Linköping University medical dissertations, No. 1358 Mind your Language, All Right? Performance‐dependent neural patterns of language Helene van Ettinger‐Veenstra Center for Medical Image Science and Visualization Division of Radiological Sciences Department of Medical and Health Sciences Linköping University, Sweden Linköping 2013 © Helene van Ettinger‐Veenstra, 2013 [email protected] Published papers have been reprinted with permission of the copyright holders Cover design: Tjeerd Veenstra www.tjeerdveenstra.nl Printed in Sweden by LiU Tryck, Linköping, Sweden, 2013 ISSN 0345‐0082 ISBN 978‐91‐7519‐668‐8 voor mijn lieve Lucas Levi They say the left side of the brain Dominates the right And the right side has to labor through The long and speechless night … Maybe I think too much ‘Think Too Much (b)’ ‐ Paul Simon ABSTRACT The main aim of this dissertation was to investigate the difference in neural language patterns related to language ability in healthy adults. The focus lies on unraveling the contributions of the right‐hemispheric homologues to Broca’s area in the inferior frontal gyrus (IFG) and Wernicke’s area in the posterior temporal and inferior parietal lobes. The functions of these regions are far from fully understood at present. Two study populations consisting of healthy adults and a small group of people with generalized epilepsy were investigated. Individual performance scores in tests of language ability were correlated with brain activation obtained with functional magnetic resonance imaging during semantic and word fluency tasks. Performance‐dependent differences were expected in the left‐hemispheric Broca’s and Wernicke’s area and in their right‐hemispheric counterparts. PAPER I revealed a shift in laterality towards right‐hemispheric IFG and posterior temporal lobe activation, related to high semantic performance. The whole‐brain analysis results of PAPER II revealed numerous candidate regions for language ability modulation. PAPER II also confirmed the finding of PAPER I, by showing several performance‐dependent regions in the right‐hemispheric IFG and the posterior temporal lobe. In PAPER III, a new study population of healthy adults was tested. Again, the right posterior temporal lobe was related to high semantic performance. A decrease in left‐ hemispheric IFG activation could be linked to high word fluency ability. In addition, task difficulty was modulated. Increased task complexity showed to correlate positively with bilateral IFG activation. Lastly, PAPER IV investigated anti‐correlated regions. These regions are commonly known as the default mode network (DMN) and are normally suppressed during cognitive tasks. It was found that people with generalized epilepsy had an inadequate suppression of regions in the DMN, and showed poorer performance in a complex language test. The results point to neural adaptability in the IFG and temporal lobe. Decreased left‐lateralization of the IFG and increased right‐ lateralization of the posterior temporal lobe are proposed as characteristics of individuals with high language ability. I II SAMMANFATTNING Som vuxna människor är vi, även då vi är friska, väldigt olika, med olika förmågor. Så är det också med språklig förmåga. Det varierar betydligt mellan olika personer hur bra läsförståelse man har, eller hur lätt man har att hitta på ord. Denna avhandling bygger på att dessa mätbara språkliga skillnader också kan synliggöras i hjärnan med hjälp av hjärnscanning, så kallad funktionell magnetresonanstomografi. Hjärnaktivering vid språkfunktion är ofta koncentrerad i den vänstra hjärnhalvan; i nedersta delen av pannloben samt i bakre delen av tinningloben, men även den högra hjärnhalvan kan aktiveras av flera olika språkfunktioner. Speciellt finns de funktioner som får en person att förstå komplicerade språkkomponenter, till exempel bildspråk eller andra typer av underliggande betydelser i språket, i den högra hjärnhalvan. I studierna som ligger till grund för denna avhandling förväntades att hjärnaktiveringen i vanliga språkområden i den vänstra hjärnhalvan skulle variera med språklig förmåga. Om personer som är bättre på språk har en hjärna som fungerar mer effektivt, så skulle det visa sig som mindre aktivering i vänstersidiga språkområden. Å andra sidan, om personer som presterar bra har bättre kognitiv förmåga än sämre presterande, skulle det kunna synas som mer aktivering i de understödjande språkområdena i höger hjärnhalva. Resultaten som framgår i denna avhandling är framför allt att aktivering i höger tinninglob är involverad i bättre språklig förmåga. Det finns också antydningar att nedre delen av den högra pannloben är mer aktiverad när man är bra på språk. Resultaten visade sig dock att variera med språkuppgift; det finns bevis för mer aktivering i höger pannlob i samband med bättre språkförståelse och för mindre aktivering i vänster pannlob i samband med bättre förmåga att generera ord. Dessutom är den nedre delen av pannloben mer aktiv vid svårare språkförståelseuppgifter. Slutsatsen av dessa studier är att aktivering i den nedre pannloben är beroende av kognitiv kapacitet, men att aktivering i den högersidiga bakre tinningloben är specifik för språkförståelse. De studier som är inkluderade i avhandlingen visar att desto bättre man är på språk, desto mindre använder man enbart den vänstra hjärnhalvan när man läser eller genererar ord. III IV LIST OF PUBLICATIONS This dissertation is based on the following original papers, which are referred to throughout the text by their Roman numerals: PAPER I Van Ettinger‐Veenstra HM, Ragnehed M, Hällgren M, Karlsson T, Landtblom A‐M, Lundberg P, and Engström M (2010). Right‐hemispheric brain activation correlates to language performance. NeuroImage 49(4): 3481–3488. PAPER II Van Ettinger‐Veenstra HM, Ragnehed M, McAllister A, Lundberg P, and Engström M (2012). Right‐hemispheric cortical contributions to language ability in healthy adults. Brain and Language 120(3): 395–400. PAPER III Gauffin H*, Van Ettinger‐Veenstra HM*, Landtblom A‐M, Ulrici D, McAllister A, Karlsson T, and Engström M. Impaired language function in generalized epilepsy: Inadequate suppression of the default mode network. Accepted in Epilepsy & Behavior, 2013. PAPER IV Van Ettinger‐Veenstra HM, Karlsson T, McAllister A, Lundberg P, and Engström M. Laterality shifts in neural activation coupled to language ability. Submitted to PLoS ONE, 2013. * The first two authors contributed equally to this paper Related Peer‐Reviewed Conference Abstracts Veenstra HM, Ragnehed M, Hällgren M, Lundberg P, and Engström M. Brain lateralization assessed by fMRI and dichotic listening. Paper presented at the 15th Annual Meeting of the Organization for Human Brain Mapping, California, USA, 2009. Veenstra HM, Pettersson J, Nelli C, Ragnehed M, McAllister A, Lundberg P, and Engström M. Influence of performance‐related language ability on cortical activation. Paper presented at the 15th Annual Meeting of the Organization for Human Brain Mapping, California, USA, 2009. Van Ettinger‐Veenstra H, Karlsson T, Ulrici D, Gauffin H, Landtblom AM, and Engström M. Language ability in healthy and epilepsy participants: an fMRI investigation. Paper presented at the 43rd European Brain and Behaviour Society Meeting, Seville, Spain, 2011. Van Ettinger‐Veenstra H, Gauffin H, McAllister A, Lundberg P, Ulrici D, Landtblom A‐M, and Engström M. Language deficits in Epilepsy, an fMRI study. Paper presented at the 18th Annual Meeting of the Organization for Human Brain Mapping, Beijing, China, 2012. V AT A GLANCE PAPER (study) METHODS I 14 healthy adults. fMRI: Lateralization Index (A) from sentence reading (SENCO) task was correlated with Read, BeSS, FAS & BNT performance scores. Also, Dichotic Listening laterality measurements were investigated. II (A) III 18 healthy adults. Whole‐brain analyses from sentence reading (SENCO) and word fluency (WORGE); activation was correlated with Read, BeSS, FAS & BNT performance scores. 27 healthy adults. Lateralization Index from (B) ROI analyses of sentence reading (SEN) and word fluency (WORD), correlated with performance scores on BeSS and FAS. Also, task difficulty related brain activation was investigated with multiple regression. 27 healthy & 11 Generalized Epilepsy IV (B) participants. Investigated for deactivation in the default mode network during sentence reading (SEN). Also, language performance measurements of the epilepsy group. VI CONCLUSIONS RESULTS Both dichotic listening and fMRI results point to a right‐hemispheric activation as a characteristic for high language ability. Activation in the right‐hemispheric ROIs was more pronounced for high performance. This correlated with the dichotic listening results. Especially high BeSS and Read scores correlated with increased right‐lateralization. Regions in inferior frontal gyrus (BA 47) and middle temporal gyrus (BA 21) are related to high semantic language ability. Several clusters in right IFG and temporal lobe showed to correlate with BeSS and Read on the sentence reading fMRI task. No such results for word fluency. Activation in the inferior frontal gyrus is modulated by semantic difficulty, while right temporal lobe activation is specific for semantic ability. Activation in the temporal lobe was more right‐lateralized for high BeSS performance. Activation in left IFG was less left‐lateralized for high FAS performance. The difficult incongruent sentence reading condition was characterized by bilateral IFG activation People with Generalized Epilepsy experience language difficulties. This could be explained by aberrant suppression of activation in the default mode network. A failure to suppress default mode network activation is disturbing for cognitive functioning. People with Generalized Epilepsy showed worse performance in BeSS than healthy controls. They also showed diminished DMN deactivation, notable was the decreased left temporal lobe deactivation and increased hippocampal activation. VII VIII ABBREVIATIONS BA BeSS BNT Brodmann Area “Bedömning av Subtila Språkstörningar” – Assessment of Subtle Language Deficits Boston Naming Test BOLD Blood Oxygen Level Dependent DMN Default Mode Network fMRI functional Magnetic Resonance Imaging FWE Family‐Wise Error GE Generalized Epilepsy GLM General Linear Model IFG Inferior Frontal Gyrus LI Laterality Index MNI Montreal Neurological Institute MRI Magnetic Resonance Imaging P‐FIT Parieto‐Frontal Integration Theory ROI Region of Interest SEN sentence reading fMRI task used in PAPER III & PAPER IV SENCO sentence completion fMRI task used in PAPER I & PAPER II WORD word generation fMRI task used in PAPER III WORGE word generation fMRI task used in PAPER II IX CONTENTS ABSTRACT I SAMMANFATTNING III LIST OF PUBLICATIONS V AT A GLANCE VI ABBREVIATIONS IX 1 INTRODUCTION 1.1 LANGUAGE ABILITY 1.1.1 Language Abilities 1.1.2 Language Dysfunctions 1.2 NEURAL CORRELATES TO LANGUAGE 1.2.1 Language Models 1.2.2 Semantics 1.2.3 Word Fluency 1.2.4 Right‐Hemispheric Influences 1.2.5 Laterality 1.2.6 Anti‐correlated Brain Activation 1.3 INTELLIGENCE MODELS FOR LANGUAGE ABILITY 1.3.1 Relation Language Ability and Intelligence 1.3.2 Intelligence Models 1.4 AIMS 2 METHODS 2.1 NEUROLINGUISTIC MEASURES 2.1.1 Tests of Language Ability 2.1.2 Dichotic Listening 2.1.3 fMRI Language Paradigms 2.1.4 Study Population 2.1.5 Generalized Epilepsy 2.2 FUNCTIONAL MRI 2.2.1 Properties of Functional MRI 1 2 2 3 4 4 8 8 8 9 10 11 11 11 13 15 15 15 16 16 17 17 18 18 2.2.2 Region of Interest Analysis 2.2.3 Laterality Index Analysis 3 RESULTS 3.1 3.2 3.3 3.4 19 20 23 MULTIPLE REGRESSION ANALYSES LATERALITY ANALYSES TASK DIFFICULTY MODULATION LANGUAGE DYSFUNCTIONS IN EPILEPSY 4 DISCUSSION 24 27 28 29 31 4.1 NEURAL CORRELATES TO PERFORMANCE 4.1.1 Multiple Regression Analyses 4.1.2 Laterality Analyses 4.1.3 Task Difficulty Modulation 4.1.4 Language Dysfunctions in Epilepsy 4.2 HEALTHY ADULTS 4.3 INTERPRETATION OF ACTIVATION PATTERNS 4.4 FUTURE DIRECTIONS 31 31 33 34 35 36 37 42 5 CONCLUSIONS 45 ACKNOWLEDGMENTS 46 REFERENCES 49 PAPER I PAPER II PAPER III PAPER IV Big black cloud On a yellow plain Sure enough it Looks like rain Packin' up all our Faith and trust Me and the wanderlust ‘Wanderlust’ ‐ Mark Knopfler 1 INTRODUCTION Mapping of language disability patterns requires a thorough understanding of language ability patterns. The neural pathways for perceiving and generating language are slowly being unraveled, but the exact contributions of typical left‐hemispheric language areas (Broca’s and Wernicke’s area) are not yet completely clear. Neither is the role of language‐related regions in the – usually non‐ dominant – right hemisphere. The opinion about how right‐hemispheric regions influence language has changed. In the past, activation in the right hemisphere during language tasks was largely overlooked; but over time, researchers gained an understanding of the emotional content processing aspects. At present, additional roles of the right hemisphere in language are being explored, including language comprehension aspects. Evidence of these right‐hemispheric comprehensive aspects is presented in this dissertation within a framework of manifestations of language ability in the brain. This dissertation presents four papers that investigated language ability, which was defined as language production and comprehension abilities. The first three papers describe how healthy adults were tested for brain activation evoked by neurolinguistic functional magnetic resonance imaging (fMRI) tasks. These fMRI tasks measured semantic processing and word fluency activations. The results were related to individual performance measurements in various tests of language ability, including reading, word fluency, picture naming and use of complex language. The fourth paper discusses how the brains of people with generalized epilepsy can express altered activation patterns in relation to lower language ability. 1 1. INTRODUCTION 1.1 Language Ability 1.1.1 Language Abilities The ability to produce language enables one to communicate one’s own thoughts and express oneself. Comprehension of language will enable one to perceive information that might be new or interesting. As in all skills; individual differences are present. The origins of these differences might be attributed to the amount of exposure to language, or to one’s own interests in reading or verbal expression. Whenever people manifest differences in behavior, neuroimagers will look for the neural correlates to these differences. Indeed, the rationale behind the performed experiments that led to this dissertation was to visualize language ability differences in healthy subjects. The current sub‐chapter will present previous research on language ability variation. In the following sub‐chapter, ‘Neural Correlates to Language’, a more detailed framework for language ability will be introduced. Language discussions often refer to the classical language areas of Broca’s area in the left inferior frontal gyrus (IFG) and Wernicke’s area in the left posterior temporal lobe. It is also known that other functional regions are involved in language processes; these will be explored in the next sub‐chapter. It seems that differences in language performance can be – at least partly – explained by differentiations in activation in Broca’s and Wernicke’s language areas, although their exact contribution is not yet clear. Studies investigating high performance in word fluency have shown an increase of left‐hemispheric IFG activation for high performance (Wood et al., 2001), but also no difference at all (Dräger et al., 2004). When semantic tasks are studied, increased activation of posterior temporal and parietal regions is shown for high performance (Booth et al., 2003; Meyler et al; 2007; Weber et al., 2006). However, an opposing view emerges from an increasing number of works revealing a relationship between reading and sentence comprehension and decreased activation in left hemispheric language areas (Reichle et al., 2000; Prat et al., 2007; 2011, Prat & Just, 2011). The mechanism behind this activation reduction is thought to be a more efficient neural functioning. Efficacy in recruiting neural regions or pathways enables a person to re‐attribute cognitive resources guided by task demand. Thus, a person skilled in language may use his or her brain in a more optimal way for the presented task. Furthermore, there is evidence of a specific role of the right‐hemispheric homologues of Broca’s and Wernicke’s area in high language performance. Many of the results presented in the papers that are included in this dissertation point also to a right‐hemispheric contribution to high language ability. If people with a high language ability recruit additional language‐supporting areas, this may indicate that a high adaptability of neural resources is an explanatory mechanism for language ability differences. Research supporting the theories of neural adaptability and neural efficiency as 2 1. INTRODUCTION explicatory for high language ability will be presented in the sub‐chapter ‘Intelligence models for Language Ability’ 1.1.2 Language Dysfunctions The introduction started out by stating that knowledge of language ability will lead to an understanding of language disability. PAPER IV presents a group of people with epilepsy showing subtle language disabilities, and compares them with healthy subjects performing on a normal level. The reverse statement to the one above is also true; upon investigating language disabilities, a model for language abilities can be created. Much of our knowledge about the language system has been gained from lesion studies notably those on left‐hemispheric lesioned patients showing word production problems, as presented a little later in this section. Language impairment can have a variety of underlying causes; impaired language functioning, cognitive ability, or sensory/motoric abilities, or lack of training or exposure to language. A disruption in any component of language production or comprehension in the language model1 evidently will result in a disruption of language ability. Since the studies included in this dissertation measure word generation and sentence reading, this section discusses reading impairment (dyslexia) and production problems. Developmental dyslexia is characterized by various neurological differences throughout the brain, probably caused by anomalies during the development of language systems in the brain (Catts & Kamhi, 2005; Démonet et al., 2005). It has been suggested that this type of dyslexia is related to abnormal dominance patterns or abnormal development of dominance (Heim et al., 2010), but the causes are though probably multiple and more complex (Crystal 2010). Acquired dyslexia can occur after a lesion in one out of various brain regions (Price et al., 2003). Functional imaging studies on the neurological differences between people with dyslexia and normal performers show a diminished activation in temporal and parietal regions (Salmelin et al., 1996; Shaywitz et al., 1998), and an increase in inferior frontal activation (Shaywitz et al., 1998). Both the presence of expected activation and the absence of unexpected activation in the right hemisphere have been observed to act as distinguishers of people with dyslexia from people without reading impairment (Paulesu et al., 1996; Simos et al., 2000). Word production problems are often not development‐related but result from lesions in the language‐dominant hemisphere. Problems with word fluency are seen in people with dementia and with left temporal lobe epilepsy (Ruff et al., 1997). Named after the location of brain damage, aphasia 1 e.g. the space station model presented in the following sub‐chapter ‘Brain Functioning’ 3 1. INTRODUCTION can be classified as Broca’s aphasia, Wernicke’s aphasia or global aphasia – the latter being a combination of Broca’s and Wernicke’s aphasia. It is now known that in Broca’s aphasia, brain regions posterior to Broca’s area are often damaged; and that in Wernicke’s aphasia the location of damage can vary (Crystal 2010). Broca’s aphasia results in deficits in expressive abilities and is characterized by non‐fluent speech which is grammatically incorrect. Wernicke’s aphasia occurs when receptive systems are damaged and results in both comprehension problems and problems producing intelligible speech, even though it appears to be fluent. Furthermore, word retrieval problems are a common deficiency (Crystal 2010). Studies on language disabilities can help us to find regions of interest for the investigation of language abilities. Lesion studies that have led to an understanding of language disabilities have shown that disruption of language functioning in the language‐dominant hemisphere has a much higher impact than a disruption in the non‐dominant hemisphere. Thus, the language functions in the non‐dominant hemisphere may not be compulsory for language production, but may support complex processing. 1.2 Neural Correlates to Language 1.2.1 Language Models There are many possible theoretical models to describe the complex structure of language. Often, these models use similar distinctions between word forms, word structure, word meaning and understanding of text or speech. In other words, many models describe language as a process defining the range of linguistic information from small building blocks to complex meaningful communication. To understand language in the context of this dissertation, a useful model is the space station model as presented by Crystal (2010), and represented in Figure 1. This model describes an interactive framework integrating the components of language that are investigated in the papers included in this dissertation. The different components are: phonetics (pronunciation attributes) and phonology (sounds that convey different meanings), morphology (word structure) and syntax (sentence structure), semantics (meaningful content) and pragmatics (discourse information). The connection between these components is not uni‐directional, but rather interconnected as represented in the space station model. This is consistent with the neural organization of language, where both top‐down and bottom‐up processes can be observed during language processes (Friederici 2012). 4 1. INTRODUCTION Figure 1. Representation of the Space Station Language Model. The linguistic levels presented in the circles are interconnected, indicating free exchange of linguistic information between levels; thus all information is available at once for an external researcher. Figure adapted from Crystal (2010). Measures of language ability preferably test for many linguistic components, including production and perception of language, and have a high enough difficulty level to measure variability in language skills. On the other hand, the total test duration should be kept to a minimum as to impose only minimally on the participants, especially on those with cognitive disabilities. The tests used in our studies, (see also Methods section for their description), show two approaches towards this goal. First; established tests such as the Boston Naming Test (Kaplan et al., 1983) or word fluency tests – testing word retrieval and word production skills – are used in many research studies that describe the neural mechanisms that lie behind. Moreover, these tests are easily translated to the magnetic resonance scanner environment without much adapting. However, both tasks are very focused; they do not test for the full spectrum of language ability. Other tests, such as comprehensive reading, investigate language perception and comprehension and could be translated to the scanner environment with some modification. A second approach is to gather multiple language ability tests in a battery, such as the Assessment of Subtle Language Deficits or BeSS test (Laakso et al., 2000). This relatively new complex language ability test is not yet established, but can detect subtle language dysfunctions without showing a ceiling effect (as the results of our papers will show). Moreover, this is a compact test, so that language ability can be assessed quickly without too much imposing on the 5 1. INTRODUCTION concentration skills of people with language dysfunctions (such as the people with generalized epilepsy from our PAPER IV). However, this test is less practical in a scanner environment. Neurological models are often based on the classical Wernicke‐Geschwind model (Geschwind 1965), which describes the neurological dissociation between language production/speech attributed to Broca’s area, and language semantic comprehension (semantics) attributed to Wernicke’s area. Many later studies have shown that this description is insufficient, as it does not take into account other functional areas, nor does it describe accurately the precise boundaries of linguistic functional areas (Price 2000; 2012; Démonet et al., 2005; Smits et al., 2006). An overview of the segregation in left‐hemispheric language areas is given in Figure 2. For instance, Broca’s area contains regions involved in semantics as well as in syntax processing (cf. Price 2012). Interestingly, although language studies often focus on the language‐dominant left hemisphere (Vigneau et al., 2006), the right hemisphere often shows a similar activation pattern (Démonet et al., 2005). Nevertheless, aspects of neural correlates to the Wernicke‐Geschwind model are supported by recent lesion studies investigating aphasia (Yang et al., 2008) and by functional imaging studies (Price 2000; Bookheimer 2002). Therefore, Broca’s and Wernicke’s area are used as regions of interest in several of our analyses, in combination with other regions that were found in relation to semantic and word fluency tasks. When using the labels of Broca’s and Wernicke’s areas, it is important to define their extent; the definition of Wernicke’s area in particular can vary from including only the posterior superior temporal gyrus to the inclusion of large parts of the parietal and temporal cortex. Throughout this dissertation, including all articles, the definition used is as follows: Broca’s area comprises the left IFG; specifically Brodmann areas (BA) 44 and 45. Wernicke’s area comprises the left posterior superior temporal gyrus (BA 22) and the posterior part of BA 21, as well as the posterior perisylvian2 region which consists of the left angular gyrus and the supramarginal gyrus (BA 39 & inferior BA 40). The right‐hemispheric counterparts of these areas are referred to as Broca’s and Wernicke’s area homologues. Language production and perception are by no means controlled solely by these regions3. The regions important for language will be discussed in the following sections which introduce an overview of activation related to semantic and word fluency tasks. Since the topic of this dissertation is language ability, neural processes not directly related to language are not introduced here. 2 Perisylvian indicates the region around the Sylvian fissure. This fissure divides the frontal and parietal lobules from the temporal lobe. 3 An example is given by (Dronkers et al., 2007), who found that the patients of Paul Broca – whose brains evidenced the theory of speech production located in left IFG – had lesions that were spread over a wider region than just Broca’s area. 6 1. INTRODUCTION Figure 2. Finite overview (based on imaging studies by Cathy Price) of the segregation of functional languagerelated areas in the left hemisphere. The colored areas each refer to different tasks, either differing in modality (auditory/visual) or in linguistic component. Figure reprinted with permission. See Price (2012) for details. 7 1. INTRODUCTION 1.2.2 Semantics Our studies have used semantic sentence reading fMRI tasks, either requiring completion of sentences or reading of congruent/incongruent sentences. Semantic tasks such as reading (Price 2000), and sentence and story comprehension (Sakai et al., 2001; Kaan & Swaab, 2002) typically activate Broca’s and Wernicke’s area in the left hemisphere (Price et al., 2003; overview in Binder et al., 2009). In the left IFG, BA 47 plays also a role in semantic processing (Dapretto & Bookheimer, 1999; Bookheimer 2002). Furthermore, the anterior temporal cortex and the fusiform gyrus are involved in semantic processing (Price et al., 2003; overview in Price 2012). Activation in the parietal perisylvian region has been shown to correlate with linguistic complexity in sentences (Carpenter et al., 1999) and semantic associating (Price 2000). Semantic processing often also activates right‐ hemispheric IFG and temporal lobe (Bookheimer 2002), which will be discussed in the section ‘Right‐ Hemispheric Influences’. 1.2.3 Word Fluency Word generation (or: word fluency) tasks are frequently used to determine language lateralization by fMRI (Cuenod et al., 1995; Hertz‐Pannier et al., 1997). The generation of words evokes activation in the left middle and inferior frontal gyrus (Fu et al., 2002; Costafreda et al., 2006), with a particularly important role for the pars opercularis (Price 2000). Furthermore is activation observed in the inferior temporal cortex and in the adjacent fusiform area (Price 2000), and in the anterior cingulate cortex (Fu et al., 2002) The sub‐regions in the IFG have specific roles and the activation pattern is dependent on the nature of the fluency task (Heim et al., 2009). 1.2.4 Right‐Hemispheric Influences Most language tasks evoke activation in bilateral frontal, temporal or parietal areas; the specific role of right‐hemispheric language areas is often interpreted as abstract linguistic functioning. Although lesion studies often indicate that the right‐hemisphere is not indispensable for language production, neuroimaging studies show that the right hemisphere plays an important and often distinct role, something we found evidence of in our studies as well. Vigneau and colleagues (2011) discuss in their meta‐analysis the right hemisphere in relation to language processing. They conclude that the right‐ hemispheric IFG seems to have no access to phonemic representations, unlike the left IFG. Activation in the right IFG is observed during processing of metaphors (Schmidt & Seger, 2009) and the perception of prosody (Buchanan et al., 2000). Furthermore, the right IFG is active when information is conflicting during complex language tasks; this is related to figurative language and increasing 8 1. INTRODUCTION ambiguity (Bookheimer 2002; Snijders et al., 2009). Bookheimer suggests that the role of the right IFG might be to help making decisions based on linguistic information. The right hemisphere is also important for understanding and integrating spoken and written information (Bookheimer 2002). In particular, the understanding of context processing or pragmatics – which is necessary for interpreting for example ambiguous or emotionally loaded information – is attributed to the right temporal lobe (Vigneau et al., 2011). Examples of right temporal lobe activation are seen in studies investigating the interpretation of prosody (Vigneau et al., 2011), the integration of semantic information (Caplan & Dapretto, 2001), or the processing of metaphors (Bottini et al., 1994; Mashal et al., 2005; Ahrens et al., 2007). The neural activation resulting from the processing of metaphors is possibly related to the metaphors being perceived as nonsensical or containing novel semantic information (Mashal et al., 2009). The right hemisphere is thus involved in pragmatic processing on a meta‐syntactic level (Mitchell & Crow, 2005). 1.2.5 Laterality The dominance of a hemisphere in language processing can be quantified as the degree of lateralization. A non‐typical degree of lateralization has been attributed to both language abilities and disabilities (cf. the first section ‘Language Abilities’). Knecht and colleagues (2000) tested 188 healthy right‐handed adults for language lateralization in the brain with a word generation fMRI task. This task has been widely reported to be a powerful and effective paradigm for generating language production (Neils‐Strunjas 1998). Language lateralization study results have indicated that there is no difference in language lateralization ratios between males and females. Furthermore, a left‐ to right‐hemispheric dominance ratio of 13 to 1 was established (Knecht et al., 2000). Besides fMRI, dichotic listening is an alternative and feasible non‐invasive method to test for language lateralization (Hugdahl 2011). The dichotic listening method is based on the notion that bi‐aural auditory stimuli travel more easily to the contralateral rather than ipsilateral hemisphere, due to more extensive contralateral than ipsilateral pathways from the ear to the auditory cortex. Also, there is a blocking of ipsilateral pathways during conflicting input. After travelling to the contralateral cortex, the auditive signals are processed more automatically in the hemisphere that is dominant for language. Ergo, the language‐dominant hemisphere presumably resides contralateral to the ear that processes more stimuli during bi‐aural stimulation (Kimura, 2011). 9 1. INTRODUCTION Differences between methods to test for laterality are discussed by Abou‐Khalil (2007), who concluded that fMRI was one of the most realizable techniques4. The clear advantage of fMRI over dichotic listening is that fMRI can localize activation. Nonetheless, dichotic listening is superior in practicality, both in terms of costs and of convenience. It is also important to realize that the laterality measurements obtained by fMRI are very much dependent on which language task is chosen. Both word fluency and sentence comprehension seem to be indicative of determining language lateralization (Niskanen et al., 2012). Besides ear dominance, hand dominance is also seen to have a direct connection to the contralateral hemispheric. Right‐handedness is highly correlated with left‐hemispheric language dominance (in 94 – 96 % of right‐handers). In left‐handers, it is slightly more common to have right‐hemispheric dominance, yet 78 % of the left‐handed population is also left dominant for language (Szaflarski et al., 2002). Language lateralization is thought to correlate with differences in gray matter between hemispheres, and when the cortex is damaged, language lateralization for expressive language functions can change (Lee et al., 2008). Josse and colleagues (2009) investigated how gray matter differences could predict language lateralization, and showed that when gray matter is analyzed with a voxel‐by‐voxel method, structural asymmetry correlated well with language lateralization. However, these correlations were lost when global lateralization was compared with regional gray matter asymmetries. Nowadays, local lateralization is of interest and many researchers prefer to investigate the lateralization of separate regions (Seghier et al., 2011b). A strong lateralization of cognition has been linked to high cognitive performance (Güntürkün et al., 2000). Recently, an opposing view has emerged, namely that the optimal degree of lateralization for high cognitive performance was small. In other words; a higher degree of bilaterality might be more favorable for performance (Hirnstein et al., 2010). 1.2.6 Anti‐correlated Brain Activation In PAPER IV we examine activation that is correlated negatively with language tasks; this can be labeled as deactivation. Deactivation is the decrease of signal in regions that are activated during rest but not during task condition, thus functions in these regions are thought to be suppressed. Some of these regions form a network that is consistently activated during rest and deactivated during tasks; this is called the Default Mode Network (DMN). DMN activation is associated with ‘free thinking’ 4 cf. (Medina et al., 2007), who presents an overview of the reliability of fMRI‐obtained laterality measurement. 10 1. INTRODUCTION processes – often referred to as thinking about the day, shopping lists, and what’s for dinner – therefore the suppression of DMN activation enables a person to allocate more cognitive power to the task. Heterogeneity of the anti‐correlation during a semantic task in the different regions of the DMN is to be expected (Seghier & Price, 2012). A difference in suppression of the DMN between the task and control condition can also be expected, depending on how engaging the control condition is. Deactivation patterns might be just as necessary as activation patterns to explain brain functioning (Binder 2012). 1.3 Intelligence models for Language Ability 1.3.1 Relation Language Ability and Intelligence There is an, although limited, correlation between language ability and intelligence (e.g. word fluency: Haier et al., 1992; Roca et al., 2010; semantics: Prat et al., 2007). Some intelligence models describe processes that can be applied to language ability as well, and help to understand the differences in language performance observed in previous and our current work. Intelligence is attributed to a parieto‐frontal network that includes several regions and connections that are shared with language processing functions. This network is described in the Parieto‐Frontal Integration Theory of intelligence (Jung & Haier, 2007). A second intelligence theory is the neural efficiency hypothesis of intelligence (Haier et al., 1992). This theory describes how well‐developed skills can be characterized by a more effective manner of processing in the brain. Thus; high‐skilled individuals will show a decreased brain activation compared with lower‐skilled persons. This reasoning can be applied to language skills as well, as will be put forward in the next section. Lastly, neural adaptability is discussed; this is a trait observed in high‐skilled individuals. These theories together may explain the functional activation patterns observed in high performers (e.g. Prat 2011; Langer et al., 2012). 1.3.2 Intelligence Models The ParietoFrontal Integration Theory (PFIT) of intelligence is a summation of regions in a network found to show activation dependent on intelligence level (Jung & Haier, 2007). It has been known that neural correlates to high intelligence are located in the prefrontal cortex (Thompson et al., 2001), and that increased gray and white matter is observed in both frontal and parietal regions in correlation 11 1. INTRODUCTION with high intelligence (Neubauer & Fink, 2009). The P‐FIT of intelligence states that it takes a network of interactive regions to provide high abilities. The functions are divided within this network from caudally located rule generating processes, to rostral functions such like selecting, and testing of answers. The network includes the language processing areas in the posterior perisylvian region. The Neural efficiency hypothesis of intelligence states that networks for cognitive functions work in a more efficient manner in intelligent brains. Therefore, intelligent brains will show less activation in task‐specific networks during imaging studies. Haier and colleagues (1992) state that the mechanism behind neural efficiency might be deactivation of irrelevant brain areas, or a more specific use of task‐related areas. The neural efficiency hypothesis of intelligence appears to be limited to frontal regions, and conditional on task as well as task‐difficulty (Neubauer & Fink, 2009). Predominantly frontal activation patterns in high performers show efficient behavior during easy to moderately difficult tasks. Activation in the frontal region has previously been shown to decrease upon automation of processes (Ramsey et al., 2004). When demands get high, this is no longer true; high performers then recruit more brain regions to solve the task. The high intelligent individuals might have more adaptive strategies than low performers and can – depending on task demand – either use their brain efficiently or call in the help of supporting brain regions (Doppelmayr et al., 2005). Neural efficiency patterns have been observed in high capacity readers during sentence comprehension (Maxwell et al., 1974; Prat et al., 2007; Prat & Just, 2011). The additional recruitment of supporting neural resources whenever a task is difficult may be described as Neural adaptability (Prat et al., 2007). It is hypothesized that individuals highly proficient in language show more neural adaptability compared with people with lower proficiency. This can be observed as activation in language‐related regions, either in main language regions or in additional supportive regions. Evidently, the theories above outline a varied pattern of the relation between high performance and neural activation or deactivation. This pattern is dependent on task, task demands and functional region. In the Discussion the considerations concerning the interpretation of brain activation will be further explored. 12 1. INTRODUCTION 1.4 Aims Language ability in healthy adults was expected to be visualized as a modulation of activation in language‐related regions, with respect to the level of activation, but also the degree of lateralization between hemispheres. PAPER I aimed to determine regional lateralization of semantic language functions in relation to performance in tests of language ability. It was expected to find laterality differences related to performance in the IFG and posterior temporal lobe, for both fMRI‐obtained laterality and for dichotic listening. PAPER II aimed to find the neural correlates to language ability throughout the whole brain. The expectation was to find specific regions in the right IFG and posterior temporal lobe activated during from a semantic task that were related to high performance in tests of language ability. Furthermore, brain activation during word fluency was investigated and compared with semantic results, in order to find whether there were similarities in activation patterns related to high language ability. PAPER III aimed to reproduce the findings of PAPER I and PAPER II in a new study population. Thus, activation during semantic and word fluency tasks that emerged in the right‐hemispheric homologues of Broca’s and Wernicke’s area were investigated for their correlation with high performance in tests of language ability. In addition, activation related to task demand was investigated. Brain activation patterns related to high performance were expected to show neural efficiency for low‐demand tasks in the IFG. Furthermore, high language ability was expected to be characterized by neural adaptability; i.e. increased right‐hemispheric contributions. PAPER IV aimed to investigate language deficits in people with generalized epilepsy. This group was also expected to show an inadequate suppression of the default mode network that is normally highly anti‐correlated with the task. 13 Strength and courage overrides The privileged and weary eyes Of river poet search naiveté Pick up here and chase the ride The river empties to the tide All of this is coming your way ‘Find the River’ – Bill Berry, Michael Stipe, Peter Buck, Michael Mills 14 2 METHODS 2.1 Neurolinguistic Measures 2.1.1 Tests of Language Ability In PAPER I and PAPER II, four tests to measure language ability were used: FAS and BNT measured word retrieval abilities, and BeSS and Read measured language comprehension abilities. In PAPER III and IV, only BeSS and FAS were used. FAS is a phonemic word generation test in which participants are cued with a letter (F, A, S), and have to generate as many words as possible, starting with the cue letter. Total score is the number of generated words for all three letters. BNT is the established Boston Naming Test. During the test, the participant is presented with 60 pictures that have to be named. BeSS ( “Bedömning av Subtila Språkstörningar” or Assessment of Subtle Language Deficits) tests for the use of complex language by means of seven subtasks (Laakso et al., 2000). Those subtasks are: REP repetition of long sentences (9‐16 words) CON sentence construction (from three words, with given context, under time pressure) INF inferential reasoning (based on a read text) COM comprehension of complex embedded sentences GAR comprehension of garden‐path or ambiguous sentences MET comprehension of metaphors VOC vocabulary – word definition 15 2. METHODS Maximum score was 210 points. The Read test is selected from a Swedish exam for university students. Participants had to read three texts and answer four questions on each text. The total score was the number of correctly answered questions. 2.1.2 Dichotic Listening Dichotic Listening scores were acquired in PAPER I with the use of a version of the Bergen Dichotic Listening Test (Hugdahl 1995), which is a consonant‐vowel test. Auditive stimuli created from the combination of a stop consonant and the vowel ‘a’ (e.g. ba – ga – pa) were presented bi‐aurally to the participants. Depending on the instructions, the participants had to report the stimuli; either heard in the left or the right ear; in both ears; or the most salient stimulus. The results were calculated as a right ear advantage; subtracting correct responses perceived by the left ear from those heard in the right ear, then dividing this figure by the number of total correct responses. A high right ear advantage meant that the subject was better at reproducing stimuli heard in the right ear, compared with the left ear. This was interpreted as a lateralization index for language; a high right ear advantage meant strong left‐hemispheric lateralization. 2.1.3 fMRI Language Paradigms The word generation task WORGE from PAPER II was as described in (Engström et al., 2010) but with moderation of the control condition. The participants were cued with a letter taken from the Swedish alphabet, excluding C, Q, W, X, Y, Z, Å, Ä, and Ö. They were instructed to generate words with the cued letter, as many as possible within the given time of 5 s. The cue letters were varied and presented in blocks containing three to five letters, pseudorandomly ordered. The baseline or control task consisted of presentation of an asterisk alternated with a row of asterisks. The word generation task WORD is described in PAPER III. Similarly to WORGE, a cue letter was presented, but this time the cue letters were divided into two difficulty categories; ‘easy’ (frequent starting letter in a Swedish word list) and ‘hard’ (infrequent starting letter). The letters were presented per category in a block of seven letters, alternating with control blocks. The control block differed from WORGE in the sense that only one asterisk was presented each trial. The sentence completion task SENCO is described in PAPER I. This was a cloze task; the participant had to silently generate the missing last word of a sentence. The sentences were presented in blocks, 16 2. METHODS the presentation duration of a sentence was 3 s followed by display of an asterisk for 2 s. The control condition consisted of asterisks mimicking a short sentence. The congruent/incongruent sentence reading task SEN is described in PAPER III. The participants were presented with blocks differing in difficulty level; either congruent (‘easy’ condition) or incongruent (‘hard’ condition) sentences, or control blocks containing a row of asterisks and arrows. The participants had to judge whether the situation described in the sentence took place inside or outside. During the control condition, the participants had to report in which direction the arrow was pointing. 2.1.4 Study Population Study A investigated a healthy adult population of 18 participants: nine females and nine males aged 21‐64 (mean age: 40). For PAPER 1, a subset of 14 participants (seven females, seven males) were investigated, aged 21‐55 (mean age: 36.9). Study B investigated two groups. First, a healthy adult population of 27 participants: 14 females and 13 males aged 18‐35 (mean age: 25.5) was investigated. The analyses from PAPER III were performed on data from this group. For PAPER IV; the healthy control group was compared with a group of 11 people with generalized epilepsy: six females and five males, with an age range of 20‐35 years (mean age: 26.5). In both the healthy control group and in the group of people with generalized epilepsy there was a left‐handed individual. All participants had Swedish as their first language and were screened by means of a questionnaire on the absence of neurological, cognitive or psychiatric disorders and magnetic resonance contra‐ indications. 2.1.5 Generalized Epilepsy The different types of epilepsy can be classified according to etiology. This results in a distinction between generalized epilepsies with genetically inherited origin, and focal epilepsies (Berg et al., 2010; Poduri & Lowenstein, 2011). People with generalized epilepsy (GE) show a widespread atypical cortical activity (Marini et al., 2003) and may experience language problems (Chaix et al., 2006; Caplan et al., 2009). GE is also related to an abnormal connectivity in the default mode network (McGill et al., 2012). 17 2. METHODS 2.2 Functional MRI 2.2.1 Properties of Functional MRI Functional MRI can detect susceptibility changes in the blood that arise depending on the amount oxygen that is present. Neurons that are activated exchange neurotransmitters, and this exchange process consumes oxygen. This is overcompensated by transport of an abundance of oxygenated blood5 to the activated area, the oxygenated blood differs from the surrounding deoxygenated blood in magnetic properties. This process is called the blood oxygen level dependent (BOLD) response and is measured using susceptibility sensitive magnetic resonance sequences6. Since changes in blood flow are slow, the fMRI signal has a low temporal aspect. Furthermore, the magnetization difference is very subtle, with a low signal‐to‐noise ratio. Therefore, a common approach is to repeat the action or stimulus that evokes the pattern of interest many times, and calculate the average of the response. The highest power is obtained when stimuli are presented in blocks, and the blocks for different conditions and the baseline are presented in an alternating sequence. To get a measure of neural activation per condition in each spatial unit (i.e. voxel), a standard approach is to model the expected BOLD response with the general linear model (GLM), which is then fitted to the data. This model is time‐variant. An equation for the GLM is given as Y = Xβ + ε , in which Y is the data represented by the design matrix X (the design matrix models aspects of the experiment such as conditions or performance covariates) times the parameter estimates β (estimates for the data that explain as much as possible). The ε is the residual error term. In our studies, we used statistical parametric mapping (SPM)7 to model the GLM on our data. All our studies were collected with a Philips Achieva 1.5 tesla scanner, using gradient‐echo planar imaging sequences. The obtained images were all normalized to a standard brain with coordinates in Montreal Neurological Institute (MNI) space. The activation pattern for each condition can be quantified by subtracting the number of activated voxels in one condition from another. Most often are task conditions compared to a baseline condition. Subsequently, the significance of the first‐level analysis results (testing individuals) can be tested by, for example, t‐tests. Thus, testing for activation related to a certain condition can be done by subtracting baseline activation from activation during the condition. Testing for deactivation can 5 To be precise; it is the hemoglobin protein that transports oxygen in the blood. Hence the term ‘hemodynamic response function’ that is used to describe the overcompensation of oxygen transport to active neurons. 6 Paramagnetic deoxygenated blood disturbs the magnetic resonance signal, by hastening the dephasing of protons that emit this signal. If the amount of oxygenated blood increases, the measured signal increases as well. 7 www.fil.ion.ucl.ac.uk/spm/software 18 2. METHODS be done by subtracting condition activation from baseline activation. The resulting statistical maps can be entered into a second‐level analysis to test on group level. Two groups can be compared with a two‐sample t‐test, or if data fluctuation depending on individual performance scores is investigated, a multiple regression approach can be taken. For our multiple regression analysis, we corrected for age by modeling age as a covariate and tested for individual performance differences by modeling performance score as a covariate of interest. 2.2.2 Region of Interest Analysis If the location of expected activation is reasonably certain, and an analysis of the whole brain is not required, the analysis can be restricted to regions of interest (ROIs). In our studies, ROIs were obtained in different ways, a posteriori and a priori, to answer different questions. In PAPER II, the whole‐brain analysis results were used to guide placement of small spherical ROIs at significant peaks of activation. Parameter estimates were calculated from an ROI analysis and then tested for their correlation strength with performance. To report the strength of these correlations as a measure of significance would give an inflated measurement, since this is a second correlation of fMRI data with performance scores. Therefore, our posthoc results were merely used to filter out low‐significant correlations from the regions that were significant in the multiple regression analysis with a p‐value threshold of 0.01, corrected for multiple measurements by means of the false discovery rate In Study B that led to PAPER III and PAPER IV, we had an expectation of which regions would be active. Therefore, we were able to restrict our statistical tests to include only the voxels in the predicted regions and thus correct the significance calculation for the small volumes used. For the unpublished results related to the healthy population in Study B that are discussed in this dissertation, we used the following ROIs: the IFG pars opercularis (BA 44), IFG pars triangularis (BA 45), IFG pars orbitalis (BA 47), the middle and superior temporal gyri – described as the ‘posterior temporal lobe’, and the angular gyrus (BA 39). Here, only results significant at p < 0.05 were reported, and the family‐wise error (FWE) rate was used to correct for multiple measurements. In PAPER I and PAPER III, we used ROIs for the laterality index analysis as well. In these analyses, the bilateral ROIs were created to be mirror‐symmetrical so that they were equal in number of voxels. In PAPER I, the used ROIs were: the IFG including the pars opercularis and pars triangularis, the temporal lobe including the middle and superior temporal gyrus – this ROI was divided into the anterior temporal lobe and posterior temporal lobe – , the anterior cingulate cortex, and the superior parietal lobe. The ROIs used in the laterality index analysis PAPER III were based on results of PAPER I and PAPER II; IFG including the pars opercularis, pars triangularis and pars orbitalis; the angular 19 2. METHODS gyrus; and the posterior temporal lobe including the middle and superior temporal gyri (excluding the temporal pole). This last ROI is less restrictive than the ‘posterior temporal lobe’ ROI from PAPER I. The ROIs used for analysis of the default mode network in PAPER IV were also bilateral: the medial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, precuneus, inferior parietal lobe, middle temporal gyrus, superior temporal gyrus, hippocampus, and parahippocampus. 2.2.3 Laterality Index Analysis Often, a laterality index (LI) is defined as the result of a subtraction of activated voxels in the left hemisphere of the brain from the activated voxels in the right hemisphere. In our studies, the laterality index analysis is calculated not for the whole brain but for separate regions. Also, since calculation by this simple subtraction makes an LI sensitive to choice of threshold, we used a weighted LI that was derived from varying thresholds (see PAPER I for details). 20 21 Half of the time we’re gone but we don’t know where And we don’t know where Here I am ‘Only Living Boy in New York’ – Paul Simon 22 3 RESULTS Performance differences in healthy subjects were explored in our first study; Study A. We tested the fMRI tasks sentence completion SENCO and word generation WORGE in relation to performance measurements in tests of language ability. This led to the publication of PAPER I and PAPER II. Guided by our findings, we examined a new study population in Study B for performance differences, and furthermore for difficulty‐related activation. The fMRI acquisition in Study B was done on tasks investigating sentence reading of congruent and incongruent sentences (SEN) and, again, word generation (WORD); this is presented in PAPER III and PAPER IV. All performance scores were obtained from tests of language ability performed off‐line, i.e. not during the fMRI scanning session. The ‘Results’ chapter is divided into four sections. First, the multiple regression analysis relating performance to brain activation during tasks of language ability from PAPER I will be described. The second sub‐chapter presents how language ability is characterized by laterality differences between regions of interest in both hemispheres, as presented in PAPER II and PAPER III. Then, results from PAPER III on the neural differences related to task difficulty are described. Lastly, our research on patterns in the default mode network that are anti‐correlated with sentence reading from PAPER IV is reported. The DMN deactivation is investigated both for healthy adults and people with generalized epilepsy, in relation to performance differences and task difficulty. 23 3. RESULTS 3.1 Multiple Regression Analyses Prior to PAPER I and PAPER II, the Swedish test for complex language functioning BeSS had not been used to test neural correlates related to language performance differences. Moreover, most literature on language performance and brain activation was based on non‐Swedish populations. In PAPER II, we therefore adapted an unconstrained, whole‐brain analysis approach. We measured how neural activation, related to sentence completion and word generation varied in relation to the off‐line performance measures (FAS and BNT for WORGE, BeSS and Read for SENCO). The typical activation patterns for sentence reading and word fluency in the whole group can be seen in Figure 3 (SENCO) and Figure 4 (WORGE). In PAPER II, we observed a mainly right‐hemispheric contribution to high language performance during our multiple regression analysis (overview in Table I of PAPER II). This contribution was most evident for the SENCO task with the BeSS performance score as a covariate of interest. We observed increased activation in the right IFG pars orbitalis (BA 47) and the right middle temporal gyrus (BA 21) to correlate with high performance in BeSS and Read. High Read performance was related to activation in several regions in the right lateral frontal lobe (dorsolateral prefrontal cortex) and middle temporal gyrus; a cluster of activation in the left middle temporal gyrus was also observed. In addition, the right fusiform gyrus was increasingly activated in participants with high Read performance. The increased activation characterizing high performance was also observed for WORGE, where word generation activation in the right IFG increased for participants with high BNT scores. However, high FAS scores only correlated with left medial frontal gyrus activation, and not with any right‐hemispheric clusters or with activation in Broca’s or Wernicke’s areas. 24 3. RESULTS Figure 3. Neural activation (redyellow) and deactivation (blue) during sentence reading on the SENCO fMRI task in a healthy participant group. The scale indicates the Zvalue of activation strength, the numbers indicate the z coordinate of each slice in the MNI coordinate system. L = left hemisphere, R = right hemisphere. 25 3. RESULTS Figure 4. Neural activation (redyellow) and deactivation (blue) during word fluency on the WORGE fMRI task in a healthy participant group. The scales indicate the Zvalue of activation strength, the numbers indicate the z coordinate of each slice in the MNI coordinate system. L = left hemisphere, R = right hemisphere. 26 3. RESULTS The findings of performance‐dependent right‐hemispheric IFG, dorsolateral prefrontal cortex and temporal lobe activation, regions of interest were created in these areas to test again with a multiple regression analysis of performance influences on brain activation patterns in a new study population (Study B). Here, some unpublished results are discussed first. A multiple regression analysis of activation in the predefined ROIs showed correlations between high performance and activation in the left, rather than in the right hemisphere. Activation in the left posterior temporal lobe during the hard condition of SEN correlated with high BeSS performance (Peak Z: 4.89; p < 0.05 FWE corrected; MNI coordinates: ‐40, ‐56, 14) (Figure 5, left). During the hard condition of WORD, activation in the left angular gyrus correlated with high FAS performance (Peak Z: 4.11; p < 0.05 FWE corrected; MNI coordinates: ‐54, ‐66, 28) (Figure 5, right). Figure 5. Brain rendering showing locus of activation (with peakvalue of activation in red) in the left hemisphere. Left: posterior temporal lobe activation during difficult sentence reading (SEN task) correlated with high BeSS performance. Right: angular gyrus activation during word fluency (WORD task) correlated with high FAS performance. 3.2 Laterality Analyses We tested how laterality in regions of interest varied with performance scores. Therefore, we used a threshold‐independent approach to calculate a laterality index in ROIs in both study populations; in PAPER I from Study A and PAPER III from Study B. The ROIs in Broca’s and Wernicke’s area from PAPER I were re‐used in PAPER III, with the addition of an ROI defining the angular gyrus. PAPER I investigated only sentence completion, and showed that the right posterior temporal ROI was more active than the left when high Read scores were achieved. High BeSS scores were 27 3. RESULTS correlated with more activation in the right than in the left IFG. These results were confirmed by the results of the dichotic listening investigation. The dichotic listening test elicited a decreased right ear advantage during bi‐aural stimulus perception in correlation with high scores on the Read, BeSS, FAS and BNT tests. This high language performance correlation was found for free‐report (stimuli reported from either or both ears) and for directed‐report‐left (stimuli reported from the left ear) conditions. The directed‐report‐left condition also correlated with the fMRI LI in the posterior temporal lobe; participants that showed a right‐hemispheric or bilateral language activation also could attend better to, and give more responses heard with the left ear. LI analysis of the sentence reading task in PAPER III reproduced this result in a new study population. In PAPER III, we found that the right posterior temporal ROI was more active than the left in correlation with high BeSS performance scores. We also applied an LI analysis to the word generation data in PAPER III. We now observed that the LI in the IFG correlated negatively with performance in FAS. This negative correlation was characterized as a decreased left‐hemispheric IFG activation in relation with high fluency performance rather than an increase in right‐hemispheric activation (see Figure 2A in PAPER III). 3.3 Task Difficulty Modulation In PAPER III, we modified fMRI task difficulty; this was done by taking the contrast of the complex versus the simple condition (Hard > Easy contrasts). We wished to investigate if, and how, difficulty‐ related activation would differ from performance‐related modulations of activation patterns. We showed that the activation patterns related to the increased complexity of incongruent sentence reading were located in the bilateral IFG. An increase in difficulty of word generation did not relate to a change in brain activation patterns. No interactions between task difficulty and performance were observed. The analysis of healthy adults in PAPER IV showed that the deactivation patterns in the DMN related to the complex incongruent sentence reading condition were augmented in the pregenual anterior cingulate cortex bordering the medial frontal cortex. 28 3. RESULTS 3.4 Language Dysfunctions in Epilepsy A group of people with generalized epilepsy were tested with the BeSS and FAS tests for language ability. The people with GE performed worse than healthy controls in the BeSS test; performance was lower in all subtests of BeSS, except in the inference subtest (INF). The correlation of lower performance in FAS for people with GE tested just above significance. The reaction times of people with GE in all conditions of the SEN fMRI task were significantly longer than in healthy controls. The people with GE did not show similar suppression patterns in DMN regions as the healthy controls had. In a direct comparison between brain deactivation patterns of people with GE and healthy controls, the people with GE showed less suppression of the posterior cingulate cortex and the left anterior temporal cortex during reading of congruent sentences. Furthermore, people with GE showed activation rather than deactivation in the right parahippocampal gyrus. – The healthy controls did not show any activation or deactivation at all in that region. 29 It is important in life to measure yourself at least once … with nothing to help you but your own hands and your own head paraphrased after Primo Levi immortalized by Alexander Supertramp 30 4 DISCUSSION 4.1 Neural Correlates to Performance 4.1.1 Multiple Regression Analyses From the multiple regression analyses no performance‐dependent similarities between the study populations from PAPER II (Study A) and the unpublished results related to PAPER III (Study B) emerged. This might not come as a surprise, since there were several key differences between the word fluency and semantic tasks used in the two studies. The word generation task was slightly different in each study, but the sentence reading tasks differed substantially from each other. SENCO (PAPER I & PAPER II) was a cloze test, presenting incomplete congruent sentences that lacked a last word. In SEN (PAPER III & PAPER IV); the sentences were complete and congruent in the easy condition, and complete but incongruent in de difficult condition. The subtraction of baseline activation from sentence reading on SEN did not yield significant results. The implications of these divergent results are twofold. First; the semantic language ability correlates vary depending on the choice of task. This indicates that the identified regions from PAPER II in e.g. BA 47 and BA 22 may not be representative of semantic ability per se. This is not to say that we could not relate brain activation to language ability; this will be discussed in the next section ‘Laterality Analyses’. Second, as discussed by Newman and colleagues (2001) and Binder (2012), the choice of baseline condition – often a form of rest – is pivotal for the activation pattern resulting from subtracting designs in an fMRI analysis. A clear example is seen in the SEN results of PAPER III when we subtracted the difficult condition, incongruent sentence reading, from congruent sentence reading. We obtained very different results compared with subtraction of baseline activation from congruent sentence reading – this will be discussed in more detail in the next section. The choice of control condition is very important because, as is now well‐known, a baseline condition that is not engaging is not equal 31 4. DISCUSSION to a resting state of the brain. Rather, the opportunity of letting the mind wander evokes a highly interconnected network supporting cognitive processes; this is described as the default mode network. This network will be discussed later in connection with language ability results obtained from people with epilepsy. The baseline conditions in our sentence reading experiments did control for the visual aspects of the sentence by presenting clusters of symbols. In addition, in the baseline condition of SEN there was a judgment aspect similar to the task condition, in which a button press from the participant was required. The baseline conditions were kept simple and might not have engaged the participants in a high degree as our intention was to image all aspects of language processing related to the tasks instead of filtering out some of these processes. However, according to Binder (2012) this could have the consequence that conceptual processes – shared between the resting network and the language network – were masked because the participants’ attention was not occupied during the baseline condition. Since the baseline conditions differed between the semantic tasks, this could, together with the task differences, account for the differences in the results between studies. The word generation tasks were rather similar. A possible explanation for the different results is simply that the study populations differed from each other. First, there was a substantial difference in the age range of the included participants. Adults up to 65 years of age were allowed to participate in the first study that comprised PAPER I and PAPER II, as our interest in language ability included the whole healthy adult population. However, since our study samples were rather small, for the next study we reduced the age range to 18‐35. This would help to obtain more power in our study, by diminishing intra‐subject variability. The variation between the word generation tasks also needs to be addressed. The WORGE task used in PAPER II presented letters for 5 s each, the order was randomized within the blocks. The WORD task, used in PAPER III was divided into high and low frequency letter blocks, with a presentation time of only 2 s per letter. This has the implication that the WORD task, especially in the difficult condition which contained only infrequent letters, was more difficult than WORGE. When investigating this difficult WORD condition, the activation in the left angular gyrus showed to be related to high FAS performance. Activation in the left posterior temporal lobe in the difficult SEN condition was related to high BeSS performance. These regions are concurrent with the P‐FIT theory and the activation might be linked to higher intelligence. Increased activation in these regions may be an indication of neural adaptability in high performing individuals. According to the neural adaptability theory (e.g. Prat et al., 2007), it can be expected that high performers change their strategy depending on task difficulty, and thus show different brain activation patterns for easy compared with difficult conditions. This adaptable activation may be absent in low performers because they do not have the possibility to adapt their neural activation, or because they simply stopped participating while high performers might continue. The results of neural adaptability evoking right‐hemispheric activation for high performers, as seen in PAPER I and 32 4. DISCUSSION PAPER II, did not emerge from the multiple regression analysis of WORD; however the laterality analysis did show this correlation, as will be discussed next. 4.1.2 Laterality Analyses Although we could not confirm the specific focalization of correlates to language ability, the observation of semantic performance‐dependent activity increase in the right‐hemispheric posterior temporal lobe has repeatedly been made in our studies. We reproduced these results with different fMRI activation measures (multiple regression on activation in the whole brain in PAPER II, and laterality index calculation on regions of interest in PAPER I and PAPER III), and with different laterality measures (LI in PAPER I and PAPER III, and dichotic listening in PAPER I). Since PAPER III was based on a different study population from PAPER I and PAPER II, this also meant a reproduction in a new study population. Activation in the right temporal lobe has been discussed by Bookheimer (2002) to represent visual imagery, related to earlier findings that were close to the region that we found to drive this lateralization difference (Bookheimer et al., 1995; Keihl et al., 1999). Next to this right temporal lobe involvement in language ability, we found some evidence in PAPER II that activation in the right IFG during sentence completion was indicative of high performance. In our subsequent PAPER III, we however observed that a decrease in dominance of the left IFG during word generation was related to high performance. It is tempting to speculate that the level of dominance has a relation to language ability; a reasoning that has been postulated before. The argument that high lateralization is indicative of high performance has been made repeatedly by Annett (1998), who proposed the right‐shift theory in relation to language performance; and stated that language dysfunctions in several disorders are linked to atypical (i.e. not left‐hemispheric) language dominance. This has been observed for schizophrenia (Crow 2000; Ocklenburg et al., 2013), epilepsy (Springer et al., 1999) and dyslexia (Crystal 2010). Also, it is known that during the development of language in children, lateralization increases with age (Szaflarski et al., 2006) and the degree of lateralization in children seems to be related to performance (Groen et al., 2012). Nonetheless, our results are not the first contra‐indications for cognitive advantages of a decreased left‐hemispheric lateralization. Hirnstein and colleagues (2010) suggest that a high degree of lateralization is not favorable for high performance; this has been observed more often in adults (Lust et al., 2011). Our studies indicate that indeed for word generation, left lateralization correlates negatively with high performance in the IFG. However, our results from PAPER II do not show any performance‐ dependent activation modulation in Broca’s and Wernicke’s area in the left hemisphere, and the most consistent result is that the activation level of the right hemisphere drives the performance‐ dependent results. This could be interpreted as neural adaptability in the high performing brain. The adaptability seen in the IFG is observed for both word fluency and sentence reading, but not in all 33 4. DISCUSSION studies. In the next paragraph, the adaptability of the IFG in relation to increased sentence difficulty will be discussed in relation to the observed performance‐dependent laterality differences. The adaptability of the right‐temporal lobe, however, is consistent for semantic tasks. Previously, the right‐temporal involvment in pragmatics (Mitchell & Crow, 2005; Vigneau et al., 2011) and visual imagery (Bookheimer et al., 1995; Keihl et al., 1999) were discussed, and a probable explanation is that these functions are be more evolved in the participants that score high on the BeSS test. The right‐lateralized semantic activation pattern for high language ability does not seem to be dependent on task difficulty, unlike would be expected according to the neural efficiency hypothesis (Neubauer & Fink, 2009). This might be explained by the very nature of the semantic tasks. Peelle and colleagues (2004) concluded that a semantic task is per definition complex. Participants therefore may already in the easy condition experience considerable task demands, and already manifest language ability‐related activation patterns. In conclusion; there appears to be evidence that language ability is connected with the degree of language lateralization. It could also be that laterality is not a static but a dynamic property of the brain. The flow of laterality could be regulated by external input and interhemispheric interactions (Seghier et al., 2011a). If so, individuals with high language ability might modulate this regulation towards an optimal interaction. 4.1.3 Task Difficulty Modulation Before discussing the results of our task difficulty modulation from PAPER III, it is interesting to take a closer look at the dichotic listening results from PAPER I in light of an article on cognitive control and dichotic listening by Hugdahl and colleagues (2009). In PAPER I, the dichotic listening results show a correlation between right‐hemispheric processing and high language performance, this correlation was in concordance with our fMRI laterality results. In particular, this correlation appeared for our directed‐report‐left condition, which is similar to the forced left condition from Hugdahl and colleagues. Whenever a person is forced to attend to the non‐dominant left ear, a successful report of this ear can only be achieved by means of top‐down cognitive control (Hugdahl et al., 2009). This implies that increased cognitive control, and not increased language ability in specific, could underlie the observed decreased left‐hemispheric lateralization of language. The task difficulty modulation in our language ability investigation of PAPER III would therefore help to understand whether the observed right‐hemispheric influences on performance might be modulated by cognitive control rather than language ability. Increased difficulty of the semantic task evoked bilateral IFG activation. This result met our expectations that were based on similar difficulty‐dependent findings (Just et al., 1996), possibly 34 4. DISCUSSION related to increased working memory demands which activate the inferior and prefrontal gyrus bilaterally (Cabeza & Nyberg, 2000). Better cognitive control during word retrieval would help the participant suppressing unwanted answers like already generated words, and thus favor high performance. During the more difficult word fluency task condition with less frequent starting letters, more cognitive control is required to properly generate words, since less words are available. Alternatively to language ability driving right‐hemispheric IFG activation, the IFG activation could be modulated by cognitive control. Unlike Just and colleagues (1996) found in their study, we did not observe a difficulty‐dependent increase in the temporal lobe. We also found no interaction effect between task difficulty and performance. Therefore, in regard to semantic difficulty modulation, we find no grounds for an alternative explanation that the increased right‐hemispheric temporal lobe activation would be driven by task demand. We can therefore defend our hypothesis that language ability, or at least semantic ability, is influenced by the degree of lateralization of the posterior temporal lobe. Task difficulty modulation of the deactivation pattern of the DMN during the sentence reading task was also investigated for the healthy adults in PAPER III. When the SEN task became more difficult; the suppression of activation of the anterior cingulate cortex and adjacent medial frontal cortex was even stronger. This is in line with a study from McKiernan and colleagues (2006), that showed that an increase in task demands would result in an increase of deactivation in the DMN. The medial frontal gyrus deactivation seems to be in the same region as the region described as the anterior‐ventral medial prefrontal gyrus by Seghier and Price (2012). In their study, the medial frontal gyrus was deactivated during semantic processing; this deactivation could not be explained by an increase in demands alone. The authors hypothesized that this deactivation was a further suppression of the ‘free thinking’ function of the DMN, in order to “focus the semantic system toward the external salient information” (Seghier & Price, 2012, pp 11). The augmentation of deactivation in the pregenual anterior cingulate cortex was bordering the medial frontal cortex. This pregenual activation is presumably related to task switching, in which the anterior cingulate cortex plays an essential role (Botvinick et al., 1999). 4.1.4 Language Dysfunctions in Epilepsy In PAPER IV, we presented evidence of language dysfunctions in people with GE; something that has not been the focus of the research on epilepsy. Subtle language dysfunctions may have a great impact on daily functioning (Sturniolo & Galletti, 1994), and, importantly, may negatively affect the life of people with epilepsy (Gauffin et al., 2011). We also investigated whether these language dysfunctions were related to atypical activation patterns in the DMN. In healthy adults, the DMN is suppressed during cognitive tasks; this suppression was also observed during the semantic task SEN. This 35 4. DISCUSSION deactivation of the interconnected DMN supports cognitive processes (Fox et al., 2005; Binder 2012). In people with GE, the suppression showed to be not uniform; several regions did not exhibit deactivation. A lack of deactivation has been linked to a decrease in cognitive performance (Kelly et al., 2008). In a direct comparison between people with GE and healthy adults, the decrease in DMN activation differed significantly in the posterior cingulate cortex – a central processing node in the DMN (Fransson & Marrelec, 2008) – and the left anterior temporal cortex. Our results point to a reduced functional segregation of the DMN which could explain the subtle language impairments that people with GE have, and which were described in PAPER IV (McGill et al., 2012). A second explanation for the impairment of complex language functions as measured by BeSS can be found in the aberrant hippocampal and parahippocampal activation in people with GE, which could impair semantic retrieval functioning (Greenberg et al., 2009; Sheldon & Moscovitch, 2012). 4.2 Healthy Adults One of the main issues in this dissertation is the variability in language ability between healthy adults. In experiments, researchers try to keep the inter‐subject variability at the lowest level possible, since findings related to the variable of interest could easily be obscured by this variability. This is especially the case when groups are small, as is usual in fMRI studies. As is the case in our presented studies, the study group is often controlled for: age, gender, handedness, concomitant medical, neurological, or psychiatric illnesses, and the use of psychoactive drugs. Between our study populations, there were differences in the age range of the healthy participants. This difference in age could bring out a greater variance in performance scores, but could also obscure results by introducing more inter‐subject variability. We included both males and females in our experiments but found no significant difference in performance between these groups. This is not to say that gender‐related performance differences are not to be expected; it has been shown that females outperform males in language tasks, especially in verbal fluency tasks (Kimura 1992). Interestingly, improved performance might not necessarily have a gender‐related neural cause (Sommer et al., 2008; Allendorfer et al., 2012). In future research, it might be necessary to gather more detailed information about participants. Several studies have investigated hormonal influences – which vary depending on the menstrual cycle – in relation to performance (Fernández et al., 2003; Simić & Santini, 2012). They conclude that indeed language performance varies depending on the menstrual phase, but not uniformly for task or region. Even the 36 4. DISCUSSION lateralization of language has been shown to vary depending on the menstrual phase (Hjelmervik et al., 2012). Whereas inter‐subject variability in brain regions related to word generation has found to be low (Xiong et al., 2000), this is naturally not the case when participants who have right hemisphere dominance for language are included. Controlling for handedness is an indirect control for language lateralization. However, as has been observed throughout this dissertation, the level of hemispheric dominance is highly variable amongst right‐handed individuals and between regions. Moreover, the majority of left‐handers (who are often excluded from language fMRI research) have also left‐ hemispheric dominance for language, while right‐handers could have right‐hemispheric dominance. It is though shown in a study combining fMRI and diffusion tensor imaging, that handedness is directly related not only to laterality but also to hemispheric asymmetry (Propper et al., 2010). Of course, assessing handedness gives a cheap and quick indication of language laterality; however, when assessing control groups it is important to consider all the factors that influence language ability that are not controlled for. 4.3 Interpretation of Activation Patterns Brain functioning measured by non‐invasive neuroimaging studies like fMRI cannot easily generate as much incontestable evidence as could be obtained from lesion or intracranial recording studies. In fMRI studies, several assumptions are made, these are also addressed in the Methods chapter. Some of these assumptions are: a) neural functioning is characterized by the BOLD response b) neural functioning can be visualized by subtraction of activation in a baseline task from activation in a cognitive task c) the measured activation is related to brain functioning, rather than to noise d) the results can be generalized outside of the study population The discussion of assumption a) is a fundamental one; how is the BOLD activation that we see in our images related to activity on a neuronal level? That there is a relation is no longer in doubt (Buckner 37 4. DISCUSSION 2003); however the nature of this relation is far from clear. The research group of Logothetis has very recently discussed the current state of knowledge about the representation of the BOLD signal on a neuronal level (Goense et al., 2012). They state that there is evidence for underlying contributions both from local field potentials as well as from smaller neuronal populations8; both from excitatory as well as from inhibitory neural activity; and also for contribution from different neurotransmitters. To complicate the view on the relationship between the BOLD response and neuronal signals even more; the authors conclude that “the relationship may differ depending on area, task, or behavioral state of the subject”. The neuronal underpinnings of complex language functioning can therefore not yet be explained, and this assumption thus remains unproven. Assumption b) takes the discussion a level higher by asking if the paradigm used and the analysis thereof indeed measures the cognitive function of interest. The fact that activation is observed in a region does not mean that the related cognitive function is located in that area. A parallel can be drawn with the language dysfunctions discussed in the Introduction; dysfunctions following a lesion do not prove that the lesioned region is solely and selectively responsible for the execution of that function. The region could just as well be a small part of a serial network, or contain interconnecting fibers from two executive areas (Roskies et al., 2001). Whereas the presented literature under assumption a) indicated that it is reasonable to assume that neurons and not neuronal connections give rise to observed BOLD responses, the exact nature of the contribution of the activated area could not be determined from our studies. To understand the right‐hemispheric activation observed throughout the work reported in this dissertation, the interpretation needs to be based on literature findings on language disability and language functions in the left hemisphere as presented in the Introduction. Furthermore, a closer look at subtraction analyses is needed. Obviously, subtracting a baseline symbol‐viewing condition from a complex linguistic condition leaves activation that could be related to many components of the linguistic model. This is further illustrated by our analyses in PAPER III, where the subtraction of the ‘hard’ from the ‘easy’ condition, namely incongruent from congruent sentence reading, provided very different results from when we subtracted the baseline condition. The analysis that investigated sentence reading in comparison to the baseline did not result in any significant activation, likely because of most activation that is task related is shared between individuals. Only when investigating specific aspects of sentence reading, individual differences emerged. These differences could be representative for strategy differences related to language skill. The aim of this dissertation was to 8 Local field potentials can be roughly defined as the averaged input signal of a neural population measured over a few millimeters, while multi‐unit activation can be measured on smaller neural populations and represents neuronal output signals (Logothetis et al., 2001) 38 4. DISCUSSION generalize language ability contributions rather than to define separate linguistic components. Therefore, the subtraction method was suitable for our analyses. Of course, a reverse subtraction, namely subtracting the task condition from baseline, can also be done. This contrast will visualize anti‐correlated patterns. It is less common to look at deactivation patterns than at activation patterns, although neural suppression can provide valuable information as observed in PAPER IV. The representation of language models in neurolinguistic results is critically reviewed by Van Lancker‐Sidtis (2006) and Sidtis (2007), and rightfully so. Several questions underlying assumption b) are often taken for granted in neuroimaging. Some of these questions are whether language components have a functional correlate in the brain, or whether increased or decreased activation represents better or worse performance. It is plausible that there is no universal theory to describe neural functioning in the brain, but that activation should be interpreted with regard to region and task9. It is also likely that other methods than the GLM should be used to answer questions such as ‘How is language ability represented in the brain?’ in more detail. The GLM is a robust model but not the right choice when the underlying brain activation is expected to deviate in physiological properties or interconnectivity with other regions. Instead, a network model may be used; this can be based on ROIs or be unguided10. The possibilities of using network models include: creating an optimal model specific to the tested study population; detecting activation patterns that were not expected a priori, and visualizing how different regions have a shared correlation or anti‐ correlation with the task. Such an analysis could for instance shed light on the possibly aberrant interactions of the DMN in people with GE Assumption c) is per definition not completely true if no counter‐measurements are taken. Since a whole‐brain fMRI dataset usually contains over 100 thousand voxels, and a GLM tests for the significance of activation in each voxel, the chance of obtaining false positives is substantial. It is necessary to at least apply a stringent p‐value, and preferably apply a correction for multiple comparisons. A pitfall related to the amount of noise present in the data is the inflation of the amount of false positives when testing on a non‐independent selected sample. This inflation was popularized as ‘voodoo correlations’ (Vul et al., 2009)11, and although it referred to social science studies in particular, the article raises a valid point regarding selection of regions of interest. The approach that we adopted in PAPER II – selecting ROIs based on data results, and extraction of parameter estimates only from these selected ROIs – has to be used cautiously. The reported significance can only be based on the initial selection, and not on subsequent correlation tests that would only re‐test the 9 e.g. in the frontal lobe may the neural efficiency hypothesis of intelligence be applied, see also ‘Intelligence Models’ in the Introduction. such as an independent component analysis 11 ‘Voodoo correlations’ was a definition used in the pre‐published title, this was vehemently discussed online; overview at: www.edvul.com/voodoocorr.php 10 39 4. DISCUSSION already determined correlation. Therefore, in PAPER II, we used this method not to select but rather to deselect activated ROIs whose statistical significance was already ensured in the initial analysis by applying a correction for multiple comparisons. Multiple comparisons are not only made within a data set, but also when running different analyses on data from one experiment; when running different experiments on the same study population; or, arguably, when including different studies within one dissertation. With every new measurement, the chance of finding a false positive result increases. How can we be sure our results really represent reality instead of random noise? Of importance is the fact that the different analyses of the same study population, (PAPER I on the population of Study A and PAPER III & PAPER IV on the population of Study B) are based on pre‐defined selection of regions of interest; thus the analyses are only guided by previous, independent research. PAPER II had a different approach. This paper started with an unconstrained whole‐brain analysis, which, unguided by the researcher or other input, reproduced the ROI‐restricted findings of PAPER I. Clearly, PAPER I and PAPER II are interdependent since they examine people from the same participant pool; therefore the results show similar patterns. These patterns could be due either to noise or to performance‐related activation. As is the case with fMRI research, all activation should be regarded as spurious unless reproduced over and over again. A strong evidence for results to be reliable, is reproduction over methods or study populations. In PAPER I, our results from the fMRI analysis were congruent with our dichotic listening results; both indicated that increased right‐lateralization was correlated with high performance. Study B was performed for the reason of reproduction of the results from PAPER I and PAPER II in a new study population. Some of our results from PAPER II remained unconfirmed, however we did reproduce findings that increased right temporal lobe activation and decreased left IFG activation were dependent of high performance. Therefore, these independently and repeatedly obtained results became the focus of this dissertation. PAPER III and PAPER IV investigate different regions of interest in the same healthy adult study population, and our view broadened, from differences in the healthy population, to include the investigation of differences between healthy participants and people with epilepsy. Even though the healthy participant group is the same, the hypotheses and tests between papers are divergent. Moreover, the t‐tests of PAPER IV and multiple regression analyses of PAPER III were corrected for multiple comparisons with use of the stringent family‐wise error rate. The laterality index correlations of PAPER III are based on comparisons between two regions of interest, this already reduced the statistical comparison from thousands of voxels to the few tested ROIs. Assumption d) is again best proved by reproduction of results, as is done in PAPER III; by reproducing findings of PAPER I and PAPER II. Some of our analyses are performed on a relatively small study population, even by fMRI standards. This population may or may not have been representative of other healthy adults. Because of the small group size, the outcomes are rather 40 4. DISCUSSION sensitive to outliers, especially in multiple regression analyses. The problems with small group sizes are discussed by Thirion and colleagues (2007), who suggested that around 20 participants was an acceptable group size. Some of our analyses are whole‐group analyses, on up to 27 participants, but some other analyses investigate within‐group differences, and therefore have less detection power12. Less detection power results in a higher possibility of many false negatives; thus there was a greater chance that we failed to find existing neural correlates to language performance. However, the found results are significant, because false positives were kept to a minimum by applying correct stringent p‐values, and by applying corrections for multiple measurements on the multiple regression analyses. To come back to the sensitivity to outliers in small groups, this was addressed with an additional analysis of data from Study A, which was the study with the smallest study population. The results in PAPER II from the multiple regression analysis, that showed activation during SENCO related to high performance in the BeSS test and that was underlying our hypotheses in PAPER III, were re‐analyzed, this time with a two‐sample t‐test. The two samples, high performers and low performers, were participants that had performed above and under the mean score for BeSS respectively. The results, presented uncorrected in Figure 6, show activation in the right IFG (pars orbitalis, BA 47) and right middle temporal gyrus (BA 21). This activation pattern proved to be significant at p < 0.05, FWE corrected, when the ROIs from PAPER III were applied. Figure 6. Brain activation during the sentence completion SENCO task, when contrasting high BeSS performers to low BeSS performers. Activation is observed in the right hemisphere, in the inferior frontal gyrus pars orbitalis and in the middle temporal gyrus. This activation was significant at p<0.05, FWE corrected after small volume correction on predefined regions of interest. 12 It should be noted however that our experiments were done using equipment and software from post2007, which contributed to improved signal detection. 41 4. DISCUSSION 4.4 Future Directions Notably, several of the presented results, either unpublished or presented in our papers, were not according to our expectations. The results together cannot reveal a sufficient model of the neural correlates to language ability, since the concept appears to be too intricate. Nonetheless, our results provide important clues how to obtain an even better understanding. The most consistent findings in previous literature that is presented in the Introduction – ‘Right‐ hemispheric Influences’ and that is discussed in detail in PAPER III, indicate an important role of the right hemisphere in understanding language context and integrating linguistic information. The right hemisphere is activated especially when the language used is ambiguous or full of imagery such as in metaphors. In our sentence reading task SEN, the participants had to imagine where situations took place, this required spatial thinking and evoked right‐hemispheric activation (Brown & Kosslyn, 1993). Spatial thinking is not unique for our semantic task; in fact, a great deal of language understanding requires the use of spatial concepts (Zwaan & Radvansky, 1998). It is tempting to hypothesize how our findings not only indicate a neural correlate to sentence understanding in the right posterior temporal lobe, but may be linked to imagery or spatial thinking involved in language tasks. Thus, the absence of performance‐modulated right‐hemispheric activation in relation to our fluency task might be because of the nature of this task. Future studies could introduce a different fluency task that incorporates spatial thinking. This could be a verbal divergent thinking tasks, such as the brick task (‘How many things can you do with a brick’) (Guilford et al., 1978). Carlsson and colleagues (2000) presented a study that gives a promising base for this hypothesis. The study found that the Brick test in comparison to the FAS test activates the right frontal lobe significantly more in highly creative participants than in low creative participants. The FAS performance scores however did not vary between those two groups. Brain activation obtained during such a divergent thinking task, or an other spatial thinking based task, might be sensitive to right‐hemispheric modulations related to language performance. Possibly, language ability should be measured not with the FAS test but with the complex language functioning BeSS test, since the latter test investigates more components of language. It would also be valuable to test for intercorrelated networks in relation to performance. It is reasonable to suspect that high language ability might be characterized not only by neural adaptable regions but also by adaptable connectivity, thus a change in correlation between activated brain regions. Dynamic causal modeling of our regions of interest would give an answer to that hypothesis. 42 4. DISCUSSION In addition, functional brain activation images of our participant groups during rest have been collected. As discussed before; the brain is far from resting during rest, but rather shows activation in the default mode network. Since we observed a diminished suppression of this network during task in people with generalized epilepsy, there may be connectivity differences as well that are related to language dysfunctions or even to the level of language ability. Again, a dynamic causal model might help to answer this hypothesis and determine whether language ability level can be visualized not only as divergent neural correlates but also as divergent neural interaction. Another method to visualize connection between brain regions is diffusion tensor imaging, which visualizes the neural pathways and intra‐ and interhemispheric connections in the brain (Glasser & Rilling, 2008). Diffusion tensor imaging could reveal properties of neurons and neuronal pathways that may distinguish high language ability (e.g. Konrad et al., 2012), and underlie the functional differences observed throughout this work. 43 Seal my heart and break my pride I've nowhere to stand and now nowhere to hide Align my heart, my body, my mind To face what I've done and do my time ‘Dust Bowl Dance’ – Mumford & Sons 44 5 CONCLUSIONS The results presented in this dissertation consistently show that activation in the right posterior temporal lobe is correlated with high language ability in healthy adults. The mechanism behind high performance could be a better adaptation of right‐hemispheric temporal activation, and stronger pragmatic or visual imagery skills. PAPER I aimed to relate regional lateralization of semantic language functions to language ability. Dichotic listening laterality results showed that increased right‐hemispheric laterality correlated with high language performance. The fMRI findings revealed that specifically activation in the right IFG and right posterior temporal lobe correlated with high language ability. The aim of PAPER II was to both reproduce these findings and test for other neural correlates to language ability. The most consistent finding was the confirmation of the contribution of the right‐ hemispheric IFG and posterior temporal lobe to high language ability. In PAPER III, a new study population was investigated and tested for reproducibility of our previous results. Indeed, increased semantic activation in the right‐hemispheric posterior temporal lobe correlated with high performance in a complex language test. It was also revealed that it was decreased left‐hemispheric rather than increased right‐hemispheric IFG activation during word generation that correlated with increased word fluency ability. These results were congruent with the hypothesis of neural adaptability as a language ability characteristic. Furthermore, when task difficulty was modulated, the bilateral IFG was active only when task demands increased, this effect was not expected but not observed in Wernicke’s area. Lastly, PAPER IV investigated the default mode network that is anti‐correlated with a task. It was found that people with generalized epilepsy show poor anti‐correlation patterns of this network. This might explain the diminished performance scores for complex language ability that the group containing people with GE showed in comparison to healthy adults. 45 Acknowledgments Acknowledgments should probably not be written just a few days before print. There are so many I would like to thank. But I’ll drink coffee like there’s no tomorrow and try to name you all. First, I’d like to thank my supervisors, our work together under your guidance has led to these publications on which my dissertation stands. Thomas Karlsson; I’d never have thought that spending all those evenings at work could be so pleasant. Go Skellefteå! Coffee, wise words, and jokes; all of these in abundance; until I had to run for the train. I hope we will continue in collaboration on our fMRI journey that has led us so far. Peter Lundberg ; whenever I thought something was obvious, you’d ask me: “Why is that?” Very true; nothing in the brain is obvious, it is fascinating! It was because of your connection with Bas that I came in Sweden to the in the rest of the world rather unknown –fjärde storstadsregion!!‐ . At first I was so confused by your Skånsk, however now I’m pretty confident we’ll work together great in exciting studies that are yet to come. Anita McAllister; I’ve greatly enjoyed your enthusiasm; on everything from language, to the use of your voice (I will practice before presenting this book the 17th!), to lovely stories like the one about Pulvermüller. And above all; Maria Engström; you were the most dedicated supervisor a PhD‐student could ask for, it is thanks to you that I am where I am now. I’ve had the pleasure of meeting your lovely family and enjoying your company in Barcelona and Sevilla. You’ve learned me to appreciate contemporary art, and we share a strong passion for the fjäll; if we won’t meet at work, we’ll meet there! I also would like to thank all of my co‐authors, with you I’ve spend considerable time brainstorming and wondering over weird results. Mattias Ragnehed; when I began, I got your dissertation with the text “Lycka till med din egen”; well, here it is! Mathias Hällgren; thanks for your work on dichotic listening; a great complement to our fMRI work. Daniel Ulrici; you’ve put so much work into our Epilepsy study, thanks for making it a success. AnneMarie Landtblom; we’ve discovered these interesting things, and you were always curious for more, bedankt! And it’s a shame the fjällugglor didn’t make it into our paper. Helena Gauffin; I’ve had great fun and lots of laughs when working with you, but even more when we did not work. Thanks to all the volunteers that participated, especially thanks to the epilepsy patients, for their valuable time and their patience. My colleagues at, and through research linked to the CMIV or Radiological Sciences; who live by the adagio “great work deserves great coffee breaks”. You’ve truly learned me how to fika like a Swede. I’d like thank all you guys and mention specifically Anders T (thanks for introducing me to spex and cheap movies when I was just arrived in Sweden), Maria M, Chunliang (and of course little David; I hope I’ll meet him again!), Filipe, Olof, Anders P, Örjan S, Marcel, and Håkan G. Some of my partners in fMRI‐crime: Mats L, Örjan D, Susanna, let’s meet at the FBI! My (ex‐) roommates Anders G, Danne, Rodrigo, Jonatan and Karin; we’ve had a wonderful time in Beijing with fried ice cream and playing guess‐what’s‐on‐the‐menu. For the Future!. The people who made things work; Anna, Annika, Henrik E, Ingela A, Ingela E, Johan, Lillian and Maria K; I’d seriously be lost without your help. 46 Thanks to DOMFiL and the people with who I had the privilege to represent the Health Sciences PhD students; Axel, Sven, Alma, Daniel and Stefan. I’ve learned many things that I never knew and more things that I immediately forgot, but it was great fun! I enjoy thinking back on the time when I started all fresh and naïve with research in Utrecht. Thanks to Ryota Kanai who I did my very first experiments with. I’ve sat hours and hours adapting to moving stimuli, only to figure out that the experiment should be done otherwise once again. And yet it was fascinating, exciting, and a good training in how data collection would be. Thanks to Bas Neggers who supervised me into something half decent as a researcher, and who guided me to my very first publications. Not only did you believe in me, we even had awesome times on vacation ‐ I mean conferences. BBQ, snorkeling, and beer; living in apartments instead of boring hotels; life was good. Sometimes I miss playing with strong magnets. Thanks to my great former (and first!) neuroscience colleagues from Utrecht, who I enjoy meeting for beer, bbq, (it seems to be a recurring theme with dutchies) and road trips during conferences: Antoin; not only I remember that you could evoke thumb twitches while applying TMS on your own head with your other hand, but also that Australia road trip was epic. Together with Kelly; I’d like to add to the story from the last thesis about our 5000 km drive without proper preparation and the fact that we accidentally lost a day. Because what about the impromptu camp fires, birds and kangaroos literally everywhere, and the fact that we did the whole trip with only ONE cd (The Class of ’55) that rocked as much as you guys do. Tjerk & Willem, thanks for doing sweet studies and writing sweet papers together with me. Mariët; you were a great friend, we’ve had good talks, best roommate ever! Cédric, Remko; I really hope we’ll meet again, it’s great fun going out with you! Thanks to my friends; for the extra support during this crazy period, for lending me your brain (Andreas) or your time & help (Stacy, Emily). Thanks for your friendship David & Natasha, David B, Frida & David L, Elin, Britta, Jonathab (sic), Sune & Karin, Caroline, Emilie, Denes & Margit and all of my friends in the Immanuelskyrkan. Thanks for the wine, whiskey and cheese, the laughs and the help with moving, most of all thanks for your warm hearts. I can’t wait to make ‘social life’ a daily thing again. Thanks to Richtje & Jeroen, and Natascha & Wouter for your visits and love; you guys are true friends and I hope on many (more) snow and hike getaways together. Also, sometimes I wish I could kidnap all your kids, but I’m glad I didn’t do it because I’d never have finished writing this thing. Joel; you became a good friend after we’ve only talked for a few minutes. That’s exceptional. Let’s do some ski touring. Thanks to Matthijs; Bob might be your brother from another mother, but you ‘re my colleague in an other country; we’ve shared a whole career from the navy to psychology. Your work is great, your enthusiasm inspiring, and your stories; they are hilarious. Thanks to my family. Thanks to Tjeerd & Ienke, my parents who taught me that I could become whatever I wanted. And you were there with me; whether it was on a windy boat or in a noisy magnet. Thanks for the care packages and the design of this book. Thanks to Dick & Edith, my parents‐in‐law, for always being there and lending a hand with whatever crazy things we’d think of. Thanks to Marlies (thanks for your visits and practical help!), Pauline & Karin; my sisters and sister‐in‐law for everything, but most of all for being devoted aunts to Lucas. Finally; thanks to Bob. Thanks for being my last‐resort guinea pig (freely interpreting task instructions and suffering through EEG‐try‐outs). We’ve delivered baby Lucas last year, a dissertation this year; we probably should take it easy for a while. But we won’t. With every adventure, I love to take the leap, but it is because of you that I don’t crash. My life wouldn’t be awesome without you. 47 References Abou–Khalil B (2007). Review Methods for determination of language dominance: the Wada test and proposed noninvasive alternatives. Current Neurology and Neuroscience Reports 7(6): 483–490. Ahrens K, Liu H, Lee C, Gong S, Fang S, and Hsu Y (2007). Functional MRI of conventional and anomalous metaphors in Mandarin Chinese. Brain and Language 100: 163–171. 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