Darius Rountree-Harrison

EEG analyst, BSc, GDPA, MPhil, QEEG-T, PhD (Candidate)







About

Darius Rountree-Harrison is ANFI EEG analyst responsible for EEG/QEEG reports and consultations. He studied neuroscience at the University of Sydney, psychology at Monash University and learned to analyse QEEG under the supervision of Jay Gunkelman and Mirjana Askovic. Darius is an accredited QEEG Diplomate and is a psychology doctorate candidate, looking at HRV biomarkers for PTSD. This work stems from Darius’ interest in stress responses, homeostatic processes and neuronal function.

During his time at ANFI, Darius has delivered training in HRV, mentored psychologists and students to analyse QEEG/HRV data and has been involved with clinical PTSD research. He routinely uses winEEG, Neuroguide, LORETA, Kubios, Thought Technology, EEGer Spectrum, Heart Math and many other software and biofeedback systems for analysis, clinical intervention and training.

Darius’ greatest strength lies in his ability to integrate a wide range of neuropsychophysiological research and practices, with presenting symptoms, individual EEG and HRV data to set integrated treatment plans. His clinical insights have aided the recovery of hundreds of clients of all ages and psychopathologies. He is currently employed at STARTTS as an EEG Analyst.

Presenting at the 2025 ANSA Conference

Comparison of EEGs from Traumatised Refugees to Age and gender matched Normative data and machine learning symptom associations

My presentation explores EEG biomarkers for posttraumatic stress disorder derived from comparing clinical data to the iSyncBrain normative database. The analyses revealed distinct biomarkers associated with the eyes open and closed conditions, gender differences and limited differences between clinical and sub-clinical groups. I will discuss the diversity of PTSD presentations, highlight limitations of the current data and conceptual understanding of PTSD, and provide details of my plans for more detailed analyses using phenotypic clusters and machine learning methods. This presentation will help clinicians better understand clinical presentations and their treatment implications.





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