Detection of evoked resonant neural activity in Parkinson's disease.

Objective. This study investigated a machine-learning approach to detect the presence of evoked resonant neural activity (ERNA) recorded during deep brain stimulation (DBS) of the subthalamic nucleus (STN) in people with Parkinson's disease.Approach. Seven binary classifiers were trained to distinguish ERNA from the background neural activity using eight different time-domain signal features.Main results. Nested cross-validation revealed a strong classification performance of 99.1% accuracy, with 99.6% specificity and 98.7% sensitivity to detect ERNA. Using a semi-simulated ERNA dataset, the results show that a signal-to-noise ratio of 15 dB is required to maintain a 90% classifier sensitivity. ERNA detection is feasible with an appropriate combination of signal processing, feature extraction and classifier. Future work should consider reducing the computational complexity for use in real-time applications.Significance. The presence of ERNA can be used to indicate the location of a DBS electrode array during implantation surgery. The confidence score of the detector could be useful for assisting clinicians to adjust the position of the DBS electrode array inside/outside the STN.

Contrast sensitivity and visual acuity of Queensland fruit flies (Bactrocera tryoni).

This study examines the visual acuity of Queensland fruit flies (Bactrocera tryoni) by analysing their turning responses to an immersive visual stimulus consisting of a pattern of vertical stripes presented at various angular periods and rotational rates. The results infer that these flies possess an interommatidial angle of approximately [Formula: see text], and an ommatidial acceptance angle of approximately [Formula: see text]. This suggests that the visual acuity of Queensland fruit flies is substantially better than that of the classical vinegar fly (Drosophila melanogaster), and is comparable to those of the housefly (Musca domestica) and the honeybee (Apis mellifera). The contrast sensitivity of Queensland fruit flies is comparable to that of the housefly.

Flight control of fruit flies: dynamic response to optic flow and headwind.

Insects are magnificent fliers that are capable of performing many complex tasks such as speed regulation, smooth landings and collision avoidance, even though their computational abilities are limited by their small brain. To investigate how flying insects respond to changes in wind speed and surrounding optic flow, the open-loop sensorimotor response of female Queensland fruit flies (Bactrocera tryoni) was examined. A total of 136 flies were exposed to stimuli comprising sinusoidally varying optic flow and air flow (simulating forward movement) under tethered conditions in a virtual reality arena. Two responses were measured: the thrust and the abdomen pitch. The dynamics of the responses to optic flow and air flow were measured at various frequencies, and modelled as a multicompartment linear system, which accurately captured the behavioural responses of the fruit flies. The results indicate that these two behavioural responses are concurrently sensitive to changes of optic flow as well as wind. The abdomen pitch showed a streamlining response, where the abdomen was raised higher as the magnitude of either stimulus was increased. The thrust, in contrast, exhibited a counter-phase response where maximum thrust occurred when the optic flow or wind flow was at a minimum, indicating that the flies were attempting to maintain an ideal flight speed. When the changes in the wind and optic flow were in phase (i.e. did not contradict each other), the net responses (thrust and abdomen pitch) were well approximated by an equally weighted sum of the responses to the individual stimuli. However, when the optic flow and wind stimuli were presented in counterphase, the flies seemed to respond to only one stimulus or the other, demonstrating a form of 'selective attention'.