# COMPONENTS OF THE LANGUAGE-READY BRAIN

EDITED BY: Cedric Boeckx and Antonio Benítez-Burraco PUBLISHED IN: Frontiers in Psychology

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ISSN 1664-8714 ISBN 978-2-88919-893-1 DOI 10.3389/978-2-88919-893-1

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# **COMPONENTS OF THE LANGUAGE-READY BRAIN**

Topic Editors: **Cedric Boeckx,** University of Barcelona & Catalan Institute for Research and Advanced Studies (ICREA), Spain **Antonio Benítez-Burraco**, University of Huelva, Spain

This volume highlights new avenues of research in the language sciences, and particularly, in the neurobiology of language. The term "language-ready brain" stresses, on the one hand, the importance of a brain-based description of our species' linguistic capacity, and, on the other, the need to appreciate the crucial role culture plays in shaping the linguistic systems children acquire and adults use. For this reason, the focus is not put on language per se, but on our learning biases and cognitive pre-dispositions toward language. Both brain and culture are considered at two crucial levels of inquiry: phylogeny and ontogeny. In a fast-growing field like the language sciences and specifically, language evolution studies, this book has tried to capture several of the most exciting topics explored currently, sowing seeds for future investigations.

**Citation:** Boeckx, C., Benítez-Burraco, A., eds. (2016). Components of the Language-Ready Brain. Lausanne: Frontiers Media. doi: 10.3389/978-2-88919-893-1

# Table of Contents

*04 Editorial: Components of the Language-Ready Brain* Cedric Boeckx and Antonio Benítez-Burraco

### **Section 1: Genetics**

*05 Retinoic Acid Signaling: A New Piece in the Spoken Language Puzzle* Jon-Ruben van Rhijn and Sonja C. Vernes

### **Section 2: The brain**

*13 Merge in the Human Brain: A Sub-Region Based Functional Investigation in the Left Pars Opercularis*

Emiliano Zaccarella and Angela D. Friederici

*22 'Syntactic Perturbation' During Production Activates the Right IFG, but not Broca's Area or the ATL*

William Matchin and Gregory Hickok

## **Section 3: Development**

*69 The "Globularization Hypothesis" of the Language-ready Brain as a Developmental Frame for Prosodic Bootstrapping Theories of Language Acquisition*

Aritz Irurtzun

*76 Temporal Attention as a Scaffold for Language Development* Ruth de Diego-Balaguer, Anna Martinez-Alvarez and Ferran Pons

## **Section 4: Evolution**

# Editorial: Components of the Language-Ready Brain

Cedric Boeckx 1 \* and Antonio Benítez-Burraco2

1 Catalan Institute for Research and Advanced Studies (ICREA)/Department of General Linguistics, Universitat de Barcelona, Barcelona, Spain, 2 Department of Philology and its Didactics, University of Huelva, Huelva, Spain

Keywords: language, language development, evolution, neurolinguistics, evolutionary biology

**The Editorial on the Research Topic**

**Components of the Language-Ready Brain**

Our intention in putting together this volume was to exemplify and highlight new avenues of research in the language sciences concerning the neurobiology of language. We chose the term "language-ready brain" for our Research Topic, like we did for Boeckx and Benitez-Burraco, because we think it is high time to stress, on the one hand, the importance of a brain-based description of our species' linguistic capacity, and, on the other, the need to appreciate the crucial role culture plays in shaping the linguistic systems children acquire and adults use. In this sense, the focus of neurobiological investigations should not be "language," but our learning biases and cognitive pre-dispositions toward language (i.e., "language-readiness"). Both brain and culture considerations ought to shape research at all levels of inquiry: phylogeny and ontogeny.

The contributions to this research topic break new grounds, by either revisiting long-standing issues (such as the role of Broca's region, the relevance of lateralization, the evolutionary origins of phonology, the role of basic cognitive and perceptive abilities in language acquisition, or the functions performed by language), or by examining closely issues that we are sure will rise to prominence in the near future (like the translational models of language processing into specific patterns of brain oscillations or the nature of the gene networks in which known "language genes" are found integrated). Taken together, the papers collected here shed light on language at the level of the genetics (van Rhijn and Vernes), brain connectivity (Murphy; Theofanopoulou), and physiology (Matchin and Hickok; Zaccarella and Friederici), cognition (de Boer; de Diego-Balaguer et al.), and behavior (Bouchard; Irurtzun; Reboul; Samuels).

In a fast-growing field like the language sciences, Research topics cannot hope to capture all relevant aspects of the field, but we hope that the present volume offers a snapshot that some of the most exciting research taking place today, sowing seeds for future investigations.

# AUTHOR CONTRIBUTIONS

Both authors wrote the editorial.

# FUNDING

Preparation of this work was supported by funds from the Spanish Ministry of Economy and Competitiveness (grants FFI2013-43823-P and FFI2014-61888-EXP), as well as funds from the Generalitat de Catalunya (2014-SGR-200).

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Boeckx and Benítez-Burraco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

#### Edited and reviewed by:

Manuel Carreiras, Basque Center on Cognition, Brain and Language, Spain

> \*Correspondence: Cedric Boeckx<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

Received: 25 April 2016 Accepted: 06 May 2016 Published: 23 May 2016

#### Citation:

Boeckx C and Benítez-Burraco A (2016) Editorial: Components of the Language-Ready Brain. Front. Psychol. 7:762. doi: 10.3389/fpsyg.2016.00762

# Retinoic Acid Signaling: A New Piece in the Spoken Language Puzzle

*Jon-Ruben van Rhijn1,2 and Sonja C. Vernes1,3\**

*1 Department of Language and Genetics, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands, 2 Molecular Neurophysiology Group, Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands, 3 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands*

Speech requires precise motor control and rapid sequencing of highly complex vocal musculature. Despite its complexity, most people produce spoken language effortlessly. This is due to activity in distributed neuronal circuitry including cortico-striato-thalamic loops that control speech–motor output. Understanding the neuro-genetic mechanisms involved in the correct development and function of these pathways will shed light on how humans can effortlessly and innately use spoken language and help to elucidate what goes wrong in speech-language disorders. *FOXP2* was the first single gene identified to cause speech and language disorder. Individuals with *FOXP2* mutations display a severe speech deficit that includes receptive and expressive language impairments. The neuro-molecular mechanisms controlled by *FOXP2* will give insight into our capacity for speech–motor control, but are only beginning to be unraveled. Recently FOXP2 was found to regulate genes involved in retinoic acid (RA) signaling and to modify the cellular response to RA, a key regulator of brain development. Here we explore evidence that FOXP2 and RA function in overlapping pathways. We summate evidence at molecular, cellular, and behavioral levels that suggest an interplay between FOXP2 and RA that may be important for fine motor control and speech–motor output. We propose RA signaling is an exciting new angle from which to investigate how neuro-genetic mechanisms can contribute to the (spoken) language ready brain.

#### *Edited by:*

*Antonio Benítez-Burraco, University of Huelva, Spain*

#### *Reviewed by:*

*Constance Scharff, Freie Universität Berlin, Germany Tomokazu Tomo Fukuda, Tohoku University, Japan*

*Christina Roeske contributed to the review of Constance Scharff*

> *\*Correspondence: Sonja C. Vernes<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology*

*Received: 01 September 2015 Accepted: 10 November 2015 Published: 26 November 2015*

#### *Citation:*

*van Rhijn JR and Vernes SC (2015) Retinoic Acid Signaling: A New Piece in the Spoken Language Puzzle. Front. Psychol. 6:1816. doi: 10.3389/fpsyg.2015.01816*

Keywords: retinoic acid, FoxP2, synaptic plasticity, development, motor skills, striatum, dopamine receptor

# SPEECH AND SPOKEN LANGUAGE

Speech is the primary modality by which humans use language, and human orofacial morphology is uniquely suited to the production of intricate vocalizations needed for spoken language (Lieberman, 2007). The orofacial musculature is one of the most complex muscle systems in the body and in order to successfully produce meaningful speech these muscles must be controlled and coordinated in rapid sequences involving distributed neuronal circuitry. This motor activity is generated in several neural loops that select appropriate actions and generate the necessary motor patterns. One crucial circuit, the cortico-basal ganglia loop, sends activity from the motor cortex to the striatum (a component of the basal ganglia) where activity is integrated. Subsequently, outputs from here modulate activity in several thalamic nuclei. Activity from the thalamus is then sent back to the motor cortex, where a specialized population of output neurons organizes the complex thalamocortical inputs (Kravitz and Kreitzer, 2012; Calabresi et al., 2014). These cortical output neurons send the information, via the pyramidal tract, to motor neurons directly controlling muscle tissue. These neurons are either located in the spinal cord (controlling limb and body movements), or in the brainstem's cranial nerve nuclei

(controlling facial and vocal tract movements). An illustration of the cortico-basal ganglia loop (in the rodent brain) is given in **Figure 1A**. Proper connectivity within this pathway is necessary to enable the precise outputs needed for orofacial muscle control.

The striatum can be seen as a central hub within the motor pathway, making it one of the most intriguing regions in which to investigate properties of motor circuitry and orofacial control. Striatal activity is especially important for fine motor behavior and motor skill learning (Doyon et al., 2003) and cortical and subcortical circuitry, including the striatum, has been established as highly important for speech–motor control (Lieberman, 2002). Furthermore, increased activation of the basal ganglia (which incorporates the striatum) has been shown via functional brain imaging (fMRI) in specific speech–motor language tasks (Wildgruber et al., 2001; Booth et al., 2007). Lastly, morphological changes in the striatum have been described in individuals with speech problems such as stuttering (Craig-McQuaide et al., 2014) and non-fluent aphasia (Ogar et al., 2007).

The principal cell type in the striatum is the medium spiny neuron (MSN), which makes up approximately 98% of all striatal cells (Kemp and Powell, 1971; Huang et al., 1992; for review, see Kreitzer and Malenka, 2008). MSNs can be further divided into two categories of neurons that have different connectivity and opposing functions: dopamine receptor type 1 (D1R) and dopamine receptor type 2 (D2R) expressing cells (**Figure 1A**). D1R expressing MSNs connect to thalamic nuclei via the "direct pathway" which results in excitation of the motor cortex. D2R expressing MSNs form an "indirect pathway" that connects to the thalamus via multiple subcortical regions leading to inhibition of the thalamus and thus reduced cortical input (**Figure 1A**), (Albin et al., 1989; Kravitz and Kreitzer, 2012; Calabresi et al., 2014). This balance between excitation (resulting in more movement) and inhibition (less movement) is crucial for coordinated motor function (Calabresi et al., 2014) including fine orofacial motor control.

In order to unravel the fundamental components that enable humans to effortlessly use spoken language, we will need to understand the neuro-genetic mechanisms involved in establishment, function, and maintenance of speech–motor pathways.

# SPOKEN LANGUAGE AND FOXP2

A breakthrough in speech and language genetics came with the identification of the first gene to cause a speech/language disorder: *FOXP2* (Lai et al., 2001). Mutations in *FOXP2* were found in a large pedigree known as the KE family (Hurst et al., 1990; Fisher et al., 1998; Lai et al., 2001). Affected family members were diagnosed with a severe speech impairment known as developmental verbal dyspraxia (also known as childhood apraxia of speech; OMIM: 602081) and carried a mutation in one copy of their *FOXP2* gene. In addition to speech impairments, affected family members demonstrated receptive and expressive language problems (Watkins et al., 2002a). Although rare, *FOXP2* mutations have been found in a number of unrelated families and individuals with similar speech/language phenotypes (MacDermot et al., 2005; Feuk et al., 2006; Shriberg et al., 2006; Lennon et al., 2007; Palka et al., 2012; Rice et al., 2012; Zilina et al., 2012; for review, see Bacon and Rappold, 2012). In depth investigations of the KE family phenotype indicated a severe impairment in orofacial praxis tasks (Vargha-Khadem et al., 1995; Lai et al., 2001; Watkins et al., 2002a). In addition, impairments in language production tasks (e.g., phoneme addition, word repetition) were found between control and affected individuals (Vargha-Khadem et al., 1995). Different aspects of speech are thus impaired in KE family members (Watkins et al., 2002a). Orofacial praxis deficits underlie impaired lexicon building and subvocal (internal) speech representations which can affect irregular verb grammar (Doyon et al., 2003) and rule based grammar learning (Ullman, 2001). Thus, some of the language impairments in the KE family could be related to the core speech production deficits observed.

FOXP2, and its murine homolog Foxp2, are found across many regions of the developing and postnatal brain (FoxP2 will be used when referring to both species). Intriguing is the high expression of FoxP2 throughout the mouse and human cortico-striato-thalamic motor circuitry (Lai et al., 2003). During early development FoxP2 is broadly expressed in these regions, but in later developmental and postnatal stages expression becomes more restricted (**Figure 1B** depicts Foxp2 expression in the postnatal mouse brain). In adults, Foxp2 is limited to deep layer cortical neurons (layer 5 motor cortex and layer 6 throughout; Ferland et al., 2003; Morikawa et al., 2009; Hisaoka et al., 2010; Tomassy et al., 2010; Reimers-Kipping et al., 2011; Tsui et al., 2013). Within the striatum, Foxp2 is highly expressed in both types of MSN, though more commonly in D1R MSNs compared to D2R neurons (Vernes et al., 2011). Corresponding with its expression pattern, imaging studies have shown humans with *FOXP2* mutations display structural and functional differences in motor areas. Affected members of the KE family showed structural gray matter volume differences in the motor cortex and striatum (Watkins et al., 2002b). Furthermore, functional imaging studies showed an underactivation of the striatum and altered cortical activation (including speech/motor areas such as the left anterior insular cortex) during word generation and word repetition tasks (Liegeois et al., 2003).

Converging evidence from FoxP2 expression pattern studies and phenotypic characterization of human mutations suggests that FOXP2 may play an important role in the development of the speech–motor pathway. The high expression of Foxp2 in a specific subset of neurons (D1R MSNs) in the striatum indicates a functional specificity related to motor tasks requiring the striatothalamic connections of the direct pathway. Malfunctions within this pathway could ultimately affect aspects of the motor circuitry related to fine motor control and contribute to the observed speech–motor deficit in humans.

# FOXP2 AS A MOLECULAR ENTRY POINT INTO SPEECH–MOTOR PATHWAYS

FoxP2 is a transcription factor; its molecular function is to regulate the expression of other genes, switching them on or off in a temporally and spatially controlled manner. FoxP2 has been shown to regulate 100s of different genes involved in processes crucial to brain development and function, ranging from neurogenesis and migration, to neurite outgrowth and synaptic activity (Spiteri et al., 2007; Vernes et al., 2007, 2011; Konopka et al., 2009; Devanna et al., 2014). Recently, evidence has suggested that FOXP2 regulates a number of genes involved in the retinoic acid (RA) signaling pathway (Devanna et al., 2014). RA is a vitamin-A derivative essential to mammalian development. Disruption of the RA signaling pathway (caused by genetic disruptions or dietary deficiencies) can have severe consequences during development and adulthood (Holson et al., 1997; Krezel et al., 1998)

Retinoic acid induces genetic and morphological changes in cells. When neuronal precursors (cells that generate neurons during development) differentiate into neurons they switch on genes normally found in mature neurons, stop dividing and grow long processes known as neurites (Siegenthaler et al., 2009; Korecka et al., 2013). We previously compared how neuronlike cells with or without FOXP2 responded to RA and found that cells showed stronger genetic and morphological changes in response to RA if FOXP2 was present (Devanna et al., 2014). In addition we discovered that FOXP2 changed the expression of RA receptors – proteins that directly control the cellular response to RA (Devanna et al., 2014). Of particular interest, FOXP2 upregulated retinoic acid receptor β (RARβ) and a number of other genes involved in transport or modification of RA were also transcriptionally regulated (e.g., RORβ, CRABPII, and ASCL1). These experiments suggest an intriguing link between FOXP2 and the RA pathway, in which FOXP2 seems to contribute to or modify the cellular response to RA.

Given the importance of the RA pathway for development, this raises new questions about how FOXP2 might mediate its effects on brain and neural circuit development. Could the relationship between FOXP2 and the RA pathway be relevant for (1) normal motor circuitry development and function, and/or (2) effects of FOXP2 dysfunction in patients? To address these questions, we need to understand how FoxP2 and the RA pathway might interact, and in what way FoxP2 mutations might affect the RA pathway on a cellular, functional and behavioral level.

# RA, FOXP2, AND MOTOR BEHAVIOR

Retinoic acid is a key compound during embryogenesis, affecting a multitude of critical developmental pathways. Precise control of RA levels is essential for normal brain development as either an excess or a deficiency of RA results in widespread adverse effects on the brain.

Gestational treatment of rats with excess RA results in behavioral deficits in learning, memory and motor function (Holson et al., 1997). Rats treated with excess RA displayed poor generalized motor control including impairments in the 'righting reflex' (the ability to return to upright position), and the ability to sit only on the back paws. In addition, gestationally treated adult rats showed problems with learning and memory, such as decreased learning rates in a water filled T maze (Butcher et al., 1972; Holson et al., 1997). Rats lacking dietary vitamin A (of which RA is a metabolite) also perform poorly on motor learning and motor performance tasks (Carta et al., 2006). Furthermore, mice engineered to lack a key facilitator of RA signaling (RARβ) develop severe locomotion deficits and are highly impaired on motor learning tasks (Krezel et al., 1998).

The displayed motor deficits are similar to phenotypes observed in mouse models of Foxp2 dysfunction. Mouse models of two well characterized patient mutations of FOXP2 have been created that have comparable phenotypes. One mouse model reflects the R553H missense mutation found in the KE family (Lai et al., 2001). The second mouse model mirrors an early stop codon in exon 7 introduced by a non-sense mutation that leads to a loss of FOXP2 protein in an independent family with speech/language disorder (MacDermot et al., 2005; Groszer et al., 2008). Mice that have a homozygous Foxp2 mutation show severe general motor impairments, reminiscent of animals treated with excess RA. However these Foxp2 homozygous mutants do not survive beyond 3–4 weeks after birth, possibly due to a requirement for Foxp2 in other organs such as the lungs or heart (Groszer et al., 2008). In mice where a single copy of Foxp2 is affected (as per the heterozygous state of the mutations observed in patients) general motor control is normal but motor learning is impaired (Groszer et al., 2008; French et al., 2012). This more subtle phenotype closely resembles the motor learning phenotype observed in RA deprived rats (Carta et al., 2006). For an overview of the different phenotypes exhibited by Foxp2 mutation, RAR mutation, and RA treatment, see **Table 1**.

# FOXP2 AND RA SIGNALING AFFECT NEURONAL FUNCTION

In addition to the behavioral deficits, vitamin A deprivation/supplementation adversely affects striatal development and function. Cells in the developing lateral ganglionic eminence (the precursor region of the striatum) do not differentiate into the appropriate neuronal subtypes when RA signaling is blocked (Toresson et al., 1999; Chatzi et al., 2011). However restoring RA levels rescued this phenotype and resulted in normal differentiation into appropriate neuronal cell types (Chatzi et al., 2011). Separately, mice engineered to knockout the *RAR*β gene display gross morphological striatal defects including impaired neurogenesis and deficits in acquiring proper neuronal identities (Liao et al., 2008). Lastly, chronic postnatal vitamin A supplementation has been linked to oxidative cell toxicity in the striatum (de Oliveira et al., 2007).

Foxp2 also contributes to striatal cell morphology and function. Foxp2 mutant neurons exhibit reduced neurite growth and branching in primary striatal cultures (Vernes et al., 2011) and the *in vivo* striatum displays aberrant neuronal activity. Mice with a heterozygous Foxp2 mutation showed unusually high activity in the dorsomedial striatum during active motor behavior (French et al., 2012). This suggests striatal cells can no longer properly modulate their activity following input from motor areas when lacking Foxp2. Moreover, the increased striatal activity normally seen when animals perform motor learning tasks was absent in mutant mice. Instead, a decrease in firing rate was seen, again suggesting aberrant modulation of responses to cortical and/or thalamic input (French et al., 2012). Additionally, extracellular measurements on striatal brain slices from heterozygous Foxp2 mutant animals show these cells fail to respond to induction of long term depression (LTD; Groszer et al., 2008). An inability to induce long term plasticity [either LTD or long term potentiation (LTP)] has debilitating consequences as scaled activity (plasticity) is necessary for circuits to properly regulate their input and output. Synaptic long term plasticity changes underlie information storage and are necessary for learning and memory (Novkovic et al., 2015; Zhu et al., 2015). Interestingly, in the striatum, synaptic plasticity has been strongly linked to motor learning (Dang et al., 2006; Kreitzer and Malenka, 2007). Defects specifically related to striatal LTD and LTP are known to affect procedural motor learning and the acquisition of new motor paradigms (Gubellini et al., 2004).

Aberrant induction of synaptic scaling has also been found in mice following acute RA depletion, which results in a complete lack of hippocampal LTP or LTD (Misner et al., 2001). This phenotype was specific to RA depletion and was reversible, as vitamin A supplementation rapidly restored normal synaptic plasticity (Misner et al., 2001). At a molecular level, RA signaling is mediated by the action of RA receptors (RARs; RARα, RARβ, and RARγ) and similar plasticity defects have been shown for mice lacking RARα (Sarti et al., 2012) or RARβ (Chiang et al., 1998). Hippocampal cells from these mice fail to establish LTD when subjected to low frequency stimulation – the paradigm necessary to induce LTD in the hippocampus. By contrast, excess RA induced the reverse effect in cultured hippocampal slices, where increased excitatory activity was observed (Aoto et al., 2008). It is not yet known if RA signaling affects synaptic plasticity in the striatum. However, the similarity in synaptic activity phenotypes between Foxp2-, RARα-, and RARβ-deficient animals (albeit focusing on different brain regions) does indicate these transcription factors may play a role in similar intracellular pathways regulating neuronal activity and synaptic plasticity.

The aforementioned plasticity (LTD and/or LTP) deficits in Foxp2, RARα, and RARβ mutant animals suggests an improper reaction of neuronal circuits to changes in external input. Induction of LTD or LTP leads to a decrease or an increase, respectively, in the amount of glutamate receptors (of the AMPAreceptor class) at the synaptic membrane (Seidenman et al., 2003; Briand et al., 2014; for review, see Luscher and Huber, 2010). This change in AMPA receptor abundance modifies the response strength of a cell when it is excited. The change in stimulus–response strength is transient, and in time the normal AMPA receptor distribution will be restored, returning synaptic responses to normal levels. RA treatment of hippocampal cultures has shown an increase of AMPA receptors on the cell surface (Aoto et al., 2008), but no data on the striatum is currently present. The shared synaptic plasticity defect following disruption of RA signaling pathways or Foxp2 mutation does suggest that they both may influence receptor abundance or localization at the synapse in the striatum, an intriguing area for further study.

*–, no effect;* +*, mild effect;* ++*, strong effect; N/A, not applicable; NT, not tested.*

A thorough investigation of the mechanisms leading to LTD and LTP deficits resulting from RA/RAR and Foxp2 malfunction will be necessary to understand if they function in the same pathways. Understanding the molecular mechanisms underlying striatal function, especially related to complex motor circuitry function, will lead to a better understanding of striatal speech– motor control.

# MOLECULAR LINKS BETWEEN RARs AND FOXP2

Retinoic acid receptors canonically function as transcription factors, regulating genes responsible for directing normal embryogenesis and brain development. Interestingly, FoxP2 and RARs share some of the same target genes (Balmer and Blomhoff, 2002; Delacroix et al., 2010; Devanna et al., 2014).  Notably high expression of RARs can be found throughout the motor circuitry, including cortical, striatal, and multiple thalamic regions (Krezel et al., 1999), (**Figure 1B**). We focus on two key receptors found in the motor circuitry: RARα and RARβ. RARα is found in layer 5 of the cortex and in the thalamus – both regions that overlap with murine Foxp2 expression (Krezel et al., 1999; Zetterstrom et al., 1999; Ferland et al., 2003; Lai et al., 2003; Hisaoka et al., 2010). Interestingly, Foxp2 only overlaps with RARα in the motor cortex layer 5, because Foxp2 expression is largely restricted to layer 6 of other mature cortical areas. RARβ is strongly expressed only in the striatum, another site where Foxp2 expression is highest (**Figure 1B**). Notably, FOXP2 has been shown to directly drive RARβ expression in human cells (Vernes et al., 2007; Devanna et al., 2014), although this is yet to be shown in the striatum. This high level of overlap, combined with shared target genes and molecular interactions, strongly supports interplay between FoxP2 and RARs in motor pathways.

# CONCLUDING REMARKS

In addition to its canonical role during embryogenesis, studies described here suggest RA signaling plays a specific role in the development and function of striatal motor circuitry and may link to FoxP2 function. Disruption of the RA pathway results in strikingly similar phenotypes to FoxP2 mutation on multiple levels, which suggests a potential mechanistic interaction. FoxP2 and RARs can regulate some common target genes, affect similar cellular phenotypes and show highly overlapping expression patterns in the cortico-striato-thalamic motor circuitry. In the striatum, aberrant function of Foxp2 and RA signaling contributes to altered development and, in the case of mutations of mouse Foxp2, altered synaptic plasticity similar to that seen in the hippocampus of RARα mutant animals. Given that RARβ is predominantly expressed in the postnatal striatum, it seems likely that its disruption will also affect striatal plasticity, however, this is yet to be experimentally determined. Lastly, animals with mutated Foxp2 or RA signaling defects show comparable motor control/learning impairments. Thus at multiple levels (molecular, cellular, circuit, and behavioral) there is evidence that interplay between FoxP2 and RA signaling may facilitate proper development and function of motor circuitry. This evidence from mice is strengthened by findings in songbirds which show both FoxP2 and RA influence song learning by acting in circuits that have parallels with human vocal-motor pathways (Haesler et al., 2007; Wood et al., 2008). In the future it will be of great value to understand if these signaling cascades interact to influence neuronal mechanisms related to song learning or speech–motor control, and if RA signaling deficits are involved in aberrant speech–motor development in humans. The capacity for human speech and spoken language is dependent on multiple molecular and neural building blocks. With the link between FoxP2 and RA signaling, a new block has been suggested, giving us new opportunities to investigate the evolution and development of the (spoken) language ready brain.

## REFERENCES

#### ACKNOWLEDGMENTS

This work was supported by a grant from the Donders Institute, Radboud University Nijmegen, a Marie Curie career development grant awarded to SCV and by the Max Planck Society. We would like to thank Moritz Negwer for valuable comments on the manuscript.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 van Rhijn and Vernes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Merge in the Human Brain: A Sub-Region Based Functional Investigation in the Left Pars Opercularis

#### Emiliano Zaccarella1, 2 \* and Angela D. Friederici 1, 2

1 Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2 Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany

Language is thought to represent one of the most complex cognitive functions in humans. Here we break down complexity of language to its most basic syntactic computation which hierarchically binds single words together to form larger phrases and sentences. So far, the neural implementation of this basic operation has only been inferred indirectly from studies investigating more complex linguistic phenomena. In the present sub-region based functional magnetic resonance imaging (fMRI) study we directly assessed the neuroanatomical nature of this process. Our results showed that syntactic phrases—compared to word-list sequences—corresponded to increased neural activity in the ventral-anterior portion of the left pars opercularis [Brodmann Area (BA) 44], whereas the adjacently located deep frontal operculum/anterior insula (FOP/aINS), a phylogenetically older and less specialized region, was found to be equally active for both conditions. Crucially, the functional activity of syntactic binding was confined to one out of five clusters proposed by a recent fine-grained sub-anatomical parcellation for BA 44, with consistency across individuals. Neuroanatomically, the present results call for a redefinition of BA 44 as a region with internal functional specializations. Neurocomputationally, they support the idea of invariance within BA 44 in the location of activation across participants for basic syntactic building processing.

Keywords: pars opercularis, clusters, syntax, merge, fMRI

# INTRODUCTION

Traditionally language is thought of as one of the most complex cognitive functions. Recently, it has been claimed, however, that the human capacity to process complex syntactic structures is based on a very basic binary process which syntactically binds words together hierarchically to form larger structures. Because of the fundamental nature of this computation, called merge in theoretical linguistics (Chomsky, 1999; Adger, 2003), the determination of its neural implementation would constitute the neurobiological basis of a process which is at the root of any complex syntactic structure (Berwick et al., 2013). Up to now, the operation has almost never been directly studied in isolation, as syntax usually has been studied in more complex sentential contexts (Just et al., 1996; Stromswold et al., 1996; Moro et al., 2001; Cooke et al., 2002; Röder et al., 2002; Ben-Shachar et al., 2003, 2004; Constable et al., 2004; Bornkessel et al., 2005; Fiebach et al., 2005; Grewe et al., 2005; Friederici et al., 2006b; Santi and Grodzinsky, 2007, 2010; Caplan et al., 2008; Kinno et al., 2008;

#### Edited by:

Cedric Boeckx, Catalan Institute for Research and Advanced Studies (ICREA) and Universitat de Barcelona, Spain

#### Reviewed by:

Narly Golestani, Université de Genève, Switzerland Evie Malaia, University of Texas at Arlington, USA

> \*Correspondence: Emiliano Zaccarella<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

Received: 15 September 2015 Accepted: 10 November 2015 Published: 27 November 2015

#### Citation:

Zaccarella E and Friederici AD (2015) Merge in the Human Brain: A Sub-Region Based Functional Investigation in the Left Pars Opercularis. Front. Psychol. 6:1818. doi: 10.3389/fpsyg.2015.01818 Newman et al., 2010). These and other studies across different languages indicate that the larger region in and around Broca's area in the inferior frontal cortex (IFG) supports syntactic processes (for reviews see Vigneau et al., 2006; Friederici, 2011).

A second region to be considered in the frontal cortex is the frontal operculum (FOP) which is a phylogenetically older than Brodmann Area (BA) 44 (Sanides, 1962; Friederici, 2006) and has been shown to be involved in syntactic classification as well as word based processing (Grasby et al., 1994; Stowe et al., 1998; Friederici et al., 2000b, 2006a). The adjacent anterior insula (aINS) is involved in the processing of short two-word sequences independent of whether they constitute a phrase or not (Zaccarella and Friederici, 2015). Other studies trying to localize syntactic processes reported the involvement of temporal regions, i.e., the left posterior superior and anterior temporal lobe (ATL) regions rather than inferior frontal regions (Bottini et al., 1994; Stowe et al., 1999; Vandenberghe et al., 2002; Humphries et al., 2005, 2006). These studies, however, used long sentences and compared these to word-lists often allowing minimal syntactic processes. The lack of any ATL activity in studies comparing sentences that only differed in syntactic complexity (Friederici et al., 2006b) speaks in favor of a compositional semantic role rather than a syntactic role of this area (Barsalou, 1982; Humphries et al., 2007). Indeed, a series of recent magnetoencephalography (MEG) experiments looking at conceptual compositionality effects at the phrasal level, found the ATL to be active during the construction of complex semantic representations, when color concepts had to be combined together with real objects, or when the same colors had to be combined with nouns (object labels) carrying semantic information (Bemis and Pylkkänen, 2011, 2013; Del Prato and Pylkkanen, 2014). Semantic sensitivity for the area is strongly confirmed by a recent fMRI study showing that activity change in the ATL varied as a function of the presence of lexicosemantic information, but not of syntactic variables, during the construction of progressively increasing linguistic structures (Pallier et al., 2011).

The goal of the present study was to identify the neural basis of the most basic syntactic computation, upon which any more complex hierarchical structure can be derived. By this computation two words, i.e., this and ship, are bound together to a hierarchical phrase containing both words—i.e., this ship with this dominating ship. By applying the same mechanism again, we then recombine this phrase with the closest element occurring in the sentence, to form increasing syntactic hierarchies—i.e., this ship sinks. Phrases of two-word length—like this ship—are the ideal level to investigate this most basic process of syntactic binding, as the amount of cognitive load required to process such small constructions is very limited. This means that—after classifying this as a determiner and ship as a noun—only the operation of merge is necessary to make it a phrase. Crucially, at the very same two-word level, it is also possible to create contexts consisting of simple lists of words—like stone, ship—in which no phrase can be created, as no syntactic relationship holds. Because of this minimal opposition, two-word manipulations are an ideal level to identify the most basic and essential syntactic computation of merge. We hypothesize that this computation should be located in Broca's area, as this region has been found to support syntactic processes (Vigneau et al., 2006; Friederici, 2011; Hagoort and Indefrey, 2014) and further that if its assumed fundamental nature holds, it should be localizable (a) in a very confined subregion within this area, and (b) with little variance across individuals.

Neuroanatomically, Broca's area can be subdivided into BA 45 and BA 44, whose borders were first mapped based on the cellular organization of their regional tissues (Brodmann, 1909), and then redefined using observer-independent cell density profiles over histological slices of postmortem brains (Amunts et al., 1999). More recently, a multireceptor-based analysis separated BA 44 into an anterior dorsal part, and a posterior ventral part (Zilles and Amunts, 2009; Amunts et al., 2010). A very recent meta-analytic functional connectivity-based parcellation (CBP) approach even proposed a decomposition of BA 44 into five separate subregions, called clusters (Cs) two of which located in its more posterior part (C1 and C4), another two in the more anterior part (C2 and C3), and a third one in the inferior frontal junction (C5; Clos et al., 2013). The CBP approach first identifies the whole-brain co-activation pattern for each voxel contained in BA 44 across several thousands fMRI studies, and then groups together those voxels into distinct clusters, according to the similarity of their co-activation patterns across the brain. While the functional specificity of these five clusters as indicated by the metaanalysis is low, with each of the clusters' functional domain ranging from action, working memory, switching to other cognitive tasks, the fine-grained subdivision of BA 44 may allow a precise localization of the most fundamental syntactic process assumed for any natural language. Here we hypothesize, that if the high functional specificity of merge as a fundamental syntactic computation holds, this mechanism should to be localizable in one of the sub-clusters within BA 44, with little inter-individual variance.

This hypothesis was investigated in an fMRI study using twoword sequences either allowing hierarchical syntactic binding to apply (phrase trials) or not (list trials), which were presented visually. Phrase trials and list trials were constructed as parallel as possible, only varying in the possible application of syntactic binding. They differed in their first element, which was either a determiner (e.g., this) or a noun (apple), while the second element was always a phonotactically legal pseudoword (flirk) to drastically reduce conceptual-semantic processing in both conditions (Bemis and Pylkkänen, 2011). Two corresponding one-word conditions were also included, in which the pseudowords were substituted with a series of X's (e.g., XXXXX) to explore the effect of number of words. Thus, the experiment included two factors: type of STRUCTURE ["phrase" (PH), allowing syntactic binding, vs. "list" (LS) not allowing syntactic binding] and number of WORDS ("2-words" vs. "1 word plus Xs"). We employed three progressively regionstringent levels of data analysis to localize syntactic binding: (i) a whole-brain analysis to know whether BA 44 and FOP and/or the aINS show activity during two-word processing; (ii) a more restricted volume-of-interest analysis for BA 44 to directly contrast phrase processing vs. word-list processing; and (iii) a cluster-of-interest analysis within BA 44 to localize syntactic

binding at the individual subject level. These three analyses allow us to test whether: (i) BA 44 is highly sensitive to structure formation even at the lowest level of phrase structure building; (ii) the fundamental nature of syntactic binding is expressed by a stringent localization in a subregion within BA 44, using a clusterbased approach in which the five clusters by Clos et al. (2013) are used to test sub-regional sensitivity for merge within BA 44; (iii) the invariant character of syntactic binding assuming little variance of the localization within this subcluster of BA 44 across individuals.

# MATERIALS AND METHODS

#### Participants

We tested 27 right-handed subjects (Oldfield, 1971), but only 22 subjects (11 female; mean age 28.5 years, standard deviation (SD) 3.62 years; all native German speakers) were included in the analysis. Four subjects were excluded because of poor behavioral performance. One additional subject was excluded because the trial list file was corrupted. The local ethics committee of the University of Leipzig approved all procedures used during the experiment. Written informed consent was obtained from all subjects.

# Stimuli Construction

At the 2-words level, phrasal syntactic contexts (2-PH) comprised eight adjectival determiners of two-syllables in length to appear as first word—jede/jedes (each), eure/euer (your), jene/jenes (that), diese/dieses (this) followed by 48 different pseudowords (e.g., DIESE FLIRK). List contexts (2-LS) comprised eight nouns selected from the CELEX corpus for German (Baayen et al., 1995) which were matched to the determiners for syllabic length, letter length, and syllabic stress—Apfel (apple), Käse (cheese), Ofen (oven), Efeu (ivy), Motor (motor), Kiwi (kiwi), Haken (hook), Koffer (suitcase). A corresponding example of list context was APFEL, FLIRK. These words and the pseudowords were constructed and controlled according to all relevant psycholinguistic parameters, and for the pseudowords we strongly avoided associative effects with real words by using an automatized screening procedure based on the same CELEX corpus, followed by a final filtering selection done by three mother-tongue German speakers (see Appendix A–Stimuli construction for detailed information). Importantly, the use of pseudowords in either phrasal or list contexts at two-word level, was directly intended to reduce potential interactions due to semantic activity, given indeed that we were explicitly interested in finding neural correlates of syntactic processing in the brain. In doing so, we took advantage of the intrinsic linguistic distinction between the syntactically prominent functional lexicon (e.g., determiners, prepositions, conjunctions) and the semantically prominent contentive lexicon (e.g., nouns, verbs, adjectives), and coupled it together with the semantic-free nature of the pseudowords themselves. In this respect, since determiners have less semantic content that do nouns, the choice to use pseudowords instead of real words helped us to: (1) keep syntactic activity at work in determiner-pseudoword contexts, while removing semantic information; (2) remove syntactic and semantic information in the noun-pseudoword contexts, by reducing compounding effects via head de-lexicalization (see Supplementary Material); (3) shield further light on the role of BA 44 as syntactic-sensitive area. Finally, at the 1-word level, the pseudowords were substituted with a series of X's (e.g., XXXXX) to obtain 1-word phrasal contexts (1-PH: DIESE XXXXX) and 1-word list contexts (APFEL XXXXX).

# Procedure

Before entering the scanning room, participants performed a short practice session of the actual experiment on a desktop computer located just outside the MR unit area. None of the stimuli used in the instruction session were used during the experimental session. Once in the scanner, stimuli were presented visually using the software package Presentation R (Neurobehavioral Systems, Inc., Albany, CA, USA) with a Sanyo PLC-XP50L LCD XGA (Sanyo Electric Co., Ltd., Moriguchi, Japan; pixels = 1024 × 768; refresh rate = 100 Hz) backmirror system mounted on the head-coil. Although the projector was already adjusted for minimal luminance, a white font/gray background was used, and preferred by all subjects. A monospaced font (Courier) was used (capitalized letters; 45 pt.). A single trial consisted of a white fixation cross which remained at the center of the screen until a random jitter of either 0 or 1000 ms after volume acquisition started the visual stimulation. Stimulusonset-asynchrony was 8.6 s on average. All trials had a total duration of 900 ms and the items were presented sequentially on the screen one after the other (Supplementary Figure 1). Given that our stimulus construction was syllable-constrained, the first bi-syllabic word remained on the screen for 600 ms, while the second monosyllabic word/X string lasted 300 ms. As soon as the fixation cross reappeared, immediately after the second item within the trial had been shown, subjects were requested to perform a simple sequence judgment task similar to the one used in Friederici et al. (2000a), as quickly as possible, by indicating via triple-choice button-pressing whether the two words together formed a phrase (e.g., DIESE FLIRK = yes), whether they did not form a phrase, but just a list of two nouns (APFEL FLIRK = no), or whether it was a trash trial with X strings (DIESE XXXXX/APFEL XXXXX = trash). We used a fully counter-balanced stimulus exposure across conditions, such that in half of the cases the determiner (or the noun) was followed by a pseudoword, and in the other half of the cases by a sequence of Xs. Therefore, subjects could not discriminate between conditions on occurrence of the first word of the trial, rather they were forced to pay attention to the second word to solve the task. Subjects were requested to use the right index finger, the right middle finger, and the right ring finger to accomplish the task. The order of both buttons and trials were fully randomized across subjects. Each experimental dataset collection lasted ∼42 min.

## Behavioral Data Analysis

Mean reaction times for correct responses (RTs) and accuracy rates were calculated for each condition of each participant and were analyzed using a Two-way within-subject analysis of variance (ANOVA), with factors STRUCTURE (phrase: PH vs. list: LS) and number of WORDS (2- vs. 1-word). Missing responses were counted as non-correct responses.

# fMRI Data Acquisition

Functional images were acquired with a 3T whole-body Bruker Medspec 3000 Scanner. The functional data were acquired using a T2∗ -weighted gradient-echo echo-planar-imaging (EPI) sequence, with the following parameters: TR = 2.0 s, TE = 30 ms, flip angle = 90◦ , FOV = 19.2 × 19.2 cm2 , in-plane resolution = 3×3 mm2 ; data matrix = 64×64; slice thickness = 3 mm; interslice gap = 1 mm; number of slices = 30 (axial slices, parallel to AC-PC line/whole-brain coverage, ascending direction), number of volumes = 1270 volumes. T1-weighted 3D MP-RAGE (magnetization-prepared rapid gradient echo) images (Mugler and Brookeman, 1990)—TI = 650 ms; TR = 1300 ms; alpha = 10◦ ; FOV = 256 × 240 mm—were previously acquired with a non-selective inversion pulse to be used for preprocessing of the functional data.

# Functional Imaging Data Analysis

Functional data were analyzed using the SPM8 software package (http://www.fil.ion.ucl.ac.uk/spm/). In the pre-processing session, subject-specific functional volumes were co-registered with corresponding structural T1-weighted images. Functional time series were further realigned to the first image to correct for motion artifacts, and resliced for timing correction. A gray-matter segmentation-based procedure was used for normalization to the standard MR template included in the SPM software package. A Gaussian filter of 8 mm3 FWHM was used to smooth the data. A high pass filter of 128 s was used to attenuate slow global signal changes. These data entered in a number of analyses described below, they were also used for an additional analysis focusing on the insula and its subregions presented in a separate article (Zaccarella and Friederici, 2015).

#### fMRI Whole-brain Data Analysis

The SPM8 software package was then used to perform a twostage random-effects analysis to ensure result generalizability over the population level (Penny and Holmes, 2004). The first five volumes from each dataset were excluded to allow for magnetic saturation effect. Subject-specific general linear models were assessed using the hemodynamic response function from the SPM software (Friston et al., 1995). Single stimulus functions were modeled according to their timing onsets. Error trials and fillers trials were modeled as distinct conditions, and movement parameters were treated as regressors of no interest. Contrast estimates for the four experimental conditions (compared against the global mean) were obtained using first-level statistics. The contrast estimates were then used in a second-level withinsubjects ANOVA to assess group contrasts. Statistical inferences were drawn at P < 0.05, with a Family-Wise Error (FWE) correction.

#### Volume-of-Interest Analysis

Following our initial hypothesis that BA 44 is responsible for merge processing, we focused on the left infero-frontal region alone, by performing a finer-grained analysis in BA 44 to assess the specific effect of phrases compared to lists, directly (2-PH > 2-LS). The cytoarchitectonically-defined BA 44 from the maximum probability map (MPM; Supplementary Figure 2) of the SPM Anatomy Toolbox (Eickhoff et al., 2005) served as an independent search space to avoid selection bias (Kriegeskorte et al., 2009; Vul and Kanwisher, 2010). A smallvolume correction (SVC) was used to threshold the results, at P < 0.05, FWE-corrected.

#### Cluster-of-Interest Analysis

The multi-modal CBP map for BA 44 proposed in Clos et al. (2013) served as mask for the Cluster-of-Interest (COI) analysis. Here we first wanted to simply localize the cortical distribution of the active voxels to the contrast 2-PH > 2-LS, which we found at P < 0.05, FWE-corrected in the SVC analysis discussed above. This map, which is bounded by the same cytoarchitectonic region (MPM) that was used in the above SVC analysis, consists of five sub-regional BA 44 clusters comprising a posterior-dorsal cluster (C1), an anterior-dorsal cluster (C2), an anterior-ventral cluster (C3), a posterior-ventral cluster (C4), and an inferior frontal junction cluster (C5; see also Supplementary Figure 3). For our purposes, the activation mass obtained from the SVC analysis was first transformed into a binary image of zeroes (not-active voxels) and ones (active voxels), and then dot-multiplied with each cluster volume from the BA 44 parcellation map described above. Following this procedure we then counted the total number of ones (active-voxels) falling within each cluster to determine the overlapping region. Additionally, the five CBP clusters were further used as seed sub-regions to extract signal intensity, to evaluate the mean activity distribution of the syntactic binding effect across the different clusters. Mean signal extraction from the five clusters was done using Marsbar 0.41 for SPM (available at http://marsbar.sourceforge.net).

#### Individual Peak Activity Distribution Analysis

Finally, we were interested in assessing whether at the individual level, the peak distribution of neural activity across individual subjects was homogeneously spread within BA 44, or rather gathered around one of the CBP clusters described above, therefore showing little variance over space. To evaluate cluster sensitivity at the subject level, we again used the map for BA 44 as a binary searchable space, and dot-multiplied it with the subject-specific contrast we obtained as {T} maps from first-level statistics, for the contrast (2-PH > 2-LS). For each subject, we then extracted a unique 3D coordinate maximum corresponding to one voxel. Each 3D coordinate was in turn localized as belonging to a particular cluster, using each BA 44 sub-regional cluster as independent mask, following an analogous counting procedure of the one described in the COI analysis above. From the resulting distribution, we first performed a standard chisquare distribution test. We then employed a randomization test of goodness-of-fit to strengthen, or possibly weaken, the significance of our cluster-sensitivity. We therefore drew 10,000 random samples from a population with our known proportions we obtained from the data, re-calculated the chi-square for each replicate sample, and then counted how many times a larger chi-square value was obtained during randomization (McDonald, 2009). The proportion of replicates with chi-square values equal to or greater than the first observed value was then taken as final p-value. A threshold of p = 0.05 (5% of times) was chosen.

# RESULTS

# Behavioral Results

No significant effect for accuracy was found. A significant effect for STRUCTURE [F(1, 21) = 25.003; p < 0.0001] and an interaction between WORDS and STRUCTURE [F(1, 21) = 5.896; p < 0.05] were found for the reaction time data. A series of paired t-tests revealed that subjects were slower for 2-LS compared to 2-PH (t = 3.93; p < 0.001). An almost significant difference for LS: 2 > 1 was found (p = 0.059), while there was no significant difference for the contrast PH: 2 > 1 (p > 0.1; Supplementary Figure 4).

#### Whole-brain Analysis

We found a main effect of WORDS in the left and right FOP/adINS at x = −33; y = 23; z = −2 and x = 36; y = 23; z = −2, respectively, and in the left BA 44 at x = −48; y = 11; z = 7. The main effect of STRUCTURE, as well the WORDS × STRUCTURE interaction did not yield any significant clusters that survived standard statistical thresholds (Supplementary Table 1).

#### Volume-of-Interest Analysis

The hypothesis-driven analysis in BA 44 revealed a significant cluster for phrase compared to list at two-words level (2-PH > 2- LS) in the ventral anterior part of BA 44 at x = −48; y = 17; z = 16, using a small volume correction analysis (SVC) in the area. No significant voxel was found active for the opposite contrast (2-PH < 2-LS), even at more liberal thresholds. To note, prior direct comparison between phrase and list at two-words level, we run an ANOVA with factors WORDS and STRUCTURE within BA 44 to gain information about a possible interaction within the region. Interestingly, we detected within the region the 3D voxel showing the highest peak by downloading the unthresholded WORDS × STRUCTURE activation map we obtained from the SPM group-averaged output (x = −51; y = 20; z = 13). From this 3D coordinates we then extracted signal intensity for all four conditions to verify whether an interaction between WORDS and STRUCTURE would have survived statistical control. To note, we found a significant interaction between the two factors at p = 0.039 level [F(1, 21) = 4.83]. Direct comparison between 2-PH and 1-PH was significant at p < 0.001 level (t = 3.87), as it was between 2-PH and 2-LS at p = 0.007 level (t = 2.94). Direct comparison between 2-LS and 1-LS was not significant (t = 1.67; p = 0.11; see **Figure 1A**. See also Supplementary Table 1 and Supplementary Figure 5 for the interaction effect). We further performed additional independent analyses in the other regions that were found to be active for the main effect of WORDS, to evaluate whether syntactic binding was specifically performed in BA 44 alone, or whether additional portions of the cortex were also involved. There was no difference between phrases and lists in the other regions under analysis (see also Appendix B–Volume-of-interest Analysis for more information). To gain further exploratory indication on the relative functional contribution of phrases and lists to the main effect of WORDS in the inferior frontal regions, and to verify the results we obtained from the SVC analysis, we went then back to our full brain datasets and performed two distinct planned contrasts at the two levels of the STRUCTURE factor. For the contrast lists vs. oneword list condition (LS: 2 > 1) we found activity in the opercula only at x = −33; y = 23; z = −2 and x = 36; y = 23; z = −2. For the contrast phrases vs. one-word phrase condition (PH: 2 > 1) we found additional recruitment of the ventralanterior portion of left BA 44 at x = −51; y = 11; z = 7, which, together with the LS: 2 > 1 results, suggests a stronger involvement of BA 44 for phrases than for lists. At the wholebrain level, however there was no significant difference in activity for the contrast phrases and lists (2-PH > 2-LS) using standard threshold methods.

#### Cluster-of-Interest Analysis within BA 44

All voxels active for the contrast phrase vs. list at twowords level (2-PH > 2-LS) in the SVC in BA 44 fell within the anterior–ventral cluster C3 (100% overlap; 12/12 voxels; see **Figure 1B**). Remarkably, paired t-tests for signal intensity revealed a significant difference in activity between phrases and lists (2-PH > 2-LS) in the C3 cluster only [t(21) = 2.97, p = 0.007, surviving Bonferroni-correction for the number of tests [p = (0.05/5 tests) = 0.01], and in no other sub-region (see **Figure 2** and Supplementary Figure 6).

#### Individual Peak Activity Distribution Analysis

A strong cluster-sensitive distribution of the individual peak activity for the contrast (2-PH > 2-LS) was found in C3, as compared to the other BA 44 sub-regions [χ 2 (4) = 13.45, p = 0.009, confirmed after 10,000 randomization tests for goodnessof-fit; see **Figure 3**].

#### DISCUSSION

The goal of the present fMRI study was to identify the neuroanatomical basis of the most fundamental syntactic computation, which is at the root of all natural languages (Chomsky, 1995; Berwick et al., 2013). This basic computation, called merge, which binds two words together syntactically, allows to build up syntactic structures with increasing hierarchy. Here we found that this most basic process of syntactic binding corresponded to increased activity in a most confined brain region, i.e., the anterior section of the ventral left pars opercularis, BA 44 at the posterior part of Broca's area. Conversely, a phylogenetically older area, the FOP/adINS, was found to be equally active for both phrasal structures and unstructured wordlists, not discriminating between these.

With respect to the FOP/aINS, the present analysis indicates that this area's function—previously identified by a region-ofinterest analysis of the insula—is also identifiable at the whole brain analysis, thereby extending results from the same study (Zaccarella and Friederici, 2015). Its involvement revealed from the contrast between two- and one-word stimuli may reflect word-accumulation processes during which the categorical information and the grammatical status of the word is first accessed (Friederici et al., 2000b) and then shortly maintained on hold (Grasby et al., 1994), before further processing takes place. A similar activity pattern found for both phrases and lists is not surprising, given the low degree of functional specialization that the FOP/adINS has inside the language processing system (Saygin et al., 2004; Mutschler et al., 2009). This lower specialization of the left FOP/adINS compared to BA 44 also finds support in another fMRI study which showed that this area, in contrast to BA 44, was not able to distinguish between grammar types, but only able to detect an error in the order of syllables in sequences (Friederici et al., 2006a).

In BA44 we found that the basic operation of syntactic merge was sensitive to a specific cluster within the region, such that only the anterior-ventral cluster as one among five sub-regions discriminated between phrases and lists. Crucially, we discovered that the localization of this activity within the same anteriorventral cluster was highly consistent across participants. While a

Zaccarella and Friederici Merge in the Left Pars Opercularis

previous functional connectivity-based meta-analysis described this subregion as being associated with all kinds of language processes (Clos et al., 2013), the present study is the first to delineate this subregion from other regions within BA 44 in its function in language. This particular subregion appears to be responsible for the most basic syntactic computation at the root of all syntactic hierarchies. Remarkably, within neurolinguistic literature, the contribution of parts of Broca's area, in particular BA 44, to syntactic processing has mostly been discussed in terms of syntactic complexity at the sentential level, since the area was found to be crucial for the processing of syntactically more complex sentential hierarchies, compared to simpler ones (Röder et al., 2002; Friederici et al., 2006a,b; Bahlmann et al., 2008). The present data are in line with the view of BA 44 being activated as a function of structural hierarchy, but they clearly go beyond this view by demonstrating that the most basic syntactic computation upon which more complex hierarchies are built, can be neuroanatomically located in a sub-region of BA 44. This means that because both complex and simpler linguistic hierarchies necessarily share the same computational merging algorithm, BA 44 activates as a function of structural hierarchy regardless of the linguistic complexity itself.

The sustained cluster-sensitivity of the merge computation in the anterior-ventral cluster of BA 44 both at the group and the individual level points toward a fundamental and constrained nature of merge at the neural level. While we acknowledge that the present work only tested for one single language, the low interindividual spatial variability we found across our representative set of subjects, might be taken as a first approximate indication in favor of the fundamental character of the computation itself. The present finding of a sub-regional specificity and invariability of the most basic process closely resembles the neural organization of other basic sensory processes, as for example in the visual system (Downing et al., 2006), while the low inter-individual variability of the basic processes gives rise to the assumption that their function to structure relation is predetermined. In this respect, because of the essential nature of merge as being the shared computation across all human languages, future studies should systematically focus their attention on the neural implementation of very basic linguistic processes in multiple languages. This would ultimately prove whether the universality of merge at theoretical level (Chomsky, 1995) adequately corresponds to some neuroanatomical generalizability at the neural level suggested here.

From an evolutionary perspective, no clear evidence for a human-like language syntax in nonhuman species has been presented so far (Fitch and Hauser, 2004; Bolhuis et al., 2014). Structural studies have shown that the FOP is a phylogenetically older cortex, which is fully represented in monkeys (Sanides, 1962), while BA 44 seems to be more expressed in humans than in monkeys, in which it plays a role in orofacial somatomotor processes (Petrides et al., 2005). The activation pattern we report in our study closely resembles the one proposed for human and non-human artificial grammar processing in the adult brain, in which violations to transition probabilities are found to activate FOP, while violations to more rules activate BA 44 (Friederici et al., 2006a). This functional split between the labor of FOP/aINS and that of BA 44 is particularly intriguingly if put into relation with recent theoretical linguistic models, which propose that the specificity of merge in language should reside in the property that words have to create constituents where the lexical label of the single dominant word (e.g., determiner) is reflected as a hierarchical influence onto the newly created syntactic constituent (e.g., determiner phrase; Boeckx, 2010; Chomsky, 2013; Murphy, 2015). At the interface between linguistic theory and neurolinguistics, the merge mechanism would then consist of two phases: one in which linguistic elements are strung together without any hierarchical dimension in the FOP/aINS, and a labeling phase in which the dominant lexical element transforms the string into a hierarchically labeled syntactic structure in the anterior-dorsal BA 44. We believe that this speculation can constitute a testable model for the evolution of the language faculty, in which behavioral, functional and anatomical data can be put together in a comparative perspective within across human and animal species (Murphy, 2015).

# CONCLUSION

The sub-anatomical specificity for the process of syntactic binding, called merge, has a strict neural basis in the anteriorventral cluster of BA 44. The profoundly constrained regional localization of this syntactic operation converges on the conclusion that the computation at the root of our syntactic knowledge has strict neural basis. The constraint localization of the activity and its consistency across the participants point toward the fundamental neurobiological nature of the operation of merge itself, thereby providing a novel view on the relation between linguistic theory and neurobiology.

# ACKNOWLEDGMENTS

We wish to thank all participants for their involvement, Mandy Jochemko, Anke Kummer, and Simone Wipper for MRI data acquisition, Lars Meyer for his valuable support during the analysis of the functional data, Michiru Makuuchi, Katharina von Kriegstein, and Isabell Wartenburger for their comments on earlier versions of the manuscript, Francie Manhardt for evaluating the stimulus material, and Kerstin Flake for helping us creating the figures. This work was supported by a grant of the European Research Council (ERC-2010-AdG 20100407 awarded to AF) and by the Berlin School of Mind and Brain (EZ).

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpsyg. 2015.01818

# REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Zaccarella and Friederici. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# 'Syntactic Perturbation' During Production Activates the Right IFG, but not Broca's Area or the ATL

#### William Matchin1 \* and Gregory Hickok2

1 Cognitive Neuroscience of Language Laboratory, Department of Linguistics, University of Maryland, College Park, MD, USA, 2 Auditory and Language Neuroscience Laboratory, Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA

Research on the neural organization of syntax – the core structure-building component of language – has focused on Broca's area and the anterior temporal lobe (ATL) as the chief candidates for syntactic processing. However, these proposals have received considerable challenges. In order to better understand the neural basis of syntactic processing, we performed a functional magnetic resonance imaging experiment using a constrained sentence production task. We examined the BOLD response to sentence production for active and passive sentences, unstructured word lists, and syntactic perturbation. Perturbation involved cued restructuring of the planned syntax of a sentence mid utterance. Perturbation was designed to capture the effects of syntactic violations previously studied in sentence comprehension. Our experiment showed that Broca's area and the ATL did not exhibit response profiles consistent with syntactic operations – we found no increase of activation in these areas for sentences > lists or for perturbation. Syntactic perturbation activated a cortical-subcortical network including robust activation of the right inferior frontal gyrus (RIFG). This network is similar to one previously shown to be involved in motor response inhibition. We hypothesize that RIFG activation in our study and in previous studies of sentence comprehension is due to an inhibition mechanism that may facilitate efficient syntactic restructuring.

#### Edited by:

Cedric Boeckx, Catalan Institution for Research and Advanced Studies/Universitat de Barcelona, Spain

#### Reviewed by:

Olaf Hauk, MRC Cognition and Brain Sciences Unit, UK Ina Bornkessel-Schlesewsky, University of South Australia, Australia

> \*Correspondence: William Matchin<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

Received: 12 December 2015 Accepted: 05 February 2016 Published: 23 February 2016

#### Citation:

Matchin W and Hickok G (2016) 'Syntactic Perturbation' During Production Activates the Right IFG, but not Broca's Area or the ATL. Front. Psychol. 7:241. doi: 10.3389/fpsyg.2016.00241

Keywords: syntax, sentence processing, language, fMRI, inferior frontal gyrus, Broca's area, ATL, production

# INTRODUCTION

Language can be analyzed as a cognitive faculty consisting of several components, including a core structure-building system – syntax – that operates over stored lexical atoms (Chomsky, 1982, 1995; Hauser et al., 2002). Much work attempting to localize syntactic operations has focused on Broca's area (Stromswold et al., 1996; Hagoort, 2005; Grodzinsky and Santi, 2008; Friederici, 2011) in the left inferior frontal gyrus (LIFG; Brodmann areas 44 and 45) and the anterior temporal lobe (ATL; Rogalsky and Hickok, 2009; Bemis and Pylkkänen, 2011; Brennan et al., 2012). However, the response profile of Broca's area during sentence comprehension appears to be more compatible with a domain-general function such as working memory or cognitive control than with syntax (Kaan and Swaab, 2002; Novick et al., 2005; Rogalsky and Hickok, 2011; Bornkessel-Schlesewsky and Schlesewsky, 2013), although this is still a hotly debated issue (Hickok and Rogalsky, 2011; Fedorenko et al., 2012b). Similarly, recent neuroimaging and neuropsychological studies have implicated the ATL in semantic rather than syntactic processes (Rogalsky and Hickok, 2009; Pallier et al., 2011; Del Prato and Pylkkanen, 2014; Wilson et al., 2014).

We chose to perform a functional magnetic resonance imaging (fMRI) experiment during sentence production to contribute to this debate. Sentence production studies in fMRI and magnetoencephalography (MEG) have revealed large overlap with the activation patterns found in comprehension, suggesting that similar neural networks underlie sentence processing in both modalities (Braun et al., 2001; Blank et al., 2002; Haller et al., 2005; Golestani et al., 2006; Menenti et al., 2011; Segaert et al., 2012; Del Prato and Pylkkanen, 2014; Pylkkänen et al., 2014). These studies are informative with respect to the neurobiology of sentence production; however, few production studies have manipulated syntactic variables compared to the vast literature on syntactic processing in comprehension. Syntax production studies will be important to provide complementary evidence to the comprehension literature to better understand syntactic processing in the brain.

We attempted to parallel the effects of syntactic violations that several researchers have used to study syntax in comprehension (e.g., Embick et al., 2000; Moro et al., 2001; Friederici et al., 2003). However, there are significant obstacles in extending the violation approach to production. Instructing subjects to produce artificial syntactic errors means that subjects will expect the upcoming violation. This may eliminate the effect of interest because expectation lessens the strength of the neural response to syntactic violations (Lau et al., 2006). The short time constraints of fMRI make difficult capturing infrequent natural errors or using a paradigm to induce subjects to produce them (Ferreira and Swets, 2005). Because of these reasons, we forced subjects to intermittently and unexpectedly switch their planned syntactic structure mid-utterance. The logic is that switching structures increases demands on the neural resources involved in syntactic processing (as well for other mechanisms). We expected to capture this effect in the blood-oxygen level-dependent (BOLD) response during scanning.

In the present study, we controlled the syntactic structure of the subjects' utterances with a constrained sentence elicitation task, similar to Caplan and Hanna (1998). To induce syntactic restructuring, we borrowed the target perturbation paradigm from motor control research (Paulignan et al., 1991; Elliott et al., 1995; Izawa et al., 2008). In this paradigm subjects attempt to hit a target, e.g., reaching from one point to another on a screen. On most trials the subject's target and/or sensory feedback remain constant throughout the trial. On a smaller proportion of random trials, the subject's target or sensory feedback is altered midmovement. For example, the target location changes, or a force is applied to the subject's arm. On such trials, the subject must adapt and correct the movement trajectory online to reach the goal. We adapted this approach to syntax, dubbing our paradigm "syntactic perturbation."

We trained subjects to produce either active sentences (e.g., Susan is following Charlie) or passive sentences (e.g., Charlie is being followed by Susan). On most trials (standard trials, 80%) subjects did not switch their planned structure. On a smaller proportion of random trials (switch trials, 20%) a cue prompted subjects to switch structures mid-utterance. In other words, on switch trials the cue prompted the subject to switch from active to passive or from passive to active. This task was artificial, raising questions about the ecological validity of our experiment – such considerations should be kept in mind when evaluating the results. However, we assumed subjects would update their syntactic structure regardless of this artificial nature.

The key assumptions of our experiment are the following: (1) the planned syntactic structure of an utterance is built in advance of speech production (at least for mono-clausal active and passive sentences), and (2) this plan can be dynamically updated during speech production. The first assumption is supported by the fact that juxtaposition errors often occur for words or phrases of the same syntactic category and from the same syntactic position (Fromkin, 1971) – to account for this regularity, speakers must have built the syntactic structure in advance of articulation. The second assumption is supported by an experiment showing that speakers decrease their rate of speech predictively if the structure they ultimately utter contains a syntactic violation (Ferreira and Swets, 2005).

Our design consisted of two main contrasts: STRUCTURE (sentences > word lists) and PERTURBATION (switch trials > standard trials), and one secondary contrast: COMPLEXITY (passive sentences > active sentences). Using our novel paradigm in production rather than comprehension, we examined the response profile of these contrasts in areas traditionally associated with syntax. Our main goal was to further inform the debate on the role of these regions in syntactic processing. Also, we believe that the discussion of the neurobiology of syntax has focused overwhelmingly on the ATL and Broca's area because of the repeated use of similar experimental manipulations in comprehension. We sought to determine whether our experiment, differing in modality and task, found activation in areas outside of these regions, potentially indicating a role for them in syntactic processing. We discuss our predictions for each of these contrasts in turn.

The contrast of sentence > word lists in comprehension has frequently revealed activation in the ATL, often bilaterally but also left lateralized (e.g., Mazoyer et al., 1993; Humphries et al., 2005; Rogalsky and Hickok, 2009). Two recent MEG studies found increased activation for preparation to produce two-word phrases compared to production of single words (Pylkkänen et al., 2014) and two-word lists (Del Prato and Pylkkanen, 2014). Current research supports a semantic interpretation of ATL function that drives these effects. We expected that the contrast of STRUCTURE in our study would also activate the ATL because the production of sentences presumably requires semantic processing that the production of lists does not.

The sentence > list contrast in comprehension occasionally activates Broca's area (Bedny et al., 2011; Fedorenko et al., 2011; Pallier et al., 2011), but these effects are much less consistent than for the ATL (Rogalsky and Hickok, 2011). This suggests that this activation reflects the contribution of working memory resources or cognitive control mechanisms needed to parse difficult input rather than fundamental syntactic operations (Novick et al., 2005; Rogalsky et al., 2008). Our sentences are short, simple in structure, and guided by a strict template; we believe this minimizes demands on these mechanisms. Therefore we did not expect STRUCTURE to activate Broca's area.

For similar reasons, we also expected that Broca's area would not show a significant effect of PERTURBATION. During switch trials, subjects had an unambiguous selection of the alternative sentence construction, which should minimize selection demands (Miller and Cohen, 2001). With respect to working memory, these resources are taxed in conditions that place heavy demands on maintenance of information or retrieval across intervening material (Baddeley, 1992; Gibson, 2000; Lewis et al., 2006). During switch trials, subjects had to quickly restructure their utterance but did not have to maintain additional material or retrieve information across long distance.

We did not have equally strong predictions for the PERTURBATION contrast in the ATL. Although the existing evidence does not support a role for the ATL in syntax, changes in syntactic structure lead to changes in semantic interpretation (Chomsky, 2014). If the ATL plays a role in combinatory semantics, syntactic restructuring might induce activation in this region for semantic processes. Therefore we expected to potentially see increased activation for PERTURBATION in the ATL.

Significant effects of PERTURBATION outside these areas could reflect syntactic operations in areas not traditionally associated with syntax, such as subcortical areas (see Lieberman, 2001; Ullman, 2004; Boeckx and Benítez-Burraco, 2014 for these non-standard proposals). Activation for this contrast could also reflect non-syntactic mechanisms. These could be linguistic (e.g., reanalysis of thematic role assignment), or non-linguistic (e.g., error detection, attention). In the discussion section we discuss robust activation of the right IFG for PERTURBATION in the context of the literature on action inhibition and the role it may play in syntactic restructuring.

The secondary COMPLEXITY contrast (passive/complex > active/simple) is an extension of previous work that has shown increased activation in Broca's area for passive sentences (e.g., Ye and Zhou, 2009; Mack et al., 2013). The standard interpretation of this finding is that increased syntactic processing resources are used to process passive sentences. However, while historical approaches of generative grammar (Chomsky, 1982, 2002, 2014) posited a syntactic complexity difference between passives and actives (application of a movement operation in passives), modern syntactic theory does not (largely due to the VPinternal subject hypothesis – active sentences also involve movement of the subject, Kitagawa, 1994). Any complexity difference between passives and actives therefore likely lies in non-syntactic factors, such as the mapping of arguments to thematic roles (we thank an anonymous reviewer for pointing this out). The fact that Broca's area does show increased activation for passives during comprehension supports a non-syntactic interpretation of the function of this region. Whether this region shows increased activation for passive compared to active sentences during production is an open question and should inform hypotheses of this region's function. We did not have strong predictions for this contrast, but included it because it allowed comparison with previous research in comprehension.

# MATERIALS AND METHODS

# Subjects

Twenty-one right-handed, native speakers of English (age 19– 33, 10 female) volunteered for participation. Subjects had normal or corrected-to-normal vision, no hearing impairment, and reported no history of neurological disorder. Subjects were paid \$10 for participation in a 1-hour behavioral training session. One subject was excluded from the fMRI portion of the experiment due to difficulty with the task during the behavioral session resulting in 20 remaining subjects in the fMRI experiment. Subjects were paid \$30 an hour for participation in the fMRI session. Consent was acquired from each subject before participation and all procedures were approved by the Institutional Review Board of UC Irvine.

# Stimuli

The stimulus for each trial consisted of a cue that progressed through three stages: PREP, GO, and FINISH. Every stimulus had the same basic appearance: simple line drawings of the people engaged in the target sentence, the names of the people in large font next to the drawings, the verb to be used in the sentence in the middle of the screen, and an arrow underneath the verb pointing to the right or the left (**Figure 1**). Identical stimulus presentation was used for both sentences and lists – only the subject's task changed. Twenty different transitive verbs were used. Verb length varied from one to three syllables. Verbs were selected for a mix of articulatory complexity. Here is the complete list of verbs: admire, deceive, examine, follow, frighten, greet, harass, help, hug, kick, kiss, pinch, poke, protect, punch, push, rob, scare, tease, tickle.

Verbs were randomly distributed throughout the experimental runs. Four people were used with these names: Mary, Susan, Charlie, and Kevin. The first person was always a different gender than the final person, and people were randomly distributed in different positions throughout the experimental runs. Three people appeared on each cue: one person on the left (START) and two on the right (END). The END people were displayed vertically, one above the other. During the first stage, the PREP stage, a rectangular box surrounded the START person. The arrow, in black color, pointed from the START person horizontally toward the middle of the END people, and not directly toward either of them. During the second stage, the GO stage, the rectangular box disappeared, serving as a "go" signal for the subject to begin articulating. During the third stage, the FINISH stage, the arrow turned blue and tilted up or down to point to the target END individual for that trial. This design forced the subject to begin articulating without knowing which person to end the sentence with and to use the information provided at the FINISH stage to complete the sentence with the correct person. The PREP stage lasted for 500 ms. We chose this time to give subjects enough time to process the information and plan their utterances. The GO stage began immediately after the 500 ms, and subjects began articulating in synchrony with the disappearance of the box. The interval between the GO stage and the FINISH stage was 300 ms, and

the FINISH stage remained on the screen for 1000 ms, followed by fixation until the next trial. During the behavioral training session, the subject would initiate the next trial whenever ready. During the fMRI session, the inter-trial-interval was fixed at 4200 ms, for a total trial duration/inter-trial interval of 6 s.

#### Task

The task was production of either sentences or lists and to restructure appropriately to the switch cue. This resulted in a 2 × 2 design: **STRUCTURE** (sentence, list) and **PERTURBATION** (standard, switch). In the sentence condition, subjects produced sentences with either active or passive construction using the template detailed below. These two constructions comprised an additional sub-factor within the sentence condition, **COMPLEXITY** (active/simple, passive/complex). Active sentences were cued with an arrow pointing away from the first person, and passive sentences were cued with an arrow pointing toward the first person (**Figure 1**). Active sentences were produced with this template: (person 1) **is** (verb)**ing** (person 2). e.g., Mary is following Charlie. Passive sentences were produced with this template: (person 1) **is being** (verb)**ed by** (person 2). e.g., Mary is being followed by Charlie. We instructed subjects to use the progressive aspect on every trial and not to deviate from the template. In the list condition, subjects produced a list of words based on the information from the cue. Subjects ignored the identity of the particular verb on the cue and did not use it in their lists. When the arrow pointed to the right (as in active sentences), subjects produced a list with this template: (person 1) "word **right** arrow" (person 2), .e.g., "Mary word right arrow Charlie." When the arrow pointed to the left (as in passive sentences), subjects produced a list with this template: (person 1) "word **left** arrow" (person 2). e.g., "Mary word left arrow Charlie." We chose the word word to approximately control for the duration of planning and articulation that would take place for the word is in the sentence condition. This timing was relevant to when the subjects were cued to restructure during their utterance (we discuss this in more detail below). Subjects made their utterances at a natural speaking rate.

Subjects did not know how to complete the sentence/list at the beginning of each trial. The FINISH stage indicated which person (top or bottom) would be the second person in the sentence. Subjects were instructed to begin their utterances at the GO stage and use the information on the FINISH stage to determine which name to produce. We set the ISI between the GO stage and the FINISH stage to be 300 ms to allow subjects enough time while speaking naturally to update their utterance without making mistakes on the switch trials. As an example, if the target sentence were "Mary is following Charlie," at the GO stage subjects started speaking "Mary is following . . .", then 300 ms later at the FINISH stage they updated their plan to include "Charlie" and finished. Similarly for the list condition, if the target list were "Mary word left arrow Charlie," they would start speaking "Mary word left

arrow. . ." at the GO stage, and update at the FINISH stage to include "Charlie."

Standard trials occurred as described above; switch trials involved not only updating person 2, but also switching the orientation of the arrow mid-production (**Figure 2**). On sentence switch trials, subjects switched their target sentence from active to passive or vice versa, e.g., Mary is following (person 2) → Mary is **being followed by Charlie**. During list switch trials, subjects needed to switch whether they said right arrow or left arrow, e.g., Mary word left arrow (person 2) → Mary word **right arrow Charlie**. Standard and switch trials were presented at a 4/1 ratio and in random order within each run, such that subjects could not predict what the next trial would be. We used this ratio because this approximate ratio was used in previous studies of target perturbation and fMRI (3/1 ratio used by Tourville et al., 2008), and a smaller ratio of standard to switch trials might have resulted in anticipation of switch trials. We did not want subjects to use a strategy of not committing to a syntactic plan on every trial in order to avoid errors.

The sentence and list conditions were presented in separate runs to avoid confusion and task-switching effects. To balance the spatial orientation of the cues, we counterbalanced across sides by presenting subjects with cues that flowed from left to right (depicted in **Figures 1** and **2**) and cues that flowed from right to left (active sentences correspondingly began with a left arrow instead of a right arrow). Subjects always received two runs from either the sentence or list condition in a row, one each of left and right cue orientation (order counterbalanced across subjects), and we collapsed all analyses across the two orientations.

# Behavioral Training Session

Before running the experiment in the fMRI scanner, we familiarized subjects on the task in a behavioral training session. We wanted subjects to be well prepared for the task in the fMRI scanner to limit variance in performance as well as minimize effects of exposure. In the training session we explained the task to the subjects, including a demonstration by the experimenter on several trials. Then, subjects were asked to perform the task themselves. In the first several trials, the experimenter remained in the testing room to give feedback and instruction. When the subject grasped the task, the experimenter left the room and the subject proceeded self-paced. Subjects performed both tasks with both orientations for a total of four experimental runs, consisting of 50 trials apiece, for a total of 100 trials in the sentence condition and 100 trials in the list condition. The subjects' utterances were recorded and their performance was analyzed. A subject's response was considered an error if they produced the incorrect sentence construction (e.g., active instead of passive), produced the word right instead of the word left (or vice versa), or if they made a speech error during the trial (e.g., produced the wrong speech sound, extensive delays, etc.). Substituting the names of people (e.g., Mary instead of Susan) or substituting one verb for

construction (in red) when that cue was presented. Only the image within the large black rectangles was part of the stimulus. LEFT: PREP stage, during which the subject prepared to begin producing the sentence with either active or passive construction. MIDDLE: GO stage, during which subject was cued to begin producing the incomplete sentence. RIGHT: FINISH stage, during which the completing information was presented. On switch trials, the subject would have to change from one structure to another.

another (e.g., push instead of punch) were not counted as errors, unless the subject also made an additional error as described above. We were only able to collect and analyze behavioral data from 14 out of 20 subjects due to equipment issues. To assess the effect of perturbation on behavior, we averaged across constructions in the sentence conditions and direction in the list conditions. We then performed a 2 × 2 ANOVA (STRUCTURE × PERTURBATION). Subjects underwent the fMRI portion of the experiment after completing the behavioral session, either the same day or on a subsequent day, within a week after the behavioral session.

# fMRI Experiment

Before scanning, subjects were briefly re-familiarized with the task by performing a few trials in each condition outside the scanner. Subjects were instructed to produce their utterances out loud in the scanner, but quietly and with minimal articulation. Subjects received 12 total experimental runs during the experiment (six sentence, six list, counterbalanced by orientation). During the experiment, a fixation cross was displayed on a screen in-between trials. Stimuli were delivered with Matlab software (Mathworks, Inc, USA) utilizing Psychtoolbox (Brainard, 1997; Kleiner et al., 2007). Subjects were given ear covers and foam earplugs to attenuate scanner noise. Each run contained 40 standard trials and 10 switch trials in random order with no explicit rest trials. Presentation order of sentence and list runs was counterbalanced along with cue orientation across subjects. Active/passive constructions and left/right arrow lists were presented at equal frequency. The high-resolution anatomical image was collected following the experimental runs. The scanning session lasted about 1 h and 15 min in total.

# fMRI Data Collection and Analysis

MR images were obtained in a Philips Achieva 3T (Philips Medical Systems, Andover, MA, USA) fitted with an eightchannel RF receiver head coil at the high field scanning facility at UC Irvine. We first collected a total of 1896 T2\*-weighted EPI volumes over 12 runs using Fast Echo EPI in ascending order (TR = 2 s, TE = 25 ms, flip angle = 90◦ , in-plane resolution = 1.95 mm × 1.95 mm, slice thickness = 3 mm with 0.5 mm gap). The first four volumes of each run were collected before stimulus presentation and discarded to control for T1 saturation effects. The high-resolution T1-weighted anatomical image was acquired in the axial plane (TR = 8 ms, TE = 3.7 ms, flip angle = 8 ◦ , size = 1 mm isotropic).

Slice-timing correction, motion correction, and spatial smoothing were performed using AFNI software (http://afni. nimh.nih.gov/afni). Motion correction was achieved by using a 6-parameter rigid-body transformation, with each functional volume in a run first aligned to a single volume in that run. Functional volumes were aligned to the anatomical image, and subsequently aligned to Talairach space (Talairach and Tournoux, 1988). Functional images were resampled to 2.5 mm isotropic voxels and spatially smoothed using a Gaussian kernel of 6 mm FWHM. Finally, functional images were rescaled to reflect percent signal change from the mean signal during each run.

First-level analyses were performed on each individual subject's data using AFNI's 3dDeconvolve function. The regression analysis was performed to find parameter estimates that best explained variability in the data. Each predictor variable representing the time course of activity associated with the task was entered into a deconvolution analysis that estimated parameters best representing the timecourse of the hemodynamic response function in percent signal change values. Timecourse estimates were modeled beginning with the onset of the PREP stage, i.e., when the subject began planning the sentence. The following eight regressors of interest were used in the experimental analysis: sentence active, sentence passive, list left, list right, sentence switch: active to passive, sentence switch: passive to active, list switch: left to right, and list switch right to left. The six motion parameters were included as regressors of no interest. Second-level group analyses were then performed. The values from the experimental contrasts from each subject and condition were entered into a mixed-effects analysis with subjects as random variables using AFNI's 3dMEMA function. We tested the following contrasts: sentence vs. list (**STRUCTURE**), active vs. passive (**COMPLEXITY**), and switch vs. standard (**PERTURBATION**). Because we were particularly interested in switch effects for the sentence condition, we examined the effects of PERTURBATION for sentences and lists separately in addition to the interaction of STRUCTURE and PERTURBATION. We corrected for multiple comparisons though Monte Carlo simulation using AFNI's 3dClustSim function to hold the family-wise error (FWE) rate to less than 0.05. We estimated smoothness in the data from the residual error time series for each subject's first-level analysis using AFNI's 3dFWHMx function. These estimates were averaged across participants for input to 3dClustSim (simulations were restricted to in-brain voxels). Activations were considered significant with a per-voxel threshold of p < 0.001 (one-tailed) and a cluster size threshold of 610 mm3 (39 voxels).

# ROI Analyses

Given the extensive literature documenting a relationship between Broca's area, the ATL, and sentence processing, we performed ROI analyses on these regions. We extracted percent signal change values within structural ROIs for the left and right ATL, Broca's area, and the right hemisphere homolog of Broca's area, the right inferior frontal gyrus (RIFG) and ran statistical analyses. For Broca's area and the RIFG, we used templates in Talairach space for BA44 and BA45 provided by AFNI based on the cytoarchitectonic probability maps of Amunts et al. (1999). We included every voxel in each map and combined both maps together to form a single mask for Broca's area and a single mask for the RIFG. The relevant functional regions of interest for the ATL do not align well to probability maps based on cytoarchitectonics; we constructed left and right ATL ROIs based on coordinates reported in the neuroimaging literature. We obtained the center of mass coordinates reported by Rogalsky and Hickok (2009) for the sentence > list contrast in the left and right ATL, and created spheres with radius 10 mm around the coordinates. We averaged across all voxels within each ROI and analyzed the average percent signal change values across

the entire estimated timecourse. We first analyzed the effect of COMPLEXITY (passive > active) within each ROI with paired t-tests. We then collapsed our analyses across constructions in the sentence conditions and direction in the list conditions, resulting in 2 × 2 ANOVAs for each ROI (STRUCTURE x PERTURBATION).

# RESULTS

## Behavioral Performance

To reiterate, we only collected behavioral data during the behavioral training session before the fMRI session. **Figure 3** shows the behavioral performance of the 14 subjects for whom we collected data. For non-switch standard trials, subjects performed near ceiling for the sentence and list conditions. The clear outlier is the sentence switch condition. Even though subjects' performance dropped during switch sentence trials, their performance was still above 80%, indicating that they could successfully perform the task. A 2 × 2 ANOVA revealed a significant main effect of STRUCTURE, F(1,13) = 5.282, p = 0.039, η 2 = 0.289, no significant main effect of PERTURBATION, F(1,13) = 3.232, p = 0.095, η 2 = 0.199, and a significant interaction, F(1,13) = 5.353, p = 0.038, η 2 = 0.292. Follow-up two-tailed t-tests (α = 0.025) revealed a marginally significant effect of PERTURBATION for sentences, t(1,13) = 2.077, p = 0.058, Cohen's d = 0.555, and no effect of PERTURBATION for lists, t(1,13) = 0.668, p = 0.516, Cohen's d = 0.169. These results confirm that performance was only impaired during the sentence switch condition.

collapsed across constructions in the sentence conditions and right/left arrow in the list conditions. Analysis revealed a significant main effect of STRUCTURE and a significant interaction between STRUCTURE and PERTURBATION. ∼: marginally significant simple effect of PERTURBATION for sentences (p = 0.058) at p < 0.025. Error bars indicate standard error of the mean. See text for details of statistical analyses.

# Whole-Brain fMRI Analyses

The whole-brain contrasts of STRUCTURE and COMPLEXITY did not reveal activation in the ATL or Broca's area. The effect of STRUCTURE (sentences > lists) revealed increased activation for sentences in left visual cortex, right precentral gyrus, right postcentral gyrus, and bilateral middle frontal gyrus (**Figure 4**). The effect of COMPLEXITY (passive > active sentences) revealed one cluster in the left postcentral gyrus (**Figure 4**). See **Table 1** for Talairach coordinates for each significant cluster of activation for these contrasts.

The effect of PERTURBATION in the sentence condition (sentence switch > sentence control) revealed increased activation during the switch condition in a network including areas typically found for experiments of response selection/inhibition as in the Go/No-Go task (Simmonds et al., 2008; Swann et al., 2009). The GO/No-Go task requires subjects to inhibit a planned motor response when a "stop" signal appears, as well in areas found for perturbation in low-level motor control (Diedrichsen et al., 2005; Tourville et al., 2008; **Figure 5**). Particularly strong activation was observed in the right IFG and anterior insula that has been shown to be involved in "stopping," or the cancelation of a planned response (Aron et al., 2003, 2014). Activations for this contrast also included the supplementary motor area (SMA), pre-SMA, basal ganglia (right caudate nucleus), left inferior parietal cortex, right STS, and right IFG/MFG (**Figure 5**). The effect of PERTURBATION in the list condition (switch lists > standard lists) revealed one cluster in the left cerebellum (**Figure 5**, bottom). See **Table 2** for Talairach coordinates for each significant cluster of activation for these contrasts.

The interaction contrast of PERTURBATION with STRUCTURE did not reveal any significant clusters when cluster-corrected for multiple comparisons, suggesting that there was a similar switch effect across the sentence and list conditions in the brain, although the separate contrasts for these conditions activated different sets of areas.

# ROI Analyses

For the ROI analyses, based on our expectations from the literature, we separately examined the effect of COMPLEXITY (passive > active sentences) using a one-way t-test and performed a 2 × 2 ANOVA of PERTURBATION (switch vs. control) and STRUCTURE (sentences vs. lists).

There was no effect of COMPLEXITY (passive > active) for any of the ROIs (all reported tests are one-tailed t-tests). Broca's area: t(1,19) = −0.059, p = 0.477; RIFG: t(1,19) = 0.069, p = 0.473; left ATL: t(1,19) = 1.746, p = 0.952; right ATL: t(1,19) = 0.799, p = 0.783. The high t-value of the left ATL indicates that there was a possibility of higher activation for active – less complex – sentences.

In Broca's area, there was no significant effect of STRUCTURE, F(1,19) = 1.443, p = 0.244, or PERTURBATION, F(1,19) = 0.714, p = 0.408, and no significant interaction, F(1,19) = 0.164, p = 0.408. In the RIFG, there was no significant effect of STRUCTURE, F(1,19) = 0.005, p = 0.946, a significant effect of PERTURBATION, F(1,19) = 13.541, p = 0.002, and no significant

N = 20. FWE cluster-corrected p < 0.05; individual voxel threshold p < 0.001, cluster size threshold 610 mm3 . Coordinates reflect the center of mass of each significant cluster. Coordinates are reported in Talairach space.

effect of the interaction, F(1,19) = 0.663, p = 0.426. Activations for each of these conditions in Broca's area and the RIFG are displayed in **Figure 6**.

Both ATL regions showed a significant main effect of PERTURBATION (reduced activity for perturbation), no main effect of STRUCTURE, and no interaction. Left ATL: STRUCTURE, F(1,19) = 0.597, p = 0.449; PERTURBATION, F(1,19) = 6.963, p = 0.016; interaction, F(1,19) = 2.820, p = 0.110. Right ATL: STRUCTURE, F(1,19) = 0.123, p = 0.729; PERTURBATION, F(1,19) = 13.161, p = 0.002; interaction, F(1,19) = 0.396, p = 0.537. Activation for each of these conditions in left and right ATL ROIs are displayed in **Figure 7**. While the test of the interaction between STRUCTURE and PERTURBATION in the left ATL was not significant, it should be noted that this effect trended toward significance.

## DISCUSSION

We performed a novel investigation in the effort to understand the neural bases of syntax: a constrained speech production task, including two different sentence constructions (active and passive), unstructured lists, and a "syntactic perturbation" paradigm. One goal was to probe the response profile of the traditional candidates for syntactic processing and their right hemisphere homologs with this novel PERTURBATION paradigm and a contrast of STRUCTURE (sentences > word lists). We also included a secondary contrast of COMPLEXITY (passive > active sentences) to determine if this effect previously found for comprehension in Broca's area extended to production. Finally, another goal of the experiment was exploratory – to determine whether networks outside of the traditional candidate regions for syntax would activate to syntactic perturbation. We will first focus our discussion on the activation profiles of Broca's area and the ATL. Following this we discuss the effects we obtained for the whole-brain contrasts, particularly the activation we obtained for PERTURBATION in the right IFG and the potential role this region plays in sentence processing.

# The Activation Profile of Broca's Area

The domain-general hypotheses of Broca's area suggest that this region underlies a non-syntactic mechanism during sentence processing, either resolving representational conflict through cognitive control (Novick et al., 2005) or providing working memory resources (Rogalsky and Hickok, 2011). The lack of effects for our PERTURBATION contrast in this region is consistent with these accounts, and contrary to the expectations of a region involved in syntax, as perturbation was expected to tax syntactic processing.

We also did not observe a significant effect of STRUCTURE in Broca's area. Previous work has shown that this contrast is observable in small subregions directly adjacent to regions that do not show this contrast (Hickok and Rogalsky, 2011; Fedorenko et al., 2012a). Our structural ROIs may have contained sentence-selective and non-selective subregions, thus weakening our power to detect effects of STRUCTURE. Regardless, the contrast was clearly not robust, and combined with the fact that the PERTURBATION contrast did not approach significance in this region speaks against a syntactic function.

We did not replicate previous findings for passive > active sentences in Broca's area in comprehension (Ye and Zhou, 2009; Mack et al., 2013). Hypotheses of Broca's area function in sentence processing should take this disparity into account, while noting that the task constraints of our study may have substantially

reduced our ability to detect activation differences between these constructions.

# The Activation Profile of the Anterior Temporal Lobe (ATL)

Our whole-brain analysis did not reveal any effects of COMPLEXITY and STRUCTURE in the left or right ATL. However, the ROI analysis did reveal a PERTURBATION effect for the ATL bilaterally – decreased activity for perturbation. We attribute the null effect of STRUCTURE and the decreased activity for PERTURBATION to a semantic rather than syntactic function of the ATL and decreased attention to semantic content in our study.

Our ROI plots showed less activity for switch sentences than for natural sentences, which reduced the sensitivity of our analyses to detect a main effect of STRUCTURE. This reduction can be explained by decreased attention to the semantic content of the stimulus for switch trials. Rogalsky and Hickok (2009) showed that attention substantially affects activation to semantic content in the ATL. The demanding nature of our task may have distracted subjects away from the semantic content of the sentences, reducing the difference in semantic processing between lists and sentences. Our stimuli also had limited semantic content generally. We used proper nouns instead of common nouns (e.g., Mary instead of the acrobat), and simplistic line drawings devoid of detail rather than pictures of actual people engaging in action as used in other studies (e.g., Menenti et al., 2011; Segaert et al., 2012). Future studies seeking to obtain effects of structure in the ATL during speech production should enrich the semantic nature of the materials and choose a task that does not require heavy attentional demands.

.

The decreased activity for PERTURBATION is contrary to the expectations of a region involved in syntax, but compatible with a role for semantics. Any effect of PERTURBATION would presumably increase demands on syntactic structure building, rather than decrease them. The increased attentional demands of switching syntactic structures, however, likely reduced attention

#### TABLE 2 | Effects of PERTURBATION.

fpsyg-07-00241 February 19, 2016 Time: 21:36 # 10

N = 20. FWE cluster-corrected p < 0.05; individual voxel threshold p < 0.001, cluster size threshold 610 mm3 . Coordinates reflect the center of mass of each significant cluster. Coordinates are reported in Talairach space.

to the semantic content of the sentences, accounting for a reduction of activity in the ATL as discussed above.

The major piece of data in support of a basic syntactic function of the ATL is the observation that the structural effect in the ATL can be found for sentences with the content words replaced by non-words, retaining the structural "feel" but with greatly impoverished semantic content (i.e., jabberwocky stimuli; Mazoyer et al., 1993; Humphries et al., 2006; Rogalsky et al., 2011). However, this effect is much less robust than for full sentences, with some studies failing to observe it at all (Pallier et al., 2011; Fedorenko et al., 2012c). Future research could determine the source of these discrepancies, including testing the notion that there may be a functional-anatomical subdivision within the ATL between syntactic and semantic processing (Rogalsky and Hickok, 2009).

# Whole-Brain Contrasts of Complexity and Structure

We first discuss the whole brain contrasts of COMPLEXITY and STRUCTURE. The whole-brain contrast of COMPLEXITY revealed one significant cluster in the left post-central gyrus. Since passive sentences are longer than active sentences, requiring additional articulation, this cluster likely reflects the increased motor speech output and corresponding somatosensory input rather than any core linguistic function. The whole-brain contrast of STRUCTURE only revealed activity in visual cortex and bilateral superior frontal areas. These regions have been previously associated with visual attention (Kastner and Ungerleider, 2000; Corbetta and Shulman, 2002). This suggests that demands on visual attention were stronger during the sentence condition than during the list condition, which is supported by the behavioral data.

The lack of additional effects in language-related regions for these contrasts deserves explanation. We have already discussed Broca's area and the ATL; other language-related areas that are typically activated by this contrast include the left posterior temporal lobe and the angular gyrus (Bedny et al., 2011; Pallier et al., 2011; Fedorenko et al., 2012c). The difference between our results and previous studies cannot be attributed solely to the differences between production and comprehension; several production studies have revealed effects in these areas (Menenti et al., 2011, 2012; Segaert et al., 2012, 2013). As discussed in the introduction, the structurally simple and short sentences that we used minimized demands on working memory and cognitive control, and our stimuli did not encourage rich semantic processing. It may be the case that effects in these languagerelated regions are due to these processes. Previous research points to a role for the posterior temporal lobe in working memory and cognitive control (Hickok et al., 2003; Glaser et al., 2013) and the angular gyrus in semantic processing (Binder et al., 2009; Price et al., 2015), consistent with this speculation.

# Syntactic Perturbation Reveals a Network for Response Selection, Action Inhibition, and Motor Control

While syntactic PERTURBATION did not activate traditional language areas of the left hemisphere, it did activate other brain regions, including medial frontal areas (SMA, pre-SMA), the right caudate nucleus, the right posterior STS, the right IFG, and the right anterior insula. These are regions that have been reported in studies of perturbation and motor control in other domains (Diedrichsen et al., 2005; Suminski et al., 2007; Tourville et al., 2008) and studies of response selection/action inhibition implementing go/no-go designs (Simmonds et al., 2008). The list PERTURBATION contrast activated only the cerebellum. This disparity of results between the sentence and list conditions must be treated carefully, as the interaction contrast did not reveal a significant statistical interaction between STRUCTURE and PERTURBATION in any regions. This suggests that there were similar activation patterns for both conditions, but that the effect was somewhat stronger in the sentence condition.

The activation of the right caudate nucleus is consistent with the suggestion that the basal ganglia are involved in syntactic operations (Lieberman, 2001; Ullman, 2004). However, we do not believe that this activation in our study reflects syntax. This is because the right basal ganglia are part of a larger network that is strongly implicated in stopping, discussed below.

While RIFG activation is sometimes reported for syntactic manipulations (Embick et al., 2000; Friederici et al., 2000; Meyer et al., 2000; Fiebach et al., 2005; Tyler et al., 2010), it is not common for experiments of basic sentence processing, and the aphasia literature does not support a strong association between deficits in sentence processing and the RIFG (Damasio, 1992; but see Caplan et al., 1996). The effect of PERTURBATION in this region therefore likely reflects non-syntactic mechanisms. The operative mechanism may be action inhibition, or "stopping," which has been attributed specifically to the RIFG in conjunction with the other areas activated by the PERTURBATION contrast (Aron et al., 2003, 2014). Under this hypothesis, the RIFG operates as a "brake." We can apply this braking hypothesis to the

current study through reverse inference. After subjects planned to produce a sentence with a given sentence construction, on switch trials they utilized the brake to inhibit this plan. When subjects planned to produce a list of words, they also relied on the brake, but less so.

Our study provides insight into a surprisingly large amount of previous studies of syntax and sentence comprehension that report activation of the RIFG. Such studies can be divided into two groups: studies of complex/non-canonical sentence constructions and garden-path sentences (Meyer et al., 2000; Fiebach et al., 2005; Grewe et al., 2006; Bornkessel-Schlesewsky et al., 2012; Chan et al., 2012), and studies involving syntactic violations (Embick et al., 2000; Moro et al., 2001; Ben-Shachar et al., 2003; Friederici et al., 2003, 2006; Bahlmann et al., 2008). The fact that our task explicitly involved stopping suggests that this mechanism may account for RIFG activations in these previous studies. When subjects process a sentence with non-canonical sentence structure or syntactic violations, they must revise their initial parse to arrive at the correct interpretation. This revision may rely on an inhibition function to quickly reject the current parse in favor of a new one. Supporting this hypothesis, Caplan et al. (1996) found that patients with right hemisphere lesions had significantly worse sentence comprehension than control subjects, particularly for complex sentence constructions (although these effects were not as strong as in patients with left hemisphere lesions). Future research could further investigate the hypothesis of a "braking" function during sentence comprehension.

# CONCLUSION

The present study sought to implement a novel paradigm in the study of syntax and the brain: a constrained sentence production

# REFERENCES

task with a perturbation paradigm applied to syntactic structure. While our activations point to a possibility of a stopping mechanism in the RIFG that facilitates structural revision, it is difficult to make any firm conclusions based on this study alone. The lack of effects for syntactic PERTURBATION and STRUCTURE in Broca's area suggest that this region performs a non-syntactic function during sentence processing. This supports the previous body of evidence against a role for syntax in Broca's area (Rogalsky and Hickok, 2011). Finally, we did not extend previous effects of sentences > word lists in the ATL to production, although the lack of an effect may have been due to reduced activity in this region during perturbation. This is consistent with a role for the ATL in combinatorial semantics.

# AUTHOR CONTRIBUTIONS

WM and GH conceptualized and designed the experiment. WM created the stimuli and collected and analyzed the data. WM and GH wrote and revised the manuscript.

## FUNDING

This research was supported by NIH Grant # DC03681 awarded to GH.

### ACKNOWLEDGMENTS

We would like to thank Jonathan Venezia for useful suggestions, Maral Aghvinian for assistance with data analysis, and two anonymous reviewers for their comments.

magnetic resonance imaging. Psychol. Sci. 14, 433–440. doi: 10.1111/1467- 9280.01459

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Matchin and Hickok. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Brain asymmetry in the white matter making and globularity

#### *Constantina Theofanopoulou\**

*Department of General Linguistics, Universitat de Barcelona, Barcelona, Spain*

Recent studies from the field of language genetics and evolutionary anthropology have put forward the hypothesis that the emergence of our species-specific brain is to be understood not in terms of size, but in light of developmental changes that gave rise to a more globular braincase configuration after the split from Neanderthals-Denisovans. On the grounds that (i) white matter myelination is delayed relative to other brain structures and, in humans, is protracted compared with other primates and that (ii) neural connectivity is linked genetically to our brain/skull morphology and language-ready brain, I argue that one significant evolutionary change in *Homo sapiens'* lineage is the interhemispheric connectivity mediated by the Corpus Callosum. The size, myelination and fiber caliber of the Corpus Callosum present an anterior-to-posterior increase, in a way that inter-hemispheric connectivity is more prominent in the sensory motor areas, whereas "high- order" areas are more intra-hemispherically connected. Building on evidence from language-processing studies that account for this asymmetry ('lateralization') in terms of brain rhythms, I present an evo-devo hypothesis according to which the myelination of the Corpus Callosum, Brain Asymmetry, and Globularity are conjectured to make up the angles of a co-evolutionary triangle that gave rise to our language-ready brain.

#### *Edited by:*

*Antonio Benítez-Burraco, University of Huelva, Spain*

#### *Reviewed by:*

*Pedro Tiago Martins, Pompeu Fabra University, Spain Bridget Samuels, Pomona College, USA*

#### *\*Correspondence:*

*Constantina Theofanopoulou, Department of General Linguistics, Universitat de Barcelona, Edifici Josep Carner, 5a Planta, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology*

*Received: 27 July 2015 Accepted: 24 August 2015 Published: 10 September 2015*

#### *Citation:*

*Theofanopoulou C (2015) Brain asymmetry in the white matter making and globularity. Front. Psychol. 6:1355. doi: 10.3389/fpsyg.2015.01355* Keywords: brain asymmetry, lateralization, skull, globularity, corpus callosum, white matter, brain rhythms, language

# Introduction