and detect subtle group differences and have shown promise in recent studies. 62 , 63 …”

Section: Discussionmentioning

confidence: 99%

“… 61 Two recent studies have used machine learning to differentiate rs-fMRI signal from successful cognitive agers and controls in participants 60 and older 62 and 80 and older. 63 However, results are difficult to interpret because neither study has longitudinal data to ensure that participants were free from emergent neurodegenerative disease.…”

Section: Introductionmentioning

confidence: 99%

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SuperAging functional connectomics from resting-state functional MRI

Diamond,

Sridhar,

Maier

et al. 2024

Brain Communications

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Understanding the relationship between functional connectivity (FC) of higher-order neurocognitive networks and age-related cognitive decline is a complex and evolving field of research. Decreases in FC have been associated with cognitive decline in persons with Alzheimer’s disease and related dementias (ADRD). However, the contributions of FC have been less straightforward in typical cognitive aging. Some investigations suggest relatively robust FC within neurocognitive networks differentiates unusually successful cognitive aging from average aging, while others do not. Methodologic limitations in data processing and varying definitions of “successful aging” may have contributed to the inconsistent results to date. The current study seeks to address previous limitations by optimized MRI methods to examine FC in the well-established SuperAging phenotype, defined by age and cognitive performance as individuals 80 and older with episodic memory performance equal to or better than 50-to-60-year-olds. Within- and between-network FC of large-scale neurocognitive networks were compared between 24 SuperAgers and 16 cognitively average older-aged control (OACs) with stable cognitive profiles using resting state functional MRI (rs-fMRI) from a single visit. Group classification was determined based on measures of episodic memory, executive functioning, verbal fluency, and picture naming. Inclusion criteria required stable cognitive status across two visits. First, we investigated the FC within and between seven resting state networks from a common atlas parcellation. A separate index of network segregation was also compared between groups. Second, we investigated the FC between six subcomponents of the default mode network (DMN), the neurocognitive network commonly associated with memory performance and disrupted in persons with ADRD. For each analysis, FCs were compared across groups using two-sample independent t-tests and corrected for multiple comparisons. There were no significant between-group differences in demographic characteristics including age, sex, and education. At the group-level, within-network FC, between-network FC, and segregation measurements of seven large-scale networks, including subcomponents of the DMN, were not a primary differentiator between cognitively average aging and SuperAging phenotypes. Thus, FC within or between large-scale networks does not appear to be a primary driver of the exceptional memory performance observed in SuperAgers. These results have relevance for differentiating the role of FC changes associated with cognitive aging from those associated with ADRD.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SuperAging functional connectomics from resting-state functional MRI

Diamond,

Sridhar,

Maier

et al. 2024

Brain Communications

View full text Add to dashboard Cite

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Phenotyping superagers using machine learning algorithms on whole brain connectivity resting-state fMRI studies

de Godoy,

de Paula,

Min

et al. 2024

Preprint

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Superagers, older adults with memory performance similar to middle-aged individuals, were studied to identify key neural networks responsible for their brain function connectivity. Using a previously published resting-state fMRI (rs-fMRI) dataset from 31 participants (14 superagers and 17 controls) examined at 3 and 7 Tesla (T) scanners, we cross-validated the findings from an Elastic Net regression model using a Random Forest algorithm. Important nodes were identified based on Mean Decrease Gini and Mean Decrease Accuracy measures. Superagers were initially phenotyped in six key preselected networks and then across eleven whole-brain networks. The study confirmed the importance of the salience and default mode networks in classifying superagers, identifying significant nodes in the precuneus, posterior cingulate cortex, prefrontal cortex, temporo-occipital junction, and extrastriate superior cortex. Whole-brain analysis highlighted novel relevant networks, including auditory, visual-lateral, and visual-medial networks. Results showed that 7T rs-fMRI provided more discriminative nodes and better predictive performance than 3T. The findings underscore the role of particular brain regions and networks related to memory and cognition in superagers and suggest that additional nodes in auditory and visual networks contribute to their cognitive resilience. These insights enhance understanding of brain resilience and preserved cognitive abilities in older adults.

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Phenotyping Superagers Using Resting-State fMRI

Cited by 2 publications

References 58 publications

SuperAging functional connectomics from resting-state functional MRI

SuperAging functional connectomics from resting-state functional MRI

Phenotyping superagers using machine learning algorithms on whole brain connectivity resting-state fMRI studies

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