The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database.
Hanneke van DijkGuido A van WingenDamiaan DenysSebastian OlbrichRosalinde van RuthMartijn ArnsPublished in: Scientific data (2022)
In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment response. At the same time neuroscience is becoming more data-driven, made possible by computational advances. In support of biomarker development and methodologies such as training Artificial Intelligent (AI) networks we present the extensive Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG database. This clinical lifespan database (5-89 years) contains resting-state, raw EEG-data complemented with relevant clinical and demographic data of a heterogenous collection of 1274 psychiatric patients collected between 2001 to 2021. Main indications included are Major Depressive Disorder (MDD; N = 426), attention deficit hyperactivity disorder (ADHD; N = 271), Subjective Memory Complaints (SMC: N = 119) and obsessive-compulsive disorder (OCD; N = 75). Demographic-, personality- and day of measurement data are included in the database. Thirty percent of clinical and treatment outcome data will remain blinded for prospective validation and replication purposes. The TDBRAIN database and code are available on the Brainclinics Foundation website at www.brainclinics.com/resources and on Synapse at www.synapse.org/TDBRAIN .
Keyphrases
- resting state
- attention deficit hyperactivity disorder
- major depressive disorder
- functional connectivity
- working memory
- obsessive compulsive disorder
- electronic health record
- big data
- autism spectrum disorder
- adverse drug
- bipolar disorder
- end stage renal disease
- ejection fraction
- mental health
- newly diagnosed
- randomized controlled trial
- chronic kidney disease
- clinical trial
- prognostic factors
- deep brain stimulation
- study protocol
- machine learning
- peritoneal dialysis