There is a strong emphasis on developing novel neuroscience technologies, in particular on recording from more neurons. There has thus been increasing discussion about how to analyze the resulting big datasets. What has received less attention is that over the last 30 years, papers in neuroscience have progressively integrated more approaches, such as electrophysiology, anatomy, and genetics. As such, there has been little discussion on how to combine and analyze this multimodal data.Here, we describe the growth of multimodal approaches, and discuss the needed analysis advancements to make sense of this data.
Evolution of Neuroscience Technologies and Questions
The development of neuroscience technology has been rapidly advancing (Stevenson and Kording, 2011; Insel et al., 2013; Kandel et al., 2013; Marblestone et al., 2013) across many approaches, including those that investigate neural activity (Kording, 2011; Prevedel et al., 2014; Schwarz et al., 2014; Van Horn and Toga, 2014; Vladimirov et al., 2014; Hamel et al., 2015; Lemon et al., 2015), neuroanatomy (Zador et al., 2012; Helmstaedter, 2013; Van Essen, 2013; Oh et al., 2014; Glaser et al., 2015), and gene expression and genetics (Cahoy et al., 2008; Stein et al., 2012; Lee et al., 2014; Medland et al., 2014). Advancing technologies allow us to answer more complex questions. For instance, with single electrodes, researchers could only ask about how individual neurons respond to stimuli and relate to behavior (Hubel and Wiesel, 1959; O’Keefe and Dostrovsky, 1971; Georgopoulos et al., 1982). With the invention of electrode arrays (Maynard et al., 1997; Schwarz et al., 2014; Siegel et al., 2015) and large-scale optical recording techniques (Prevedel et al., 2014; Vladimirov et al., 2014; Hamel et al., 2015), many now ask how neurons interact with each other (Cohen and Kohn, 2011; Stevenson and Kording, 2011; Cunningham and Yu, 2014). Data analysis techniques have been extended to make sense of this growing neural data (e.g., Pfau et al., 2013; Cunningham and Yu, 2014; Freeman et al., 2014; Gao and Ganguli, 2015), which has led to many important insights about the brain.
Along with developing new technologies and increasing the scalability of existing technologies, another way to answer more complex questions is to combine multiple approaches (e.g., using electrophysiology and neuroanatomy together). The brain is a complex system whose function depends on the interplay between countless structures and actions, all spanning different spatial and temporal scales. Combining multiple approaches is critical for understanding how different aspects of the brain relate to each other, e.g., how the morphology of a neuron influences its activity. Moreover, combining multiple approaches is critical for understanding how the brain operates at multiple scales, e.g., how the spikes of individual neurons are related to waves of activity spread across the brain. Data analysis techniques to make sense of this “multimodal” data will be very important going forward.