Structural Polymorphism in Tau Filaments: An Implication for Neurodegenerative Diseases

Tau filaments are the pathological hallmark of >20 neurodegenerative diseases including Alzheimer's disease, Pick's disease, and progressive supranuclear palsy. In the adult human brain, six isoforms of tau are expressed that differ by presence or absence of the second of the four semiconserved repeats. As a consequence, half of the tau isoforms have three repeats (3R tau), whereas the other half has four repeats (4R tau). Site-directed spin labeling of recombinant tau in conjunction with electron paramagnetic resonance spectroscopy was used to obtain structural insights into tau filaments. The studies showed that the filaments of 4R tau and 3R tau share a highly ordered core structure in the third repeat with parallel, in-register arrangement of beta-strands. This structure in 3R and 4R is conserved regardless of whether full-length isoforms (htau40 and htau23) or truncated constructs (K18 and K19) are used. When mixed, 3R tau and 4R tau coassembled into heterogeneous filaments. Hence, these findings indicate that there are at least three compositionally distinct types of filaments: homogeneous 3R tau, homogeneous 4R tau, and heterogeneous 3R/4R tau. In vitro experiments show that the seeded filament growth, a prerequisite for tau spreading in tissue culture and brain, is crucially dependent on the isoform composition of individual seeds. Seeds of 3R tau and 3R/4R tau recruit both types of isoforms whereas seeds of 4R tau can recruit 4R tau, but not 3R tau, establishing an asymmetric barrier. Conformational templating of 4R tau onto 3R tau seeds eliminates this barrier, giving rise to a new type of tau filament. Conformational studies at the molecular level of tau filaments were done using Double electron-electron resonance spectroscopy, which allows the determination of distances between pairs of spin labels. These studies revealed structural differences between filaments of 3R tau and 4R tau. Furthermore, they indicated that 4R tau assumed the conformation of 3R tau when templated on 3R tau seeds. Our measurements have also provided insights into the heterogeneity of tau filament structure. Conformational differences due to variation in filament composition and seeding properties of tau filaments have shown that they are structurally polymorphic in nature. This structural polymorphism of tau filaments has widespread implications in understanding and treatment of neurodegenerative diseases.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.