Phenotype and imaging features associated with APP duplications.

BACKGROUND: APP duplication is a rare genetic cause of Alzheimer disease and cerebral amyloid angiopathy (CAA). We aimed to evaluate the phenotypes of APP duplications carriers. METHODS: Clinical, radiological, and neuropathological features of 43 APP duplication carriers from 24 French families were retrospectively analyzed, and MRI features and cerebrospinal fluid (CSF) biomarkers were compared to 40 APP-negative CAA controls. RESULTS: Major neurocognitive disorders were found in 90.2% symptomatic APP duplication carriers, with prominent behavioral impairment in 9.7%. Symptomatic intracerebral hemorrhages were reported in 29.2% and seizures in 51.2%. CSF Aß42 levels were abnormal in 18/19 patients and 14/19 patients fulfilled MRI radiological criteria for CAA, while only 5 displayed no hemorrhagic features. We found no correlation between CAA radiological signs and duplication size. Compared to CAA controls, APP duplication carriers showed less disseminated cortical superficial siderosis (0% vs 37.5%, p = 0.004 adjusted for the delay between symptoms onset and MRI). Deep microbleeds were found in two APP duplication carriers. In addition to neurofibrillary tangles and senile plaques, CAA was diffuse and severe with thickening of leptomeningeal vessels in all 9 autopsies. Lewy bodies were found in substantia nigra, locus coeruleus, and cortical structures of 2/9 patients, and one presented vascular amyloid deposits in basal ganglia. DISCUSSION: Phenotypes associated with APP duplications were heterogeneous with different clinical presentations including dementia, hemorrhage, and seizure and different radiological presentations, even within families. No apparent correlation with duplication size was found. Amyloid burden was severe and widely extended to cerebral vessels as suggested by hemorrhagic features on MRI and neuropathological data, making APP duplication an interesting model of CAA.

Improvement of Pyramidal Tract Side Effect Prediction Using a Data-Driven Method in Subthalamic Stimulation.

OBJECTIVE: subthalamic nucleus deep brain stimulation (STN DBS) is limited by the occurrence of a pyramidal tract side effect (PTSE) induced by electrical activation of the pyramidal tract. Predictive models are needed to assist the surgeon during the electrode trajectory preplanning. The objective of the study was to compare two methods of PTSE prediction based on clinical assessment of PTSE induced by STN DBS in patients with Parkinson's disease. METHODS: two clinicians assessed PTSE postoperatively in 20 patients implanted for at least three months in the STN. The resulting dataset of electroclinical tests was used to evaluate two methods of PTSE prediction. The first method was based on the volume of tissue activated (VTA) modeling and the second one was a data-driven-based method named Pyramidal tract side effect Model based on Artificial Neural network (PyMAN) developed in our laboratory. This method was based on the nonlinear correlation between the PTSE current threshold and the 3-D electrode coordinates. PTSE prediction from both methods was compared using Mann-Whitney U test. RESULTS: 1696 electroclinical tests were used to design and compare the two methods. Sensitivity, specificity, positive- and negative-predictive values were significantly higher with the PyMAN method than with the VTA-based method (P < 0.05). CONCLUSION: the PyMAN method was more effective than the VTA-based method to predict PTSE. SIGNIFICANCE: this data-driven tool could help the neurosurgeon in predicting adverse side effects induced by DBS during the electrode trajectory preplanning.

Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation: proof of concept and application to the pyramidal tract side effect induced by pallidal stimulation.

Deep brain stimulation of the medial globus pallidus (GPm) is a surgical procedure for treating patients suffering from Parkinson's disease. Its therapeutic effect may be limited by the presence of pyramidal tract side effect (PTSE). PTSE is a contraction time-locked to the stimulation when the current spreading reaches the motor fibers of the pyramidal tract within the internal capsule. The objective of the study was to propose a preoperative predictive model of PTSE. A machine learning-based method called PyMAN (PTSE model based on artificial neural network) accounting for the current used in stimulation, the three-dimensional electrode coordinates and the angle of the trajectory, was designed to predict the occurrence of PTSE. Ten patients implanted in the GPm have been tested by a clinician to create a labeled dataset of the stimulation parameters that trigger PTSE. The kappa index value between the data predicted by PyMAN and the labeled data was 0.78. Further evaluation studies are desirable to confirm whether PyMAN could be a reliable tool for assisting the surgeon to prevent PTSE during the preoperative planning.