RESUMO
BACKGROUND: Word-finding difficulty is prevalent but poorly understood in persons with relapsing-remitting multiple sclerosis (RRMS). OBJECTIVE: The objective was to investigate our hypothesis that phonological processing ability is below expectations and related to word-finding difficulty in patients with RRMS. METHOD: Data were analyzed from patients with RRMS (n = 50) on patient-reported word-finding difficulty (PR-WFD) and objective performance on Wechsler Individual Achievement Test, Fourth Edition (WIAT-4) Phonemic Proficiency (PP; analysis of phonemes within words), Word Reading (WR; proxy of premorbid literacy and verbal ability), and Sentence Repetition (SR; auditory processing of word-level information). RESULTS: Performance (mean (95% confidence interval)) was reliably lower than normative expectations for PP (-0.41 (-0.69, -0.13)) but not for WR (0.02 (-0.21, 0.25)) or SR (0.08 (-0.15, 0.31). Within-subjects performance was worse on PP than on both WR (t(49) = 4.00, p < 0.001, d = 0.47) and SR (t(49) =3.76, p < 0.001, d = 0.54). Worse PR-WFD was specifically related to lower PP (F2,47 = 6.24, p = 0.004, η2 = 0.21); worse PP performance at PR-WFD Often (n = 13; -1.16 (-1.49, -0.83)) than Sometimes (n = 17; -0.14 (-0.68, 0.41)) or Rarely (n = 20; -0.16 (-0.58, 0.27). PR-WFD was unrelated to WR or SR (ps > 0.25). CONCLUSION: Phonological processing was below expectations and specifically linked to word-finding difficulty in RRMS. Findings are consistent with early disease-related cortical changes within the posterior superior temporal/supramarginal region. Results inform our developing model of multiple sclerosis-related word-finding difficulty.
RESUMO
Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 ± 3.64 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 ± 6.51 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.