No abstract

A key challenge in Functional Magnetic Resonance Imaging (fMRI) is the detection of activation areas in the brain. We introduce a new method of M R I signal detection, using an approach termed the Periodicity Transform. The technique is based on temporal data analysis. A search for periodicity is camed out in the tMRI time series data. The method is applicable to block design experiments. In the block paradigm, the stimulus period is known and it is possible to use this information for searching periodicities in the time series data. We present the results for the periodicity detection in the time series of the simulated phantom as well as clinical fMR1 data from the finger tapping experiment. No assumptions have been made about the amplitude and frequency of the activation signal. The algorithm extracts arbitrary harmonics at the periodicity defined by thc stimulus function.

Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn’t require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat—wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI.