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

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.

Biomarkers for dementia in Latin American countries: Gaps and opportunities

“…Even respondents who do not use it are interested and see this as a useful tool supporting clinical and research settings. This result may be explained by the growing body of evidence supporting the use of neuroimaging biomarkers throughout the dementia continuum (MCI 75 ) and care pathway including diagnosis, 76 progression, 77 and predicting treatment response 78 …”

“…This result may be explained by the growing body of evidence supporting the use of neuroimaging biomarkers throughout the dementia continuum (MCI 75 ) and care pathway including diagnosis, 76 progression, 77 and predicting treatment response. 78 Neither peripheral nor genetic biomarkers are currently reported as being used for clinical purposes. A barrier to the clinical implementation of peripheral biomarkers might be the lack of adequate centers and standardized protocols for sample processing.…”

Exploring cognitive reserve’s influence: unveiling the dynamics of digital telerehabilitation in Parkinson’s Disease Resilience

“…Identifying factors influencing rehabilitation response in chronic neurological diseases is a relevant field of investigation paving the way for treatment personalization and customization 1,2 , with the potential to maximize efficacy. Previous studies revealed the role of demographics, clinical conditions and psychosocial variables in modulating rehabilitation outcomes [1][2][3][4] .…”

“…Identifying factors influencing rehabilitation response in chronic neurological diseases is a relevant field of investigation paving the way for treatment personalization and customization 1,2 , with the potential to maximize efficacy. Previous studies revealed the role of demographics, clinical conditions and psychosocial variables in modulating rehabilitation outcomes [1][2][3][4] . Besides these factors, the latest evidence supported the role of life-long stimulating activities in influencing patients' attitudes and responses to nonpharmacological interventions in neurological disorders [4][5][6][7][8][9][10] .…”

The Neural Correlates of Metacognitive Awareness

“…Research on neurodegeneration and aging has highlighted the importance of identifying objective measures of biological processes that characterize medical states and clinical outcomes (Belleville & Bherer, 2012;di Tella et al, 2021;Horvath et al, 2018;Meghdadi et al, 2021;Nuzzo et al, 2014;Parnetti et al, 2019;Poil et al, 2013;Solís-Vivanco et al, 2015). Biomarkers (Strimbu & Tavel, 2010) of neurocognitive dysfunctions are commonly obtained using neuroimaging techniques, such as functional magnetic resonance (fMRI) and electroencephalography (EEG).…”