Brief of Amici Curiae Scholars of the Constitutional Rights of Children in Support of Petitioners in Obergefell v. Hodges

Supreme Court precedent establishes that the government may not punish children for matters beyond their control. Same-sex marriage bans and non-recognition laws (“marriage bans”) do precisely this. The states argue that marriage is good for children, yet marriage bans categorically exclude an entire class of children – children of same-sex couples – from the legal, economic and social benefits of marriage. This amicus brief recounts a powerful body of equal protection jurisprudence that prohibits punishing children to reflect moral disapproval of parental conduct or to incentivize adult behavior. We then explain that marriage bans punish children of same-sex couples because they: 1) foreclose their central legal route to family formation; 2) categorically void their existing legal parent-child relationships incident to out-of-state marriages; 3) deny them economic rights and benefits; and 4) inflict psychological and stigmatic harm. States cannot justify marriage bans as good for children and then exclude children of same-sex couples based on moral disapproval of their same-sex parents’ relationships or to incentivize opposite-sex couples to “procreate” within the bounds of marriage. To do so, severs the connection between legal burdens and individual responsibility and creates a permanent class or caste distinction.

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.