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Deep brain stimulation (DBS) within the subcallosal cingulate cortex (SCC) alleviates symptoms of depression through an unclear therapeutic mechanism. Precise stimulation of SCC white matter (SCCwm) is thought to be necessary to achieve therapeutic response, and clinical recordings can now be used to test this hypothesis. In this paper we characterized the where, what, and how of SCCwm-DBS immediate effects, its network action, at therapeutic stimulation frequencies. First, using simultaneous LFP and EEG, we determined whether the effects of SCCwm-DBS are local at the SCC and/or remote at downstream cortical regions. We then charactized the spatial pattern effected by DBS across high-density EEG, finding multi-oscillatory response modes. Finally, we demonstrated that these modes are spatially consistent with white matter tracts targeted during surgical implantation. These results clarify the immediate actions of SCCwm-DBS as broad low-frequency power increases in brain regions downstream to stimulated white matter. This quantitative characterization of SCCwm-DBS network action has implications for future clinical trials, and may accelerate adaptive therapy optimization.

Most real-world behaviors are performed with incomplete information. Odor-guided locomotion, an ecologically important behavior essential to an animal’s survival, is an example of such a behavior. Different odors activate different patterns of olfactory receptor neuron (ORN) classes providing information about which odor is present but does not provide any navigational information. In this study, we investigate the sensorimotor transformation that relates ORN activation to locomotion changes in Drosophila by optogenetically activating different combinations of ORN classes and measuring the resulting changes in locomotion. Three features describe this sensorimotor transformation: First, locomotion depends on both the instantaneous firing frequency (f) and its change (df); the two together serve as short-term memory that allows the fly to automatically adapt its motor program to sensory context. Second, the mapping between f-df and locomotor parameters such as speed or curvature is distinct for each pattern of activated ORNs. Finally, the sensorimotor mapping changes with time after odor exposure allowing integration of information over a longer timescale.

Mechanisms that control movements range from navigational mechanisms, in which the animal employs directional cues to reach a specific destination, to search movements during which there are little or no environmental cues. Even though most real-world movements result from an interplay between these mechanisms, an experimental system and theoretical framework for the study of interplay of these mechanisms is not available. Here, we rectify this deficit. We create a new method to stimulate the olfactory system in Drosophila or fruit flies. As flies explore a circular arena, their olfactory receptor neuron (ORNs) are optogenetically activated within a central region making this region attractive to the flies without emitting any clear directional signals outside this central region. In the absence of ORN activation, the fly’s locomotion can be described by a random walk model where a fly’s movement is described by its speed and turn-rate (or kinematics). Upon optogenetic stimulation, the fly’s behavior changes dramatically in two respects. First, there are large kinematic changes. Second, there are more turns at the border between light-zone and no-light-zone and these turns have an inward bias. Surprisingly, there is no increase in turn-rate, rather a large decrease in speed that makes it appear that the flies are turning at the border. Similarly, the inward bias of the turns is a result of the increase in turn angle. These two mechanisms entirely account for the change in a fly’s locomotion. No complex mechanisms such as path-integration or a careful evaluation of gradients are necessary., Author summary The strategy an animal employs to explore the environment and to find and return to the location where it has previously found food or mates is an important part of its behavior. In nature, animals have incomplete information about their environment, and must use this incomplete information to navigate. In most laboratory experiments, there is usually clear directional information making it difficult to infer an animal’s real strategy from laboratory behavioral experiments. In this study, we devise a new behavioral task wherein we remotely activate olfactory neurons when fruit flies are in a given location. This activation makes a given location attractive to the flies without providing any directional information and allows us to assess how flies navigate under these conditions. We find that flies navigate towards the activated location using two simple mechanisms: First, its speed in the activated region and its turn rate is much lower than it is elsewhere. Second, at the boundary of the odor-zone, its speed decreases dramatically and its turns become much sharper. Essentially, these simple mechanisms appear to be extremely robust.

Most behaviors such as making tea are not stereotypical but have an obvious structure. However, analytical methods to objectively extract structure from non-stereotyped behaviors are immature. In this study, we analyze the locomotion of fruit flies and show that this non-stereotyped behavior is well-described by a Hierarchical Hidden Markov Model (HHMM). HHMM shows that a fly's locomotion can be decomposed into a few locomotor features, and odors modulate locomotion by altering the time a fly spends performing different locomotor features. Importantly, although all flies in our dataset use the same set of locomotor features, individual flies vary considerably in how often they employ a given locomotor feature, and how this usage is modulated by odor. This variation is so large that the behavior of individual flies is best understood as being grouped into at least three to five distinct clusters, rather than variations around an average fly., eLife digest Many behaviors that we perform everyday, including something as familiar as making a peanut-butter sandwich, consist of a sequence of recognizable acts. These acts may include, for example, holding a knife and opening a jar. Yet often neither the sequence nor the individual acts are always performed in the exact same way. For example, there are many ways to hold a knife and there are many ways to open a jar, meaning neither of these actions could be called “stereotyped”. A lack of stereotypy makes it difficult for a computer to automatically recognize the individual acts in a sequence. This same problem would apply to other common behaviors, such as walking around somewhere you have not visited before. While we easily recognize it when we see it, walking is not a stereotyped behavior. It consists of a series of movements that differ between individuals, and even in the same individual at different times. So how can someone automatically recognize the individual acts in a non-stereotyped behavior like walking? To begin to find out, Tao et al. developed a mathematical model that can recognize the walking behavior of a fruit fly. Existing recordings of fruit flies walking were analyzed using a type of mathematical model called a Hierarchical Hidden Markov Model (often shortened to HHMM). Such models assume that there are hidden states that influence the behaviors we can see. For example, someone’s chances of going skiing (an observable behavior) depend on whether or not it is winter (a hidden state). The HHMM revealed that the seemingly random wanderings of a fly consist of ten types of movement. These include the “meander”, the “stop-and-walk”, as well as right turns and left turns. Exposing the flies to a pleasant odor – in this case, apple cider vinegar – altered how the flies walked by changing the time they spent performing each of the different types of movement. All flies in the dataset used the same ten movements, but in different proportions. This means that each fly showed an individual pattern of movement. In fact, the differences between flies are so great that Tao et al. argue that there is no such thing as an average walk for a fruit fly. The model represents a complete description of how fruit flies walk. It thus provides clues to the processes that transform an animal’s sensory experiences into behavior. But it also has potential clinical applications. Similar models for human behaviors could help reveal behaviors that are abnormal because of disease. Normal behaviors also show variability, and some diseases increase or decrease this variability. By making it easier to detect these changes, mathematical models could support earlier diagnosis of medical conditions.