Predicting Working Memory Training Responsiveness in Parkinson’s Disease: Both “System Hardware” and Room for Improvement Are Needed

Abstract

Background. Patients with Parkinson’s disease (PD) are highly vulnerable to develop cognitive dysfunctions, and the mitigating potential of early cognitive training (CT) is increasingly recognized. Predictors of CT responsiveness, which could help to tailor interventions individually, have rarely been studied in PD. This study aimed to examine individual characteristics of patients with PD associated with responsiveness to targeted working memory training (WMT). Methods. Data of 75 patients with PD (age: 63.99 ± 9.74 years, 93% Hoehn & Yahr stage 2) without cognitive dysfunctions from a randomized controlled trial were analyzed using structural equation modeling. Latent change score models with and without covariates were estimated and compared between the WMT group ( n = 37), who participated in a 5-week adaptive WMT, and a waiting list control group ( n = 38). Results. Latent change score models yielded adequate model fit (χ2-test p > .05, SRMR ≤ .08, CFI ≥ .95). For the near-transfer working memory composite, lower baseline performance, younger age, higher education, and higher fluid intelligence were found to significantly predict higher latent change scores in the WMT group, but not in the control group. For the far-transfer executive function composite, higher self-efficacy expectancy tended to significantly predict larger latent change scores. Conclusions. The identified associations between individual characteristics and WMT responsiveness indicate that there has to be room for improvement (e.g., lower baseline performance) and also sufficient “hardware” (e.g., younger age, higher intelligence) to benefit in training-related cognitive plasticity. Our findings are discussed within the compensation versus magnification account. They need to be replicated by methodological high-quality research applying advanced statistical methods with larger samples.

Cognitive Performance and Learning Parameters Predict Response to Working Memory Training in Parkinson’s Disease

“…The increased predictive value of the learning parameters at FU is also reflected in higher ranks of feature importance. This is in accordance with findings of previous correlational WMT analyses [ 12 ] that revealed how individual differences in demographic, cognitive, and clinical variables become less important from immediate to 3-month FU examination. The present study including learning parameters therefore may contribute to closing the gap in understanding the factors influencing response to WMT in patients with PD by revealing associative relationships between individual patient characteristics and cognitive performance.…”

Memory enhancement by multidomain group cognitive training in patients with Parkinson’s disease and mild cognitive impairment: long-term effects of a multicenter randomized controlled trial

“…Additional, higher respective lower age, female respective male sex, higher education level, lower baseline motor status, lower baseline LEDD, and positive respective negative ApoE4 status predicted training gains after 6 or 12 months in the CT group for selected outcomes. Previous PD studies detected lower baseline scores [ 12 , 14 ], higher global cognitive status [ 11 ], higher fluid intelligence and higher self-efficacy expectancy [ 14 ], MCI diagnosis [ 13 ], higher educational level [ 11 , 14 ], longer [ 10 ] or shorter disease duration [ 11 ], younger age [ 14 ], and younger age at PD diagnosis [ 10 ] as predictive for enhancements in cognitive functions immediately or 3 months after CT. These inconsistent results may be explained by study-specific differences (e.g., sample size and heterogeneity, cognitive tests used), but may also indicate the challenge of predicting CT responsiveness in cognitively impaired PD patients.…”

“…Moreover, little research has been done in the past to identify predictors of CT responsiveness in PD patients. Few previous studies systematically investigated a variety of sociodemographic, clinical, genetic, and neuropsychological factors [10][11][12][13][14], however, inconsistent results were reported for most predictors.…”

Working Memory Training Responsiveness in Parkinson’s Disease Is Not Determined by Cortical Thickness or White Matter Lesions

“…Cognitive training might be a way of non-pharmacological intervention to preserve a high level of cognitive function and independence [ 1 ]. In line with this, our group has previously shown that 5 weeks of computerized working memory training (WMT) could boost cognitive performance for visual-spatial and verbal working memory in a cohort of cognitively healthy patients with PD over a 3-months follow-up period [ 2–4 ].…”

“…In the light of precision medicine working towards tailored treatment approaches, it is essential to understand who can benefit most from WMT. While younger age, higher intelligence and lower cognitive baseline performance seem to predict better training responsiveness [ 2 ], it is currently unknown how preexisting structural brain properties might influence training success. While it has previously been shown, that a higher load of white matter lesions was associated with lower training-induced change in processing speed— but no other cognitive domain— in healthy elderly, such evidence is missing in patients with PD [ 7 ].…”