Jivesh Ramduny

Abstract Population neuroscience datasets allow researchers to estimate reliable effect sizes for brain-behavior associations because of their large sample sizes. However, these datasets undergo strict quality control to mitigate sources of noise, such as head motion. This practice often excludes a disproportionate number of minoritized individuals.

Abstract Reproducible functional connectivity-based biomarkers have remained elusive despite the promise of deeply phenotyped consortia. An important component of reproducibility is reliability over repeated measures, often measured by the intraclass correlation coefficient (ICC). Here, we test the use of functional connectome fingerprinting as a way to select pre- and post-processing parameters.

Abstract Recognition of the detrimental impact of participant motion on functional connectivity measures has led to the adoption of increasingly stringent quality control standards to minimize potential motion artifacts. These stringent standards can lead to the exclusion of many participants, creating a tension with a countervailing requirement for large sample sizes that can provide adequate statistical power, particularly for brain-behavior association studies.