Aims: The outer mitochondrial membrane protein Miro1 is a crucial player in mitochondrial dynamics and calcium homeostasis. Recent evidence indicated that Miro1 mediates calcium-induced mitochondrial shape transition, which is a prerequisite for the initiation of mitophagy. Moreover, altered Miro1 protein levels have emerged as a shared feature of monogenic and sporadic Parkinson's disease (PD), but, so far, no disease-associated variants in RHOT1 have been identified. Here, we aim to explore the genetic and functional contribution of RHOT1 mutations to PD in patient-derived cellular models.Results: For the first time, we describe heterozygous RHOT1 mutations in two PD patients (het c.815G>A; het c.1348C>T) and identified mitochondrial phenotypes with reduced mitochondrial mass in patient fibroblasts. Both mutations led to decreased endoplasmic reticulum-mitochondrial contact sites and calcium dyshomeostasis. As a consequence, energy metabolism was impaired, which in turn caused increased mitophagy.Innovation and Conclusion: Our study provides functional evidence that ROTH1 is a genetic risk factor for PD, further implicating Miro1 in calcium homeostasis and mitochondrial quality control.
Saccades are ballistic eye movements that rapidly shift gaze from one location of visual space to another. Detecting saccades in eye movement recordings is important not only for studying the neural mechanisms underlying sensory, motor, and cognitive processes, but also as a clinical and diagnostic tool. However, automatically detecting saccades can be difficult, particularly when such saccades are generated in coordination with other tracking eye movements, like smooth pursuits, or when the saccade amplitude is close to eye tracker noise levels, like with microsaccades. In such cases, labeling by human experts is required, but this is a tedious task prone to variability and error. We developed a convolutional neural network to automatically detect saccades at human-level accuracy and with minimal training examples. Our algorithm surpasses state of the art according to common performance metrics and could facilitate studies of neurophysiological processes underlying saccade generation and visual processing. NEW & NOTEWORTHY Detecting saccades in eye movement recordings can be a difficult task, but it is a necessary first step in many applications. We present a convolutional neural network that can automatically identify saccades with human-level accuracy and with minimal training examples. We show that our algorithm performs better than other available algorithms, by comparing performance on a wide range of data sets. We offer an open-source implementation of the algorithm as well as a web service.
Purkinje cell “complex spikes,” fired at perplexingly low rates, play a crucial role in cerebellum-based motor learning. Careful interpretations of these spikes require manually detecting them, since conventional online or offline spike sorting algorithms are optimized for classifying much simpler waveform morphologies. We present a novel deep learning approach for identifying complex spikes, which also measures additional relevant neurophysiological features, with an accuracy level matching that of human experts yet with very little time expenditure.
Saccades are ballistic eye movements that rapidly shift gaze from one location of visual space to another. Detecting saccades in eye movement recordings is important not only for studying the neural mechanisms underlying sensory, motor, and cognitive processes, but also as a clinical and diagnostic tool. However, automatically detecting saccades can be difficult, particularly when such saccades are generated in coordination with other tracking eye movements, like smooth pursuits, or when the saccade amplitude is close to eye tracker noise levels, like with microsaccades. In such cases, labeling by human experts is required, but this is a tedious task prone to variability and error. We developed a convolutional neural network (CNN) to automatically detect saccades at human-level performance accuracy. Our algorithm surpasses state of the art according to common performance metrics, and will facilitate studies of neurophysiological processes underlying saccade generation and visual processing.
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