<p>Functional<strong> </strong>magnetic resonance imaging (fMRI) is a popular technique to study brain function and neural networks. Functional MRI studies are often characterized by small sample sizes and rarely consider statistical power when setting a sample size. This could lead to data dredging, and hence false positive findings. With the widespread use of fMRI studies in clinical disorders, the vulnerability of participants points to an ethical imperative for reliable results so as to uphold promises typically made to participants that the study results will help understand their conditions. While important, power-based sample size calculations can be challenging. The majority of fMRI studies are observational, i.e., are not designed to randomize participants to test efficacy and safety of any therapeutic intervention. My PhD thesis therefore addresses two objectives: firstly, to identify potential obstacles to implementing sample size calculations, and secondly to provide solutions to these obstacles in observational clinical fMRI studies. This thesis contains three projects.</p> <p>Implementing a power-based sample size calculation requires specifications of effect sizes and variances. Typically in health research, these input parameters for the calculation are estimated from results of previous studies, however these often seem to be lacking in the fMRI literature. Project 1 addresses the first objective through a systematic review of 100 fMRI studies with clinical participants, examining how often observed input parameters were reported in the results section so as to help design a new well-powered study. Results confirmed that both input estimates and sample size calculations were rarely reported. The omission of observed inputs in the results section is an impediment to carrying out sample size calculations for future studies.</p> <p>Uncertainty in input parameters is typically dealt with using sensitivity analysis; however this can result in a wide range of candidate sample sizes, leading to difficulty in setting a sample size. Project 2 suggests a cost-efficiency approach as a short-term strategy to deal with the uncertainty in input data and, through an example, illustrates how it narrowed the range to choose a sample size on the basis of maximizing return on investment.</p> <p>Routine reporting of the input estimates can thus facilitate sample size calculations for future studies. Moreover, increasing the overall quality of reporting in fMRI studies helps reduce bias in reported input estimates and hence helps ensure a rigorous sample size calculation in the long run. Project 3 is a systematic review of overall reporting quality of observational clinical fMRI studies, highlighting under-reported areas for improvement and suggesting creating a shortened version of the checklist which contains essential details adapted from the guidelines proposed by Poldrack et al. (2008) to accommodate strict word limits for reporting observational clinical fMRI studies.</p> <p>In conclusion, this PhD thesis facilitates future sample size and power calculations in the fMRI literature by identifying impediments, by providing a short-term solution to overcome the impediments using a cost-efficiency approach in conjunction with conventional methods, and by suggesting a long-term strategy to ensure a rigorous sample size calculation through improving the overall quality of reporting.</p> / Doctor of Philosophy (PhD)

<p dir="ltr">The brain is a dynamic tissue that is passively driven by a combination of the cardiac cycle, respiration, and slow wave oscillations. The function of the brain relies on its ability to maintain a normal homeostatic balance between its mechanical environment and metabolic demands, which can be greatly altered in the cases of neurodegeneration or traumatic brain injury. It has been a challenge in the field to quantify the dynamics of the tissue and cerebrospinal fluid flow in human subjects on a patient-specific basis over the many spatial and temporal scales that it relies upon. Non-invasive imaging tools like structural, functional, and dynamic MRI sequences provide modern researchers with an unprecedented view into the human brain. Our work leverages these sequences by developing novel, open-source pipelines to 1) quantify the biomechanical environment of the brain tissue over 133 functional brain regions, and 2) estimate real-time cerebrospinal fluid velocity from flow artifacts on functional MRI by employing breathing regimens to enhance fluid motion. These pipelines provide a comprehensive view of the macroscale tissue and fluid motion in a given patient. Additionally, we sought to understand how the transmission of macroscale forces, in the context of traumatic brain injury, contribute to neuronal damage by 3) developing a digital twin to simulate 30-200 g-force loading of 2D neuronal cultures and observing the morphological and electrophysiological consequences of these impacts in vitro by our collaborators. Taken together, we believe these works are a steppingstone that will enable future researchers to deeply understand the mechanical contributions that underly clinical neurological outcomes and perhaps lead to the development of earlier diagnostics, which is of dire need in the case of neurodegenerative diseases.</p>