Activation in neural networks controlling ingestive behaviors: What does it mean, and how do we map and measure it?

Physiology & Behavior 89 (2006) 501 – 510

Activation in neural networks controlling ingestive behaviors: What does it mean, and how do we map and measure it? Alan G. Watts ⁎, Arshad M. Khan, Graciela Sanchez-Watts, Dawna Salter, Christina M. Neuner Neuroscience Research Institute and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089-2520, United States Received 27 February 2006; received in revised form 5 May 2006; accepted 25 May 2006

Abstract Over the past thirty years many of different methods have been developed that use markers to track or image the activity of the neurons within the central networks that control ingestive behaviors. The ultimate goal of these experiments is to identify the location of neurons that participate in the response to an identified stimulus, and more widely to define the structure and function of the networks that control specific aspects of ingestive behavior. Some of these markers depend upon the rapid accumulation of proteins, while others reflect altered energy metabolism as neurons change their firing rates. These methods are widely used in behavioral neuroscience, but the way results are interpreted within the context of defining neural networks is constrained by how we answer the following questions. How well can the structure of the behavior be documented? What do we know about the processes that lead to the accumulation of the marker? What is the function of the marker within the neuron? How closely in time does the marker accumulation track the stimulus? How long does the marker persist after the stimulus is removed? We will review how these questions can be addressed with regard to ingestive and related behaviors. We will also discuss the importance of plotting the location of labeled cells using standardized atlases to facilitate the presentation and comparison of data between experiments and laboratories. Finally, we emphasize the importance of comprehensive and accurate mapping for using newly emerging technologies in neuroinfomatics. © 2006 Elsevier Inc. All rights reserved. Keywords: Fos; Neuroinfomatics; MAPK; Hypothalamus; Atlas; Rat; Anorexia; Brain maps

1. Introduction A major focus for behavioral neuroscience is to understand the complete functional organization of the neural networks that initiate, maintain, and terminate specific behaviors. Clarifying neural network organization for ingestive behaviors would reveal which neurons contribute to the behavioral sequence, when they contribute, and would provide the basis for further functional investigations into how collectively they can control eating and drinking. How can we clarify the organization of these networks in a way that will help us understand their function? In this review, we will address this question from the perspective of mapping patterns of neural activation. We will first discuss a general model for ⁎ Corresponding author. Neuroscience Research Institute, Hedco Neuroscience Building, MC 2520, University of Southern California, Los Angeles, CA 90089-2520, United States. Tel.: +1 213 740 1497; fax: +1 213 741 0561. E-mail address: [email protected] (A.G. Watts). 0031-9384/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.physbeh.2006.05.025

considering the organization of central control networks in behavior, and then consider the meaning of the term ‘activation’ from the viewpoint of neural physiology. We then describe the use and limitations of various cellular markers that are currently used to map neural activation. Finally, we will discuss strategies that we can use to incorporate this type of data into standardized atlases and databases in a way that will enable investigators to share and compare these complex datasets in a meaningful way, and to construct testable models of behavioral control networks. 2. Behavioral control networks and changes in neural activation 2.1. Behavioral control networks The sequence of motor actions that make up a behavior ultimately derives from the changing signaling patterns within networks that control sensory transduction, central integration, and motor selection and execution (Fig. 1; [1,2]). Our

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constituent neurons are the building blocks of network function. Thus, if we can identify the location, chemical phenotype, and projections of the neurons that change their firing rates–or as is often more generally stated, their ‘activity’–at critical times of the behavioral sequence then it should be possible to begin defining the nature of the networks responsible for behavioral initiation, maintenance, and termination. Before we discuss tools and strategies, we will briefly consider the meaning of the term neural activation. An accurate definition becomes important for considering how the most commonly used cellular markers are related to neural function. 2.2. Neural activation

Fig. 1. A schematic representation of the neural systems and their interactions involved with controlling reflex (A) and motivated behaviors (B). Central neural connections are shown in black, and hormonal and feedback signals as dashed lines. The regions that constitute the central behavioral control networks in the brain are enclosed within the gray box in (B). Adapted from [49].

understanding is clearest at the sensory and motor parts of the network because the nature of the sensory mechanisms in the brain and periphery is fairly easily probed using specific homeostatic and metabolic signals. For example, we know that angiotensin II acts on neurons in the subfornical organ (SFO) to stimulate drinking, and that leptin, insulin, and ghrelin acting on neurons in the arcuate nucleus (ARH) have profound effects on feeding behavior [3,4]. Similarly, the neural bases of the simple motor actions in the consummatory phase are relatively easily tracked in the hindbrain, and we know with some degree of detail how several of the motor networks in the hindbrain that control chewing and swallowing are organized [5–9]. But located between sensory transduction and motor output are the control networks that add the adaptive value to behavior and allow animals to survive successfully in their environment (Fig. 1). Despite their importance to the organization of behavior, the constituency and function of these critical circuits are very poorly understood, for the most part because of their great complexity and the lack of suitable tools for investigation. Fig. 1 posits that the exterosensory and interosensory signals that initiate motivated–as opposed to reflex–behaviors require sophisticated processing within central control networks. In terms of network function at the absolute simplest level, a specific behavior is initiated when input signal processing causes some neurons within a central control network to increase their firing rate while others are inhibited. These firing rate changes are key parts of the integration process within the network that ultimately initiates a specific motor sequence. The way that a behavior emerges from the activity of these control networks is very poorly understood. In fact for all ingestive behaviors our knowledge of the constituent neurons within central networks together with their structure/function relations, is rudimentary at best. However, what is clear is that changes in the nature and release rates of chemical signals (fastacting transmitters, peptides, and neuromodulators) from

The term ‘activation’ when applied to neurons (or indeed to any cell type), implies that some aspect of their function–but usually output–increases over a defined time period. Although ‘activation’ is useful for general discussion, it is often used synonymously with increased firing rate, which is considered the neuron's primary functional output. Closely examining the function of the most commonly used cellular markers together with their associated mechanisms shows that different physiological processes are being tracked by each of these markers. Moreover, it is generally accepted that the function of many commonly used markers is not necessarily related directly to changes in firing rate. Whether a neuron changes its activation state is determined by how it integrates its afferent inputs. Part of this integration process involves changing the state of signal transduction pathways, which in turn can alter two fundamental processes (Fig. 2): neuronal excitability, by way of changes in membrane potential; and the biosynthesis of a variety of proteins and peptides, which include transcription factors, proteins involved with transmitter release, receptor proteins, etc. A third cellular process that is intimately involved with changes in neural activity is the neuron's energy metabolism (Fig. 2). Increases in firing rate require significant energy expenditure, particularly for maintaining Na+/K+-ATPase activity [10]. 2.3. Tracking neural activation How do we track changes in neural activity relative to the development of a behavior? And if we can do this, what will the results tell us about the structure and function of the networks? There are three main strategies that experimentalists use to track changes in neural activity relative to the development of a behavior. One is direct electrophysiological recording to correlate changes in neural firing patterns in particular brain regions with the expression of a behavior. A disadvantage with regard to overall network structure is that we need to know the constituents of a given neural control network in advance and with some degree of certainty; i.e. where to place the electrodes. Electrophysiology can produce complex and sophisticated sets of data that are extremely useful for determining the signaling properties of a network [11,12]. These types of data are critical to understanding how behaviors are controlled because they provide a way to analyze network function in real time. Furthermore, multielectrode assemblies are now able to simultaneously gather data

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Fig. 2. Three processes within neurons have components that are amenable for use as markers of neural activation: biosynthesis, excitability, and energy metabolism. Within these processes, markers vary in the mechanisms that lead to their accumulation, the time frames over which they are detectable, and their downstream functions.

about neural firing rates in a way that is providing real insights about how neural networks, particularly in the cortex, are put together and operate to develop complex behaviors [13,14]. But even with this level of technical sophistication our ability to determine the actual function of the group of neurons from which recordings are taken currently remains relatively rudimentary within the context of behavioral control [12]. A second strategy that provides resolution on a broader scale than electrophysiology is to track changes in energy utilization within defined regions of the brain. There are a number of markers associated with energy metabolism that have been used in this manner, e.g. cytochrome oxidase [15,16]. But the most wellknown is to measure changes in the uptake of [14C]2-deoxy-Dglucose (2DG) in response to a stimulus. 2DG is a glucose analog that is not isomerized to fructose-6-phosphate after its phosphorylation by hexokinase. Upon administration into the circulation [14C]2DG is taken up by all cells in proportion to their glucose utilization. Measuring the accumulation of [14C]2DG after a fixed time interval reveals brain areas that alter their energy expenditure following an experimental manipulation. [14C]2DG autoradiography was pioneered by Sokoloff in the early 1970s and has been applied to a number of neuroscientific problems [17,18]. For many years it provided the only way to image neural activation patterns throughout the brain in a manner that is now more commonly tracked using c-Fos in small animals, and fMRI in humans. Of course tracking changes in metabolic activity provides indices of neural function that are inherently different to those monitored by electrophysiology or immunocytochemistry (Fig. 2), and these methods do not have the cellular resolution of immunocytochemical techniques. The third strategy for tracking changing patterns of neural activity–and the one upon which we will focus our attention for the rest of this article–is to map the location, chemical phenotype, and connections of all the neurons whose activity changes before and during the initiation of a behavioral episode. This requires a multi-

dimensional approach using a combination of behavioral analysis, a variety of neuroanatomical and biochemical techniques, along with newly emerging techniques in neuroinfomatics [19–21]. If we can identify which neurons change their activity as a behavior develops, we might be in a position to clarify the organization of the networks responsible for different aspects of behavioral control. Currently our ability to do this for any behavior is very poor. 3. Markers Fig. 2 illustrates the cellular events that follow transmitter binding to receptors on the neural membrane. It highlights the fact that many components in the biosynthetic cascades initiated by afferent integration might serve as indices of neural activation. Changes in the levels of these components are going to reflect changes in the state of signal transduction pathways regulated by ligand–receptor binding. However, their utility as markers depends upon whether these levels change in a manner that can track the stimulus in a meaningful way, and upon the availability of sufficiently sensitive detection methods. Three sets of molecules have been used as markers to determine neural activation response patterns to a multitude of stimuli: phosphorylated proteins; primary and mature RNA transcripts; and the peptides/proteins that are the end products of biosynthesis (Fig. 2). The nature of these molecules means that in situ hybridization and/or immunocytochemistry are the most common methods for identifying neurons where the levels of a particular marker change following a stimulus. Each of these molecules has characteristics that make them more or less useful for tracking specific aspects of neural activation. For example, some protein kinases are widely distributed in the brain and are rapidly phosphorylated following a stimulus making them potentially very useful for identifying neurons that quickly become involved in organizing behavioral responses. We will describe some of our recent results showing the utility of one such marker–the

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phosphorylated (p) forms of p44/p42 mitogen-activated protein (MAP) kinases (also known as ERKs 1/2)–in tracking rapid neural responses. The discovery in the mid-1980s that certain proteins (immediate–early gene products [22]) quickly accumulate in neurons following an extracellular stimulus provided the breakthrough that led to their use as activation markers [23]. The protooncogene c-Fos [24] is by far the most commonly used cellular marker, although others are also used [25–27]. Cellular markers, by their nature, have been used almost exclusively to track how neurons and circuits respond to a single stimulus. Because endpoints are detected histologically, experimental design is generally linear within a set of animals: stimulus application, tissue processing, data gathering and interpretation. However, we should note that it is possible to track the convergence of two stimuli given at discrete intervals onto neural networks of the same animal. The clearest examples of this approach are the use of Arc and Homer 1a gene products. These immediate early genes have rapid (Arc) or slower (Homer 1a) activation and decay patterns, meaning that detecting their mRNAs can separately track two stimuli that are presented 20– 30 min apart [26−28]. Further developing this type of technology will dramatically improve our ability to understand how behavioral networks are organized. Having identified the categories of molecules that can act as cellular markers, we should consider both the cellular mechanisms that lead to marker accumulation, and the functions that the markers have in neurons. 4. Mechanisms Two considerations are central to understanding what changes in the concentrations of cellular markers can reveal about neural network organization. First, what are the cellular events that culminate in the accumulation of the marker? And second, what function does the marker itself have within the neuron? The downstream products of the signal transduction pathways engaged by a neuron's afferent inputs are critical cellular events regulating the accumulation of the marker. Understanding these mechanisms instructs us about the processes to which a marker may or may not respond, and they constrain the way we can interpret what the neuron's overall function might be in relation to the stimulus. A good example is the fact that c-Fos biosynthesis tracks stimuli that increase intracellular calcium following the occupation of a variety of receptors in a way that is related to frequency of excitatory neural inputs [22,30,29]. Furthermore, this property means that increased c-Fos expression is not invariably coupled to neuronal firing rates (see also [22,31,32]); a feature that also applies to all currently used cellular activation markers. It is also important to note that these mechanistic issues mean that the absence of a cellular marker does not exclude the neuron's potential contribution to behavioral control. The physiological role that a particular marker plays within a neuron provides the context for recognizing how changes in its levels relate to neural activity. This is an important consideration when trying to place the neuron within the overall context of network function. For example, because a great deal is known about the signaling properties of p44/42 MAP kinases

[33,34], we can use this information to consider the type of cellular process engaged by a particular stimulus if levels of pERK1/2 increase. The function of other cellular markers in neurons is much less clear. In this regard c-Fos acts as a transcription factor for a large number of genes that regulate cell growth, proliferation, and differentiation [35]. However, the role of these types of long-term adaptive processes in neurons is unknown. 5. Resolution The utility of the data generated from experiments using cellular markers depends upon at least three analytical resolution issues. First, how well can we temporally correlate the patterns of marker expression with the identified components of a behavior? This is determined largely by the kinetics of marker accumulation and degradation, and can be considered as the marker's temporal resolution. Second, how accurately can we describe and resolve the sequence of motor events that make up the behavior being investigated? Third, how accurately can we represent (i.e. map) the location of activated neurons within the brain? 5.1. Temporal resolution If the distribution of a particular marker within the brain is to provide a meaningful picture of network structure during a behavioral sequence then we need to understand its response kinetics (Fig. 2). How long does it take for a marker to accumulate in a neuron, and what is its half-life? Some markers respond to a stimulus very rapidly and are only transiently detectable. Changes in membrane potential occur within milliseconds of an afferent stimulus, and can be equally brief in duration once the stimulus is removed. Changes in the activity of some biosynthetic pathway components also occur rapidly—protein phosphorylation, for example. Other events, such as the accumulation of newly synthesized bioactive proteins and peptides, occur much more slowly and are more persistent in neurons. c-Fos and the fos-related antigens (FRA) are examples of markers of this type. Their accumulation can take 30 min or longer, and in the case of FosB, can persist for many hours [36]. Each type of marker thus provides a different picture of network structure and function at a given time of the behavioral sequence. For example, different expression patterns are seen if we examine the c-Fos in the brain 30 min and 2 h following a stimulus that triggers feeding [37]. If the behavior is made up of a complex sequence of motor events, then the complete map obtained at a time well into the sequence will be a useful composite of the neural networks involved with the events expressed up to that point [38]. The pattern of c-Fos distribution seen within the brain 2 h after a challenge that leads to feeding will likely be different again to what is revealed by a marker that accumulates within 5 min of the challenge (see below). In the latter case, because of the rapidity with which the marker appears, it should be possible to correlate the resulting expression patterns much more tightly to a specific aspect of the behavior than with a marker such as cFos. This point leads us now to consider that the utility of the maps derived from different markers is determined by how well we can resolve the structure of the behavior.

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5.2. Behavioral resolution The structure of the behavior chosen for study will determine how well data can be defined within the context of network organization. All behaviors consist of a temporally arranged series of sensory-motor events [2]. For some behaviors this sequence will be relatively fixed and easily documented, while for others it will vary considerably each time they are expressed, particularly in the appetitive phase. Those behaviors that are more easily dissected will be those that have simple, well defined, and easily quantified stimulus-response characteristics. An example of an ostensibly simple ingestive behavior is the set of motor actions utilized when a rat approaches a water source to drink. Despite its overt simplicity a quite complex sequence of motor actions is revealed when this behavior is dissected into its component parts (Table 1 [39–41]). Another example of a simple ingestive behavioral sequence is the compensatory feeding that occurs when dehydration–anorexia is reversed [42]. Within any of these behavioral sequences, each component set of sensory-motor events will involve its own pattern of neural activation. Other ingestive behaviors are more varied and complex in their initiation and expression; the predatory behavior expressed as an animal searches of food, for example [38]. The

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structure of these complex behaviors means that it is more difficult to correlate expression patterns of cellular markers with a particular aspect of the behavior. However, the analysis of ensuing c-Fos expression patterns is beginning to provide real insight into the different networks that contribute to these complex behaviors [38]. A further analytical complexity is that some experimental manipulations that stimulate ingestive behaviors are accompanied by a wide range of autonomic and/or neuroendocrine activities. For example, consider the feeding that results from an injection of norepinephrine (NE) into the region of the paraventricular nucleus of the hypothalamus (PVH). Feeding begins within a few minutes of injection [43] meaning that changes in the relevant feeding control networks are initiated quickly. However, NE applied to the PVH has other significant physiological effects that are not directly related to the overt motor actions of feeding. First, NE injections increase the release of ACTH secretogogues from CRH neuroendocrine neuronal terminals in the median eminence, and ultimately glucocorticoid from the adrenal cortex [44]; second, NE injections have significant cardiovascular effects [45]. Each of these motor events will have their own sensory-motor outcomes and associated patterns of neural activation. To probe the circuits involved with NE-associated feeding, a typical experiment might

Table 1 A summary of some of the motor actions, motor systems, associated sensory inputs, and time frames engaged as a rat approaches a drinking spout to drink fluid (see [39–41] for more details) Behavior phase Initiation Potential Initiators

Motor action

Motor system

Sensory input

Time frame

Increased plasma osmolality Decreased plasma volume Circadian Incentive (cognitive) Dry mouth (eg. from eating) Inhibition of salivation

– –

ANS ANS

Osmoreceptors Angiotensin II/baroreceptors Suprachiasmatic nucleus Visual/olfactory/gustatory Trigeminal Trigeminal

Foraging/exploration/purposive locomotion Body and head orientation to fluid source

Whole body Whole body/head/neck

Visual/olfactory etc. Visual/olfactory etc.

min/s s

Body orientation to source Head/snout orientation to source

Whole body Head/neck

Visual/olfactory etc. Visual/olfactory etc.

s s/ms

Contact of perioral vibrissae with fluid source Forward movement of snout Contact of upper lip with fluid source Contact of lower lip with fluid source

Head/neck Head/neck/trigeminal Head/neck/trigeminal Head/neck/trigeminal

Trigeminal Trigeminal Trigeminal Trigeminal

s/ms ms ms ms

Opening of mouth Extension of tongue Initial exploratory licking (tongue extension–retraction)

Trigeminal Hypoglossal Hypoglossal

Trigeminal Trigeminal/gustatory Trigeminal/gustatory

ms ms s/ms

Licking Swallowing

Hypoglossal

Trigeminal/gustatory

s/ms

Retraction of tongue Closure of mouth Withdrawal from fluid source

Hypoglossal Trigeminal Head/whole body

Trigeminal Trigeminal Visual/olfactory etc.

ms ms s

Appetitive

Consummatory Approach

Preparation

Identification & confirmation

Performance

Termination

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use immunocytochemistry to reveal c-Fos expression patterns 90–120 min after the NE injection. As we analyze these data, the question will arise as to which c-Fos labeled neurons are directly related to feeding and which ones derive from the initiation and consequences of the neuroendocrine and autonomic events. Clearly, understanding the complete motor sequence–behavior, autonomic and neuroendocrine–for the selected behavior will significantly improve the resolution of the analysis. Ultimately, how accurately a particular behavior is described will determine how well the resulting patterns of neural activation can be correlated with the behavioral sequence. A clear and detailed understanding of the behavioral sequence is essential for interpreting the ensuing expression patterns in the brain; a welldefined behavior is going to be easier to correlate with neural activation patterns than one that is poorly defined or documented. 5.3. Examples To illustrate the utility of these various markers, we now discuss two examples where we have used cellular markers to explore the structure of central control networks. 5.3.1. Neural activation following an intravenous injection of 2-deoxyglucose Systemic injections of 2DG in rats generate a triad of motor responses aimed at normalizing energy metabolism [46]: increased sympathoadrenal output that elevates adrenaline secretion from the adrenal medulla; increased release of CRH from neurons in the hypothalamic paraventricular nucleus (PVH) that stimulates ACTH and then glucocorticoid secretion from the adrenal cortex; and feeding. We have recently used immunocytochemistry to detect the phosphorylated form of the ERK1/2 MAP kinases (pERK) as part of a study aimed at mapping the central responses to reduced glucose availability. As discussed earlier, phosphorylation of these widely distributed kinases rapidly follows a range of ligand–receptor interactions. We have shown previously that both iv 2DG and halothane anesthesia elevate pERK1/2 levels in PVH CRH neurons within 10 min [47]. However, we have now gone on to show that within 5 min of an iv 2DG pERK1/2 is seen in the PVH, the lateral part of the central nucleus of the amygdala (CEAl), the oval nucleus (ov) in the dorsolateral part of the bed nuclei of the stria terminalis (BST), and part of the insular cortex (IC). These results (Fig. 3 [48]) emphasize the utility of pERK1/ 2 for identifying neurons that are rapidly activated by a stimulus. 5.3.2. Neural activation during the reversal of dehydration–anorexia Rats become anorexic during the consumption of hypertonic saline as part of a behavioral reflex aimed at protecting their fluid balance [1,49]. Anorexia becomes particularly pronounced after 4–5 days, when nocturnal food intake drops to about 25– 35% of baseline levels. Returning drinking water stimulates a stereotypic behavioral sequence that begins with drinking, followed after about 8 or 9 min by a robust bout of eating that lasts about 20–30 min [42]. Animals that have been drinking hypertonic saline for 5 days will eat about 25% of their normal

Fig. 3. Photomicrographs of phospho(p)ERK1/2 immunoreactivity in the oval nucleus of the bed nuclei of the stria terminalis (A, A′), lateral part of the central nucleus of the amygdala (B, B′), and paraventricular nucleus of the hypothalamus (C, C′) from animals killed 5 min after being injected intravenously with either saline (A, B, C) or 200 mg/kg of 2-deoxy-D-glucose (2-DG; A′, B′, C′).

nocturnal food intake in the hour following the return of water. The behavioral sequence is activated by a simple stimulus, is highly predictable, and prominently features eating during its initial phases, making it an excellent candidate for investigating the organization of the central circuits that initiate feeding. To this end, we have used c-Fos and pERK1/2 immunocytochemistry to determine the location and phenotype of neurons that are activated after water is returned to dehydrated (DE)–anorexic rats. Our first study [50] used c-Fos immunocytochemistry to clarify the role of a population of CRH neurons in the LHA [51] during both the development and reversal of DE–anorexia. Because CRH neurons are found in the same LHA regions as orexin and MCH neurons [52], we also examined the behavior of these neurons as DE–anorexia develops and is then reversed. We found that 5 days of DE increase c-Fos-immunoreactivity (ir) in large numbers of neurons in the LHA, some of which also show increased CRH, but not orexin or MCH gene expression. We also showed that the behavioral sequence exhibited by DE animals in the minutes following water drinking is accompanied by a further increase in the number of c-Fos-ir nuclei, which was independent of

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Fig. 4. Photomicrographs of phospho(p)ERK1/2 immunoreactivity in the insular cortex (A, B), paraventricular nucleus of the hypothalamus (C), and lateral hypothalamic area (D, E, F) of animals given 2.5% saline to drink for 5 days, and then either given water to drink or maintained on saline and killed 10 min later. Note the increase in pERK1/2 immunoreactivity in the insular cortex and lateral hypothalamic area but not the paraventricular nucleus 10 min after the return of water.

whether animals ate or were denied access to food. c-Fos-ir significantly increased in orexin neurons but not CRH or MCH neurons. Together these data implicate CRH but not orexin or MCH neurons in the motor events accompanying the development of DE– anorexia; and orexin but not CRH or MCH neurons in controlling behavioral sequence that is stimulated by drinking water. In a broader study (A.G. Watts, C. Neuner and D. Salter, unpublished observations) we have recently used pERK1/2 immunocytochemistry to identify neurons that are activated during the first 10 min after water is returned. We posit that rather than waiting 45 min or longer after water is returned (as is required for c-Fos expression), a tighter correlation between behavioral onset and the identity of central circuits can be achieved if we identify neurons at the same time as the animal is in the process of initiating feeding. With this technique we find that there is increased pERK1/2-ir in the insular cortex and lateral hypothalamic area, but not the PVH or arcuate nucleus 10 min after the return of water (Fig. 4). These data show that pERK immunocytochemistry can be used to track changes of neural activation with a temporal resolution that is much closer to the time of stimulus onset than that achieved using c-Fos as a cellular marker.

each positively labeled neuron on a drawing or a map taken from a standardized brain atlas, and use photomicrographs as adjuncts for data representation (e.g. [50]). Ideally, maps should accurately represent the area of interest in terms of nuclear boundaries and the position of major fiber tracts etc. The other point to consider is

5.4. Mapping activation patterns and the spatial resolution of data analysis The third important aspect of resolution concerns how data from neuroanatomical experiments are represented, analyzed, and compared across experiments and laboratories. Considered at the simplest level, after sections containing the positively labeled neurons are processed then it is a relatively straightforward task to photograph and publish them. Although this technique provides an exact representation of the data, it often offers little utility to other investigators for comparison with their datasets. A more sophisticated and useful representation is to plot the position of

Fig. 5. Plate 18 of the Swanson brain atlas [54] with the pERK immunoreactivity from Fig. 3 placed in approximately the correct alignment (left plate). The border on the right delineates the equivalent BST regions. Abbreviations: al, anterolateral area of the BST; am, anteromedial area of the BST; BST, bed nuclei of the stria terminalis; fu, fusiform nucleus of the BST; ju, juxtacapsular nucleus of the BST; ov, oval nucleus of the BST; PS, parastrial nucleus.

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that the entire brain should be mapped with a particular marker, not just favored regions. Only if we know the location of all the cells that change their activity will we have a complete picture of the regions engaged in the processing of information required to control an ingestive behavior. But however the data are represented, how can they be most easily interpreted and used by others in the field? Neuroanatomical experiments are no different to those that generate parametric data in that maps showing the locations of labeled neurons must also be replicated and independently verified to become usefully established in the literature. To do this, data from independent experiments must be represented in a manner that other investigators can accurately compare. Simply put, two independent sets of data showing the distribution of labeled cells can only be compared if they share common reference points and fiducials. Currently the best way to do this is to plot data on maps from a standardized brain atlas, the most widely used of which are Paxinos and Watson [53] and Swanson [54]. A second advantage of accurately plotting the positions of labeled neurons on standardized atlas maps is that the investigator can access published information about projections and chemical

phenotype. This is particularly important when deriving data from complex heterogeneously organized brain regions. As an example, consider the neurons showing pERK1/2 labeling in the dorsolateral part of the BST 5 min after a 2DG injection (Fig. 5). These neurons are found in the oval (ov) nucleus of the BST, and because of the complexity of this part of the BST it is critical to be able to locate these neurons with a precise descriptor, rather than a more general term such as the ‘dorsolateral BST’. Fig. 6 shows the projection array of all the subnuclei in the dorsolateral BST (see also [55]). With this in mind, if we compare the projection patterns of the BSTam, BSTal, and BSTju with the BSTov, we see that there is less likelihood of pERK1/2-labeled neurons in the BSTov projecting to the PVH, than if we had seen labeling slightly more medially in the BSTam, or more ventrally in the BSTal (Figs. 5 and 6). Fig. 6 shows other examples of differential projection patterns of these BST nuclei to the lateral hypothalamic area, parabrachial, and dorsomedial nuclei. These differences in projections are crucial if we wish to construct the detailed neural network that is involved with generating responses to 2DG. However, determining the final structure of these control networks will require data derived from experiments that combine

Fig. 6. A comparison of the projections from the anteromedial group (amg) anterior lateral group (alg) of the bed nucleus of the stria terminalis (BST) derived from anterograde and retrograde tracing studies. The horizontal bars highlight the projections of the juxtacapsular (ju), the oval nucleus (ov), anterolateral area (al), and anteromedial area (am) of the BST. The size of the dots represents the density of the projection. (See text for more details, and [55] for references and a full description of the connections.) Abbreviations: ACBc, nucleus accumbens, core; ACBp, nucleus accumbens, posterior part; ACBsd, nucleus accumbens, dorsal shell; ACBsv, nucleus accumbens, ventral shell; ADP, anterodorsal preoptic nucleus; AHN, anterior hypothalamic nucleus; AHNc, anterior hypothalamic nucleus, central part; AHNp, anterior hypothalamic nucleus, posterior part; AMBd, nucleus ambiguus, dorsal division; AVP, anteroventral preoptic nucleus; AVPV, anteroventral periventricular nucleus hypothalamus; AVPV, anteroventral periventricular nucleus hypothalamus; B, Barrington's nucleus; CEAc, central amygdalar nucleus, capsular part; CEAl, central amygdalar nucleus, lateral part; CEAm, central amygdalar nucleus, medial part; CP, caudoputamen; DMHa, dorsomedial hypothalamic nucleus, anterior part; DMHp, dorsomedial hypothalamic nucleus, posterior part; DMHv, dorsomedial hypothalamic nucleus, ventral part; DMX, dorsal motor nucleus vagus nerve; EW, Edinger-Westphal nucleus; I, internuclear area, hypothalamic periventricular region; ISN, inferior salivatory nucleus; LCN, lateral cervical nucleus; LHAjd, lateral hypothalamic area, juxtadorsomedial region; LHAjvd, lateral hypothalamic area, juxtaventromedial region, dorsal zone; LHAjvv, lateral hypothalamic area, juxtaventromedial region, ventral zone; LHAs, lateral hypothalamic area, suprafornical region; LHAsfa, lateral hypothalamic area, subfornical region, rostral zone; LHAsfp, lateral hypothalamic area, subfornical region, posterior zone; LSc, lateral septal nucleus, caudal (caudodorsal) part; LSr, lateral septal nucleus, rostral (rostroventral) part; LSv, lateral septal nucleus, ventral part; MEPO, median preoptic nucleus; MEV, midbrain trigeminal nucleus; MM, medial mammillary nucleus, body; MPNc, medial preoptic nucleus, central part; MPNl, medial preoptic nucleus, lateral part; MPNm, medial preoptic nucleus, medial part; MPO, medial preoptic area; MRNm, midbrain reticular nucleus, magnocellular part; NTSco, nucleus of the solitary tract, commissural part; NTSl, nucleus of the solitary tract, lateral part; NTSm, nucleus of the solitary tract, medial part; PAGvl, periaqueductal gray, ventrolateral division; PARN, parvicellular reticular nucleus; PBl, parabrachial nucleus, lateral division; PBm, parabrachial nucleus, medial division; PD, posterodorsal preoptic nucleus; PGRNl, paragigantocellular reticular nucleus, lateral part; PMd, dorsal premammillary nucleus; PMv, ventral premammillary nucleus; PS, parastrial nucleus; PSCH, suprachiasmatic preoptic nucleus; PSTN, parasubthalamic nucleus; PVHap, paraventricular hypothalamic nucleus, anterior parvicellular part; PVHd, paraventricular hypothalamic nucleus, descending division; PVHdp, paraventricular hypothalamic nucleus, dorsal parvicellular part; PVHlp, paraventricular hypothalamic nucleus, lateral parvicellular part; PVHmpv, paraventricular hypothalamic nucleus, medial parvicellular part, ventral zone; PVi, periventricular hypothalamic nucleus, intermediate part; PVp, periventricular hypothalamic nucleus, posterior part; PVpo, preoptic periventricular nucleus; RR, midbrain reticular nucleus, retrorubral area; SFO, subfornical organ [Pines]; SNr, substantia nigra, reticular part; SSN, superior salivatory nucleus; SUT, supratrigeminal nucleus; V, motor nucleus of the trigeminal nerve; VMHdm, ventromedial nucleus hypothalamus, dorsomedial part; VMHvl, ventromedial nucleus hypothalamus, ventrolateral part; VTA, ventral tegmental area.

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tract-tracing methodologies with the identification of markers for neuronal activation (eg. [56]). Finally, accurate data representation using standardized atlases opens up the possibility of using newly emerging neuroinfomatics tools to generate and compare distribution patterns of activated neurons during behavior [19–21,57,58]. Given the fact that there are an estimated 500–1000 identified brain regions and 25,000 connections in the brain [19], it goes without saying that analyzing and correlating data from cellular markers with components in a behavioral sequence are going to require informatics tools that rank in complexity and utility along side those used for probing and mapping mammalian genomes. Two neuroinfomatics systems that our lab is currently using are Neuroscholar (http://chasseur.usc.edu/website/index. html) and the Brain Architecture Management System (http:// brancusi.usc.edu/bkms/). 6. Conclusion Brain imaging techniques have come a long way in the 50 years since the idea that monitoring local blood flow and glucose utilization could give us a dynamic picture of brain activation. Today, to define the organization of the neural networks that control ingestive behavior we are steadily improving the resolution of our methods in regard to cellular markers and behavioral analysis. Newly emerging techniques are providing fascinating pointers towards what can be achieved with cellular markers that respond in the same time domain as the behavior. But if we want to delineate the structure and function of entire neural networks perhaps the biggest challenge for behavioral neuroscientists and neuroanatomists is to analyze, compare, and share the large quantities of data that are generated by these methods. The complexity of the approaches needed to achieve this goal is of the same magnitude as problems in genomics, proteomics, metabolomics etc., and will require the same sophisticated computational approaches to data analysis and presentation. Acknowledgements The work from the authors laboratory was supported by PHS grants NS29728, MH66168, MH67392, and MH71108. We would like to thank Larry Swanson and Hong-Wei Dong for permission to use part of their original figure and the unpublished data shown in Fig. 6. References [1] Watts AG. Neuropeptides and the integration of motor responses to dehydration. Annu Rev Neurosci 2001;24:357–84. [2] Watts AG, Swanson LW. Anatomy of motivational systems. In: Pashler Hal, Gallistell Randy, editors. ‘Stevens’ handbook of experimental psychology, volume 3, learning, motivation, and emotion. Third edition. John Wiley & Sons; 2002. p. 563–632. [3] Cowley MA. Hypothalamic melanocortin neurons integrate signals of energy state. Eur J Pharmacol 2003;480:3–11. [4] Niswender KD, Baskin DG, Schwartz MW. Insulin and its evolving partnership with leptin in the hypothalamic control of energy homeostasis. Trends Endocrinol Metab 2004;15:362–9.

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