Title: The use of somatosensory discrimination with respect to motor and functional recovery of the upper limb after stroke : a study with functional MRI
Other Titles: Somatosensorische discriminatie in het kader van de motorische en functionele recuperatie van het bovenste lidmaat na CVA : een fMRI- studie
Authors: Van de Winckel, Ann
Issue Date: 17-May-2006
An acute sensorimotor hemiparesis of the contralateral upper and/or lower limb occurs in 73% to 88% of all patients with cerebral infarctions. Fifty-five to 75% of these patients still suffer from both motor and functional deficits, three to six months after stroke. As a result, 30% to 60% of the patients remain dependent in some activities of daily living and only 25-45% of patients regain functional recovery of the arm after stroke.
One of these new treatment concepts is the “Cognitive Therapeutic Exercises”, developed by Perfetti. Cognitive elements are added to the sensorimotor training, represented by tactile and kinesthetic discrimination tasks. During sensorimotor discrimination tasks, patients are encouraged to create mental sensorimotor representations of the movement in a structured motor learning environment. The tasks are programmed with increasing complexity throughout the rehabilitation process and aim at motor recovery, avoiding both inadequate adaptation of tone and inefficient muscle contractions. Sensory feedback, such as proprioception, has a crucial effect on motor ability at cortical and spinal levels as it reshapes sensorimotor reorganisation. The goal of this therapeutic concept is to teach the patient to move in a qualitatively correct way. Quality of movement also depends on ‘sense of touch’, because it is an essential key for transmitting information between the human body and its environment. One of the most frequently used body parts to transmit this information, are the hands. Consequently, therapists who use this way of rehabilitating patients pay more attention to the rehabilitation of the upper limb, and particularly to the hand, in comparison to therapists who use other treatment concepts.

Many therapists use scales to record functioning or activity in the arm after stroke and to document changes after therapy. There is concern amongst therapists because the existing scales do not take sufficiently the quality of movement into account.

Several scales have been specifically developed to monitor the motor activity level of the upper limb. All have been tested for reliability and validity. However, few have been thoroughly verified for other statistical properties.

Rasch analysis has recently been introduced in the rehabilitation sciences and is increasingly used for scale development and to verify scaling properties of existing scales. Rasch analysis examines the validity and unidimensionality of the scale. It has the power to verify whether the data are fitting the Rasch model, transforms the original ordinal scale into an interval scale, where both the ability of the patient and the difficulty of the items are displayed on the same metric. It also shows which items or persons are not behaving according to the model.

In Chapter I, the Rasch analysis on the “Motor Evaluation Scale for Upper Extremity in Stroke Patients” (MESUPES) has been performed.
To answer the need for taking into account the quality of movement, the “Motor Evaluation Scale for Upper Extremity in Stroke Patients” (MESUPES) was developed. The scale was investigated for validity and unidimensionality using the Rasch measurement model. Differential item functioning was performed to test the stability of item hierarchy on several variables. Inter-rater reliability was examined with kappa-values, weighted percentage agreement and intra-class correlation coefficients (ICC).
Three hundred ninety-six patients (average age: 63.38 +/- 12.89 years) participated in the Rasch study and 56 patients (average age: 65.68 +/-12.75 years) in the reliability study. They were recruited in twelve hospitals and rehabilitation centres in Belgium, Germany and Switzerland. Differential item functioning (DIF) established the stability of the items for gender, age, country, side of hemiplegia, type of stroke and time since stroke.

Based on Rasch analysis, 5 items were removed from the original scale of 22 items and the scale has been divided into a MESUPES-arm test (8 items) and a MESUPES-hand test (9 items). Both scales fitted the Rasch model. Kappa values and weighted percentage agreements were high on all individual items, and intra-class correlation coefficients were high on group level, confirming the reliable measurement of arm and hand performance. The items were stable across all subgroups of the sample.

In sum, the MESUPES-arm and -hand test measure the upper limb function in stroke patients taking into account the quality of the movement performance. The MESUPES-arm and -hand meet the statistical properties of reliability, internal construct validity and unidimensionality.

In Chapter II, the “Rivermead Motor Assessment (RMA) Arm Section” has been evaluated with Rasch analysis.
The RMA Arm section is widely used in research. Validity and unidimensionality was investigated with Rasch analysis. Principal component analysis on the Rasch data gave additional information on unidimensionality. Differential item functioning (DIF) provided evidence of the stability of the items across gender, age, country, side of hemiplegia, type of stroke and time after stroke.

Two hundred fifty-two stroke patients with an average age of 63.5 years (SD 13.2) participated to the study. They were recruited in 12 stroke units and rehabilitation centres across Belgium, Germany, and Switzerland.
Eleven of the original 15 items fitted the Rasch model. Principal component analysis revealed two hidden dimensions in the scale. Therefore, the Revised RMA Arm section (RRMA Arm Section) has been divided into two main subjects: “static and dynamic control of movement” and “coordination”. The two subsets showed no DIF, meaning that the hierarchy of the items was maintained across the subgroups of the sample. The RRMA Arm Section is therefore a valid tool to evaluate upper limb function after stroke.

A better knowledge of the mechanisms regulating the long-term (partial) recovery observed for most neurological problems after neural damage, might prompt new and more efficacious therapeutic and rehabilitative strategies for neurological diseases as well as a better understanding of the mechanisms of existing therapies in terms of how intense they need to be applied and to which group of stroke patients they might benefit the most.
The “Cognitive Therapeutic Exercises” of Perfetti, is a neurocognitive rehabilitation concept that makes use of cognitive sensorimotor discrimination tasks and mental representation of the perceived movement. The tasks are of gradually increasing complexity, avoiding both inadequate muscle tone and inefficient muscle contractions. Attention, problem solving, memory, language and perception play an important role. The therapist teaches patients how to use their cognitive and attention capabilities. This exchange of information between motor activity and sensory feedback is essential to programme movements. Patients with poor or absent recovery of hand motor control after a stroke have a severely reduced metabolism in the sensory thalamic relay, which suggests that appropriate sensory feedback from the paretic hand is essential for motor recovery.
The goal of this study was to get more in-depth knowledge of the mechanisms involved when solving passive somatosensory discrimination tasks. Passive somatosensory discrimination tasks are used during the rehabilitation of hemiplegic stroke patients. This process involves cognitive operations such as receiving, storing and processing sensory information. Passive somatosensory discrimination exercises can be applied early on to stimulate the sensorimotor system, and thus to improve motor outcome. However, it is unclear how these neural networks operate, either in healthy subjects or in patients.

In Chapter I we explored which areas of the brain where involved in passive somatosensory discrimination tasks in healthy volunteers.
We applied functional magnetic resonance imaging (fMRI) while the right index finger of ten healthy subjects was passively moved along various shapes and lengths by an fMRI compatible robot. This robot was specifically developed to mimic the task, normally done by the therapist. During the experimental condition, patients had to discriminate either (1) two unfamiliar shapes (quadrilateral shapes with unparallel and unequal sides; (2) two familiar shapes (rectangles or triangles); or (3) two lengths. For the length discrimination, the finger of the participant was moved along a wooden bar, which was fixed to the robot. For the discrimination of shapes, the finger was moved in the air. The participants had their eyes closed and had to decide whether the two movements were the same or not. We wanted to identify the neural networks involved during the cognitive process of discriminating spatial features, and not the areas related to movement, or to general discrimination. Therefore, we added a control condition, in which movement was presented, but not attended to. To make sure that participants did not pay attention to the movement, we asked them to discriminate music fragments. In both the experimental and control condition, passive movements were provided together with music fragments. In the experimental condition, passive movements were discriminated and music was not attended to. In the control condition, music fragments were discriminated and movements were not attended to.

Comparing passive somatosensory to music discrimination, a bilateral parieto-frontal network was identified, including the precuneus, superior parietal gyrus, postcentral gyrus, rostral intraparietal sulcus and supramarginal gyrus as well as the supplementary motor area (SMA), dorsal premotor (PMd) and ventral premotor (PMv) areas. Additionally we compared the discrimination of different spatial features, i.e. discrimination of length versus familiar and unfamiliar geometric shapes. Length discrimination activated mainly medially located superior parietal and dorsal premotor circuits whereas discrimination of familiar geometric shapes activated a more lateral network including inferior parietal and ventral premotor regions. These results indicate that differential parieto-frontal circuits are involved in the discrimination of length versus familiar shapes and, thus, provide new insights into the neural basis of extracting spatial features from somatosensory input.

In Chapter II we explored which neural networks were operating during passive somatosensory discrimination tasks in recovered subcortical stroke patients. Second, we verified whether those networks differed from those found in age-matched healthy volunteers.
Eight patients with subcortical stroke were asked to discriminate familiar shapes (rectangles or triangles) or differences in length, while the index finger of the right hand was passively moved by an MRI compatible robot. Passive movements were provided together with music fragments. In the experimental condition, patients discriminated shape or length, whereas in the control condition, patients discriminated music fragments, to divert attention from the movement. Then, we contrasted familiar shape and length discrimination to music discrimination. Familiar shape discrimination activated the more laterally located inferior parietal sulcus along with a minor PMv activation, while the length discrimination divulged a medially located superior parietal activation, along with a minor PMd activation. Cerebellar activation was present in length discrimination and to a minor extent also in the familiar shape discrimination.

This study is a first step to understand how different types of passive somatosensory discrimination operate in the brain of recovered subcortical stroke patients.
This may be a stepping stone to set up a lesion-specific rehabilitation program for stroke patients.

Second, the results did not differ statistically from the results of a similar previous study in age-matched healthy volunteers. The findings suggest that the cognitive mechanisms related to solving discrimination tasks remain intact and can therefore be implemented to improve motor function during rehabilitation.
Table of Contents: PART I: RASCH ANALYSIS
Introduction 3
1. Stroke: Symptoms and treatment programmes 5
2. Measurement of upper limb in stroke 6
3. Rasch analysis 7
3.1. Theoretical concept of Rasch analysis 8
3.2. Practical use of Rasch analysis 13
3.3. Conclusion 17
References 19
Chapter I 25
Can quality of movement be measured? Rasch analysis and inter-rater
reliability of the “Motor Evaluation Scale for Upper Extremity in Stroke Patients” (MESUPES)
Ann Van de Winckel, Hilde Feys, Suzan van der Knaap, Ruth Messerli, Fabio Baronti,
Ruth Lehmann, Bart Van Hemelrijk, Franca Pantè, Carlo Perfetti, Willy De Weerdt
Clinical Rehabilitation 2006, 20(9).
Chapter II 53
Assessment of arm function in stroke patients: Rivermead Motor
Assessment (RMA) Arm section revised with Rasch analysis
Ann Van de Winckel, Hilde Feys, Nadina Lincoln, Willy De Weerdt
Clinical Rehabilitation (submitted)
Summary and discussion 73
Summary 75
Discussion 78
1. Implications of the study 78
2. Future considerations 79
References 81

Introduction 87
1. Brain areas related to movement 89
1.1. Simple versus complex movements 90
1.2. Active versus passive movements 90
1.3. Imagined movements 91
2. Motor recovery 92
2.1. Motor recovery after stroke 92
2.2 Motor recovery after rehabilitation 94
3. Present study 98
3.1. Rationale for the study 98
3.2. Aim and set-up of the study 101
References 105

Chapter I 113
Passive Somatosensory Discrimination Tasks in Healthy Volunteers:
differential networks involved in familiar versus unfamiliar shape and
length discrimination.
Ann Van de Winckel, Stefan Sunaert, Nicole Wenderoth, Ron Peeters, Paul Van Hecke, Hilde Feys, Els Horemans, Guy Marchal, Stephan P. Swinnen, Carlo Perfetti, Willy De Weerdt
NeuroImage 2005, 26: 441-453.
Chapter II 153
Passive Somatosensory Discrimination in Subcortical Stroke Patients: differential neural network in familiar shape versus length discrimination.
Ann Van de Winckel, Stefan Sunaert, Nicole Wenderoth, Ron Peeters, Hilde Feys,
Els Horemans, Guy Marchal, Vincent Thijs, Stephan P. Swinnen, Carlo Perfetti, Willy De Weerdt
in preparation for submission
Summary and discussion 193
Summary 195
Discussion 198
1. Implications of the study 198
2. fMRI and current therapeutic concepts 199
References 202
Publication status: published
KU Leuven publication type: TH
Appears in Collections:Research Group for Neuromotor Rehabilitation
Department of Rehabilitation Sciences - miscellaneous

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