Ahmad Ashoori

Parkinson's disease rigidity: relation to brain connectivity and motor performance

Objective: (1) To determine the brain connectivity pattern associated with clinical rigidity scores in Parkinson’s disease (PD) and (2) to determine the relation between clinically assessed rigidity and quantitative metrics of motor performance. Background: Rigidity, the resistance to passive movement, is exacerbated in PD by asking the subject to move the contralateral limb, implying that rigidity involves a distributed brain network. Rigidity mainly affects subjects when they attempt to move; yet the relation between clinical rigidity scores and quantitative aspects of motor performance are unknown. Methods: Ten clinically diagnosed PD patients (off-medication) and 10 controls were recruited to perform an fMRI squeeze-bulb tracking task that included both visually guided and internally guided features. The direct functional connectivity between anatomically defined regions of interest was assessed with Dynamic Bayesian Networks (DBNs). Tracking performance was assessed by fitting Linear Dynamical System (LDS) models to the motor performance, and was compared to the clinical rigidity scores. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) regression method was used to determine the brain connectivity network that best predicted clinical rigidity scores. Results: The damping ratio of the LDS models significantly correlated with clinical rigidity scores (p = 0.014). An fMRI connectivity network in subcortical and primary and premotor cortical regions accurately predicted clinical rigidity scores (p < 10−5). Conclusion: A widely distributed cortical/subcortical network is associated with rigidity observed in PD patients, which reinforces the importance of altered functional connectivity in the pathophysiology of PD. PD subjects with higher rigidity scores tend to have less overshoot in their tracking performance, and damping ratio may represent a robust, quantitative marker of the motoric effects of increasing rigidity.

Mode Detection in switched pursuit tracking tasks: Hybrid estimation to measure performance in Parkinson's disease

Parkinsons disease (PD) is a neurodegenerative disorder that impairs motor skills, speech, and other voluntary movement, and may be associated with cognitive inflexibility. Fourteen PD subjects (both on and off medication) and 10 normal subjects performed a manual pursuit tracking task, in which the dynamics of the task suddenly change without explicit enunciation. The task dynamics have three modes, in which the error (the difference between the target and the users cursor) is attenuated, exaggerated, or unchanged - hence we model the subject performing the tracking task as a hybrid system with arbitrary switching. Second-order stochastic LTI models of tracking performance in each mode are first obtained through system identification. We then use a multiple model adaptive estimation (MMAE) algorithm to determine a) whether each subject successfully adapted to the sudden change in tracking dynamics, and if so, b) the delay in switching to the new mode. These parameters were analyzed for all subjects, and found to be statistically significant across groups. While normal subjects consistently detected the change in task dynamics, PD subjects show considerably more difficulty in detecting the switch (especially off medication), and did not switch into the new mode as quickly as normal subjects. Our results suggest that PD subjects have considerable impairment in adapting to changing motor environments.

A novel algorithm for edge enhancement based on Hilbert Matrix

In this paper, the performance of image filtering using the Hilbert Matrix and its inverse is studied first and according to its results, a new edge enhancement and embossing algorithm is proposed. Most of the Laplacian and Sobel-based edge detection methods use small masks. The algorithm suggested here not only enhances and sharpens the edges of the image, but also makes it possible to use customized masks in Hilbert matrix and its inverse. Practical results show that this algorithm can be exploited in different fields such as angiography. The proposed method has been compared with sharpening method using Laplacin and Sobel operators.

Fuzzy image fusion application in detecting coronary layers in IVUS pictures

Intra Vascular UltraSound (IVUS) is a medical imaging technique, which is based on inserting an ultrasound catheter inside a vessel and producing real-time high-resolution images from the inner side of the vessel. In this paper, processing of fused IVUS frames in order to automatically detecting the coronary inner layers is provided. To reach this goal, preprocessing actions including substitution of the catheter region with the average brightness of the whole image, wavelet transform and edge-preserving smoothing are performed first. Then detecting the borders applying deformable models (distance-potential snake is used according to these images topology) has been displayed. Finally, using fuzzy integral operators for fusion in three levels including data-level, feature- level and decision-level, the mentioned process is done again and the good effect of image fusion on the IVUS frames of NIOC hospital data bank has been displayed. This method has the advantage of being fully automated, without needing the initial contour to be manually determined. The merit of this method comparing with similar methods is also its acceptable executing time, which is very important for curing patients.

Detecting Coronary Layers in IVUS Pictures Using Image Fusion Approach

Coronary artery disease is considered as the most important cause of death in most developed or semi-developed societies. It is known as a silent disease because it develops gradually without any serious symptoms and is recognized only after patient sudden death or serious infarction. The main cause of this type of disease is the plaque integration inside the coronary arteries. This obstructs blood circulation and cardiac muscles nutrition. Hence, finding methods for detecting vessels obstruction and curing it in time would be important to prevent complete obstruction. X-ray Angiography is one of the common methods to this end, which is an invasive method with a high risk due to X-ray radiations. In addition, this method is not strong enough to determine the quantity and the kind of the plaques (Fibrous Tissue, Necrotic Core classification, and/or Fibro-Fatty) (Agostoni et al., 2004). One of the recent methods emerged for detecting vessels obstruction is Intra Vascular Ultra Sound (IVUS) imaging technique, which is based on inserting an ultrasound catheter inside a vessel and producing real-time cross-sectional images from the inner side of the vessel (Schoenhagen and Stillman, 2005). This semi-invasive method has not the X-ray harms and provides more accurate information from the vessel wall (Schoenhagen and White, 2003). In addition to its safety, IVUS is a reproducible method for imaging the vessel walls and determining the quantity of the vessel obstruction by the plaques. Fig. 1 shows a sample IVUS picture. IVUS imaging is carried out by inserting a catheter into a vessel, which travels through and reaches the artery. Since the area of this catheter is larger than the one of coronary vessels, it stops there and a fine probe (0.961.17 mm long) emerges from it and penetrates to the end of the vessel. The probe is then pulled backward with a constant velocity and meanwhile IVUS frames are captured. Common frequency for imaging is 20-40 MHz. An increase in the frequency may improve the resolution; But due to energy absorption in tissues, quality of the images are low. For medical usage of IVUS images, the borders of the inside and outside of a vessel and also plaque layers must be determined. This is usually done manually by a specialist, which is a time-consuming and error-prone procedure. Moreover, due to different noises such as motion artifact, ring-down, and speckle noise, automatic processing (Terzopoulos and Fleischer, 1988) of these images is one of the difficult problems in image processing. Lots of

Control relevant identification for controlling a continuous-stream bioreactor with unknown dynamics

This paper presents an iterative procedure for control relevant identification for closed-loop system with unknown dynamics. At each step of iteration, an optimal controller is designed for the worst case model that obtained from identification task. The proposed framework uses simple model for identification task and an optimal Hinfin controller will be designed. This method is also applied on a continuous stream bioreactor and the result was quite satisfactory.

Model predictive control of a nonlinear fed-batch fermentation process

Bioprocesses, which are involved in producing different antibiotics and other pharmaceutical products, may be conveniently classified according to the mode chosen for the process: either batch, fed-batch or continuous. From the control engineers viewpoint it is the fed-batch processes, however, which present the greatest challenge to get a pure product with a high concentration. To achieve this goal, control of the following parameters has significant importance dealing with these processes: temperature, pH, dissolved oxygen (DO2). Bioprocesses have complicated dynamics. Hence, their control is a delicate task; Nonlinearity and non-stationarity, which make modeling and parameter estimation particularly difficult perturbs such processes. Moreover, the scarcity of on-line measurements of the component concentrations (essential substrates, biomass and products of interest) makes this task more sophisticated. In this paper, Model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor has been developed. MPC is performed via determining the control signal by minimizing a cost function in each step. The results of this controller to maximize penicillin concentration have been displayed and also compared with the results of auto-tuned PID controller used in previous works.