Early neurodevelopmental outcomes in preterm infants: memory, attention, & encoding speed

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1 University of Iowa Iowa Research Online Theses and Dissertations Spring 2017 Early neurodevelopmental outcomes in preterm infants: memory, attention, & encoding speed Amanda Michelle Benavides University of Iowa Copyright 2017 Amanda Michelle Benavides This dissertation is available at Iowa Research Online: Recommended Citation Benavides, Amanda Michelle. "Early neurodevelopmental outcomes in preterm infants: memory, attention, & encoding speed." PhD (Doctor of Philosophy) thesis, University of Iowa, Follow this and additional works at: Part of the Neuroscience and Neurobiology Commons

2 EARLY NEURODEVELOPMENTAL OUTCOMES IN PRETERM INFANTS: MEMORY, ATTENTION, & ENCODING SPEED by Amanda Michelle Benavides A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Neuroscience in the Graduate College of The University of Iowa May 2017 Thesis Supervisor: Professor Peg Nopoulos, MD

3 Copyright by AMANDA MICHELLE BENAVIDES 2017 All Rights Reserved

4 Graduate College The University of Iowa Iowa City, Iowa CERTIFICATE OF APPROVAL This is to certify that the Ph.D. thesis of PH.D. THESIS Amanda Michelle Benavides has been approved by the Examining Committee for the thesis requirement for the Doctor of Philosophy degree in Neuroscience at the May 2017 graduation. Thesis Committee: Peg Nopoulos, Thesis Supervisor Nancy C. Andreasen Alexander Bassuk Edward F. Bell Daniel Bonthius

5 ACKNOWLEDGMENTS I would like to first thank Shannon Ross-Sheehy, PhD, John Spencer, PhD, and Sammy Perone, PhD, who have each been instrumental in my understanding of the three novel cognitive assessments used in this study and their implications. Additionally, a huge thank you to the members of the Nopoulos lab, MR Research Facility, Department of Pediatrics Neonatal Research Network, and Program Project Grant Research Group that have helped with various aspects of this research and without whom this study would not have been possible: Thomasin McCoy, PhD, and Amy Conrad, PhD, in the early development of the cognitive assessment protocols, Vince Magnotta, PhD, and Andrew Metzger for their extensive work on the development of the automated pipeline for 12-month-old infant scans, Corinne Hamlin, MAT, in recruitment, scheduling, and administration of this study, Holly Dirks, RN (Brown University) for her tremendous amount of guidance and training in the infant MRI sleep protocol, MRI research technologists Autumn Craig and Marla Kleingartner for their assistance during our infant scans, Jane Brumbaugh, M.D., Diane Eastman, ARNP in the high-risk followup pediatric clinic, and clinical research coordinators Karen Johnson, BSN, and Gretchen Cress, BSN, MPH in the Department of Pediatrics for their significant role and assistance in recruitment of infant participants, members of the PPG Research Group (Project Number 5P01HL PRETERM TRANSFUSIONS: BRAIN STRUCTURE AND FUNCTION OUTCOMES ) for supporting my graduate work and providing critical evaluations and suggestions for direction over the past four years,, and Russell Valentin for his assistance in the coding of cognitive tasks. Finally, I would like to thank my mentor, Peg Nopoulos, for her foremost and invaluable role in my graduate training and scientific development over the past four years, and ii

6 even more importantly, for always being the positive and encouraging source for me as I progressed through the sometimes difficult stages of graduate school. iii

7 ABSTRACT Due to a steady increase in preterm birth rates over the past 20 years, prematurity has emerged as an important public health concern. Among the morbidities observed in this preterm population is a predominance of cognitive and behavioral deficits, suggesting the study of brain development, in particular, as an area of importance. The current study investigates neurodevelopmental outcomes in the first years of life of both brain structure, with the use of structural magnetic resonance imaging (MRI), and function, with the use of three novel cognitive tasks: (1) Visual Working Memory (VWM) task 1, a variant of changedetection paradigms commonly used in adults to assess VWM capacity; (2) Infant Orienting With Attention (IOWA) task, a spatial-cueing task that assesses visual orienting and attention; and (3) Encoding speed task (EST), a continuous familiarization paradigm assessing speed of information processing. Healthy 12-month-old full-term and preterm infants were recruited through University of Iowa s High-Risk Follow-up program and Neonatal Admissions Registry. [Demographics of participants: mean corrected age: months, gestational age (GA) range: weeks.]. An MRI imaging acquisition protocol was developed in order to scan infants during their naptime without sedation. A novel automated atlas-based segmentation approach to tissue classification developed at the University of Iowa was implemented in order to generate structural brain volumes of gray matter (GM), white matter (WM), cerebrospinal fluid, (CSF) and blood for each of the following regions of interest (ROI): total intracranial, cerebrum, cerebellum, striatum (consisting of caudate and putamen), globus pallidus, thalamus, and hippocampus, and all four cerebral lobes (frontal, occipital, parietal, temporal). Performance measures for each of the cognitive tasks were based on recorded infant eye movements (latency & direction), and included: Change preference scores, shift rate, and total looking iv

8 time (VWM task); visual spatial orienting measures (mean reaction time, mean error rate [MER]), and attention proficiency scores (cue facilitation, cue interference, and cue sensitivity) (IOWA task); Number of trials to reach criterion, novelty score, shift rate, and average look duration (EST). Linear regression analyses demonstrated gestational age (GA) as a significant predictor of performance in all three cognitive tasks: (1) On the more challenging multi-item VWM task, older GA preterm infants had significantly shorter total looking times than early preterm infants; (2) from the IOWA task, both MER and two attentional proficiency measures (cue facilitation and cue sensitivity) were associated with GA, again with superior performance from the older GA infants; and (3) two measures of processing speed on the EST were associated with GA, with older GA infants having better performance. Neuroimaging data demonstrated that increased GA predicted increased cerebral GM volumes (in particular, occipital and temporal lobe GM), in addition to increased striatal and thalamic volumes. Brain structure-function analyses showed associations of decreased occipital GM volume with increased total looking time (VWM task), while temporal GM and thalamic volumes were directly correlated with the two measures of attention: cue facilitation and cue sensitivity scores (IOWA task). Decreased volumes in multiple subcortical and cortical areas were correlated with worse performance on the EST, including temporal GM, occipital GM, striatum, and thalamus. Therefore, results of this study support the use of these cognitive tasks as sensitive enough to detect specific functional differences in performance based on GA early in development, and that performance may be directly related to underlying brain structures. 12- month-old infants born at earlier GAs demonstrate selective functional deficits, as opposed to the global cognitive delay currently assessable by Bayley-III in clinics 2. Therefore, these tasks may be potential early markers of risk in preterm and/or other high-risk infant populations. Continued investigations using more direct evaluations of the underlying brain structural v

9 correlates of memory, attention, and processing speed, through the use of multi-modality structural and functional brain imaging currently available, will provide a deeper understanding of early brain-behavior relationships during this critical period of dynamic maturation in the infant. vi

10 PUBLIC ABSTRACT Due to a steady increase in the number of babies born prematurely over the past 20 years, prematurity (a birth occurring before 37 weeks gestation) has emerged as an important public health concern. Even with improved survival of these infants, they remain at risk for many unfavorable health outcomes. Most of those risks include cognitive and behavioral deficits that show up later in life, highlighting the importance of studying the development of the brain, in particular. The current study investigates brain development outcomes in the first years of life using: (1) structural magnetic resonance imaging (MRI) to study brain structure, and (2) three novel cognitive assessments of visual working memory, attention, and speed of processing information. Healthy 12-month-old infants were recruited through University of Iowa s Neonatal Admissions Registry. An MRI imaging acquisition protocol was developed in order to scan infants during their naptime without sedation. Additionally, a new automatic approach to classifying areas of the brain was developed at the University of Iowa Department of Radiology for 12-month-old brain images. These novel cognitive assessments are based on infant eye movements (including how long it takes for an infant to react to certain stimuli and the direction of their looking). Results from this study support the use of these cognitive tasks to detect specific functional changes in performance based on gestational age. Therefore, these tasks may be potential early markers of risk in preterm populations, but continued investigations are necessary to fully elucidate early brain outcomes during this critical period of development. vii

11 TABLE OF CONTENTS LIST OF TABLES... ix LIST OF FIGURES... xi CHAPTER 1: INTRODUCTION... 1 CHAPTER 2: VISUAL WORKING MEMORY... 7 CHAPTER 3: ATTENTION CHAPTER 4: ENCODING (PROCESSING) SPEED CHAPTER 5: BRAIN STRUCTURE IN THE INFANT CHAPTER 6: METHODS CHAPTER 7: RESULTS CHAPTER 8: DISCUSSION APPENDIX REFERENCES viii

12 LIST OF TABLES TABLE 1. DEMOGRAPHIC AND NEONATAL CHARACTERISTICS OF INFANT PARTICIPANTS (N=69) TABLE 2. INDEPENDENT SAMPLES T-TEST COMPARING DEMOGRAPHIC AND NEONATAL CHARACTERISTICS BETWEEN MALE (N=40) AND FEMALE (N=29) INFANT PARTICIPANTS TABLE 3. DEMOGRAPHICS OF VWM TASK PARTICIPANTS (N=46) TABLE 4. DEMOGRAPHICS OF IOWA TASK PARTICIPANTS (N=54) TABLE 5. DEMOGRAPHICS OF EST PARTICIPANTS (N=41) TABLE 6. DEMOGRAPHICS OF PARTICIPANTS WITH NEUROIMAGING DATA (N=24) TABLE 7. VWM TASK PERFORMANCE: LINEAR REGRESSION ANALYSIS WITH GA TABLE 8. VWM TASK PERFORMANCE: COMPOSITION OF EXTREME GROUPS TABLE 9. IOWA TASK PERFORMANCE: LINEAR REGRESSION ANALYSIS WITH GA (ALL PARTICIPANTS; N=54) TABLE 10. IOWA TASK PERFORMANCE: LINEAR REGRESSION ANALYSIS WITH GA (PRETERM ONLY; N=41) TABLE 11. IOWA TASK PERFORMANCE: COMPOSITION OF EXTREME GROUP ANALYSIS TABLE 12. EST PERFORMANCE: LINEAR REGRESSION ANALYSIS WITH GA TABLE 13. EST PERFORMANCE: COMPOSITION OF EXTREME GROUP ANALYSIS TABLE 14. EFFECTS OF GA ON BRAIN MEASURES: LINEAR REGRESSION ANALYSIS TABLE 15. COMPOSITION OF EXTREME GROUP ANALYSIS FOR NEUROIMAGING DATA (N=24) ix

13 TABLE 16. NEUROIMAGING: RESULTS OF EXTREME GROUP ANALYSIS USING ANOVA TABLE 17. VWM TASK: STRUCTURE/FUNCTION RELATIONSHIP (TOTAL LOOKING TIME [SS2]; N=12) TABLE 18. VWM TASK: STRUCTURE/FUNCTION RELATIONSHIP (TOTAL LOOKING TIME [SS6]; N=12) TABLE 19. VWM TASK: COMPOSITION OF MEDIAN SPLIT GROUP ANALYSIS (N=12) FOR NEUROIMAGING DATA TABLE 20. IOWA TASK: STRUCTURE/FUNCTION RELATIONSHIP (MEAN ERROR RATE; N=18) TABLE 21. IOWA TASK: STRUCTURE/FUNCTION RELATIONSHIP (CUE FACILITATION; N=18) TABLE 22. IOWA TASK: STRUCTURE/FUNCTION RELATIONSHIP (CUE SENSITIVITY; N=18) TABLE 23. IOWA TASK: COMPOSITION OF MEDIAN SPLIT GROUP ANALYSIS (N= 18) FOR NEUROIMAGING DATA TABLE 24. EST: STRUCTURE/FUNCTION RELATIONSHIP (NUMBER OF TRIALS TO CRITERION, N=13) TABLE 25. EST: STRUCTURE/FUNCTION RELATIONSHIP (NOVELTY SCORE; N=13) TABLE 26. EST: COMPOSITION OF MEDIAN SPLIT GROUP ANALYSIS (N=13) FOR NEUROIMAGING DATA TABLE 27. EST: RESULTS OF MEDIAN SPLIT GROUP ANALYSIS USING ANOVA (NUMBER OF TRIALS TO CRITERION) x

14 LIST OF FIGURES FIGURE 1. SCHEMATIC REPRESENTATION OF A TRIAL USED FOR NOVEL VWM TASK FIGURE 2. SCHEMATIC REPRESENTATION OF EXPERIMENTAL AND BASELINE (CONTROL) CONDITIONS FOR THE IOWA TASK (AS PUBLISHED BY ROSS-SHEEHY, ET AL. 2015) FIGURE 3. SCHEMATIC REPRESENTATION OF THREE EXAMPLE TRIALS OF THE EST (36 TRIALS TOTAL) FIGURE 4. RELATIONSHIP BETWEEN GESTATIONAL AGE AND TOTAL LOOKING TIME (SS2) FIGURE 5. RELATIONSHIP BETWEEN GESTATIONAL AGE AND TOTAL LOOKING TIME (SS6) FIGURE 6. VWM TASK: EXTREME GROUP COMPARISON (TOTAL LOOKING TIMES) FIGURE 7. IOWA TASK: GROUP ACCURACY MEASURES FIGURE 8. RELATIONSHIP BETWEEN GESTATIONAL AGE AND MEAN ERROR RATE (FULL SAMPLE) FIGURE 9. RELATIONSHIP BETWEEN GESTATIONAL AGE AND CUE FACILITATION (FULL SAMPLE) FIGURE 10. RELATIONSHIP BETWEEN GESTATIONAL AGE AND CUE SENSITIVITY (FULL SAMPLE) FIGURE 11. RELATIONSHIP BETWEEN GESTATIONAL AGE AND MEAN ERROR RATE (PRETERM ONLY) FIGURE 12. RELATIONSHIP BETWEEN GESTATIONAL AGE AND CUE FACILITATION (PRETERM ONLY) FIGURE 13. RELATIONSHIP BETWEEN GESTATIONAL AGE AND CUE SENSITIVITY (PRETERM ONLY) FIGURE 14. IOWA TASK: EXTREME GROUP COMPARISON (MEAN ERROR RATE) xi

15 FIGURE 15. IOWA TASK: EXTREME GROUP COMPARISON (CUE FACILITATION AND CUE SENSITIVITY) FIGURE 16. RELATIONSHIP BETWEEN GESTATIONAL AGE AND NUMBER OF TRIALS TO CRITERION FIGURE 17. RELATIONSHIP BETWEEN GESTATIONAL AGE AND NOVELTY SCORE FIGURE 18. EST: EXTREME GROUP COMPARISON (NUMBER OF TRIALS TO CRITERION) FIGURE 19. EST: EXTREME GROUP COMPARISON (NOVELTY SCORE) FIGURE 20. NEUROIMAGING EXTREME GROUP COMPARISON FIGURE 21. VWM TASK: MEDIAN SPLIT GROUP COMPARISON (SS6 TLT) FIGURE 22. IOWA TASK: MEDIAN SPLIT GROUP COMPARISON (CUE SENSITIVITY) FIGURE 23. EST: MEDIAN SPLIT GROUP COMPARISON (NUMBER OF TRIALS TO CRITERION) xii

16 CHAPTER 1: INTRODUCTION Unlike many other health problems, the preterm birth rate, defined as an infant born before 37 weeks gestation, has seen a steady increase by more than 20 percent between the years of 1990 and ,4. Despite recent declines in rates since 2006, total preterm births remain at percent of all live births in the United States. Preterm infants have emerged as an important public health concern in the United States, with considerable implications for health care and insurance, education, and social support services, as well as for the annual societal economic burden, which is estimated to be at least $26.2 billion, equal to $51,600 per infant born preterm 5. An estimated $18.1 billion in health care costs for newborn care in the United States is attributable to infants born prematurely, or nearly half of total hospital charges. Preterm infants may be classified according to gestational age and birth weight, with infants weighing <1500g defined as very low birth weight (VLBW) and those weighing <1000g defined as extremely low birth weight (ELBW). Over the past three decades, advances in perinatal medicine, such as earlier and increased use of steroid therapy during pregnancy, assisted ventilation techniques, surfactant therapy, and enhanced nutrition have resulted in markedly improved survival of ELBW and VLBW infants and those born at the limits of viability (22 to 25 weeks gestation) 3,4. Despite improvements in mortality for premature infants, there has not been a concomitant decrease in morbidities among these infants 6-9. Rather, the incidence of most major short-term (within first few years of life) complications associated with prematurity have remained relatively stable 10. It is widely accepted that preterm infants have higher rates 1

17 of multiple neonatal morbidities than term infants, including respiratory distress, apnea, hypoglycemia, seizures, periventricular leukomalacia, and rehospitalizations 11. Additionally, preterm infants continue to be at risk of developing a wide spectrum of long-term (childhood through adulthood) morbidities as well, which primarily include deficits of the motor, sensorial, cognitive, and behavioral domains 11-17, suggesting brain development in particular as an area of vital importance. There is increasing recognition that preterm infants are at higher risk for what is known as high prevalence, low severity deficits later in life in multiple areas of cognitive function such as learning, visuomotor function, language, executive function, attention, memory, and behavior 18. These deficits, are termed high prevalence as they affect as many as 50-70% of VLBW infants 19 ; The deficits are considered low severity based on the notion that the deficits are not at the level of intellectual deficiency (newer term for mental retardation). However, these deficits should not be underestimated in functional impact as they appear relatively stable during development but persist into adolescence and young adulthood, during which time dysfunction becomes more obvious and impairing as the child grows older. This discrepancy between improved mortality, yet increased cognitive and behavioral morbidities, highlights a critical need for both short-term and long-term neurodevelopmental outcome studies in preterm infants. In order to improve long-term neurodevelopmental outcomes for these infants, it is important to identify the early markers of risk for these later high prevalence, low severity deficits of cognitive functioning. Additionally, understanding the underlying neural correlates of abnormal neurodevelopmental sequelae, including early mechanisms 2

18 of cerebral injury and their effects on subsequent development, may provide the opportunity for timely and targeted interventions. The most widely and commonly used measures for early cognitive function in high-risk and preterm infants are the Bayley Scales of Infant Development (BSID) and its revisions 2,20,21. The primary scales used in the earlier versions of the BSID were the mental developmental index (MDI), which evaluates early cognitive and language functioning, and the psychomotor developmental index (PDI), which measures early fine and gross motor development. The BSID-II has received much criticism for the broad nature of the MDI and PDI scales and its lack of sub-scale standardized scores 22, as well as its poor predictive validity for cognitive functions at school age 23. The newest edition, Bayley-III 2, has attempted to address these limitations by refining its measures to include five sub-scales assessing cognition, language, motor skills, social-emotional, and adaptive behavior, in addition to extending its floors and ceilings to assess development in lower-functioning and impaired infant populations. However, even with these improvements in this well-standardized and precise assessment, the Bayley-III may still be a poor indicator of neurodevelopmental impairment in the early years of life for many reasons. First, brain structure and function is nascent early in life; an infant at birth and in the first few years of life is not manifesting the cognitive repertoire that can be measured later in life (e.g. by school-aged years). Therefore, currently available developmental assessments such as the Bayley-III may not precisely measure early neurodevelopment because the brain functions they aim to evaluate have simply not developed yet. Indeed, a recent study demonstrated that the Bayley-III may still underestimate developmental delay in ELBW at two years of age 24. 3

19 Secondly, given the rapid development of the brain in these first few years of life, specific brain areas and tissues may have an increased risk of damage, making it imperative to obtain more precise or direct measures of brain structure and/or function. The Bayley-III gives generalized measures of development that may mask specific functional deficits in infant cognition, such as visual working memory, processing speed, attention, and executive function. Therefore, there remains the need for more sensitive and specific neurodevelopmental assessments to be utilized efficiently in high-risk and preterm infants that may provide clinically useful information relating to early development. This study was designed to investigate early neurodevelopmental outcomes of cognitive function of both normal healthy and preterm infants in the first year of life, with the use of more specific and sensitive measures of early brain development. Three cognitive assessment tasks will be evaluated: a visual working memory (VWM) task, the Infant Orienting With Attention (IOWA) task, and an encoding speed task (EST). In addition, the utilization of structural magnetic resonance imaging (MRI) in these infants will provide more direct evaluation of the underlying brain structural correlates of these cognitive tasks. Findings from this study will establish markers of underlying cortical and sub-cortical functioning, further supporting their use as early neurocognitive evaluation tools for preterm infants. FUNCTION (Aim #1): This study will provide further insight into specific early cognitive function in infants (at 12 months old corrected age), using three novel cognitive assessments. STRUCTURE (Aim #2): This study will directly examine the concomitant neural correlates of performance of three early 4

20 developmental cognitive abilities (visual working memory, attention, and processing speed) in healthy preterm infants using a non-invasive imaging modality: structural magnetic resonance imaging (MRI). We hypothesize that increased functional deficits on measures of attention, visual working memory, and processing speed development will be directly related to underlying structural brain abnormalities. The current study has the potential impact for improving scientific knowledge and directing therapeutic interventions in the first few years of life for many reasons. First, this study aims to investigate structural and functional outcomes early in a preterm infant s life, a critical period for insight into early development, but even more importantly, for significant implications about adult disease. In fact, the period of birth to 2 years of age might be the most dynamic period of postnatal brain development in humans. This concept is based upon the fetal origins of adult disease hypothesis proposed by Dr. David Barker 25,26, which theorizes that insults early in development, such as malnutrition, inflammation, infection, hypoxia, stress, or toxins, may result in permanent changes in structure, physiology, and metabolism that persist throughout life and may have a significant impact on future adult disease. This theory is in contrast to the widely accepted idea of developmental plasticity, which suggests that the human brain has a critical period early in life when it is able to adaptively respond to its environmental conditions, followed by a period of fixed function and the loss of plasticity. For the human brain, the period of 24 weeks gestation to ~ 2 years after birth is characterized by rapid growth and differentiation, which also makes it extremely vulnerable to injury during this time 27. This has important implications for connectivity in the brain of these preterm infants, because connectivity to areas of the brain that 5

21 support higher functioning, such as the frontal lobe, may depend on developmental events early in life. Therefore, assessment of brain structure in preterm infants, specifically at 12 months old (corrected age), provides the opportunity for a clearer understanding of the developing preterm infant s brain anatomy and physiology, both early and as expressed over time. That is, short-term outcome studies have potential to significantly impact understanding of long-term outcomes as well. Secondly, findings from this proposal investigating short-term functional outcomes using more precise assessments of visual working memory, attention, and processing speed are fundamental in advancing our current understanding of the brain in the premature infant; knowledge gained from quick and inexpensive neurocognitive assessments may help to establish guidelines for therapeutic intervention, both in the neonatal period and in the first few years of life. In the realm of early cognitive developmental measurements in the pediatric clinic, there currently is lacking sensitive and specific neurodevelopmental assessments available for high-risk and preterm infants to provide clinically useful information relating to early development. The novel cognitive assessments discussed here and their variants may provide a better understanding of the cognitive outcomes and differences early in a preterm infant s life. 6

22 CHAPTER 2: VISUAL WORKING MEMORY Memory may be conceptualized as traces of experiences that are created, maintained, later retrieved and referenced, and ultimately, have a significant influence on human behavior. Over the past century, the study of the processes involved in memory formation, maintenance, and retrieval has seen transitions first from the behaviorism era of 1930s-1960s, in which stimuli-response relationships and reinforcement schedules were believed to underlie the observation of learned behaviors, to the modal model memory theories, in which information was thought to move into memory through separate stores (e.g. first into sensory stores, then into a limited capacity, directly accessible short-term memory (STM), and finally into a more permanent long-term memory (LTM) with unlimited capacity that requires retrieval through STM stores) 28. The more recent models of memory are increasingly dynamic in nature, with a greater emphasis on how information moves into and out of the focus of attention where it has the opportunity to exert conscious influence on behavior. 28 The component(s) of memory that are essential both early in human development and in the early phases of the life of a memory are STM and/or working memory (WM). According to Bauer & Fivush (2014), a more broad definition of these systems are processes [that] are the means by which the products of experience are maintained in attention or consciousness long enough to initiate the transformation that turns immediate experience into memory traces that can be accessed at a later point in time. 28. In another way, WM may be defined as a critical early cognitive process with a highly limited capacity and duration; that is, a memory system that is used for temporary storage 7

23 and manipulation of information in the service of complex tasks 29. One component of working memory which is fundamental during infant development and has been the most extensively studied is visual working memory (VWM) or visual short-term memory (VSTM), which allows one to integrate views of the world that are separated by rapid eye movements (saccades) and blinks, as well as to compare or discover relationships between objects that cannot be foveated simultaneously; thus, VWM is necessary to rapidly link the visual information acquired during one period of fixation with the information available in the next period, stored only momentarily, and used every time the eye moves thousands of times daily 30. Historically, the common view of these STM systems is that they must: (1) create memory representations rapidly (on the order of milliseconds), (2) store only a handful of representations at any one time (i.e. highly limited storage capacity), and (3) require active rehearsal to maintain the representations beyond a few seconds (i.e. short retention intervals). In order to assess these memory systems, there have been a variety of experimental paradigms utilized. Many studies investigating VWM in adults have frequently used variants of the change-detection task, in which observers are first presented with an arrays of simple shapes, followed by a short delay, a second array is shown, and finally, subjects must indicate whether the two arrays observed were identical. Using this approach, it has been shown that adults can maintain three or four simple colors (or three to four multifeature objects) in short-term memory 31. Similarly and probably most commonly, multiple different versions of the delayed response task have been used in investigations of infant memory over the years, with countless variations in hiding location, delay imposed, type of response required, etc. 32. As one of 8

24 the earliest studies of this type, Walter Samuel Hunter tested his daughter at months old by hiding a toy in one of three boxes, imposing a delay (and distraction), and then allowing her to search for the toy. As an extension to the delayed response task, Jean Piaget introduced the A not B task, in which a toy was hidden at location B several times, before hiding at a new location A and prompting search after a delay during this test trial. This task requires an infant to not only remember the spatial location of an object, but to tap into executive function inhibitory control of previously reinforced responses 32,33. Investigations of these same memory systems in infants have also utilized habituation or novelty preference procedures, in which infants are first shown one stimulus during a familiarization or habituation phase, and subsequently, their memory for that stimulus is tested by comparing time of looking by the infants for the now-familiar versus novel stimulus; the familiarization time, nature of the stimuli, and type of discrimination utilized are all varied depending on the amount of time needed by most infants to encode the stimuli. For these tasks, the assumption is that a memory has been formed for the familiar stimulus by the infants if they look longer at the novel stimulus than the now-familiar object. However, there are multiple limitations and practical difficulties inherent in using many of these experimental designs in infants. First, common change-detection paradigms in adults require verbal feedback from the subject on whether the arrays are similar or different, an inherent component of the experimental model that is not translatable to infants. Second, in habituation/novelty preference procedures, infants are usually presented with a familiarization stimulus for tens of seconds or more (or trained over many minutes and days), and the memory is tested over relatively short retention 9

25 periods (a few seconds), both of which may be much longer than the assumed timing of STM systems for creation of memory representations (less than 500 milliseconds [ms]) and retention interval (less than 1000 ms). For example, a study of infants VWM in a span task utilized short familiarization phases of 3-10 seconds for each item (depending on the age of infants between 5 and 12 months), and subsequently assessed the infant s novelty preference for between one and four items present sequentially 34 ; memory for each of the items was determined by presenting each familiar item with a novel item, in the same order of presentation as during the familiarization phase. It was shown that 12- month-old infants were able to remember all four items, while younger infants had memory spans of only one or two items, providing evidence of a limited capacity memory system. However, it remains unclear the ability of this task to isolate the relative contributions of STM and LTM, primarily due to its exposure and retention durations, especially in the four item trials. Even more so, it is not definitive whether this familiar versus novel experimental model taps into a recognition memory that is simply based on previous experience with a stimulus, and not an actual memory representation of that stimulus (i.e. VSTM). Therefore, it may be that these procedures are not actually isolating and assessing VWM processes exclusively, and are more likely tapping into components of LTM systems, as well. It is these very components of VSTM that much of the current research and recent experimental paradigms have focused on, in order to isolate VWM: memories that are formed quickly, not retained for a long time, and of particular interest to my current thesis work, of limited capacity. One novel experimental paradigm utilized by Shannon Ross-Sheehy, John Spencer, & colleagues at the University of Iowa DeLTA Center to study memory of 10

26 limited capacity in infants is based on the change-detection procedure that is commonly used in adults to isolate VSTM and may reliably address many of the limitations of these previous experimental paradigms. As a variant of the paired-comparison procedure, this paradigm rests on the assumption that given the choice of two similar displays, infants will look longer to the display that imposes a greater informational load 35. Infants will prefer to look at changing displays compared with a nonchanging display, but only if they can form a memory of the changing colors (STM requirement #1: create memory representations rapidly) and keep those colors active in memory across a 300 ms delay (STM requirement #2: require active rehearsal to maintain the representation beyond a few seconds). There are many advantages of this experimental paradigm over previous experimental methods utilized in infants, with particular emphasis on extracting the use of short-term memory systems and minimizing the contribution of long-term memory systems 1. First, each display is presented for 500 ms, which maximizes the need for rapid memory formation, especially for arrays containing multiple items. Second, although the stimulus arrays are presented for many cycles over a 20 second interval, a given color is not repeated very many times before changing to a different color; this small total exposure duration before a change minimizes contributions from long-term memory. Third, retention period is only 300 ms, which is short enough to be well within the expected duration of STM. Finally, all the stimulus items are simple colored squares, which leads to high intra- and inter-trial similarity and provides substantial interference with long-term memory representations. 11

27 This VWM task has been previously used to characterize normal development of VSTM capacity 1 and the binding of features 36 in infants. Recent investigations aimed at understanding the nature and development of VWM using this task during infancy have suggested rapid development between 6.5 and 10 months, with VWM capacity for singlefeature objects approaching the adult capacity of three or four objects within the first year of life 1,34. In another study investigating feature-binding (e.g., object identity [color] and location) in infancy, this novel VWM paradigm demonstrated that 6.5-month-old infants could not detect changes in color-location combinations, but 7.5-month-old infants could just as effectively bind color and location (i.e., showed strong preference for changes in color/location) as 12.5-month-old infants. Therefore, it appears that the ability to represent multiple objects and the ability to bind features in VSTM may develop during the same time period. This novel VWM assessment has the potential to give a clearer isolation of short-memory, minimize contributions from long-term memory, and provide for direct comparisons to previous studies of VWM in adults in order to evaluate the developmental origins of adult short-term memory systems. 12

28 CHAPTER 3: ATTENTION In the realm of psychology, selective attention has long been thought to play a key role in animal behavior. Attention may be defined as the ability to organize the sensory, perceptual, or spatial resources of the brain in order to optimize performance towards goal-directed behaviors 37. These attentional processes involve analyzing the most significant and task-relevant stimuli and directing brain processes towards locations that may require action. One of the earliest assumed manifestations of attention in infancy is the attainment of a state of alertness, or a state of preparedness in order to process high priority signals, a concept that goes back to Moruzzi & Magoun (1949) In adults, psychologists have focused on the ability to maintain this alertness, also known as sustained attention; however, in infancy, the focus of early developmental studies has been on the attainment of such states of alertness, which is infrequent in the first month of life and most likely does not manifest until 4 to 8 or 10 weeks postnatally. Because this early cognitive function may be intimately related to arousal states, subcortical structures (such as the brainstem and its reticular activating systems) and their influences on higher-order structures may underlie these early attentional functions very early in postnatal life 38. However, another component of attention that has emerged and gained increased interest in the developmental literature over the last two decades is the study of visual attention and spatial orienting in infancy, particularly due to a concerted effort to 13

29 understand and correlate early measures of attention with later cognitive functioning in childhood. In studies of visual attention, orienting involves several stages of processing, including engagement of visual attention to a particular stimulus, disengagement of attention from the currently-fixated stimulus, and shifting of visual attention from one stimulus to another (or, saccade execution). One of the earliest appearing components of visual attention are the automatic or reflexive saccades exhibited by young infants, in which they tend to make eye movements in response to visually striking items, the sudden appearance of unexpected object in the world, items with high contrast boundaries, or objects that are moving. These early-appearing reflexive saccades are thought to be predominantly mediated by subcortical structures that are relatively mature at birth, such as the superior colliculus, and subsequently, increasing synaptic connections from these structures to the frontal eye fields (FEFs) and primary visual cortex to allow for improved visual acuity over the first two months of postnatal life 41,42. In addition to these reflexive eye movements, an infant in the first few months after birth will also undergo significant improvement in his or her voluntary (or endogenous) saccades, as well as inhibitory control of reflexive saccades (to an attentional pre-cue) in order to facilitate their speed of response to a second stimulus by 4 months of age; other estimates postulate that both reflexive and inhibitory control visual functions may undergo significant changes between 3 months and 6 months 38,42,43. These voluntary saccades may rely on prefrontal cortex areas. Both reflexive and voluntary eye movements allow for deeper understanding of later endogenous attention mechanisms in 14

30 infancy, both important precursors of goal-directed behaviors such as reaching, grasping, and locomotion, and thus may be an early indicator of attention. Spatial cueing tasks are paradigms that measure overt, reflexive saccades, and have been used to assess endogenous attention in special populations, such as very young infants, preterm infants, or infants with developmental deficits, in order to overcome the practical difficulties of conditioning or training in these subjects 38. These tasks rely on a spatial cueing effect, in which there is a tendency for infants saccades to be faster and more accurate to targets that are preceded by a brief spatial cue, than to targets with no spatial pre-cue. Use of these spatial cueing tasks, in particular, has provided for better understanding of both covert orienting mechanisms, in which attentional shifts are observed prior to initiating an eye movement to the stimulus (e.g., facilitation of an infant s eye movements to a target stimulus when a cue is presented prior to the target, or marking the future location of the stimulus), as well as overt orienting, which involves the actual eye movement and saccade execution. For example, as a correlate to the discussion of VWM in infancy in the last chapter, selective attention may also contribute to the infants ability to create VSTM representations of multiple single-feature objects in this task. In one study, Ross-Sheehy (2011) et al. used cues to direct attention to the nonchanging or changing displays. The question was: does an infant prefer a changing over nonchanging display if a cue directs attention to it (i.e. valid trial)? What about if a cue directs attention to the nonchanging display instead (i.e. invalid trial)? Results of this study demonstrated developmental changes in the interaction between attention and VSTM: 10-month-old infants preferred the valid trials over invalid trials, but 5-month-olds did not 44. Therefore, 15

31 attention was able to facilitate preferences for changes in multiple-object arrays only in older infants, much like in adults. In the current study, a novel visual orienting paradigm, the Infant Orienting With Attention (IOWA) task, will be used to evaluate early differences in visual orienting proficiency (speed and accuracy), as well as visual attention proficiency, in healthy fullterm and preterm infants. This task consists of several different spatial cue conditions with varying degrees of visual competition to detect and measure an infant s ability to covertly shift attention and make fast and accurate eye movements to peripherally appearing targets. Data obtained from this task provides very detailed information about the development of covert attention, planning of eye movements, visual competition, and orienting speeds. Preliminary studies using the IOWA task in a 5- and 10-month old preterm infant population (gestational age range: weeks, birth weight range: grams) showed that preterm infants were comparable to their full-term counterparts on measures of speed and accuracy, yet exhibited significant deficits in spatial attention that did not manifest until 10 months of age 45. This suggests emergent attentional deficiencies may be related to and detectable early in infant development (as early as 10 months of age). 16

32 CHAPTER 4: ENCODING (PROCESSING) SPEED Age-related changes in speed of information processing appear to be quite pronounced from early childhood through adolescence, and is theorized to play a pivotal role as the foundation of other higher-order executive functions and cognitive abilities later in development. Faster reaction times (RT) and inspection time (IT) have long been associated with individual differences in general intelligence over childhood development (IT is defined as the minimum amount of time needed for someone to reliably discriminate between two stimuli presented for only extremely brief extremely intervals at a rapid rate) 46,47 RTs increase linearly with age for a number of diverse childhood cognitive tasks such memory search, mental rotation, visual search, analogical reasoning, and mental arithmetic 48. Consistent patterns of age differences across such diverse tasks suggests processing speed may function as a global ability and may be a fundamental to the entire developing information-processing system. Evaluation of information processing has primarily been measured only indirectly in infancy, with paired-comparison and habituation paradigms discussed previously in Chapter 2 (VWM). As described, infants are first shown one stimulus during a familiarization phase, and subsequently, on test, their memory for that stimulus is tested by comparing time of looking for the now-familiar versus novel stimulus; the familiarization/habituation time, nature of the stimuli, and type of discrimination utilized are all varied depending on the amount of time expected for most infants in the group to encode the stimuli. For these tasks, the assumption is that the infant has encoded a memory for the familiar stimulus if they look longer at the novel stimulus than the now- 17

33 familiar (& encoded) object. When used in the context of evaluating processing speed in infants, because group novelty scores tend to vary directly as a function of familiarization time, encoding speed can only be inferred from these novelty scores; that is, this is not a direct index of how quickly the individual infant is processing the target; some infants may not have had sufficient time to encode the stimuli, while other who have already processed the information may be forced to continue attending. Additionally, differences in attention may also be influencing processing speed. Difference in short lookers and long lookers infants suggested that short lookers may process information more efficiently and rapidly than long lookers, and long lookers may de delayed in their abilities to disengage and shift visual attention 49. The procedure utilized in this study is a variation of the novel continuous familiarization technique previously described in Rose, et al (2002) 50. Recent findings suggest that individual differences in processing speed may have their roots in infancy: variation of this processing speed tasks have demonstrated that measures of processing speed at 7-36 months were significantly predictive of global outcomes of processing speed and IQ, as well as measures of executive functioning, at 11 years old 51,52. Additionally, in a developmental study of processing speed in of 5-, 7-, and 10-month-old infants, preterm infants required about 20% more trials and 30% more time to reach criterion than their full-term counterparts 50. In the current adaptation of the task, infants are presented with a series of paired stimuli presented simultaneously, with one remaining the same from trial to trial. This procedure terminates trials only when the infant has reached a specified amount of consistent preference for the novel stimuli. Because this task can be used with infants of 18

34 varying age and risk status, developmental differences can be assessed with a direct metric of whether the stimulus has been processed sufficiently (i.e. the infant can recognize as familiar). 19

35 CHAPTER 5: BRAIN STRUCTURE IN THE INFANT Due to the highly evolving nature of the infant and adolescent brain, more precise measures of brain structure are necessary for evaluation of neurodevelopment in these infants. Magnetic resonance imaging (MRI) of the brain offers the opportunity to directly quantify brain structures to provide more accurate information regarding brain development. Over the past 20 years, thousands of studies using MRI have been used to obtain quantitative measures of brain structure in school-aged children and adults, in both normal development and pathologic conditions. Though, the use of MRI in the neonatal and early infancy period is a relatively new venture. One specific aim of this study will be evaluation of brain development in the preterm infant. Recently, a structural MRI study of normal brain development from birth to 2 years showed a robust growth of the brain (by 101% increase in the first year, and 15% increase in the second year); hemispheric growth was driven primarily by gray matter (GM), with an 149% increase versus 11% increase for white matter (WM) 53. There is a rapid elaboration of synapses within the first two years of life, which also corresponds to increasing GM volumes to a lifetime maximum around age 2. Several studies have identified early structural brain abnormalities with the use of neonatal MRI and their associations with neurodevelopmental impairments In this population, a form of white-matter brain injury known as periventricular leukomalacia (PVL) is known to be the most common injury to the preterm infant s brain 58. PVL is caused by events such as hypoxia, subsequent tissue ischemia, or infection, all of which may also have deleterious effect on later brain maturation But a previously underrecognized accompaniment of this primary disease is neuronal and axonal deficits in four 20

36 primary areas: thalamus, basal ganglia, cerebral cortex, and cerebral WM. Multiple volumetric and neuropathologic studies have demonstrated the decreased volumes of and neuronal loss to all of these structures as early as term-equivalent age, but later into childhood, adolescence, and adulthood as well Additionally, multiple studies have also identified early structural brain abnormalities with the use of neonatal MRI and their associations with neurodevelopmental impairments For example, WM abnormalities (present in 21% of infants) and GM abnormalities (present in 45% of infants) in a sample of very preterm infants at term-equivalence were found to be predictive of cognitive delay, motor delay, cerebral palsy, and neurosensory impairment at two years of age 54, as well as predictive for general intellectual ability, language development, and executive functioning at four and six years old 55. The neural underpinnings of the specific cognitive functions discussed in Chapters 2-4 (namely, visual working memory, attention, and encoding speed) are not fully understood, but are beginning to be elucidated. For VWM, two areas have been proposed to underlie change detection differences: prefrontal cortex (proposed to have elevated responses in delayed response memory tasks), and parietal cortex (an area known to be responsible for binding dorsal and ventral visual streams in adults) 65,66. In studies of visual attention, orienting involves several stages of processing, including engagement of visual attention to a particular stimulus, disengagement of attention from the currently-fixated stimulus, and shifting of visual attention from one stimulus to another (or, saccade execution). Each of these subfunctions has been attributed to structures in the posterior or dorsal attention system (sometimes referred to as the 21

37 where system), including the thalamus, parietal lobe, and temporoparietal junction, respectively 38,39. More recent work hypothesizes a remote influence of these dorsal attention areas on the sensory-specific areas (i.e., primary visual cortex and extrastriate regions in the temporal lobe); that is, synchronization between the attention network may lead to greater sensitivity in the visual system, allowing for faster responses to visual targets 67. In regard to processing speed, although there are emerging associations of measures of processing speed with various higher-level cognitive processes throughout development, such as fluid intelligence, reasoning, memory, and executive function, much less is known regarding underlying brain correlates. In fact, results of this study may be among the first for outlining specific neural correlates of processing speed in infancy Exploration of brain structure and its associations with early cognitive functions in preterm infants is necessary to identify when and how structural characteristics of brain development may influence the formation of these functions. The concurrent behavioral correlates and functional significance of these structural abnormalities are not entirely understood, particularly due to the practical difficulties of developing and administering neurocognitive assessments for high-risk neonatal subjects with limited cognitive and behavioral repertoires and high inattention levels, as well as subject cooperation and motion for brain imaging studies. It is hypothesized that performance on IOWA, VWM, and EST tasks will be strongly predicted by individual differences in brain development in areas associated with these functions in later development. The use of three novel neurocognitive assessments in conjunction with structural MRI will allow 22

38 for understanding of early brain-behavior relationships in preterm infants, with the potential to provide early indicators of impending deficits. 23

39 CHAPTER 6: METHODS Participant Recruitment The goal of sample recruitment was to obtain one sample in which there was a large variance in gestational age (GA). That is, our strategy was not to recruit a preterm and a full term sample for direct group comparison, but instead to acquire one large sample where within that sample, effects of GA could be assessed (a within-group strategy). Normal healthy full-term (GA greater than 37 weeks) and preterm infants (GA completed weeks) at 12-months-old corrected age (i.e., infant s age from expected date of birth) were recruited through their participation in University of Iowa (UIHC) Children s Hospital s: (1) High-Risk follow-up program at their 9-12 month old clinic visit, and (2) neonatal admissions registry; that is, if an infant is 14 months chronologically, but born two months early, their corrected age is 12 months old. Healthy full-term infants were also recruited through noon news, , and posted recruitment letters on pediatric nurse bulletins and outpatient clinics. Exclusionary criteria included significant co-morbidities such as epilepsy, severe eye or retinal disease, stroke, major birth defects, or a history of neonatal surgical closure of patent ductus arterioles using a metal clip (open patent ductus arteriosus is a common condition in ELBW and LBW preterm infants). Interested parents were scheduled to return to the hospital for a single hour testing session, which included time for cognitive testing, parent questionnaires, and structural MRI scan time. As the study included both cognitive testing (lasting about 30 to 40 minutes) and structural imaging to be scanned during the infant s naptime, families were instructed to wake their child a little earlier than normal, and to refrain from giving the child a 24

40 morning nap or falling asleep in the car ride to the hospital in order to maximize the chances of a successful scanning session. All travel, parking, and meal expenses were provided or reimbursed to the families for the day of the study. A total of 69 infant participants completed the cognitive battery and/or structural MRI. This total includes two infant participants of the same corrected age from a previous feasibility study (who completed the novel cognitive battery, but not an MRI). Of the 69 participants, 33 infants completed high-quality MRI scans. Reasons for not obtaining MRI scans included: 32 infants woke up during the transfer to scanner or while scanning, one infant had a PDA clip (discovered upon further pre-screening and medical record review) and was therefore, ineligible for the scan, and one participant declined the MRI portion of the study. Demographic and Neonatal Characteristics Demographics characteristics of the 69 infant participants are shown in Table 1. Of the total 69 infants, 40 were male (58%). The distribution of gestational ages among participants was as follows: six infants born between 22 and 27 weeks, 22 infants born between 27 and 32 weeks, 22 infants born between 32 and 36 weeks, and 17 infants born between 37 and 42 weeks. Due to the increased likelihood of visual impairments early in life for this high-risk population, retinopathy of prematurity (ROP) diagnoses were obtained from medical records. Of the 52 preterm participants, 26 had a history of ROP (at the worst stage diagnosed: 16 Stage 0, five Stage 1, three Stage 2, and two Stage 3). However, 24 of the 26 infants with a history of ROP were resolved by time of participation (two infants status is unknown due to no follow-up at UIHC). There were no significant differences between males and females in GA, chronological or corrected 25

41 age, birth weight, or head circumference at the time of participation (see Table 2); males were significantly taller (p=0.022) and weighed more (p=0.007) than females at the time of participation. Table 1. Demographic and Neonatal Characteristics of Infant Participants (N=69) Minimum Maximum Mean SD Gestational Age (GA), weeks Chronological Age, months Corrected Age, months Birth weight, grams Baby Height, inches Baby Weight, pounds Baby Head Circumference, inches Socioeconomic Status (SES) Table 2. Independent Samples T-test comparing Demographic and Neonatal Characteristics between Male (N=40) and Female (N=29) Infant Participants Males Females (n=40) (n=29) Mean (SD) Mean (SD) F P GA, weeks Chronological Age, months Corrected Age, months Birth weight, grams Baby Height, inches * Baby Weight, pounds * Baby Head Circumference, inches SES *Significant at p <.05 level Novel Cognitive Assessments All three novel cognitive tasks were presented to infants on a 40 SONY LCD TV monitor, viewable through an opening in a black occluding curtain. Experimenters administered appropriate stimuli (Adobe Director programs) using a 27 imac desktop 26

42 computer situated behind the curtain. During the experimental session, infants sat on their parent s lap ~100 centimeters (cm) in front of the TV monitor. Parents were instructed to close their eyes and refrain from talking to the infant or directing them in any way during the tasks. A trained experimenter sat out of sight of the infant (behind the curtains) and recorded infant eye movements (latency & direction) via live video feed. Videos of infants performing the three tasks were recorded on Sony camera capable of 60 frames per second and saved for offline coding (as necessary). Visual Working Memory (VWM) Task For this task, infants were presented with two simultaneous displays that blink on and off repetitively over a period of 20 seconds for each of six trials. These arrays consisted of 2, 4, or 6 colored squares (stimuli) presented on a gray background. The colors of the squares were selected from a set of eight highly discriminable values (red, green, cyan, white, yellow, black, brown, and blue). On one display, the colors of the squares remained constant from presentation to presentation, nonchanging display. On the other display, one color was changed in each new presentation, the changing display (the square that changed color on a given presentation was selected at random). The two displays appeared simultaneously for a brief period (500 ms) and were separated by a brief delay (300 ms), and continued appearing and disappearing in this manner for a total of 20s per trial. The set size (the number of squares on each display, e.g. 2, 4, or 6) was identical for the two displays and remains constant throughout a trial. The colors within a display were always different from each other, but colors could be repeated across the two displays. Infants completed 6 trials (of 20 seconds each), with two trials at each of the three set sizes, one with the changing display on the left and the nonchanging 27

43 display on the right, and one with the sides reversed. The order of trials was randomized across participants. A trained observer sat behind the curtain, initiated each trial with a button press, and recorded the looking time of infants fixations to each of the two displays. The observer was unaware of the set size or the side of the changing displays on each trial. Experimenter pressed a key once he/she judges that the infant is fixating the flashing red dot in the middle of the display, simultaneously turning off the central red fixation and initiating the changing and nonchanging displays. The experimenter recorded the duration of infants fixation to each of the displays by pressing two additional keys. Neither button was pressed when the infant was not looking toward either display. If an infant does not look at either display for the entire 20s, the trial was repeated. For a schematic representation of a trial for this task, see Figure 1. Trials in which the infant did not look for at least 5000 ms, as well as trials in which looking was only to one side, were excluded from analyses. 28

44 Figure 1. Schematic Representation of a trial used for novel VWM Task. NO CHANGE (left display) CHANGE (right display) 500 ms 300 ms seconds Trials for this task consisted of 2, 4, or 6 colored changing or nonchanging squares (six trials total). VWM Task Participants 46 infants successfully completed the VWM task (25 males, 21 females). Demographics of VWM Task participants are shown in Table 3. Table 3. Demographics of VWM Task Participants (N=46) Minimum Maximum Mean SD Gestational Age (GA), weeks Chronological Age, months Corrected Age, months Birth weight, grams Baby Height, inches Baby Weight, pounds Baby Head Circumference, inches Socioeconomic Status (SES)

45 Cognitive Performance Measures All performance measures for this task were calculated based on the amount of time infants spend looking at the changing and nonchanging displays on each trial: (1) Total Looking Time (TLT) average of total duration of looking on trials of the same set size; includes looking to both changing and nonchanging displays (i.e. does not include time NOT spent looking at either display). (2) Change Preference Score (CPS) - duration of looking to the changing display as a ratio of the total duration of looking to the two displays; e.g., a CPS score of 0.5 indicates that an infant looked equally to the changing and nonchanging displays, and a CPS score of 0.7 indicates that an infant looked at the changing display for 70% of the total looking duration for that trial. Equation 1. Change Preference Score (CPS) Computation: CPS = looking time to changing display TOTAL looking time to displays (3) Shift Rate 1 (SR1) - rate of gaze switching relative to looking time; e.g., higher shift rates indicate decreased memory being formed for the colors on the changing display Equation 2. Shift Rate 1 (SR1) Computation: SR1 = # of switches btwn displays TOTAL looking time to displays 1000 Hypothesis: Within a sample of 12 month-old infants born over a wide range of gestational ages (23-41 weeks), GA will predict performance: infants born later (i.e. older GA) will have better performance scores on the VWM task than infants born very 30

46 preterm (i.e. younger GA) at 12 months of corrected age (e.g. lower TLT, higher CPS, lower SR1 at larger set sizes). Infants Orienting With Attention (IOWA) Task This novel spatial cueing task consists of 120 trials of three different experimental conditions (i.e., valid, invalid, and double cue) and two baseline control conditions (i.e., audio tone only, no tone) (see Figure 2 for schematic representation of trials as published in Ross-Sheehy, et al ). Each experimental condition contained a 100 ms spatial pre-cue (small black dot measuring 1 degree in diameter) paired with a 50 Hz pure tone, either in same location as target (valid), contralateral to target (invalid), or on both sides (double); neither baseline control condition contained a spatial pre-cue: the tone condition contained only the 100ms audio tone, and the no cue condition contained neither a spatial nor tone cue. Spatial cues provided different experimental conditions to evaluate underlying attentional mechanisms in the infant, while auditory tone cues provided an alerting mechanism to prime the infant for a behavioral response. Targets in each condition consisted of colorful images of everyday objects (e.g., cheeseburger, mailbox, coffee mug, etc.) drawn randomly from a pool of 52 images, with novelty, total area, contour, chromatic and luminance contrast varied randomly across targets. Before each trial, a central fixation stimulus was presented at the center of monitor to attract the infant s attention to that location. Once the infant looks to central fixation, a button is pressed to end the fixation stimulus and initiate a 100ms pre-cue period, followed by a 100ms delay interval, then finally the presentation of the target to the left or right of fixation. The target remains visible until the infant makes an eye movement. Experimenter made speeded button presses indicating a look to either the left 31

47 or right, which simultaneously ends the trial, and re-initiates the central fixation stimulus. If an infant did not make an eye movement to the target within 2000ms of the target presentation, the trial was ended and repeated (beginning with the fixation stimulus). Figure 2. Schematic representation of experimental and baseline (control) conditions for the IOWA task (as published by Ross-Sheehy, et al. 2015). Infants are administered 120 trials containing all five conditions presented in random order. IOWA Task Participants 54 infants successfully completed the IOWA task (31 males, 23 females). Demographics of IOWA Task participants are shown in Table 4. Table 4. Demographics of IOWA Task Participants (N=54) Minimum Maximum Mean SD Gestational Age (GA), weeks Chronological Age, months Corrected Age, months Birth weight, grams

48 Baby Height, inches Baby Weight, pounds Baby Head Circumference, inches Socioeconomic Status (SES) Offline Frame-By-Frame (FBF) Coding Each Adobe Director output file from the IOWA task was pasted into Excel spreadsheet containing a FBF coding template provided by Dr. Shannon Ross-Sheehy for offline coding of the timing (reaction times [RT]) and direction (e.g., left or right) of the infant s first eye movement after the start of each trial. Responses were coded as correct if the infant s first eye movement is to the side of the display with the target; trials in which the infant s first look is not to the side of the target are coded as incorrect. Trials in which there was a blink or no perceivable look after the start of trial were deleted from analysis. RT (milliseconds) was calculated by counting the number of frames from the beginning of the target presentation to the first frame in which an infant eye movement to either left or right side stopped. Before analysis, looks that were deemed too fast (i.e., <100 ms) or too slow (i.e., >1000 ms) to be a reactive saccade were filtered out in order to ensure that all remaining trials were RTs in response to the appearance of a target and not the cue or other distracting elements, or off-task behavior. This pre-analysis filtering of RTs resulted in 784 trials of too fast saccades (13.3% trial loss) and 7 trials of too slow saccades (0.01% trial loss) to be excluded from analyses. 33

49 Reliability Offline FBF-coding was completed by two independent observers on 25% of subjects in order to provide intra-subject observer reliability, which revealed a correlation of between look frame number (Average Mean Difference of 2.837, SD 3.439). Cognitive Performance Measures Several unique composite visual orienting and attention scores were calculated for each infant, based on RTs and accuracy across all cue conditions, in order to facilitate comparisons. Each of the scores indicated with asterisk (*) include only correct trials in its computation. Each score allowed for different aspects of visual attention and orienting to be assessed, regardless of differences in overall speed between individual infants. Due to the difficulty in interpreting performance measures of RT and accuracy across multiple conditions, composite attention scores were created by normalizing to each infant s own baseline RT. While difference scores of raw RTs may provide information about absolute visual orienting, normalized difference composite attention scores (described below) allow for comparisons of spatial cueing effects above individual differences in raw orienting speed. (1) Visual Orienting Proficiency Scores a. Mean RT * average of RTs across all five conditions (i.e., a measure of accuracy); higher scores reflect slower eye movements to the target. Equation 3. Mean RT Computation: MeanRT = ValidRT + InvalidRT + DoubleRT + ToneRT + NoneRT 5 34

50 b. Mean Error Rate (MER) average of error rates across all five conditions (i.e., a measure of accuracy); higher scores indicate a higher sensitivity to the spatial cue, and thus increased amount of errors by the infant. Equation 4. Mean Error Rate (MER) Computation: MER = 1 Valid%Correct + Invalid%Correct + Double%Correct + Tone%Correct + None%Correct 5 (2) Attentional Proficiency Composite Scores a. Cue Facilitation* - proportion decrease in RT (i.e., increase in speed) for the valid cue condition relative to the baseline tone cue condition, normalized to mean RT (or tone RT); e.g., higher facilitation scores indicate a large facilitation effect (infant s RTs were substantially faster by the presence of an ipsilateral attentional pre-cue) Equation 5. Cue Facilitation Score Computation: Facilitation = ToneRT ValidRT MeanRT b. Cue Interference* - proportion increase in RT (i.e., decrease in speed) for the invalid cue condition relative to the baseline tone cue conditions, normalized to mean RT (or tone RT); e.g., higher interference scores indicate a large interference effect (infant s RTs were substantially slower by the presence of contralateral attentional pre-cue) Equation 6. Cue Interference Score Computation: Interference = InvalidRT ToneRT MeanRT c. Cue Sensitivity* - a general measure of how sensitive an infant is to visual cues, regardless of whether the impact is due to facilitation, interference, or 35

51 both; e.g., higher sensitivity scores indicate a greater impact of cue on infant s RTs. Equation 7. Cue Sensitivity Score Computation: Sensitivity = InvalidRT ValidRT MeanRT Hypothesis: Within a sample of 12 month-old infants born over a wide range of gestational ages (23-41 weeks), GA will predict performance: infants born later (i.e. older GA) will have better performance scores on the IOWA task than infants born very preterm (i.e. younger GA). Older GA infants will have visual orienting scores that reflect decreased mean RTs [faster] and decreased MERs [more accurate]; composite attention scores, will reflect spatial cueing effects, indicating decreased cue interference, cue sensitivity, with increased cue facilitation Encoding Speed Task (EST) In this continuous familiarization task (see Figure 3), infants were presented with pairs of stimuli simultaneously a familiar target is presented with a different novel one on each trial. Stimuli consisted of everyday objects (e.g. ball, car, cup, bear, etc.). The left-right placement of novel & familiar targets was randomized across trials, with the novel stimuli not appearing on the same side on more than two successive trials. Much as the administration of the VWM task, a trained experimenter sat behind the curtain, pressed a key once he/she judges that the infant is fixating the dancing star in the middle of the display, simultaneously turning off the star and initiating the paired stimuli display, and recorded the looking time of infants fixations to each of the two stimuli on the screen. Trials began with the first look to either of the paired targets and ended when the infant has accumulated four seconds of looking to the display. The experimenter recorded 36

52 the duration of infants fixation to each of the stimuli by pressing two additional keys. The observer was unaware of the left or right placement of the novel stimulus. Testing continued until criterion is met; criterion is defined as three out of four consecutive trials in which the infant spent >55% of the trial looking at the novel stimulus. Additionally, there had to be some looking directed toward both targets to ensure active comparison between them for a trial to be marked as completed. In the event that criterion was not met, the infant completed a maximum of 36 trials. Neither button was pressed when the infant was not looking toward either stimulus; if an infant does not look at either display for the entire four seconds, the trial is repeated. Figure 3. Schematic representation of three example trials of the EST (36 trials total). Central Attention Getter (start of task & between trials) Trial 1 Trial 2... Trial 3 Trials for this task consisted of two stimuli presented simultaneously for four seconds: a familiar stimulus (i.e. cat) paired with a novel stimulus (i.e. banana, polar bear, spaceship, etc.). 37

53 EST Participants 41 infants successfully completed the EST (19 males, 22 female). Demographics of EST participants are shown in Table 5. Table 5. Demographics of EST Participants (N=41) Minimum Maximum Mean SD Gestational Age (GA), weeks Chronological Age, months Corrected Age, months Birth weight, grams Baby Height, inches Baby Weight, pounds Baby Head Circumference, inches Socioeconomic Status (SES) Cognitive Performance Measures (1) Trials to Criterion the number of trials (out of 36) taken to reach criterion; e.g., higher trials to criterion indicates a slower processing speed to have a preference to the novel stimuli. (2) Novelty Score (NS) amount of time looking to the novel item averaged across trials completed; e.g., higher novelty score indicates faster processing speed of the novel stimuli. (3) Shift Rate 2 (SR2) - rate of gaze switching relative to total looking time; e.g., higher shift rates indicate active comparison of the two stimuli, and thus, faster encoding speed (NOTE: this is converse of SR1 in VWM task). Equation 8. Shift Rate 2 (SR2) Computation SR2 = # of shifts between stimuli TOTAL looking time = 4s 38

54 (4) Average Look Duration (ALD) the average duration of looks on each trial (averaged over trials); e.g., higher average look durations indicate less comparison of the two stimuli, and thus, faster encoding speed (NOTE: this is inverse of SR2 above). Hypothesis: Within a sample of 12 month-old infants born over a wide range of gestational ages (23-41 weeks), GA will predict performance: infants born later (i.e. older GA) will have better performance scores on the EST than infants born very preterm (i.e. younger GA) at 12 months of corrected age (e.g., less number of trials to reach criterion, increased novelty scores, higher shift rates, and decreased ALD). Transformation of Performance Variables In order to simplify interpretation, some performance scores for all three cognitive tasks were transformed to ensure that a higher score indicated a better performance (by multiplying the score times -1 for those scores in which a higher score meant poorer performance). The following variables were transformed in this way: total looking times (TLT) and shift rate (SR1) for the VWM task, all five measures for the IOWA task, and Trials to Criterion and Average Look Duration (ALD) for EST. Structural Magnetic Resonance Imaging (MRI) MRI Scanning of Infants Due to the practical difficulty of imaging infants (that often move), as well as difficulty in processing images for research purposes, the Nopoulos lab collaborated with neonatal and infant imaging experts at the University of North Carolina (including consultant John Gilmore) and Brown University (including consultants Sean Deoni and 39

55 Holly Dirks). These colleagues have successfully imaged over 500 neonates, as well as obtained follow-up scans for one- and two-year old infants. With their guidance, an image acquisition protocol has been developed for structural imaging in the infant here at the University of Iowa. In order to reduce risks to the children, no sedation was used. The protocol used for this study is based on procedures practiced and perfected by the research nurses at both UNC and Brown through many years of practice and attention to detail, and involved the following steps: (1) child is brought in at naptime, (2) mother put baby to sleep by either rocking to sleep in the scanner room, or allowing the child to fall asleep in a Pack N Play in a separate rom, (3) mother gently placed child in scanner (if rocked to sleep) or infant is buckled into immobilizer apparatus within a Pack N Play and subsequently transferred on an MRI compatible cart to the scanner room (see Dean, D.C., et al 2014 for more information on this protocol and pictures of setup 68 ), (4) electrodynamic headphones were placed over the child s ears, and memory foam cushions were used to secure head positioning, and (5) images were obtained while the infant was asleep. Total scan time was approximately 30 minutes. MR Image Acquisition All MRI data were acquired on a 3T Siemens Trio scanner (Siemens, Erlangen, Germany). The protocol acquired a 3-dimensional T1-weighted magnetization-prepared rapid-acquisition gradient-echo sequence in the coronal plane with 1-mm isotropic resolution. A turbo spin-echo T2-weighted sequence was obtained in the coronal plane with 1-mm isotropic resolution. 40

56 Automated Pipeline for Tissue Classification and Quantitative Volumetric Data To provide unbiased quantitative volumetric data for structural analysis, an automated atlas-based segmentation approach to tissue classification was implemented. The automated pipeline consisted of an initial registration to bring the atlas into subjectspecific coordinates, tissue classification was done using an expectation maximization (EM) algorithm followed by a level set segmentation developed to enhance white/gray matter differentiation in the cerebral cortex, and isolation of results to functionally specific regions for analysis. With the exception of the level set segmentation, all programs used for the pipeline are available through the open source software suite BRAINSTools 69. An age appropriate atlas was developed to account for the spatial likelihood of structural morphology specific to the stage of development of interest. The atlas was comprised of eight manually corrected tissue maps of one-year-old subjects taken randomly from the sample population. The pipeline was applied to the images from each subject with successful T1- and T2-weighted magnetic resonance scans. Quantitative volumetric data was obtained by examining tissue measurements within the entire head as well as within functionally distinct regions (e.g., the temporal lobe) determined by Talairach atlas space 70. Initial Registration To apply the spatial likelihoods of each tissue type contained in the atlas to a subject, the atlas and the subject must be registered to a common space. The accuracy of this registration is one of the most important factors in determining the accuracy of tissue classification. A stepwise process of incrementally improving registration was utilized. 41

57 Each subject was first aligned in AC-PC space, where the origin is defined as the center point of the anterior commissure (AC) and the anterior to posterior axis runs through the posterior commissure (PC) and lies in the mid sagittal plane (MSP). This was done with BRAINSConstellationDetector, a program which automatically identifies predefined anatomical landmarks of interest 71. After AC-PC alignment, the atlas was registered to the subject using an affine transformation optimized for Mattes mutual information metric. This was done by comparing the T1-weighted subject image to a T1 reference image contained in the atlas. The results of the affine transformation were applied to the atlas as the input for a more accurate, high-dimensional registration, symmetric image normalization (SyN) registration 72. The results from each registration were concatenated to obtain a combined transform. Tissue Classification The tool used for primary tissue classification was BRAINSABC 73. BRAINSABC utilizes an EM algorithm for segmentation and iteratively improves bias correction, tissue classification, and atlas registration. The algorithm was given the atlas in subject space as well as T1- and T2-weighted images. Multimodal information greatly enhances segmentation results. Due to the large variability of distal cortical white and gray matter structure, the spatial priors included in the atlas were not sufficient for obtaining satisfactory segmentations in those areas. To improve segmentation a level set segmentation method was developed. An approach optimal for images with the intensity inhomogeneity commonly found in MRI was adapted for three-dimensional volumes and specified for 42

58 white/gray matter differentiation 74. The level set method was initialized with results from the EM algorithm and the distal cortical region of the resulting labels were used to the correct the original label map. Neuroimaging Participants Of the 33 infant participants with successful MRI scans, nine infant s data were excluded from analyses due to: unsuccessful initial registration in the automated pipeline (six infants), unsuccessful AC-PC alignment (one infant), bad initial T1 & T2 scans (one infant), and undersegmentation of cerebellum & putamen with oversegmentation of cortical white matter (one infant). Demographics of the remaining 24 infants with neuroimaging data are shown in Table 6. Table 6. Demographics of Participants with Neuroimaging Data (N=24) Minimum Maximum Mean SD Gestational Age (GA), weeks Chronological Age, months Corrected Age, months Birth weight, grams Baby Height, inches± Baby Weight, pounds± Baby Head Circumference, inches* Socioeconomic Status (SES) Structural Measures of Interest Volumes of gray matter (GM), white matter (WM), cerebrospinal fluid, and blood were generated for each of the following regions of interest (ROI): total intracranial, cerebrum, cerebellum, striatum (consisting of caudate and putamen), globus pallidus, thalamus, and hippocampus, and all cerebral four lobes (frontal, occipital, parietal, temporal). One additional brain variable that was computed was a cortex volume variable (consisting of GM of all four lobes only). 43

59 Statistical Analysis All analyses were performed by using SPSS Statistics (IBM Corp. Released IBM SPSS Statistics for Mac, Version Armonk, NY: IBM Corp). Cognitive Performance Measures Analyses Gestational age (GA) was used as a continuous measure and linear regression analyses were used to directly assess the relationship between GA and all cognitive performance measures for the IOWA, VWM, and EST. A two-tailed p value was used. Main effects of sex and chronological age were explored and if these factors had a significant effect on the cognitive/brain measure, they were kept in the model as covariates. If they had no effect on the cognitive/brain measure, they were removed from the model. In follow-up to the linear regression analyses, post-hoc extreme group analyses were performed using an analysis of variance (ANOVA) procedure to directly compare the early GA to later GA infants for each significant performance measure. Extreme groups were constructed by ranking the sample on GA and then assigning the top quartile of subjects (those with the highest GA) to the later GA and the bottom quartile of subjects to the early GA group. To avoid a type II error, analyses were limited to the measures that were significantly predicted by GA. Brain Structure Measures Analyses for all brain measures obtained from the structural MRI automated pipeline for tissue classification and quantitative volumetric data (see above) were parallel to those for the cognitive tests: gestational age (GA) was used as a continuous measure and linear regression analyses were used to directly assess the relationship 44

60 between GA and brain measures. A two-tailed p value was used. Main effects of sex and chronological age were explored and if these factors had a significant effect on the cognitive/brain measure, they were kept in the model as covariates. If they had no effect on the cognitive/brain measure, they were removed from the model. Intracranial Volume (ICV) is a measure that represents tissue within the cranium. In order to control for total brain size, all regional brain measures (cerebrum, cerebral gray, cerebral white, cerebellum, striatum, globus pallidus, thalamus, and hippocampus) and regional GM volumes (including frontal, temporal, parietal, and occipital lobes) were adjusted for ICV by using a brain measure:icv ratio. In follow-up to the linear regression analyses, post-hoc extreme group analyses were performed using an analysis of variance (ANOVA) procedure to directly compare the early GA to later GA infants for each significant brain measure. Extreme groups were constructed by ranking the sample on GA and then assigning the top quartile of subjects (those with the highest GA) to the later GA and the bottom quartile of subjects to the early GA group. Structure-Function Analyses All brain measures obtained from the structural MRI automated pipeline for tissue classification and quantitative volumetric data (see above) were used as continuous measures of brain volume morphology and linear regressions were used to assess their relationship with cognitive performance measures of the VWM task, IOWA task, and EST. In order to minimize the number of tests, structure-function analyses were done only for the brain structural areas and cognitive performance measures found to be significantly predicted by GA on each of the previous regression analyses. 45

61 In follow-up to the structure-function linear regression analyses, post-hoc median split analyses were performed using an analysis of variance (ANOVA) procedure to directly compare the brain structure of infants that performed significantly different from each other on those measures of interest from each cognitive task. To avoid a type II error, analyses were limited to the measures that were significantly predicted by GA. 46

62 CHAPTER 7: RESULTS Main Effects of Sex and Age: All cognitive and brain measures were evaluated for main effecrs of sex and corrected age (age at the time of testing). For the cognitive measures (VWM, IOWA, EST), there were no significant main effects of sex or age on any variables. Therefore these variables were not included in the analysis. For the brain variables, the only measure with a significant main effect of sex was ICV (F(1,22)=4.609, p=0.043) with males having larger volumes. Therefore, for the linear regression model, sex was included for the assessment of ICV. VWM Table 7 shows results of linear regression analysis for VWM task performance measures with GA. Total Looking Time (TLT) at all set sizes were predicted by GA, reaching significance for set size 2 [SS2] (β= , p=0.029) and set size 6 [SS6] (β = , p=0.037). Infants with the lowest GA had the longest total looking times at those set sizes. Figures 4 and 5 show this relationship between GA and TLT for SS2 and SS6, respectively. CPS and shift rates were not significantly predicted by GA at any set size in this sample of infants. A post-hoc extreme group analysis of TLT at SS2 and SS6 confirmed these findings (see Table 8 for group compositions and Figure 6 for ANOVA); later GA infants had significantly decreased looking times at both set sizes as compared to infants born at an earlier GA. As the significant group difference was observed for these two measures, they were used in structure-function analyses. 47

63 Table 7. VWM Task Performance: Linear Regression Analysis with GA β (s.e.) Sig. (2-tailed) Total Looking Time (TLT) Set Size 2 (SS2) (85.542) Set Size 4 (SS4) ( ) Set Size 6 (SS6) (86.826) Change Preference Score (CPS) SS (0.004) SS (0.005) SS (0.004) Shift Rate (SR1) SS (0.006) SS (0.008) SS (0.008) Figure 4. Relationship between Gestational Age and Total Looking Time (SS2). 48

64 Figure 5. Relationship between Gestational Age and Total Looking Time (SS6). 49

65 Table 8. VWM Task Performance: Composition of Extreme Groups GA Mean (sd) GA Minimum GA Maximum Early GA (n = 11) (2.23) Late GA (n = 11) (0.94) Figure 6. VWM Task: Extreme Group Comparison (Total Looking Times). Total Looking Time (ms) - Transformed SS2_TLT* SS6_TLT** Early GA Late GA * ANOVA F = 5.63; p = **ANOVA F = 4.44; p = IOWA Figure 7 shows mean reaction times (RTs) by cue condition and proportion correct for each cue condition as measures of group accuracy. Participants were fastest in the condition which contained a valid spatial cue, and slowest for the invalid cue condition, with the two baseline control conditions (tone and no tone) and double cue conditions having intermediate RTs (7a). Also, as expected, overall percent correct was 50

66 highest for conditions that contained either a valid spatial cue or no spatial cue (two baseline control conditions), and lowest for the condition with an invalid spatial cue, with the double cue condition having an intermediate proportion correct (7b). Table 9 shows results of regression analyses for IOWA task performance measures with GA for all participants. Mean Error Rate (MER) was significantly predicted by GA (β=0.001, p=0.015), with older GA making less errors (Figure 8 shows the quadratic relationship between GA and MER). In terms of attentional proficiency scores, GA predicted both cue facilitation (β=0.0020, p=0.049) and cue sensitivity (β=0.0040, p=0.018). Figures 9 and 10 show the quadratic relationship between GA and cue facilitation and cue sensitivity, respectively. Because of this non-linear relationship for multiple measures, regressions were then performed using only the preterm infants in the sample (results are shown in Table 10). Again, performance on these three measures were confirmed and the effect of GA was even stronger: GA significantly predicted MER (β=0.010, p=0.003), cue facilitation (β=0.018, p=0.042), and cue sensitivity (β=0.031, p=0.016). Figures show the relationship between GA and each of these measures, respectively, in the sample of preterm infants only. In summary, these results demonstrate that infants born at later GA made fewer errors, were less facilitated by the cue in the valid conditions, and less sensitive to the cue in general (i.e., regardless of whether it was cue interference or cue facilitation). Post-hoc extreme group analysis of these three measures confirmed these findings (see Table 11 for group compositions and Figures 14 and 15 for ANOVA results); later GA infants had significantly decreased mean error rates and significantly decreased cue sensitivity as compared to infants born at 51

67 an earlier GA. These three significant IOWA performance measures were used in structure-function analyses. Figure 7. IOWA Task: Group Accuracy Measures. (a) Average Reaction Times (correct trials only) for each Cue Condition 52

68 (b) Proportion correct (all trials) for each Cue Condition Table 9. IOWA Task Performance: Linear Regression Analysis with GA (All Participants; N=54) β (s.e.) Sig. (2-tailed) Visual Orienting Proficiency Mean RT (0.995) Mean Error Rate (MER) (0.0004)* Attentional Proficiency Composite Scores Cue Facilitation (0.001)* Cue Interference (0.007) Cue Sensitivity (0.002)* *Quadratic model is best fit 53

69 Figure 8. Relationship between Gestational Age and Mean Error Rate (Full Sample). 54

70 Figure 9. Relationship between Gestational Age and Cue Facilitation (Full Sample). 55

71 Figure 10. Relationship between Gestational Age and Cue Sensitivity (Full Sample). 56

72 Table 10. IOWA Task Performance: Linear Regression Analysis with GA (Preterm Only; N=41) β (s.e.) Sig. (2-tailed) Visual Orienting Proficiency Mean RT (1.601) Mean Error Rate (MER) (0.003) Attentional Proficiency Composite Scores Cue Facilitation (0.009)* Cue Interference (0.010) Cue Sensitivity (0.012)* Figure 11. Relationship between Gestational Age and Mean Error Rate (Preterm Only). 57

73 Figure 12. Relationship between Gestational Age and Cue Facilitation (Preterm Only). 58

74 Figure 13. Relationship between Gestational Age and Cue Sensitivity (Preterm Only). 59

75 Table 11. IOWA Task Performance: Composition of Extreme Group Analysis GA Mean (sd) GA Minimum GA Maximum Early Preterm (n = 10) (2.06) Late Preterm (n = 10) (0.62) Figure 14. IOWA Task: Extreme Group Comparison (Mean Error Rate). 0 Mean Error Rate - Transformed Early Preterm Late Preterm -0.3 *ANOVA; F = 8.687, p =

76 Figure 15. IOWA Task: Extreme Group Comparison (Cue Facilitation and Cue Sensitivity) Facilitation* Cue Sensitivity** Attention Scores - Transformed Early Preterm Late Preterm *ANOVA; F=2.54, p = 0.12 **ANOVA; F = 5.24, p =

77 EST Table 12 shows results of linear regression analysis for EST performance measures with GA. As hypothesized, both the number of trials taken to reach criterion (β=0.617, p=0.048) and novelty score (β=0.009, p=0.001) were significantly predicted by GA in this sample of infants, shown in Figures 16 and 17, respectively. Shift rate (SR2) and average look duration (ALD) did not reach significance, though both measures were in expected correlational directions. Post-hoc extreme group ANOVA analyses confirmed these findings, with novelty score reaching significance for group differences (see Table 13 for group compositions and Figures 18 and 19 for ANOVA results). Taken together, these results confirm that infants born at later GAs had less trials taken to reach criterion, as well as increased novelty scores, than infants born early preterm. Therefore, only these two measures were used in structure-function analyses. Table 12. EST Performance: Linear Regression Analysis with GA β (s.e.) Sig. (2-tailed) Trial Criterion Met On (0.303) Novelty Score (0.002) Shift Rate (SR2) (0.004) Average Look Duration (ALD) (7.389)

78 Figure 16. Relationship between Gestational Age and Number of Trials to Criterion. 63

79 Figure 17. Relationship between Gestational Age and Novelty Score. 64

80 Table 13. EST Performance: Composition of Extreme Group Analysis GA Mean (sd) GA Minimum GA Maximum Early GA (n = 11) (2.37) Late GA (n = 10) (2.31) Figure 18. EST: Extreme Group Comparison (Number of Trials to Criterion). Trials To Criterion (Transformed)* Early GA Late GA *ANOVA F = 2.897; p =

81 Figure 19. EST: Extreme Group Comparison (Novelty Score) Novelty Score* Early GA Late GA *ANOVA F = 9.048; p =

82 Brain Structure Table 14 shows the results of linear regression analyses for all brain measures. While ICV was not significantly predicted by GA (when controlling for sex), overall cerebral GM volume (β=0.328, p=0.001) and cortex volume (β=0.002, p=0.005) were found to be significantly predicted by GA; specifically, GA also predicted occipital lobe GM (β=0.107, p=0.000) and temporal lobe GM (β=0.083, p=0.009). Infants born at an older GA had increased volumes in these areas. Additionally, GA significantly predicted two subcortical structures: striatum (β=0.021, p=0.011) and thalamus (β=0.008, p=0.038). Again, older GA infants had increased volumes in these brain areas. Post-hoc extreme group ANOVA analyses confirmed these findings (see Table 15 for group compositions, and Figure 20 and Table 16 for ANOVA results). Group comparisons showed that there were significant differences in each area, except striatum (which approached significance). These results confirm that infants born at a later GA had increased volumes in multiple GM measures, and two subcortical areas. Therefore, regional GM of occipital and temporal lobes and the two subcortical measures were used in structurefunction analyses. 67

83 Table 14. Effects of GA on Brain Measures: Linear Regression Analysis β (s.e.) Sig. (2-tailed) Intracranial Volume (ICV) -1, (4, ) Cerebellum (0.040) Cerebrum (0.021) Cerebrum White Matter (0.059) Cerebrum Gray matter (0.092) Cortex (0.000) Frontal Lobe Cortex (0.051) Parietal Lobe Cortex (0.046) Temporal Lobe Cortex (0.027) Occipital Lobe Cortex (0.029) Sub-Cortical Structures Striatum (0.008) Thalamus (0.003) Globus Pallidus (0.002) Hippocampus (0.006)

84 Table 15. Composition of Extreme Group Analysis for Neuroimaging Data (N=24) GA Mean (sd) GA Minimum GA Maximum Early Preterm (n = 6) (2.04) Late Preterm (n = 8) (0.82) Figure 20. Neuroimaging Extreme Group Comparison. 0.6 Z Scores of Brain Variables Early Preterm Late Preterm -0.8 Table 16. Neuroimaging: Results of Extreme Group Analysis using ANOVA F (se) p Cerebral Gray 6.78 (2.16) 0.02 Cortex 4.50 (0.02) 0.05 Temporal Gray 7.67 (0.85) 0.01 Occipital Gray 5.57 (0.74) 0.03 Striatum 3.89 (0.23) 0.07 Thalamus 5.28 (0.07)

85 Structure-Function Correlations VWM Task Tables 17 and 18 show the two significant cognitive performance measures from the VWM task (total looking time [TLT] at set size 2 [SS2] and TLT at set size 6 [SS6]) and their correlations with brain volumetric measures, respectively. TLT at SS2 was not significantly predicted by any brain volume. TLT at SS6 was significantly predicted by occipital GM (β=0.178 x 10-3, p=0.024): the lower the GM in the occipital lobe, the lower the TLT at this hardest set size. Post-hoc median split ANOVA analyses of high looking time versus low looking time infants confirmed these findings, though did not reach significance (see Table 19 for group compositions, and Figure 21 for ANOVA results of occipital lobe GM). This group comparison demonstrated that the difference in TLT at the hardest set size (SS6) may be directly related to differences in occipital lobe GM: infants with high looking time had decreased volumes of occipital GM as compared to short looking time infants. Table 17. VWM Task: Structure/Function Relationship (Total Looking Time [SS2]; N=12) β (s.e.) p Temporal Gray x 10-4 (0.824 x 10-4 ) Occipital Gray x 10-3 (0.684 x 10-4 ) Striatum x 10-6 (0.240 x 10-6 ) Thalamus x 10-7 (0.900 x 10-7 ) Table 18. VWM Task: Structure/Function Relationship (Total Looking Time [SS6]; N=12) β (s.e.) p Temporal Gray x 10-4 (0.977 x 10-4 ) Occipital Gray x 10-3 (0.676 x 10-4 ) Striatum x 10-6 (0.260 x 10-6 ) Thalamus x 10-6 (0.900 x 10-7 )

86 Table 19. VWM Task: Composition of Median Split Group Analysis (N=12) for Neuroimaging Data SS6 TLT Mean (sd) SS6 TLT Minimum SS6 TLT Maximum High Looking Time (n = 6) (1530) Low Looking Time (n = 6) (2146) Figure 21. VWM Task: Median Split Group Comparison (SS6 TLT). Volume of Occipital Gray matter (x10 3 cc) Occipital Gray* Long Looking Time Short Looking Time *ANOVA, F = 1.39 (0.834), p =

87 IOWA Task Tables show the three significant cognitive performance measures from the IOWA task (mean error rate [MER], cue facilitation, cue sensitivity) and their correlations with brain volumetric measures, respectively. MER was not significantly predicted by any brain area. Cue facilitation was significantly predicted by thalamic volume (β= p=0.015): the smaller the thalamus, the higher the cue facilitation scores. Cue sensitivity was significantly predicted by temporal lobe GM (β=10.797, p=0.028) and thalamic volume (β=90.648, p=0.017): the smaller the volumes of temporal lobe GM and thalamus, the higher the cue sensitivity scores. Post-hoc median split ANOVA analyses of infants with high cue sensitivity versus low cue sensitivity confirmed these findings, with thalamic volume difference reaching significance in group comparisons (see Table 23 for group compositions, and Figure 22 for ANOVA with brain measures). This group comparison demonstrated that the difference in cue sensitivity may be directly related to differences in temporal lobe GM and thalamus: infants with high cue sensitivity had decreased volumes of temporal lobe GM and thalamus as compared to low cue sensitivity infants. Table 20. IOWA Task: Structure/Function Relationship (Mean Error Rate; N=18) β (s.e.) p Temporal Gray ( ) Occipital Gray (201.06) Striatum ( ) Thalamus ( )

88 Table 21. IOWA Task: Structure/Function Relationship (Cue Facilitation; N=18) β (s.e.) p Temporal Gray (6.043) Occipital Gray (6.368) Striatum (21.151) Thalamus (42.843) Table 22. IOWA Task: Structure/Function Relationship (Cue Sensitivity; N=18) β (s.e.) p Temporal Gray (4.491) Occipital Gray (4.998) 0529 Striatum (17.160) Thalamus (34.360) Table 23. IOWA Task: Composition of Median Split Group Analysis (N= 18) for Neuroimaging Data Cue Sensitivity Mean (sd) Cue Sensitivity Minimum Cue Sensitivity Maximum High Cue Sensitivity (n = 9) (0.086) Low Cue Sensitivity (n = 9) (0.080)

89 Figure 22. IOWA Task: Median Split Group Comparison (Cue Sensitivity) Z Scores of Brain Variables Cue Sensitive Cue Insensitive Temporal Gray* Thalamus** *ANOVA, F = 2.40, p = **ANOVA, F = 6.74, p =

90 Encoding Speed Task (EST) Tables 24 and 25 show the two significant cognitive performance measures from the EST task (number of trials to criterion, novelty score) and their correlations with brain volumetric measures, respectively. Trials to criterion was significantly predicted by four brain measures (occipital lobe GM [β= , p=0.000]; temporal lobe GM [β= , p=0.049]; striatum [β= , p=0.000]; thalamus [β= , p=0.024]): the smaller the volumes, the more trials taken to reach criterion (i.e., the slower to encode). Additionally, striatal volume was significantly predictive of novelty score (β=21.839, p=0.045); that is, increased volume of the striatum correlated with increased novelty scores (i.e., faster encoding speed). Post-hoc median split ANOVA analyses of infants with high number of trials to criterion (or slow encoders) versus low number of trials to criterion (or fast encoders) showed that temporal GM was the most predictive of performance on EST, though not reaching significance in group comparisons (see Table 26 for group compositions, and Figure 23 and Table 27 for ANOVA results with brain measures). This group comparison demonstrated that the difference in encoding speed (as measured by trials to criterion) may be directly related to differences in temporal lobe GM: infants with high number of trials to criterion had decreased volumes of temporal lobe GM, occipital lobe GM, striatum, and thalamus as compared to infants with low number of trials to criterion. Table 24. EST: Structure/Function Relationship (Number of Trials to Criterion, N=13) β (s.e.) p Temporal Gray ( ) Occipital Gray ( ) Striatum ( ) Thalamus ( )

91 Table 25. EST: Structure/Function Relationship (Novelty Score; N=13) β (s.e.) p Temporal Gray (2.973) Occipital Gray (3.051) Striatum (9.672) Thalamus (40.571) Table 26. EST: Composition of Median Split Group Analysis (N=13) for Neuroimaging Data Trials to Criterion Mean (sd) Trials to Criterion Minimum Trials to Criterion Maximum High Trials to Criterion (n = 6), or slow (10.65) 4 6 Low Trials to Criterion (n = 7), or fast 5.29 (0.95)

92 Figure 23. EST: Median Split Group Comparison (Number of Trials to Criterion). 0.6 Z Scores of Brain Variables Slow Fast -0.6 Temporal Gray Occipital Gray Striatum Thalamus Table 27. EST: Results of Median Split Group Analysis using ANOVA (Number of Trials to Criterion) F (se) p Temporal Gray 3.78 (0.007) 0.07 Occipital Gray 1.20 (0.008) 0.29 Striatum 2.74 (0.002) 0.12 Thalamus 1.33 (0.000)

93 CHAPTER 8: DISCUSSION Over the past few decades, the study of infant cognition and development early in life and into childhood and later has demonstrated multiple areas of risk for infants born preterm, particularly deficits in memory, attention, processing speed, and later executive functioning. In the realm of early cognitive developmental measurements in the pediatric clinic, there currently lacks neurodevelopmental assessments available for high-risk and preterm infants with the ability to provide clinically useful information relating to early development of these functions. Three novel cognitive assessments were evaluated in a sample of preterm and healthy full term infants across a wide GA at the age of 12 months old to gain a better understanding of specific cognitive outcomes and differences early in a preterm infant s life. Results from the novel VWM task revealed that infants born at early GA had longer total looking times at set sizes 2 and 6, but no differences in change preference score (CPS) or shift rate. This finding suggest that even with equal VWM performance (in terms of CPS), infants born at an older GA may be more efficient (i.e., their decreased TLT at SS2 and SS6 indicates they did it in less time). These results are in line with systematic research on look durations that has found that longer fixation durations may be associated with the need for longer stimulus exposure in order to encode visual stimuli 75. That is, short lookers may have better visual memory than long lookers. Decreased total looking time at the hardest set size was directly correlated with increased GM in the occipital lobe (a brain area that was also shown to have significant gestational age effects in our sample). Therefore, while overall performance in CPS was 78

94 not significantly predicted by GA at 12 months old, differential VWM ability may still be elucidated by this task. Cognitive performance results from the IOWA task in this study offer a novel approach to the study of attention in infancy. In particular, results from this task present very detailed information about several functionally distinct aspects of attention as they emerge within the first year of life in a high risk infant population: the development of covert attention, planning of eye movements, visual competition, and orienting speed. Because the IOWA task was designed to provide a robust evaluation of attention at the level of an individual infant, its composite attention scores allow for direct comparisons between infants, regardless of average orienting and speed differences. Group accuracy measures (mean RTs and proportion correct) demonstrated that our sample of 12-monthold infants were performing as expected on each of the cue conditions and showed evidence of spatial cueing effects: faster eye movements to the valid cue condition (slower to invalid and double cue conditions) and higher proportion of correct trials to conditions with either a valid cue or no spatial cue. GA significantly predicted multiple measures of visual orienting and attentional proficiency (including MER, Cue Facilitation, and Cue Sensitivity), and these effects were even more pronounced in an only preterm infant sample. That is, older preterm infants had decreased error rates (i.e., improved visual orienting accuracy) and decreased attention composite scores. These findings are consistent with preliminary analyses of the IOWA task in a preterm sample, in which attentional scores were significantly decreased in a low risk (later GA) infant group at 10 months of age 76. Taken together, these results suggest that preterm infants born at an older GA have improved visual orienting accuracy, as exemplified by a 79

95 decreased MER, while also exhibiting decreased spatial cueing effects on all attention measures, as exemplified by significantly decreased measures of attention. Older GA preterm infants were less sensitive to the spatial cues, regardless of whether the cue elicited interference or facilitation to the target, than early GA preterm infants. Results of brain structure analyses demonstrated that differences in cue sensitivity may be directly related to temporal lobe GM and thalamic volumes; that is, lower cue sensitivity scores correlated with increased temporal lobe GM and thalamic volumes. This may have important implications given findings of thalamic involvement in the early alertness aspect of attention, and its later association to engagement of locations in space and filtering out of irrelevant information in the visual field with increasing synaptic connections to extrastriate visual cortices 67,77,78. Temporal lobe areas have also been implicated in the shifting of focus of attention from a cued location to a target when the target is miscued 67. In summary, these performance results lend further support for the use of IOWA task to study individual differences in attention and perhaps, later functional outcomes. Finally, in the novel EST, two discrete measures of processing speed were directly correlated with GA in 12-month-old infants. Older GA infants had decreased number of trials taken to reach criterion and increased novelty scores (i.e., those infants born earlier exhibited slower processing speeds of novel interesting stimuli). While two additional measures of encoding speed were not significantly predicted by GA in this sample, they were in the hypothesized correlational directions. Results of structurefunction studies demonstrated that improved measures of processing speed were significantly correlated with volumes of multiple cortical and subcortical areas, including 80

96 cerebral GM, cortex (and specifically temporal and occipital lobe GM), striatum, and thalamus; decreased volumes in these areas correlated with worse performance on the EST. Consistent with the concept of processing speed as a central limiting factor in multiple areas of executive function, the findings from this novel EST demonstrate that: (1) differences in processing speed abilities may be ascertained very early in life, and (2) deficits in processing speed may be correlated with multiple subcortical and cerebral brain areas. These results may be among the first evidence of neurodevelopmental deficits associated with processing speed differences in infants born preterm. There are a number of limitations inherent to the present study. First, the aim of this study was to provide further insight into specific early cognitive function in preterm infants (at 12 months old corrected age) using novel cognitive paradigms, as well as to directly examine the concomitant neural correlates of these early developmental cognitive abilities. The infant sample population chosen for this study was one in which there was a large variance in GA in order to directly examine the effect of GA on brain function and structure. However, because healthy, full-term 12-month-old infants were not included, the results of this study may not provide definitive conclusions on early cognitive function and brain development differences as related to prematurity versus normal full-term development. Further studies are warranted to address this question. Another limitation to the present study is the small number of infants with neuroimaging data (N=24). Even within this sample, however, the results of linear regression analyses for the quantitative volumetric brain measures demonstrated that GA significantly predicted a number of GM and subcortical volumes, with two-tailed p values < Ongoing work to optimize the automated atlas-based segmentation approach to tissue 81

97 classification will provide further quantitative volumetric data processing for structural analysis. There are still many questions to be addressed and significant areas of future investigation in cognitive neuroscience for a complete understanding of VWM, attention, and processing speed throughout development, both early life and as they relate to later adult cognitive processes. First, much of the literature of infant cognition is focused on visual information, but the extension of those processes to other sensory modalities will be critical, as many adult paradigms actually utilize STM and attention to auditory information). Secondly, an emerging issue in the literature is the development of appropriate tasks that increase our understanding of how early manifestations of cognitive functions in infancy are related to later functioning in adulthood; many of these paradigms have been utilized within the first year of life, but the extension of how these systems develop beyond and as a precursor to similar cognitive systems in childhood is necessary. Finally, and possibly most critically, there remains the need for a deeper understanding of the mechanism(s) behind the developmental changes observed in infancy 30. While there are a number of practical difficulties in evaluating early cognitive abilities with appropriate paradigms in these infants, in addition to the many inherent challenges for administration of these tasks (while effectively controlling for all distracting environmental factors to the infant, particularly auditory or visual stimuli), results from a small sample of infants using these novel assessments lend support to the use of these three tasks as sensitive to detect changes in performance based on gestational age. Continued investigations using more precise and direct evaluations of the 82

98 underlying brain structural correlates of memory, attention, and processing speed tasks through the use of multi-modality structural and functional brain imaging currently available will provide a deeper understanding of early brain-behavior relationships during this critical period of dynamic maturation in the infant. 83

99 APPENDIX Supplementary Figure 1. Basal Ganglia Mask 84

100 Supplementary Figure 2. Brain Mask 85

101 Supplementary Figure 3. Brainstem Mask 86

102 Supplementary Figure 4. Cerebellum Mask 87

103 Supplementary Figure 5. Cerebrum Mask 88

104 Supplementary Figure 6. Cerebrum2 Mask 89

105 Supplementary Figure 7. Cerebrum3 Mask 90

106 Supplementary Figure 8. Cerebrum4 Mask 91

107 Supplementary Figure 9. Cran Mask 92

108 Supplementary Figure 10. Frontal Lobe Mask 93

109 Supplementary Figure 11. Hippocampal Mask 94

110 Supplementary Figure 12. Occipital Lobe Mask 95

111 Supplementary Figure 13. Parietal Lobe Mask 96

112 Supplementary Figure 14. Temporal Lobe Mask 97

113 Supplementary Figure 15. Thalamus Mask 98

114 Supplementary Figure 16. Vent2 Mask 99

115 Supplementary Figure 17. Ventricle Mask 100