The Case for Incorporating EEG in Clinical TMS Practice

Abstract

TMS psychiatrists are seeking methods to identify subtypes of clinically complex treatment-resistant depression (TRD), requiring rigorous, in-depth assessment. Advances in affordable neurotechnology and software have made it possible to record, process, and analyze EEGs within the clinic. Psychiatrists can learn to decipher the electroencephalograph (EEG) according to the EEG phenotype model developed by Johnston, Gunkelman, & Lunt (2005). We reviewed and processed raw EEG recordings, using WinEEG software, from 50 recent TRD TMS patients. Their primary diagnoses included: MDD recurrent (54%), MDD recurrent with mixed features (22%), bipolar 1 depression (4%), and bipolar 2 depression (20%). Secondary diagnoses included chronic PTSD (72%), dysthymia (32%), comorbid anxiety disorder (48%), OCD (6%), substance use disorder in full remission (18%), alcohol use disorder (6%), cannabis use disorder (4%), personality disorders (10%), and ADHD (12%). Baseline EEGs showed the following features, in ranking order: dysfunctional insular involvement seen in independent component analysis (80%), a marker of severity of depression; hypercoherent frontal alpha, reflecting diminished cognitive and emotional control (80%); excess alpha at right temporoparietal junction (76%), likely a marker relating to comorbid chronic PTSD; and tonic midline theta (38%), a marker of anterior cingulate involvement, often reflecting fear and dread, and excess beta abnormalities (80%) reflecting issues of overarousal. We currently use EEG data to inform medication choices and TMS protocol indications and contraindications. We believe these findings have helped guide successful treatment choices and hope to validate that observation with these and an additional 300 patients’ EEG, clinical, and TMS treatment response data.