Retraining Reaching in Cerebellar Ataxia

NCT ID: NCT03879018

Last Updated: 2025-09-05

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 18 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2019-08-01
• Study Completion Date: 2026-05-01

Brief Summary

The purpose of this study is to test for benefits of reinforcement based training paradigm versus standard practice over weeks for improving reaching movements in people with ataxia.

Detailed Description

Damage to the cerebellum produces characteristic deficits in movement coordination, known as "ataxia." Reaching movements become curved, tremulous, and over- or undershoot targets, thus affecting nearly all activities of daily living. Sitting and standing balance becomes unsteady, and walking has a characteristic 'drunken' appearance with lateral veering and a widening of the base of support. People with many types of neurological diseases (e.g. autosomal dominant ataxias (e.g. SCAs), multiple sclerosis, cerebral palsy, stroke, Freidreich's ataxia) often have disabling ataxia.

In past work the investigators have shown that many individuals with ataxia from cerebellar disease can learn simple visuomotor tasks using reinforcement learning paradigms. The investigators do not know if individuals with ataxia from cerebellar disease can improve more complex motor patterns. In general, there are few rehabilitation studies on ataxia, with most focusing on balance and walking. Yet, arm ataxia is a significant problem that affects most all activities of daily living (e.g. eating, cooking, bathing, dressing, working). Many studies have assessed reaching ataxia on single days in order to try to better understand the fundamental basis for ataxic arm movements. Based on previous literary searches, there are only a couple of small studies that have tested whether training over weeks can mitigate arm ataxia. Each of these was a case series of either 3 or 4 people, and all patients had ataxia from lesions that included structures outside of the cerebellum. Both showed some positive effects but responses varied across patients. This work that the investigators propose will look at the affects of a longer training regimen of upper limb reaching in people with cerebellar ataxia.

The investigators will study cerebellar patients that have shown the ability to learn from previous work. Subjects with cerebellar ataxia will be randomized into two groups to receive either reinforcement training or standard practice training over a 12 week period. Subjects will train for 45 minutes a day, 3 times per week for two weeks for each type of training, with a two week 'rest' period in between. After training, subjects will be asked to return for two visits to test for retention. On each training day, reinforcement training (or standard practice) will be done using an Oculus Rift and Touch 3D headset. Training encompasses reaching to a 3D target with either online visual feedback or binary feedback 400 times. Motion tracking sensors will be placed on the shoulder, elbow, wrist, and finger, in order to track movement data in real time. These studies will provide important new information about upper limb long term training with visual feedback in individuals with Cerebellar Ataxia

Conditions

Cerebellar Ataxia

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: CROSSOVER
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

Reinforcement Training

Reach training with visual feedback. During each training session, participants will first be familiarized with the task and then will reach from a home position to 4 virtual targets that are presented in the front of the participant and within the workspace where most natural arm movements are performed. During training the participant will reach a total of 400 times. For reinforcement training, participants will not see their hand or a cursor, but instead participants will receive target-specific binary feedback after each reach (i.e. based on running average of last 10 reaches to that target). Binary feedback indicates only whether the reach was successful or unsuccessful and provides no specific information about the location of the hand.

Group Type: EXPERIMENTAL

Reach training with visual feedback

Intervention Type: BEHAVIORAL

Reach training will be accomplished using an Oculus Rift and Touch 3D headset. Active markers will be placed on the shoulder, elbow, wrist, and finger in order to capture limb movement in real time. During each training session, participants will first be familiarized with the task and then will reach from a home position to 4 virtual targets that are presented in the front of the participant and within the workspace where most natural arm movements are performed.Targets will be presented in a pseudorandom order and participants will reach a total of 400 times

Standard Practice Training

Reach training with visual feedback. During each training session, participants will first be familiarized with the task and then will reach from a home position to 4 virtual targets that are presented in the front of the participant and within the workspace where most natural arm movements are performed. During training the participant will reach a total of 400 times. For standard practice, participants will be able to see a cursor that represents the position of the hand at all times and try to make straight reaches to the targets. This type of feedback provided specific information about the location of the hand.

Group Type: EXPERIMENTAL

Reach training with visual feedback

Intervention Type: BEHAVIORAL

Reach training will be accomplished using an Oculus Rift and Touch 3D headset. Active markers will be placed on the shoulder, elbow, wrist, and finger in order to capture limb movement in real time. During each training session, participants will first be familiarized with the task and then will reach from a home position to 4 virtual targets that are presented in the front of the participant and within the workspace where most natural arm movements are performed.Targets will be presented in a pseudorandom order and participants will reach a total of 400 times

Interventions

Reach training with visual feedback

Reach training will be accomplished using an Oculus Rift and Touch 3D headset. Active markers will be placed on the shoulder, elbow, wrist, and finger in order to capture limb movement in real time. During each training session, participants will first be familiarized with the task and then will reach from a home position to 4 virtual targets that are presented in the front of the participant and within the workspace where most natural arm movements are performed.Targets will be presented in a pseudorandom order and participants will reach a total of 400 times

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• Cerebellar damage from stroke, tumor or degeneration
• Age 22-80

Exclusion Criteria

• Clinical or MRI evidence of damage to extracerebellar brain (e.g. multiple system atrophy)
• Extrapyramidal symptoms, peripheral vestibular loss, or sensory neuropathy
• Dementia ( Mini-Mental State exam > 22)
• Pain that interferes with the tasks
• Vision loss that interferes with the tasks

• Minimum Eligible Age: 22 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institutes of Health (NIH)

NIH

Sponsor Role: collaborator

Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

NIH

Sponsor Role: collaborator

Hugo W. Moser Research Institute at Kennedy Krieger, Inc.

OTHER

Sponsor Role: lead

Responsible Party

Amy J. Bastian, Ph.D.

Professor of Neuroscience

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Amy J Bastian, PhD, PT

Role: PRINCIPAL_INVESTIGATOR

Kennedy Krieger Institute and Johns Hopkins School of Medicine

Locations

Motion Analysis Lab in the Kennedy Krieger Institute

Baltimore, Maryland, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Anthony J Gonzalez, BS

Role: CONTACT

agonza30@jhmi.edu

4439232716

Facility Contacts

Anthony J Gonzalez, BS

Role: primary

gonzalezan@kennedykrieger.org

443-923-2716

Other Identifiers

2R01HD040289-15A1

Identifier Type: NIH

Identifier Source: secondary_id

View Link

IRB00182673

Identifier Type: -

Identifier Source: org_study_id