Yale Childhood Aggression CBT fMRI
Using CBT to Examine Circuitry of Frustrative Non-reward in Aggressive Children (RDoC-CBT, NCT01965184)
Task fMRI of children with clinically significant aggression across psychiatric diagnoses and healthy controls, scanned before a randomized trial of cognitive-behavioral therapy versus supportive psychotherapy at the Yale Child Study Center. 100 aggressive children and 29 controls aged 8 to 16.
Overview
This cohort comes from an NIMH-funded randomized trial at the Yale Child Study Center (NCT01965184, grant R01MH101514) that compared 12 sessions of cognitive-behavioral therapy with 12 sessions of supportive psychotherapy for irritability, anger and aggression in children and adolescents. The study follows the NIMH Research Domain Criteria approach: aggression is treated as a dimension that cuts across diagnoses, and the trial asks whether change in emotion regulation circuitry tracks response to therapy. Children were scanned with fMRI and recorded with EEG before treatment, and the trial protocol planned a second session at the end of treatment. The data are held in NIMH Data Archive collection 2139, which lists 96 shared subjects.
Composition
The baseline analysis by Ibrahim et al. includes 100 children with aggressive behavior (27 girls) and 29 healthy controls (13 girls), aged 8 to 16, with a mean age of 11.9 years. Of 136 children scanned, 7 were excluded for head motion or a computer error. All children in the aggression group scored at least 65 on the CBCL Aggressive Behavior scale and met criteria for oppositional defiant disorder, conduct disorder or disruptive mood dysregulation disorder. Co-occurring diagnoses were common: 78 had ADHD, 26 an anxiety disorder and 18 autism spectrum disorder. Controls had no psychiatric or neurological history. The trial registry reports 101 enrolled participants.
Acquisition
Imaging used a Siemens MAGNETOM Tim Trio at 3 T. The analysed run is a 4.7 minute block-design task of fearful and calm faces from the NimStim set, during which children judged the gender of each face. The NDA collection description also names a frustration-inducing GoNoGo task in fMRI and EEG; its scan counts are not reported in the paper.
Annotations
There are no image labels. Subjects carry K-SADS-PL diagnoses, CBCL, Reactive-Proactive Aggression Questionnaire, SRS-2 and WASI IQ scores, and the trial adds treatment arm and outcome ratings.
Known limitations
Structural scan details, the follow-up scans and the EEG are described in a supplement or the protocol and are not broken down here. The NDA collection shares fewer subjects than the paper analysed, and which runs it contains is not listed publicly. The control group is small and the sample comes from one site.
Cohort
Aggregate numbers from the sources below. Bars are relative to the 129 subjects.
Sex
- Female 40 31%
- Male 89 69%
Participants in Ibrahim et al. 2022, connectome-based predictive modeling of childhood aggression, Mol Psychiatry (author manuscript PMC9035467)
Condition
Groups can overlap
- Aggressive behavior 100 78%Age mean 11.7 ± 2.3
- ADHD 78 60%
- Oppositional defiant disorder 76 59%
- Healthy control 29 22%Age mean 12.8 ± 1.8
- Anxiety disorder 26 20%
- Disruptive mood dysregulation disorder 18 14%
- Autism spectrum disorder 18 14%
- Conduct disorder 4 3%
- Major depressive disorder 4 3%
Table 1, Participants in Ibrahim et al. 2022, connectome-based predictive modeling of childhood aggression, Mol Psychiatry (author manuscript PMC9035467)
Condition by sex
Reported cross table. A dot marks a cell the source does not give.
| Female | Male | |
|---|---|---|
| Aggressive behavior | 27 | 73 |
| Healthy control | 13 | 16 |
Participants, Table 1 in Ibrahim et al. 2022, connectome-based predictive modeling of childhood aggression, Mol Psychiatry (author manuscript PMC9035467)
Age
mean 11.9 ± 2.2, range 8 to 16
No age bins reported.
Table 1, Participants in Ibrahim et al. 2022, connectome-based predictive modeling of childhood aggression, Mol Psychiatry (author manuscript PMC9035467)
Contrast / sequence
Groups can overlap
- BOLD fMRI 129 100%
Data Acquisition and Preprocessing in Ibrahim et al. 2022, connectome-based predictive modeling of childhood aggression, Mol Psychiatry (author manuscript PMC9035467)
Scanner vendor
- Siemens Healthineers 129 100%
Data Acquisition and Preprocessing in Ibrahim et al. 2022, connectome-based predictive modeling of childhood aggression, Mol Psychiatry (author manuscript PMC9035467)
Field strength
- 3 T 129 100%
Data Acquisition and Preprocessing in Ibrahim et al. 2022, connectome-based predictive modeling of childhood aggression, Mol Psychiatry (author manuscript PMC9035467)
Country
- United States 129 100%
Participants in Ibrahim et al. 2022, connectome-based predictive modeling of childhood aggression, Mol Psychiatry (author manuscript PMC9035467)
License and access
Our reading of the license, not legal advice. Before you use the data, read the original license and confirm that your use is allowed. We take no responsibility for how you use a dataset. Full disclaimer
A research proposal is reviewed and approved
NIMH Data Archive Data Use Terms and Conditions (Data Use Certification)
Terms every NIMH Data Archive data access request accepts. Data are for research, scholarship or teaching; they may only be shared with collaborators who hold their own access; re-identification is forbidden; each publication needs an NDA Study linked to the data and an acknowledgement; downloaded data must be deleted when the research ends or after the one-year access period.
The study data are NIMH Data Archive collection 2139. Access needs an approved NDA data access request; the paper also offers anonymized data from the authors on reasonable request.
What you can do
- Not stated
- Not stated
- Not stated
- Conditional
- Conditional
What you can share
- No
- Conditional
- Share trained models Not stated
What you must do
- Yes
- Share alike No
- Yes
- Conditional
- Manuscript review No
- No
- Yes
- Yes
Limits
- Yes
- Location limits No
Citation
Ibrahim K, Noble S, He G, Lacadie C, Crowley MJ, McCarthy G, Scheinost D, Sukhodolsky DG. Large-scale functional brain networks of maladaptive childhood aggression identified by connectome-based predictive modeling. Mol Psychiatry 27(2):985-999 (2022). doi:10.1038/s41380-021-01317-5
Sources
Every number on this page comes from one of these documents. Each chart names the table or page it is taken from. The raw numbers are in stats.csv.