Qualitative Evaluation of the Immediate Copy of the Rey–Osterrieth Complex Figure: Comparison Between Vascular and Degenerative MCI Patients

Salvadori, Emilia; Dieci, Francesca; Caffarra, Paolo; Pantoni, Leonardo

doi:10.1093/arclin/acy010

Cited by 24 publications

(18 citation statements)

References 34 publications

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“…The score is the time in seconds required to complete; higher scores represent worse performance.Color Word Stroop Test, for selective attention and response inhibition, requires the participants to selectively process the color features of written words, while continuously blocking out the processing of reading (Caffarra et al, 2002b). The interference effect is evaluated by means of execution time in seconds; higher scores represent worse performance.Immediate copy of the Rey-Osterrieth Complex Figure (ROCF); despite this test is commonly used for the assessment of visuo-spatial abilities, there is growing consensus on the fact that its complexity requires an integrative cognitive approach, and its reproduction involves also planning and organizational strategies that are related to executive functions (Caffarra et al, 2002a; Elderkin-Thompson et al, 2004; Freeman et al, 2000; Salvadori et al, 2018; Shin et al, 2006). The score (range 0–36) is based on the presence and accuracy of 18 units of the figure; higher scores represent better performance.…”

Section: Methodsmentioning

confidence: 99%

“…Immediate copy of the Rey-Osterrieth Complex Figure (ROCF); despite this test is commonly used for the assessment of visuo-spatial abilities, there is growing consensus on the fact that its complexity requires an integrative cognitive approach, and its reproduction involves also planning and organizational strategies that are related to executive functions (Caffarra et al, 2002a; Elderkin-Thompson et al, 2004; Freeman et al, 2000; Salvadori et al, 2018; Shin et al, 2006). The score (range 0–36) is based on the presence and accuracy of 18 units of the figure; higher scores represent better performance.…”

Section: Methodsmentioning

confidence: 99%

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Fractal dimension of cerebral white matter: A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Pantoni

Marzi

Poggesi

et al. 2019

NeuroImage: Clinical

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Patients with cerebral small vessel disease (SVD) frequently show decline in cognitive performance. However, neuroimaging in SVD patients discloses a wide range of brain lesions and alterations so that it is often difficult to understand which of these changes are the most relevant for cognitive decline. It has also become evident that visually-rated alterations do not fully explain the neuroimaging correlates of cognitive decline in SVD. Fractal dimension (FD), a unitless feature of structural complexity that can be computed from high-resolution T 1 -weighted images, has been recently applied to the neuroimaging evaluation of the human brain. Indeed, white matter (WM) and cortical gray matter (GM) exhibit an inherent structural complexity that can be measured through the FD. In our study, we included 64 patients (mean age ± standard deviation, 74.6 ± 6.9, education 7.9 ± 4.2 years, 53% males) with SVD and mild cognitive impairment (MCI), and a control group of 24 healthy subjects (mean age ± standard deviation, 72.3 ± 4.4 years, 50% males). With the aim of assessing whether the FD values of cerebral WM (WM FD) and cortical GM (GM FD) could be valuable structural predictors of cognitive performance in patients with SVD and MCI, we employed a machine learning strategy based on LASSO (least absolute shrinkage and selection operator) regression applied on a set of standard and advanced neuroimaging features in a nested cross-validation (CV) loop. This approach was aimed at 1) choosing the best predictive models, able to reliably predict the individual neuropsychological scores sensitive to attention and executive dysfunctions (prominent features of subcortical vascular cognitive impairment) and 2) identifying a features ranking according to their importance in the model through the assessment of the out-of-sample error. For each neuropsychological test, using 1000 repetitions of LASSO regression and 5000 random permutations, we found that the statistically significant models were those for the Montreal Cognitive Assessment scores ( p -value = .039), Symbol Digit Modalities Test scores ( p -value = .039), and Trail Making Test Part A scores ( p -value = .025). Significant prediction of these scores was obtained using different sets of neuroimaging features in which the WM FD was the most frequently selected feature. In conclusion, we showed that a machine learning approach could be useful in SVD research field using standard and advanced neuroimaging features. Our study results raise the possibility that FD may represent a consistent feature in predicting cognitive decline in SVD that can complement standard imaging.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Fractal dimension of cerebral white matter: A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Pantoni

Marzi

Poggesi

et al. 2019

NeuroImage: Clinical

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“…This test evaluates visuo-constructional praxis, but also executive functions. A recent study [43] shows that patients with vascular MCI scored worse than patients with MCI associated with a degenerative cause.…”

Section: Discussionmentioning

confidence: 98%

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

et al. 2018

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Depression and cognitive impairment are intimately associated, especially in elderly people. However, the association between late-life depression (LLD) and mild cognitive impairment (MCI) is complex and currently unclear. In general, it can be said that LLD and cognitive impairment can be due to a common cause, such as a vascular disease, or simply co-exist in time but have different causes. To contribute to the understanding of the evolution and prognosis of these two diseases, this study’s primary intent was to explore the ability of artificial neural networks (ANNs) to identify an MCI subtype associated with depression as an entity by using the scores of an extensive neurological examination. The sample consisted of 96 patients classified into two groups: 42 MCI with depression and 54 MCI without depression. According to our results, ANNs can identify an MCI that is highly associated with depression distinguishable from the non-depressed MCI patients (accuracy = 86%, sensitivity = 82%, specificity = 89%). These results provide data in favor of a cognitive frontal profile of patients with LLD, distinct and distinguishable from other cognitive impairments. Therefore, it should be taken into account in the classification of MCI subtypes for future research, including depression as an essential variable in the classification of a patient with cognitive impairment.

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“…Ongoing and future research should focus on human- and animal-based studies of these interactions and on multidisciplinary consortia exploring potential biomarkers and clinical targets for intervention. Another relevant issue is the need to learn how to best evaluate and qualify cognitive performance as to outline cognitive components that are more specific of the vascular contribution 45 and whether this approach would be clinically meaningful. In the meanwhile, it is reasonable for all patients being assessed or managed for CI, to also be assessed for vascular brain injury and risk for it, and follow relevant published guidelines for primary or secondary prevention of cardio-cerebrovascular disease as applicable.…”

Section: Conclusion and Suggestions For Future Researchmentioning

confidence: 99%

Cerebrovascular disease in patients with cognitive impairment: A white paper from the ESO dementia committee – A practical point of view with suggestions for the management of cerebrovascular diseases in memory clinics

Verdelho

Biessels

Chabriat

et al. 2021

European Stroke Journal

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Purpose Practical suggestions on clinical decisions about vascular disease management in patients with cognitive impairment are proposed. Methods The document was produced by the Dementia Committee of the European Stroke Organisation (ESO) based on the evidence from the literature where available and on the clinical experience of the Committee members. This paper was endorsed by the ESO. Findings Vascular risk factors and cerebrovascular disease are frequent in patients with cognitive impairment. While acute stroke treatment has evolved substantially in last decades, evidence of management of cerebrovascular pathology beyond stroke in patients with cognitive impairment and dementia is quite limited. Additionally, trials to test some daily-life clinical decisions are likely to be complex, difficult to undertake and take many years to provide sufficient evidence to produce recommendations. This document was conceived to provide some suggestions until data from field trials are available. It was conceived for the use of clinicians from memory clinics or involved specifically in cognitive disorders, addressing practical aspects on diagnostic tools, vascular risk management and suggestions on some therapeutic options. Discussion and conclusions The authors did not aim to do an exhaustive or systematic review or to cover all current evidence. The document approach in a very practical way frequent issues concerning cerebrovascular disease in patients with known cognitive impairment.

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Qualitative Evaluation of the Immediate Copy of the Rey–Osterrieth Complex Figure: Comparison Between Vascular and Degenerative MCI Patients

Cited by 24 publications

References 34 publications

Fractal dimension of cerebral white matter: A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Fractal dimension of cerebral white matter: A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

Cerebrovascular disease in patients with cognitive impairment: A white paper from the ESO dementia committee – A practical point of view with suggestions for the management of cerebrovascular diseases in memory clinics

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