Methods Development

calcFD Toolbox

calcFD is a MATLAB toolbox for calculating the fractal dimensionality of a 3D structure, designed to work with intermediate files from FreeSurfer analysis pipeline, but can also use other volumes. I have shown that applying this measure to cortical and subcortical structures relates to age-related differences better than extant measures such as cortical thickness, gyrification, and subcortical volume.
For a copy of the toolbox, go to

Related Publications

Madan, C. R., & Kensinger, E. A. (2016). Cortical complexity as a measure of age-related brain atrophy. NeuroImage, 134, 617-629. doi:10.1016/j.neuroimage.2016.04.029

Madan, C. R., & Kensinger, E. A. (2017). Age-related differences in the structural complexity of subcortical and ventricular structures. Neurobiology of Aging, 50, 87-95. doi:10.1016/j.neurobiolaging.2016.10.023

Madan, C. R., & Kensinger, E. A. (in press). Test-retest reliability of brain morphology estimates. Brain Informatics. doi:10.1007/s40708-016-0060-4

Test of Ability in Movement Imagery (TAMI)

TAMI is an objective test of ability in movement imagery. TAMI consists of 10 questions (preceded by one practice question) in which participants are asked to imagine a series of motor movements. Participants are then presented with several images and are asked to select the image that corresponds to their final body positioning. This booklet consists of the TAMI questionnaire and response sheet, along with pertinent details regarding the administration and scoring of TAMI.
For a copy of the TAMI administration manual, please email me (

Related Publications

Madan, C. R., & Singhal, A. (2013). Introducing TAMI: An objective test of ability in movement imagery. Journal of Motor Behavior, 45, 153-166. doi:10.1080/00222895.2013.763764

Madan, C. R., & Singhal, A. (2014). Improving the TAMI for use with athletes. Journal of Sports Sciences, 32, 1351-1356. doi:10.1080/02640414.2014.889847

Madan, C. R., & Singhal, A. (2015). No sex differences in the TAMI. Cognitive Processing, 16, 203-209. doi:10.1007/s10339-014-0644-y

Breathe Easy EDA (BEEDA)

Electrodermal activity (EDA) methods evaluate fluctuations in skin electrical conductance, providing a measure of sympathetic nervous system arousal. Respiration influences EDA, such that irregular breathing causes EDA fluctuations which cannot be unambiguously distinguished from changes related to psychophysiological arousal. Thus, it is crucial to control for respiration-induced EDA artifacts. Here we developed a MATLAB toolbox for eliminating EDA respiration artifacts and analyzing EDA data, which we freely distribute: Breathe Easy EDA or ‘BEEDA’. BEEDA’s artifact removal GUI allows users to quickly clean EDA data, greatly facilitating EDA analysis. Additionally, BEEDA’s analysis functions allow users to seamlessly analyze the cleaned data. The EDA analysis capabilities include tonic and phasic EDA measurements, following from standard methodological implementations. The toolbox is suitable for any experiment recording both EDA and respiration data, and flexibly adjusts to experiment-specific parameters (e.g., trial structure and desired analysis parameters).
John Ksander (currently a grad student at Brandeis University) is the lead on this project, and it was recently presented at the 2016 annual meeting of the Psychonomic Society.
The toolbox isn't ready for distribution yet, but you can sign-up here to be notified when it is ready for distribution.

Spectral time-lapse (STL) Toolbox

The spectral time-lapse (STL) algorithm is designed to be a simple and efficient technique for analyzing and presenting both spatial and temporal information of animal movements within a two-dimensional image representation. The STL algorithm re-codes an animal's position at every time point with a time-specific color, and overlaid it over a reference frame of the video, to produce a summary image. It additionally incorporates automated motion tracking, such that the animal's position can be extracted and summary statistics such as path length and duration can be calculated, as well as instantaneous velocity and acceleration. This toolbox implements the STL algorithm as a MATLAB toolbox and allows for a large degree of end-user control and flexibility.
For a copy of the toolbox, go to

Related Publications

Madan, C. R., & Spetch, M. L. (2014). Visualizing and quantifying movement from pre-recorded videos: The spectral time-lapse (STL) algorithm. F1000Research, 3, 19. doi:10.12688/f1000research.3-19

Baron, D. M., Ramirez, A. J., Bulitko, V., Madan, C. R., Greiner, A., Hurd, P. L., & Spetch, M. L. (2015). Practice makes proficient: Pigeons (Columba livia) learn efficient routes on full-circuit navigational traveling salesperson problems. Animal Cognition, 18, 53-64. doi:10.1007/s10071-014-0776-6