For the brain to function correctly, the activities of multiple regions must be coordinated. This coordination is thought to be carried out by waves of electrical activity in the brain. One of the most prominent signals within these waves is called the theta rhythm.

The theta rhythm is thought to help coordinate neural activity between the regions of the brain that are involved in learning and memory. However, theta rhythms also appear when subjects encounter emotional stimuli, which suggests that they might have a role in social cognition. Consistent with this idea, theta rhythms are reduced in individuals with autism spectrum disorders, but the exact nature of the relationship between theta rhythms and social behavior has remained unclear.

Tendler and Wagner have now addressed this question directly by implanting electrodes into five brain regions that are active when rats engage in social interactions. Exposing a rat to a social stimulus, such as an unfamiliar visitor rat, caused the intensity of theta rhythms to increase in this network. This change was temporary, with the theta rhythms gradually returning to normal as the novelty of the visitor wore off.

An increase in the intensity of theta rhythms also occurred in the same network when the rats encountered a fearful stimulus, such as a tone that had previously signaled the delivery of a mild electric shock. Notably, however, the fearful stimulus led to an increase in low frequency theta rhythms, whereas the social stimulus led to an increase in high frequency theta rhythms.

These results suggest that social and fearful stimuli give rise to two different forms of alertness or arousal, which are reflected by the two types of theta rhythms in this network within the brain. Tendler and Wagner also suggest that the distinct frequencies of theta rhythms might be used to support different forms of communication between various regions of the brain, depending on the emotional value of the stimuli (for example, are they social or fearful stimuli?) encountered by the animal. This means that emotional states might be dictating cognitive processes such as learning and memory.

Oscillatory brain activity, mostly categorized to the theta (3–12 Hz), beta (12–30 Hz), and gamma (30–80 Hz) bands, is thought to coordinate neural activity in vast neuronal assemblies dispersed over different brain regions (Buzsáki and Draguhn, 2004). This type of coordination may underlie high level cognitive functions, such as speech and social communication (Uhlhaas and Singer, 2006; Uhlhaas et al., 2009) that are impaired in autism spectrum disorders (ASD) (Geschwind, 2009). Increasing evidence suggest that individuals with ASD show deficits in long-range neuronal communication associated with low-frequency rhythms, such as the theta rhythm (Geschwind and Levitt, 2007; Rippon et al., 2007; Wass, 2011). Nonetheless, a clear connection between rhythmic brain activity and social behavior has not yet been established.

Mammalian social organization depends on the ability to recognize and remember individual conspecifics (Wiley, 2013). This social recognition memory (SRM) can be assessed in rodents using their innate tendency to investigate novel conspecifics more persistently than familiar ones (Gheusi et al., 1994). In the SRM habituation–dishabituation test, social memory is assessed by the gradual reduction in the amount of time the animal spends investigating a particular social stimulus during consecutive encounters (Ferguson et al., 2002). This short-term memory was shown to be mediated mainly by chemical cues (semiochemicals) perceived via the main and accessory olfactory systems (Dulac and Torello, 2003). Upon binding of semiochemicals to the receptors expressed by the sensory neurons of the main olfactory epithelium and the vomeronasal organ, sensory information is conveyed to the main (MOB) and accessory (AOB) olfactory bulbs, respectively (Dulac and Wagner, 2006). Both bulbs then project, directly and indirectly, to the medial amygdala (MeA) (Pro-Sistiaga et al., 2007; Kang et al., 2011) that is thought to transfer the information to the hippocampus through the lateral septum (LS) (Bielsky and Young, 2004). The MOB projects also to several cortical areas comprising the primary olfactory cortex, of which the piriform cortex (Pir) is best characterized (Wilson and Sullivan, 2011) (Figure 1).

Figure 1

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Here we hypothesized that social behavior is associated with an elevation of rhythmic activity in the network of brain areas that process social stimuli. To examine this hypothesis, we recorded electrophysiological activity from the brains of freely behaving adult male rats performing the SRM paradigm (Video 1). A telemetric system was used to record from wire electrodes chronically implanted in the five aforementioned brain regions: MOB, AOB, MeA, LS, and Pir (Dulac and Wagner, 2006). We found that social encounters were associated with enhancement of brain rhythmic activity, specifically at 7–10 Hz range, in all brain regions. This enhancement that was proportional to the degree of novelty of the social stimulus appeared to reflect an internal brain-state associated with social arousal. In contrast, a fear-conditioned tone, which is associated with fear arousal, induced rhythmicity in the low theta range (3–7 Hz) in the same network of brain regions. Moreover, social and fearful stimuli elicited different patterns of change in coherence between the distinct brain regions. We hypothesize that these two types of stimuli induce distinct arousal states in the animal, which are reflected by the different kinds of theta rhythmicity. We further suggest that the distinct types of theta rhythmicity support different modes of communication between the various brain areas. These in turn may modify cognitive processes such as memory acquisition and recall depending on the value and saliency of the stimulus by enhancing synchronous neuronal activity between remote neuronal assemblies.

Video 1

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This study demonstrates that an encounter with a social stimulus causes increased LFP rhythmicity in the high theta range (7–10 Hz), in a network of limbic brain areas associated with social memory. Strikingly, the change in theta rhythmicity is directly proportional to the novelty of the social partner, and may thus be considered a neuronal correlate of short-term social memory (Liebe et al., 2012). Since the modulation of theta rhythmicity is observed even when anesthetized stimuli are used, we infer that it does not depend on the behavior of the social stimulus. Despite the similarity in investigative behavior, such modulation of theta rhythmicity is not observed with object stimuli, suggesting that it is social specific. Since the augmented theta rhythmicity and the associated increase in theta coherence persist beyond the removal of the social stimulus itself, we conclude that these parameters do not mirror sensory inputs but rather reflect a state of arousal that slowly fades away. This is in agreement with the fact that the increase in theta power occurs prior to the actual introduction of the social stimulus in the arena, suggesting increased arousal due to the anticipated social encounter. Finally, since the change in theta rhythmicity during the SRM test correlates with the novelty of the social stimulus, we posit that it reflects a graded level of arousal, which is proportional to the stimulus saliency.

One of the questions that arise from the study is whether the social encounter-induced state of arousal is elicited by the ‘social’ quality of the stimulus or whether it simply results from the complexity of the stimulus. Notably, the social stimulus is much more complex than the single object or odor stimuli that we used as controls. It emits a complex mixture of odors and semiochemicals, and in addition to the main and accessory olfactory systems, it also stimulates the visual, auditory, and somatosensory systems. It is not likely that the full complexity of the social stimulus may be mimicked by the use of any artificial mixture of odors, hence the possibility that the arousal state results from the complexity of the stimulus cannot be excluded. On the other hand, at least as regards to fear-associated arousal, it is well documented (Takahashi et al., 2008) that a very simple cue is sufficient to evoke a state of arousal, such that is observed by the freezing of rodents in response to the pure odorant 2,3,5-Trimethyl-3-thiazoline (TMT), a component of fox odor (Fendt et al., 2003), or to a pure tone in a fear conditioning paradigm (Rogan et al., 1997). This suggests that the factor that determines the state of arousal is not the complexity of the stimulus but rather the information it embodies with regards to the natural environment of the animal.

Many studies, both in animals and humans, have linked brain theta rhythmicity to the processing of emotional cues (Sainsbury and Montoya, 1984; Sainsbury et al., 1987a; Sainsbury et al., 1987b; Aftanas et al., 2001; Balconi and Pozzoli, 2009; Knyazev et al., 2009; Maratos et al., 2009; Luo et al., 2013). In animals theta rhythmicity was mostly studied in the hippocampus (Buzsáki, 2002), where it was classified into two types, Type 1 and Type 2. The atropine-insensitive Type 1 theta rhythmicity shows higher frequency (8–12 Hz) and is thought to be associated mainly with voluntary movement. In contrast, atropine-sensitive Type 2 rhythmicity is characterized by lower frequency (4–8 Hz) and is thought to be linked to arousal during states of immobility (Bland, 1986; Sainsbury, 1998). Notably, Type 2 rhythmicity was mostly studied using states of fear and aversive stimuli and was shown to be induced by neutral stimuli if conditioned by fear or introduced in the presence of predators (Sainsbury and Montoya, 1984; Sainsbury et al., 1987a; Sainsbury et al., 1987b). The relationship of the two types of hippocampal theta rhythmicity and similar rhythms recorded from other brain regions, such as in our case, should be cautiously examined for several reasons. First, recent studies showed that in the hippocampus itself there are differences in the profile of theta rhythmicity between the earlier studied dorsal hippocampus and the more recently studied ventral hippocampus (Adhikari et al., 2010), the latter of which shows theta rhythmicity with stronger association to the one recoded in the mPFC (Jacinto et al., 2013), and may be dissociated from the dorsal hippocampus under certain conditions such as decision making (Schmidt et al., 2013). Second, even for the dorsal hippocampus the dichotomy between the two types of theta rhythmicity is far from being perfect with Type 2 rhythmicity reported to reach 12 Hz at some states and Type 1 rhythmicity reported to disappear during certain movements (Sainsbury, 1998). Interestingly, researchers reported that in cats the correlation between movement and Type 1 rhythmicity was good at the beginning of the experiments, when a lot of exploratory and object manipulation behavior was observed, but deteriorated towards the end of the experiments, when the animals were still moving but were uninterested in the task (Sainsbury, 1998). This might suggest that in the hippocampus too, high frequency Type 1 theta may be associated with sensory information processing during ‘positive’ arousal states associated with motivational voluntary movements, such as exploration, while low frequency Type 2 theta may be linked to ‘negative’ arousal states, such as those caused by fear, which is usually associated with freezing.

Regardless of the nature of hippocampal theta oscillations, theta rhythmicity associated with emotional states was reported in several other brain areas (Bland et al., 1993; Bland and Oddie, 2001; Pignatelli et al., 2012). Of particular interest is the finding that theta rhythmicity in a limbic network that includes the hippocampus, medial prefrontal cortex and the basolateral complex of the amygdala (lateral and basolateral amygdala) is associated with fear memories. Importantly, the consolidation and recall of long-term fear memory was found to be associated with elevated coherence of the theta rhythmicity in this network (Paré and Collins, 2000; Seidenbecher et al., 2003; Pape et al., 2005; Popa et al., 2010; Lesting et al., 2013), while its extinction was associated with a decline in coherence, in a brain-region dependent manner (Narayanan et al., 2007). Moreover, interfering with theta coherence through local electrical micro-stimulation affected fear-memory recall and extinction depending on theta phase (Lesting et al., 2013). Thus, coordinated arousal-induced theta rhythmicity within this network seems to be involved in consolidation and recall of aversive memories (Popa et al., 2010; Lesting et al., 2013). Here, we demonstrated for the first time that similar phenomena occur in a distinct network of limbic areas that are linked to social memory, in the course of social encounters. Importantly, a comparison of the theta activity between social and fearful stimuli revealed that although both cause a state of arousal, the patterns of theta rhythmicity and coherence within the same network are completely different. First, in agreement with previous studies (Paré and Collins, 2000; Seidenbecher et al., 2003; Pape et al., 2005; Popa et al., 2010; Lesting et al., 2013), the recall of fear memory causes rhythmicity in the low theta range, while a social encounter elicits rhythmic activity in the high theta range. This suggests the existence of two types of arousal: fear-associated arousal and social-related arousal. Second, each of these conditions caused a distinct pattern of coherence changes between the same regions of the network. Given these results, we hypothesize that the distinct types of theta rhythmicity promote different communication protocols (Kepecs et al., 2006) for the coordination of neural activity in the network, which depends on the emotional state of the animal. Our results are in agreement with the hypothesis that theta rhythmicity facilitates cognitive processes such as memory formation that are associated with emotionally salient stimuli (Pelletier and Paré, 2004).

The source and distribution of theta rhythms in the mammalian brain are not fully understood (Pignatelli et al., 2012). This issue was extensively studied in the hippocampus (Buzsáki, 2002), which was shown to have the capacity to self-generate theta rhythmicity (Goutagny et al., 2009). Yet, as described above theta rhythmicity also exists in various cortical and limbic areas, where it shows dynamic coherence with the hippocampal theta rhythm. One area shown to display robust theta rhythmicity is the olfactory bulb, where it follows the rhythm of respiration (‘sniff cycle’) (Rojas-Libano et al., 2014). Sniffing, similarly to whisking, is a sensory sampling activity, the rate of which dynamically changes throughout the theta band and is strongly influenced by internal arousal and motivational state of the animal (Clarke and Trowill, 1971; Chang, 1992). Specifically, high-frequency sniffing (8–12 Hz) develops in anticipation of reward delivery (Freeman et al., 1983; Monod et al., 1989; Kepecs et al., 2007; Wesson et al., 2008). The olfactory bulb theta rhythm and sniffing are not usually coherent with the hippocampal rhythm. However, in some odor-based learning tasks these rhythms do become transiently coherent (Macrides et al., 1982; Kay, 2005; Martin et al., 2007), a process that was suggested to be mediated by cholinergic neurons in the medial septum (Tsanov et al., 2014). Interestingly, whisking was shown to get occasionally phase locked with the sniff cycle (Cao et al., 2012; Ranade et al., 2013) or with the hippocampal theta rhythm (Komisaruk, 1970) during exploratory behavior. Thus, various generators of theta rhythmicity in the brain, such as those reflected by sniffing, whisking, or the hippocampal theta rhythm may become dynamically coupled by the brain neuromodulatory systems. While we did not monitor sniffing in our experiments, several recent studies reported changes in sniffing during both social interactions (Assini et al., 2013; Wesson, 2013) and fear conditioning (Shionoya et al., 2013). These studies showed that the sniff cycle adopt high-range theta rhythmicity during social interactions and low-range rhythmicity during fear conditioning. These differences are probably reflected by the distinct rates of theta rhythmicity that we record in the MOB and AOB during these conditions. This may explain our observation of high coherence between MOB-AOB and the low coherence each of them display with all other regions. Moreover, while the coherence between the MOB-AOB is increased during exploration of both social and object stimuli, the coherence between the LS–MeA increases only during social interactions. Thus, the theta rhythmicity displayed by the AOB and MOB probably emerges from a distinct generator, most likely the sniff cycle, that is separate from the one causing rhythmicity in higher brain areas. Furthermore, the significant differences in correlation and coherence dynamics between the various limbic areas suggest the involvement of distinct generators as well. For example, neither paradigm showed significant coherence changes between the LS-NAcc, as opposed to a significant increase in coherence between the LS-MeA or LS-Pir during social interactions. It should be noted that these differences cannot not be accounted for by local diffusion of LFP signals, since the LS is much closer to the NAcc than to the MeA or Pir. Direct synaptic connections cannot explain these differences either as the MeA shows very low coherence with the AOB, despite the strong bidirectional connections between them, but rather displays the highest coherence with the contralateral MeA, despite the lack of direct synaptic pathway (Canteras et al., 1995). Therefore, the differential coherence changes between distinct pairs of brain regions during the various conditions are most likely mediated by either a common input to these regions or via brain-region specific neuromudulatory systems. However, the arousal-driven modulation of theta rhythmicity which seems to be common to all brain regions is probably mediated by a general, brain-wide neuromodulatory mechanism such as neurohormonal activity (Lee and Dan, 2012; Marder, 2012).

An ever growing body of evidence implies rhythmic brain activity in various cognitive processes, particularly in memory acquisition and recall (Fell and Axmacher, 2011; Buzsáki and Watson, 2012; Cannon et al., 2014). Specifically, slow frequency rhythms such as the theta rhythm, are hypothesized to mediate communication between brain regions and to promote the temporal binding of neural assemblies in these areas into coherent networks subserving specific cognitive processes (Buzsáki and Draguhn, 2004; Jutras and Buffalo, 2010; Benchenane et al., 2011; Fell and Axmacher, 2011). During the last decade, several prominent theories implied a disordered or weak communication among brain regions as a major deficit underlying ASD etiology and symptoms (Brock et al., 2002; Uhlhaas and Singer, 2006; Geschwind and Levitt, 2007; Kana et al., 2011; Wass, 2011). Indeed, multiple recent studies found reduction in the power and coherence of slow brain rhythms, such as the alpha and theta rhythms, in ASD individuals (Murias et al., 2007; Coben et al., 2008; Isler et al., 2010; Barttfeld et al., 2013; Doesburg et al., 2013; Machado et al., 2013; Kikuchi et al., 2015). In agreement with these findings, our results suggest that arousal-driven theta rhythmicity may help bind correlated neuronal assemblies in distinct brain areas participating in cognitive and emotional processes underlying social behavior. A disruption of the correlated neuronal activity associated with the theta rhythmicity is likely to impair these processes (Uhlhaas and Singer, 2006; Geschwind and Levitt, 2007; Buzsáki and Watson, 2012) resulting in atypical social behaviors.

1. 1. Adhikari A
   2. Topiwala MA
   3. Gordon JA

   (2010) Synchronized activity between the ventral hippocampus and the medial prefrontal cortex during anxiety

   Neuron 65:257–269.

   https://doi.org/10.1016/j.neuron.2009.12.002

   • Google Scholar
2. 1. Aftanas LI
   2. Varlamov AA
   3. Pavlov SV
   4. Makhnev VP
   5. Reva NV

   (2001) Affective picture processing: event-related synchronization within individually defined human theta band is modulated by valence dimension

   Neuroscience Letters 303:115–118.

   https://doi.org/10.1016/S0304-3940(01)01703-7

   • Google Scholar
3. 1. Assini R
   2. Sirotin YB
   3. Laplagne DA

   (2013) Rapid triggering of vocalizations following social interactions

   Current Biology 23:R996–R997.

   https://doi.org/10.1016/j.cub.2013.10.007

   • Google Scholar
4. 1. Balconi M
   2. Pozzoli U

   (2009) Arousal effect on emotional face comprehension: frequency band changes in different time intervals

   Physiology & Behavior 97:455–462.

   https://doi.org/10.1016/j.physbeh.2009.03.023

   • Google Scholar
5. 1. Barttfeld P
   2. Amoruso L
   3. Ais J
   4. Cukier S
   5. Bavassi L
   6. Tomio A
   7. Manes F
   8. Ibanez A
   9. Sigman M

   (2013) Organization of brain networks governed by long-range connections index autistic traits in the general population

   Journal of Neurodevelopmental Disorders 5:16.

   https://doi.org/10.1186/1866-1955-5-16

   • Google Scholar
6. 1. Benchenane K
   2. Tiesinga PH
   3. Battaglia FP

   (2011) Oscillations in the prefrontal cortex: a gateway to memory and attention

   Current Opinion in Neurobiology 21:475–485.

   https://doi.org/10.1016/j.conb.2011.01.004

   • Google Scholar
7. 1. Bielsky IF
   2. Young LJ

   (2004) Oxytocin, vasopressin, and social recognition in mammals

   Peptides 25:1565–1574.

   https://doi.org/10.1016/j.peptides.2004.05.019

   • Google Scholar
8. 1. Bland BH

   (1986) The physiology and pharmacology of hippocampal formation theta rhythms

   Progress in Neurobiology 26:1–54.

   https://doi.org/10.1016/0301-0082(86)90019-5

   • Google Scholar
9. 1. Bland BH
   2. Colom LV

   (1993) Extrinsic and intrinsic properties underlying oscillation and synchrony in limbic cortex

   Progress in Neurobiology 41:157–208.

   https://doi.org/10.1016/0301-0082(93)90007-F

   • Google Scholar
10. 1. Bland BH
    2. Oddie SD

    (2001) Theta band oscillation and synchrony in the hippocampal formation and associated structures: the case for its role in sensorimotor integration

    Behavioural Brain Research 127:119–136.

    https://doi.org/10.1016/S0166-4328(01)00358-8

    • Google Scholar
11. 1. Brock J
    2. Brown CC
    3. Boucher J
    4. Rippon G

    (2002) The temporal binding deficit hypothesis of autism

    Development and Psychopathology 14:209–224.

    https://doi.org/10.1017/S0954579402002018

    • Google Scholar
12. 1. Buzsáki G

    (2002) Theta oscillations in the hippocampus

    Neuron 33:325–340.

    https://doi.org/10.1016/S0896-6273(02)00586-X

    • Google Scholar
13. 1. Buzsáki G
    2. Draguhn A

    (2004) Neuronal oscillations in cortical networks

    Science 304:1926–1929.

    https://doi.org/10.1126/science.1099745

    • Google Scholar
14. 1. Buzsáki G
    2. Watson BO

    (2012)

    Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease

    Dialogues in Clinical Neuroscience 14:345–367.

    • Google Scholar
15. 1. Cannon J
    2. McCarthy MM
    3. Lee S
    4. Lee J
    5. Borgers C
    6. Whittington MA
    7. Kopell N

    (2014) Neurosystems: brain rhythms and cognitive processing

    The European Journal of Neuroscience 39:705–719.

    https://doi.org/10.1111/ejn.12453

    • Google Scholar
16. 1. Canteras NS
    2. Simerly RB
    3. Swanson LW

    (1995) Organization of projections from the medial nucleus of the amygdala: a PHAL study in the rat

    The Journal of Comparative Neurology 360:213–245.

    https://doi.org/10.1002/cne.903600203

    • Google Scholar
17. 1. Cao Y
    2. Roy S
    3. Sachdev RN
    4. Heck DH

    (2012) Dynamic correlation between whisking and breathing rhythms in mice

    The Journal of Neuroscience 32:1653–1659.

    https://doi.org/10.1523/JNEUROSCI.4395-11.2012

    • Google Scholar
18. 1. Chang FC

    (1992) Modification of medullary respiratory-related discharge patterns by behaviors and states of arousal

    Brain Research 571:281–292.

    https://doi.org/10.1016/0006-8993(92)90666-W

    • Google Scholar
19. 1. Clarke S
    2. Trowill JA

    (1971) Sniffing and motivated behavior in the rat

    Physiology & Behavior 6:49–52.

    https://doi.org/10.1016/0031-9384(71)90013-8

    • Google Scholar
20. 1. Coben R
    2. Clarke AR
    3. Hudspeth W
    4. Barry RJ

    (2008) EEG power and coherence in autistic spectrum disorder

    Clinical Neurophysiology 119:1002–1009.

    https://doi.org/10.1016/j.clinph.2008.01.013

    • Google Scholar
21. 1. Cohen Y
    2. Wilson DA
    3. Barkai E

    (2013) Differential modifications of synaptic weights during odor rule learning: dynamics of interaction between the piriform cortex with lower and higher brain areas

    Cerebral Cortex 25:180–191.

    https://doi.org/10.1093/cercor/bht215

    • Google Scholar
22. 1. Doesburg SM
    2. Vidal J
    3. Taylor MJ

    (2013) Reduced theta connectivity during set-shifting in children with autism

    Frontiers in Human Neuroscience 7:785.

    https://doi.org/10.3389/fnhum.2013.00785

    • Google Scholar
23. 1. Dölen G
    2. Darvishzadeh A
    3. Huang KW
    4. Malenka RC

    (2013) Social reward requires coordinated activity of nucleus accumbens oxytocin and serotonin

    Nature 501:179–184.

    https://doi.org/10.1038/nature12518

    • Google Scholar
24. 1. Dulac C
    2. Torello AT

    (2003) Molecular detection of pheromone signals in mammals: from genes to behaviour

    Nature Reviews Neuroscience 4:551–562.

    https://doi.org/10.1038/nrn1140

    • Google Scholar
25. 1. Dulac C
    2. Wagner S

    (2006) Genetic analysis of brain circuits underlying pheromone signaling

    Annual Review of Genetics 40:449–467.

    https://doi.org/10.1146/annurev.genet.39.073003.093937

    • Google Scholar
26. 1. Fell J
    2. Axmacher N

    (2011) The role of phase synchronization in memory processes

    Nature Reviews Neuroscience 12:105–118.

    https://doi.org/10.1038/nrn2979

    • Google Scholar
27. 1. Fendt M
    2. Endres T
    3. Apfelbach R

    (2003)

    Temporary inactivation of the bed nucleus of the stria terminalis but not of the amygdala blocks freezing induced by trimethylthiazoline, a component of fox feces

    The Journal of Neuroscience 23:23–28.

    • Google Scholar
28. 1. Ferguson JN
    2. Young LJ
    3. Insel TR

    (2002) The neuroendocrine basis of social recognition

    Frontiers in Neuroendocrinology 23:200–224.

    https://doi.org/10.1006/frne.2002.0229

    • Google Scholar
29. 1. Freeman WJ
    2. Viana Di Prisco G
    3. Davis GW
    4. Whitney TM

    (1983) Conditioning of relative frequency of sniffing by rabbits to odors

    Journal of Comparative Psychology 97:12–23.

    https://doi.org/10.1037/0735-7036.97.1.12

    • Google Scholar
30. 1. Geschwind DH

    (2009) Advances in autism

    Annual Review of Medicine 60:367–380.

    https://doi.org/10.1146/annurev.med.60.053107.121225

    • Google Scholar
31. 1. Geschwind DH
    2. Levitt P

    (2007) Autism spectrum disorders: developmental disconnection syndromes

    Current Opinion in Neurobiology 17:103–111.

    https://doi.org/10.1016/j.conb.2007.01.009

    • Google Scholar
32. 1. Gheusi G
    2. Bluthé RM
    3. Goodall G
    4. Dantzer R

    (1994) Social and individual recognition in rodents: Methodological aspects and neurobiological bases

    Behavioural Processes 33:59–88.

    https://doi.org/10.1016/0376-6357(94)90060-4

    • Google Scholar
33. 1. Goutagny R
    2. Jackson J
    3. Williams S

    (2009) Self-generated theta oscillations in the hippocampus

    Nature Neuroscience 12:1491–1493.

    https://doi.org/10.1038/nn.2440

    • Google Scholar
34. 1. Gunaydin LA
    2. Grosenick L
    3. Finkelstein JC
    4. Kauvar IV
    5. Fenno LE
    6. Adhikari A
    7. Lammel S
    8. Mirzabekov JJ
    9. Airan RD
    10. Zalocusky KA
    11. Tye KM
    12. Anikeeva P
    13. Malenka RC
    14. Deisseroth K

    (2014) Natural neural projection dynamics underlying social behavior

    Cell 157:1535–1551.

    https://doi.org/10.1016/j.cell.2014.05.017

    • Google Scholar
35. 1. Gur R
    2. Tendler A
    3. Wagner S

    (2014) Long-term social recognition memory is mediated by oxytocin-dependent synaptic plasticity in the medial amygdala

    Biological Psychiatry 76:377–386.

    https://doi.org/10.1016/j.biopsych.2014.03.022

    • Google Scholar
36. 1. Isler JR
    2. Martien KM
    3. Grieve PG
    4. Stark RI
    5. Herbert MR

    (2010) Reduced functional connectivity in visual evoked potentials in children with autism spectrum disorder

    Clinical Neurophysiology 121:2035–2043.

    https://doi.org/10.1016/j.clinph.2010.05.004

    • Google Scholar
37. 1. Jacinto LR
    2. Reis JS
    3. Dias NS
    4. Cerqueira JJ
    5. Correia JH
    6. Sousa N

    (2013) Stress affects theta activity in limbic networks and impairs novelty-induced exploration and familiarization

    Frontiers in Behavioral Neuroscience 7:127.

    https://doi.org/10.3389/fnbeh.2013.00127

    • Google Scholar
38. 1. Jutras MJ
    2. Buffalo EA

    (2010) Synchronous neural activity and memory formation

    Current Opinion in Neurobiology 20:150–155.

    https://doi.org/10.1016/j.conb.2010.02.006

    • Google Scholar
39. 1. Kana RK
    2. Libero LE
    3. Moore MS

    (2011) Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders

    Physics of Life Reviews 8:410–437.

    https://doi.org/10.1016/j.plrev.2011.10.001

    • Google Scholar
40. 1. Kang N
    2. Baum MJ
    3. Cherry JA

    (2011) Different profiles of main and accessory olfactory bulb mitral/tufted cell projections revealed in mice using an anterograde tracer and a whole-mount, flattened cortex preparation

    Chemical Senses 36:251–260.

    https://doi.org/10.1093/chemse/bjq120

    • Google Scholar
41. 1. Kay LM

    (2005) Theta oscillations and sensorimotor performance

    Proceedings of the National Academy of Sciences of USA 102:3863–3868.

    https://doi.org/10.1073/pnas.0407920102

    • Google Scholar
42. 1. Kepecs A
    2. Uchida N
    3. Mainen ZF

    (2006) The sniff as a unit of olfactory processing

    Chemical Senses 31:167–179.

    https://doi.org/10.1093/chemse/bjj016

    • Google Scholar
43. 1. Kepecs A
    2. Uchida N
    3. Mainen ZF

    (2007) Rapid and precise control of sniffing during olfactory discrimination in rats

    Journal of Neurophysiology 98:205–213.

    https://doi.org/10.1152/jn.00071.2007

    • Google Scholar
44. 1. Kikuchi M
    2. Yoshimura Y
    3. Hiraishi H
    4. Munesue T
    5. Hashimoto T
    6. Tsubokawa T
    7. Takahashi T
    8. Suzuki M
    9. Higashida H
    10. Minabe Y

    (2015) Reduced long-range functional connectivity in young children with autism spectrum disorder

    Social Cognitive and Affective Neuroscience 10:248–254.

    https://doi.org/10.1093/scan/nsu049

    • Google Scholar
45. 1. Knyazev GG

    (2007) Motivation, emotion, and their inhibitory control mirrored in brain oscillations

    Neuroscience and Biobehavioral Reviews 31:377–395.

    https://doi.org/10.1016/j.neubiorev.2006.10.004

    • Google Scholar
46. 1. Knyazev GG
    2. Slobodskoj-Plusnin JY
    3. Bocharov AV

    (2009) Event-related delta and theta synchronization during explicit and implicit emotion processing

    Neuroscience 164:1588–1600.

    https://doi.org/10.1016/j.neuroscience.2009.09.057

    • Google Scholar
47. 1. Komisaruk BR

    (1970) Synchrony between limbic system theta activity and rhythmical behavior in rats

    Journal of comparative and physiological psychology 70:482–492.

    https://doi.org/10.1037/h0028709

    • Google Scholar
48. 1. Lee SH
    2. Dan Y

    (2012) Neuromodulation of brain states

    Neuron 76:209–222.

    https://doi.org/10.1016/j.neuron.2012.09.012

    • Google Scholar
49. 1. Lesting J
    2. Daldrup T
    3. Narayanan V
    4. Himpe C
    5. Seidenbecher T
    6. Pape HC

    (2013) Directional theta coherence in prefrontal cortical to amygdalo-hippocampal pathways signals fear extinction

    PLOS ONE 8:e77707.

    https://doi.org/10.1371/journal.pone.0077707

    • Google Scholar
50. 1. Liebe S
    2. Hoerzer GM
    3. Logothetis NK
    4. Rainer G

    (2012) Theta coupling between V4 and prefrontal cortex predicts visual short-term memory performance

    Nature Neuroscience 15:456–462, S1–S2.

    https://doi.org/10.1038/nn.3038

    • Google Scholar
51. 1. Luo Q
    2. Cheng X
    3. Holroyd T
    4. Xu D
    5. Carver F
    6. Blair RJ

    (2013) Theta band activity in response to emotional expressions and its relationship with gamma band activity as revealed by MEG and advanced beamformer source imaging

    Frontiers in Human Neuroscience 7:940.

    https://doi.org/10.3389/fnhum.2013.00940

    • Google Scholar
52. 1. Machado C
    2. Estévez M
    3. Leisman G
    4. Melillo R
    5. Rodriguez R
    6. Defina P
    7. Hernández A
    8. Pérez-Nellar J
    9. Naranjo R
    10. Chinchilla M
    11. Garófalo N
    12. Vargas J
    13. Beltrán C

    (2013) QEEG spectral and coherence assessment of autistic children in three different experimental conditions

    Journal of Autism and Developmental Disorders 45:406–424.

    https://doi.org/10.1007/s10803-013-1909-5

    • Google Scholar
53. 1. Macrides F
    2. Eichenbaum HB
    3. Forbes WB

    (1982)

    Temporal relationship between sniffing and the limbic theta rhythm during odor discrimination reversal learning

    The Journal of Neuroscience 2:1705–1717.

    • Google Scholar
54. 1. Maratos FA
    2. Mogg K
    3. Bradley BP
    4. Rippon G
    5. Senior C

    (2009) Coarse threat images reveal theta oscillations in the amygdala: a magnetoencephalography study

    Cognitive, Affective & Behavioral Neuroscience 9:133–143.

    https://doi.org/10.3758/CABN.9.2.133

    • Google Scholar
55. 1. Marder E

    (2012) Neuromodulation of neuronal circuits: back to the future

    Neuron 76:1–11.

    https://doi.org/10.1016/j.neuron.2012.09.010

    • Google Scholar
56. 1. Martin C
    2. Beshel J
    3. Kay LM

    (2007) An olfacto-hippocampal network is dynamically involved in odor-discrimination learning

    Journal of Neurophysiology 98:2196–2205.

    https://doi.org/10.1152/jn.00524.2007

    • Google Scholar
57. 1. Mitra P
    2. Bokil H

    (2008)

    Observed brain dynamics

    Observed brain dynamics, Oxford, NY, Oxford University Press, xxii, 381 p. p.

    • Google Scholar
58. 1. Monod B
    2. Mouly AM
    3. Vigouroux M
    4. Holley A

    (1989) An investigation of some temporal aspects of olfactory coding with the model of multi-site electrical stimulation of the olfactory bulb in the rat

    Behavioural Brain Research 33:51–63.

    https://doi.org/10.1016/S0166-4328(89)80018-X

    • Google Scholar
59. 1. Murias M
    2. Webb SJ
    3. Greenson J
    4. Dawson G

    (2007) Resting state cortical connectivity reflected in EEG coherence in individuals with autism

    Biological Psychiatry 62:270–273.

    https://doi.org/10.1016/j.biopsych.2006.11.012

    • Google Scholar
60. 1. Narayanan RT
    2. Seidenbecher T
    3. Kluge C
    4. Bergado J
    5. Stork O
    6. Pape HC

    (2007) Dissociated theta phase synchronization in amygdalo- hippocampal circuits during various stages of fear memory

    The European Journal of Neuroscience 25:1823–1831.

    https://doi.org/10.1111/j.1460-9568.2007.05437.x

    • Google Scholar
61. 1. Pape HC
    2. Narayanan RT
    3. Smid J
    4. Stork O
    5. Seidenbecher T

    (2005) Theta activity in neurons and networks of the amygdala related to long-term fear memory

    Hippocampus 15:874–880.

    https://doi.org/10.1002/hipo.20120

    • Google Scholar
62. 1. Pape HC
    2. Pare D

    (2010) Plastic synaptic networks of the amygdala for the acquisition, expression, and extinction of conditioned fear

    Physiological Reviews 90:419–463.

    https://doi.org/10.1152/physrev.00037.2009

    • Google Scholar
63. 1. Paré D
    2. Collins DR

    (2000)

    Neuronal correlates of fear in the lateral amygdala: multiple extracellular recordings in conscious cats

    The Journal of Neuroscience 20:2701–2710.

    • Google Scholar
64. 1. Paré D
    2. Collins DR
    3. Pelletier JG

    (2002) Amygdala oscillations and the consolidation of emotional memories

    Trends in Cognitive Sciences 6:306–314.

    https://doi.org/10.1016/S1364-6613(02)01924-1

    • Google Scholar
65. Book

    1. Paxinos G
    2. Watson C

    (2007)

    The rat brain in stereotaxic coordinates (6th edition)

    Amsterdam, Boston: Academic Press/Elsevier.

    • Google Scholar
66. 1. Pelletier JG
    2. Paré D

    (2004) Role of amygdala oscillations in the consolidation of emotional memories

    Biological Psychiatry 55:559–562.

    https://doi.org/10.1016/j.biopsych.2003.08.019

    • Google Scholar
67. 1. Pignatelli M
    2. Beyeler A
    3. Leinekugel X

    (2012) Neural circuits underlying the generation of theta oscillations

    Journal of Physiology 106:81–92.

    https://doi.org/10.1016/j.jphysparis.2011.09.007

    • Google Scholar
68. 1. Popa D
    2. Duvarci S
    3. Popescu AT
    4. Léna C
    5. Pare D

    (2010) Coherent amygdalocortical theta promotes fear memory consolidation during paradoxical sleep

    Proceedings of the National Academy of Sciences of USA 107:6516–6519.

    https://doi.org/10.1073/pnas.0913016107

    • Google Scholar
69. 1. Pro-Sistiaga P
    2. Mohedano-Moriano A
    3. Ubeda-Banon I
    4. Del Mar Arroyo-Jimenez M
    5. Marcos P
    6. Artacho-Perula E
    7. Artacho-Pérula E
    8. Crespo C
    9. Insausti R
    10. Martinez-Marcos A

    (2007) Convergence of olfactory and vomeronasal projections in the rat basal telencephalon

    The Journal of Comparative Neurology 504:346–362.

    https://doi.org/10.1002/cne.21455

    • Google Scholar
70. 1. Ranade S
    2. Hangya B
    3. Kepecs A

    (2013) Multiple modes of phase locking between sniffing and whisking during active exploration

    The Journal of Neuroscience 33:8250–8256.

    https://doi.org/10.1523/JNEUROSCI.3874-12.2013

    • Google Scholar
71. 1. Rippon G
    2. Brock J
    3. Brown C
    4. Boucher J

    (2007) Disordered connectivity in the autistic brain: challenges for the ‘new psychophysiology’

    International Journal of Psychophysiology 63:164–172.

    https://doi.org/10.1016/j.ijpsycho.2006.03.012

    • Google Scholar
72. 1. Rogan MT
    2. Stäubli UV
    3. LeDoux JE

    (1997) Fear conditioning induces associative long-term potentiation in the amygdala

    Nature 390:604–607.

    https://doi.org/10.1038/37601

    • Google Scholar
73. 1. Rojas-Libano D
    2. Frederick DE
    3. Egana JI
    4. Kay LM

    (2014) The olfactory bulb theta rhythm follows all frequencies of diaphragmatic respiration in the freely behaving rat

    Frontiers in Behavioral Neuroscience 8:214.

    https://doi.org/10.3389/fnbeh.2014.00214

    • Google Scholar
74. 1. Sainsbury RS

    (1998) Hippocampal theta: a sensory-inhibition theory of function

    Neuroscience and Biobehavioral Reviews 22:237–241.

    https://doi.org/10.1016/S0149-7634(97)00011-0

    • Google Scholar
75. 1. Sainsbury RS
    2. Harris JL
    3. Rowland GL

    (1987a) Sensitization and hippocampal type 2 theta in the rat

    Physiology & Behavior 41:489–493.

    https://doi.org/10.1016/0031-9384(87)90085-0

    • Google Scholar
76. 1. Sainsbury RS
    2. Heynen A
    3. Montoya CP

    (1987b) Behavioral correlates of hippocampal type 2 theta in the rat

    Physiology & Behavior 39:513–519.

    https://doi.org/10.1016/0031-9384(87)90382-9

    • Google Scholar
77. 1. Sainsbury RS
    2. Montoya CP

    (1984) The relationship between type 2 theta and behavior

    Physiology & Behavior 33:621–626.

    https://doi.org/10.1016/0031-9384(84)90381-0

    • Google Scholar
78. 1. Schmidt B
    2. Hinman JR
    3. Jacobson TK
    4. Szkudlarek E
    5. Argraves M
    6. Escabi MA
    7. Markus EJ

    (2013) Dissociation between dorsal and ventral hippocampal theta oscillations during decision-making

    Journal of Neuroscience 33:6212–6224.

    https://doi.org/10.1523/JNEUROSCI.2915-12.2013

    • Google Scholar
79. 1. Seidenbecher T
    2. Laxmi TR
    3. Stork O
    4. Pape HC

    (2003) Amygdalar and hippocampal theta rhythm synchronization during fear memory retrieval

    Science 301:846–850.

    https://doi.org/10.1126/science.1085818

    • Google Scholar
80. 1. Shionoya K
    2. Hegoburu C
    3. Brown BL
    4. Sullivan RM
    5. Doyere V
    6. Mouly AM

    (2013) It's time to fear! Interval timing in odor fear conditioning in rats

    Frontiers in Behavioral Neuroscience 7:128.

    https://doi.org/10.3389/fnbeh.2013.00128

    • Google Scholar
81. 1. Takahashi LK
    2. Chan MM
    3. Pilar ML

    (2008) Predator odor fear conditioning: current perspectives and new directions

    Neuroscience and Biobehavioral Reviews 32:1218–1227.

    https://doi.org/10.1016/j.neubiorev.2008.06.001

    • Google Scholar
82. 1. Thomson DJ

    (1982)

    Spectrum Estimation and Harmonic-Analysis

    P Ieee 70:1055–1096.

    • Google Scholar
83. 1. Tsanov M
    2. Chah E
    3. Reilly R
    4. O'Mara SM

    (2014) Respiratory cycle entrainment of septal neurons mediates the fast coupling of sniffing rate and hippocampal theta rhythm

    The European Journal of Neuroscience 39:957–974.

    https://doi.org/10.1111/ejn.12449

    • Google Scholar
84. 1. Uhlhaas PJ
    2. Pipa G
    3. Lima B
    4. Melloni L
    5. Neuenschwander S
    6. Nikolić D
    7. Singer W

    (2009) Neural synchrony in cortical networks: history, concept and current status

    Frontiers in Integrative Neuroscience 3:17.

    https://doi.org/10.3389/neuro.07.017.2009

    • Google Scholar
85. 1. Uhlhaas PJ
    2. Singer W

    (2006) Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology

    Neuron 52:155–168.

    https://doi.org/10.1016/j.neuron.2006.09.020

    • Google Scholar
86. 1. Wass S

    (2011) Distortions and disconnections: disrupted brain connectivity in autism

    Brain and Cognition 75:18–28.

    https://doi.org/10.1016/j.bandc.2010.10.005

    • Google Scholar
87. 1. Wesson DW

    (2013) Sniffing behavior communicates social hierarchy

    Current Biology 23:575–580.

    https://doi.org/10.1016/j.cub.2013.02.012

    • Google Scholar
88. 1. Wesson DW
    2. Donahou TN
    3. Johnson MO
    4. Wachowiak M

    (2008) Sniffing behavior of mice during performance in odor-guided tasks

    Chemical Senses 33:581–596.

    https://doi.org/10.1093/chemse/bjn029

    • Google Scholar
89. 1. Wiley RH

    (2013) Specificity and multiplicity in the recognition of individuals: implications for the evolution of social behaviour

    Biological Reviews of the Cambridge Philosophical Society 88:179–195.

    https://doi.org/10.1111/j.1469-185X.2012.00246.x

    • Google Scholar
90. 1. Wilson DA
    2. Sullivan RM

    (2011) Cortical processing of odor objects

    Neuron 72:506–519.

    https://doi.org/10.1016/j.neuron.2011.10.027

    • Google Scholar

Author details

1. Alex Tendler

   Sagol Department of Neurobiology, University of Haifa, Haifa, Israel

   Contribution

   AT, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Shlomo Wagner

   Sagol Department of Neurobiology, University of Haifa, Haifa, Israel

   Contribution

   SW, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   shlomow@research.haifa.ac.il

   Competing interests

   The authors declare that no competing interests exist.

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