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This lab’s research philosophy is rooted in an integrative and multidisciplinary approach to scientific research. Broadly, Nicholson’s research program includes using neuroimaging applications to answer fundamental questions within psychiatry and neuroscience regarding symptom manifestation and maintenance, as well as recovery from illness and treatment efficacy. In order to accomplish these research goals, Nicholson’s research network is highly collaborative across multiple centres of excellence, and notably includes Dr. Ruth Lanius (Western University), Dr. Frank Scharnowski (University of Vienna), Dr. Paul Frewen (Western University), and Dr. Brigitte Lueger-Schuster (University of Vienna).


Abstract Surface



Posttraumatic stress disorder (PTSD) is a debilitating psychiatric condition that can develop after exposure to traumatic events and involves symptoms of persistent intrusive recollections (vivid unwanted memories, flashbacks, and nightmares), avoidance of trauma-related stimuli (thoughts, feelings, and external reminders), alterations in cognition and mood (negative self-beliefs and expectations, difficulty concentrating, and an inability to experience positive emotions), and alterations in arousal and reactivity (aggression, destructive behaviour, hypervigilance, and problems sleeping). Recently, a dissociative subtype of PTSD has been recognized, characterizing individuals experiencing significant emotional detachment and hypo-emotionality. Dissociation involves detachment from immediate somatic or environmental experience and often occurs during trauma, modulating its immediate psychophysiological impact. Typically, individuals with the dissociative subtype of PTSD have a history of more severe early-life trauma, higher PTSD severity, and single-nucleotide polymorphisms associated with dissociation.


The primary focus of our research is on characterizing aberrant neural mechanisms underlying PTSD. Nicholson’s research network examines differential biomarkers of PTSD and the dissociative subtype of PTSD using neuroimaging methods, and additionally examines transdiagnostic biomarkers of other psychiatric disorders. Here, our research efforts explore a variety of domains pertaining to clinical, behavioural, and computational neuroscience. Our lab utilizes a wide range of neuroimaging statistical approaches in order to accomplish these research goals, such as dynamic causal modelling, and machine learning applications. Recently, we have utilized machine learning computations to better characterize and predict PTSD diagnoses based on “big data” neuroimaging samples. Moving forward, it is of critical importance that we develop new models to better understand the neurobiological basis of PTSD heterogeneity in order to prevent illness and suicide, improve treatment, and provide optimal functional recovery among patients.



Neurofeedback is a non-invasive approach used in the treatment of a wide range of neuropsychiatric disorders, including PTSD. Neurofeedback with both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represents an emerging adjunctive treatment that allows patients to self-regulate neural states through a brain computer interface. The underlying benefit of this treatment practice is that one can directly entrain and regulate neural activity along with associated psychological symptoms. Neurofeedback represents a closed-loop design, meaning that continuous sensory representations of  brain activity are provided to individuals in real-time with the aim of controlling this activity. 


Currently, mixed response rates of psychotherapy and suboptimal response to pharmacological treatments have been reported in PTSD. Furthermore, dropout rates from psychological therapies remain a critical barrier to recovery and are significantly higher for trauma-focused therapies. It is clear that not every therapeutic approach will benefit every patient in the same way and that novel adjunctive treatments are in high demand for the treatment of PTSD. Observations of altered patterns of neural functioning within PTSD patients have driven efforts to develop novel non-invasive treatment interventions that target dysregulated brain areas. Given the diversity of brain circuits that may be involved in PTSD, modern neurofeedback technology may facilitate a more personalized approach to medicine when treating patients with PTSD and could also help to improve symptoms in those individuals resistant to treatment in the past.


Of importance, our research group was the first lab to publish a real-time fMRI neurofeedback study in PTSD. Moving forward, we are currently analyzing clinical trials of neurofeedback in patients with PTSD.


Neuroimaging machine learning applications can learn from spatially distributed patterns within fMRI data in order to make clinical predictions. These methods can generate models based on complex sources of information which provide a powerful avenue that leverages technology in order to evaluate risk-factors, identify illness subtypes, classify patients, and predict response to treatment. Specifically, machine learning computations for fMRI are sensitive enough to make inferences at the single-subject level. In other words, machine learning models can be generalized to individual patients in order to make clinical predictions, a capacity with critical implications for clinical diagnosis, treatment, and prediction of psychiatric prognosis. Recently, a growing number of studies have applied machine learning methods to neuroimaging data in order to predict and characterize PTSD. Indeed, we have shown recently that machine learning algorithms were able to accurately classify PTSD, PTSD subtypes, and healthy individuals using brain activation with over 90% accuracy. Future clinical implementation of these cutting-edge tools may not only allow for earlier and more accurate PTSD classification, but may also revolutionize further the field of personalized medicine by pairing patients with the optimal treatment.


There is overwhelming evidence, now more than ever, that systemic discrimination negatively affects the physical and mental health of minority groups. Minority stress theory posits that societal stigma and marginalization compromises individual health through several psychosocial stress processes. These stressors are thought to be mediated through several shared pathways of PTSD and stress/trauma-related disorders, including: stress reactivity, avoidance, hypervigilance, disrupted attachment, self-awareness, social cognition, emotion dysregulation, and alterations in the sense-of-self. Indeed, these cognitive, affective, and behavioural minority stress processes are associated with mental health problems and several health-risk behaviours, one of many include increased prevalence of PTSD and suicidality. Our lab aims to better understand these underlying mechanisms on the neural level among racial, ethnic, gender, and sexual minority (LGBTQ+) populations. 

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