Dataset Information

"Will you draw me a pelvis?? Dynamic neuro-cognitive imagery improves pelvic schema and graphic-metric representation in people with Parkinson's Disease: A randomized controlled trial.

ABSTRACT: BACKGROUND:Body schema (i.e., the mental representations of the body), vital for motor and cognitive functions, is often distorted in people with Parkinson's disease (PD). Deficits in body, and especially pelvic, schema can further exacerbate motor and cognitive deficits associated with PD. Such deficits, including those in graphic and metric misjudgments, can manifest in drawing tasks. Mental imagery is a recommended approach for PD rehabilitation with potential for ameliorating body schema. OBJECTIVE:To investigate the effect of a two-week dynamic neuro-cognitive imagery (DNI) training versus in-home learning and exercise control (learning/exercise) on pelvic schema and graphic representation (i.e., drawing height and width). DESIGN:Twenty participants with idiopathic PD (Hoehn&Yahr I-III; M age: 65.75?±?10.13) were randomly allocated into either a DNI or a learning/exercise group. Participants were asked to complete the "Draw Your Pelvis? test in which they drew their pelvis at pre- and post-intervention. Drawings were assessed for pelvic schema score and drawing dimensions (i.e., height and weight). INTERVENTION:DNI anatomical and metaphorical imagery focusing on pelvic anatomy and biomechanics. RESULTS:No difference (p?>?.05) was detected at baseline between drawn pelvis height and width. Following intervention, improvements were greater in the DNI group for pelvic schema (p?

SUBMITTER: Abraham A

PROVIDER: S-EPMC6455960 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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"Will you draw me a pelvis?ˮ Dynamic neuro-cognitive imagery improves pelvic schema and graphic-metric representation in people with Parkinson's Disease: A randomized controlled trial.

Abraham Amit A Hart Ariel A Dickstein Ruth R Hackney Madeleine E ME

Complementary therapies in medicine 20190103

<h4>Background</h4>Body schema (i.e., the mental representations of the body), vital for motor and cognitive functions, is often distorted in people with Parkinson's disease (PD). Deficits in body, and especially pelvic, schema can further exacerbate motor and cognitive deficits associated with PD. Such deficits, including those in graphic and metric misjudgments, can manifest in drawing tasks. Mental imagery is a recommended approach for PD rehabilitation with potential for ameliorating body sc ...[more]

PMID: 30935544

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Project description:Alzheimer's disease (AD) is the kind of dementia that affects the most people around the world. Therefore, an early identification supporting effective treatments is required to increase the life quality of a wide number of patients. Recently, computer-aided diagnosis tools for dementia using Magnetic Resonance Imaging scans have been successfully proposed to discriminate between patients with AD, mild cognitive impairment, and healthy controls. Most of the attention has been given to the clinical data, provided by initiatives as the ADNI, supporting reliable researches on intervention, prevention, and treatments of AD. Therefore, there is a need for improving the performance of classification machines. In this paper, we propose a kernel framework for learning metrics that enhances conventional machines and supports the diagnosis of dementia. Our framework aims at building discriminative spaces through the maximization of center kernel alignment function, aiming at improving the discrimination of the three considered neurological classes. The proposed metric learning performance is evaluated on the widely-known ADNI database using three supervised classification machines (k-nn, SVM and NNs) for multi-class and bi-class scenarios from structural MRIs. Specifically, from ADNI collection 286 AD patients, 379 MCI patients and 231 healthy controls are used for development and validation of our proposed metric learning framework. For the experimental validation, we split the data into two subsets: 30% of subjects used like a blindfolded assessment and 70% employed for parameter tuning. Then, in the preprocessing stage, each structural MRI scan a total of 310 morphological measurements are automatically extracted from by FreeSurfer software package and concatenated to build an input feature matrix. Obtained test performance results, show that including a supervised metric learning improves the compared baseline classifiers in both scenarios. In the multi-class scenario, we achieve the best performance (accuracy 60.1%) for pretrained 1-layered NN, and we obtain measures over 90% in the average for HC vs. AD task. From the machine learning point of view, our proposal enhances the classifier performance by building spaces with a better class separability. From the clinical application, our enhancement results in a more balanced performance in each class than the compared approaches from the CADDementia challenge by increasing the sensitivity of pathological groups and the specificity of healthy controls.

Dataset Information

"Will you draw me a pelvis?? Dynamic neuro-cognitive imagery improves pelvic schema and graphic-metric representation in people with Parkinson's Disease: A randomized controlled trial.

Publications

"Will you draw me a pelvis?ˮ Dynamic neuro-cognitive imagery improves pelvic schema and graphic-metric representation in people with Parkinson's Disease: A randomized controlled trial.

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