Brain Dynamics Underlying the Development of Cognitive Flexibility

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Kupis, L. (2021). Brain Dynamics Underlying the Development of Cognitive Flexibility [University of Miami]. https://scholarship.miami.edu/discovery/fulldisplay/alma991031583588402976/01UOML_INST:ResearchR epository

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UNIVERSITY OF MIAMI

BRAIN DYNAMICS UNDERLYING THE DEVELOPMENT OF COGNITIVE FLEXIBILITY

Lauren Kupis

A THESIS

Submitted to the Faculty of the University of Miami in partial fulfillment of the requirements for the degree of Master of Science

Coral Gables, Florida

August 2021

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science

BRAIN DYNAMICS UNDERLYING THE DEVELOPMENT OF COGNITIVE FLEXIBILITY

Lauren Kupis

Approved:

Lucina Uddin, Ph.D. Aaron Heller, Ph.D. Professor of Psychology Professor of Psychology

Manish Saggar, Ph.D. Guillermo Prado, Ph.D. Stanford University Dean of the Graduate School LAUREN KUPIS (M.S., Psychology) Brain Dynamics Underlying the Development (August 2021) of Cognitive Flexibility

Abstract of a thesis at the University of Miami.

Thesis supervised by Professor Lucina Uddin. No. of pages in text. (68)

Cognitive flexibility, or the ability to mentally switch according to changing environmental demands, supports optimal outcomes across development. Despite the importance of cognitive flexibility for development, little is known regarding the neural mechanisms underlying it. The goal of the current study was to uncover developmental differences in the neural systems supporting cognitive flexibility using a functional MRI task designed to elicit switching mechanisms in both children and adults and by using a novel method called co-activation pattern (CAP) analysis. The current study examined neural differences in brain activation and dynamic brain states between children and adults during a cognitive flexibility task, and the relationships between brain dynamics and behavior. The CAP analysis revealed that children as compared with adults dwelled longer in brain states consisting of hybrid brain states consisting of between-network coupling. Additionally, in both children and adults, more frequent occurrence of a brain state consisting of coupling between the default and central executive networks was associated with better cognitive flexibility as measured by the Behavior Rating Inventory of Executive Function. This study provides the first evidence of the developmental changes associated with brain dynamic changes during cognitive flexibility and links brain function with a real-world measure of cognitive flexibility, thereby paving the way for future research of neurodevelopmental disorders characterized by atypical cognitive flexibility. TABLE OF CONTENTS

Page

LIST OF FIGURES ...... iv

LIST OF TABLES ...... v

Chapter

1 BACKGROUND ...... 1 Cognitive flexibility… ...... 1 Executive functions needed to implement cognitive flexibility… ...... 6 Development of cognitive flexibility ...... 11 Brain network findings in cognitive flexibility ...... 19 Specific aims and hypotheses ...... 23

2 METHODS ...... 26 Behavioral measures ...... 27 Data acquisition ...... 30 Data preprocessing ...... 30 Analytic plan...... 32

3 RESULTS ...... 34 Brain activation ...... 34 Co-activation pattern analysis (CAP)...... 38 Age differences in CAPs ...... 38 Brain-behavior relationships with CAPs ...... 39

4 DISCUSSION ...... 41 Brain activation ...... 41 Co-activation pattern analysis (CAP) ...... 43 Limitations and future directions ...... 48 Conclusion ...... 49

FIGURES ...... 50

REFERENCES ...... 56

iii LIST OF FIGURES FIGURE 1…………………………………………………………………………….. 50

FIGURE 2…………………………………………………………………………….. 51

FIGURE 3…………………………………………………………………………….. 52

FIGURE 4…………………………………………………………………………….. 53

FIGURE 5…………………………………………………………………………….. 54

iv LIST OF TABLES

TABLE 1…………………………………………………………………………….. 26

TABLE 2…………………………………………………………………………….. 35

v CHAPTER 1: BACKGROUND

Flexible cognition and behavior are required to adaptively respond to changing environmental demands. This process is enabled by cognitive flexibility, the mental readiness to switch to initiate flexible behavioral responses (Dajani & Uddin, 2015).

Cognitive flexibility is a core feature of executive functioning (Diamond, 2013; Logue &

Gould, 2014) and is associated with positive life outcomes including the successful transition into adulthood (Burt & Paysnick, 2012), resilience to negative life events

(Genet & Siemer, 2011), and better quality of life (Davis et al., 2010). Cognitive flexibility is also critical for developmental outcomes, including reading and math skills

(Yeniad et al., 2013), social competence (Ciairano et al., 2006), and overall academic achievement (Titz & Karbach, 2014). Despite the importance of cognitive flexibility across the lifespan, little is known regarding the neural mechanisms supporting the development of this ability.

Cognitive flexibility

Examples of cognitive flexibility include the ability to think differently about a situation, and quickly switch between tasks or strategies to solve a problem. Being flexible ultimately aids creativity (Lu et al., 2017, 2019), problem-solving (Ionescu,

2012), learning (Kehagia et al., 2010), and resilience to negative life events (Genet &

Siemer, 2011). Cognitive flexibility benefits adaptation via the ability to quickly transition from different activities or change perspective. Therefore, cognitive flexibility is an important feature of daily functioning.

Cognitive flexibility is also associated with positive life outcomes including academic achievement such as reading and math skills (Yeniad et al., 2013), and the

2 successful transition into adulthood (Burt & Paysnick, 2012). Conversely, cognitive inflexibility may be a risk factor for repetitive or ruminative thoughts (Deveney &

Deldin, 2006; Genet et al., 2013; Whitmer & Banich, 2007), which underlie psychological disorders such as anxiety, depression, obsessive-compulsive disorder

(OCD), and autism spectrum disorder (ASD) (Keenan et al., 2018; McDougle et al.,

1995; van Steensel et al., 2011). Ultimately, cognitive flexibility is important during childhood and adolescence because these periods are accompanied by learning, susceptibility to psychological disorders (e.g., anxiety and depression; (Côté et al., 2009), and increased substance use (Rose et al., 2019). In all cases, cognitive flexibility may buffer against negative effects.

Although cognitive flexibility contributes to positive adaptation and learning across development, the underlying brain regions supporting developmental changes of cognitive flexibility are not fully understood. Characterizing the brain regions involved in cognitive flexibility across development may clarify the mechanisms underlying the cognitive and behavioral changes observed.

The lateral frontoparietal network (FPN) has been found to be important in the development of executive function broadly (Dajani & Uddin, 2015). Further, flexible interactions among brain regions and neural networks may contribute to greater cognitive flexibility (Nomi, Bolt, et al., 2017). However, these neuroimaging findings are primarily in adults, and reflect the neural processes associated with mature cognitive flexibility. A more complete understanding of the neural mechanisms underlying cognitive flexibility may lead to the creation of targeted treatments for neurodevelopmental disorders, the

updating of classroom curricula, and the creation of individualized preventative measures for psychological disorders or substance use (Lucina Q. Uddin, 2021).

Terminology

A variety of terminologies exist to describe tasks that have been developed to study cognitive flexibility. Cognitive flexibility is first considered to be the ‘umbrella’ term describing a cognitive construct that is investigated using ‘set-shifting’ and ‘task switching’ tasks (Konishi et al., 1998; Monsell, 2003). Relatedly, cognitive flexibility is referred to as ‘shifting’ in latent models of components of executive function (A. Miyake et al., 2000).

Task switching and set shifting

Two types of tasks commonly used to assess cognitive flexibility are ‘task switching’ and ‘set-shifting’ paradigms. Task switching paradigms involve alternating between tasks, and performance is measured by a ‘switch cost’, or the difference in task performance (e.g., reaction time) in switching versus non-switching task blocks (Monsell,

2003; Vandierendonck et al., 2010). An example of task switching are rule-switching tasks, which require participants to switch their response selection (or task) based on the presented rule (Wendelken et al., 2012). Set-shifting, on the other hand, involves shifting or switching within a task (Dajani & Uddin, 2015). For example, a commonly used set- shifting task requires participants to shift attention between color and shape dimensions and choose the unique attribute (Casey et al., 2004). Although these tasks differ in terms of switching within versus between tasks, they both are thought to rely on cognitive flexibility. However, these subtle differences may have important implications in the neural processes underlying the different types of shifting processes occurring.

There are different types of task switching and set-shifting paradigms used to study cognitive flexibility in developmental neuroimaging studies. Broadly, the categories of these various tasks include: attention-shifting, complex set-shifting, rule- switching, and performance-based switching tasks. These tasks are often adaptations of common neuropsychological test batteries that assess cognitive flexibility including the

Dimensional Change Card Sort (DCCS; Zelazo, 2006), the Wisconsin Card Sorting Task

(WCST; Heaton RK et al., 1993), and shifting tasks within the Delis-Kaplan Executive

Function System (D-KEFS; Delis et al., 2001). In the DCCS, usually given to children between 3 and 5 years, children are presented with two cards (e.g., blue rabbit and red boat) and are asked to match a series of cards to the two cards based on one dimension such as color or shape (e.g., blue boat is matched with the blue rabbit) (Zelazo, 2006).

The WCST can be given to individuals from 6.5 to 89 years of age, and requires cards to be sorted based on one of three dimensions (color, shape, or number) and update the sorting criteria based on the experimenter’s feedback (Heaton RK, Chelune GJ, Talley

JL, Kay GG, Curtiss G, 1993). Lastly, the D-KEFS consists of several tasks (color-word interference, card sorting, design fluency, and verbal fluency tasks) that test shifting abilities.

Functional Magnetic Resonance Imaging cognitive flexibility tasks

Four general categories of fMRI tasks that have been used to study cognitive flexibility include: attention shifting, complex set-shifting, rule-switching, and performance-based switching tasks. These tasks range in difficulty in that some have only one task demand (e.g., attention) or include many demands (e.g., attention, working memory, and switching). The fMRI cognitive flexibility tasks also differ greatly in the

5 type of switching required (e.g., attention versus rule-based switching). The following section describes in more detail these categories of tasks.

Attention Shifting

In attention shifting tasks, participants shift attention between stimuli dimensions

(e.g., color, shape) within a trial. This type of task has been primarily utilized in developmental studies, as it is generally easy for young children to understand the simple instructions (Casey et al., 2004; Dirks et al., 2020; Morton et al., 2009; Yerys et al.,

2015). Attention-shifting tasks are often an adaptation of the DCCS test battery (Ezekiel et al., 2013; Morton et al., 2009), and therefore, can be given to children as young as 3 years (Hanania & Smith, 2010).

Complex Set-Shifting

In complex set-shifting tasks, participants are still required to shift within a trial, but utilize more cognitive abilities than simply shifting attention (Yasumura et al., 2015).

One example of a complex set-shifting task is the flexible item selection task (FIST;

Jacques & Zelazo, 2001). Participants are presented with three cards and are instructed to select 2 cards that match on one dimension (e.g., number, color, size) and then shift by selecting another matching pair based on a different dimension (Jacques & Zelazo, 2001).

Moreover, the FIST is similar to the DCCS and WCST, but can vary in difficulty by including more abstract/complex dimensional shifts, which can be further altered by increasing dimensions or shifts within a trial (Dajani et al., 2020; Dick, 2014).

Additionally, other cognitive processes such as working memory may be recruited to complete the task depending on the number of switches needed to complete the trial.

Recruitment of other cognitive processes such as working memory and inhibition in

6 addition to shifting are thought to support cognitive flexibility (Dajani & Uddin, 2015), and may better represent real-world scenarios. Because the FIST can be altered to make it more difficult, and may be more representative of real-world scenarios, children tend to perform less well compared with adults (Dick, 2014). Therefore, complex set-shifting tasks may require greater cognitive demands to complete the task.

Rule-Switching

Rule-switching tasks require participants to flexibly respond to a stimulus based on the given rule (e.g., respond based on color or direction) (Wendelken et al., 2012).

Similar to complex set-shifting tasks, rule switching tasks sometimes require higher order processes (i.e., working memory) when participants must mentally maintain the rules

(Dajani & Uddin, 2015).

Performance-Based Switching

Performance-based switching tasks typically involve: 1) an adaptation of the

WCST, where participants have to switch between three possible response rules (Crone et al., 2008), and 2) a probabilistic task (Hauser et al., 2015; van den Bos et al., 2012), where participants have to switch their stimulus choice based on probabilistic feedback

(e.g., choosing stimulus A receives positive feedback 80% of the time whereas choosing

B results in only 20% positive feedback).

Executive functions needed to implement cognitive flexibility

Cognitive flexibility is a key aspect of executive functioning, or the higher order processes that enable goal-oriented behaviors. However, other executive functions may be involved in cognitive flexibility, and are thought to be distinct yet correlated constructs (Akira Miyake & Friedman, 2012). The executive functions involved in

7 cognitive flexibility processes may include working memory and inhibition, along with other processes such as salience detection and attention (Dajani & Uddin, 2015).

Common behavioral cognitive flexibility tasks, including the Wisconsin Card Sorting

Test (WCST; Heaton RK, Chelune GJ, Talley JL, Kay GG, Curtiss G, 1993), the

Dimensional Card Sort (DCCS; Zelazo, 2006), and various tasks of the Delis Kaplan

Executive Functioning (D-KEFS; Delis et al., 2012), require multiple executive functions to complete the tasks (Coulacoglou & Saklofske, 2017; Lange et al., 2016; A. Miyake et al., 2000). For example, during the WCST, participants classify cards differing on color, shape, or number of designs. Participants need to be able to orient their attention to salient features of the cards, utilize working memory to recall previously used responses

(Lange et al., 2016), inhibit prior responses (Diamond et al., 2005), and ultimately switch their response. Therefore, it is theorized that cognitive flexibility is orchestrated in accordance with other crucial executive functions of working memory and inhibition

(Diamond et al., 2005), and salience/attention processes (Chen et al., 2016).

Salience detection

Salience detection is the process of determining which stimuli are salient and subsequently drawing attention to the stimuli (Lucina Q. Uddin, 2015). Salience detection is supported by the mid-cingulo insular network (M-CIN; Salience network) (Lucina Q.

Uddin et al., 2019), including the anterior insula (AI), dorsal anterior cingulate cortex

(dACC), and subcortical and limbic structures (Seeley et al., 2007). The M-CIN integrates external sensory information and internal emotional and body states that are used to guide behavior. The M-CIN additionally plays a role as a flexible hub, facilitating brain network interactions, and allowing salient information to be processed and

8 responded to (Sridharan et al., 2008). Specifically, the posterior and mid-insula integrate and transmit interoceptive signals, whereas the AI orchestrates the brain network dynamic interactions (Dajani & Uddin, 2015). The flexible variability in the M-CIN is thought to be an important contributor to flexible behavior, and it has been previously shown to predict individual differences in cognitive flexibility (Chen et al., 2016).

Bottom-up/top-down attention

Attention processes can be partitioned into dorsal and ventral pathways in the brain, contributing to goal-oriented (top-down) and stimulus-driven (bottom-up) attention

(Chica et al., 2011). The dorsal-attention network (DAN), comprised of the intraparietal sulcus (IPS), superior parietal lobule, and frontal eye fields (FEF), supports top-down processing, whereas the ventral attention network (VAN) comprised of the ventrolateral prefrontal cortex (vlPFC) and temporoparietal junction, supports bottom-up processing

(Corbetta et al., 2008). The DAN is therefore important for spatial orienting of attention and feature-based attention (Vossel et al., 2014), useful in cognitive flexibility tasks such as during attention-shifting.

Working memory

Working memory, or the ability to mentally maintain information needed for an ongoing task or process (Oberauer, 2019), is largely supported by the lateral-frontal parietal network (L-FPN; central executive network) or areas of the dorsolateral PFC, vlPFC, premotor and parietal cortices (Thomason et al., 2009). The L-FPN broadly supports goal-directed systems and control of information flow in the brain (Niendam et al., 2012). Working memory is an important aspect in cognitive flexibility tasks, and a potential confounder if not properly accounted for. The Flexible Item Search Task (FIST)

9 is a cognitive flexibility task that requires participants to match stimuli based on a shared dimension and flexibly switch to make a new dimensional match (Dick, 2014). Further, as more matches are required, greater working memory is needed to prevent repeating prior matches. Greater working memory load is also indicative of task performance, primarily in children as they typically perform worse on tasks requiring high working memory loads, compared with older children and adults (Cowan et al., 2010). However, in the FIST, working memory was found not to impact cognitive flexibility performance when the working memory load was age-appropriate. Overall, working memory is an important aspect of performing cognitive flexibility tasks, but age-related confounds need to be accounted for.

Inhibition

Inhibition is regarded as an important function for cognitive flexibility (Davidson et al., 2006) because prior tasks or responses need to be inhibited prior to switching.

Inhibition is most commonly supported by the brain regions of the right vlPFC (Aron et al., 2014), AI, and the inferior frontal junction (IFJ) (Aron & Poldrack, 2006). The right vlPFC plays a role primarily in response inhibition (Sebastian et al., 2016), the rAI plays a role in detecting behaviorally relevant events and an important region in the M-CIN, and the IFJ plays a role in detecting behaviorally relevant stimuli (Sebastian et al., 2016).

Inhibition therefore allows for the previous task set to be inhibited prior to switching.

Isolating cognitive flexibility from other executive functions in neuroimaging studies

A challenge with studying cognitive flexibility involves isolating cognitive flexibility from other core executive functions (i.e., inhibition and working memory).

Difficulty in isolating the process may impact our understanding of the brain regions

10 underlying cognitive flexibility across development. Executive functions enable goal- oriented behaviors and extant literature suggests core executive functions are independent constructs yet highly related (A. Miyake et al., 2000). Therefore, isolating executive functions from each other becomes difficult, and maybe impossible, because many of these processes are related and in some cases may even depend on each other. For example, many of the flexibility tasks described above involve working memory to keep the task rules in mind, and inhibition to prevent repetition of previously answered responses. These more complex tasks, however, are thought to be more representative of real-world applications as real world situations integrate multiple processes (e.g., visual, sensorimotor, attention, salience, various executive functions) (Matusz et al., 2019;

Bottenhorn et al., 2019). Additionally, in adult studies of the neural substrates underlying cognitive flexibility, there are brain regions activated solely during cognitive flexibility tasks and brain regions activated across tasks of executive functions. Because cognitive flexibility tasks sometimes involve many executive functions, and has been seen to impact the findings in adult fMRI cognitive flexibility studies, studies during childhood and adolescence may not always be testing pure cognitive flexibility. However, study of

‘pure’ cognitive flexibility may not be representative of real-world switching (Bottenhorn et al., 2019). Because of this, multiple considerations need to be made when assessing the results from the developmental fMRI studies of cognitive flexibility including the type of task used, executive functions required to complete the task, and developmental stage of the participants since different components of executive function have different developmental trajectories.

Development of cognitive flexibility

Behavioral Findings

Cognitive flexibility and its associated executive functions exhibit different developmental trajectories across childhood (Anderson, 2002). In general, these skills begin to emerge during preschool in children as young as 3 years (Hughes, 1998).

Cognitive flexibility skills in preschool children represent lower order forms, such as shifting, evidenced by children’s ability to perform well on shifting tasks such as the

DCCS (Diamond et al., 2005). Further, integration of other executive functions such as working memory and inhibition with cognitive flexibility are not yet fully developed in preschool-aged children, revealed by poor performance on tasks requiring the integration of various executive functions (Diamond et al., 2005). For example, 4.5 year-old children successfully perform the DCCS task but perform poorly on the WCST (Chelune & Baer,

1986; Dick, 2014), suggesting cognitive flexibility can be altered by task difficulty, and higher order flexibility takes longer to develop. Through elementary school age, cognitive flexibility takes a protracted development compared with other executive functions (Davidson et al., 2006), and reaches similar behavioral performance as adults by 10 years of age (Chelune & Baer, 1986; Dick, 2014; Rosselli & Ardila, 1993; Welsh et al., 1991). However, cognitive flexibility skills continue to develop into adolescence and adulthood (Anderson, 2002), peaking between 21 and 30 years (Cepeda et al., 2001).

Brain Regions in neurotypical adults

Greater insight into the neurodevelopmental processes associated with cognitive flexibility can be gleaned from studies of the adult brain. Findings from functional MRI studies of adults using age-adjusted versions of cognitive flexibility tasks have shown

12 core brain regions and networks underlying cognitive flexibility (Dajani et al., 2016), as well as task-dependent brain regions (C. Kim et al., 2011; Chobok Kim et al., 2012)

(Figure 3). Brain regions found to be involved in core cognitive flexibility across all task types include the ventrolateral prefrontal cortex (vlPFC), dorsolateral PFC (dlPFC), anterior cingulate cortex (ACC), right anterior insula (AI), inferior frontal junction (IFJ), premotor cortex, inferior and superior parietal cortices, inferior temporal cortex, occipital cortex, and subcortical structures such as the caudate and thalamus (Dajani & Uddin,

2015; Chobok Kim et al., 2012; Niendam et al., 2012). Brain regions involved in cognitive flexibility further differentiate depending on the type of task (C. Kim et al.,

2011; Chobok Kim et al., 2012). More abstract switching recruits anterior-PFC regions; moderately abstract switching recruits mid-PFC; and constrained switching recruit posterior-PFC regions (C. Kim et al., 2011). Results from Kim et al., (2012) meta- analysis further revealed region-specific activation during attention-shifting tasks in the dorsal portion of the premotor cortex, and during set-shifting tasks in the frontopolar cortex. Overall, the findings from adult neuroimaging studies of cognitive flexibility reveal brain regions associated with cognitive flexibility that may differentiate depending on the task.

Neural correlates in developmental cohorts

Developmental neuroimaging studies suggest cognitive flexibility takes a protracted developmental trajectory, as paralleled in behavioral findings. A prolonged development among the brain regions supporting cognitive flexibility, mainly the frontoparietal network (Barber & Carter, 2005; Cole & Schneider, 2007), is also observed in structural findings suggesting the gray matter in those regions mature later than other

13 regions (Casey et al., 2000; Giedd et al., 1999). Similarly to adult findings, certain brain regions are commonly activated in children across switching tasks among the frontoparietal network such as the dlPFC and the pre-SMA (Morton et al., 2009;

Wendelken et al., 2012), and the basal ganglia (Casey et al., 2004; Crone, Wendelken, et al., 2006; Rubia et al., 2006). However, activation among the frontoparietal network and insula increases in activation strength across development and most strongly in adults, suggesting as these regions develop, cognitive flexibility strengthens (Rubia et al., 2006;

Taylor et al., 2012; Wendelken et al., 2012).

Attention-shifting

Cognitive flexibility can also be observed differentially depending on the switching/shifting task, as observed in adult studies of cognitive flexibility (Chobok Kim et al., 2012). Attention-shifting, shifting attention between stimuli dimensions (e.g., color, shape), has been primarily studied across development (Casey et al., 2004; Dirks et al.,

2020; Morton et al., 2009; Yerys et al., 2015). Children and adults share common brain activation regions during attention-shifting including the superior parietal cortex, dlPFC,

IFJ, pre-SMA region (Morton et al., 2009), and the caudate nucleus (Casey et al., 2004).

Key developmental differences between children and adults are also seen during attention-shifting. In Morton et al., (2009) children (11-13 years) had unique activation among the R superior frontal sulcus, whereas adults had unique activation in the L superior parietal cortex and R thalamus. This finding implies children may have different switching strategies compared to adults resulting in differing brain activation patterns. In another study, Casey et al., (2004) observed more prefrontal and parietal regions in adults compared to children (7-11 years) suggesting greater recruitment of these cortical regions

14 across development. In a study of typically developing (TD) children compared with children with autism spectrum disorder (ASD), utilizing the same task from Casey et al.,

(2004), TD children (7-12 years) had brain activation in the L posterior supramarginal gyrus/angular gyrus during shift trials (Dirks et al., 2020). The discrepancies in attention- shifting findings may be due to small participation sample sizes and the shifting task, previously shown to be a poor indicator of cognitive flexibility (Dirks et al., 2020).

Set-shifting

Complex set-shifting tasks require different brain regions to complete the task compared with lower level shifting tasks. Attention-shifting studies primarily utilize the

DCCS, shown to be relatively easy for children at young ages, but the WCST (set- shifting) appears to be more difficult for children, and in some cases adults (Dajani et al.,

2020; Dick, 2014). Shifting tasks that require greater cognitive flexibility include more abstract/complex dimensional shifts or rules requiring a greater number of dimensions or switches (Dajani et al., 2020; Dick, 2014). Further, the requirements during the shift are also more complex and require using more cognitive abilities (Yasumura et al., 2015).

In one study that examined set-shifting abilities in children using the WCST task, children had activation in the right insula, a region important for switching between brain networks to enable flexible behavior (Menon & Uddin, 2010), with increasing activation with age (Taylor et al., 2012). Only one study in adults has used the Flexible Item

Selection Task (FIST), a complex version of a set-shifting task that requires subjects to abstract a matching dimension and switch flexibly to a new matching dimension (Dajani et al., 2020; Dick, 2014). Although the FIST has only been studied in adults, behavioral

15 evidence reveals age-related changes at least until 10 years of age, suggesting age-related neural differences in set-shifting at least until 10 years (Dick, 2014).

Rule-switching

Rule-switching is another cognitive flexibility task that requires higher order processes particularly in rule switching that requires working memory to mentally maintain the rules (Wendelken et al., 2012). The earliest study found adolescents had similar brain activation as adults during rule-switching, among regions of the pre-

SMA/SMA (Crone, Donohue, et al., 2006). In a recent rule-switching study, the authors utilized a task that required participants to switch flexibly from one task rule to another

(Wendelken et al., 2012). In children (8-13 years) and adults, brain regions of the L dlPFC, L PPC, and pre-SMA regions were involved in rule-switching trials. The dlPFC and SMA were similarly activated as in other cognitive flexibility studies. Further, there were key developmental differences seen between children and adults in their brain activations during the task. Adults had greater activation overall and among the pre-

SMA, PMC, and L PPC. Children also had regions more activated than adults among the right inferior parietal lobe and the cingulate gyrus. Additionally, children engaged the L superior temporal gyrus and the R middle temporal gyrus, in the switch trials compared with repeating trials. However, this pattern of activation was not seen in adults, suggesting rule switching may create greater cognitive demands and may be more difficult for children compared to adults. Further, children had more initial dlPFC activation driven by the previous trial rule, suggesting children are more likely to maintain previous rules when it is no longer relevant and may have difficulties with switching to a new rule set (Wendelken et al., 2012).

Performance-based switching

Performance-based switching studies have revealed many behavioral and neurodevelopmental differences between children and adults (Crone et al., 2008; Hauser et al., 2015; van den Bos et al., 2012). The type of tasks used to test performance-based switching involve an adaptation of the WCST, where participants had to spatially switch between three possible response rules (Crone et al., 2008) and a probabilistic task

(Hauser et al., 2015; van den Bos et al., 2012), where participants had to switch their stimulus choice based on probabilistic feedback (e.g., choosing stimulus A receives positive feedback 80% of the time whereas choosing B results in only 20% positive feedback). The insula appears to be an important contributor to performance-switching as indicated by activation during the WCST adapted task and in adolescents, where greater activation occurred in the anterior insula for reward prediction errors prior to switching

(Crone et al., 2008). This is also in line with findings in a meta-analysis of developmental executive functions, where the right anterior insular cortex was shown to have age-related involvement among inhibition, switching, and working memory (Houdé et al., 2010).

Other regions were important during performance-switching including the DLPFC, mPFC, ACC, striatum, vmPFC, amygdala, L PCC, L Putamen, R precentral gyrus, L

SFG, and L IPL. The mPFC was commonly observed across the three developmental studies, consistent with reports of the role of the PFC in cognitive flexibility (Rougier et al., 2005).

Overview of neurodevelopmental findings

The neurodevelopmental studies of cognitive flexibility provide initial insight into the developmental processes surrounding switching/shifting, an ability important for life

17 outcomes. The regions commonly activated in children during switching/shifting across task types include the dlPFC and the pre-SMA/SMA. The dlPFC is one region of the frontoparietal network involved in switching in adults. The dlPFC is primarily thought to be involved with working memory (Thomason et al., 2009), therefore children may be using working memory to complete the tasks. However, in cognitive flexibility tasks with higher loads of working memory, children typically perform less accurately than adults

(Thomason et al., 2009; Wendelken et al., 2012), suggesting the integration of working memory and switching processes are not fully developed during childhood. The pre-

SMA/SMA was also discovered to be activated in children across multiple developmental cognitive flexibility studies. Activation of the pre-SMA is commonly seen in set-shifting tasks (Barber & Carter, 2005) in adults and broadly during executive functioning tasks

(Duncan & Owen, 2000). The Pre-SMA is overall thought to be involved in task-set reconfiguration, suppression of previous responses, and error likelihood estimation

(Morton et al., 2009).

The developmental neuroimaging studies reviewed also provided insight into the regions that were not commonly activated in children during cognitive flexibility tasks.

For example, only one study found evidence of activation of the IFJ in both children and adults (Morton et al., 2009). The IFJ was found to be implemented across various cognitive flexibility tasks in adults (Dajani et al., 2020; C. Kim et al., 2011). The IFJ was additionally found to coordinate switches among brain regions during cognitive flexibility (Dajani et al., 2020). Specifically, the IFJ gets activated first, and leads to activations in other regions involved in cognitive flexibility. Since this region was not

18 commonly activated in cognitive flexibility studies with children, it suggests the ability to coordinate switching undergoes developmental changes.

Another set of brain regions found to be commonly observed in adults is the M-

CIN (dACC, AI, insula). There were mixed findings surrounding the M-CIN among the children studies with some instances with increased activation of the insula in children

(Ezekiel et al., 2013; Mogadam et al., 2018; Rodehacke et al., 2014) and conversely some instances of decreased or no activation of the insula during switching (Crone, Donohue, et al., 2006; Crone et al., 2008; Dibbets et al., 2006; Dirks et al., 2020; Nelson et al.,

2007; Taylor et al., 2012; Wendelken et al., 2012). The M-CIN supports coordination among large-scale networks (M-FPN/DMN and L-FPN/CEN) and is thought to enable flexible behavior (Lucina Q. Uddin et al., 2015). The mixed findings regarding the M-

CIN brain regions in children may be due to the various tasks (i.e., attention-shifting vs. performance-switching) or varying age groups across the studies (i.e., from 3 years to 17 years). In most cases, children and adolescents were separately grouped, and generally adolescents had more insula activation than children, suggesting an age-related increase of activation of the insula (Taylor et al., 2012) associated with better behavioral performance (Chen et al., 2016). The studies reviewed provide valuable insight into the development of cognitive flexibility but there are still many questions to be answered, including how the brain organization of these brain regions enable cognitive flexibility and changes across development.

Brain network findings in cognitive flexibility

Brain networks enabling flexibility

A growing body of literature has begun to explore the dynamic brain network topology that enables cognitive flexibility and flexible behavior. Prior evidence reveals adaptive behavior is supported by flexible network interactions among the L-FPN and M-

CIN (Braun et al., 2015; Cohen et al., 2014; Cole et al., 2013; Shine et al., 2016). The L-

FPN and M-CIN have the most flexible connections with other brain regions compared with other brain networks (Cocuzza et al., 2020; Mattar et al., 2016). The medial frontoparietal network (M-FPN or default network) appears to have the greatest flexibility using static FC methods (Cole et al., 2013), where the FC is averaged across the duration of the scan, but recent advanced dynamic FC methods that account for time- varying FC across the duration of the scan, reveal the M-CIN has flexible connections

(Nomi et al., 2016). Specifically, the right dAIC of the M-CIN acts as a switcher hub, facilitating flexible interactions between the M-FPN and L-FPN (Nomi et al., 2016;

Sridharan et al., 2008; L. Q. Uddin et al., 2011). The temporal flexibility of the M-CIN has also been shown to predict individual differences in cognitive flexibility in adults

(Chen et al., 2016).

Functional/Dynamic connectivity cognitive flexibility

Brain network associations, identified using FC or correlations between separate brain regions (Eickhoff & Müller, 2015), have revealed network interactions in cognitive flexibility not otherwise seen using brain activation methods. One study evaluated the FC during a resting-state fMRI scan to task performance on a set-shifting task performed outside of the scanner. Greater posterior cingulate cortex/precuneus (M-FPN) connectivity with the ventromedial striatopallidum (basal ganglia) was correlated with fewer total errors on the set-shifting task, suggesting the relationship between the M-FPN

20 and basal ganglia is important for cognitive flexibility (Vatansever et al., 2016).

However, another study found evidence for state-dependent FC relationships between the

L-FPN and M-FPN such that greater L-FPN and M-FPN connectivity during task-state is associated with cognitive flexibility, whereas greater L-FPN and M-FPN connectivity during resting-state relates to poorer performance (Douw et al., 2016). Together, these studies reveal the importance of considering resting- or task-states when investigating brain network relationships with cognitive flexibility and considering the role of the M-

FPN in cognitive flexibility.

A recent fMRI study examined the neural correlates of cognitive flexibility in adults using the FIST (Dajani et al., 2020; Dick, 2014). A variety of brain regions were activated in flexibility versus control comparisons including the M-FPN and M-CIN, such as the lIFJ extending into the left IFG, dlPFC, FEFs, AI, dACC/pre-SMA and IPL

(Dajani et al., 2020). The regions activated by the flexibility versus control comparisons were also analyzed using FC network analyses and an extended unified structural equal modeling approach to estimate ROI to ROI directed network connectivity (Dajani et al.,

2020). They found only the lIFJ was directly activated and other activations seen were due to their functional connections with the lIFJ. The lIFJ therefore was hypothesized to be the first region to activate in response to engagement with cognitive flexibility and leads to engagement among other regions of the prefrontal, parietal and cerebellar regions. Therefore, the lIFJ modulates cognitive flexibility due to its role in updating task sets during switching (Chobok Kim et al., 2012). Dajani et al., (2020) additionally found roles for the dlPFC in maintaining information such as during working memory tasks, the

AI and dACC of the M-CIN as being an important connection to the lIFJ during cognitive

21 flexibility, and the lAG of the M-FPN in task performance as reported in the literature

(Douw et al., 2016; Vatansever et al., 2016).

Development

A network approach has also provided greater insight to the brain related changes associated with cognitive flexibility across development. In a study using the DCCS, an attention-shifting task, age-related differences were seen among the FC of the lPFC with the anterior cingulate, inferior parietal cortex, and the ventral tegmental area, showing stronger activation in adults compared with children (12 years mean age) (Ezekiel et al.,

2013). Children had greater FC between the frontal pole and insula. Together, these findings suggest children may utilize a different cognitive strategy to implement attention-shifting as shown by the connection among the frontal pole and insula, and may develop FC among the lPFC later (Ezekiel et al., 2013).

Brain network flexibility is also shown to change across development, and reflects changes in cognitive functioning. In children (8-15 years), brain signal variability increases with age and corresponds to a reduction in behavioral variability and greater accuracy (McIntosh et al., 2008). Across the lifespan, brain signal variability decreases across most regions of the brain, but these decreases are offset by increases in select brain regions including the anterior insula of the M-CIN (Nomi, Bolt, et al., 2017). There are also age-related changes in dwell time and frequency of certain brain states, and increases in the number of brain state transitions during task-elicited states across development

(Hutchison & Morton, 2015), suggesting the brain becomes more flexible with age (9 years to 32 years). Additionally, in separate study with a sample of 7-16 year old children, there were age related increases in temporal variability of FC among the M-

CIN, L-FPN, and M-FPN (Marusak et al., 2017). The same study also found highly variable connections between the L-FPN and M-FPN, indicating these regions integrate early in life (Marusak et al., 2017). Additional work found the functional organization of the whole brain in children (9-12 years) during resting-state is similar to adults, and variability in network organization among the L-FPN and M-CIN can be observed in late childhood (Le et al., 2020). This finding aligns with behavioral evidence suggesting children reach similar behavioral performance as adults on set-shifting tasks around 10 years of age (Dick, 2014). The dynamic coordination within the brain and primarily among large-scale neurocognitive networks support the development of cognitive flexibility.

Co-activation Pattern Analysis (CAP)

Although the studies reviewed provide insight into the dynamic network changes seen across development and their relationship with cognitive performance, the methods used rely on either ‘static’ methods (i.e., FC), where the time series is averaged across the fMRI scan (Biswal et al., 1995), or time-varying methods (i.e., dynamic FC), where the moment to moment changes in FC is captured (Chang & Glover, 2010). FC methods in particular do not capture time-varying representations of the brain. Further, dFC relies on the ‘sliding window’ approach (Chang & Glover, 2010) to capture FC changes across a fixed window length (Preti et al., 2017). FC and dFC methods, although valuable, rely on many assumptions and arbitrarily collapse data into time and space. Novel dynamic methods such as co-activation pattern (CAP) analysis (Liu et al., 2013) are increasingly utilized (Kupis et al., 2020, 2021) because they capture time-varying brain state alterations not otherwise observed using static methods and in some instances reveal

23 more brain and behavior relationships compared with static methods (Lurie et al., 2020).

For the first time, this study aims to examine the neural correlates of cognitive flexibility across development using a novel brain dynamic method and link brain function during a cognitive flexibility task with behavior.

Specific aims and hypotheses

Aim 1: To understand the neural bases of cognitive flexibility in neurotypical children and adults during task elicited brain states.

Aim1a. To examine brain activation responses in children compared with adults during a flexible item selection task that requires flexible switching between stimulus dimensions (Jacques & Zelazo, 2001).

Aim1b. To examine dynamic brain state metrics (dwell time, frequency, and transitions) in children compared with adults during a flexible item selection task that requires flexible switching between stimulus dimensions (Jacques & Zelazo, 2001).

Hypotheses: Children will have reduced activation in regions previously associated with flexibility in adults (IFJ, dACC, Cerebellum, AI), but not in regions of the dlPFC, L PPC, and pre-SMA. The IFJ, dACC, cerebellum, and AI are previously shown to be activated in adults during cognitive flexibility tasks (Dajani et al., 2020; Dajani & Uddin, 2015;

Chobok Kim et al., 2012). Conversely, the dlPFC, L PPC, and pre-SMA have been shown to be as strongly activated in children when compared to adults in cognitive flexibility tasks (Crone, Donohue, et al., 2006; Crone et al., 2008; Morton et al., 2009;

Wendelken et al., 2012).

Children will additionally exhibit longer dwell times and less frequently occurring brain states associated with brain dynamics during the cognitive flexibility task, and

24 have fewer transitions during flexibility trials. Previous research shows children have longer dwell times (Ryali et al., 2016) and less frequent occurrences of certain brain states during rest fMRI (Kupis et al., under review). Additionally, children are hypothesized to have fewer transitions in brain network configurations compared with adults due to growing evidence demonstrating brain state transitions increase across development and during task states (Hutchison & Morton, 2015).

Aim 2: To investigate developmental differences in behavioral measures of flexibility

(BRIEF) (Dajani et al., 2020; Gioia G. A., Isquith P. K., Guy S. C., Kenworthy L.., 2000;

Roth R. M., Isquith P. K., Gioia G. A.., 2005) in children and adults and behavioral associations (reaction time, accuracy, BRIEF) with dynamic brain state metrics during the cognitive flexibility task.

Hypotheses: Children will have higher (poorer) scores on the BRIEF shift scales compared with adults. Children will also have less brain activation in the IFJ, dACC, and AI associated with poorer (lower) scores on the BRIEF shift scales. Prior studies found the IFJ to be an important hub for cognitive flexibility tasks in adults (Dajani et al.,

2020; Chobok Kim et al., 2012), indicating this region may undergo the greatest developmental changes to support switching mechanisms. Additionally, the dACC and

AI of the M-CIN is hypothesized to be less activated and associated with behavioral performance and the BRIEF due to a growing body of literature suggesting this area has the most age-related changes during development during cognitive flexibility tasks

(Crone et al., 2008; Hauser et al., 2015; Rubia et al., 2006; Taylor et al., 2012).

Additionally, there is prior evidence showing the flexibility within the M-CIN predicts individual differences in cognitive flexibility in adults (Chen et al., 2016). Therefore, it is

25 hypothesized that lower activation in the dACC and AI will be associated with poorer cognitive flexibility behavior in children.

Secondly, using CAP analysis, children will have reduced brain dynamics as measured by dwell time, frequency of occurrence, and transitions during the cognitive flexibility task, associated with poorer behavioral measures of flexibility compared with adults. Specifically, longer dwell times, less frequently occurring states, and fewer transitions is predicted to be associated with poorer performance on the cognitive task and lower BRIEF scores. Many prior studies have observed increased brain flexibility with age corresponding with greater task performance (Burzynska et al., 2015; Hutchison

& Morton, 2015; McIntosh et al., 2008).

CHAPTER 2: METHODS

Participants included 32 adults (19-46 years) and 25 typically developing (TD) children (7-12 years) recruited from the University of Miami and the wider Miami community (Table 1). Exclusionary criteria included 1) less than 2 usable task runs and

2) incidental findings. Subjects additionally underwent a visual Quality Control inspection and were excluded if they had one or more visually identifiable artifacts including but not limited to: excessive motion, ringing, blurring, ghosting, wrapping, signal loss, and head coverage. All participants were right-handed as determined by self or parental report, with no history of psychological disorders.

Table 1

Children (N = 25) Adults (N = 32) p-value

Age (year) 10.20 ± 1.50 (7 - 12) 24.78 ± 6.46 (19 - 46) < .001

Sex 16M 9F 17M 15F .409

Ethnicity 16 (Not 15 (Not .053 Hispanic/Latino) 8 Hispanic/Latino) 11 (Hispanic/Latino) 1 (Hispanic/Latino) 6 (NR) (NR)

Race 15 (White) 0 (Black 16 (White) 0 (Black or .002 or African American) African American) 0 0 (Asian) 6 (More (Asian) 0 (More than than one race) 3 one race) 4 (Other) 12 (Other) 1 (NR) (NR)

Mean FD (mm) .21 ± .11 (.07 - .47) .09 ± .03 (.05 - .15) < .001

BRIEF Shift T 44.84 ± 6.97 (36 - 45.19 ± 9.87 (39 - 77) .882 score 63) Mean ± sd (minimum - maximum)

Note: M, male; F, female; Mean FD, mean framewise displacement; NR, not reported; BRIEF, Behavior Rating Inventory of Executive Function.

Participant preparation for MRI

Children participants viewed a 5-minute videotape of a child volunteer undergoing a fMRI scan to familiarize them of the procedure before their scan date. All participants underwent a training procedure the day of their scheduled scan. Participants were trained on the FIST/cFIST by an experimenter on a computer desktop and practiced a computer-based task and fMRI adapted task before undergoing a mock MRI training.

During the mock MRI, participants completed the same fMRI task. Scanner training was followed by functional and structural brain imaging along with a task refresher before the first task run in the scanner. For more details of the experimental procedure see Dajani et al., 2020.

Behavioral measures

Behavior Rating Inventory of Executive Function (BRIEF)

For children, the Behavior Rating Inventory of Executive Function-2 (BRIEF-2) was completed by parents (Gioia G. A., Isquith P. K., Guy S. C., Kenworthy L.., 2000).

The BRIEF-2 is a 86-item parent report questionnaire used to assess executive function and organizational skills in children. The adults were administered the BRIEF-A, the adult version of the BRIEF. The BRIEF-A is a 75-item self-report measure that assesses executive function and organizational skills in adults (Roth et al., 2005). Both the BRIEF-

2 and BRIEF-A include a shift subscale and T scores were examined as shifting skills are a measure of cognitive flexibility (Dajani & Uddin, 2015). T scores at or above 70 are considered clinically elevated.

Flexible Item Selection Task (FIST)

The FIST used in this study is an adapted version of the 4-match FIST developed by Dick, 2014. For each trial, participants were presented with four white, vertical cards on a light gray background. Each card consisted of images that varied in four dimensions: color (blue, green, red), shape (boat, flower, rabbit), size (large, 1.82 in; medium, 0.83 in; small, 0.38 in), and number of images (one, two, three). See Figure 1 for a visual of the task.

Participants were trained to select a pair of cards that were the “same” in one dimension and select two more pairs that were the “same but in a different way”. They made a total of three selections per trial. Participants used a four-button rectangular box to make their selections using their right hand. The task was administered in a computer- based version and fMRI-adapted version to facilitate training outside of the scanner and use while in the scanner. The computer-based task was used to test accuracy before the fMRI scan to allow further training if necessary.

fMRI-adapted Task: Adults

Participants completed up to 4 runs of the FIST. They made three selections as quickly as possible during a fixed trial duration (8 sec). Trial timing was determined by testing 4 adult pilots. There were 20 Flexibility trials containing unique combinations of color, shape, size and number of stimuli. Participants additionally had control trials where they were provided the same set of four cards as given during the Flexibility trials, but were instructed to “follow along” and pressed the button corresponding to the cards outlined in black. The two outlined cards appeared for 2.6 sec, and three different selections appeared, totaling 8 sec, the same length as the Flexibility trials. A 12 sec

29 interstimulus interval (ISI) also displayed in-between each 8 sec Flexibility and control trial.

Each functional task run lasted 4 minutes, and each run consisted of 10 Flexibility trials and 10 Control trials. Runs 1 and 2, and 3 and 4 consisted of the same trails but in a randomized order.

fMRI-adapted Task: Children

Children participants completed up to 4 runs of the FIST. They made three selections as quickly as possible during a fixed trial duration (12 sec). Trial timing was based on pilot testing and found to be optimal for children participants. There were 20

Flexibility trials containing unique combinations of color, shape, size and number of stimuli. Participants additionally had control trials where they were provided the same set of four cards as given during the Flexibility trials, but were instructed to “follow along” and pressed the button corresponding to the cards outlined in black. The two outlined cards appeared for 2.6 sec, and three different selections appeared, totaling 12 sec, the same length as the Flexibility trials. A 12 sec interstimulus interval (ISI) also displayed in-between each 12 sec Flexibility and control trial.

Each functional task run lasted 6 minutes, and each run consisted of 10 Flexibility trials and 10 Control trials. Runs 1 and 2, and 3 and 4 consisted of the same trails but in a randomized order.

Accuracy and Reaction Time (RT):

Accuracy was calculated for all participants on Flexibility and control trials based on recorded button presses. A Flexibility trial is accurate if the participant made six correct button presses, corresponding to the three paired card choices, within 8 sec

(adults) or 12 sec (children). Each of the six button presses were divided into three pairs, and each pair counted as one selection. If six button presses were not recorded or if the participant selected a card that was not similar on one of the four dimensions, the trial was scored as incorrect. For a control trial to be accurate, all three selections must have been correctly corresponding to the outlined cards as part of the “follow along”.

RT were recorded for Flexibility trials only as participants were able to select freely within the trial duration. RT for control trials were not used.

Data acquisition

MRI Data acquisition

Task fMRI data were acquired for participants on a 3T GE scanner using an EPI sequence and a 32-channel head coil (repetition time [TR] = 2 sec, echo time = 30 sec, flip angle = 75°, 3.4 mm slices, voxel size = 3.4 isotropic mm). The first five volumes were immediately discarded to account for gradient stabilization, resulting in 122 volumes per task run for adults and 182 volumes per task run for children. T-1 weighted

FSPGR BRAVO scans were acquired for registration of the functional image to standard space (inversion time = 650 msec, flip angle = 12°, field of view = 25.6 cm, 1 mm isotropic voxels).

Data preprocessing

Preprocessing was conducted in FSL 5.0.9 and were the following steps: brain extraction using BET, rigid body motion correction with MCFLIRT using FEAT, slice time correction, smoothing with a 6-mm kernel, high pass filtering (100 sec), coregistration to the structural image, and normalization to the 2 mm MNI template.

Next, functional data underwent nuisance signal correction including 1) linear and

31 quadratic trends 2) mean-time series from the white matter (WM) and cerebrospinal fluid

(CSF) and 3) 24 motion parameters obtained by motion correction. For the CAP analysis, the last step included censoring time components > .2 mm FD for each subject. The censored time series were visually inspected using carpet plots to ensure the time points removed were not entirely task-related.

Co-activation Pattern Analysis

The time series were extracted from 100 nodes for each subject using the Schaefer parcellation (Schaefer et al., 2018), and were converted to z-statistics and concatenated into one (nodes x timepoints) matrix where the number of timepoints is based on how many TRs remained after scrubbing per participant ([Total TRs x 57 subjects] x 100 nodes). The matrix was then subjected to k-means clustering to determine the optimal number of clusters. The elbow criterion was applied to the cluster validity index (the ratio between within-cluster to between-cluster distance) for values of k = 2-20 and an optimal value of k = 5 was determined.

K-means clustering (squared Euclidean distance) was then applied to the matrix using the optimal k = 5 to produce 5 CAPs (“brain states”). CAP metrics were calculated and included: a) dwell time (DT), calculated as the average number of continuous TRs that a participant stayed in a given brain state, b) frequency of occurrence of brain states, calculated as an overall percentage that the brain state occurred throughout the duration of the resting-state fMRI scan compared to other brain states, and c) the number of transitions, calculated as the number of switches between any two brain states.

Analytic plan

BRIEF

An independent sample t-test was conducted between children and adults using the shifting T scaled scores on the BRIEF.

Data analyses: Task-based fMRI data

General Linear Model (GLM)

Flexibility and control trials were modeled at the individual level for each run using a gamma hemodynamic response function (double-gamma HRF with 0 phases).

Additionally, the six rigid motion estimates were included as nuisance regressors at the run level. Each task run was combined at the individual level using a fixed effects analysis. A group level analysis was conducted using a mixed-effects design using

FLAME 1 to identify brain regions activated for contrasts including: Flexibility >

Control. The main effects of Flexibility and Control trials were modeled relative to rest

(i.e., fixation trials). One-sample t-tests were conducted to determine within-group activations in children and adults. Independent sample t-tests were conducted to determine between-group activations in children and adults. Significant voxels were identified using a voxel-level threshold at a family-wise error (FWE)-corrected p < 0.05

(Dajani et al., 2020; Eklund et al., 2016)

Co-activation Pattern Analysis (CAPs)

Age

The relationship between children and adults in their brain dynamic metrics

(dwell time, frequency, transition) for each CAP were assessed using independent sample t-tests. Age relationships with the brain dynamic metrics were also tested using a linear regression with a continuous age and covariates including sex and head motion.

Equation:

Ŷ = B0 + B1(Age) + Bn(Covariates)

Brain-Behavior Relationships

The relationship between behavior (accuracy on the flexibility task and BRIEF shift) and brain dynamic metrics (dwell time, frequency, transition) for each CAP were assessed using multiple regressions. Covariates included sex and head motion and age was coded dichotomously so children (0) and adults (1) were separated into two groups.

Age was also tested as a continuous variable. Fist, we tested age as a moderator for the relationship between behavior and the brain dynamic metrics for each CAP. Testing age as a moderator allows us to see if the brain-behavior relationships are different depending on the age group (children and adults). Following significant interactions, the simple slopes were examined to aid interpretation of the interaction. For categorical age, the slopes were tested for both children and adults. For continuous age, three ages were tested including 7 years, 18 years (average), and 46 years. Following non-significant interactions, we examined the relationship between behavior and brain dynamic metrics for each CAP, while controlling for sex, head motion, and age. BRIEF shift T scores were compared between children and adults to ensure their scores could be included within the same regression.

Equations:

Ŷ = B0 + B1(Behavior) + B2(Age) + B3(Behavior x Age) + Bn(Covariates) [Moderation]

Ŷ = B0 + B1(Behavior) + Bn(Covariates) [Main Effects]

CHAPTER 3: RESULTS

Brain activation

Task contrasts combined across all four runs were investigated at a voxel threshold of FWE-corrected p < .05 and at z-threshold > 3.1. Results are presented in

Figure 2.

Brain activation was observed in adults most strongly during Flexibility trials compared with Control trials within the paracingulate gyrus, anterior cingulate gyrus, temporal fusiform cortex, temporal occipital fusiform cortex, lateral occipital cortex, cerebellum, supramarginal gyrus, posterior parietal lobule, angular gyrus, and anterior supramarginal gyrus (Figure 2a).

Brain activation was observed in children most strongly during Flexibility trails compared with Control trials within the lateral occipital cortex, paracingulate gyrus, anterior cingulate gyrus, middle, inferior and superior frontal gyrus, cerebellum, frontal orbital cortex, insular cortex, pallidum, angular gyrus, intracalcarine cortex, supramarginal gyrus, precuneus cortex, temporal gyrus, and precentral gyrus. (Figure

2b).

Group comparisons demonstrated greater brain activation in adults compared with children during the Flexibility trials compared with Control trials in the temporal fusiform cortex, temporal occipital fusiform cortex, cerebellum, supramarginal gyrus, superior parietal lobule, angular gyrus, inferior and middle frontal gyrus, precuneus cortex, paracingulate gyrus, insular cortex, anterior cingulate gyrus, frontal pole, and supplementary motor cortex (Figure 2c). Children did not have any brain regions more strongly activated compared with adults during the Flexibility trials compared with

Control trials (Figure 2d). These findings support our hypothesis that children will exhibit reduced activation in brain regions typically activated during flexibility trials.

Table 2

Comparison Brain region Network Peak Z MNI Coordinates

x y z

Adult Group L paracingulate gyrus, L-FPN 9.62 -2 24 38 anterior cingulate gyrus

L temporal fusiform Visual 9.46 -34 -44 -24 Cortex, temporal occipital fusiform cortex

L lateral occipital Dorsal 9.25 -28 -68 40 cortex Attention

R crus II, VIIb Cerebellum 9.21 6 -78 -38

L posterior L-FPN 9.17 -40 -46 38 supramarginal gyrus, superior parietal lobule, angular gyrus, anterior supramarginal gyrus

R crus II, vermis crus Cerebellum 9.11 6 -72 -30 II, vermis VI, VIIb

L lateral occipital Dorsal 6.46 -30 -66 42 Children cortex Attention Group L paracingulate gyrus, L-FPN 6.39 -4 26 38 superior frontal gyrus, anterior cingulate gyrus

L middle and superior L-FPN 6.18 -26 12 54 frontal gyrus

R crus II, VI, VIIb Cerebellum 6.07 8 -70 -30

R crus I Cerebellum 5.9 34 -62 -32

R frontal orbital cortex, L-FPN 5.71 -30 26 -4 insular cortex

L middle and inferior L-FPN 5.3 -40 30 22 frontal gyrus

R temporal occipital Visual 5.28 38 -58 -18 fusiform cortex

Vermis VI Cerebellum 5.13 -4 -70 -28

R paracingulate gyrus, M-FPN/ 5.08 10 28 34 anterior cingulate M-CIN gyrus, superior frontal gyrus

R pallidum Subcortical 5.05 14 0 6

L angular gyrus L-FPN 4.89 -36 -52 46

R intracalcarine cortex Visual 4.75 8 -68 10

R anterior cingulate M-CIN 4.72 12 32 22 gyrus, paracingulate gyrus

L posterior and anterior L-FPN 4.71 -46 -42 46 supramarginal gyrus

R precuneus cortex M-FPN 4.51 8 -64 44

L precuneus cortex M-FPN 4.48 -8 -70 50

L inferior temporal Dorsal 4.42 -46 -48 -10 gyrus Attention

L pallidum Subcortical 4.27 -18 -4 0

L crus II, VIIb Cerebellum 4.12 -6 -76 -38

L middle frontal gyrus, L-FPN 4.09 -40 10 30

precentral gyrus, inferior frontal gyrus

L intracalcarine cortex Visual 4.07 -14 -80 12

Adult > L temporal fusiform Visual 6.1 -36 -42 -26 Children cortex

L temporal occipital Visual 5.82 -42 -48 -24 fusiform cortex

L superior lateral Visual 5.65 -32 -84 32 occipital cortex

Vermix IX Cerebellum 5.56 0 -60 -40

L V Cerebellum 5.49 0 -56 -20

L supramarginal gyrus, L-FPN 5.41 -40 -46 38 superior parietal lobule, angular gyrus

L inferior lateral Visual 5.35 -46 -70 -20 occipital cortex

L I-IV Cerebellum 5.17 0 -46 38

L inferior frontal gyrus L-FPN 5.14 -50 20 24

R superior lateral Visual 4.97 36 -64 40 occipital cortex

R precuneus cortex M-FPN 4.78 2 -70 52

L paracingulate gyrus L-FPN 4.74 -2 24 22

R insular cortex M-CIN 4.73 36 18 -2

L insular cortex M-CIN 4.57 -32 16 0

R anterior cingulate M-CIN 4.38 2 2 68 gyrus

R inferior frontal gyrus L-FPN 4.33 52 16 14

R frontal pole, middle L-FPN 4.14 50 36 18 frontal gyrus

R middle frontal gyrus L-FPN 4.12 36 12 52

R supplementary motor Somato- 4.06 2 2 68 cortex motor

Children > None Adults

Note: Results are voxel-level thresholded at FWE-corrected p < .05 and Z > 3.1. Coordinates in white matter, brainstem, or outside the brain are not included and only results with Z > 4.0 are presented. Results are organized by peak Z value in descending order for each group or comparison. R = right; L = Left; L-FPN, lateral frontoparietal network; M-FPN, medial frontoparietal network; M-CIN, midcingulo insular network.

Co-activation pattern analysis (CAP)

Using elbow criterion, the optimal value of brain states was k = 5. The five dynamically re-occurring brain states were observed across all children and adult participants (Figure 3). CAP 1 was characterized by co-activation among the lateral frontoparietal (L-FPN; executive control), and medial frontoparietal network (M-FPN; default) nodes. CAP 2 was characterized by co-activation among the visual, somatomotor, dorsal attention (DAN), midcingulo-insular (M-CIN; salience), and L-FPN network nodes. CAP 3 was characterized by co-activation among the somatomotor, M- CIN, M-FPN, DAN, and temporoparietal network nodes. CAP 4 was characterized by co- activation among the limbic, L-FPN, and M-FPN network nodes. CAP 5 was characterized by co-activation among the visual network nodes.

Age differences in CAPs

Group differences between children and adults in brain dynamic metrics for each

CAP were first tested using independent sample t-tests. Children and adults had significantly different CAP 2 dwell times, (t(55) = 3.33, p = .002); CAP 4 dwell times,

(t(55) = 2.20, p = .032); and CAP 5 frequency of occurrence, (t(55) = -2.88, p = .006).

CAP 2 was characterized by co-activation among the visual, somatomotor, DAN, M-

CIN, and L-FPN network nodes; CAP 4 was characterized by co-activation among the limbic, L-FPN, and M-FPN network nodes; and CAP 5 was characterized by co- activation among the visual network nodes. Children had longer dwell times of CAP 2 and CAP 4, and a less frequently occurring CAP 5 (Figure 4).

Multiple regressions were examined using a continuous age predictor with covariates. Results were consistent with the categorical approach such that a continuous measure of age predicted CAP 2 dwell time, b = -0.01, SE = 0.005, p = .046; CAP 2 frequency, b = -0.001, SE = 0.001, p = .015; CAP 4 frequency, b = -0.001, SE = < 0.001, p = .016; and CAP 5 frequency, b = 0.002, SE = < 0.001, p = .002 (Figure 5).

Brain-behavior relationships with CAPs

Brain-behavior relationships were tested using categorical and continuous age moderators. Age (children = 0 and adults = 1) was first tested as a moderator of brain- behavior relationships. Age moderated the relationship between BRIEF shift and CAP 1 frequency, b = -0.003, SE = 0.001, p = .007. CAP 1 was characterized by co-activation among the L-FPN and M-FPN nodes. Simple slope analysis revealed the relationship between BRIEF shift and CAP 1 frequency was not significant in children, b = -0.001, SE

= 0.001, p = .224, but was significant in adults, b = 0.02, SE = 0.001, p = .001. The relationship between BRIEF shift and the brain dynamic metrics were also tested using age as a continuous moderator. This resulted in the same finding such that age moderated the relationship between BRIEF shift and CAP 1 frequency, b = < 0.001, SE = < 0.001, p

= .038. Simple slope analysis (-1 SD, mean, + 1 SD) revealed the relationship between

BRIEF Shift and CAP 1 frequency was not significant at -1 SD (9 years), b = -0.003, SE

= 0.001, p = .670; or at the mean age (18 years), b = 0.001, SE = < 0.001, p = .088; but was significant at + 1 SD (27 years), b = 0.002, SE = 0.001, p = .005 (Figure 5D). There were no significant interactions between age and behavioral performance on the cognitive flexibility task (p’s > .05).

Following non-significant interactions, brain-behavior relationships between CAP brain dynamic metrics and BRIEF shift and behavioral performance on the cognitive flexibility task was assessed while controlling for sex, age, and head motion. There were no significant relationships between CAP dynamics and behavioral performance

(accuracy or reaction time), on the cognitive flexibility task for adults, p’s > .05.

However, there were significant relationships between BRIEF shift and CAP 2 frequency, b = -0.001, SE = < 0.001, p = .046 (Figure 5E); and CAP 4 frequency, b = -

0.001, SE = < 0.001, p = .004 for both children and adults (Figure 5F). CAP 2 was characterized by co-activation among the visual, somatomotor, DAN, M-CIN, and L-FPN network nodes; and CAP 4 was characterized by co-activation among the limbic, L-FPN, and M-FPN network nodes.

CHAPTER 4: DISCUSSION

Cognitive flexibility is an aspect of executive function, or goal-directed process, important for adapting to environmental changes and for developmental outcomes such as academic achievement. Cognitive flexibility deficits are observed in various neurodevelopmental disorders such as autism spectrum disorder (Lucina Q. Uddin,

2020). Therefore, it is important to understand the neural correlates associated with cognitive flexibility throughout development to aid treatments and optimize outcomes.

The current study examined brain activation and brain dynamic differences in children and adults during a cognitive flexibility task. As predicted, both groups had brain activation in networks important for cognitive flexibility including the lateral frontoparietal (L-FPN; central executive) and midcingulo-insular (M-CIN; salience) networks, and adults had greater activation compared with children in these networks.

We additionally demonstrate brain dynamics among some brain states consisting of multiple networks dwelled longer and were more frequent in children compared with adults, that age moderated the relationship between BRIEF shifting and the frequency of the L-FPN/medial frontoparietal (M-FPN; default) state, and some states consisting of multiple networks were important for shifting abilities in the real-world as measured by the Behavior Rating Inventory of Executive Function (BRIEF; Gioia G. A., Isquith P. K.,

Guy S. C., Kenworthy L.., 2000).

Brain activation

In adults, we found robust activation among regions important for cognitive flexibility including the paracingulate gyrus, anterior cingulate gyrus, temporal fusiform cortex, temporal occipital fusiform cortex, lateral occipital cortex, cerebellum,

42 supramarginal gyrus, posterior parietal lobule, angular gyrus, and anterior supramarginal gyrus. In children, similar brain regions were active as compared with adults but were less robust and more diffuse. This is consistent with previous theories suggesting that fine tuning of relevant neural systems in regions important for task learning occurs across development (Durston et al., 2006; Levitt, 2003). The adult activation findings have previously been published (Dajani et al., 2020). Here we extend this previous work by examining the effects in children. The results for the children group are consistent with previous findings (D’Cruz et al., 2016; Wendelken et al., 2012). For example, previous research found similar brain regions active in children and adults during a cognitive flexibility task including the anterior cingulate cortex, prefrontal cortex, precuneus, and the visual cortex (D’Cruz et al., 2016; Wendelken et al., 2012).

Adult > Children group differences revealed that adults had greater brain activation in key cognitive flexibility brain regions compared with children within the insular cortex, anterior cingulate gyrus, and superior and middle frontal gyrus. Previous studies support our findings (Casey et al., 2004; Crone et al., 2008; Ezekiel et al., 2013;

Rubia et al., 2006), as these studies also found adults had greater activation compared with children during cognitive flexibility tasks in the superior frontal gyrus (Casey et al.,

2004), anterior cingulate gyrus (Crone et al., 2008; Ezekiel et al., 2013; Rubia et al.,

2006), and insula (Crone et al., 2008; Rubia et al., 2006). The superior frontal gyrus is thought to play a prominent role in task switching (Crone, Wendelken, et al., 2006; Cutini et al., 2008) and working memory (du Boisgueheneuc et al., 2006). The anterior cingulate cortex and insula make up the salience network (M-CIN), and together serve to integrate external sensory information with internal states (e.g., emotions or goals) (Seeley et al.,

2007). The insula is thought to be broadly associated with subjective salience associated with homeostatic, emotional, and cognitive factors (Craig & (Bud) Craig, 2009; Lucina

Q. Uddin, 2015) while the anterior cingulate gyrus monitors conflicts, detects errors, and is implicated in emotions (Rolls, 2019). Our findings extend previous work since the

FIST is thought to better resemble real-world situations and is a reliable measure of cognitive flexibility (Dajani et al., 2020). Overall, our work extends previous research by revealing key developmental brain regions implicated in cognitive flexibility.

Co-activation pattern analysis (CAP)

Understanding temporal changes in brain states during a task has implications for real-world scenarios and may reveal more information about developmental changes than previous ‘static’ work. Brain dynamic patterns during the FIST were examined in children and adults. First, we examined age relationships with each CAP and found age was significantly associated with multiple CAP dynamics during the cognitive flexibility task including CAP 2 dwell time, CAP 4 dwell time, and CAP 5 frequency. CAP 2 was characterized by co-activation among the visual, somatomotor, dorsal attention network

(DAN), M-CIN, and L-FPN network nodes. CAP 4 was characterized by co-activation among the limbic, L-FPN, and M-FPN network nodes. Lastly, CAP 5 was characterized by co-activation among the visual network nodes. In the CAPs with a hybrid brain network coupling (CAP 2 and CAP 4), children had longer dwell times and more frequently occurring states compared with adults. Conversely, in the state consisting of one network (CAP 5; visual), this state occurred less frequently in children compared with adults. Our findings are consistent with the notion that task-related patterns of brain activation change from more diffuse to more focal across development (Durston et al.,

2006), such that brain regions important for a task become more efficient and rely on fewer other brain regions. Our results are also consistent with other papers assessing functional connectivity and brain dynamics changes in children and adults during rest and task states. Previous work has shown that children (9-18 years) may express certain states more frequently than adults (19-32 years), while adults express other states more frequently (Hutchison & Morton, 2015). As in the current study, how frequently a state occurs across development depends on the state. Further, connectivity among networks such as the M-FPN, L-FPN, and M-CIN become increasingly anticorrelated and separated across development (Sherman et al., 2014). Similarly, brain dynamic patterns during task states become more efficient or less variable across development (Hutchison

& Morton, 2015). Therefore, brain states with hybrid coupling of networks may occur less frequently and dwell less across development as they may not be as relevant to the task, whereas engagement of a state with one network may be more efficient and relevant to the task. Our results demonstrate children may express certain hybrid states more frequently compared with adults during a cognitive flexibility task.

Age Moderates real-world cognitive flexibility and Brain Dynamics

We also examined brain-behavior relationships, and tested age as a moderator of cognitive flexibility and dynamic brain states. Age moderated the relationship between

BRIEF shift and CAP 1 frequency, which consisted of co-activation among the L-FPN

(central executive) and M-FPN (default) nodes. In one of our previous studies, we found a resting-state CAP consisting of co-activation between the L-FPN and M-FPN was critical across the lifespan and for cognitive flexibility (Kupis et al., Under Revision).

Other work reveals that L-FPN/M-FPN coupling is important across neurocognitive

45 aging, as it may reflect the interactions between cognitive fluidity and crystalized abilities

(Spreng et al., 2018). For example, the L-FPN is thought to support goal-oriented behavior (Koechlin et al., 2003) and is involved in task-set switching (Dreher & Berman,

2002), and the M-FPN is thought to support internally directed cognitive processes such as storing knowledge representations and experiences (Andrews-Hanna et al., 2014); therefore, coupling strength of these networks may represent the balance between cognitive control and recollecting autobiographical memories or internal thought processes (Spreng et al., 2018).

In the current study, we found that in children (7-12 years years) and young adults

(~ 19 years) the relationships between how frequently the M-FPN/L-FPN state occurred and cognitive flexibility as indexed by the BRIEF shift was similar. The L-FPN/M-FPN state occurred more frequently in young adults than in children, but in both ages poorer shift scores were associated with fewer occurrences of CAP 1 (Figure 5d). Conversely, in older adults, CAP 1 occurred more frequently regardless of the BRIEF shift score. These findings are consistent with our previous work such that the frequency of the L-FPN/M-

FPN state increased through childhood and occurred most frequently during middle age

(~40 years). Additionally, middle age-was associated with optimal cognitive flexibility performance regardless of how long the M-FPN/L-FPN state dwelled for (Kupis et al.,

Under Review). The current study extends previous work by revealing when the L-

FPN/M-FPN state occurs less frequently during childhood it is associated with poorer cognitive flexibility. Additionally, our findings suggest this state is important during task- evoked cognitive flexibility states. Coupling of the L-FPN/M-FPN is important for neurocognitive aging (Spreng et al., 2018; Turner & Nathan Spreng, 2015), and appears

46 to be important for task-engaged cognitive flexibility across development (Kupis et al.,

Under Review).

Brain dynamics during a cognitive flexibility task relate to real-world cognitive flexibility

We also found brain-behavior relationships between CAP brain dynamic metrics and BRIEF shift for CAP 2 frequency, and CAP 4 dwell time and frequency. Children did not significantly differ from adults on their BRIEF shift scores, and both groups were included in the brain-behavior analyses. In both brain states, greater frequency of occurrences was associated with enhanced cognitive flexibility regardless of age. Greater dynamic brain flexibility is increasingly associated with enhanced cognitive performance

(Battaglia et al., 2020; Braun et al., 2015; Jia et al., 2014; Nomi, Vij, et al., 2017; Xia et al., 2019). Similar to our findings, a previous study examining brain dynamics during a resting-state associated with executive function performance in young adults found elevated cognitive flexibility performance was associated with greater episodes of more frequently occurring states (Nomi, Vij, et al., 2017). Further, in the current study, brain dynamic patterns were not associated with behavioral performance during the task, but instead associated with real-world switching as measured by the BRIEF. The brain states

(CAP 2 and CAP 4) that were associated with shifting included co-activation of multiple networks. In real-world cognitive experiences, neural activity may reflect the interactions among motor, social, emotional, cognitive, and perceptual processes (Bottenhorn et al.,

2019). Additionally, a growing idea is that complex behaviors are facilitated by interactions among several networks (Barrett & Satpute, 2013; Lindquist et al., 2012;

Mišić & Sporns, 2016; Spreng et al., 2013).

CAP 2 and CAP 4 relationships with BRIEF shifting reflect the notion that multiple networks interact to support complex behavioral processes. CAP 2 included co- activation among the visual, somatomotor, DAN, M-CIN and L-FPN networks. Previous research suggests sensory input through visual and somatomotor regions occur during real life processing, attentional processes gate irrelevant stimulus information, and information processing further occurs based on salience detection (M-CIN) and ultimately promotes task specific regions (L-FPN) (Bottenhorn et al., 2019). CAP 2 suggests these processes are interacting to facilitate real-life cognitive flexibility. There is also evidence that emotional processes interact with cognitive flexibility. For example, having a positive affect may benefit cognitive flexibility (Dreisbach & Goschke, 2004), whereas a negative affect may result in poorer cognitive flexibility performance (Wang et al., 2017). In the current study, CAP 4 included co-activation among the limbic

(orbitofrontal cortex and temporal poles), L-FPN, and M-FPN networks. Since the M-

FPN connections with the L-FPN are thought to be associated with cognitive abilities

(Spreng et al., 2018) such as cognitive flexibility (Kupis et al., Under Review), and limbic regions are associated with emotional processes (Barbas, 2007), connectivity among those networks may support the integration of emotions and emotional memories with decision-making and action in complex behavior such as cognitive flexibility.

Interestingly, CAPs 2 and 4 were not associated with behavioral performance on the cognitive flexibility task. Previous research reveals cognitive performance on tasks do not always correlate with real-world executive functions as measured by the BRIEF

(Toplak et al., 2013). One hypothesis why this may occur is that behavioral performance on a task may be related to efficiency and information processing in the brain, whereas

48 real-world executive functions may be reflective of the individual’s goals and beliefs

(Toplak et al., 2013). The CAPs found to be associated with the real-world measure of shifting may be reflective of this idea. Our novel findings suggest brain dynamics during a cognitive task may be associated with real-world abilities.

Limitations and future directions

Various limitations are important to note in this study. First, the age sample is limited to 7-12 year old children and 19-46 year old adults. Future work should include a larger age range. Additionally, our sample size is limited, therefore, future work should include a larger sample of participants. Head motion is another limitation and particularly a concern for dynamic brain analyses, however, this was accounted for by including head motion regressors and removal of time points with excessive head motion during the preprocessing steps. Additionally, future work should consider preprocessing pipelines that do not censor time points or use of a more lenient censoring cut off. Another potential limitation is the focus on cognitive flexibility rather than exploring associations with other executive functions or executive functions broadly. Prior work examining the relationships between brain dynamic patterns and executive function found that although cognitive flexibility is correlated with other executive functions, certain brain dynamic patterns were only associated with cognitive flexibility (Nomi, Vij, et al., 2017). Future work is needed to continue to connect brain dynamic patterns with executive functioning.

Lastly, novel brain dynamic methods exist that do not arbitrarily collapse data into space or time (Saggar et al., 2018) and should be used alongside CAPs to explore how different aspects of brain function relate to executive function.

Conclusion

The current study demonstrates a relationship between brain dynamic patterns and development during task-evoked states. Our findings reveal the importance of assessing temporal shifts in brain organization that occur during a task across development.

Additionally, we found age-related differences in a brain state important for cognitive aging as it related to shifting in the real-world. Lastly, the current study demonstrated relationships between brain state dynamics and shifting in the real world suggesting naturalistic cognitive flexibility requires interactions across modalities and reference to the individual’s goals and memories. Overall, these results reveal the unique contributions brain dynamic approaches provide to our understanding of the neural changes associated with cognitive flexibility development, and can serve as a framework for future investigations exploring real-world cognitive flexibility deficits in clinical populations.

FIGURES

Figure 1.

Figure 1. Flexible Item Search Task (FIST)

Top: A schematic of the fMRI-adapted 4-Match FIST for adults. Bottom: Control trials and Flexibility trials. During the Flexibility trials, participants were asked to choose three successive pairs of cards that matched in one dimension (i.e., shape, color, size,

51 number). Control trials consisted of pairs highlighted in black and participants were instructed to select the highlighted pairs using a corresponding button press. Fixation trials were jittered to optimize the presentation times for an event-related design. The FIST for children was similarly designed but the task durations were 12 seconds rather than 8 seconds, and control trials lasted 12 seconds rather than 8 seconds. Figure adapted from (Dajani et al., 2020).

Figure 2

Figure 2.

A) Adults within-group brain activation for Flexibility > Control contrast and voxel level corrected z > 3.1, cluster level p < .05. B) Children within-group activation for Flexibility > Control contrast and voxel level corrected z > 3.1, cluster level p < .05. C) Group comparison adults > children brain activation for Flexibility > Control contrast and voxel level corrected z > 3.1, cluster level p < .05. D) Group comparison children > adults brain activation for Flexibility > Control contrast and voxel level corrected z > 3.1, cluster level p < .05.

Figure 3

Figure 3.

Five dynamically re-occurring co-activation pattern (CAP) brain states. SomMot, somatomotor network; DAN, dorsal attention network; M-CIN, midcingulo-insular network (Salience); L-FPN, lateral frontoparietal network (central executive); M-FPN, medial frontoparietal network (default); TempPar, temporoparietal network.

Figure 4

Figure 4.

Significant independent sample t-tests comparing children and adults on brain dynamic metrics for CAP 2, CAP 4, and CAP 5 brain states. CAP 2 was characterized by co-activation among the visual, somatomotor, DAN, M-CIN, and L-FPN network nodes; CAP 4 was characterized by co-activation among the limbic, L-FPN, and M-FPN network nodes; and CAP 5 was characterized by co-activation among the visual network nodes.

Figure 5

Figure 5.

All regressions include sex and head motion covariates, and e) and f) included age as a covariate. A) Linear regression between age and dwell time of co-activation pattern (CAP) 2. As age increased, the dwell time of CAP 2 decreased. CAP 2 was characterized

55 by co-activation among the visual, somatomotor, dorsal attention, M-CIN (salience), and L-FPN (executive control) network nodes. B) Linear regression between age and frequency of CAP 2. As age increased, the frequency of CAP 2 decreased. C) Linear regression between Age and frequency of CAP 4. As age increased, the frequency of CAP 4 decreased. CAP 4 was characterized by co-activation among the limbic, L-FPN, and M-FPN network nodes. D) Linear regression between Age and frequency of CAP 5. As age increased, frequency of CAP 5 increased. CAP 5 was characterized by co- activation among the visual network nodes. E) Age moderated cognitive flexibility, as indexed by the shift T scores, and frequency of CAP 1. 7 year olds and 18 year olds had similar slopes such that CAP 1 occurred less frequently as shift scores increased. 46 years was associated with a frequently occurring CAP 1 regardless of increasing shift scores. CAP 1 was characterized by co-activation among the L-FPN (executive control), and M- FPN (default) nodes. F) Linear regression between Shift T scores and frequency of CAP 2. CAP 2 occurred less frequently as shift scores increased. G) Linear regression between Shift T scores and frequency of CAP 4. CAP 4 occurred less frequently as shift scores increased.

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