Neuroimaging Biomarkers Predicting the Efficacy of Multimodal Rehabilitative Intervention in the Alzheimer’s Dementia Continuum Pathology

Sonia Di Tella*, Monia Cabinio, Sara Isernia, Valeria Blasi, Federica Rossetto, Francesca Lea Saibene, Margherita Alberoni, Maria Caterina Silveri, Sandro Sorbi, Mario Clerici, Francesca Baglio

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

In this work we aimed to identify neural predictors of the efficacy of multimodal rehabilitative interventions in AD-continuum patients in the attempt to identify ideal candidates to improve the treatment outcome. Subjects in the AD continuum who participated in a multimodal rehabilitative treatment were included in the analysis [n = 82, 38 Males, mean age = 76 ± 5.30, mean education years = 9.09 ± 3.81, Mini Mental State Examination (MMSE) mean score = 23.31 ± 3.81]. All subjects underwent an MRI acquisition (1.5T) at baseline (T0) and a neuropsychological evaluation before (T0) and after intervention (T1). All subjects underwent an intensive multimodal cognitive rehabilitation (8–10 weeks). The MMSE and Neuropsychiatric Inventory (NPI) scores were considered as the main cognitive and behavioral outcome measures, and Delta change scores (T1–T0) were categorized in Improved (ΔMMSE > 0; ΔNPI < 0) and Not Improved (ΔMMSE ≤ 0; ΔNPI ≥ 0). Logistic Regression (LR) and Random Forest classification models were performed including neural markers (Medial Temporal Brain; Posterior Brain (PB); Frontal Brain (FB), Subcortical Brain indexes), neuropsychological (MMSE, NPI, verbal fluencies), and demographical variables (sex, age, education) at baseline. More than 50% of patients showed a positive effect of the treatment (ΔMMSE > 0: 51%, ΔNPI < 0: 52%). LR model on ΔMMSE (Improved vs. Not Improved) indicate a predictive role for MMSE score (p = 0.003) and PB index (p = 0.005), especially the right PB (p = 0.002) at baseline. The Random Forest analysis correctly classified 77% of cognitively improved and not improved AD patients. Concerning the NPI, LR model on ΔNPI (Improved vs. Not Improved) showed a predictive role of sex (p = 0.002), NPI (p = 0.005), PB index (p = 0.006), and FB index (p = 0.039) at baseline. The Random Forest reported a classification accuracy of 86%. Our data indicate that cognitive and behavioral status alone are not sufficient to identify best responders to a multidomain rehabilitation treatment. Increased neural reserve, especially in the parietal areas, is also relevant for the compensatory mechanisms activated by rehabilitative treatment. These data are relevant to support clinical decision by identifying target patients with high probability of success after rehabilitative programs on cognitive and behavioral functioning.
Lingua originaleEnglish
pagine (da-a)735508-N/A
RivistaFrontiers in Aging Neuroscience
Volume13
DOI
Stato di pubblicazionePubblicato - 2021

Keywords

  • MRI
  • biomarker
  • brain reserve
  • cognitive reserve
  • dementia
  • neurodegenerative diseases
  • rehabilitation

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