<p>Modulation of neural activity with electrical stimulation is a widespread therapy for treating neurological disorders and diseases. Two notable applications that have had striking clinical success are deep brain stimulation (DBS) for the treatment of movement disorders (e.g., Parkinson's disease) and spinal cord stimulation (SCS) for the treatment of chronic low back and limb pain. In these therapies, the battery life of the stimulators is much less than the required duration of treatment, requiring patients to undergo repeated battery replacement surgeries, which are costly and obligate them to incur repeatedly the risks associated with surgery. Further, deviations in lead position of 2-3 mm can preclude some or all potential clinical benefits, and in some cases, generate side-effects by stimulation of non-target regions. Therefore, despite the success of DBS and SCS, their efficiency and ability to activate target neural elements over non-target elements, termed selectivity, are inadequate and need improvement.</p><p>We combined computational models of volume conduction in the brain and spine with cable models of neurons to design novel electrode configurations for efficient and selective electrical stimulation of nervous tissue. We measured the efficiency and selectivity of prototype electrode designs in vitro and in vivo. Stimulation efficiency was increased by increasing electrode area and/or perimeter, but the effect of increasing perimeter was not as pronounced as increasing area. Cylindrical electrodes with aspect (height to diameter) ratios of > 5 were the most efficient for stimulating neural elements oriented perpendicular to the axis of the electrode, whereas electrodes with aspect ratios of < 2 were the most efficient for stimulating parallel neural elements.</p><p>Stimulation selectivity was increased by combining two or more electrodes in multipolar configurations. Asymmetric bipolar configurations were optimal for activating parallel axons over perpendicular axons; arrays of cathodes with short interelectrode spacing were optimal for activating perpendicular axons over parallel axons; anodes displaced from the center of the target region were optimal for selectively activating terminating axons over passing axons; and symmetric tripolar configurations were optimal for activating neural elements based on their proximity to the electrode. The performance of the efficient and selective designs was not be explained solely by differences in their electrical properties, suggesting that field-shaping effects from changing electrode geometry and polarity can be as large as or larger than the effects of decreasing electrode impedance.</p><p>Advancing our understanding of the features of electrode geometry that are important for increasing stimulation efficiency and selectivity facilitates the design of the next generation of stimulation electrodes for the brain and spinal cord. Increased stimulation efficiency will increase the battery life of IPGs, increase the recharge interval of rechargeable IPGs, and potentially reduce stimulator volume. Greater selectivity may improve the success rate of DBS and SCS by mitigating the sensitivity of clinical outcomes to malpositioning of the electrode.</p> / Dissertation

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Reliable chronic neural recording from focal deep brain structures is impeded by insertion injury and foreign body response, the magnitude of which is correlated with the mechanical mismatch between the electrode and tissue. Thin and flexible neural electrodes cause less glial scarring and record longer than stiff electrodes. However, the insertion of flexible microelectrodes in the brain has been a challenge. A novel insertion method is proposed, and demonstrated, for precise targeting deep brain structures using flexible micro-wire electrodes. A novel electrode guiding system is designed based on the principles governing the buckling strength of electrodes. The proposed guide significantly increases the critical buckling force of the microelectrode. The electrode insertion mechanism involves spinning of the electrode during insertion. The spinning electrode is slowly inserted in the brain through the electrode guide. The electrode guide does not penetrate into cortex. The electrode is inserted in the brain without stiffening it by coating with foreign material or by attaching a rigid support and hence the method is less invasive. Based on two new mechanisms, namely spinning and guided insertion, it is possible to insert ultra-thin micro-wire flexible electrodes in rodent brains without buckling. I have demonstrated successful insertion of 25 µm platinum micro-wire electrodes about 10 mm deep in rat brain. A novel micro-motion compensated ultra-thin flexible platinum microelectrode has been presented for chronic single unit recording. Since manual insertion of the proposed microelectrode is not possible, I have developed a microelectrode insertion device based on the proposed method. A low power low noise 16 channel programmable neural amplifier ASIC has been designed and used to record the neural spikes. The ability to record neural activity during insertion is a unique feature of the developed inserter. In vivo implantation process of the microelectrode has been demonstrated. Microelectrodes were inserted in the Botzinger complex of rat and long term respiratory related neural activity was recorded from live rats. The developed microelectrode has also been used to study brain activity during seizures. In-vivo experimental results show that the proposed method and the prototype insertion system can be used to implant flexible microelectrode in deep brain structures of rodent for brain studies.

The relationship between post-mortem state and changes of biological tissue impedance has been investigated to serve as a basis for developing an in-vitro measurement method for monitoring the freshness of meat. The main challenges thereby are the reproducible measurement of the impedance of biological tissues and the classification method of their type and state. In order to realize reproducible tissue bio-impedance measurements, a suitable sensor taking into account the anisotropy of the biological tissue has been developed. It consists of cylindrical penetrating multi electrodes realizing good contacts between electrodes and the tissue. Experimental measurements have been carried out with different tissues and for a long period of time in order to monitor the state degradation with time. Measured results have been evaluated by means of the modified Fricke-Cole-Cole model. Results are reproducible and correspond to the expected behavior due to aging. An appropriate method for feature extraction and classification has been proposed using model parameters as features as input for classification using neural networks and fuzzy logic. A Multilayer Perceptron neural network (MLP) has been proposed for muscle type computing and the age computing and respectively freshness state of the meat. The designed neural network is able to generalize and to correctly classify new testing data with a high performance index of recognition. It reaches successful results of test equal to 100% for 972 created inputs for each muscle. An investigation of the influence of noise on the classification algorithm shows, that the MLP neural network has the ability to correctly classify the noisy testing inputs especially when the parameter noise is less than 0.6%. The success of classification is 100% for the muscles Longissimus Dorsi (LD) of beef, Semi-Membraneous (SM) of beef and Longissimus Dorsi (LD) of veal and 92.3% for the muscle Rectus Abdominis (RA) of veal. Fuzzy logic provides a successful alternative for easy classification. Using the Gaussian membership functions for the muscle type detection and trapezoidal member function for the classifiers related to the freshness detection, fuzzy logic realized an easy method of classification and generalizes correctly the inputs to the corresponding classes with a high level of recognition equal to 100% for meat type detection and with high accuracy for freshness computing equal to 84.62% for the muscle LD beef, 92.31 % for the muscle RA beef, 100 % for the muscle SM veal and 61.54% for the muscle LD veal. / Auf der Basis von Impedanzspektroskopie wurde ein neuartiges in-vitro-Messverfahren zur Überwachung der Frische von biologischem Gewebe entwickelt. Die wichtigsten Herausforderungen stellen dabei die Reproduzierbarkeit der Impedanzmessung und die Klassifizierung der Gewebeart sowie dessen Zustands dar. Für die Reproduzierbarkeit von Impedanzmessungen an biologischen Geweben, wurde ein zylindrischer Multielektrodensensor realisiert, der die 2D-Anisotropie des Gewebes berücksichtigt und einen guten Kontakt zum Gewebe realisiert. Experimentelle Untersuchungen wurden an verschiedenen Geweben über einen längeren Zeitraum durchgeführt und mittels eines modifizierten Fricke-Cole-Cole-Modells analysiert. Die Ergebnisse sind reproduzierbar und entsprechen dem physikalisch-basierten erwarteten Verhalten. Als Merkmale für die Klassifikation wurden die Modellparameter genutzt.

The relationship between post-mortem state and changes of biological tissue impedance has been investigated to serve as a basis for developing an in-vitro measurement method for monitoring the freshness of meat. The main challenges thereby are the reproducible measurement of the impedance of biological tissues and the classification method of their type and state. In order to realize reproducible tissue bio-impedance measurements, a suitable sensor taking into account the anisotropy of the biological tissue has been developed. It consists of cylindrical penetrating multi electrodes realizing good contacts between electrodes and the tissue. Experimental measurements have been carried out with different tissues and for a long period of time in order to monitor the state degradation with time. Measured results have been evaluated by means of the modified Fricke-Cole-Cole model. Results are reproducible and correspond to the expected behavior due to aging. An appropriate method for feature extraction and classification has been proposed using model parameters as features as input for classification using neural networks and fuzzy logic. A Multilayer Perceptron neural network (MLP) has been proposed for muscle type computing and the age computing and respectively freshness state of the meat. The designed neural network is able to generalize and to correctly classify new testing data with a high performance index of recognition. It reaches successful results of test equal to 100% for 972 created inputs for each muscle. An investigation of the influence of noise on the classification algorithm shows, that the MLP neural network has the ability to correctly classify the noisy testing inputs especially when the parameter noise is less than 0.6%. The success of classification is 100% for the muscles Longissimus Dorsi (LD) of beef, Semi-Membraneous (SM) of beef and Longissimus Dorsi (LD) of veal and 92.3% for the muscle Rectus Abdominis (RA) of veal. Fuzzy logic provides a successful alternative for easy classification. Using the Gaussian membership functions for the muscle type detection and trapezoidal member function for the classifiers related to the freshness detection, fuzzy logic realized an easy method of classification and generalizes correctly the inputs to the corresponding classes with a high level of recognition equal to 100% for meat type detection and with high accuracy for freshness computing equal to 84.62% for the muscle LD beef, 92.31 % for the muscle RA beef, 100 % for the muscle SM veal and 61.54% for the muscle LD veal. / Auf der Basis von Impedanzspektroskopie wurde ein neuartiges in-vitro-Messverfahren zur Überwachung der Frische von biologischem Gewebe entwickelt. Die wichtigsten Herausforderungen stellen dabei die Reproduzierbarkeit der Impedanzmessung und die Klassifizierung der Gewebeart sowie dessen Zustands dar. Für die Reproduzierbarkeit von Impedanzmessungen an biologischen Geweben, wurde ein zylindrischer Multielektrodensensor realisiert, der die 2D-Anisotropie des Gewebes berücksichtigt und einen guten Kontakt zum Gewebe realisiert. Experimentelle Untersuchungen wurden an verschiedenen Geweben über einen längeren Zeitraum durchgeführt und mittels eines modifizierten Fricke-Cole-Cole-Modells analysiert. Die Ergebnisse sind reproduzierbar und entsprechen dem physikalisch-basierten erwarteten Verhalten. Als Merkmale für die Klassifikation wurden die Modellparameter genutzt.