Nicole Law. Copyright by Nicole Law 2009

Transcription

1 Exploring the Neural Basis of Working Memory: Using Probabilistic Tractography to Examine White Matter Integrity and its Association to Working Memory in Paediatric Brain Tumor Patients by Nicole Law A thesis submitted in conformity with the requirements for the degree of Master of Arts Graduate Department of Psychology University of Toronto Copyright by Nicole Law 2009

2 Exploring the Neural Basis of Working Memory: Using Probabilistic Tractography to Examine White Matter Integrity and its Association to Working Memory in Paediatric Brain Tumor Patients Abstract Nicole Law Master of Arts Department of Psychology University of Toronto 2009 Paediatric posterior fossa tumors are often effectively controlled with a combination of radiation, chemotherapy and surgery. However, therapeutic craniospinal radiation has been associated with widespread cognitive late effects. Working memory is one such cognitive ability that has yet to be fully examined in this clinical population. Bilateral tracts connecting the cerebellum with the DLPFC were delineated using DTI tractography in all participants, replicating the cerebrocerebellar pathway outlined in an animal model. There were observable differences in white matter integrity (quantified by DTI measures of anisotropy, and mean, axial, and radial diffusivity) of the cerebellum-dlpfc pathway in patients versus controls. Additionally, working memory deficits that were found in patients were correlated with DTI indices pertaining to the cerebellum-dlpfc pathway. Therefore, this thesis is the first to explore the possible relations between white matter integrity of this pathway following treatment for paediatric posterior fossa tumors and working memory function. ii

3 Acknowledgments I would like to thank my supervisor Dr. Don Mabbott as well as my committee members, Dr. Mary Lou Smith and Dr. Maureen Dennis, for all of their help and guidance over the course of this year. I would also like to thank Conrad Rockel, Dr. Nadia Scantlebury, Jolynn Dickson, Dallas Card, and Dr. Garland Jones for their input and suggestions along the way. Lastly, I would like to thank my family and friends for all of their encouragement and support during this process. iii

4 Table of Contents Abstract ii Acknowledgements.. iii List of Appendices v Introduction. 1 Methods Participants.. 22 The Impact of Demographic and Medical Variables.. 23 Materials and Procedure. 26 Reliability of Scanning Protocol. 26 Image Processing. 26 Behavioural Measures. 34 Statistics.. 36 Results. 38 Overall IQ Differences 38 Cerebellum-DLPFC Tracts. 39 Group Differences in White Matter Integrity. 41 Group Differences in Working Memory Outcome. 43 Working Memory Outcome and White Matter Integrity 44 Path Model Analyses for the Cerebellum-DLPFC Pathways. 47 Discussion References Appendices.. 70 iv

5 List of Appendices Appendix A: Analyses for average parental education Appendix B: Scan parameters by site for T1-weighted images Appendix C: Scan parameters by site for DTI images Appendix D: Analyses for site of MRI scan Appendix E: ANCOVA results for CSR and surgery only brain tumor subgroups; DTI analyses controlling for time since diagnosis Appendix F: ANCOVA results for CSR and surgery only brain tumor subgroups; WMI, DST, LNST controlling for time since diagnosis Appendix G: Control group correlations for DTI indices of each cerebellum-dlpfc pathway and WMI, excluding two outliers v

6 1 Brain tumors are the leading cause of death and disability from childhood disease in developed countries (Bleyer, 1999); their incidence accounts for 3.9 to 4.03 cases per 100,000 in The United States and Canada alone (Miltenburg, Louw & Sutherland, 1996). Approximately 55% of all brain tumors in childhood are located within the posterior fossa, an area in the brain containing the cerebellum, pons, and medulla (Kline & Sevier, 2003). Tumors growing in the area of the posterior fossa can block the flow of cerebrospinal fluid and cause increased pressure on the brain and spinal chord (Kline & Sevier, 2003). The most frequent diagnoses of tumors in the posterior fossa are low grade gliomas (astrocytomas), medulloblastomas, and ependymomas, respectively (Yachnis, 1997). As the most common pediatric brain tumor, gliomas (or astrocytomas) account for 20% to 25% of all posterior fossa tumors in children (Schott, Naidich & Gan, 1983). Gliomas arise from glial cells, and there are particular glial cells (astrocytes) that astrocytomas arise from (Yachnis, 1997; Mamelak & Jacoby, 2007). The first line of treatment for children with lowgrade gliomas is surgery, namely gross tumor resection (GTR) (Mueller & Chang, 2009). In general, patients presenting low-grade gliomas who undergo complete resection of the tumor do not receive adjuvant therapy (i.e. radiation or chemotherapy), unless there is presence of disease recurrence or progression (Mueller & Chang, 2009). Medulloblastomas are malignant tumors that arise from primitive neuroectodermal tissue and account for 30% of all pediatric brain tumors and 20% to 50% of posterior fossa tumors (Schott, Naidich & Gan, 1983). As medulloblastomas are radiosensitive in nature, radiation is the most common adjuvant therapy (Mueller & Chang, 2009). Currently, the standard of treatment for posterior fossa medulloblastomas includes postoperative craniospinal radiation (CSR) with a boost to the posterior fossa, followed by 12 months of chemotherapy (Mueller & Chang, 2009; Packer, Goldwein & Nicholson et al., 1999).

7 2 Ependymomas are glial tumors that arise from radial glial cells or ependymal cells within the central nervous system (CNS) (Yachnis, 1997; Hany et al., 1998) and account for 5% to 12% of all posterior fossa tumors (Schott, Naidich & Gan, 1983). Treatment for ependymomas includes a combination of surgical resection and radiation targeted at the posterior fossa (i.e. focal radiation, consisting of a boost dose only) (Packer, Goldwein & Nicholson et al., 1999). With treatment advances such as neurosurgery, radiation therapy, and chemotherapy in the last 25 years, survival rates have improved dramatically (Bleyer, 1999), but survival is often achieved at a considerable cost. Therapeutic craniospinal radiation (CSR) can have adverse late effects on many bodily structures, including endocrine, skeletal, and central nervous systems (Schultheiss, Kun, Ang & Stephens, 1995; Goldwein, Radcliffe & Johnson et al., 1996). Late effects (i.e. symptoms occurring after recovery from early onset disorders) are for the most part irreversible and thus the most destructive (Shun Wong & Van der Kogel, 2004). Late effects associated with radiation injury to the spinal chord include permanent sensory and motor deficits (Shun Wong & Van der Kogel, 2004). Although CSR is frequently required for effective tumor control, it has been associated with significant neuro-toxicity, including white matter damage and minor-to-severe neuro-cognitive morbidity (Schultheiss et al., 1995; Edwards-Brown & Jakacki, 1999; Shun Wong & Van der Kogel, 2004). Specifically, it has been found that white matter abnormalities, including decline in white matter volume, are often seen following this treatment, even in normal appearing white matter (Edwards-Brown & Jakacki, 1999; Mulhern et al., 2004; Khong et al., 2003; Mabbott, Noseworthy, Bouffet, Rockel & Laughlin, 2006). Thus, focusing on the effects of treatment for posterior fossa tumors on development and integrity of white matter is of chief interest. Understanding these effects is crucial for early identification of treatment related damage, including radiation injury. Such effects may give insight into understanding the complex relations between mechanisms of radiation injury and

8 3 neuro-cognitive outcome. Accordingly, in this thesis, the impact of treatment for posterior fossa tumors on a proposed white matter pathway (connecting the cerebellum in the posterior fossa with frontal cortical regions) was examined. Diffusion tensor imaging (DTI) was used to aid in investigating whether compromise of this pathway is associated with impaired working memory. White matter is composed of glial cells that provide structural and physiological support within the central nervous system and form myelin to insulate axons (Kolb, 1990). This tissue is essential for cognitive efficiency as it propagates the transmission of electrical signals along axons (Kolb, 1990). It is important to examine the relationship between white matter and brain injury (i.e. tumors, radiation, surgical resection) in the pediatric central nervous system precisely because the growth of white matter is a key source of brain development in children (Barnea- Goraly, Menon & Eckert et al., 2005). Glial cells in the brain may be susceptible to damage by radiation due to their continuously proliferating nature as well as being prone to cell cycle disruption (Mulhern, Palmer & Reddick et al., 2001). Accordingly, due to their nature, these growing cells are preferentially targeted by radiation. Changes in brain tissue following CSR can be observed histopathologically and include glial atrophy, vascular changes in white and grey matter, demyelination, and white matter-specific necrosis (Schultheiss et al., 1995; Shun Wong & Van der Kogel, 2004). Such changes have also been associated with vast behavioral deficits (i.e. spatial learning and working memory). It has been hypothesized that CSR also inhibits neurogenesis, which is thought to persist even though adulthood (Shun Wong & Van der Kogel, 2004). Therefore it may be the inhibition of neurogenesis that contributes to neurocognitive deficits following CSR. To measure subtle compromise in white matter integrity and statistically relate such compromise to neuro-cognitive outcome, quantitative indices of underlying tissue properties are needed and diffusion tensor imaging (DTI) is an ideal tool to use for this purpose.

9 4 DTI, a novel quantitative neuroimaging technique, allows for the attainment of information on the diffusion of water within white matter because diffusion of water in white matter is affected by myelin and the orientation and regularity of fibers (Beaulieu, 2002). This technique is important to this thesis because it can be used to acquire quantitative information about white matter, and hence interrogate the role of CSR in white matter pathology (leading to myelin and axon dysfunction). Diffusion properties can be used to measure white matter organization as well as detect any deterioration (i.e. a lack of myelination, axonal breakdown, or a dysfunction in normal myelin formation). A diffusion tensor matrix is generated from the data acquired from diffusion-weighted images and three separate diffusivities or directions (i.e. eigenvalues (λ 1, λ 2, λ 3 )), are calculated by matrix diagonalization (Basser, 1995; Song, Sun & Ramsbottom et al., 2002). The first eigenvalue (λ 1 ) is thought to be a measure of axial diffusivity (λ ), namely diffusion parallel to the axonal fibres (Basser, 1995), and is measured in mm 2 /sec. An increase in axial diffusivity is thought to reflect axon degeneration (Song et al., 2002). The second and third eigenvalues represent diffusion perpendicular to axonal fibres (Basser, 1995). Radial diffusivity (λ ), also measured in mm 2 /sec, is used an overall measure of this perpendicular diffusion and is calculated by taking the average of the second and third eigenvalues, (λ 2 + λ 3 )/2 (Basser, 1995). Radial diffusivity is important in that it serves to give insight into the integrity of the myelin of axons. Song et al. (2002) demonstrated that dysmyelination of shiverer mice (mice homozygous for a recessive mutation of myelin basic protein, leading to incomplete myelin formation in the CNS) brain white matter results in significant increase in λ, but has no effect on λ. The eigenvalues can also be combined into summary parameters that reflect a simplified measure of water diffusion characteristics. An apparent diffusion coefficient (ADC) or mean diffusivity (MD) reflects how free water is able to travel or diffuse and can be defined as the

10 5 magnitude of water diffusion, measured in mm 2 per second (Kingsley, 2006). MD is essentially an average taken of three separate eigenvalues. Anisotropy, or preferentially oriented diffusion, provides information on the shape or direction of water diffusion (a reflection of the three directional eigenvalues), as diffusion of water occurs more readily parallel to a set of aligned fibers (i.e. in areas free of obstruction) than perpendicular to them (Kingsley, 2006). Fractional anisotropy (FA) values range from 0 to 1 (0 indicating equal diffusion in all directions, and 1 indicating diffusion in a single direction), providing an index of directionality (Kingsley, 2006; Beaulieu, 2002). White matter FA has been shown to increase nonlinearly with age; and is attributed to myelination, as well as increases in numbers of axons, axonal diameter, and fibre coherence (Nomura, Sakuma, Tagami, Okuda & Nakagawa, 1994). Thus, water diffusion anisotropy of axonal fibre tracts likely reflects myelin sheath and axonal integrity (Basser, 1995; Beaulieu, 2002). It has been proposed that a reduction in anisotropy (decline in FA) indicates a breakdown of myelin as well as axonal fibre degeneration (Beaulieu, Does, Snyder & Allen, 1996). One application of DTI is tractography. Tractography is a tool used to define white matter pathways and is sensitive to pathological abnormalities (i.e. caused by posterior fossa tumors and/or treatment methods) (Basser, 1995). A specific type of tractography is probabilistic tractography which relies on the alignments of white matter between the dominant orientation of local water diffusion and the orientation of fibres (Beaulieu, 2002). Thus, white matter pathways delineated by probabilistic tractography reflect fibre organization in the brain. Probabilistic tractography infers the continuity of fibres from voxel to voxel and produces a connectivity index along such white matter pathways (Basser, 1995; Beaulieu, 2002), aiding in generating maps of fibre connectivity between brain regions.

11 6 Structural integrity of the brain (i.e. certain areas or pathways, myelination, or axon formation) can influence cognitive ability. Hence, a central assumption of this thesis is that if the brain undergoes structural changes, function may also be affected. This assumption is particularly relevant when examining the type of treatment obtained in patients with posterior fossa tumors. Since a treatment plan for many of these patients includes CSR, and CSR has been found to lead to white matter damage (Edwards-Brown & Jakacki, 1999; Mulhern et al., 2004; Khong et al., 2003; Mabbott et al., 2006), it is likely that there is an association between these white matter structural changes and cognitive function. In fact, CSR has been associated with cognitive morbidity (Edwards-Brown & Jakacki, 1999). Thus, examining the relation between white matter injury and neuro-cognitive late effects gives insight into how to determine and implement feasible and less harmful modes of treatment to patient populations, as well as to identify those at highest risk for cognitive decline. Multiple studies have examined core neuro-cognitive functions in children treated for posterior fossa tumors. The majority of children treated with cranial radiation often have problems maintaining their premorbid levels of intellectual development and academic achievement (Mulhern et al., 2004). In fact, increased impairments in spelling, reading and arithmetic ability are often apparent following CSR (Mulhern, Palmer & Merchant et al., 2005). Mabbott, Spiegler, Greenberg, Rutka, Hyder and Bouffet (2005) found an association between cranial radiation and declines in academic ability, social skills, and attention, which emerge over time. Mabbott, Witol, Penkman, Strother and Bouffet (2008) found that patients treated with cranial radiation demonstrated lowered measures of IQ along with slow information processing speeds as compared to patients treated with surgery only and healthy controls. Furthermore, in 94% of children treated with craniospinal radiation, declines of two to four IQ points per year have consistently been documented (Spiegler et al., 2004).

12 7 Additionally, in the years subsequent to treatment for medulloblastoma, patients are at risk for neuro-cognitive deficits that may impair academic performance, employment opportunities, and overall quality of life (Mulhern, Kepler & Thomas et al., 1998; Ris, Packer, Goldwein, Jones-Wallace & Boyett, 2001). It has been suggested that such cognitive deficits in patients treated with CSR reflect a reduced ability to obtain novel information from their surroundings (Palmer et al., 2001). As attention, processing speed, short-term and working memory and visuo-motor coordination are all required to gain knowledge and learn from environmental cues, cognitive processing mechanisms in the brain underlying these processes are likely to be impaired in this population (Schatz, Kramer, Ablin & Matthay, 2000). Thus, the observation that widespread cognitive deficits are present in patients treated with CSR serves as a basis for this thesis, as the purpose is to examine and quantify underlying changes in the brain and compare these changes to developmental neurocognititve outcome. Accordingly, neuroimaging measures of white matter tissue damage, specifically measured in normal appearing white matter volume, have been found to predict adverse intellectual function following cranial radiation. In an attempt to discern if treatment with aggressive therapy leads to lower volumes of normal appearing white matter, Reddick, Glass & Palmer et al. (2005) compared white matter volumes from the MRI scans of patients that had been treated for medulloblastoma with craniospinal radiation to those of a healthy control cohort. It was found that there was a discernible decrease in normal appearing white matter volume in patients as compared to controls. These patients also exhibited lower scores on tasks of IQ, verbal memory, and sustained attention relative to controls. A decrease in normal appearing white matter volume was associated with significantly lower IQ scores in the patient population. Thus, these neurocognitive deficits may be accounted for by the deficiency in normal development of white matter

13 8 following CSR treatment. Probing further, if CSR disrupts normal axon myelination in these patients, a reduction in signal transfer efficiency in the brain may follow, causing a longer delay between stimulus and response time in testing situations (Reddick et al., 2005). These factors contribute to difficulties in learning, interpreting, and storing new information, resulting in patients not performing at par with healthy controls. Palmer, Reddick and Glass et al. (2002) hypothesized that the white matter volume of the corpus callosum (the largest white matter commissure in the brain) would be adversely affected in patients treated for medulloblastoma with CSR. Because regions of the corpus callosum are normally expected to have the highest rate of growth during childhood, disruption or degeneration of white matter (signified by lower white matter volumes) in such an area may lead to neuropsychological consequences. Using a longitudinal MRI paradigm, it was found that the total area of the corpus callosum in patients decreased over time since the commencement of CSR. This finding in patients was contrasted with significant increases in corpus callosum white matter volume (i.e. normal development) in healthy controls. In addition to the examination of white matter volume, DTI indices provide another form of quantification to provide insight into white matter integrity. DTI indices of white matter integrity in children diagnosed with brain tumors show increased mean diffusivity (MD values) and decreased anisotropy (FA values) relative to normal control children (Khong, Leung & Fung et al., 2006; Mabbott et al., 2006). Specifically, Khong et al. (2006) utilized DTI to determine if declines in FA (if present) correlated with IQ scores in children treated for medulloblastoma and children with acute lymphoblastic leukemia (ALL). In this whole brain study, it was found that loss of FA in the white matter of such children was significantly correlated with measures of IQ. Similarly, Mabbott et al. (2006) examined FA and MD values for multiple regions of interest (focusing in on specific areas in the brain in addition to whole brain white matter) in

14 9 children with medulloblastoma treated with CSR, as well as their associations with IQ. Overall mean FA was lower and mean MD higher in patients as compared to controls. Adding to the findings by Palmer et al. (2002), it was found that the genu of the corpus callosum followed this pattern (i.e. lower FA and higher MD in patients versus controls), as did the anterior and posterior limbs of the internal capsule and frontal white matter. These DTI indices were also found to correlate with overall IQ (with patients performing worse than controls). Therefore, it was found that tissue compromise, as measured by microscopic damage in normal appearing white matter, was related to poor intellectual outcome, as brought on by CSR. Based on the literature, it is evident that children with posterior fossa tumors that are treated with CSR are prone to white matter damage and have problems in cognitive processing. This is to be expected, as normal functioning white matter is essential for cognitive efficiency. Although deficits in a variety of cognitive functions have been documented in children diagnosed with posterior fossa tumors (Palmer et al., 2001; Mabbott et al., 2005; Mabbott et al., 2008), potential working memory impairments in this population have yet to be examined fully. Findings have been somewhat mixed with regard to working memory outcome in patients with posterior fossa tumors treated with CSR. Although Mabbott et al. (2008) found that treatment for posterior fossa tumors with CSR was associated with overall declines in IQ, working memory ability in their sample was relatively intact. This finding remained in patients treated with surgery only (no CSR). This finding differs from that of Schatz et al. (2000), however. In children with ALL treated with CSR, it was found that working memory function was poorer as compared to controls. It was also postulated that IQ deficits present in these patients following CSR may have been due to a gross interruption of the normal development of working memory. Differences in findings between the two studies may have been due to

15 10 discrepancies in the way each party chose to measure working memory function, or as a result of differing patient populations themselves. Understanding the cognitive impact of treatment in children with posterior fossa tumors is essential, as their quality of life after treatment depends on it. Normal age-related working memory and processing speed development has been found to account for nearly half of the agerelated improvements in intelligence (Fry & Hale, 1996). As such, poor working memory outcome following CSR may have a great impact on behaviour in a school or home setting, leading to disrupted daily functioning. Consequently, the goal of this thesis is to evaluate working memory outcome as a function of treatment for pediatric tumors. Information regarding white matter and working memory can shape the course of treatment planning for individuals with brain tumors to feasibly minimize neuro-toxicity from treatment and maximize remedial help available to the individual to serve in counteracting potential cognitive declines. The concept of working memory proposes that a committed system underlies human thought processes by maintaining and storing information in the short-term (Baddeley, 2003). Working memory capacity is the amount of information one can keep in mind for a short period of time (Baddeley, 2003) and develops throughout childhood and adolescence (Olesen, Nagy, Westerberg & Klingberg et al., 2003). Essential to reasoning and problem solving, working memory also can be thought of as one s ability to assimilate diverse forms of information (Prabhakaran, Narayanan, Zhao & Gabrieli, 2000). Among many theories, Baddeley (2003) outlines a concise, dedicated, and functional working memory system, consisting of multiple specific components. This most current view of the underlying concepts of working memory proposes a functioning central executive control system, and two storage systems; the phonological loop and the visuospatial sketchpad. Contained within the phonological loop, the phonological storehouse holds memory traces for several seconds before they fade, and an

16 11 articulatory rehearsal process acts as a form of subvocal speech. The visuospatial sketchpad holds and manipulates visuospatial representations that contain information regarding the appearance of objects. The central executive is an attentional controller, focusing, dividing, and switching attention. It also acts as a processing center, connecting working memory with long term memory. Therefore, all parts function as a limited capacity system, temporarily maintaining and storing information, and potentially serve the basis for human thought, planning, and action. There are several psychometric tools available to aid in quantifying working memory capacity. The Wechsler Intelligence Scale for Children - Fourth Edition (WISC-IV) is an intelligence test for children between the ages of six and sixteen that can be completed without reading or writing (Wechsler, 2003). The WISC-IV emphasizes the processes involved in learning such as verbal abilities, perceptual reasoning and organization, attention, concentration, processing speed, and working memory (Wechsler, 2003). Of the four indices of the WISC-IV, verbal comprehension (VCI), perceptual reasoning (PRI), processing speed (PSI), and working memory (WMI), the latter will be of prime focus in this thesis. The WMI scale is auditory in nature and its emphasis is on the short-term retrieval of memory (Wechsler, 2003). Within this scale, there are two subtests; a digit span task and a letter-number sequencing task. The digit span task is comprised of two separate tasks digit span forward and digit span backward, in which children are asked to recall a series of numbers in forward or backward sequence, respectively. The letter-number sequencing task requires children to manipulate a series of letters and numbers presented orally so that the numbers are said first (in numerical order) followed by the letters (in alphabetical order). Both of these aggregate subtests are thought to measure memory span or the ability of one to retain information for recall after a brief period of time (Wechsler, 2003). Furthermore, aspects of attention, concentration, retention, rehearsal, and

17 12 organization are also believed to be engaged during these tasks as these processes underlie working memory (Wechsler, 2003). Many neuroimaging studies have found that working memory (verbal and spatial) is mediated by several specific frontal cortical regions, depending on the type of information to be maintained (Prabhakaran et al., 2000; Courtney, Petit, Maisog, Ungerleider & Haxby, 1998; Smith & Jonides, 1999; McCarthy, Puce & Constable et al., 1996; Curtis & D Esposito, 2003). Specifically, during verbal working memory tasks, the prefrontal cortex shows bilateral activation (Prabhakaran et al., 2000; Smith & Jonides, 1999). Furthermore the middle frontal gyrus in the right hemisphere is preferentially activated during spatial location working memory tasks while the middle frontal gyrus is bilaterally activated during non-spatial working memory tasks (Prabhakaran et al., 2000; McCarthy et al., 1996). In addition to the aforementioned neuroimaging studies of healthy subjects, studies based on lesion location in patients indicate that the components of working memory (i.e. central executive, phonological loop) are localized in several different brain regions. Lesions of the dorsolateral prefrontal cortex (DLPFC), especially those located within and surrounding the principal sulcus (Broadmann s area 46) impair working memory performance (Goldman & Rosvold, 1970; Bauer & Fuster, 1976; Funahashi, Bruce & Goldman-Rakie, 1993; Curtis & D Esposito, 2003). It has also been found that the central executive component of working memory is likely to engage multiple brain regions in a functional network, which includes the dorsolateral prefrontal cortex (Baddeley, 2003). The sum of the literature indicates that working memory function is strongly associated with the DLPFC. The DLPFC is a region in the prefrontal cortex comprised fully or partially of Brodmann s areas (BA) 8 and 9 (superior frontal gyrus); 46 (middle frontal gyrus); 44 (pars opercularis of the inferior frontal gyrus) and 45 (pars triangularis of the inferior frontal gyrus);

18 13 the lateral portion of area 47 and the dorsolateral part of area 10 (both are areas of the orbitofrontal cortex as well) (Gazzaniga, Ivry & Mangun, 1998; Petrides & Pandya, 1999). The human DLPFC is the last area to develop and be fully myelinated in the cerebrum (Gazzaniga, Ivry & Mangun, 1998). In addition to functional and cortical lesion studies, there have been several studies examining the relations between working memory and white matter. Nagy, Westerberg and Klingberg (2004) examined white matter development in relation to the development of cognitive functions in healthy subjects during childhood. In particular, development of working memory capacity was positively correlated with fractional anisotropy in two regions in the left frontal lobe, including a region between the superior frontal and parietal cortices (Nagy, Westerberg & Klingberg, 2004). This study lends support to the notion that maturation of white matter is an important part of brain maturation during childhood, specifically that the maturation of relatively restricted regions of white matter are correlated with the development of specific cognitive functions. In a study combining DTI and fmri data, using healthy subjects, Olesen et al. (2003) measured brain activity using blood oxygen level-dependent (BOLD) contrast (to quantify grey matter activity) with fmri during a working memory task. White matter microstructure was investigated using DTI to obtain an indication of myelination and axon thickness. It was found that in several cortical and sub-cortical regions (i.e. fronto-parietal white matter) there were positive correlations between maturation of white matter integrity and increased brain activity. Specifically, it was found that FA values in fronto-parietal white matter correlated with BOLD response in adjacent grey matter in the superior frontal sulcus and inferior parietal lobe, areas that could form a significant functional network underlying working memory function.

19 14 As mentioned above, structural changes during the development of working memory involve maturation of white matter in the prefrontal lobe (Nagy, Westerberg & Klingberg, 2004). It has also been shown that the prefrontal cortex, specifically the DLPFC, is preferentially activated during tasks of working memory. However, in examining studies on other possible brain regions related to working memory function, an interesting finding has emerged implicating the involvement of the cerebellum in such tasks. It has been universally thought that the cerebellum is responsible for/implicated in the coordination and control of motor activity (Brooks & Thach, 1981). However, it is now apparent that in addition to influencing/acting on motor areas of the cerebral cortex, the anatomical substrate exists for cerebellar output to non-motor areas (Dum & Strick, 2003; Middleton & Strick, 2001). Attempting to dissociate verbal and spatial working memory with respect to areas of brain activation, Smith, Jonides and Koeppe (1996) used position emission tomography (PET) and tasks of verbal and spatial working memory. For spatial working memory tasks, the right ventrolateral frontal cortex (mostly area 47) showed activation. For tasks of verbal working memory, left frontal regions were activated as well as areas of the right cerebellar hemisphere. Focusing on the cerebellum only, Kim, Ugurbil and Strick (1994) found that bilateral ventral portions of the dentate (nuclei located within the deep white matter of the cerebellum) are activated during a variety of tasks involving short-term working memory. Striving to determine whether damage to the cerebellum is associated with impairments in a range of verbal working memory tasks, Ravizza, McCormick, Schlerf, Justus, Ivry and Fiez (2006) compared adult patients with isolated cerebellar lesions or resections with healthy adults. Selective deficits in tasks of verbal working memory were found in the former group, leading to a proposal that the cerebellum may contribute to verbal working memory during initial

20 15 phonological encoding (Ravizza et al., 2006). Therefore, an impaired rehearsal mechanism (a component involved in the process of phonological encoding) may lead to such working memory deficits, mediated by areas in the cerebellum. In light of Baddeley s (2003) model of working memory, the process of articulatory rehearsal is thought to be mediated by areas in the right cerebellar hemisphere (Ravizza et al., 2006). Moreover, Ziemus et al. (2007) explored fmri activation patterns during working memory and attention tasks in children with isolated cerebellar infarct as compared to healthy controls. Significant increased activation in posterior parietal regions of the brain in patients with cerebellar infarct was found, leading to the conclusion that compensatory mechanisms are at play and that cerebellar lesions affect remote cortical regions that are a part of a cortico-cerebellar network. However, it is problematic to generalize findings in patients with other diseases or injuries to patients with brain tumors (Mabbott et al., 2008), due to the unique impact that brain tumors and their treatment can have on brain function. It is more practical to compare results within similarly diagnosed patient populations (Mabbott et al., 2008). Based on recent studies, it is evident that working memory is dependent on areas in the frontal cortex (i.e. DLPFC) and cerebellum. In patient populations, it has been shown that damage to these brain areas lead to deficits in working memory. Because the cerebellum is the general location of the tumor in children with posterior fossa tumors, this may also contribute to cause a defect in working memory. Following in this vein, examining the integrity of white matter pathways that connect these two brain areas prominently involved in working memory is of great interest, especially in children with posterior fossa tumors. If frontal-cerebellar white matter pathways are evident, this thesis then strives to examine associations between white matter integrity of these connections (as quantified by DTI indices) and working memory outcome. In order to do so, however, a sufficient model is needed to constrain the pathway.

21 16 There is considerable evidence supporting the connection between the cerebellum and higher-order regions in the frontal lobe by way of a cerebrocerebellar circuit. The cerebrocerebellar circuit is comprised of a feedforward (afferent) limb and a feedback (efferent) limb (Schmahmann, 1996). The feedforward limb consists of multiple corticopontine and pontocerebellar mossy fibre projections, while the feedback limb includes the loop between cerebellothalamic and thalamocortical pathways (Schmahmann, 1996). From these findings, Schmahmann (1996) proposed a bilateral cerebello-thalamo-cortico pathway (i.e. feedback limb) that serves to redirect information from the cerebellum back to higher order areas of the cerebral cortex. Of great interest to this thesis is Schmahmann s (1996) feedback limb; specifically, the route from the dentate interpositus fastigial nuclear region of the cerebellum (as implicated in Dum & Strick, 2003; Middleton & Strick, 2001), through to the red nucleus of the rostral midbrain on the contralateral side (via the cerebellar peduncles). From there, the pathway routes ipsilaterally through nuclei of the thalamus and up into higher cortical areas (i.e. prefrontal cortex) (Figure 1). This pathway will serve as a basis for tractography seed, way, and termination point placement (see below) in this thesis.

22 17 Fig. 1: Diagrammatic representation of the feedforward (A and B) and feedback (D and E) limbs of the cerebellocerebral circuit in a non-human primate (modified from Schmahmann, 1996). Using retrograde transneuronal transport of the McIntyre-B strain of herpes simplex virus type 1 (neurotropic virus HSV1), Dum and Strick (2003) investigated possible motor and nonmotor pathways originating from the dentate nucleus of the cerebellum and terminating in the cerebral cortex. The HSV1 tracer was injected into regions of the cerebral cortex of cebus monkeys. Notably, the tracer injected into area 46 and area 9 (the main regions comprising the DLPFC) resulted in labeled neurons ventrally at mid rostrocaudal levels of the dentate. Furthermore, from these injection sites, the pathway was traced into first-order neurons of the thalamus (akin to the thalamocortical pathway proposed by Schmahmann, 1996; see below). Virus transport from these neurons in the thalamus demonstrated that the pathway concludes in neurons in the dentate nucleus of the cerebellum (analogous to the cerebellothalamic pathway

23 18 proposed by Schmahmann, 1996; see below). Accordingly, only a fraction of the output from the dentate nucleus region of the cerebellum projects to the motor cortex, with a substantial portion of the output projecting to areas of the prefrontal cortex (Dum & Strick, 2003). It is estimated that 20% or greater of the volume of the dentate contains output channels that innervate portions of areas 9 and 46 (Dum & Strick, 2003), which provides further strength to the implication that the DLPFC is one such area innervated by the dentate. Furthermore, cerebellar activation observed during working memory tasks may reflect input to articulatory control centers in the frontal lobes (Desmond, Gabrieli, Wagner, Ginier & Glover, 1997). The cerebellum could serve to initiate function in the frontal lobes (as it has been observed to be activated prior to frontal regions), modulating the feedback limb and facilitating functioning of the phonological loop when working memory is involved (Desmond et al., 1997). The link between working memory outcomes and DTI measures of white matter integrity and tractography is one area of research that has not yet been explored. It is important to study the effects of white matter integrity following CSR and its association with working memory outcomes in clinical populations in order to provide a structural and functional model of working memory. In developing a brain structure model (through DTI), and applying it to brain function (measures of working memory), clinical research can give insight to patients and family members of the possible cognitive deficits and the outcome of brain tumor treatment. To date, there have been only a few studies utilizing DTI tractography to relate localized white matter bundles with working memory. Audoin et al. (2007) examined the diffusion characteristics of white matter bundles involved in working memory in patients with early multiple sclerosis (MS), a multifocal demyelinating disease associated with working memory deficits, using DTI. Anatomical regions of interest included bilateral Brodmann s areas 9, 45, and 46, as well as the anterior cingulate and right and left thalamus (all regions implicated in the

24 19 model for the executive system of verbal working memory). DTI tractography findings from this study showed that white matter connections (i.e. white matter bundles linking the left BA 9 to the left BA 45/46, and white matter bundles connecting the right BA 45/46 to the right thalamus) were structurally impaired in early MS patients, as FA was lower and MD was higher for these pathways in patients relative to controls. Thus, it can be reasoned that working memory impairment seen in this population may be due at least in part to white matter bundle damage in brain areas required for proper working memory function. A similar methodology was used in a study by Johansen-Berg, Behrens, and Sillery et al. (2005). DTI was utilized to parcellate the thalamus into regions that are activated during tasks of working memory in healthy participants. By way of probabilistic tractography, activation centers involved in memory tasks that fell within thalamic regions were found to have a high probability of connection to the prefrontal cortex. Moreover, Karlsgodt, van Erp, and Poldrack et al. (2008) examined anatomical changes in the superior longitudinal fasciculus (SLF) (the main frontalparietal white matter connection) in patients with recent-onset schizophrenia. Schizophrenia patients showed lower FA values as compared to healthy controls across the SLF bundle. These values were also correlated with verbal working memory performance in both patient and control groups in the left but not the right SLF bundle. Therefore, the results obtained and methodology utilized in the above studies could likely be applied to patients with posterior fossa tumors. The proposed thesis will strive to do so. Thus, this thesis is unique in that it used DTI to evaluate white matter tract integrity in the brain and its relation to working memory function in children diagnosed with posterior fossa brain tumors. Furthermore, in addition to examining children that have been treated with cranial radiation, this study utilized a surgical control group (surgery only, no radiation) as well as a healthy control group. A relatively large sample size was also used in order to provide a greater

25 20 generalizability of results. To lessen cognitive morbidity of the growing number of pediatric brain tumor survivors, particularly those treated with cranial radiation, it is necessary to understand the relationship between underlying central nervous system damage and neurocognitive deficits. Gaining knowledge in this area may serve to aid medical practitioners in altering medical treatment of such tumors to avoid greater injury, identify children at risk for showing difficulties in cognitive processes and initiate early intervention, as well as assess new medical techniques for protecting or re-growing white matter. For this thesis, DTI measures will be related to working memory, an important neurocognitive process that is crucial for the association between perception and controlled action (Baddeley, 2003), in a sample of pediatric patients with posterior fossa tumors. The first goal of this study is to use DTI tractography to demonstrate that the cerebello-thalamo-cortico pathway exists and can be identified and traced in humans, especially in a clinical population. Because working memory is a process mediated by the frontal lobe (Fletcher & Henson, 2001) and has been shown to be affected by cerebellar damage (Ziemus et al., 2007; Ravizza et al., 2006) the second goal of this study is to examine the integrity of frontal-cerebellar connections and their relation to working memory function. It is expected that if frontal-cerebellar white matter connections are identified, they will be disrupted in patients with posterior fossa tumors, precisely because of the origin of tumor locale (i.e. posterior fossa). Treatment may also be a likely contribution, because CSR has been found to lead to white matter damage, and it is possible that surgical resections may sever existing white matter fibres. It is also hypothesized that group differences will be evident in white matter integrity. Specifically, children that have received CSR as treatment for posterior fossa tumors will show the greatest compromise in frontal-cerebellar connections as compared to the two other groups. Additionally, it is hypothesized that children with posterior fossa tumors who have undergone

26 21 surgery only (no CSR) will show more white matter compromise than healthy controls (which will be reflected in the accompanying DTI measures). Therefore, it is predicted that lower FA, and higher MD, axial diffusivity and radial diffusivity values will be evident for both left and right tracts in children treated with CSR than children treated with surgery only and healthy controls (healthy controls should display the highest FA values and the lowest MD, axial diffusivity and radial diffusivity values in relation to the other two groups). Furthermore, it is hypothesized that there will be group differences in outcome of working memory. It is therefore predicted that working memory deficits (as measured by WMI, digit span task, and letter-number sequencing task scores) will be more prevalent in children with posterior fossa tumors, and more pronounced in those treated with CSR than in those treated with surgery only, as compared to healthy controls. Lastly, it is hypothesized that greater deterioration and compromise of white matter integrity of tracts will be associated with deficits in working memory. Namely, lower FA and higher MD, axial diffusivity and radial diffusivity values gleaned from frontal-cerebellar tracts will be correlated with lower WMI scores. A potential model for this hypothesis is depicted in Figure 2. It is proposed that frontal-cerebellar white matter tracts are affected by CSR in the same way that selected white matter sites (i.e. cerebellar hemispheres, pons, medulla) have been found to be affected by radiation (Mabbott et al., 2006). In summary, if white matter injury (treatment-induced or otherwise) is present and is specific to the proposed tract, then a significant reduction in FA and increase in MD, axial diffusivity and radial diffusivity should be observed, thus signifying decreased neuronal integrity and greater unrestricted water movement (fewer obstacles blocking the tract). If the integrity of the tracts connecting the frontal and cerebellar regions of the brain (which have both been found

27 22 to mediate aspects of working memory) are compromised, it is anticipated that working memory function will be compromised as well. Fig. 2: A proposed model for the association of white matter integrity and working memory. Methods Participants A retrospective cohort (N = 50) comparison design was used including brain tumor patients and control subjects. Within the brain tumor group, all patients had tumors located in the posterior fossa region of the brain and were treated according to tumor type. Of the 25 brain

28 23 tumor patients, 17 were treated with CSR, (14 treated for medulloblastoma with whole brain radiation plus posterior fossa boost and adjuvant chemotherapy; 3 treated for ependymoma with focal radiation directly targeting either the whole posterior fossa or the tumor itself), and 8 patients were treated with surgery only (no radiation or chemotherapy). For the patients treated with CSR, the average head dose was 2715 cgy, the average spine dose was 2715 cgy, and the average posterior fossa boost dose was 4080 cgy. Of these 17 patients, 15 were also given adjuvant chemotherapy. For patients receiving a posterior fossa boost only, the average dose was 5580 cgy. Of the 8 surgery only patients, 7 were treated for pilocytic astrocytoma or low grade glioma, and 1 was treated for choroid plexus papilloma. Twenty-five healthy control subjects also participated in this study. All children were assessed and/or treated at The Hospital for Sick Children (n = 37; 12 treated with radiation, 5 treated with surgery only, and 20 controls) and BC Children s Hospital (n = 13; 5 treated with radiation, 3 treated with surgery only, and 5 controls). The impact of demographic and medical variables Demographic and medical information for each participant collected at the time of assessment was examined (Table 1). Steps were taken to ensure that all participants were close in age and gender in order to reduce between-group confounds. For the brain tumor vs. control groups, there was no significant difference for gender [χ 2 (1) < 0.001, p > 0.999]. There was also no significant finding for gender between radiation and surgery only groups [χ 2 (1) = 0.019, p = 0.891]. Age at testing (in years) between brain tumor and control groups was examined and no significant differences were found [F(1, 48) = 1.339, p = 0.253]. Additionally, no significant differences within the brain tumor group were evident (i.e. radiation and surgery only groups) [F(1, 23) = 0.088, p = 0.770]. Parental education (in years) was also compared. There was a significant difference between the brain tumor and control groups for maternal education [F(1, 48) = , p < 0.001] as well as paternal education [F(1, 48) = , p = 0.001].

29 24 Specifically, the control group had the greatest mean amount (years) of maternal and paternal education as compared to the combined radiation and surgery only groups. Table 1 Subject characteristics and medical variables for tumor group (with CSR and surgery only subgroups presented separately), and healthy control group. Tumor Group (Radiation + Surgery Only Subgroups) Radiation (Tumor Subgroup) Surgery Only (Tumor Subgroup) Healthy Control Group n = 25 n = 17 n = 8 n = 25 Sex (% males) Age at testing (years) Mean Standard deviation Range Parental Education Mother (years) Mean Standard deviation Range Parental Education Father (years) Mean Standard deviation Range Age at diagnosis (years) Mean N/A Standard deviation N/A Range N/A Time since diagnosis (years) Mean N/A Standard deviation N/A Range N/A Presence of post-operative or residual complications (%) N/A Presence of multiple post-operative complications (%) N/A Attempts were made to recruit siblings of children with brain tumors, but controls were also recruited from the hospital setting, as is standard protocol for such studies. This reflects the fact that the majority of controls available to the study were children of hospital workers or doctors. This likely contributed to inflating the years of parental education for the control group.

30 25 There were no differences in both maternal and paternal education between the radiation and surgery only groups ([F(1, 23) = 0.773, p = 0.388] and [F(1, 23) < 0.001, p = 0.993], respectively). In order to simplify this group difference for use in further analyses, an average parental education was calculated. Not surprisingly, the same results held for average parental education between the tumor and control groups [F(1, 48) = , p < 0.001]. Finally, parental education was significantly correlated with site of MRI scan, all working memory measures, and all IQ measures (Appendix A). Because site of MRI scan differences were apparent between Toronto and BC (see below), site was compared across demographic variables. Patients from the BC site were found to be significantly older for age at testing than those from Toronto [F(1, 48) = 5.741, p = 0.021], but as a whole sample, showed fewer years of average parental education than Toronto [F(1, 48) = 5.588, p = 0.022]. Relevant medical variables were also compared across the radiation and surgery only groups. No significant difference between the two groups was found for age at diagnosis [F(1, 23) = 1.301, p = 0.266]. There was however a significant difference found for time since diagnosis [F(1, 23) = 5.770, p = 0.025], with the surgery only group having a shorter period between diagnosis and testing than that of the radiation group. Furthermore, the groups were compared with respect to presence of post-operative complications (i.e. hydrocephalus, nystagmus, ataxia, mutism, hemiparesis, cranial nerve deficits, etc.). If a patient had significant impairment in at least one of these areas, then they were considered to have suffered postoperative or residual complications. If a patient had impairment in two or more areas, then they were considered to have multiple post-operative complications (see Table 1 for percentages for each group). Consequently, the radiation group showed a greater incidence of post-operative

31 26 complications than the surgery only group [χ 2 (1) = 6.618, p = 0.010], but the frequency of multiple post-operative complications was no different across groups [χ 2 (1) = 3.436, p = 0.064]. Materials and Procedure Reliability of scanning protocol. Due to the multi-site nature of this study, differences in quality of data across site of MRI scan may have occurred. To account for possible inter-site variability in scan quality, a single individual (Research Coordinator for the study) was scanned in both Toronto and BC locations, providing inter-site reference scans. As there were slight differences in MRI scanning protocols between Toronto and BC (see Appendices B and C), a signal-to-noise ratio was calculated for the DTI sequences acquired and compared across site. It was found that the signal-to-noise ratio was significantly higher for the BC scan; the zero diffusion image signal-to-noise ratios being 24.7 and 43.6 for Toronto and BC, respectively, and the echo-planar images signal-to-noise ratios being 14.0 and 17.4 for Toronto and BC, respectively. As such, site of MRI scan differences among the above variables were computed (see Appendix D). Image processing. All patients and controls underwent MRI scans to produce T1- weighted images (for scan details, see Appendix A) and diffusion-weighted (DTI) images (for scan details, see Appendix B). MRI data were acquired from the scanner and transferred to a Linux operating system as DICOM files. DICOM files were then converted into 3-dimensional volumes using MRIcro (Rorden & Brett, 2000). The T1-weighted images were pre-processed using FMRIB Software Library (FSL suite software) to prepare the image for registration (see below). A brain extraction tool (BET command) (Jenkinson, Pechaud & Smith, 2005) served to eliminate most of the skull and eyes, but if needed, images were further edited manually to produce a clean brain image (no residual skull or eye parts) using Analyze software (Brain Imaging Resource, Mayo Clinic). The images were then corrected for any inhomogeneity using

32 27 FMRIB s automated segmentation tool (FAST command) (Zhang, Brady & Smith, 2001), and intensity inverted using Analyze software (Brain Imaging Resource, Mayo Clinic). This process served to create an image that resembles a T2-weighted image but is in T1 space, further preparing the image for registration (see below). Diffusion weighted tensor data were also acquired and pre-processed using the protocol above. An eddy current correction (eddy_correct command) (Behrens, Woolrich & Jenkinson et al., 2003) was first applied to diffusion weighted raw data using FMRIB s FSL suite software to produce a DTI image corrected for any distortions (eddy current) that may have occurred in the magnetic field, causing stretches or shears. Beginning the registration process, the intensityinverted T1 image and the DTI image were then co-registered to each other, creating a transformation matrix. This process involved using mathematical commands in a stepwise fashion to output an overall transformation from T1 to DTI space. Specifically, using automated image registration (AIR) software (Woods, Grafton, Watson, Sicotte & Mazziotta, 1998), a linear transformation (alignlinear) followed by a non-linear transformation (align_warp) was used to manoeuvre the two separate images into the same space. This process also served to compensate for the spatial distortions due to susceptibility artefact in the diffusion-weighted images. AIR software takes common information from each of the images (inverted T1 and DTI) and therefore is able to spot corresponding tissues within either image (because they have the same relative voxel intensities) using appropriateness of fit (subtracting one image from the other to obtain absolute difference images by use of an automatic cost function) until the position with the smallest cost function is found. This image is considered the most appropriate position and thus is used for the registration. This transformation matrix was created to serve as a method to convert tracings of ROIs done on the T1-weighted image into DTI space in order to run probabilistic tractography (see below).

33 28 FA, MD, λ 1, λ 2, and λ 3 maps were calculated using FSL Suite DTIfit software (Behrens et al., 2003). FMRIB s bedpostx (Bayesian estimation of diffusion parameters obtained using sampling techniques modeling crossing fibres) (Behrens, Berg, Jbabdi, Rushworth & Woolrich, 2007) software was run using b 0 images, 4-dimensional eddy corrected raw DTI images, and text files containing gradient direction parameters. This process served to calculate the probability of connections of each voxel defined within the image with every other voxel in the image to serve as a basis for fibre tracking analysis. Prior to running the fibre tracking software, specific regions of interest (ROIs) were defined, modeling the cerebello-thalamo-cortico pathway found in primates as proposed by Schmahmann (1996). Along with utilizing the cerebellar hemispheres and the thalamus as ROIs, the DLPFC was chosen as the frontal ROI because of its implication in working memory. Thus, the seed point (the right or left DLPFC), and two way points (left or right thalamic nuclei, and left or right cerebellar white matter), comprise the ROIs. Using FMRIB s FSLview, cerebellar way points were manually placed on the b 0 (Figure 3). The left cerebellar hemisphere and right cerebellar hemisphere were defined from the bottommost slice of the b 0, outlining and delineating cerebellar white matter for approximately 5-6 slices until the superior cerebellar peduncles were reached (the peduncles were also included in the cerebellar ROI, as per the aforementioned model). The cerebellar ROI concluded once the pons and basilar arteries appeared (the fourth ventricle is still present but diminished).

34 29 a b c Fig. 3: Cerebellum ROI. Panel a: cerebellum slice 1, slice 2 (right cerebellar ROI outlined), slice 2 (right cerebellar ROI filled in). Panel b: slice 3 (right cerebellar ROI outlined), slice 3 (right cerebellar ROI filled in), slice 4 (right cerebellar ROI outlined), slice 4 (right cerebellar ROI filled in). Panel c: slice 5 (right cerebellar ROI outlined), slice 5 (right cerebellar ROI filled in), slice 6 (right cerebellar ROI outlined), slice 6 (right cerebellar ROI filled in). The thalamic way points were also placed manually on the b 0 image (Figure 4). The left and right thalamus was defined starting at a more dorsal slice and working down the brain ventrally. With the third ventricle fully visible, the left and right thalami were outlined (separate masks) taking care to avoid the putamen, globus pallidus, internal capsule, and the striate cortex on each side, as well the third ventricle (thus defining the borders of this ROI). The head of the caudate was also used as a reference point. This ROI was defined on approximately 4-5 slices. The thalamic ROI concluded once the thalamus was diminished and the lateral ventricle had almost taken full shape (approximately 1-2 slices after the genu and splenium of the corpus callosum had taken complete shape).

35 30 a b c Fig. 4: Thalamus ROI (in orange). Panel a: slice 1 (left thalamic ROI outlined), slice 1 (left thalamic ROI filled in), slice 2 (left thalamic ROI outlined), slice 2 (left thalamic ROI filled in). Panel b: slice 3 (left thalamic ROI outlined), slice 3 (left thalamic ROI filled in), slice 4 (left thalamic ROI outlined), slice 4 (left thalamic ROI filled in). Panel c: slice 5 (left thalamic ROI outlined), slice 5 (left thalamic ROI filled in), and a coronal, saggital, and axial view of left thalamus ROI. Using Analyze software (Brain Imaging Resource, Mayo Clinic), the borders of the left and right dorsolateral prefrontal cortex (DLPFC) were manually defined on the surface of a

36 31 volume rendered T1-weighted image (Figure 5) following a diagram of the human DLPFC provided by Petrides and Pandya (1999, p. 1012). Therefore, included in this ROI were Broadmann s areas 9, 44, 45, and 46; the superior part of area 47; and the dorsolateral portion of area 10. The T1-weighted image was used in this case due to the high level of structural detail provided in this kind of image versus the b 0. The outlined T1-weighted image was then registered to the b 0 image using the transformation matrix derived previously (in DTI space). This registration yielded an outline on the surface borders of the T1 (Figure 6a) and served as a guideline to manually draw the DLPFC ROI. Thus, utilizing FMRIB s FSLview, the DTIwarped T1 image outlining the DLPFC was overlaid on the b 0 image (Figure 6b) and faded to 70% (so the b 0 images could be seen, but the white outline was still apparent). Following the white outline, the ROI was extended approximately 2mm into adjacent white matter to comprise the DLPFC ROI (Figure 7). This extension was done in order to include the entire volume of the DLPFC (not just surface DLPFC) as well as to correctly obtain tracts (as tractography is defined by white matter). An example of a completed DLPFC ROI is seen in Figure 8. Fig. 5: DLPFC ROI (in orange). A volume rendering of the left DLPFC on the surface of a T1-weighted 3-D image (outlined and then filled in).

37 32 a b Fig. 6: DLPFC ROI (surface outlined in white). Panel a: after the DLPFC is outlined on the volume rendered image, it is seen on the slices of the T1 image as a white outline (to serve as guideline for drawing full left DLPFC ROI). Panel b: the same T1-weighted DLPFC outlined image (warped into DTI space) was then laid on top of the b 0 image and faded out to 70% in order to clearly see b 0 image (for tracing and filling in left DLPFC ROI). Fig. 7: DLPFC ROI (in orange). To fill in the left DLPFC ROI, a mask was drawn on top of the white line (from outlined DLPFC from volume rendered T1) and extended into frontal white matter to comprise the full left DLPFC ROI.

38 33 Fig. 8: DLPFC ROI. The completed left DLPFC ROI (in orange) seen in coronal, saggital, and axial sections. Probabilistic tractography was then used to delineate dominant white matter pathways from the cerebellum to the contralateral cerebral cortex (via the thalamus) bilaterally, herein referred to as either the right cerebellum-left DLPFC pathway or the left cerebellum-right DLPFC pathway. One seed point (the location of the start point for tractography), and two way points (one as a through-point and one as an endpoint for tractography) were used. Tracts were run using FMRIB s FSL suite (probtrackx) (Behrens et al., 2003) with the seed point as either the right or left DLPFC ROI and the first way point as either the right or left thalamus ROI. The second way point was the left or right cerebellar hemisphere ROI. Once tracts were clearly identified, all were thresholded at 10% of the maximum intensity of the tract in order to eliminate false positives (i.e. constrain the number of successful streamlines/tracts and eliminate diffuse tracts), and, if necessary, manually edited to remove any erroneous tracts not comprising or included in the cerebellar-dlpfc pathway. All ROIs and tracts were manually inspected by three other parties in the laboratory with the purpose of ensuring proper and consistent seed and way point placement. Mean and standard deviation for FA, MD, axial diffusivity (λ ), and radial diffusivity (λ ) were calculated for each tract based on the FA, MD, λ 1, λ 2, and λ 3 maps outputted by the DTIfit software (Behrens et al., 2003). ROI placement was conducted on 9 participants by an independent party to ensure reproducibility of tracts based on manually placed ROIs.

39 34 Accordingly, interclass correlations coefficients were calculated using mean FA of both left and right tracts. Cronenbach s Alpha interclass correlations coefficient was 0.912, signifying high reliability in placement of ROIs to generate tracts (i.e. minimal operator error). Behavioural measures. Neuro-cognitive assessment included evaluation of working memory utilizing the Wechsler Intelligence Scale for Children Fourth Edition (WISC-IV), focusing on the WMI scales. For the purposes of this thesis, the overall measure of working memory (WMI) was used for the analyses, as well as its subscales (digit span task and letternumber sequencing task), because WMI is designed to reliably measure working memory. The digit span task is subdivided into digit span forward and digit span backward tasks. For the digit span forward task, children were asked to repeat orally presented numbers in forward sequence (i.e. I am going to say some numbers. Listen carefully, and when I am though, say them right after me. Just say what I say: 2 9 ; child s response: 2 9 ) (Wechsler, 2003). For the digit span backward task, children were asked to repeat orally presented numbers in backward sequence (i.e. Now I am going to say some more numbers, but this time when I stop, I want you to say them backward. If I say 8 2, what would you say? ; child s response: 2 8 ) (Wechsler, 2003). If each response was correct in its entirety, the child received a score of 1 on that particular trial. If the response was incorrect, zero points were awarded. For both the digit span forward and backward tasks, there are 8 items per task and 2 trials within each item. Items increase in difficulty as the task goes on, with a number being added each time. Testing was discontinued when a child received a score of zero on both trials of an item. The raw score was then tallied with a maximum of 32 points available to be awarded (16 on the digit span forward and 16 on the digit span backward). The forward digit span task is thought to measure repetition and attention, whereas the backward digit span task actually measures memory span. The backward digit span task calls on

40 35 not just attention, but working memory centers in the brain required for rehearsal (i.e. phonological loop). In the letter-number sequencing task, children were orally presented sequences of letters and numbers together, and were asked to manipulate them so that the numbers are first (in numerical order), and the letters are next (in alphabetical order) (Wechsler, 2003) (i.e. I am going to say a group of numbers and letters. After I say them, I want you to tell me the numbers first, in order, starting with the lowest number. Then tell me the letters in alphabetical order. A 2 ; child s response: 2 A ). Scoring is congruent with that of the digit span task (i.e. scores for each trial can range from 0-1). There are 10 items in the letter-number sequencing task, with each item consisting of 3 trials (the maximum score is 30). Items increase in difficulty, as an additional letter or number is added (in alternation, to each trial) as the task progresses. The test was concluded when all items and trials were completed or if the child received a score of zero on all three trials of one item. The letter-number sequencing task serves as a multi-component working memory task in that it requires attention, rehearsal (i.e. phonological loop), reorganization (i.e. visuospatial sketchpad), and storage (i.e. the central executive). A lower score on all of the above tasks is indicative of poorer performance. Statistics The dependent variables in this study included the output of data from DTI (the measure of integrity of white matter tracts) and the working memory task scores. The former measure was quantified with calculations of FA, MD, λ, and λ, and the latter measure was quantified by WMI scores obtained from the WISC-IV. The independent variables used in this study were treatment (either treatment with CSR or surgery only for patients with posterior fossa tumors, as

41 36 compared to healthy controls receiving no treatment) in addition to demographic and medical variables. Because there were discrepancies in signal-to-noise ratios across sites as well as differences in parental education between controls and patients, site of MRI scan and parental education were taken into account in certain subsequent analyses as covariates. First, in order to examine possible overall IQ differences between the groups, one-way ANCOVAs with group as a factor were performed. While VCI and PRI were analyzed across groups, only parental education was controlled for, as there were no relevant correlations between site of MRI scan and IQ. Secondly, chi-squared analyses were performed to determine if any possible differences existed in either the right cerebellum-left DLPFC pathway or the left cerebellum-right DLPFC pathways extension into the dentate or lobe of the cerebellum across groups. Thirdly, to examine group differences in white matter integrity, DTI indices (FA, MD, λ, and λ ) within each tract obtained from the tractography-based ROIs were examined and compared using one-way ANCOVAs with group as a factor. Site and parental education were both controlled for, as each were correlated with the right cerebellum-left DLPFC pathway and the left cerebellum-right DLPFC pathway DTI indices. Furthermore, potential group differences in working memory scores (i.e. WMI, digit span, and letter-number sequencing tasks) were investigated using one-way ANCOVAs with group as a factor, controlling for parental education only. Lastly, to determine the relations between the integrity of cerebellum-dlpfc connections and working memory function, whole group correlation analyses were conducted for all DTI measures and overall WMI scores. Subsequently, the brain tumor and control groups were each considered separately in a partial correlation analysis for all DTI measures and WMI

42 37 (controlling for VCI). Multiple regression analyses were also employed to generate a path model of how treatment with radiation (as compared to surgery only), has an impact on working memory via white matter integrity of the cerebellum-dlpfc connections (Figure 9). Path model analyses, in which beta weights from multiple regression analyses are employed to estimate the strength of relations between variables, and thus are well suited to test a minimally constrained model of how treatment, DTI indices, and working memory variables may be related to each other, as well as predict working memory outcome. Thus, the exogenous variable in the path model is treatment, while the endogenous variables are the DTI indices (as a measure of white matter integrity) and working memory measures. Controlling for site, treatment and DTI indices of each tract were regressed on WMI. DTI indices of each tract were then regressed on treatment. Each direct effect obtained contributed to the model in question. Indirect effects (i.e. how treatment impinges on the integrity of cerebellum-dlpfc connections and how both, in turn, affect working memory outcome) were manually calculated from direct effects reflected in the path model. Fig. 9: The proposed path analysis model.

43 38 Results Overall IQ differences To evaluate whether any possible differences in WMI scores in patient populations were not due to or a product of overarching cognitive discrepancies (i.e. a drop in FSIQ or vocabulary deficits), group differences in VCI and PRI were examined (Table 2). Overall IQ was not utilized in this study because WMI is a component of full-scale IQ (i.e. the two measures are not independent of each other). VCI and PRI are independent of WMI, and thus could be used instead of FSIQ. There were significant differences found between the brain tumor and control groups for mean PRI, [F(1, 46) = 5.505, p = 0.023], with the control group scoring notably higher than the patient group. There was no main group difference, however, for mean VCI score [F(1, 46) = 3.624, p = 0.063]. Interestingly, when examining mean VCI differences between surgery only and radiation brain tumor subgroups, a significant difference is apparent [F(1, 22) = 8.061, p = 0.010], with the radiation group performing worse than the surgery group. There was no difference in mean PRI between the two brain tumor groups, however [F(1, 22) = 1.052, p = 0.316]. Table 2 Means and standard deviations for PRI and VCI scores across all groups. Tumor Group (Radiation + Surgery Only Subgroups) (n = 23) Radiation Subgroup (n = 16) Surgery Only Subgroup (n = 7) Healthy Control Group (n = 26) IQ Measure Mean StDev Mean StDev Mean StDev Mean StDev PRI VCI

44 39 Cerebellum-DLPFC tracts Using DTI tractography, bilateral tracts were produced that replicate the cerebellothalamo-cortical pathway demonstrated in an animal model by Schmahmann (1996) (Figure 10). This is a novel finding and consistent across controls as well as patients in this sample. However, as tracts were all thresholded and as the population in question consists of patients with cerebellar injury (i.e. surgical or radiation damage), not every tract proceeded into the cerebellar lobe or the deep dentate nuclei of the cerebellum (Table 3). However, there were no differences between patients and controls with respect to whether the right cerebellum-left DLPFC [χ 2 (1) = 0.012, p = 0.913] or left cerebellum-right DLPFC [χ 2 (1) = 0.008, p = 0.928] tract routed into the cerebellar lobe. Overall, all tracts obtained reflected a reliable pathway that included the DLPFC, thalamic nuclei, red nucleus, and in the majority of cases, the cerebellar peduncles. a

45 40 b Fig. 10: Cerebellum-DLPFC tracts. Panel a: a cross section of the b 0 image depicting the two bilateral pathways; right cerebellum via left thalamus to left DLPFC (yellow), and left cerebellum via right thalamus to right DLPFC (red). Panel b: a 3-D rendering (with cut-away) of the pathway that connects the left cerebellar hemisphere with the right DLPFC via the right thalamus. Table 3 Frequency table for left and right tracts reaching into the cerebellar lobe between brain tumor (also broken down into radiation and surgery only subgroups) and control groups. Tract Tumor Group (Radiation + Surgery Only Subgroups) Control Group (n = 24*) (n = 25) Right Cerebellum-Left DLPFC Left Cerebellum-Right DLPFC Totals Totals * Note: one participant was not included as the MRI scan provided was cut off before the cerebellum was reached Group differences in white matter integrity Significant group effects were found for several of the DTI indices for both the right cerebellum-left DLPFC and left cerebellum-right DLPFC pathways (Table 4). Specifically, mean FA and mean radial diffusivity significantly differed between the tumor and control groups (Table 5) for each tract. Specifically, mean FA was notably lower in the brain tumor group as compared to controls. Conversely, mean radial diffusivity was significantly higher in the brain