Clinical Trial Details
— Status: Completed
Administrative data
NCT number |
NCT01177774 |
Other study ID # |
09-1700 |
Secondary ID |
K24MH087913 |
Status |
Completed |
Phase |
|
First received |
|
Last updated |
|
Start date |
August 2010 |
Est. completion date |
July 13, 2023 |
Study information
Verified date |
November 2023 |
Source |
Washington University School of Medicine |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
The purpose of this research is to study why most children who have tics never develop
Tourette syndrome but some do. In other words, we aim to find features that may predict whose
tics will go away and whose tics will continue or worsen, in children ages 5 through 10 years
whose first tic occurred within the past 9 months.
Description:
Up to 30% of all children will have a tic at some point. However, tics that last a whole year
(or more) occur in only 3% of the population. Thus tic persistence may be more unusual than
tic onset, yet almost no data exist on which people with recent-onset tics go on to be
diagnosable with Tourette syndrome or chronic tic disorder, versus those whose tics are only
transient.
The overall goal of this research is to identify, prospectively, what imaging, clinical or
neuropsychological features of children who just recently started ticcing will go on to
develop a chronic tic disorder (including Tourette syndrome). Hypotheses are derived
primarily from studies of patients with established tic disorders.
Aim 1. Study pathophysiology of recent-onset tics. Aim 1a. Identify clinical,
neuropsychological, and brain imaging features that differentiate children with recent tic
onset ("New Tics" group) from tic-free controls. We will test a priori hypotheses including
tic suppression, inattentiveness, caudate nucleus volume, tic severity, and premonitory urges
(see "Summary of hypotheses" on the 3rd page of Research Strategy). Secondary analyses will
apply support vector machine (SVM) learning to a rich set of data to discover novel,
multivariate differences in the New Tics group [3,45]. These data will also include tic
phenomenology, psychiatric diagnosis, habit learning, motor dexterity, structural MRI,
perfusion MRI, and resting state functional connectivity fMRI (rs-fcMRI).
Aim 1b. Compare New Tics subjects to a group of children who are matched for age but have
already had tics for ≥1 year ("Existing TS/CTD"). Since both groups have tics, this
comparison will highlight abnormalities that cannot be explained by the mere current presence
of tics, including markers of chronicity or adaptation.
Aim 2. Prospective study of tic remission. We will re-evaluate New Tics subjects at the
1-year anniversary of tic onset (the accepted duration criterion for diagnosis of TS/CTD).
Our pilot data show good variability in the change in tic symptom severity (i.e., change in
YGTSS total tic score from baseline to followup: ΔTTS), so ΔTTS will be the primary dependent
variable. We focus on outcome as a continuous variable because no reliable estimate exists
for how many New Tics subjects will remit versus go on to diagnosis with TS/CTD. Remission
rate also depends on definition and on the thoroughness of the follow-up evaluation [4].
Aim 2a. Study the physiology of tic remission by identifying changes in clinical,
neuropsychological, and brain imaging variables that correlate with changes in clinical tic
severity (ΔTTS). This Aim benefits from prospective observation and within-subject
comparisons. The primary analysis will focus on any markers identified in Aim 1. A secondary
analysis will apply machine learning methods for a data-driven approach (support vector
regression: SVR).
Aim 2b. Identify predictors of improvement or worsening, i.e. clinical, neuropsychological,
and brain imaging features at study entry that correlate significantly with ΔTTS. The 2
primary analyses will relate clinical outcome (ΔTTS) to tic suppression ability and caudate
volume at study entry. Secondary analyses will examine other predictors using an SVR machine
learning approach.