Fidel Vila-Rodriguez

Assistant Professor

Relevant Thesis-Based Degree Programs

 
 

Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Effect of non-invasive neurostimulation on hippocampal plasticity and memory (2023)

Non-invasive stimulation therapies such as electroconvulsive therapy (ECT) or transcranial magnetic stimulation (TMS) are effective therapies for treatment-resistant depression. Although ECT is more efficacious than other treatment options, it is associated with cognitive side effects. By understanding the neurobiological underpinnings of ECT and TMS, we may gain critical insights into both their therapeutic and side effect profiles.In the current project, I used adult hippocampal neurogenesis in mice as a measure of neuroplasticity induced by neurostimulation. First, I directly compared the extent of hippocampal neurogenesis generated acutely by different stimulation modalities, including electroconvulsive shock (ECS), the animal analogue of ECT, and two forms of TMS, the 10Hz repetitive TMS (rTMS) and the intermittent theta burst stimulation (iTBS). I found that ECS increased neurogenesis significantly more than either form of TMS. However, a newer pattern of TMS called intermittent theta burst stimulation (iTBS) showed a greater neurogenic potential than the traditional repetitive TMS (rTMS) when administered acutely and therefore I conducted the first study examining neurogenesis following chronic iTBS. Chronic iTBS application did not affect neurogenesis but altered the new neurons' morphology by increasing the size of pre-synaptic terminals in males. In contrast, chronic ECS induced up to a 2-fold increase in new neuron proliferation and survival, along with an enhancement of dendritic length and pre-synaptic terminal size in both males and females. These findings suggest that the stronger form of stimulation, ECS, is associated with increased neurogenesis, however new neuron addition may not be entirely beneficial. Animals that received chronic ECS were impaired in the performance of an associative memory task. While new neurons support hippocampal functions to improve future cognition, the integration of new neurons may disrupt the existing hippocampal circuit. Although chronic ECS did not decrease the number of developmentally-born neurons, I found that chronic ECS decreased the spine density of developmentally-born neurons in the ventral hippocampus. Overall, this study showed that ECS and TMS had differential effects on adult hippocampal neurogenesis and the ECS-induced neurogenesis may impair cognition by pruning existing synaptic connections.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Cognitive dysfunction in treatment-resistant depression and the longitudinal benefits of repetitive transcranial magnetic stimulation (2022)

Introduction: Cognitive dysfunction (CD) is a commonly reported symptom of Major Depressive Disorder (MDD) and recognized as a distinct symptom domain. Patients with treatment-resistant depression (TRD) tend to experience greater rates of CD, however cognition is not well-characterized in this population and treatment options remain scarce. Repetitive Transcranial Magnetic Stimulation (rTMS) is effective in treating affective symptoms in TRD, but its effect on CD in TRD has not been established. Objectives: (1) To characterize CD in TRD; (2) to assess whether rTMS is associated with cognitive improvement. Methods: This study used data from a non-inferiority clinical trial investigating two excitatory rTMS protocols to the left dorsolateral prefrontal cortex in unipolar outpatients with TRD. Cognitive testing was performed at baseline and 3 months post-treatment in patients and a demographically matched cohort of healthy volunteers (HV). A MANOVA was performed on baseline data to assess the effects of TRD on cognition using both normative and individualized adjustments. K-means clustering was performed on the patient sample to elucidate cognitive subgroups, and binomial logistic regression was subsequently performed to determine significant clinical and demographic predictors of cluster belonging. Changes in cognitive performance from baseline to post-treatment were assessed using repeated-measures ANOVA. Results: At baseline, TRD showed selective impairment compared to HV in domains of verbal memory, speeded attention, set shifting, and inhibitory control. Relative cognitive scoring revealed greater differences in scores between TRD and HV across all cognitive domains. Clustering revealed two cognitive subgroups in TRD, namely a global impairment (GI, 57%) and a selective executive dysfunction (SE, 43%) subgroup. Belonging to the GI subgroup was predicted by benzodiazepine use and older age. Only the GI subgroup showed meaningful changes in cognitive performance at 3 months post-treatment, with significant improvements in verbal memory. Further, improvement in verbal memory was associated with improvements in affective symptoms. Conclusions: This research provides new insights into the cognitive heterogeneity of TRD by identifying cognitive subgroups and predictors of cognitive functioning. Furthermore, the findings suggest that rTMS to the left DLPFC may improve verbal memory in a subgroup of TRD patients.

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Illness perception and its influence in outcome and disability in patients with treatment resistant depression receiving rTMS treatment (2021)

Background: Depressive disorders are a significant burden to patients and society, possibly leading to catastrophic damage to one's life. Unfortunately, many of these patients become resistant to treatment. Therefore, identifying possible aspects that can influence treatment responsiveness and return to life activities has become essential. Illness perceptions have been associated with many different conditions, including depression, treatment adherence, functionality, and coping behaviours. Objectives: The objectives of this study were to describe illness perceptions in a sample of patients with treatment-resistant depression (TRD) undergoing repetitive transcranial magnetic stimulation (rTMS) treatment; to evaluate its correlation with changes in the level of disability and changes in depression symptoms after treatment; to identify the possible influence of treatment on illness perceptions changes over time. Methods: Participants with a history of treatment-resistant depression were referred from primary and secondary care to receive treatment with rTMS. Measurements were done at baseline and after treatment using BIPQ, HRSD-17, and Sheehan Disability Scale (SDS). Patients were followed for a total of 16 to 18 weeks. Results: The sample consisted of 62 participants. The majority were female with severe depression. Identity, consequences, concern, and emotional representations were very high before treatment and strongly associated with one another. Life stressors, genetics, and trauma were the most perceived causes of depression. There was an indication that identity and other dimensions could explain some of the variances in HRSD-17 scores after rTMS, and perceived identity could also explain the variance in work/school, social, and family/home scores. rTMS appeared to be correlated with changes in illness dimensions after treatment. Conclusions: Depression takes over a patient's perception and life experience affecting social, professional, and personal life aspects. Most illness perceptions in TRD patients are severe and can mildly explain changes in symptoms and functioning over time. Changes in illness perception are part of the common-sense model's dynamic feedback and could partially be attributed to treatment in this sample.

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Machine learning based prediction of repetitive transcranial magnetic stimulation treatment outcome in patients with treatment-resistant depression (2020)

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder that affects millions of people. Repetitive transcranial magnetic stimulation (rTMS) has been recommended as a safe, reliable, non-invasive, neurostimulation therapy option for treatment-resistant depression (TRD). The effectiveness of rTMS treatment varies among individuals; thus, predicting the responsiveness to rTMS treatment can reduce unnecessary expenses and improve treatment capacity. In this study, we combined machine learning models with depression rating scales, clinical variables, and demographic data to predict the outcomes and effectiveness of rTMS treatment for TRD patients. Using the clinical data of 356 TRD patients who each received 20 to 30 sessions of rTMS treatment over a 4-6-week period, we examined the predictive value of different depression rating scales and models for various prediction outcomes, at multiple time points. Our optimal baseline models achieved area under the curve (AUC) values of 0.634 and 0.735 for treatment response and remission prediction, respectively, using the Elastic Net. In the longitudinal analysis, using baseline data and early treatment outcomes for 1–3 weeks, all predictive values improved compared with baseline models. In addition, predicting the percentage of symptom improvement was also feasible using longitudinal treatment outcomes, achieving coefficients of determination of 0.277, at the end of week 1, and 0.464, at the end of week 3. We found that the use of depression rating subscales, combined with clinical and demographic data, including anxiety severity, employment status, age, gender, and education level, may produce higher accuracy at baseline. In the longitudinal analysis, the total scores of depression rating scales were the most significant predictors, allowing prediction models to be built using only the total scores, which resulted in high predictive value and interpretability. This work presented a convenient and economical approach for the prediction of rTMS treatment outcomes in TRD patients, using pre-treatment clinical and demographic data alone, without requiring expensive biomarker data. The predictive value was further enhanced by adding longitudinal treatment outcomes. This method is a plausible approach that could be utilized in clinical practice for individualized treatment selection, leading to better treatment outcomes for rTMS in TRD patients.

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