Reward Value Modulation of Reach Kinematics in Mice

Motivation is simply understanding why an organism does something. Studies of motivation where subjects use goal-directed actions to obtain rewards have been demonstrated in settings such as saccadic eye movement. However, reaching, an ethologically-relevant action, has been far less studied as a way to understand motivation in learned motor behaviors. Even further, there is no evidence on the effects of reward value on such movements. Additionally, while much is known about motivation in forebrain reward networks, far less is known about how or whether these motivational processes contribute to learning and refining new behaviors. I predict these integrative processes between skill learning and motivation could have important implications, as better understanding these processes could provide insight into potential therapies associated with various neurodegenerative disorders such as Parkinson&rsquo;s disease and cerebellar ataxia. To understand these, I designed an experiment to explicitly compare how motivation can potentiate features of a newly-learned skill behavior. Here, I trained mice to reach to food pellets within a behavioral arena. I then placed two types of pellets, chocolate and regular, into individual cages with each of the mice in order to determine if they had a preference. Mice were then able to freely reach towards these pellets, which were now considered to be of differing reward values (chocolate was high-reward, regular was low-reward). Using a paw tracking software, I found the time it took to initiate the reach, or the latency, the success rate in bringing the pellet into the arena, and the velocity and speed of the reach. I expected that due to a greater motivational drive and the speed-accuracy tradeoff (SAT), reaches towards the high-reward pellets would have a shorter latency, greater velocity, and lower success rate. However, despite the latency being shorter in reaches towards the high-reward pellets, the velocity and success rate were lower as well. This may be suggestive of the value the mice place on the high-reward pellets &ndash; that is, they want to obtain the pellet faster, but slow down in order to be more &ldquo;cautious&rdquo;, thereby causing them to be less accurate. Therefore, I propose neural circuitry that differs based on perceived reward value. The ventral tegmental area may directly project onto deep cerebellar nuclei for high-reward, but first project to the prefrontal cortex, and then to deep cerebellar nuclei for low-reward. The prefrontal cortex in this circuit would be involved in planning to allow for more confident movements. Highly rewarding situations may potentially override such planning.</p

PV-0327 Patient-specific motion management and adaptive respiratory gating in Pancreatic SBRT

Digitalitzat per Artypla

Electron dose calculations using the Method of Moments

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134898/1/mp7920.pd

A dose-volume-based tool for evaluating and ranking IMRT treatment plans

External beam radiotherapy is commonly used for patients with cancer. While tumor shrinkage and palliation are frequently achieved, local control and cure remain elusive for many cancers. With regard to local control, the fundamental problem is that radiotherapy-induced normal tissue injury limits the dose that can be delivered to the tumor. While intensity-modulated radiation therapy (IMRT) allows for the delivery of higher tumor doses and the sparing of proximal critical structures, multiple competing plans can be generated based on dosimetric and/or biological constraints that need to be considered/compared. In this work, an IMRT treatment plan evaluation and ranking tool, based on dosimetric criteria, is presented. The treatment plan with the highest uncomplicated target conformity index (TCI+) is ranked at the top. The TCI + is a dose-volume-based index that considers both a target conformity index (TCI) and a normal tissue-sparing index (NTSI). TCI + is designed to assist in the process of judging the merit of a clinical treatment plan. To demonstrate the utility of this tool, several competing lung and prostate IMRT treatment plans are compared. Results show that the plan with the highest TCI + values accomplished the competing goals of tumor coverage and critical structures sparing best, among rival treatment plans for both treatment sites. The study demonstrates, first, that dose-volume-based indices, which summarize complex dose distributions through a single index, can be used to automatically select the optimal plan among competing plans, and second, that this dose-volume-based index may be appropriate for ranking IMRT dose distributions

A new framework for classification of multi-category hand grasps using EMG signals

Electromyogram (EMG) signals have had a great impact on many applications, including prosthetic or rehabilitation devices, human-machine interactions, clinical and biomedical areas. In recent years, EMG signals have been used as a popular tool to generate device control commands for rehabilitation equipment, such as robotic prostheses. This intention of this study was to design an EMG signal-based expert model for hand-grasp classification that could enhance prosthetic hand movements for people with disabilities. The study, thus, aimed to introduce an innovative framework for recognising hand movements using EMG signals. The proposed framework consists of logarithmic spectrogram-based graph signal (LSGS), AdaBoost k-means (AB-k-means) and an ensemble of feature selection (FS) techniques. First, the LSGS model is applied to analyse and extract the desirable features from EMG signals. Then, to assist in selecting the most influential features, an ensemble FS is added to the design. Finally, in the classification phase, a novel classification model, named AB-k-means, is developed to classify the selected EMG features into different hand grasps. The proposed hybrid model, LSGS-based scheme is evaluated with a publicly available EMG hand movement dataset from the UCI repository. Using the same dataset, the LSGS-AB-k-means design model is also benchmarked with several classifications including the state-of-the-art algorithms. The results demonstrate that the proposed model achieves a high classification rate and demonstrates superior results compared to several previous research works. This study, therefore, establishes that the proposed model can accurately classify EMG hand grasps and can be implemented as a control unit with low cost and a high classification rate

Evaluating Which Dose-Function Metrics Are Most Critical for Functional-Guided Radiation Therapy

PurposeFour-dimensional (4D) computed tomography (CT) ventilation imaging is increasingly being used to calculate lung ventilation and implement functional-guided radiation therapy in clinical trials.&nbsp;There has been little exhaustive work evaluating which dose-function metrics should be used for treatment planning and plan evaluation. The purpose of our study was to evaluate which dose-function metrics best predict for radiation pneumonitis (RP).Methods and materialsSeventy lung cancer patients who underwent 4D CT imaging and pneumonitis grading were assessed. Pretreatment 4D CT scans of each patient were used to calculate ventilation images. We evaluated 3 types of dose-function metrics that combined the patient's 4D CT ventilation image and treatment planning dose distribution: (1) structure-based approaches; (2) image-based approaches using the dose-function histogram; and (3) nonlinear weighting schemes. Log-likelihood methods were used to generate normal tissue complication probability models predicting grade 3 or higher (ie, grade 3+) pneumonitis for all dose-function schemes. The area under the curve (AUC) was used to assess the predictive power of the models. All techniques were compared with normal tissue complication probability models based on traditional, total lung dose metrics.ResultsThe most predictive models were structure-based approaches that focused on the volume of functional lung receiving ≥20&nbsp;Gy (AUC, 0.70). Probabilities of grade 3+&nbsp;RP of 20% and 10% correspond to V20 (percentage of volume receiving ≥20&nbsp;Gy) to the functional subvolumes of 26.8% and 9.3%, respectively.&nbsp;Imaging-based analysis with the dose-function histogram and nonlinear weighted ventilation values yielded AUCs of 0.66 and 0.67, respectively, when we evaluated the percentage of functionality receiving ≥20&nbsp;Gy.&nbsp;All dose-function metrics outperformed the traditional dose metrics (mean lung dose, AUC of 0.55).ConclusionsA full range of dose-function metrics and functional thresholds was examined. The calculated AUC values for the most predictive functional models occupied a narrow range (0.66-0.70), and all showed notable improvements over AUC from traditional lung dose metrics (0.55). Identifying the combinations most predictive of grade 3+&nbsp;RP provides valuable data to inform the functional-guided radiation therapy process

Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals

Seizure detection is a particularly difficult task for neurologists to correctly identify the Electroencephalography (EEG)-based neonatal seizures in a visual manner. There is a strong demand to recognize the seizures in more automatic manner. Developing an expert seizure detection system with an acceptable performance level can partly fill this research gap. This paper proposes a new framework for the automated detection of neonatal seizures based on the Morse Wavelet approach that is coupled with a local binary pattern algorithm, and a graph-based community detection algorithm. An ensemble classifier method is designed to detect neonatal seizures prevalent in EEG signals. Our findings show that only 59 of the texture features can exhibit the abnormal increase in an EEG amplitude and the spikes notable during a seizure. The present results demonstrate that the proposed seizure detection model is more accurate for the detection of seizures compared with some of the traditional approaches