abstract: Vocal learning is relatively common in birds but less so in mammals. Sexual selection and individual or group recognition have been identified as major forces in its evolution. While important in the development of vocal displays, vocal learning also allows signal copying in social interactions. Such copying can function in addressing or labelling selected conspecifics. Most examples of addressing in non-humans come from bird song, where matching occurs in an aggressive context. However, in other animals, addressing with learned signals is very much an affiliative signal. We studied the function of vocal copying in a mammal that shows vocal learning as well as complex cognitive and social behaviour, the bottlenose dolphin (*Tursiops truncatus*). Copying occurred almost exclusively between close associates such as mother–calf pairs and male alliances during separation and was not followed by aggression. All copies were clearly recognizable as such because copiers consistently modified some acoustic parameters of a signal when copying it. We found no evidence for the use of copying in aggression or deception. This use of vocal copying is similar to its use in human language, where the maintenance of social bonds appears to be more important than the immediate defence of resources.
author: Stephanie L. King; Laela S. Sayigh; Randall S. Wells; Wendi Fellner; Vincent M. Janike-mail: <firstname.lastname@example.com>
date: 2013-04-22
institute: 1Sea Mammal Research Unit, School of Biology, University of St Andrews, St Andrews, Fife KY16 8LB, UK; 2Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA; 3Chicago Zoological Society, c/o Mote Marine Laboratory, 1600 Ken Thompson Parkway, Sarasota, FL 34236, USA; 4The Seas, Epcot, Walt Disney World Resort, 2016 Avenue of the Stars, EC Trl. W-251, Lake Buena Vista, FL 32830, USA
references:
title: Vocal copying of individually distinctive signature whistles in bottlenose dolphins

# Introduction

Vocal production learning enables animals to copy novel sounds in their environment or to develop their own distinctive calls, avoiding overlap with those heard before \[1\]. Most commonly, vocal learning leads to convergence in sound parameters between individuals. A good example of this can be found in bird song dialects \[2\] or in the development of group-specific contact calls \[3–7\]. The exchange of such shared calls between individuals can be aggressive or affiliative in nature. While contact calls are known to be affiliative \[7\], song type matching in song birds tends to have an aggressive connotation \[8\]. Song sparrows, for example, use song type matching when defending their territory against an unknown male, but avoid it when interacting with known neighbours with whom they use more subtle repertoire matching \[9,10\]. Repertoire matching, i.e. the use of a shared song type while avoiding a reply with the same song type, may allow the addressing of a neighbour in a more affiliative or neutral way.

In most instances, these interactions occur with calls that are shared by more than one individual. In the case of contact calls, the common call belongs either to a group or a pair of animals. In bird song, animals have individual repertoires where each song type is shared with other individuals, but the overall composition of the repertoire may be unique. Production rates for each shared call or song type are usually similar across the individuals that share it. Individual call or song types survive in populations as cultural traditions that can outlive the animals that produce them at any one time \[11\].

The signature whistle of the bottlenose dolphin stands out from these examples in that it seems to be more individually specific. Bottlenose dolphins produce a large variety of narrow-band frequency-modulated whistles and pulsed sounds for communication \[12\]. As part of their repertoire, each individual also develops an individually distinctive signature whistle \[13,14\] that develops under the influence of vocal learning \[15–17\]. Individuals listen to their acoustic environment early in life and then develop their own novel frequency modulation pattern or contour for their signature whistle \[15\]. The result is a novel and unique modulation pattern that identifies the individual even in the absence of general voice cues \[18\]. Interindividual variation in signature whistles is much larger than that found in recognition signals of other species \[19\].

Bottlenose dolphins live in fluid fission–fusion societies with animals forming a variety of different social relationships \[20\]. This social organization, coupled with restrictions in underwater vision and olfaction, has led to natural selection favouring designed individual signature whistles \[12,14\] instead of relying on the by-product distinctiveness of voice features \[19\]. The signature whistle tends to be the most commonly used whistle in each individual's repertoire accounting for around 50 per cent of all whistles produced by animals in the wild \[21\]. Bottlenose dolphins are, however, able to learn new sounds throughout their lives \[22\], and conspecifics occasionally imitate the signature whistles of others \[23\]. Thus, one animal's signature whistle can form a minor part of another animal's vocal repertoire as a result of copying \[17,23,24\]. Signature whistle copying is, however, rare \[23,25–27\], albeit significantly more common than expected by chance \[25\]. As such, each signature whistle forms only a major part of one animal's repertoire, allowing it to be a label for that particular individual when copied.

Nevertheless, the function of copying events remains unclear. It has been argued that copying of signature whistle types is equivalent to addressing other individuals. Such addressing can be affiliative or aggressive. Unlike songbirds, delphinids are not territorial and do not sing. Instead, they use their acoustic signals in the context of social interactions and group cohesion \[12\]. Bottlenose dolphins have low rates of aggression towards close associates and higher ones towards social competitors, for example among male alliances \[20\]. Investigating who is copying who can therefore give us information on the signal value of copying. In addition to affiliative and aggressive functions, a third hypothesis for whistle copying is that it is used as a deceptive form of signalling \[28\]. For example, deceptive signature whistle copying by male dolphins could allow them to gain access to females guarded by other males or to avoid directed aggression from a male alliance \[29\]. It appears that copies are sufficiently rare to allow for such a use without jeopardizing the reliability of signature whistles as identity signals.

To investigate these three hypotheses, the occurrence of signature whistle copying was studied in captive and briefly captured and subsequently released wild bottlenose dolphins. We hypothesized that if signature whistle copying is affiliative it should only occur between close associates. Alternatively, copying in an aggressive context should be more common between animals that are less closely associated. Furthermore, copies used in a deceptive way should ideally not be recognizable as copies, whereas in affiliative or aggressive contexts, they could be recognizable as such. We also investigated the temporal aspects of whistle copying given the importance of signal type matching in other species.

# Material and methods

## Social and acoustic data from the wild

Data were collected from wild bottlenose dolphins around Sarasota Bay, FL, USA between 1984 and 2009. The amount of time animals are sighted together can be used to give a measure of their association. The half-weight ratio coefficients of association (CoA) \[30\] is defined as CoA = 2*N~ab~*/*N~a~* + *N~b~*~,~ in which *N~ab~* is the number of times individuals A and B have been seen together, *N~a~* is the number of times individual A has been seen without B, and *N~b~* is the number of times individual B has been seen without A. CoAs were calculated for all study animals from data gained during regular, systematic photographic identification surveys of dolphins. CoAs given for each pair of animals caught together are from the year the recordings were taken. Wild bottlenose dolphin acoustic recordings were collected during capture–release events for health assessments and life-history studies in Sarasota Bay \[31\]. One such event takes on average 108 min from the time the net is set to the time the individual is released. During these events, animals were physically restrained and frequently out of visual sight, but not acoustic range, of one another. The signature whistle of an individual is the most common whistle type emitted in such isolation conditions \[14\]. The Sarasota Dolphin Research Programme has now accumulated a catalogue of whistles from over 250 individual dolphins from the resident community in Sarasota Bay since 1975 \[14\], many of which were recorded in multiple capture–release sessions. We compared all whistles produced by an individual with the signature whistles of all others in the same capture set in order to identify copying events. Ages of animals were known from long-term observations \[32\] or from analysing growth rings in teeth \[33\].

The vocalizations of each individual were recorded via a suction cup hydrophone, allowing the identification of the caller for each recorded call. Either custom-built or SSQ94 hydrophones were used (High Tech Inc.). Between 1984 and 2004, the acoustic recordings were taken with either Marantz PMD-430 or Sony TC-D5M stereo-cassette recorders (frequency response of recording system: 0.02–18 kHz ± 5 dB) or Panasonic AG-6400 or AG-7400 video-cassette recorders (frequency response of recording system: 0.02–25 kHz ± 3 dB). For recordings taken from 2005 onwards, a Sound Devices 744T digital recorder was used (sampled at 96 kHz, 24-bit, frequency response of recording system: 0.02–48 kHz ± 1 dB).

The first step of analysis consisted of visual comparisons of spectrograms of 205 h and 23 min of acoustic recordings of temporarily caught and released, wild bottlenose dolphins by one observer in order to identify copying events within each capture set. The total recording time inspected in this way was 110 h and 55 min for pairs of animals caught together with low association levels (CoA \< 0.5) and 94 h and 28 min for pairs of animals caught together with high association levels (CoA \> 0.5). The second step involved a detailed analysis of 32 h and 12 min (table 1) of recordings where vocal copying had been found. These contained a total of 10 219 whistles, which is the dataset on which this in-depth analysis is based.

Pairs of animals involved in signature whistle copying events, with the animal producing copies in bold. The mean similarity values are given for each animal's signature whistle when compared with the vocal copy. The copier's own signature whistles had low similarity scores with the copy while the signature whistles of the copied animals had high similarity scores with the copies (see the electronic supplementary material, figure S1).

| pair | sex | relationship | CoA | age | recording time (min) | no. of vocal copies | average similarity values |
|:---|:---|:---|----|----|----|----|----|
| **1. Calvin Ranier** | MM | associates | 1^a^ | 1528 | 7070 | 13— | 1.54.5 |
| **2a. FB26** **2b. FB48** | MM | alliance partners | 0.8 | 3129 | 93101 | 385 | 1.0/3.2^b^1.0/3.5^b^ |
| **3. FB114** FB20 | MM | associates | 0.07 | 1615 | 5195 | 4— | 2.43.3 |
| **4. FB90**FB122 | FM | mothercalf | 0.98 | 254 | 9292 | 17— | 1.33.3 |
| **5. FB65**FB67 | FF | calfmother | 0.67 | 621 | 7070 | 1— | 1.23.6 |
| **6. FB228**FB65 | MF | calfmother | 0.95 | 521 | 106106 | 8— | 1.13.5 |
| **7. FB5**FB55 | FF | mothercalf | 1.0 | 293 | 8585 | 17— | 1.33.3 |
| **8a. FB358b. FB93** | FF | mothercalf | 0.9 | 323 | 9292 | 24 | 1.7/3.7^b^2.5/3.2^b^ |
| **9. FB71**FB95 | FF | mothercalf | 1.0 | 281 | 9797 | 13— | 1.03.3 |
| **10. FB5**FB155 | FF | mothercalf | 0.56 | 292 | 7979 | 40— | 1.03.5 |
| **11. FB9**FB177 | FF | mothercalf | 0.9 | 20 | 105105 | 9— | 1.23.4 |

^a^These animals were permanent residents in a captive facility.

^b^Where both animals copied one another the average similarity value for that animal's own signature with the copy it produced of the other animal's signature whistle is given first (low number) followed by the average similarity value for that animal's own signature whistle with the copy produced by the other animal in the pair (larger number).

## Social and acoustic data from captivity

To investigate the social context of copying, four captive adult males were recorded at The Seas Aquarium, Lake Buena Vista, FL, USA, during May–June 2009. One male, Ranier, was estimated to be 28 years old and was collected at approximately 3 years of age in the northern Gulf of Mexico. The other males were Calvin (15 years old), Khyber (18 years old) and Malabar (8 years old), who were all captive born. All four animals had been together for 3.5 years at the start of the study; Ranier and Calvin had been together for 6 years. Vocalizations of these dolphins were recorded with two HTI-96 MIN hydrophones (frequency response: 0.002–30 kHz±1 dB) and two CRT hydrophones (C54 series; frequency response: 0.016–44 kHz±3 dB) onto a Toshiba Satellite Pro laptop using a four channel Avisoft v. 416 UltrasoundGate recording device (sampled at 50 kHz, 8 bit).

A total recording time of 16 h for the four males was analysed. The length of recording time when copying between pairs could be identified (as determined by their positions in the pool system) was as follows: 16 h (100%) for Ranier and Calvin, Ranier and Malabar, Khyber and Calvin and Khyber and Malabar; 14 h (87.3%) for Ranier and Khyber and 2 h (12.7%) for Calvin and Malabar. The caller was identified, using passive acoustic localization \[23\]. The social association of male pairs at The Seas was evaluated by measuring synchrony in their swimming patterns \[34\]. A focal animal instantaneous sampling method was used with an observation period of 7.5 min and a 15 s interval. At each 15 s interval, the focal animal's synchrony status was assessed relative to each other animal in the group. Observations took place 5 days per week between 08.00 and 18.00, and each animal served as the focal animal once each day in an order determined by a balanced, randomly ordered schedule. Observations were made between January 2009 and June 2009 when all four dolphins were together in the same pool.

## Identifying copying events

Initially, one observer (S.L.K.) compared all whistles in a given captured or captive group with each other, and identified all occurrences where the same whistle type was being produced by more than one animal by inspecting spectrograms (fast Fourier transform (FFT) length 512, overlap 100%, Blackmann–Harris window) in Adobe Audition v. 2.0 (Adobe Systems). Five naive human observers, blind to context and animal identity, were then used to rate the similarity of each copy of a signature whistle to the original signature whistle (the whistle as produced by its owner) and to the copier's own signature whistle. Visual classification was used as it is more reliable than computer-based methods in dolphin whistle classification \[14,35\] and is frequently used in animal communication studies \[2,36\]. The five observers were given the extracted contours (frequency modulation pattern) of the whistles as plots of frequency versus time and were asked to rate whistle similarity using a five-point similarity index ranging from 1 (dissimilar) to 5 (similar). Only copied whistles that reached a mean similarity score of more than 3 with the original signature whistle and less than 3 with the copier's own signature whistle were deemed copies and included in the analysis. A value of 3 indicates a relatively high similarity as indicated in previous studies \[25,29,37\].

## Acoustic analysis

The whistle contours of every copy as well as of randomly chosen exemplars of signature whistles of both interacting individuals were extracted using a supervised contour extraction programme \[38\], with a time resolution of 5 ms. From the contours, the following parameters were measured: start frequency, end frequency, minimum frequency, maximum frequency, frequency range, duration and mean frequency. One further parameter, number of loops, was read directly from the spectrogram where applicable. A loop was defined as a repeated modulation pattern within a signature whistle that could be separated by periods of stereotyped, discrete segments of silence. These periods of silence were taken to be 250 ms or less, which is the maximum inter-loop interval found in this population \[39\].

## Statistical analysis

All statistical procedures were conducted in R (R project for statistical computing; GNU project). Acoustic parameters were analysed by first testing for normality using the Lilliefors (Kolmogorov–Smirnov) test. Depending upon the outcome, either the Mann–Whitney test or a Welch's *t*-test was used to compare differences between parameters of the copies with the original signature whistles and the copier's own signature whistle. A sampling statistic was then created by multiplying these test statistics together, which created a combined test statistic for all parameters. This allowed comparisons of overall difference between two whistle types. A permutation test was used to shuffle the acoustic parameter measurements of the copies with those of the original signature whistles within each pair of animals. This was carried out to test whether the combined acoustic parameter statistic was significantly different from a random distribution. Ten thousand permutations were performed to calculate the distribution of the test statistic under the null hypothesis (random distribution), and the observed test statistic was then compared with this random distribution. A two-tailed test was used with a Bonferroni-adjusted significance level of *p* \< 0.002. In addition, all parameters were used in a non-metric multi-dimensional scaling analysis with a good STRESS fit of 0.04.

A permutation test was also used to test whether signal copying only occurred between affiliated pairs of animals. This involved shuffling the CoAs of the pairs of animals who produced vocal copies (*n* = 11) with those that did not (*n* = 191). Many of the individuals who copied were also in pairs with other animals where copying was not present. The sampling statistic of interest was the mean CoA for the pairs involved in signal copying. Ten thousand permutations were performed to calculate the distribution of the test statistic under the null hypothesis that the CoAs of copiers were randomly distributed. The observed test statistic was then compared with the random distribution.

Permutation tests were also performed on the timing of copies after the original signature whistle. The times of copies (*n* = 108) were shuffled with the times of the copier's own signature whistles given in response to the copied signature whistles (*n* = 1651). The random distribution was calculated from 10 000 permutations under the null hypothesis that there was no difference between the timing of copies of signature whistles after the occurrence of the template whistle and the timing of the copier's own signature whistle after the occurrence of the template whistle. The observed test statistic (mean time between original signature whistle and copy) was compared with the random distribution.

# Results

## Who copies whom?

In total, 85 different capture–release events of wild dolphins were analysed, comprising 121 individuals in different group compositions. Of these individuals, 48 were sampled on more than one occasion (range: 2–7). Of the 85 capture–release events analysed, 11 consisted of single male–male pairs, 31 consisted of single mother–calf pairs and the remaining 43 consisted of groups of different compositions. These compositions included two or more adults of the same or both sexes, mother–calf pairs with other adults and groups of mother–calf pairs.

As in previous studies \[14,40\], each bottlenose dolphin almost exclusively used its own, individually distinctive signature whistle during capture–release events. Whistle rates were generally high at these events, with a mean of 5.3 whistles per minute per individual. In 10 of 85 different capture–release sets, however, individuals were found occasionally copying the signature whistle of another animal in the set (mean rate in sets with copying: 0.18 copies per minute per individual). This occurred in 10 of 179 pairs of animals recorded from 1988 through 2004, consisting of two of the 11 male–male pairings and eight of the 31 mother–calf pairs. In some instances, both members of a pair copied one another (figure 1 and table 1; electronic supplementary material, figure S1). The total number of individuals who produced vocal copies was therefore 12. The five human judges who viewed frequency contour plots to quantify similarity of the copies with both the originals and the copier's own signature whistles showed statistically significant agreement (*κ* = 0.42, *z* = 29.9, *p* \< 0.0001) \[41\]. Similarity values for all copies are given in table 1.

The results of a permutation test clearly showed that signature whistle copying occurred between closely affiliated pairs of animals (*p* = 0.0006). The mean half-weight coefficient of association (CoA; which can range from 0 to 1) for the 10 pairs of animals that copied was 0.8, whereas the mean CoA for non-copiers was 0.4 (figure 2). Interestingly, there were also three instances of copying of whistles that were not signatures between two adult, wild females of low association (see the electronic supplementary material, figure S2). These animals also produced their own signature whistles but no signature whistle copies.

In recordings of four aquarium housed males (forming six possible pairs) at The Seas, one pair also engaged in signature whistle copying. These two individuals showed high levels of synchronous behaviour (23% of 285 min of observation time) in the pool. Synchrony is a sign of social bonding in male bottlenose dolphins \[34\]. One exchange of signature whistle copying between these males was 30 s in duration: both males emitted the signature whistle of one of them in an interactive sequence consisting of 13 and 11 renditions respectively (see the electronic supplementary material, figure S3). Copying in these individuals was not accompanied by aggressive behaviour (total observation time 16 h with 13 copies produced). The synchrony of the other male pairs was generally lower (7–13% of the observation time). One other pair, however, had a high level of synchrony (26%) but did not engage in whistle copying. Thus, copying does not necessarily occur in bonded males.

## How accurate are vocal copies?

Frequency parameter measurements of copies produced by 11 animals (one captive and 10 wild animals; two wild copiers were excluded owing to small sample sizes) revealed consistent differences between signature whistle copies and the original, copied signature whistle (table 2 and figure 3). While the overall frequency modulation pattern of the copied whistle showed high similarity to the original (figure 1), copiers introduced consistent variation in single acoustic parameters such as the start or end frequency (see the electronic supplementary material, table S1). In these parameters, copies were often closer to other whistle contours than to the copied signature whistle (figure 3). Individuals varied in the parameters modified; on average 4.4 parameters (range: 1–6) differed significantly between the copy and the original signature whistle. Copies most frequently differed from the original (for 10 of 11 copiers) in mean frequency and maximum frequency (see the electronic supplementary material, table S1). Over half of the copiers also produced copies that differed significantly from the original signature whistle in end frequency (six of 11 copiers) and frequency range (seven of 11 copiers). The copies were equally likely to be higher or lower in frequency than the original. In addition to frequency parameters, one adult male, FB26, altered the number of loops in a multi-looped whistle in his copies of the signature whistle of his alliance partner, adult male FB48. Although FB48 varied his number of loops (range: 3–6), FB26 almost always produced a three-looped copy. The number of loops in FB26's copies and FB48's originals differed significantly (Mann–Whitney: *W* = 152.5, *N*~1~ = 38, *N*~2~ = 35, *p* \< 0.0001). All of the signature whistle copies also differed significantly from those of the copiers' own signature whistles in some parameters (mean number of parameters different = 3.54; range: 1–7), whereas other parameters of a copy resembled those of the copier's own signature whistle (mean = 2; range: 0–5).

Test statistics for all acoustic parameter measurements combined for each copy and original signature whistle comparison. Shown are the sampling statistic of actual combined parameter measurements (observed), and the mean test statistic of combined parameter measurements under the null hypothesis based on 10 000 permutations (expected). Differences between acoustic parameter measurements of vocal copies and original signature whistles are significant at a level of *p* \< 0.002.

|  | observed test statistic | expected test statistic | *p* |
|:---|:---|----|----|
| Ranier versus copy of Ranier | −7.52 | −0.002 | 0.002 |
| FB48 versus copy of FB48 | 0.19 | −0.007 | 0.12 |
| FB26 versus copy of FB26 | 559 | 0.025 | \<0.0001 |
| FB20 versus copy of FB20 | 166 | 0.43 | 0.0031 |
| FB122 versus copy of FB122 | 0.27 | 0.003 | 0.1 |
| FB65 versus copy of FB65 | 1004 | 0.03 | \<0.0001 |
| FB55 versus copy of FB55 | 24 000 | 0.016 | \<0.0001 |
| FB35 versus copy of FB35 | 125 | −0.01 | \<0.0001 |
| FB95 versus copy of FB95 | −1439 | −0.01 | \<0.0001 |
| FB155 versus copy of FB155 | 3 071 589 | 1.85a | \<0.0001 |
| FB177 versus copy of FB177 | −2646 | −0.0003 | \<0.0001 |

## Vocal matching

To further investigate whether copies were emitted in response to the identified model (referred to as the original signature whistle), we investigated whether they were temporally correlated and thus occurred in vocal matching interactions. Vocal matching can be described as a receiver responding to a signal by changing some features of its own vocal behaviour in order to imitate the preceding signal. Bottlenose dolphins had very high vocalization rates during these capture–release events, so it was difficult to judge whether whistles were produced in response to those of other animals. An investigation into the timing of signature whistle copies, however, revealed that the mean time between an original signature whistle and its copy was significantly less than the mean time between an original signature whistle and a copier's own signature whistle (0.94 versus 2.55 s; permutation, *p* \< 0.0001). In the long-term captive males, vocal rates were lower, and the matching pattern was clearer: almost all copying events occurred within 1 s after the emission of the original signature whistle by its owner, indicating copies were directed towards the owner of the original signature whistle.

# Discussion

We conducted a large-scale analysis on the occurrence of vocal copying in wild bottlenose dolphins that were briefly caught, sampled and released. This dataset offered a unique opportunity to study the vocal interactions between individuals whose vocal repertoires \[14,40\] and association patterns had been well documented over decades in the wild \[32,42\]. In line with previous studies \[23,25,26\], we found whistle copying to be rare. This is consistent with the idea that signature whistles are used to indicate identity, because such a system would not be sustainable with high copying rates. While a copy could be recognizable as such if it occurred only in specific contexts, aquatic organisms usually have only limited contextual information with the acoustic signals they receive. Frequent copying of signature whistles would therefore render the identity information of the whistle unreliable. The rare copying of signature whistles may, however, be particularly suited to addressing close associates \[23–25\].

We found that copying occurred primarily in matching interactions between animals with high CoAs outside aggressive contexts, demonstrating that it is an affiliative signal. All pairs of animals that produced signature whistle copies were close associates, with only one pair having a low CoA for the year prior to recording. However, these two males were each other's closest male associate in the 4 year period prior to the recording. Many of the copiers were mother–calf pairs, with both mothers and calves likely to copy one another. While most female calves' signature whistles are distinct from their mothers', males sometimes do sound like their mothers \[37\]. The signature whistles of the male calves in this study, however, did not resemble those of their mothers (see figure 1 and electronic supplementary material, figure S1). Signature whistles of male alliance partners also tend to become more alike over time \[43\]. In this study, however, males continued producing their own, non-identical, signature whistles as well as copying the finer details of each other's preferred whistle type. Thus, age, sex and relatedness were not significant factors for the results presented here.

We found no evidence for a deceptive function of signature whistle copies. In animals that are capable of vocal learning, variations can be introduced into a copied signal, allowing encoding of additional information. Bottlenose dolphins produced accurate copies of the frequency modulation pattern of a whistle (figure 1), but introduced fine-scale differences in some acoustic parameters (table 2 and figure 3). As a result, signature whistle copies were clearly recognizable as such. Copies may even carry identity information of the copier, as some individuals maintained some frequency parameters of their own signature whistles in their copies (see the electronic supplementary material, table S1). While these variations may appear subtle, they were generally outside the acoustic variations used by the signature whistle owner itself. Dolphins are clearly capable of detecting such differences in the fundamental frequency as well as the upper harmonics \[44,45\]. Hence, these copies cannot function in a deceptive manner. Only animals that are familiar with the whistle of the owner would, however, be able to recognize copies. In encounters with unknown animals, a high rate of copying would still lead to confusion, arguing for low rates of copying overall. In fact, wild bottlenose dolphins do not copy signature whistles when encountering other groups of dolphins at sea \[46\].

Three lines of evidence suggest that active selection may have resulted in the variation found in signature whistle copies. First, bottlenose dolphins are capable of producing almost perfect copies of model sounds \[22\], suggesting that the variation is not due to limits on copying performance. Second, in experimental copying studies, bottlenose dolphins sometimes alter parameters of copies from one session to the next, and subsequently only produce copies with these novel parameter values \[47\]. Third, it has been shown that some dolphins introduce novel components such as sidebands to whistle copies, while they are perfectly capable of producing whistles without sidebands at these frequencies \[24\]. Thus, it is unlikely that variations introduced to copies are merely errors or reflect limitations in copying performance.

A role of vocal learning in the development of signals used in group cohesion and the maintenance of social bonds can be found in a number of social species \[3–7,48,49\]. The bottlenose dolphin signature whistle stands out in that it is invented by its main producer and can only be shared by animals who had experience with the inventor. Besides humans, bottlenose dolphins appear to be the other main example of affiliative copying with such individually specific learned signals, although some parrot species do use vocal learning to develop labels for social companions \[50–52\] and therefore deserve further investigation in this context. Further studies are also needed to elucidate whether copying such signals is different from sharing learned contact calls or adjusting acoustic parameters in communal displays as found in other birds and primates. Bottlenose dolphins can be trained to use vocal copies of novel, arbitrary sounds to refer to objects \[22\]. It is not yet known whether they use learned signals in this way in their own communication system. However, bottlenose dolphins have been found to copy signature whistles of animals that are not present in their group \[27\]. It is possible that signature whistle copying represents a rare case of referential communication with learned signals in a communication system other than human language \[12\]. Future studies should look closely at the exact context, flexibility and role of copying in a wider selection of species to assess its significance as a potential stepping stone towards referential communication.

## Acknowledgements

We thank Ana Catarina Alves, Aasta Eik-Nes, Thomas Götz, Teresa Gridley, Mike Lonergan, Silvana Neves, Cornelia Oedekoven, Peter McGregor and Peter Tyack for help and advice during this study. Mike Beecher and three anonymous reviewers have provided helpful comments on the manuscript. We thank Walt Disney World Animals, Science and Environment and The Seas, Epcot, Walt Disney World Resorts, Lake Buena Vista, FL, USA for letting us work with their animals, especially Heidi Harley, Andy Stamper, Patrick Berry and Jane Davis. Fieldwork in Sarasota Bay would not have been possible without the efforts of a large team of biologists, veterinarians and dolphin handlers, including the staff of the Sarasota Dolphin Research Programme, Blair Irvine, Michael Scott, Jay Sweeney and a team of experienced volunteers. Ages of some of the Sarasota Bay dolphins were provided by Aleta Hohn. This work was supported by a BBSRC Doctoral Training Grant, Dolphin Quest, the Chicago Zoological Society, the National Oceanic and Atmospheric Administration (NOAA) Fisheries Service, Disney's Animals, Science and Environment, Dolphin Biology Research Institute, Mote Marine Laboratory, Harbor Branch Oceanographic Institute and a Royal Society University Research Fellowship and a Fellowship of the Wissenschaftskolleg zu Berlin to V.M.J. Work was conducted under NOAA Fisheries Service Scientific Research permit nos 417, 655, 945, 522–1569 and 522-1785 (to R.S.W.), IACUC approval through Mote Marine Laboratory, and approval of Disney's Animal Care and Welfare Committee. All data are archived and accessible at the Sea Mammal Research Unit, University of St Andrews.

# References


author: Elizabeth R. Seaquist; Dianne Figlewicz Lattemann; Roger A. DixonCorresponding author: Elizabeth R. Seaquist, <firstname.lastname@example.com>.
date: 2012-12
title: American Diabetes Association Research Symposium: Diabetes and the Brain

From 28–30 October 2011, more than 100 people assembled in Alexandria, Virginia, to participate in an American Diabetes Association–sponsored research symposium entitled "Diabetes and the Brain." The objective of the symposium was to discuss the role of the brain in normal and abnormal metabolism and to consider the impact of diabetes on cerebral structure and function. Symposium participants were particularly interested in understanding how abnormalities in brain metabolism could affect the development of diabetes and obesity and how these diseases could, in turn, affect learning and memory. The growing epidemic of diabetes brought urgency to the meeting because of the ever-increasing number of people placed at risk for the cerebral complications of the disease. In this report, we present meeting highlights in five related areas as follows: *1)* metabolism, blood flow, and epigenetics; *2*) glucose sensing and hypoglycemia counterregulation; *3)* insulin resistance and action in the brain; *4)* neurocognition and imaging; and *5*) energy homeostasis.

# METABOLISM, BLOOD FLOW, AND EPIGENETICS

The symposium opened with an outstanding review of cerebral metabolism by Gerald Dienel, PhD, from the University of Arkansas. He emphasized that glucose is the major fuel for the adult brain and that it has multifunctional roles in brain function. Metabolism of glucose via the glycolytic, pentose phosphate shunt, and tricarboxylic acid cycle pathways provides ATP for energy, NADPH for defense against oxidative stress, and carbon for biosynthesis of amino acids and sugars used for synthesis of glycoproteins and glycolipids and for de novo synthesis of tricarboxylic acid cycle-derived neurotransmitters glutamate and γ-aminobutyric acid (GABA) (Fig. 1). Glycogen is stored predominately in astrocytes, and the CO~2~ fixation reaction required for glutamate biosynthesis occurs in astrocytes. Local rates of blood flow and glucose utilization are closely linked to local activities of brain cells, and metabolic rates increase with increased functional activity and decline when cellular signaling is reduced. Energy metabolism and neurotransmission are closely interrelated because glutamate, GABA, acetylcholine, and glycine are synthesized via glucose metabolic pathways, and catecholamine synthesis and degradation rely on oxygen. Metabolic brain imaging and magnetic resonance spectroscopic studies take advantage of the obligatory use of glucose as the predominant brain fuel, and labeled glucose or labeled glucose analogs are used to assay rates of specific reactions or pathways in living brain. Different labeled precursors are used to measure major pathway fluxes in neurons (glucose) and astrocytes (acetate), and they are used for trafficking of metabolites between these two major cell types. In experimental diabetes, changes in the metabolic rate of glucose in the brain are small, but increased brain glucose levels increase production of sorbitol, elevate oxidative stress, impair gap junctional communication among astrocytes, and cause other abnormalities. Although cognitive capability and other brain functions are affected by diabetes, its impact on the brain does not appear to be as severe as it is on peripheral organs.

Eric Newman, PhD, from the University of Minnesota, discussed the regulation of cerebral blood flow in the retina and used it as a model for regulation of brain blood flow. He emphasized that neuronal activity in the brain leads to localized increases in blood flow, a process termed functional hyperemia. Recent work suggests that activity-dependent increases in blood flow are mediated largely by a feedforward mechanism, whereby transmitter release from active neurons results in release of vasodilatory agents, such as arachidonic acid and its metabolites, either from activated glial cells or from other neurons. He went on to say that the regulation of central nervous system (CNS) blood flow is disrupted in diabetic patients. In diabetes, these changes have been best-characterized in retinas in which decreases in light-evoked vasodilation are seen, even before overt retinopathy is observed. Loss of the functional hyperemia response may result in hypoxia and may contribute to development of diabetic retinopathy.

Dr. Newman presented results from a recently completed study in his laboratory, where he found that light-evoked vasodilation was substantially reduced in rats with diabetes for a duration of 7 months. Loss of vasodilation reproduced the disruption of functional hyperemia observed in patients with diabetes. Inducible nitric oxide synthase (iNOS) was upregulated in these animals. He suggested that this response could be responsible for the reduction of functional hyperemia in diabetes, particularly because inhibiting iNOS with aminoguanidine reversed its loss. Dr. Newman concluded that targeting iNOS or its downstream signaling pathway with selective inhibitors to restore functional hyperemia ultimately might be an effective therapy for treating diabetic retinopathy.

Giulio Maria Pasinetti, MD, PhD, from Mount Sinai School of Medicine, focused on epigenetics (DNA methylation) in mouse models of diet-induced obesity. In the Tg2576 mouse, a high-fat diet for up to 5 months resulted in increased soluble β-amyloid in hippocampus and cortex (key CNS sites for learning and cognition) and impaired spatial memory behavioral performance. Using a model of the metabolic syndrome (increased body weight; glucose intolerance; and elevated blood pressure, plasma insulin, and cholesterol) developed in the wild-type C57BL/6J mouse, his group observed impairment in hippocampal electrophysiological measures that may be linked to impaired memory consolidation. Dr. Pasinetti summarized progress in identification of genes that are targets for DNA epigenetic modification and altered expression in the CNS of the mouse model of metabolic syndrome. These include genes for mitochondrial energy metabolism that are downregulated by diet-induced obesity secondary to hypermethylation of specific promoter sequences. A caloric restriction mimetic, Combi-phenol, protects against or normalizes several sequelae of the metabolic syndrome in this model, including brain mitochondrial dysfunction, cognitive behavioral performance, and CNS network connectivity evaluated with diffusion tensor imaging. The Combi-phenol is being studied in phase I and II clinical trials for safety, measurement of biomarkers, and memory assessments as a potential therapeutic or therapeutic adjunct for Alzheimer disease (AD).

# GLUCOSE SENSING AND HYPOGLYCEMIA COUNTERREGULATION

Brain glucose-sensing mechanisms in the brain were the topic of the presentation by Vanessa Routh, PhD, from the New Jersey Medical School. She explained that the ventromedial hypothalamus (VMH) contains both glucose-excited and glucose-inhibited neurons that respond to physiologically relevant changes in glucose. Glucose-excited neurons increase firing as glucose is increased through a mechanism that involves closure of the K~ATP~ channel. The phenotype of these neurons remains uncertain, but some believe they may be producing pro-opiomelanocortin (POMC). Glucose-inhibited neurons reduce firing in response to an increase in glucose. This action appears to be mediated by AMP kinase and neuronal NOS and couples to a chloride channel. Glucose-inhibited neurons are distributed throughout the VMH, including approximately half of the neuropeptide Y (NPY)–producing neuronal population. The ability of the glucose-inhibited neurons to sense and respond to glucose parallels the ability of the brain to sense and respond to fasting and hypoglycemia in normal rodents. Moreover, the response of glucose-inhibited and glucose-excited neurons to decreased glucose can be blunted by satiety signals such as leptin or insulin and can be increased by the orexigenic peptide ghrelin. On the basis of these results, Dr. Routh proposed the hypothesis that glucose-sensing neurons are important for sensing decreased glucose and initiating compensatory responses under conditions of energy deficit, such as fasting or hypoglycemia. However, during normal energy balance, it is important that glucose-sensing neurons do not respond to small glucose decreases associated with meal-to-meal fluctuations in glucose because such a response could lead to inappropriate signals of overall energy deficit. As a result, satiety hormones mask and orexigenic agents enhance the responsiveness of glucose-sensing neurons to reductions in glucose.

Stephanie Amiel, MD, from King's College London School of Medicine, presented a lecture entitled "Neural Circuits Involved in Hypoglycemia Counterregulation." She reviewed the physiology underlying the counterregulatory response to hypoglycemia and then presented neuroimaging studies in humans that have helped visualize the global brain response to hypoglycemia. Many cerebral regions have been shown to participate in the detection of hypoglycemia and coordination of the counterregulatory response, including the hypothalamus, the thalamus, the hippocampus, and higher brain centers. With a new understanding of the parts of the brain involved in the counterregulatory response to hypoglycemia, she proposed that new therapeutic avenues to prevent and treat hypoglycemia may be possible.

How these different brain regions may detect and respond to hypoglycemia was discussed by E.R.S. MD, from the University of Minnesota, in her lecture entitled "Mechanisms Underlying the Development of Hypoglycemia-Associated Autonomic Failure." She began her presentation by emphasizing that hypoglycemia-associated autonomic failure (HAAF) is a clinically significant problem that occurs in patients with type 1 and advanced type 2 diabetes. Recurrent hypoglycemia leads to HAAF because it shifts the glucose threshold that elicits adrenomedullary response (i.e., lower and lower blood sugars required to elicit response) and reduces the magnitude of hormonal counterregulatory response while having no effect on glucose level associated with neuroglycopenia symptoms. HAAF is reversible by avoiding hypoglycemia.

The mechanisms responsible for the development of HAAF remain uncertain, but at least three have been proposed. One potential mechanism is that hypoglycemia induces a change in brain glucose metabolism in such a way to prevent regulatory centers from sensing a change in blood glucose concentration during systemic hypoglycemia. A second potential mechanism is that the brain may increase the utilization of alternative fuels such as lactate and glycogen during hypoglycemia and therefore may not experience energy deficit in the setting of systemic hypoglycemia. A third mechanism suggests that changes in downstream signaling of GABA and opioids that link detection of hypoglycemia to counterregulatory response are altered in the setting of recurrent hypoglycemia.

# INSULIN RESISTANCE AND INSULIN ACTION IN THE BRAIN

C. Ronald Kahn, MD, from the Joslin Clinic and the Harvard Medical School, discussed diabetes, insulin action, and the brain. One approach to identifying pathways through which insulin alters brain metabolism has been creation of mice in which the insulin receptor is knocked out only in neural tissue. These mice show increased food intake, increased adipose mass, hyperleptinemia, and insulin resistance; however, behaviorally, they are the same as wild-type mice at a young age. However, as they age, they begin to behave abnormally in open field tests (interpreted as a sign of anxiety), in tail suspension tests (interpreted as a sign of depression), and in forced swimming tests (interpreted as a loss of motivation). How the loss of insulin action causes these behavior changes remains unknown, but preliminary studies show that these mice have decreased phosphorylation of glycogen synthase kinase 3 and increased phosphorylation of τ, two proteins known to be important in the pathogenesis of AD. Dr. Kahn's group recently performed a gene expression analysis on the hypothalamus from streptozotocin-treated C57BL/6 mice (a model for insulin-deficient diabetes) and found that genes involved in cholesterol biosynthesis are suppressed relative to controls. In addition, rates of cholesterol synthesis and the synaptosomal cholesterol content are decreased in the brains of these diabetic mice, as are levels of sterol regulatory element–binding protein 2 and srebp cleavage-activating protein (SCAP) mRNA and protein, two important regulators of cholesterol synthesis.

Mark W.J. Strachan, MD, from the Western General Hospital, Edinburgh, Scotland, also discussed insulin action in the brain in his lecture entitled "Emerging Data on the Relationship Between Insulin Resistance and Neurovascular Disorders." Insulin receptors are found in high concentrations within the limbic system of the brain. In vitro, insulin affects neuronal excitability and synaptic plasticity. Direct relationships have been shown between insulin action and the pathogenesis of AD. The β-amyloid oligomers, which accumulate in the brain in AD, are intimately linked with neuronal dysfunction and cause a rapid and substantial loss of cell surface insulin receptors in hippocampal neuronal cultures. The development of insulin resistance in the brain then can set-up a vicious cycle because resultant activation of glucose synthase kinase 3 promotes hyperphosphorylation of τ protein and altered processing of amyloid precursor protein, which increases β-amyloid oligomer production. Moreover, insulin resistance (through attendant hyperinsulinemia) may reduce the ability of insulin-degrading enzyme in the brain to break-down β-amyloid oligomers. Proving the existence of these mechanisms and pathways in humans has been challenging, but insulin concentrations in the cerebrospinal fluid do appear lower in people with severe AD. In addition, administration of intravenous insulin (with concurrent glucose to maintain euglycemia) has been shown to enhance cognitive function. Direct administration of insulin via the intranasal route has a favorable impact on cognition and may provide new opportunities for treatment. Therapies designed to improve cerebral insulin action have produced mixed results. Administration of rosiglitazone was not of cognitive benefit to people with AD, perhaps because it does not cross the blood--brain barrier, whereas an exercise intervention did lead to improvement in insulin sensitivity and cognitive function in older adults.

Suzanne Craft, PhD, from the University of Washington, presented a programmatic analysis of a specific mechanism that may link type 2 diabetes with pathological brain changes, cognitive impairment, and even AD-related dementia. A key target (if not culprit) is insulin secretion and action. Dysregulation or other abnormalities in brain insulin play a role in normal aging-related cognitive decline, exacerbated cognitive decline with type 2 diabetes, and (when severe) mild cognitive impairment and AD. The two forms of insulin dysregulation that increase the risk of neurocognitive dysfunction, insulin resistance and peripheral hyperinsulinemia, also were discussed by other speakers in this symposium. Dr. Craft detailed several mechanisms through which insulin resistance and diabetes may increase the risk of cognitive impairment and AD. In addition to reduced brain insulin uptake, these specific mechanisms include: *1*) impaired glucose and lipid metabolism; *2)* disrupted regulation of β-amyloid clearance (i.e., a hallmark of AD neuropathology); *3)* increased inflammation; and *4)* compromised vascular function (e.g., microvascular lesions). Notably, insulin receptors are distributed in several critical brain regions (e.g., hippocampus, frontal lobe), and insulin performs several crucial brain cognition functions (e.g., increasing neurotransmitter levels, glucose utilization). The fact that numerous specific mechanisms can independently and perhaps interactively interrupt signaling in any of these regions creates a formidable challenge for clinical research.

If insulin resistance is a mechanism through which diabetes may compromise neurocognitive functioning, then there may be preventable or modifiable factors that lead to insulin dysregulation as well as therapeutics that may improve insulin resistance. Regarding the former, Dr. Craft reported that epidemiological studies suggest that the expanding prevalence of conditions related to insulin resistance in western societies may be attributable to decreased levels of physical activity and increased dietary intake of calories and saturated fats. Arguably, healthier lifestyles may be protective factors operating in several specific mechanisms through which insulin resistance can lead to neurocognitive dysfunction. Regarding the latter, Dr. Craft described her own research on the therapeutic strategy of administering intranasal insulin in an effort to improve neural insulin signaling. In her project known as the Study of Nasal Insulin to Fight Forgetfulness (SNIFF), she reported promising outcomes, including improved memory performance and improved performance in activities of daily living.

# NEUROCOGNITION AND IMAGING

Christopher M. Ryan, PhD, from the University of Pittsburgh, presented a historically informative tour of the major issues and strategies for assessing cognitive function in diabetic patients. Although the field has benefited from a surge of interest in recent years, it has come to recognize that improvements can be implemented across research laboratories in crucial aspects such as: *1*) establishing diagnostic standards and procedures for evaluating equivalences and systematic variations in clinical groups; *2*) reducing inconsistencies in the cognitive testing batteries deployed (e.g., standardization, theoretical breadth, measurement purity, manifest vs. latent variable representations, and validity issues); *3*) improving research sensitivity to special population characteristics that affect diabetes--cognition relationships (e.g., children, older adults, multiple impairments); and *4*) addressing the importance of longitudinal follow-up for understanding the trajectories of cognitive change. Dr. Ryan, who was an early contributor and still is a continuing contributor to the field, focused on identifying a framework for representing some of the major approaches to selecting cognitive measures for research in diabetes and reviewing a large number of frequently used cognitive batteries.

Regarding the framework, Dr. Ryan described several related approaches. One approach emphasizes identifying relatively nonspecific cognitive markers of diabetes-related dysfunction. Neurocognitive speed has proven to be the most sensitive marker across multiple studies, even while using tasks that range widely in terms of their process purity. In a complementary approach, researchers use cognitive and neuropsychological test batteries to identify neurocognitive or clinical phenotypes (e.g., mild cognitive impairment) associated with diabetes. Results using this approach have improved as the batteries become more contemporary, comprehensive, and multidimensional. However, Dr. Ryan cautioned that it is crucial to map the expected cognitive effects to the brain changes (e.g., vascular) on the basis of theoretical and clinical data and to use batteries that are age-appropriate and change-sensitive to evaluate these relationships and performances at different phases of the life span.

Brian M. Frier, MD, from the Royal Infirmary of Edinburgh, Scotland, shifted the focus from extant approaches and available test batteries to the correlative topic of observed cognitive effects of diabetes and their potential mechanisms. He began by noting that the "unsystematic" nature of early research in the area was likely attributable to cross-study variations and within-study limitations in sample sizes, cognitive batteries, and measurement of relevant covariates. Accordingly, Dr. Frier supplemented the typical organizing scheme of diabetes type. Rather than summarizing similar and dissimilar cognitive effects of type 1 and type 2 diabetes, he began by presenting the main observed (or to-be-tested) risk factors for cognitive decrements as associated with each. Both sets of risk factors include a genetic predisposition (although this remains an understudied aspect for both). For type 1 diabetes, the other risk factors include recurrent hypoglycemia, chronic hyperglycemia, long duration, and early age of onset, as well as a group of diabetes complications. Not surprisingly, for type 2 diabetes the list is much longer and more diverse (Fig. 2). It includes vascular issues (microvascular and macrovascular disease, hypertension), acute hyperglycemia, recurrent hypoglycemia, hyperinsulinemia, dyslipidemia, amyloid deposition in the brain, and inflammatory mediators, as well as drug therapy issues and psychosocial factors such as depression. The implicit message is that diabetes per se may affect neurocognitive performance in a variety of ways but does so not just through the separate prisms of diabetes type. It is also influenced within type by a diverse set of barely overlapping but likely interacting influences.

For both disease types, there is consistent evidence that neurocognitive speed is affected, particularly straightforward indicators of mean performance rates that usually show slower average speeds for diabetic patients relative to controls. In addition, some downstream indicators of basic cognitive resources (e.g., executive function) and products (e.g., episodic memory) that may require speed for efficient performance are often implicated in research with both types of diabetes. According to Dr. Frier's review, several of the promising risk factors for cognitive decrements in type 1 diabetes have not yet been confirmed empirically. One confirmed factor among children and adolescents is early disease onset (up to age 7), a factor that is also linked to progress and participation in schooling and social development. Regarding research on cognitive effects of type 2 diabetes, a key challenge is to differentiate neurocognitive changes that characterize normal aging from those exacerbated by diabetes or associated with progressive neurodegenerative decline. Researchers are increasingly considering the roles of modulating factors. In addition to age, sex, and education, these include the specific risk factors in Fig. 2, numerous comorbidities (e.g., obesity, vascular dysfunction), treatment and adherence (e.g., diet, oral agents), and exposure to risk-enhancing substances (alcohol, smoking, cholesterol). In summary, Dr. Frier's recommendations for future research included examining multiple influences on longer-term progressive or accelerated declines in cognitive functioning. We await further results from prospective studies that feature larger sample sizes, broader coverage of key cognitive functions, and markers of important risk and mediator factors, all measured across extended bands of adulthood.

The presentation by Geert Jan Biessels, MD, PhD, from the University Medical Centre Utrecht, addressed etiology, risk factors, and biomarkers of diabetes-related cognitive dysfunction with an emphasis on novel opportunities provided by longitudinal research methods. A first important point is that cognitive dysfunction in diabetes should be characterized not just in terms of implicated aspects of cognition (e.g., episodic memory or neurocognitive speed) or even magnitude of the concurrent deficit Rather because most observed decrements are modest in magnitude, dysfunction also should be defined by characteristics of change, including direction, slope, trajectory, and outcome. Whereas for some diabetic patients neurocognitive deficits may decline only slowly over long periods of adulthood, others may experience trajectories that lead beyond normal decline to frank cognitive impairment and even dementia. Dr. Biessels reported that although diabetic patients who exhibit sustained modest decrement profiles of both types of diabetes (1 and 2) and of any age, those who experience more rapid neurocognitive decline are more likely to be patients with type 2 diabetes who eventually have development of overt dementia.

Dr. Biessels' second major point was that the two types of cognitive dysfunction in diabetes may differ not only in terms of temporal and outcome characteristics but also in terms of associated biomarkers, risk factors, and etiological influences. In addition, Dr. Biessels noted that these dynamic and interactive processes can be directly examined with multiple assessments and appropriate statistical analyses. He described one promising longitudinal study that may yield data to answer many of the questions raised in his presentation. It features abundant risk factor data on large samples of type 2 diabetic patients from the Kaiser Permanente Northern California Diabetes Registry. Finally, a summary point from his biomarker and etiology discussion merits special attention. Dr. Biessels noted an emerging theory that may imply that the two types of diabetes--cognition trajectories may differ in part in the extent to which an additional (diabetes and immediate complications) "hit" from substantial brain pathology is absorbed by some patients. For example, a mild stroke or increased rates of amyloid deposition may accelerate the transition of some type 2 diabetic patients from normal cognition to impairment or dementia.

Gail Musen, PhD, from the Joslin Diabetes Center, highlighted the value of using neuroimaging techniques to shed light on neural correlates of cognitive dysfunction in diabetes. A principal rationale is that diabetes-related changes in the brain may be detectable with recent technology early in diabetes-related degenerative processes and that they may also precede and be differentially associated with specific diabetes-related cognitive manifestations and decremental patterns. Dr. Musen commented on the recent progress and future promise in this area, attributable in part to new and more widely available neuroimaging techniques. For example, early diabetes--brain research on white matter hyperintensities (often observed with magnetic resonance imaging) revealed mixed patterns across studies. Using the more sensitive, flexible, and objective diffusion tensor imaging, researchers can examine and compare a number of white matter abnormalities and connectivity patterns. These lines of investigation were not previously available because of limitations in imaging technology. In addition, voxel-based morphometry studies of cortical thickness (including both gray and white matter density) are being used to compare diabetic patients and healthy controls. Dr. Musen reported new research from her team on one promising avenue that may eventually link diabetes more closely with cognitive impairment and dementia risk. In normal adults, the default mode network is typically deactivated during goal-directed cognitive activity, with variations on activation and metabolism patterns for AD patients. She described one study in which similar disruptions occurred for type 1 diabetic patients and an effort to investigate similar issues with type 2 diabetes (a well-known risk factor for AD). Dr. Musen concluded her presentation with several forward-looking recommendations, including identification of neural mechanisms associated with potential cognitive dysfunctions in diabetes, pursuing this goal with larger-scale studies (in terms of both sample sizes and assessment waves), and collection of repeated assessments of target (neuroimaging, cognitive performance) and covariate (diabetes-related biomarkers) measures.

# ENERGY HOMEOSTASIS

D.F.L., PhD, from the University of Washington, presented translational research demonstrating the effects of insulin and leptin on reward behavior and dopamine function in the rat. Her work has focused predominately on the rewarding value of sucrose, based on findings that sucrose drives motivation for intake of mixtures of sugar, fat, and flavor. She reviewed the CNS anatomy that mediates reward behavior and energy homeostasis and made three key points. First, these sets of circuitries are substantially interconnected. Second, specific sites within both sets of circuitries play a role in mediating different aspects of reward behavior. Third, insulin and leptin can decrease reward behavior through actions on both reward and energy homeostatic circuitries. The neurotransmitter dopamine plays a central role in reward and motivation. D.F.L. reviewed the effect of insulin to increase dopamine reuptake, which curtails dopamine signaling. There is evidence that this effect is attributable to increased cell-surface cycling of the dopamine reuptake transporter, and that this cycling is dependent on activation of phosphatidylinositol 3 kinase mechanisms. Additionally, it has been shown that leptin can decrease dopamine neuronal activity. Together, both hormones act to dampen dopamine signaling in normal animals. D.F.L. finished by reporting that exposure to a moderate-fat diet results in increased motivation for sucrose and resistance to insulin and leptin effects in rats that are metabolically normal and preobese. She concluded with examples from current human imaging studies suggesting that the effects of insulin and insulin resistance on reward circuitry in rodents are relevant for humans.

Tony Lam, PhD, from the University of Michigan, reviewed work from his laboratory in a lecture entitled "Gut--Brain Signaling." He has shown that the influx of fatty acids into the duodenum leads to the release of cholecystokinin, subsequent activation of vagal afferents, and transmission of the signal to the nucleus of the solitary tract. As a result, the gut-brain-liver axis is activated to reduce hepatic glucose production. In addition, metabolism of lipids and glucose in the hypothalamus activates protein kinase C and K~ATP~ channels that regulate hepatic glucose production. Diabetes and obesity alter these nutrient-sensing pathways in both the gut and the brain, which leads to abnormalities in glucose homeostasis.

Joel Elmquist, DVM, PhD, from the University of Texas Southwestern Medical Center, reviewed the anatomical, synaptic, and cellular functions of the mediobasal hypothalamic circuitry in the coordinated regulation of multiple components of energy homeostasis. This circuitry has now been characterized for reciprocal regulatory interactions in locus, as well as for receiving and transmitting multiple signals from other CNS sites and from the periphery. Research has focused on the roles of (orexigenic) NPY/agouti-related protein (AGRP) and (anorexigenic) POMC neurons. Dr. Elmquist provided a historical perspective on the field that laid the groundwork for the utility of genetically modified mouse models to determine specific neuronal and functional aspects of leptin activity. Specific ablation of leptin receptors in POMC neurons, which produce the neuropeptide melanocortin, results in modest obesity with decreased energy expenditure and locomotor activity but no effect on food intake, along with mild glucose intolerance and insulin resistance. Specific reinstatement of functional leptin receptors in these POMC neurons normalizes peripheral glucose homeostasis and glucagon release. Interestingly, a model in which both insulin and leptin receptors were ablated in POMC neurons did not result in strong effects on body weight but showed profound glucose dyshomeostasis and reduced fertility. This suggested coordinated action of insulin and leptin signaling in POMC neurons that regulate reproductive and hepatic function. However, leptin receptor effects are distributed throughout a network; for example, the leptin receptors expressed on steroidogenic factor-1 neurons of the VMH appear to regulate energy expenditure. The melanocortin receptor subtype 4 (MCR4) mediates many of the energy homeostatic effects of melanocortin, and new evidence suggests that MCR4 activity in multiple central and peripheral nervous system sites is important for regulation of energy homeostasis. Thus, MCR4 receptors expressed in SIM1 neurons of the paraventricular nucleus of the hypothalamus and the amygdala correct hyperphagia in MCR4-null mice. MCR4 receptors expressed in cholinergic neurons of the sympathetic nervous system increase energy expenditure and regulate hepatic glucose production, and MCR4 receptors expressed in the cholinergic neurons of the parasympathetic nervous system may regulate endocrine pancreatic function.

Tamas Horvath, DVM, PhD, from Yale University, presented his work examining synaptic interactions directly between POMC and other local neurons. The AGRP/NPY neuron releases the inhibitory neurotransmitter GABA. In addition to a projection to the paraventricular nucleus of the hypothalamus, the AGRP/NPY neuron sends a GABAergic projection to local POMC neurons in the arcuate nucleus. This local circuit is a direct target for leptin action as well as for the orexigenic hormone/neuropeptide, ghrelin. Recent work from Horvath's group has demonstrated ghrelin stimulation of UCP2 expression. Studies in a UCP2 knockout model have shown an impairment of ghrelin-stimulated electrical activity and mitochondriogenesis in NPY/AGRP neurons. Ghrelin effects have been observed in other CNS sites, i.e., the hippocampus and the dopaminergic neuron in the substantia nigra pars compacta. Taken together, the group's recent findings clearly delineate a key role of the UCP2 protein in many aspects of CNS function and its modulation by nutritional and metabolic status.

The theme of hypothalamic/brain/periphery networking was further emphasized in the presentation by Joseph Bass, MD, PhD, from Northwestern University, that focused on circadian regulation of energy homeostasis. Dr. Bass reviewed the discovery of clock proteins in the 1990s, which set the stage for research on circadian function and its regulation in the subsequent years. In mammals, including humans, the CNS "master clock" has been identified as the suprachiasmatic nucleus (SCN), and circadian rhythms are encoded by the sequential and reciprocal expression of transcription activators, CLOCK/BMAL1, that induce expression of the transcription repressors, PER1–3/CRY1–3, that inhibit CLOCK/BMAL1 expression. The cycle of expression takes approximately 24 h and is entrained by light. The SCN has connections with key homeostatic nuclei in the hypothalamus, including the arcuate, paraventricular nucleus, ventromedial hypothalamus (VMH), lateral hypothalamus, and dorsal medial hypothalamus (DMH). Thus, there is direct input on behavior, activity, metabolism, and the hypothalamic-pituitary-adrenal axis, which in turn is a key diurnal regulator of activity and metabolism. Importantly, it is now appreciated that "clock proteins" are expressed in nearly all cells. Peripheral tissue clocks are entrained to food availability, adrenal glucocorticoids, and temperature, all of which can reset peripheral clocks. Although research is only beginning to uncover the complex crosstalk between stimuli and peripheral clocks, molecules that reflect metabolic status such as NAD and the nutrient sensor AMP kinase, relay information to the clocks and connectivity is bidirectional. A current line of investigation is examining the coupling of metabolic processes and CNS clock function. A high-fat diet in an animal model has been shown to increase period length of the daily activity rhythm and to phase-shift metabolic gene expression in the liver and adipose as well as in the hypothalamus. The CNS locus of this effect (SCN per se, or SCN projections) remains to be determined but clearly demonstrates metabolite/CNS clock interaction. Finally, sleep and activity abnormalities such as narcolepsy and genetic evidence in humans with altered sleep patterns suggest that pursuit of this research and links between clock activities and metabolic disease will reveal the importance of clock/environmental coordination to human metabolic health.

# CONCLUSIONS

The workshop brought together scientists and clinicians working in disparate areas such as metabolism, psychology, imaging, and neuroscience who were all united by a desire to understand how diabetes affects cerebral structure and function. In the opening lecture, Dr. Gerald Dienel reported anecdotally that early in his career it was unusual for basic neuroscientists to consider diabetes as an influence on cognitive functioning, much less a risk factor for neurodegenerative disease. By the end of the workshop, new collaborations were formed that will support future multidisciplinary research in this area. Ideally, this work should be based on the following principles: both "neuro" and "cognitive" phenomena must be clinically relevant, theoretically selected, domain-specific, and carefully measured; research should emphasize, whenever possible, continuous (rather than categorical) assessments of phenomena related to diagnosis, performance, structure, modulating factors, and clinical outcomes; investigation should attend to a broad range of potential modulators, including proximal (e.g., insulin resistance), somatic or distal (e.g., functional biomarkers), comorbidities (e.g., vascular disruptions, inflammation), therapy--social (e.g., treatment availability and adherence, depression), and genetic susceptibilities (e.g., genetic polymorphisms related to diabetes, AD, obesity); work should continue to examine the complex systems through which endocrine and neural systems regulate energy metabolism; and, whenever possible, scientists should study these relationships dynamically using longitudinal follow-up assessments and appropriate change-sensitive statistical analyses.

## ACKNOWLEDGMENTS

No potential conflicts of interest relevant to this article were reported.

E.R.S., D.F.L., and R.A.D. contributed to the drafting and revising of the manuscript.

The authors gratefully acknowledge the organizational support of Shirley Ash (American Diabetes Association).