Improving neuroimaging methods

I have been involved in a range of different projects associated with improving neuroimaging methods. These include the following.

Work to improve region-of-interest selection:

Brooks, J. L., Zoumpoulaki, A., & Bowman, H. (2017). Data‐driven region‐of‐interest selection without inflating Type I error rate. Psychophysiology, 54(1), 100-113.

Bowman, H., Brooks, J. L., Hajilou, O., Zoumpoulaki, A., & Litvak, V. (2020). Breaking the Circularity in Circular Analyses: Simulations and Formal Treatment of the Flattened Average Approach. PLoS Computational Biology.

Work to highlight potential problems of over-fitting hyper-parameters (over-hyping) in machine learning, as applied in neuroimaging:

Hosseini, M., Powell, M., Collins, J., Callahan-Flintoft, C., Jones, W., Bowman, H., & Wyble, B. (2020). I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data. Neuroscience & Biobehavioral Reviews.

Work to highlight the problems of small samples in neuroimaging:

Lorca-Puls, D. L., Gajardo-Vidal, A., White, J., Seghier, M. L., Leff, A. P., Green, D. W., ... Bowman, H & Price, C. J. (2018). The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings. Neuropsychologia, 115, 101-111.

Work to highlight problems associated with use of bootstrapping in electrophysiology research:

Zoumpoulaki, A., Alsufyani, A., & Bowman, H. (2015). Resampling the peak, some dos and don'ts. Psychophysiology, 52(3), 444-448.

Work to develop more effective methods to demonstrate latency differences between conditions:

Zoumpoulaki, A., Alsufyani, A., Filetti, M., Brammer, M., & Bowman, H. (2015). Latency as a region contrast: Measuring ERP latency differences with dynamic time warping. Psychophysiology, 52(12), 1559-1576.