compared the sequence of NHE9 in several hundred autistic subjects from non-consan-guineous families and controls, and show that single base-pair mutations in the gene are associated with autism. Their finding therefore further supports a role for NHE9 in increasing the susceptibility to this disorder.

The symptoms of autism include commu-nication and social deficits, and restricted and repetitive behaviour. Clinical features can vary widely between individuals, and may include epilepsy or developmental regression after a period of apparently normal development. To simplify their analysis of NHE9, Morrow and colleagues focused on a subset of individuals who suffered from autism-associated epilepsy. It is thought that a subset of individuals who also have a particular additional disease feature (phenotype) in common might form a more genetically similar group. Unlike most of the autistic subjects without epilepsy, these indi-viduals showed a more than fivefold increase in potentially harmful base-pair changes in the NHE9 gene sequence, including a stop codon. Therefore, NHE9 may be added to the list of genes that are associated with certain subsets of autism6.

Morrow and co-workers’ observations7 raise some intriguing questions. Five of the six homozygous deletions identified in the HMCA subjects lie in regions outside the cod-ing sequence of the implicated genes, and so are unlikely to disrupt the encoded protein sequence. Several of the deletions might dis-rupt presumptive regulatory regions, whereas another 320-kilobase deletion closest to the PCDH10 gene, which encodes the PCD10 (protocadherin 10) protein, does not lie in a coding region or in a sequence of known func-tion. The effect of these deletions on neigh-bouring genes, and any causal role in autism, therefore require further investigation. Also, the causal nature of the NHE9 mutations, which result in potentially deleterious changes at the protein level, must be established, because similar changes are observed in control subjects, albeit less frequently.

The difficulty in identifying mutations that are definitely pathogenic is an emerging theme in common neuropsychiatric disorders. Mor-row and colleagues’ study demonstrates that studying consanguineous families is a fruitful path to identifying autism genes, but also that such analysis is more challenging than might initially have been expected. This is partly due to the underlying genetic heterogeneity of autism, a facet underscored by this study. The authors find that no single recessive mutation is associated with autism in more than one family, even in consanguineous families from the same geographic region.

Luckily, advances in DNA sequencing tech-nology mean that even large genomic regions can be efficiently sequenced, and work to identify recessive mutations in single base pairs within the genomes of subjects from the HMCA group might already be under way.

Sequencing shared regions in these families could be even more informative because many mutations in these regions are probably small — perhaps single base-pair mutations — and thus might more directly implicate specific genes.

Convergent evidence from other studies, including functional and gene -expression data, will be crucial in assessing the relevance of candidate genes to autism10. Morrow et al. embrace this notion, as their putative autism susceptibility genes represent diverse func-tional classes, which at face value do not suggest common molecular mechanisms. To provide a potential functional context, they show that three of the genes — either near (PCDH10) or overlapping (c30rf58 and NHE9) the deletions — are among a significant subset that is associated with a form of neuronal activity thought to be involved in learning and memory11.

The authors suggest that investigating such genes might provide a way of unifying seem-ingly diverse molecular findings in autism. Given the nearly two dozen genes that are now associated with the disorder6, this should be a readily testable hypothesis. The way in which functional data relate to the core deficits of autism is not known, because many aspects of learning and memory are preserved in autis-tic children. The idea that autism might rep-resent a general deficit in neuronal plasticity caused by changes in gene expression needs to be reconciled with the specificity of cognitive

dysfunction observed in the condition12, as well as the preservation of general intellectual ability in many individuals. This aspect high-lights a central challenge of research in this area: connecting basic molecular pathways to the properties of complex neural circuits and cognition in humans. Spanning these diverse levels of function will require detailed analysis of the correlation between the genetic composition of individuals and their associ-ated phenotype across the spectrum of autism disorders. ■

Daniel H. Geschwind is in the Program in Neurogenetics, Department of Neurology, and at the Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, California 90095–1769, USA.e-mail: dhg@mednet.ucla.edu

1. Peltonen, L., Perola, M., Naukkarinen, J. & Palotie, A. Hum. Mol. Genet. 15, R67–R74 (2006).

2. Jamain, S. et al. Nature Genet. 34, 27–29 (2003).3. Sebat, J. et al. Science 316, 445–449 (2007).4. Jacquemont, M. L. et al. J. Med. Genet. 43, 843–849

(2006).5. Strauss, K. A. et al. N. Engl. J. Med. 354, 1370–1377 (2006).6. Abrahams, B. S. & Geschwind, D. H. Nature Rev. Genet. 9,

341–355 (2008).7. Morrow, E. M. et al. Science 321, 218–223 (2008).8. Lander, E. S. & Botstein, D. Science 236, 1567–1570 (1987).9. Marshall, C. R. et al. Am. J. Hum. Genet. 82, 477–488

(2008).10. Alarcón, M. et al. Am. J. Hum. Genet. 82, 150–159 (2008).11. Flavell, S. W. & Greenberg, M. E. Annu. Rev. Neurosci. 31,

563–590 (2008). 12. Geschwind, D. H. & Levitt, P. Curr. Opin. Neurobiol. 17,

103–111 (2007).

NEUROSCIENCE

State-sanctioned synchronyScott J. Cruikshank and Barry W. Connors

A sleepy brain pays little attention to its surroundings, and its neurons are lulled by a common oscillation. As the brain swiftly rouses from this sluggish state, its neurons function more independently.

You know the experience. One moment you are drowsy and unfocused, awake but oblivi-ous to your environment. Suddenly, something snaps you into awareness. Whether the trigger is external, like the sight or sound of someone entering the room, or internal, such as the thought of a looming deadline, you become immediately alert. Your ability to deal with most tasks, not just the stimulus that aroused you, improves, suggesting a change in your overall ‘brain state’.

Understanding the neural mechanisms of brain states has been a goal of neuroscientists for decades1–3. One long-lived suggestion is that brain states relate to the degree to which neuronal activity in the cerebral cortex is syn-chronized4. On page 881 of this issue, Poulet and Petersen5 directly address this issue, show-ing, in awake mice, that the electrical activity

of adjacent neurons becomes less synchronous when the brain enters an active state.

The long whiskers on a rodent’s snout are important sensory organs. When mice are on the go, they sweep their whiskers through the local environment to feel and identify objects, and to navigate in darkness6. Poulet and Petersen5 studied brain activity in mice during periods of active ‘whisking’, when the animals rhythmically waved their whiskers back and forth, and compared this with periods of ‘quiet wakefulness’, when the mice (and their whisk-ers) were almost motionless (Fig. 1a). The authors managed the technical feat of making simultaneous intracellular recordings from pairs of cortical neurons in awake, unanaes-thetized animals. Intracellular recordings allow measurement not only of action potentials (the ‘firing’ of neurons, signalling their output), but

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also of smaller electrical fluctuations, which mainly reflect inputs from other nerve cells. Poulet and Petersen5 found that these sub-threshold fluctuations are highly correlated among cortical cells during quiet wakefulness and relatively desynchronized during whisk-ing (Fig. 1b).

A correspondence between quiet brain states and strong cortical synchrony was the mainstream view of neuroscientists from the earliest days of electroencephalography (EEG, a method for recording extracellular voltages from the surface of the skull) until about 20 years ago. EEG pioneers referred to the large slow-waves associated with restful behaviour as ‘synchronized’. Signals from individual neurons are tiny, so it was believed that syn-chronous activity of many neurons would be needed to give large measurable signals at EEG electrodes located outside the skull4,7. More recently, simultaneous EEG and intracellular recordings within the cortex during quiescent states (anaesthesia, sleep and quiet wakeful-ness) have demonstrated that slow oscilla-tions of intracellular membrane potentials correlate well with EEG oscillations. More-over, when brain states become active, either sponta neously or after stimulation, slow EEG waves and membrane-potential fluctuations diminish simultaneously8–10.

Because the EEG and the membrane poten-tials of most cortical neurons are strongly correlated, it seems very likely that slow mem-brane-potential oscillations are synchronized among cortical cells during quiescent states. In fact, synchrony among pairs of neurons has already been directly demonstrated in anaes-thetized animals11,12. However, anaesthetics have powerful and poorly understood effects on neural circuits, and the cellular properties of the normally behaving brain have been par-ticularly difficult to measure. This is why the work of Poulet and Petersen, measuring mem-brane potentials from pairs of cortical neurons in awake animals, is so important.

The decrease in membrane-potential cor-relation, observed when mice switched from quiet wakefulness to active whisking, was mainly due to decreased synchrony of low-frequency oscillations. During whisking, there was no obvious increase in relatively high-frequency (30–100 hertz) oscillations known as ‘gamma’ rhythms, or correlations of the same. This seems at odds with several previ-ous studies of cortical activation (including one from the Petersen group8). In fact, since the late 1980s, an extensive literature has reported that activated brain states are accompanied by enhancement of gamma rhythms and their synchrony13–16.

The absence of enhanced gamma rhythms in the current study may reflect the ambiguous nature of the whisking state in these experi-ments. EEG measurements suggest that whisk-ing periods might correspond to relatively active brain states. For instance, EEG fluctua-tions during quiet wakefulness have relatively

high amplitudes and low frequencies (features classically associated with passive brain states), whereas the fluctuations during whisking tend to be smaller and more irregular8 (features of active states). However, the heads of the mice had to be fixed in position for the duration of Poulet and Petersen’s experiments5. No stimuli were provided, and the whisking apparently began and ended spontaneously, making the behavioural significance of this ‘active’ state uncertain6.

Poulet and Petersen5 also show that elimi-nating sensory feedback from the whisker follicles, by severing the infraorbital nerve, affects neither state transitions themselves nor state-dependent changes in synchrony, even though all recordings are from the area of the cortex that processes this type of sensory information. The synaptic drives that mediate low-frequency synchrony are thus triggered centrally rather than by the direct action of the

sensory input on the cortex (Fig. 1c), consistent with much earlier studies2,17. However, Poulet and Petersen did not report whether the acti-vation is cortex-wide (for example, including visual, auditory, prefrontal and limbic areas) or limited to whisker-processing regions. Some studies have shown that specific movements can be correlated with the localized activa-tion of cortical areas associated with these motions18. Something similar may be happen-ing here because the active state was defined by whisker movement itself. There may well be important mechanistic differences between the sort of activation produced by general ‘arousal’ systems of the brain2,3,9,17, and activation medi-ated by specific processes such as sensory-motor coordination.

This raises the issue of mechanisms more generally. Which neuromodulatory systems, if any, are involved in the state transitions? Where do the signals driving membrane-potential synchrony originate, and to what extent are they mediated by state-dependent excitatory or inhibitory processes19? And how do fluctuations in synchrony affect sensory processing6?

The relationship between brain state and cortical synchrony has been investigated for at least 80 years. This week’s study by Poulet and Petersen5 used direct measurements in a field that had been dominated by oblique techniques. This may, to paraphrase Winston Churchill, mark ‘the end of the beginning’ of an epic scientific problem. ■

Scott J. Cruikshank and Barry W. Connors are in the Department of Neuroscience, Brown University, Providence, Rhode Island 02912, USA.e-mails: scott_cruikshank@brown.edu; barry_connors@brown.edu

1. Berger, H. Arch. Psychiatr. Nervenkr. 87, 527–570 (1929).2. Moruzzi, G. & Magoun, H. W. Electroencephalogr. Clin.

Neurophysiol. 1, 455–473 (1949).3. Jones, B. E. Ann. NY Acad. Sci. 1129, 26–34 (2008).4. Jasper, H. H. Psychol. Bull. 34, 411–481 (1937).5. Poulet, J. F. A. & Petersen, C. C. H. Nature 454, 881–885

(2008). 6. Kleinfeld, D., Ahissar, E. & Diamond, M. E. Curr. Opin.

Neurobiol. 16, 435–444 (2006).7. Adrian, E. D. Proc. R. Soc. Med. 29, 197–200 (1936). 8. Crochet, S. & Petersen, C. C. H. Nature Neurosci. 9,

608–610 (2006).9. Metherate, R., Cox, C. L. & Ashe, J. H. J. Neurosci. 12,

4701–4711 (1992).10. Rudolph, M., Pospischil, M., Timofeev, I. & Destexhe, A.

J. Neurosci. 27, 5280–5290 (2007).11. Lampl, I., Reichova, I. & Ferster, D. Neuron 22, 361–374

(1999). 12. Petersen, C. C. H., Hahn, T. T. G., Mehta, M., Grinvald, A.

& Sakmann, B. Proc. Natl Acad. Sci. USA 100, 13638–13643 (2003).

13. Gray, C. M., König, P., Engel, A. K. & Singer, W. Nature 338, 334–337 (1989).

14. Munk, M. H. J., Roelfsema, P. R., König, P., Engel, A. K. & Singer, W. Science 272, 271–274 (1996).

15. Steriade, M., Amzica, F. & Contreras, D. J. Neurosci. 16, 392–417 (1996).

16. Fries, P., Nikolic , D. & Singer, W. Trends Neurosci. 30, 309–316 (2007).

17. Lindsley, D. B., Schreiner, L. H., Knowles, W. B. & Magoun, H. W. Electroencephalogr. Clin. Neurophysiol. 2, 483–498 (1950).

18. Rougeul-Buser, A., Bouyer, J. J. & Buser, P. Acta Neurobiol. Exp. 35, 805–819 (1975).

19. Long, M. A., Cruikshank, S. J., Jutras, M. J. & Connors, B. W. J. Neurosci. 25, 7309–7316 (2005).

Figure 1 | Brain state and cortical synchrony. a, Quiet wakeful states of the mouse brain lack whisker movements; active brain states are indicated by whisking. b, Simultaneous recording from adjacent cortical neurons (pictured in c) show synchronous oscillations in membrane potential during quiet states. This synchrony breaks down during active (whisking) states. c, The synchronous, low-frequency cortical activity is driven by a neural ‘pacemaker’ (circle). The strength of this pacemaker is regulated by brain state (stronger during quiet wakefulness, weaker during whisking). Specific, information-rich pathways (various colours) generate relatively high-frequency, asynchronous synaptic inputs to neurons that dominate during active brain states.

Quieta

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