What can MEG neuroimaging tell us about reading?

Journal of Neurolinguistics 22 (2009) 266–280

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jneurol ing

What can MEG neuroimaging tell us about reading?

Kristen PammerThe School of Psychology, The Australian National University, Canberra, ACT, 0200, Australia

a r t i c l e i n f o

Article history:Received 30 March 2008Received in revised form 12 December 2008Accepted 24 December 2008

Keywords:MEGNeuroimagingReading

E-mail address: [email protected]

0911-6044/$ – see front matter � 2009 Elsevier Ltdoi:10.1016/j.jneuroling.2008.12.004

a b s t r a c t

Learning to read is one of the most cognitively complex tasks wewill ever learn to do. Thus understanding the reading process isnot just intrinsically interesting, but can give us a number ofvaluable insights into the relationship between brain processesand cognitive behaviour. MEG neuroimaging allows us to investi-gate reading processes in terms of the spatial extent of corticalactivations when reading, the timing between brain locations, andthe frequency dynamics between different cortical areas. The bigchallenge now for neuroscience is to model all three componentsof neural behaviour in order to be able to really understand thecomplexity of human cognition.

� 2009 Elsevier Ltd. All rights reserved.

‘‘Understanding what we do when we read would almost be the acme of a psychologist’sachievements, for it would be to describe very many of the most intricate workings of the humanmind, as well as to unravel the tangled story of the most remarkable specific performance thatcivilization has learned in all its history.’’Edmund Burke Huey 1870–1913

Reading is one of the most complex cognitive tasks that the brain has to learn. Our best estimatesare that writing systems developed only around 5000 years ago; this is not long enough to haveengendered an evolutionary advantage, and therefore it is unlikely that the brain has evolved to read.This suggests that, in order to learn to read, the brain must learn to recruit a number of differentspecialised cortical areas, and it is likely that we learn to read in the same way that we learn any otherskill, such as riding a bike or playing the piano. The difference, of course, is that a child who fails to learnto ride a bike is not exposed to the same social and personal consequences that occur as a result of

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K. Pammer / Journal of Neurolinguistics 22 (2009) 266–280 267

failing to learn to read. Therefore, understanding the cortical processes involved in reading is extremelyinteresting from a neurocognitive perspective; reading is a learned skill, it has a known developmentalpattern, the brain must recruit many disparate cortical areas in order to support it, and it is sociallydesirable. Social desirability means that large cohorts of children will be exposed to the same teachinginstruction, which makes it easier to identify children who are failing to learn to read.

In order for us to read, the brain engages many different cortical areas that are each highly speci-alised. Such areas include: the visual cortex for early feature analysis (Tarkiainen, Cornelissen, & Sal-melin, 2002; Tarkiainen, Helenius, Hansen, Cornelissen, & Salmelin, 1999), the fusiform gyrus forlanguage and complex object recognition (e.g., Cohen, Jobert, LeBihan, & Dehaene, 2004; Devlin,Jamison, Gonnerman, & Matthews, 2006), Broca’s area for language processing (e.g., Brunswick,McCrory, Price, Frith, & Frith, 1999; Jobard, Crivello, & Tzourio-Mazoyer, 2003; Price, 2000; Rumsey,Horowtiz, et al., 1997; Rumsey, Nace, et al., 1997; Xiao et al., 2005), and the superior temporal gyrus forsemantic encoding (Service, Helenius, Maury, & Salmelin, 2007; Vandenberghe, Price, Wise, Josephs, &Frackowiak, 1996). Because of the speed at which fluid reading occurs, the ‘trained’ network is likely tobe highly specific such that different components of the network must communicate at the right timeand in the right sequence. Magnetoencephalographic (MEG) neuroimaging techniques are ideallysuited to identifying these different components of the network and, in addition to this, allow us toinvestigate how such disparate cortical areas might communicate in such a fluid and dynamic way.Moreover, failing to learn to read may be a consequence of deficits in a number of different aspects ofthe network i.e., a deficit in where cortical activity is occurring, a deficit in when signals are activating,a deficit in how the different cortical areas are communicating, or, indeed any combination of these.This review draws on research from our lab and others to consider the contribution MEG neuroimagingcan make to understanding the reading process within the specific framework of ‘where’, ‘when’ and‘how’ of the cortical interactions.

1. What is MEG?

Magnetoencephalography (MEG) is a neuroimaging technique that measures the magnetic fieldpatterns generated by the brain. MEG measures activity primarily from the post-synaptic potentials ofpyramidal cortical cells. When large populations of neurons fire together, as is the case when the brainresponds to some sensory input, then an electric current is measurable outside the head. This is the basisof the well-known EEG technique. The neuromagnetic correlate of the electric current measured in EEGis the magnetic field pattern which is orthogonal to the electric current. Therefore, large populations ofneurons firing at steady-state or in response to a cortical event, collectively generate a magnetic fieldpattern measurable outside the head. It is these magnetic field patterns that are measured in MEG andmodelled in the various analysis techniques. MEG is therefore completely non-invasive, as it simplydetects the neuromagnetic signals generated naturally by the brain. Because of the relationshipbetween synchronous and non-synchronous cortical generators, a small number of synchronouslyfiring cells amidst a background of non-synchronous firing can generate a measurable signal. Forexample, in a cortical area of 1 mm, approximately 1% of synchronously firing cells could generate morethan 96% of the measurable signal (Hari, Salmelin, Makela, Salenius, & Helle,1997), and typically 104–105

cells need to be active simultaneously for fields to be detectable (Wikswo, 1990). Moreover, because ofthe complex pattern of sulci and gyri in the cortex, the currents generated by the dendrites of thepyramidal cells will be tangential (on the walls of the sulci) or radial (on the crest of the gyri) withrespect to the skull. Only the tangential currents produce magnetic signals measurable outside the head.EEG techniques have the same temporal resolution as MEG, but magnetic signals are less distorted bybrain tissue and the skull than electric signals, which results in MEG having better spatial resolutionthan EEG. Typical neuromagnetic field patterns are generally represented as a contour map (red andblue dipolar patterns in Fig. 1A), representing the magnetic field coming out of (red) and going into(blue) the head with the source at the centre. The modelling of neuromagnetic sources however issubject to the inverse problem, which in its simplest form, is that a current distribution insidea conductor cannot be uniquely described by the generated magnetic field pattern outside (vonHelmholtz, 1853). In other words, there are a number (theoretically, an infinite number) of patterns ofcortical activity that might produce the magnetic field patterns we observe outside the head.

Fig. 1. Typical analyses representations over the left inferior occipital/temporal cortex for (A) a dipolar field pattern, (B) dipolesfitted for five people, and (C) a SAM analysis.

K. Pammer / Journal of Neurolinguistics 22 (2009) 266–280268

Specifically, this is an ill-posed inverse problem because it does not allow for a unique description of thecurrent generators (Daunizeau et al., 1995). Thus, because the problem remains under-determined, wemake assumptions that the neuromagnetic signals we observe reflect particular cortical activity goingon inside the head. The inverse solution is therefore constrained by knowledge about brain anatomy,physiology, and the adoption of different analysis techniques which dramatically decrease the possiblecortical solutions (refer to Hamalainen, Hari, Ilmoniemi, Knuutila, & Lounasmaa, 1993; Hari, Levanen, &Raij, 2000; Vrba & Robinson, 2001 for comprehensive reviews of MEG). For example, the magnetic fieldpatterns generated by the brain in response to the presentation of a single tone could theoretically beproduced by any number of cortical sites. However our knowledge of the anatomy and physiology of thebrain would constrain these possibilities to the auditory cortex. (For interpretation of the references tocolour in this paragraph, the reader is referred to the web version of this article.)

In a typical MEG experiment, the subject places his/her head in a helmet which contains sensorscalled Superconducting Quantum Interference Devices (SQUIDs). The neuromagnetic signal generatedby the brain induces a magnetic field in the SQUID, which then gets converted to voltage and amplifiedfor analysis. Digitised points of the skull and fiduciary points are recorded, which are used for deter-mining the head placement in the helmet and later allows the MEG measurements to be coregisteredwith the participant’s structural MRI. The experiment is then conducted and the MEG measurementstaken. Off line data analysis then usually occurs in the context of the participant’s coregistered MEG-MRI.

There are a number of different analysis techniques (refer to Vrba, 2002 for a brief review); thetraditional technique, single and multi-dipole fitting, is conducted with averaged data for a specific timewindow. The assumption here is that the cortical generators of the neuromagnetic signal can bemodelled as a current dipole. Thus, the aim is to identify the distribution of current dipoles from theobserved magnetic field patterns. In some cases this will be a single dipole, while in other, more complexcases it will be a multi-dipole model. A non-linear search (least squares error minimisation) is con-ducted that attempts to minimise the difference between the magnetic field pattern generated by themodelled distribution of dipoles, and the magnetic field patterns actually observed (refer to Fig.1 A, andB). Such discrete cortical dipoles are unlikely to actually exist, are most useful for nicely symmetricalfield patterns, and become less stable with increasing numbers of cortical generators (Supek & Aine,1993). This has prompted researchers to adopt more sophisticated techniques of analysis.

Beamformer analysis techniques use filtering, usually spatial filtering, to enhance a signal at onecortical location whilst attenuating the signal elsewhere, thereby assessing the degree to which eachvoxel in the brain contributes to the overall measured signal (Robinson & Vrba 1999; van Veen, vanDrongelen, Yuchtman, & Suzuki, 1997). Synthetic Aperture Magnetometry (SAM) is a minimum vari-ance beamforming technique. When a SAM analysis is conducted, the whole brain is divided up intospecified voxels (e.g., 1�1�1 mm3 or 5� 5� 5 mm3, depending on the required resolution). The SAMalgorithm calculates a spatial filter that is unique for each voxel – a virtual electrode – which is thenlinked to the sensor array. These virtual electrodes provide an estimate of the temporal pattern of themagnetic sources emanating from the brain. An Active (Experimental) and Passive (Control) epoch isspecified (e.g., 700 ms post stimulus onset vs. 700 ms pre-stimulus onset), as is a frequency band ofinterest. A statistical comparison of the power estimate is made between the Experimental and Control


epochs for the frequency band of interest. This is then repeated for each voxel to give a 3D power mapacross the brain of differential activation (increase in power or decrease in power) for the Experimentalstate compared to the Control state, at the specified frequency band (Barnes & Hillebrand, 2003;Barnes, Hillebrand, Fawcett, & Singh, 2004; Fawcet, Barnes, Hillebrand, & Singh, 2004; Hadjipapas,Hillebrand, Holliday, Singh, & Barnes, 2005; Hillebrand, Singh, Holliday, Furlong, & Barnes, 2005). SAMhas now been used extensively to model a number of different aspects of cognitive behaviour (e.g.,Bayless, Gaetz, Cheyne, & Taylor, 2006; Herdman et al., 2006; Hirata et al., 2004; Itier, Herdman,George, Cheyne, & Taylor, 2006; Luo, Holroyd, Jones, Hendler, & Blair, 2007; McNab, Rippon, Hillebrand,Singh, & Swithenby, 2007; Pammer et al., 2004; Pammer, Hansen, Holliday, & Cornelissen, 2006;Ukai, 2002).

2. Spatial dynamics of reading

There are a number of excellent reviews that have mapped the functional components of thereading network (e.g., Fiez & Petersen, 1998; Jobard et al., 2003; Price, 2000; Pugh et al., 2001). Thestrength of MEG lies more in being able to combine spatial and temporal dynamics, so this section willserve more as a brief site-map to the more important components of the reading network.

Neuroimaging research has demonstrated a wide and complex network of cortical sites that arerecruited in reading. While there are unique cortical activations that are specific to each experimentaldesign, most researchers would agree that the following sites are involved in the reading network:a ventral component around the occipito-temporal area, consisting of the inferior temporal gyrus,inferior occipital gyrus, fusiform gyrus, and right posterior parietal cortex, that is devoted to visual andorthographic encoding; an anterior region consisting of the inferior frontal gyrus and inferior premotorcortex which is involved in phonological coding, and language production; a posterior dorsalcomponent which appears to be involved in phonological and orthographic integration and generallyconsists of structural elements of the posterior parietal cortex, and an area around the superiortemporal gyrus, appears to be involved in comprehension.

3. Temporal dynamics of reading

Emerging MEG studies have allowed us to put tentative ‘time-tags’ on the areas of interest in thereading network, generally describing a posterior to anterior, and inferior to superior flow of activation.Earliest, pre-lexical activity occurs in the occipito-temporal areas (Pammer et al., 2004; Tarkiainenet al., 1999, 2002). Tarkiainen et al. (1999) showed distinct early components of the reading networkwithin the first 200 ms, namely a midline occipital component (dark green in Fig. 2) activated first atw100 ms, which was followed by a bilateral inferior occipital/inferior temporal activation at about150 ms (dark blue in Fig. 2). The first component, which they referred to as the Type I response, reactsindiscriminately to visual pattern, increasing in activation with increasing visual noise. The secondcomponent, termed Type II is sensitive to word-like structure, increasing in activation with increasingword discriminability, responding preferentially to letter strings over other visual objects, butresponding indiscriminately to words, pseudowords and random letter strings. It is unclear whetherthe Type II response reflects some aspect of the M170 signal identified elsewhere as a cortical signalthat is sensitive to words, but insensitive to phonotactic probability, lexicality, (Pylkkanen, Stringfellow,& Marantz, 2002) or word frequency (Embick, Hackl, Schaeffer, Kelepir, & Marantz, 2001). In the sameregion of cortex, the Visual Word Form Area (VWFA) described by Cohen et al. (2000, 2002) is an area ofcortex in the mid Fusiform gyrus devoted specifically to the visual analysis of visual word form (blueareas in Fig. 2). While it is unlikely that the VWFA exclusively processes word form (Price & Devlin,2003), it is undeniable that this area is important to orthographic word recognition. It is arguably, thesite for the pre-lexical analysis of visually-presented words and pseudowords, as it responds morestrongly to alphabetic letter strings, to checkerboard stimuli, and to words than illegal nonwords(consonant strings), it also demonstrates invariance with regards to retinal position. Thus, within250 ms of viewing a word there is a complex feed-forward wave of activity that flows anteriorly alongthe inferior occipital gyrus and fusiform gyrus that is likely to reflect different aspects of orthographic-

Fig. 2. A schematic of some of the commonly found areas of activation in the reading network, with some idea of the underlyingfunctionality and temporal activation. Refer to the text for details.


processing. (For interpretation of the references to colour in this paragraph, the reader is referred to theweb version of this article.)

Semantic encoding is likely to occur around 400 ms, with a 250–550 ms time window. Dhond, Witzel,Dale, and Halgren (2007) demonstrated cortical dissociation between reading abstract and concretewords around the left temporal region between 300 and 400 ms. This finding is consistent with EEGstudies of the well-known n400 believed to reflect semantic encoding (Kutas & Hillyard, 1980), and isconsistent with other studies of semantic encoding (Halgren et al., 2002; Marinkovic et al., 2003), refer toMarinkovic (2004) for a review. Using MEG, Service et al. (2007) recently demonstrated mid/superiortemporal sources sensitive to both semantic and syntactic errors in written sentences. Although therewere small localisation differences between the conditions, the distinction between the two types ofsentence errors were apparent primarily in latency differences. The 400 ms time window was sensitive toanomalous semantic sentences, while the 600 ms time window was sensitive almost exclusively tosyntactic violations. However, it has been suggested that research thus far has underestimated thecomplexity of the n400. The neuromagnetic correlate of the n400 – the m400 – has been suggested toconsist of a complex of activations with specific peaks at m250 and at least one – possibly two – separatepeaks at m350, located at the left superior temporal cortex and moving slightly anteriorly with the m350signal (Embick et al., 2001; Pylkkanen et al., 2002, refer to Pylkkanen & Marantz, 2003 for a review).Functionally, the earlier m250 may be sensitive to lexical components, such as high frequency phoneme/morpheme structures, but insensitive to the frequency of whole words (Embick et al., 2001; Pylkkanenet al., 2002). The later m350 signal is sensitive to lexical frequency, but is unlikely to be the site of lexicalaccess, as the latency of the signal decreases with increasing neighbourhood frequency. This contradictsthe behavioural finding that increasing neighbourhood frequency results in slower reaction times,presumably due to increasing inhibition from competing representations. It can be assumed then, that asthe m350 signal was not inhibited with increasing neighbourhood frequency, it has little role inextracting the target word from lexically competing items. The decomposition of the m400 window intoearly and late components has some ecologically validity. A brain signal that does not register themeaning of a word until 400 ms could be seen as problematic for models of reading. Word recognitionoccurs quickly. Reaction time in a lexical decision task can be in the order of 400–500 ms, and thisincludes the motor component necessary to register the response. Contextual reading in a skilled readeris similarly fast, with a typical fixation lasting 200–250 ms (Rayner, 1998). Eye movement research has


indicated that readers are sensitive to semantic incompatibility within sentences much earlier than400 ms (e.g., Rayner, Warren, Juhasz, & Liversedge, 2004), and there is some evidence to suggest thatthere may even be parafoveal pre-processing of semantically anomalous words in reading (Murray,1998;Rayner et al., 2004). Recent MEG data of online language comprehension has suggested that corticalsignals for semantically related and unrelated word pairs could be discriminated at 100–140 ms (Shtyrov& Pulvermuller, 2007). However this study used auditory presentation. Similar studies looking at readingusing visual presentation in MEG are limited, therefore spatio-temporal mapping of semantic encoding inreading has yet to be fully elucidated using MEG. The value of the studies described above, is that thetemporal and spatial sensitivity of MEG has allowed a re-evaluation of a well-known cortical signal, then400, revealing a greater complexity than was previously believed. It is not unreasonable to suspect that,with future research, similar decomposition may be possible with other cortical signals.

3.1. The importance of spatio-temporal dynamics

The emerging picture from the MEG research is that the cortical dynamics involved in reading aresubstantially more dynamic and interactive than is suggested by many models of reading. Typicalmodels of reading indicate sequential processing such that visual encoding and the orthographic-processing components of the network (those in blue/green in Fig. 2) precede lexical analysis andphonological coding (e.g., Coltheart & Rastle, 1994; refer to Jobard et al., 2003 for a review). This isreasonable, and makes sense in a linear sequence of events in which the brain must work out whatthe pattern is visually before a phonological or semantic code can be assigned. However, we (Pammeret al., 2004) have demonstrated that while basic visual coding in the midline occipital area is the firststage of processing, in fact areas in the left inferior frontal gyrus and posterior parietal cortexputatively responsible for phonological aspects of reading, are active at the same time as theposterior fusiform area, and precede activity in the visual word form area in 100–200 ms timewindows. Strong co-activation in the visual word form area and phonological regions are thenapparent in time windows up to 400 ms (refer to Fig. 3).

Findings such as these which demonstrate cascaded processing and co-activation in multiplecortical areas, are vitally important for our development of models of reading and word recognition.More than 20 years ago McClelland and Rumelhart (1981; Rumelhart & McClelland, 1982) presenteda theoretical framework for word recognition that had at its core, the concepts of cascaded anddistributed processing (refer to Grainger, 2008, for a recent review specifically within the context oforthographic encoding). The MEG neuroimaging data that we found, suggests that cognitive brainmechanisms – of which reading is only one example – are highly dynamic, with the feed-forward,feedback and cascaded processes necessary in order to capture the richness of brain dynamics. Whilelinear ‘boxes-and-arrows’ approaches to cognitive models are important for communicating ideas, weare to be mindful of the fact that cognitive models should be supported by plausible brain mechanisms.

One of the most important applications for understanding the temporal dynamics of corticalfunction in reading is in dyslexia. MEG studies provide a valuable contribution to our understanding ofdyslexia by demonstrating that the neurocognitive deficits underpinning poor reading may have asmuch to do with the temporal dynamics of the network as with the spatial pattern of activity. Many

Fig. 3. Temporal evolution of left hemisphere and ventral brain activity elicited by visual word presentation. The figure shows theSAM group analysis of brain activity measured every 25 ms with MEG (in the 10–20 Hz band) and superimposed on a canonicalbrain. Rows 1 and 2 show the activity for word stimuli. From Pammer et al. (2004) p 1822.


neuroimaging studies using techniques such as fMRI and PET have demonstrated that dyslexic readersappear to engage different components of the reading network, with dyslexic readers showing lessactivity in areas such as the temporo-occipital cortex (e.g., Brunswick et al., 1999; Rumsey, Horowtiz,et al., 1997; Rumsey, Nace, et al., 1997; Shaywitz et al., 2003; Temple et al., 2001) and extra-striateregions (e.g., Demb, Boynton, & Heeger, 1997, 1998; Temple et al., 2001; refer to Heim & Keil 2004, fora review). MEG research confirms these results but also promotes the importance of the temporaldimension. For instance, MEG studies show that, although activation in relevant cortical areas might becommon to both normal and dyslexic readers, the timing of activation in these areas differs betweenthe two groups (e.g., Helenius, Salmelin, Service, & Connolly, 1999; Salmelin, Service, Kiesila, Uutela, &Salonen, 1996; Tarkiainen et al., 1999, 2002). Because fMRI has excellent spatial resolution but rela-tively poor temporal resolution compared to MEG, it is possible that patterns of activation for dyslexicand control readers in an fMRI study could be very similar, allowing researchers to conclude that thereis no difference in cortical activation between the groups. However, the addition of temporal ‘tags’ onthe spatial pattern of activation is likely to paint a very different picture. Studies demonstrating theimportance of timing in the reading network have been conducted by Salmelin et al. (refer to Salmelin,2006, for a review). These researchers have demonstrated that signals for adult dyslexic readers in theinferior occipital region, putatively the visual word form area, or at least leading into it, were absent ordelayed by w100 ms, and the inferior frontal cortex – Broca’s area – demonstrated an early signal whenpassively reading words. Similar disruptions to the temporal components of the neural signal havebeen found in semantic encoding (Helenius et al., 1999). In this study, normal and dyslexic readers werepresented with 4–10 word sentences in which the final word in the sentence had a high cloze-prob-ability (‘probable’), a low cloze-probability (‘rare’), or was semantically inappropriate (‘anomalous’).Dyslexic participants demonstrated a spatial pattern of cortical activation in the temporal cortex forsemantic activation that was the same as for non-impaired readers. However there was a significantdelay in these signals for the dyslexic readers compared to normal readers. Similarly, a number ofstudies have demonstrated a right-hemisphere (RH) engagement in dyslexic readers, sparking spec-ulation that compensatory mechanisms are developed in dyslexic readers. Simos et al. (2000),extended these findings to demonstrate that during phonological coding tasks, dyslexic childrenindeed showed different patterns of lateralisation that were limited to later-processing windows.Stronger activation, more sources and a later onset latency in RH temporal areas were demonstrated atthe >300 ms processing window, but not at earlier time windows. This suggests that phonologicalcoding deficits characteristic of dyslexia may be mediated by higher-order cognitive mechanisms,rather than low-level sensory input. Moreover, these new MEG studies using children are particularlyinteresting, as they open a wealth of new research possibilities. Using the word recognition paradigmof Tarkiainen et al. (1999), Parvianinen et al. (2006) presented children with simple words to readwhich were imbedded in increasing amounts of visual noise. They demonstrated that the w120 msoccipito-temporal response in adults is delayed by 50–100 ms in children. Similarly, a mid/superiortemporal source characteristic of this task at w250 ms in adults was also delayed in children. However,there was a greater degree of variability in the delay of the second source across the children, anda significant correlation between age and latency, indicative of differences in neuronal maturation atdifferent cortical sites. Such findings are consistent with the Maturational Hypothesis put forth byMcArthur and Bishop (2002, 2005; Bishop & McArthur, 2005), who have suggested that developmentaldisorders of language such as dyslexia, may be partly due to slower neuronal development, and delaysin the development of cortical connectivity. A more concentrated effort is now required to conductMEG studies with dyslexic children using both chronological and reading-matched controls in order toevaluate this possibility. Similarities in the temporal pattern of activity between dyslexic children andreading-matched control children would provide support for the proposal that developmental learningdifficulties such as dyslexia have their basic aetiology in slower or delayed maturational development.Differences between dyslexic children and both reading- and age-matched control groups would beindicative of a difference in the cortical architecture in dyslexic readers. Thus, understanding thenormal developmental trajectories of connectivity within the reading network is a vital next step inunderstanding reading and reading impairment.

The significance of these studies is to highlight the fact that the neurocognitive network is highlysensitive to the timing of activation within it, and that reading failure could result from a failure in the


activation of a network component, or the timing with which activation occurs. While substantial fMRIresearch has demonstrated that dyslexic readers do fail to engage particular components of thenetwork, the MEG studies reviewed here have demonstrated that adequate reading skills are likely tobe dependent upon both the spatial and the temporal integrity of the system. Thus a dyslexic readermight demonstrate adequate signal amplitude at a particular site in the reading network, but still havedifficulty stemming from signal delay. If signal delay disrupts processing in the specific core area it willprobably also result in a ‘domino’ effect throughout the system. Indeed, even a small disruption early inthe sensory coding mechanisms may be sufficient to disrupt the feed-forward flow of informationthrough the system in a way that could perturb the subsequent synchronisation of communicatingunits in the network. To use an analogy from basic mechanics, when a spark-plug in the engine of a carfails to fire, there are serious consequences for the rest of the system in that the other components thatare dependent on the spark-plug simply cannot do their job, and the car does not start. However, if thespark-plug fires, but does so at the wrong time, the consequences are similarly dire. A neurocognitivereading network may also require all the right components to ‘fire’ in the correct sequence and at thecorrect time in order for us to read fluently.

4. Frequency dynamics – the new frontier

The reading network involves a number of spatially disparate sites and acquiring reading skillsmeans that the brain must learn to communicate between these different sites, binding relevantinformation to access a coherent lexical entry and maintaining a smooth flow of dynamic processing.An emerging theme in reading research is the role of cortical connectivity in the neural networks thatunderlie skilled reading. MEG research in this area is still in its early stages and there are as yet a smallnumber of studies that have explored this question. Thus the role of cortical oscillatory dynamics inreading is ripe for exploration, and ideally suited to MEG techniques.

Most recent conceptualisations of the mechanisms behind cortical connectivity implicatesynchronous, rhythmic neural firing as being crucial to neural binding (Gray, Konig, Engel, & Singer,1989; Schnitzler & Gross, 2005; Singer, 1999). Neural synchrony occurs when the rhythms of neuralsignals correlate; moreover, any one neuron within a population can coordinate its firing rhythmwithin one population or a different population with a different firing rhythm, as necessary (Jerma-kowicz & Casagrande, 2007; Varela, Lachaux, Rodriguez, & Martinerie, 2001). Cortical oscillations occurwhen large groups of neurons synchronise with each other and can be described in terms of oscillationfrequency, amplitude and instantaneous phase (Sauseng & Klimesch, 2008). Thus, electrical signalsemitted by the brain fluctuate at various frequencies with the dominant frequency being dependentupon behaviour or cognitive state (e.g., Barlow, 1993). For example, rhythms in the delta (0.5–2 Hz),theta (4–12 Hz) and gamma (30–80 Hz) bands are well documented and vary with behaviouralconditions (Fellous & Sejnowski, 2000; Kujala et al., 2007). While the presence of cortical frequencyoscillations have been acknowledged for many years, the functional consequences of coherentfrequency dynamics in the brain remain an enduring theme in neuro-behavioural research (e.g.,Shadlen & Movshon, 1999). Because MEG measures the synchronous firing of populations of neurons inthe cortex, reflecting the coherent post-synaptic potentials in large populations of pyramidal cells, it isan ideal method for the non-invasive measurement of synchronous neural events. A completeunderstanding of the dynamics of visual word recognition and reading requires an understanding notonly of the ‘when’ and ‘where’ of the underlying neural circuitry, but also an understanding of theoscillatory signatures between different components of the network, which MEG is ideally placed toprovide.

Oscillatory activity in the brain within specific frequency bands that occur as the result of somesensory input can be phase-locked to the sensory event. Such signals are identified reasonably easilybecause the temporal consistency over trials allows the signal to be modelled using standard Equiv-alent Current Dipole (ECD) techniques. However there are many oscillatory changes that occur withinlarge populations of neurons that are not phase-locked to the stimulus and may therefore be silent tostandard analysis techniques that are based on averaging over trials. Indeed reading and language-based tasks are not well synchronised to the stimulus event, resulting in temporal jitter in the signalover trials. This is problematic for analysis techniques that average signals – such as dipole modelling,


because a genuine signal can be ‘washed-out’ when recording epochs are averaged. Power changes inoscillatory activity are called event-related desynchronisation (ERD) and event-related synchronisa-tion (ERS). This is a fundamental distinction in cortical activity, with one state (ERD) reflectingdecreases in synchronous cortical activity in populations of neurones within a specific frequency band,in response to a stimulus event, and the other state (ERS), a synchronisation of localised general activity(Pfurtscheller & Lopes da Silva, 1999). In most research, the salient outcomes are the power bands inwhich ERS or ERD occur: alpha (approximately 8–12 Hz), beta (approximately 12–30 Hz) and gamma(>35 Hz), with most studies exploring the functional significance of the power bands. There is littleconsistent information regarding the functional consequences of ERS and ERD per se. Therefore,despite the fact they represent fundamental processing states, we know little about when to expect anERS or ERD, and in most cases observing one or the other is intrinsically tied to the frequency band ofinterest. For example alpha ERD is common with the presentation of visual stimuli (first identified byBerger, 1929), while beta ERS is frequently found after the execution of movement (e.g., Pfurtscheller,Stancak, & Neuper, 1996, refer to Pfurtscheller & Lopes da Silva, 1999, for a review). Thus, the degree towhich ERS and ERD support different aspects of cognition has yet to be determined, but is likely toreflect a complex relationship with frequency. SAM is an ideal analysis technique for identifying ERD/ERS because it does not rely on averaging over trials, and can therefore reveal power changes in non-phase-locked data. As described in a previous section, SAM volumetric maps are produced by calcu-lating power differences in Experimental and Control time windows for frequency bands of interest,which reflect either an increase in power (ERS) in the Active window compared to the Control window,or a decrease in power (ERD). Using SAM, Xiang, Wilson, Otsubo, Ishii, and Chuang (2001) demon-strated ERD in the gamma band (60–125 Hz) in the left inferior frontal cortex in a silent word-readingtask and ERS in the low gamma band (30–60 Hz) in the occipito-temporal-pariental junction (see alsoHirata et al., 2002). However these studies used very wide time windows and broad frequency bands.Studies systematically manipulating the analysis window and frequency bands are required. We havealso highlighted the distinction between ERS and ERD in reading (Pammer et al., 2006). We wereinterested in preattentive attentional shifting in word recognition. In our study we aimed to useneurophysiological evidence to evaluate the role of the dorsal pathway in reading. It is well known inthe neurophysiological literature that the dorsal pathway is intrinsic to visuospatial processing,particularly attention (e.g., Lamme & Roelfsema 2000; Shomstein & Behrmann, 2006; Vidyasagar,1999). It is also well known that a deficit in the dorsal pathway is related to reading failure (refer toPammer & Vidyasagar, 2005 for a review) Therefore, it has been suggested (e.g., Vidyasagar & Pammer,1999; Vidyasagar, 2005) that the dorsal pathway contributes to reading and dyslexia by virtue of itsrole in visuospatial attention. In reading, as in other more natural visuospatial tasks, the dorsal pro-cessing stream may act as a visual guidance mechanism, searching and selecting the salient compo-nents of text for subsequent analysis by the ventral stream, guiding the ‘spotlight’ necessary to extractand synthesise edges, corners and lines to form letters and words. In the Pammer et al. (2006) study,we presented participants with ‘normal’ words in a lexical decision task, or ‘shifted’ words in which theinternal letters were spatially displaced above and below the centre line, such as HOUSE, the ‘shifted’stimuli were used as a way of accessing the spatial coding mechanisms that we hypothesised aremediated by the dorsal pathway when reading. We found that attentional engagement modulated bythe right posterior parietal cortex – which receives substantial input from the dorsal pathway – wasassociated with ERD activity in the gamma band (35–40 Hz) at 100–300 ms, followed by a peak in thealpha band at 150–350 ms and a reoccurrence of the gamma response sustained over 200–500 ms.

We suggested that the early signal (B1 in Fig. 4) may reflect an evoked signal, phase-locked to thestimulus onset, while the latter gamma signal (D1 in Fig. 4) may reflect an induced response to atten-tional mechanisms required to assemble the component letters of the word. However further research isrequired to confirm the functional significance of the two signals. An averaging technique such as dipolefitting would have identified only the first of these gamma peaks, missing the significance of recurrentactivation. Even with a simple lexical decision task, the dominant activity in the inferior occipital,temporal and inferior frontal areas is in the alpha and low beta frequency ranges (8–16 Hz) (Pammeret al., 2003).

Cortical coherence is a measure of the synchrony of different populations of neurons. Theassumption here is that if different populations of neurons are oscillating at the same frequency, then it

Fig. 4. The region of interest identified by the SAM analysis for group data is represented in the whole-head and coronal sections;represented is the region of interest (ROI) in the right posterior parietal cortex for the Shifted-Words condition. The graph representsthe activation at the ROI identified in the sections for different time� frequency combinations. The scale represents significantactivity (pseudo t, at p< .001 corrected) relative to the pre-stimulus control period, such that a deeper shade of blue representsstronger activity in the Shifted-Words condition compared to the ‘normal’ Words condition. Activations at different time� frequencyintervals are labelled. The star indicates activation that is significantly stronger in the Words-shifted compared to the Wordsconditions. All figures follow radiological convention such that ‘Left’ is on the left side. From Pammer et al. (2006), p 2929.


is likely that they are communicating in a way that allows the transmission of information betweendisparate parts of the brain (Schnitzler & Gross, 2005). Dynamic Imaging of Coherent Sources (DICS) isthe computation of cortical coherence or activity within a given frequency range using a spatial filter,allowing the identification of neural circuitry that share dynamic oscillatory activity (Gross et al., 2002,2001). Kujala et al. (2007) used DICS to demonstrate changes in cortical coherence within the alphaband that were associated with changes in the task demands of reading. In this study participants werepresented with uninterrupted coherent text – stories – in rapid serial visual presentation (RSVP)format. The stories were presented either above-reading-rate threshold, or below threshold(comfortable reading rate), or simply as scrambled text. Within the alpha peak, we found uniqueoscillatory signatures between cortical sites that corresponded to the different reading conditions.

These results were the first to demonstrate that changes in cortical connectivity mirror readingbehaviour and therefore provide evidence that different patterns of cortical connectivity support differentreading requirements. For example, stronger synchronisation was observed between occipito-temporal(OT) and anterior temporal (AT) and orbito-frontal (ORB) areas (X’s in Fig. 5b) for the faster RSVP taskcompared to the words/non-words conditions. Moreover, a faster presentation rate in the RSVP taskresulted in enhanced synchronisation in OT, ST and ORB (red network in Fig. 5c). (For interpretation of thereferences to colour in paragraph, the reader is referred to the web version of this article.)

Understanding cortical connectivity is the next big frontier in the neuroscience of reading. We havedemonstrated that different oscillatory networks underlie single word-reading, contextual, andspeeded reading. What needs to be done is a detailed and explicit analysis of the functional compo-nents of the reading network to understand – for example – whether different oscillatory signaturesunderlie different aspects of reading (in broad terms: attention, visual processing, comprehension,lexical access, phonological decoding). While such research is already being conducted using EEG, theaim of MEG research would be to complement this research in being able to tie frequency dynamics tospecific cortical locations.

Fig. 5. Connectivity within the network and task effects. (B) Overall connectivity between the nodal points (the synchronisationindex (SI) exceeded 99% confidence level for 8 out of 9 subjects at least in one RSVP condition). The size of the nodal point indicateshow many other points it was connected with. (B) Connections for which SI was significantly higher in at least one RSVP conditionthan when reading isolated words/non-words. (C) Connections for which the SI in the RSVP tasks differed from each other. Redindicates significant effect of presentation rate on the SI (fast/medium> slow) and blue the effect of story coherence at the slow rate(meaningful> scrambled). From Kujala et al. (2007), p 1482.


5. What can MEG neuroimaging NOT tell us about reading?

One of the limitations of MEG is that accuracy in detecting sources decreases with increasingdistance from the source (Hillebrand & Barnes, 2002). In other words, deeper sources are difficult topick up and to localise accurately in MEG. This makes it more difficult to analyse contributions fromdeeper sources such as the thalamus for example. The thalamus has been consistently implicated infMRI studies of reading and word recognition (e.g., Brunswick et al., 1999; Rumsey, Horowtiz, et al.,1997; Rumsey, Nace, et al., 1997), and thalamic damage has been associated with alexia (Tamhankaret al., 2004). Crosson (1999), in reviewing cases of thalamic disturbance, has suggested that lexical-semantic processing is highly dependent upon attentional processes mediated by the thalamus andthat visuospatial mechanisms may also be dependent on the thalamus. Given the important role thethalamus plays in relaying sensory information, it is likely that processing is important for reading andtherefore, more effort needs to be expended in understanding the role of the thalamus in reading. Deepsources can be detected in MEG (e.g., Luo et al., 2007) with careful experimental designs that increasethe signal-to-noise ratio, and new algorithms are being devised to detect deep sources (e.g., Attal et al.,2007).

Another limitation of MEG is that because large populations of neurons are required to be firingsynchronously to detect the magnetic signal, sources less than 2 cm apart may not be resolvable asdistinct sources (Hari et al., 2000). This is one reason why it is very important that we start to adoptmulti-imaging techniques in order to evaluate the internal consistency of our findings. Combining MEGand high resolution fMRI will allow topographic mapping of cortical areas using the spatial sensitivityof fMRI, and the temporal sequencing of MEG. Comparing the consistency of neural signals measuredby one technique with results from different neuroimaging techniques will allow us to better char-acterise both the physiology and the representation of the neural responses.

6. Conclusion

In order to read fluently, the brain is required to recruit disparate cortical areas to function ina highly complex and dynamic way. Because MEG is sensitive to the spatial and temporal properties ofcortical signals, it can provide a valuable contribution to the cortical processes involved in reading.MEG allows us to track the evolution of the time course of the information flow through the readingnetwork, mapping the time course of the neuromagnetic signal to specific cortical locations. This isvital to our understanding of complex cognitive networks such as reading because current evidencesuggests that the neural correlates to complex cognitive behaviour are likely to consist of very specificand subtle interactions between the timing and location of cortical signals. Furthermore, it is likely thatthe cortical processes involved in reading are best described by examining the dynamic interactionsbetween cortical networks. Indeed, the neural resources that are recruited in fluid reading, and the


speed with which this occurs indicate a requirement for neural binding at an extraordinarily sophis-ticated level. Thus, our understanding of the cortical networks involved in reading is likely to benefitfrom being able to combine information regarding the time course of information flow through thenetwork, the specific cortical locations recruited, and oscillatory frequency dynamics linking thedifferent cortical sites.

Understanding the neural processing involved in reading is vital in the literacy-dependent world inwhich we live. Approximately 10% of the population fails to learn to read, despite adequate opportunityto do so, and the social and personal costs of reading failure are high. MEG research will help uselucidate the functional components of the cortical reading network, such that we can determinewhich components of the network are most important for the acquisition of reading skills. For example,no studies have systematically investigated the neurophysiological correlates of reading skills inliterate adults who have a childhood diagnosis of dyslexia, either in actual reading processes, or thecomponent skills of reading. Understanding the processes by which some dyslexic children come tolearn to read as adults provides an enormous resource for understanding the plasticity of complexcognitive networks such as reading, how neurobiological processes can adapt to meet specific cognitiverequirements, and how we might better design remedial reading treatment in children to exploit suchprocesses. For some young dyslexic readers it might be better to foster reading skills that already existrather than attempt to develop functionality in brain mechanisms which are less responsive.Demonstrating successful cortical plasticity in literate adults with a history of dyslexia addressesquestions regarding whether treatment intervention should target the relative strengths of thedyslexic reader rather than persisting in standard intervention practices. Such findings would also haveimportant consequences for how we teach reading, in that identifying intrinsic functional componentsof the cortical reading network would provide vital information to inform current debates regardingthe relative importance of whole-word vs. phonological decoding in reading instruction.

The research reviewed here suggests that the underlying neurocognitive architecture that supportsthe behavioural differences apparent in different types of linguistic stimuli (words, nonwords, verbs,nouns, sentences, concrete/abstract etc.) will ultimately be best characterised by a combination ofdifferences in signal latency, location and frequency coherence.

Finally, understanding the cortical dynamics involved in reading allows us to extrapolate to othercognitive functions. – attentional shifting, visual search, language comprehension, memory, as well aslearnt tasks such as playing music or learning a second language. It is usually the first complex cognitivetask we learn so it can give us some insight to learning mechanisms that occur early in development.What the brain does when we read is unlikely to be very different from what it does when engaging inany complex behaviour. Thus understanding the what, where and how of reading will give us a windowto understanding the full gamut of cognition that the human brain is specialised for.

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What can MEG neuroimaging tell us about reading?