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Konopkina, K.

Publications and source records attributed to Konopkina, K..

2 recordsLinked to original sources

Multimodal MRI prediction of cognitive functioning across the lifespan: separating between-person differences from within-person changes

Brain MRI shows promise for predicting cognitive functioning, but its utility depends on its capacity to capture stable between-person differences (e.g., patient stratification), longitudinal within-person changes (e.g., prognosis, treatment monitoring), or both. Using longitudinal data from 450 adults (aged 21-90; up to three waves, five years apart) in the Dallas Lifespan Brain Study, we benchmarked five modalities, task fMRI, functional connectivity (FC), structural MRI (sMRI), diffusion-weighted imaging (DWI), and arterial spin labeling (ASL), across 37 phenotypes and their combination. Stacking all MRI modalities into one marker predicted cognitive functioning with the highest accuracy (R{superscript 2}=.51), followed by DWI and FC. Variance decomposition showed MRI markers explained substantial between-person variance (up to 60.3%) but modest within-person changes (up to 17.2%) in cognitive functioning. Commonality analysis revealed most markers, except ASL, overlapped with age-related variance in cognitive functioning. These findings clarify the strengths and limitations of MRI markers for stratifying and monitoring cognitive aging.

neuroscience↗

Multimodal MRI-based neuromarkers trace longitudinal changes in cognitive functioning in ADHD

The National Institute of Mental Healths Research Domain Criteria (RDoC) framework conceptualises cognition as a core functional domain for psychopathology that should be studied across multiple units of analysis and developmental timescales. In ADHD, however, it remains unclear whether neuroimaging-derived markers can not only predict inter-individual differences in cognition but also track longitudinal cognitive development and capture cognition-psychopathology relationships. Using the longitudinal Oregon ADHD-1000 study (n = 594 participants; 1,053 observations), we developed multimodal machine-learning markers of general cognitive functioning (g) from structural and resting-state functional MRI. The multimodal marker achieved an out-of-sample correlation of r = .46 and generalised similarly across children with and without ADHD. The marker explained 25.01% of interindividual variance (r = .48) and 18.82% of intraindividual variance (r = .52) in cognitive functioning. Commonality analyses showed that it captured 60.87% of intraindividual age-related cognitive variation (5.18% of the total variance in g). The marker also accounted for substantial portions of the cognition-hyperactivity association (58.79%; 6.39% of total variance in g) and the cognition-inattention association (25.99%; 4.13% of total variance in g). These findings provide evidence that multimodal structural and functional MRI can generate RDoC-informed markers that predict cognitive functioning, track cognitive development over time, and capture meaningful links between cognition and ADHD symptoms. Although predictive performance remains below levels required for clinical application, the results establish a foundation for longitudinal, RDoC-inspired investigations of cognitive development in ADHD.

neuroscience↗