Selective Relationships Between Sensory System Connectivity And Sensory And Cognitive Function In Aged Macaques

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Authors De La Peña, Nicole Marie

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SELECTIVE RELATIONSHIPS BETWEEN SENSORY SYSTEM WHITE MATTER

CONNECTIVITY AND SENSORY AND COGNITIVE FUNCTION IN AGED

MACAQUES

By NICOLE MARIE DE LA PEÑA

______

A Thesis Submitted to the Honors College In Partial Fulfillment of the Bachelor’s Degree With Honors In Neuroscience and Cognitive Science UNIVERSITY OF ARIZONA M A Y 2 0 1 9

Approved by:

Dr. Carol Barnes, PhD Department of Psychology, Neurology and Neuroscience 2

Selective Relationships Between Sensory System White Matter Connectivity and Sensory and Cognitive Function in Aged Macaques

Authors Nicole M. De La Peña1,2, Daniel T. Gray1,2, Lavanya Umapathy3, Sara N. Burke4, James R. Engle1,2, Theodore P. Trouard2,5, Carol A. Barnes1,2,6

Author Affiliations 1Division of Neural System, Memory & Aging, University of Arizona, Tucson, AZ 2Evelyn F. McKnight Institute, University of Arizona, Tucson, AZ 3Electrical and Computer Engineering, University of Arizona, Tucson, AZ 4Evelyn F. McKnight Brain Institute, University of Florida, Gainesville, FL 5Department of Biomedical Engineering, University of Arizona, Tucson, AZ 6Departments of Psychology, Neurology and Neuroscience, University of Arizona, Tucson, AZ

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Abstract

Normative aging results in deficits in both auditory and visual function, along with degradation of select cognitive functions. Studies have shown that sensory function is a predictor of late-life cognitive abilities, though the neurobiological base of this relationship is unclear. Previously our group found that the connectivity of medial temporal lobe-associated white matter was related to better auditory processing abilities and temporal lobe-dependent cognitive functions. This study concluded that shared impacts of aging on temporal lobe structures could account for the selectivity in these relationships. However, little is known about the association between sensory system white matter connectivity and sensory and cognitive function with age. In this study, adult and aged bonnet macaque monkeys were behaviorally characterized and evaluated for auditory and visual function. Measures of auditory and visual system white matter connectivity were extracted using diffusion MRI and probabilistic tractography. We found that higher connectivity of callosal auditory fibers was associated with better auditory function, and higher connectivity of the posterior forceps and were associated with better visual function. Higher connectivity of white matter was associated with better performance on certain temporal-lobe dependent cognitive tasks. Our results support the idea that a shared impact of aging on temporal lobe structures could partially drive relationships between auditory processing and temporal lobe-dependent cognition.

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Introduction

Relationship between Sensory and Cognitive Function

The normative aging process results in decreased functioning in multiple cognitive domains, as well as in sensory systems, especially in the modalities of and vision. In fact, approximately one third of people over the age of 65 are affected by disabling hearing loss and around one fifth of adults over the age of 60 have some degree of visual impairment (Bourne et al., 2017; Brown and Barrett, 2011).

Likewise, cognitive abilities, such as executive function, perceptual speed, and memory, tend to decline with age (Madden et al., 2012). Over the past 20 years, there has been an increase in evidence suggesting that there is an association between age-related cognitive decline and sensory deterioration (e.g. Baltes and Lindenberger, 1997; Li and Lindenberger, 2002; Lindenberger and Baltes, 1994). These studies have suggested that sensory function is a strong predictor of late-life individual variability in intelligence, with visual and auditory acuity accounting for around 93% of age-related individual differences in cognitive abilities (Li and Lindenberger, 2002; Lindenberger and Baltes, 1994). When assessments of auditory, visual, and tactile temporal processing were measured, it was found that age, overall sensory processing, and overall cognitive function were significantly correlated (Humes et al., 2013). Critically, when sensory processing was held constant in the model, the correlation between age and cognitive functioning was eliminated (Humes et al., 2013). The correlation between sensory and cognitive impairment, however, was usually present after adjusting for age (Roberts and Allen, 2016). This suggests that age-related changes in cognition may be mediated by age-related changes in sensory processing (Humes et al., 2013).

Along with the correlation between sensory function and cognitive abilities, the risk of incident dementia and possibly even Alzheimer’s disease increases with the severity of age-related hearing loss, or presbycusis (Jayakody et al., 2018a; Lin et al., 2011). Additionally, presbycusis is positively correlated with late-life depression, anxiety, social isolation, and stress, which can exacerbate the aging process by increasing certain risk factors for age-associated diseases (Jayakody et al., 2018a, 2018b). Therefore, it is important to consider the prospect of sensory aids in not only correcting for sensory deficits, but also in increasing cognitive function, reducing the risk of dementia and Alzheimer’s disease, and improving the 5

quality of life for older individuals. For instance, it has been shown that older individuals who received cochlear implants have a decrease in stress and depression levels, while also improving in short term memory, spatial working memory, and executive function (Castiglione et al., 2016; Jayakody et al., 2017;

Sonnet et al., 2017).

Age-Related Changes in White Matter Connectivity

Another hallmark of the normal aging process is a decline in the microstructural integrity of white matter throughout the central nervous system, as well as an overall reduction in the volume of white matter in the cerebral cortex (Peters et al., 1996). In contrast, age-related declines in the numbers of neurons in the neocortex and hippocampus is not observed in rhesus monkeys (Makris et al., 2007; Peters et al., 1996; West et al., 1993). In humans, there similarly does not appear to be a loss of neurons with age, although some studies have shown changes in neuronal numbers (e.g. Simic et al., 1997). Decreased white matter integrity throughout the brain is thought to result in a disconnection of widely distributed neural networks and an inability to integrate information, which is important for executive function, perceptual speed, and memory (Makris et al., 2007; Madden et al., 2012). One method of obtaining quantitative estimates of white matter connectivity is to use diffusion magnetic resonance imaging

(dMRI) techniques, such as diffusion tensor imaging (DTI). DTI measures the directionality of the displacement of water molecules across brain tissue (Madden et al., 2009). One of the most frequently used summary measures of DTI is fractional anisotropy (FA), a scalar value where higher values reflect higher directional dependence of diffusion (Madden et al., 2009). It is not entirely clear what FA measures biologically, but many consider this value to be a proxy for white matter integrity (Madden et al., 2009).

Age-related changes in fractional anisotropy are thought to be more prominent in areas of the brain that become myelinated later in life being more susceptible to deterioration as compared to early myelinating regions (Bartzokis et al., 2004; Davis et al., 2009; Pfefferbaum et al., 2005). It has been shown that age is associated with a decrease in FA in the genu, rostral body, and isthmus of the corpus callosum, as well as the frontal forceps and hippocampal commissure (Bartzokis et al., 2004; Ota et al., 6

2006; Pfefferbaum et al., 2005; Zahr et al., 2009). The literature on age-related white matter decline and its impact on age-related cognitive deficits is inconsistent in its conclusions. In several studies, a decline in FA values in the anterior tracts, such as the genu of the corpus callosum, was associated with poorer performance on executive function tasks, problem-solving tasks, and processing speed (Davis et al., 2009;

Kennedy and Raz, 2009; Makris et al., 2007; Zahr et al., 2009). FA values in more posterior tracts, such as the splenium, correlated with performance on visual memory tasks and task switching (Davis et al.,

2009; Kennedy and Raz, 2009). These studies indicate that the integrity of different white matter tracts may mediate distinct aspects of cognitive decline. Other studies challenge this hypothesis, however.

Salami et al., 2012, for example, found that white matter integrity in the body of the corpus callosum mediated age-related reductions in processing speed but not visuospatial ability or fluency, suggesting that compromised white matter is not a major contributor to decreased cognitive function in normal aging.

Furthermore, some of these studies also found that age was a mediating variable in the correlation between white matter and cognition (Davis et al., 2009; Kennedy and Raz, 2009; Zahr et al., 2009), while others showed that age did not fully explain this relationship (Makris et al., 2007).

In studying the cognitive aging process, nonhuman primates are a good model organism due to multiple factors, including their similar brain anatomy as well as resemblance in age-related cognitive changes in both quality and severity to humans (Hara et al., 2012; Nagahara et al., 2010). For example, executive functioning is one of the first mental processes to decline with age in both macaques and humans (Hara et al., 2012). Another benefit is the apparent resistance that macaques have to neurodegenerative diseases that appear with age in humans, such as Alzheimer’s disease (Kohama et al.,

2012). This makes them stable models for studying normative aging without the confounding variables introduced by pathologies associated with these diseases. Lastly, using nonhuman primates as a model allows for the collection of spatially and temporally precise electrophysiological and morphological data from a single individual over a long period of time (Peters et al., 1996).

In macaques, it has previously been found that degradation in certain white matter tracts correlates with performance on specific cognitive tasks. Gray et al., 2018, for example, found that FA 7

indices of the right hemisphere uncinate fasciculus and amygdalofugal pathway, two anatomical tracts containing fibers connecting the amygdala and orbitofrontal cortex, selectively correlated with performance on a reward devaluation task, but not a reversal learning task. The specificity of the correlations between a particular behavior and white matter tract suggests that age-related declines in certain cognitive domains are not due to a general degradation of connectivity in the brain, but rather to selective changes in white matter tracts. Previous work in our group has also found that the microstructural integrity of medial temporal lobe-associated white matter, particularly the fimbria-fornix and hippocampal commissure, was related to temporal lobe-dependent cognitive functions, as well as temporal auditory processing abilities (Gray, 2019). This study concluded that shared impacts of aging on temporal lobe structures could account for the selectivity of associations between auditory processing abilities and temporal lobe-dependent cognitive functions. However, little is known about the association between the connectivity of sensory-associated white matter tracts and different aspects of sensory and cognitive function with age.

Goals of the Present Study

The first goal of the current study is to determine whether the microstructural integrity of interhemispheric and thalamocortical white matter tracts connecting visual and auditory brain regions changes with age in macaques. The second goal is to determine whether the FA of these white matter tracts correlates with estimates of sensory and cognitive function in these animals. To this end, adult and aged bonnet macaque monkeys were behaviorally characterized through multiple cognitive tasks that tested functions associated with prefrontal and temporal lobe regions. Electrophysiological assessments of auditory threshold, auditory temporal processing, and visual temporal processing were obtained. Lastly, measures of white matter connectivity were extracted from two different interhemispheric projections and three thalamocortical radiations connecting auditory and visual systems using probabilistic tractography.

The results presented here provide evidence for a sensory-modality specific relationship between the connectivity of sensory-associated white matter and sensory function. Additionally, these data further support the idea of a shared age-related structural and functional decline in the integrity of temporal lobe 8

structures as a basis for the selectivity of associations between auditory processing abilities and temporal lobe-dependent cognitive functions.

Methods

Animal Subjects

The same cohort of five aged (mean: 26 years; range 24.25 – 30.8 years) and six adult (mean:

13.3 years; range: 11.25 – 15 years) female bonnet macaque monkeys (Macaca radiata) as in Gray, 2019 were used in the present study. Data from this cohort of animals have been reported previously (Burke et al., 2011; Burke et al., 2014; Comrie et al., 2018; Gray et al., 2017; Gray et al., 2018). Semiannual health evaluations were performed on each animal by the veterinary staff at the University of Arizona (Tucson,

AZ), and no animal displayed health concerns before or during testing. All monkeys were pair-housed in a temperature- and humidity-controlled vivarium with a 12-hour light-dark cycle with ad libitum access to food and water. The monkeys underwent behavioral shaping to allow for transport between the home vivarium and the behavioral testing apparatus (described below) in a specialized nonhuman primate holding box (dimensions: 50.8 cm x 31.1 cm x 40 cm). All experimental protocols were approved by the

Institutional Animal Care and Use Committee at the University of Arizona and followed the guidelines set by the National Institutes of Health.

Cognitive Testing

Testing Apparatus

Behavioral testing was performed in a modified Wisconsin General Testing Apparatus (WGTA), which is comprised of a holding box where animals reside during testing. One wall of the box is made up of vertical metal bars that separate the monkey from a tray with three equally spaced wells used for stimulus and reward delivery. The monkeys can reach through the bars to access the stimuli and rewards located in the wells. A wooden guillotine door was used by the experimenters to control the animals’ 9

access to and visibility of the stimuli. A transparent acrylic guillotine door was also used to allow monkeys to see but not interact with the stimuli. A one-way mirror separating the experimenters from the monkeys permitted the experimenters to observe the monkeys’ performance without detection. Stimulus objects were plastic toys of similar size (~8 cm3) and food rewards consisted of fresh fruit, vegetables, and sugar free candy. The behavioral procedures have been described in detail previously (Burke et al., 2014;

Comrie et al., 2018; Gray et al., 2017; Gray et al., 2018), so they will only be briefly outlined below.

Reversal Learning

The reversal learning paradigm (Figure 1B) was characterized in Burke et al., 2014 and Gray et al., 2017, 2018 and is a test of behavioral flexibility and inhibition of prepotent responses (Izquierdo and

Jentsch, 2011). Monkeys were trained on an object discrimination (OD) task with 40 object-pair associations, where one object in the pair was baited with a food reward. The monkey could only choose to displace one object in the pair and could obtain the food from the well under it if the correct object was displaced. Once the monkey reached a 90% performance criterion over 5 consecutive sessions, the reward associations were reversed such that the previously unrewarded object became rewarded and vice versa.

The monkeys performed the reversal learning task until they reached the learning criterion of 90% performance over 5 consecutive sessions. The effects of reversal learning were quantified using a state- space model of binary trial-response data (Gray et al., 2017, 2018). This model outputs a learning curve from which an estimated learning trial can be predicted.

LEGO-based Visual Discrimination

Visual discrimination abilities were further assessed in a smaller subset of these monkeys (n = 4 adult and n = 3 aged) using a pattern recognition task with LEGOS (Billund, Denmark; Figure 1D). The full protocol for this task has previously been described in detail (Burke et al., 2011). LEGOS were used in order to systematically vary the degree of overlap between patterns to be discriminated. Overlap scores were computed by dividing the total number of LEGO nobs that were the same between the two patterns by the total number of nobs in the two patterns. Similarities of 60%, 71%, 86%, and 92% were used. As with the object discrimination task described above, one pattern was always associated with a reward, 10

whereas the other was not. Monkeys performed 30 trials a day at each similarity level until reaching a performance criterion of 90% over 3 consecutive sessions. The number of errors performed before reaching criterion is the measure of visual-pattern discrimination learning used throughout this study.

Reward Devaluation

The reward devaluation paradigm (Figure 1F) was described in detail in Burke et al., 2014 and

Gray et al., 2017, 2018. First, all monkeys completed a 14-day food preference testing paradigm to determine each monkeys’ preferred food (the two highest preferred foods were referred to as food 1 and food 2). Following this, the monkeys were trained on a set of 40 OD tasks, where the monkeys’ created associations between distinct items in the object-pairs and food rewards. Half of the rewarded objects were associated with food 1, while the other half were associated with food 2. Monkeys were trained on this task to a performance criterion (90% performance over 5 consecutive sessions). Then, the animals underwent the devaluation component, which tested the animals’ ability to use the value of a food reward to guide choices and maximize rewards. In these sessions, only rewarded objects were used. In the baseline sessions, monkeys performed the task without receiving any food prior to testing. Then, the monkeys performed the same task ~10 minutes after undergoing a selective satiation procedure for either food 1 or food 2. A second baseline testing session followed the satiation sessions. The monkeys also underwent reward devaluation tasks without objects, which served as a control for the ability of satiation to modify the monkeys’ food preferences. The effect of reward devaluation was quantified using a difference score, which was the change in choices of object/food type in selective satiation sessions as compared to baseline sessions. Positive values indicated a preference for the non-devalued object, while negative values indicated a preference for the devalued object.

Delayed Nonmatching-to-Sample Task (DNMS)

A detailed description of the DNMS paradigm (Figure 1H) is contained in Comrie et al., 2018.

This learning task is used to assess non-spatial object recognition memory. In brief, the task began with a single object placed over the middle well that was baited with food. The monkeys could displace the object and retrieve the reward in the well. The wooden guillotine door was lowered for a variable delay 11

period (10 seconds during training and 30, 120, or 600 seconds during testing). Once the door was raised, the monkeys were presented with the familiar object along with a novel object over the two lateral wells.

Only the well under the novel object was baited. Monkeys learned through trial and error that the well underneath the novel object contained the food reward and was the correct response. Monkeys were trained until they reached learning criterion (90% performance over 5 consecutive sessions). The acquisition data display how many trials were required until the learning criterion was reached. The testing phase consisted of five days where the delays were sequentially increased. The average performance data display the average proportion of correct responses across the five delay conditions.

Delayed Response Task

The delayed response task (Figure 1K) is a test of visuospatial short-term memory and is outlined in Comrie et al., 2018. Briefly, the monkeys were allowed to observe through the acrylic door as the experimenter baited one of the two lateral wells with a food reward. Then the experimenter covered both wells with two identical opaque plaques. The wooden guillotine door was dropped and commenced a delay period of either 0 or 1 seconds for the training phase or 5, 10, 15, 30, or 60 seconds for the testing phase. Following the delay, the door was lifted, and animals were allowed to displace one of the plaques, retrieving the food reward if the baited plaque was chosen. Acquisition data display the number of trials required to reach the learning criterion (90% performance over 3 consecutive 30-trial sessions) with the 1 second delay. Once learning criterion was met, the monkeys began the testing phase, where the delays were sequentially increased. The performance data display the average proportion of correct responses across the 5 delay conditions.

Composite Cognitive Score

Data from each task in this cognitive battery were z-score normalized to standardize the units of all data. Higher z-score values indicate better performance on the tasks. The composite cognitive score for each animal was determined by an average of all the z-scores.

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Auditory and Visual Sensory Measures

Auditory Brainstem Response

The auditory brainstem response (ABR; Figure 2B, 2C, 2E) procedures followed the protocols performed in previous studies (Engle et al., 2013; Fowler et al., 2010; Gray, 2019; Ng et al., 2015). Each animal was anaesthetized with ketamine (1.5–2.0 mg/kg) and DexMedetomidine (0.007– 0.01 mg/kg) and placed in a prone position with their head elevated. The skin was cleaned with alcohol and sterile 22- gauge stainless steel electrodes were positioned subcutaneously behind both ears, the forehead, and the back of the neck (Allen and Starr, 1978; Fowler et al., 2010). Soft insert earphones (etymotic ER3A transducers) were placed into each ear canal. Evoked potentials were collected with an Intelligent Hearing

System (Smart EP Win USB, v. 3.97) on a laptop computer. Auditory stimuli consisted of tone bursts with the frequencies of 2, 8, 16, or 32 kHz within a 10-millisecond trapezoidal envelope with a 2 millisecond rise/fall time. Each stimulus was repeated a minimum of 2,000 times to obtain a reliable ABR recording average. Evoked response potentials were amplified 100,000 times and bandpass filtered at

100-1500 Hz to extract the ABR waveform (Ozdamar and Kraus, 1983). The two ABR waveforms considered for analysis were the II and IV peaks, as these are most prominently observed in macaque evoked potentials (Ng et al., 2015). The latency was defined as the time between the onset of the auditory stimulus and the apex of the specific wave.

Threshold ABR

Auditory stimuli were presented binaurally at 50 Hz at an initial sound pressure peak level of 80 dB. The intensity of the sound pressure was sequentially reduced by 20 dB until a waveform was no longer observed. Once a distinguishable peak was gone, the sound pressure level was raised in 5 dB steps until a waveform appeared. The ABR threshold was measured as the average sound pressure level between the absence and the reappearance of the waveform. See Figure 2B and 2C for examples of threshold ABR recordings.

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Temporal ABR

Auditory stimuli were presented binaurally at 60 dB at an initial frequency of 50 Hz. Then the stimuli were presented at 20 Hz. Wave IV peak latencies were calculated for each frequency condition, as it has been shown that the latency shifts in these waveforms increases with decreasing intervals between auditory stimuli (Mehraei et al., 2016). This shift has been shown to correlate with alterations in temporal auditory processing (Mehraei et al., 2016, 2017; Sanguebuche et al., 2018). Therefore, an estimate of temporal auditory processing capacity was defined as the difference between the 50 Hz and 20 Hz latency. See Figure 2E for example temporal ABR recordings.

Visual Evoked Potential

Visual evoked potentials (VEP; Figure 2H) were acquired using the same Intelligent Hearing

System software (Smart EP Win USB, v 3.97) as was used during the ABR recordings. Electrode placements were directed by the guidelines set by the Smart EP manufacturer, with adjustments to fit the macaque head. Specifically, a mid-occipital scalp electrode was positioned an inch above the inion and right and left occipital electrodes were positioned on either side of the mid-occipital electrode. A reference electrode was positioned on the top of the scalp along the midline and the ground was placed posterior to the brow ridge. Animals were situated in the prone position with their heads elevated. The monkeys received a visual stimulus of a full-field checkerboard pattern delivered by the Intelligent

Hearing System VEP stimulator placed around 50 cm in front of the monkeys’ face. These stimuli were delivered at either 1 or 2 Hz. The visual stimuli were repeated a minimum of 100 times to obtain reliable average evoked potentials. Evoked potentials were amplified 100,000 times and bandpass filtered at 1-

300 Hz. Adult and aged animals reliably showed an evoked potential around 75 ms, which is referred to

as P75. The latency was defined as the time between stimulus onset and the apex of the P75. The latency data presented in this study was derived from the 1 Hz condition. The latency difference was calculated

by subtracting the P75 latency of the 1 Hz condition from the 2 Hz condition.

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Composite Sensory Scores

Data from the threshold-ABR, temporal-ABR, and VEP were z-score normalized in order to standardize the units of all data. Lower z-scores indicate better sensory function. The composite sensory z-score for each animal was determined by an average of all of the z-scores.

Diffusion Tensor Imaging and Tractography

Image Acquisition and Processing

The imaging acquisition and processing protocol is described in detail in Gray et al., 2018.

Briefly, all magnetic resonance images were obtained on a 3T GE (General Electric, Milwaukee, WI)

Signa scanner, using a body coil for the radio frequency excitation and an eight-channel head coil for reception. T2-weighted whole-brain reference scans were acquired for each monkey using a fast spin- echo sequence. 3D volumes were sectioned into coronal, sagittal, and axial slices. Diffusion-weighted

(DW) images were obtained using single shot echo planar imaging sequence. The data were acquired over

51 diffusion directions in a HARDI sampling scheme over a single shell with a b-value of 1000 s/mm2.

Lastly, high-resolution anatomical whole-brain T1-weighted images were acquired with a 3D inversion- recovery prepped spoiled gradient-echo sequence using the same parameters described in Gray et al.,

2018.

DICOM images were converted into NIFTI format and T1, T2, and dMRI images were skull stripped using drawn masks in MRIcron (https://www.nitrc.org/projects/mricron). Eddy current distortions in DW images were corrected using an iterative Gaussian Process based registration in FSL

(Andersson and Sotiropoulos, 2015; Rasmussen and Williams, 2006). Distortions from B0-field inhomogeneity were corrected using the TORTOISE software (Pierpaoli et al., 2010) by non-linearly registering the DW images to reference T2 images. Coil inhomogeneity was corrected using N4ITK bias correction software (Tustison and Gee, 2009; Tustison et al., 2010) and noise in DW images was removed using a local principal component-based noise removal algorithm outlined in Manjón et al., 2013. DW images were registered to T1 images using FSL’s Automated Segmentation Toolbox then FSL’s Linear 15

Image Registration Tool and a Boundary Based Registration algorithm (Greve and Fischl, 2009).

Fractional Anisotropy maps were generated from diffusion tensor fitting of the DW images in each monkey’s native space.

Tractography and Fractional Anisotropy

Probabilistic streamlines were generated between four left and right hemispheric cortical regions of interest (ROI) in each subject’s native space. Streamlines were generated using a multi-tensor tractography approach in FSL’s Diffusion Toolbox, ProbtrackX, as defined in Gray et al., 2018. This analysis outputs a probability map in which the value of each voxel is the weighted probability that the voxel belongs to the distinct anatomical pathway between the two seed regions. These weighted probability maps were used to extract the FA of the pathway. A mean value was calculated for each pathway to produce a probabilistically-weighted FA value.

Regions of Interest and Probabilistic Streamlines

All regions of interest were drawn on T1-weighted images using MRIcron software. The parameters for drawing the ROIs, along with the protocol for obtaining the commissural and thalamocortical fiber streamlines, are outlined below.

Superior temporal gyrus and -associated corpus callosal fibers: The posterior boundary of the superior temporal gyrus was defined as the slice in which the lateral fissure first separates the parietal and temporal lobes. The anterior boundary was defined as the slice just posterior to the appearance of the anterior commissure. The ROI was drawn in each section starting from the medial junction between the superior temporal gyrus and the insula and including both the dorsal and lateral banks of the superior temporal gyrus. Streamlines between right and left superior temporal gyri were generated to traverse through the corpus callosum. Two inclusion masks were drawn encompassing the corpus callosum in the sagittal sections just off of the midline in both hemispheres, and an exclusion mask was drawn in the coronal section just anterior to the anterior edge of the superior temporal gyri seed ROIs.

Visual cortex and the posterior forceps: The visual cortex ROIs were drawn beginning at the posterior portion of the brain in the first slice where the occipital pole appears and ending at the plane just posterior 16

to the parietooccipital sulcus. The posterior forceps were derived by generating streamlines between the right and left visual cortices to traverse through the splenium of the corpus callosum. This was done by including an inclusion mask through the midline of the splenium and exclusion masks were drawn in the coronal plane just anterior to the splenium as well as through the midline of the brain just posterior to the splenium.

Auditory cortex, medial geniculate thalamic nucleus, and acoustic radiation: The auditory cortex ROI was restricted to encompass only the dorsal bank of the superior temporal gyrus, within the lateral fissure.

The anterior border was set to the section containing the anterior-most portion of the , and the posterior border was set as the first section to contain insular cortex medial to the dorsal bank of the superior temporal gyrus. For the medial geniculate nucleus ROI, the anterior border was set as the fourth section anterior to the first section to contain thalamic tissue anterior to the splenium, which was defined as the posterior border. The medial geniculate ROI was bounded medially by the lateral extent of the , which is located just ventral to this nucleus. The lateral border was set by the lateral geniculate nucleus. Streamlines were generated between the medial geniculate nucleus ROI and the auditory cortex

ROI. Exclusion masks were set to prevent streamlines from entering the inferior parietal gyrus, as well as along the longitudinal fasciculi anterior and posterior to the auditory cortex ROI to prevent streamlines from being generated anteriorly and posteriorly.

Visual cortex, lateral geniculate thalamic nucleus, and optic radiation: The visual cortex ROI was drawn as described above. The anterior border of the lateral geniculate nucleus ROI was defined as the first section to contain the posterior aspect of the putamen, and the posterior border was the first section to contain thalamic tissue anterior to the splenium. The most medial aspect of the parasubiculum of the hippocampal complex, which is located immediately ventral to the lateral geniculate, was used to define the medial border, and the white matter of the external capsule was the lateral border. Streamlines were generated between the lateral geniculate nucleus ROIs and the visual cortex ROIs. Exclusion masks were set to prevent streamlines from entering the parietal cortex, and also along the longitudinal fasciculi to prevent streamlines from being generated anteriorly. 17

Sensorimotor cortex, ventrolateral thalamic nuclei, and superior thalamic radiation: The sensorimotor

ROI encompasses both the primary somatosensory and primary motor cortices. The anterior border of this ROI was set to the first section in which the anterior-most aspect of the central sulcus disappears.

The posterior border was set as the second section posterior to the first appearance of the posterior aspect of the central sulcus. The cingulate gyrus bounded this ROI medially, and the lateral fissure bounded it laterally. For the ventrolateral thalamic nuclei ROI, the anterior boundary was set as the first section in which the anterior aspect of the third ventricle split into dorsal and ventral components. The posterior border was set as the first section anterior to the section in which the dorsal aspect of the third ventricle rejoins the ventral aspect of the third ventricle. Medially the ventrolateral thalamic nuclei were bounded by anterior and mediodorsal thalamic nuclei, as well as the internal medullary lamina. The external medullary lamina and were the landmarks used to delineate the lateral border.

Streamlines were generated between the ventrolateral thalamic nuclei and the sensorimotor cortex ROI.

Exclusion masks were used to prevent streamlines from being generated in the superior temporal gyrus, as well as along the longitudinal fasciculi to prevent streamlines from being generated anteriorly and posteriorly.

Statistical Analyses

Behavioral Assessment

The reversal learning, reward devaluation, and visual discrimination tasks were analyzed using repeated measures ANOVAs. Post-hoc tests were performed in every case using unpaired t-tests.

Delayed nonmatching-to-sample and spatial delayed response trials to criterion and average performance across delays measures were analyzed with unpaired t-tests. In all cases an alpha level of 0.05 was used, and p-values underwent Bonferroni-Holm correction when applicable.

Assessment of sensory function

ABR thresholds, latencies, and latency differences across different stimulus frequencies were analyzed with repeated measures ANOVAs. Analyses of ABR data averaged across stimulus frequencies, 18

as well as VEP data were analyzed with unpaired t-tests. In all cases an alpha level of 0.05 was used, and p-values were Bonferroni-Holm corrected when necessary.

Fractional anisotropy comparisons

FA estimates from each commissural projection were analyzed using repeated measures unpaired t-tests. FA estimates from each thalamocortical projection were analyzed using repeated measures

ANOVAs with age group (adult and aged) and hemisphere (right and left) as factors. Post-hoc tests were performed in every case using unpaired t-tests. Again, in all cases an alpha level of 0.05 was used.

Regression analyses

The relationships between auditory and cognitive function, as well as the relationships between fractional anisotropy indices and sensory and/or cognitive function were assessed using a robust regression model. This regression method is an alternative to least-squares regression and is commonly used with comparatively smaller datasets since it is more robust in the presence of outliers. In the auditory to cognitive function correlations, auditory scores were the independent variables and cognitive scores the dependent variables. In the fractional anisotropy to cognitive/sensory function correlations, fractional anisotropy was the independent variable and cognitive/sensory scores the dependent variables. In all cases the significance criterion was p < 0.05.

Results Cognitive Assessment The animals were tested on a battery of behavioral assessments, each examining different aspects of cognition. Aged animals had significantly lower composite cognitive scores as compared to adult animals, indicating lower cognitive performance (t-test, p = 0.019, t = 2.83; Figure 1A). The results for each cognitive task tested in the cognitive battery are presented below. 19

Reversal learning: Aged animals required more trials to learn the object discrimination and reversal learning components of the task (ANOVA, F(1,12) = 7.76, p = 0.01; Figure 1C). Posthoc t-tests confirmed that aged animals required more trials to learn the reversal learning component (t-test, p =

0.043, t = -2.34), but not the object discrimination portion (t-test, p = 0.42, t = 0.83). Animals in both age groups required more trials to learn the reversal learning component compared with the object discrimination component (ANOVA, F(1,12) = 30.74, p <0.001). There was no task by age interaction

(ANOVA, F(1,12) = 1.92, p = 0.18).

LEGO-based visual discrimination: The number of errors to reach performance criterion increased with the degree of stimulus overlap (ANOVA, F(1,6) = 15.14, p = 0.0009; Figure 1E). A posthoc t-test confirmed that aged animals had more errors to criterion on the 92% similarity condition (t- test, p = 0.0049, t = -4.79).

Reward devaluation: Aged animals performed worse in the object-based reward devaluation task as compared to adult animals, where aged monkeys had lower difference scores (t-test, p = 0.04, t = -3.73;

Figure 1G). Differences scores in this object-free condition were higher in both groups compared to the object-based condition (ANOVA, F(1,7) = 8.4, p = 0.01; Figure 1G). Adult and aged animals performed comparatively in the object-free reward devaluation task (t-test, p = 0.18, t = 1.61). This suggests that the age-associated differences in the task with objects were not due to a change of food preferences caused by the satiation procedures.

Object Recognition Memory: Neither the number of trials required to reach performance criterion on the DNMS task nor the average performance across all DNMS delay conditions was significantly different between the adult and aged animals (t-test, acquisition: p = 0.15, t = -1.59, Figure 1I; average performance: p = 0.38, t = 0.92; Figure 1J).

Spatial short-term memory: The number of trials required to reach performance criterion on the

DR task was not statistically different between the age groups (t-test, p = 0.47, t = -0.75; Figure 1L). 20

Average performance across all DR delay conditions between the adult and aged monkeys also did not differ (t-test, p = 0.57, t = 0.59; Figure 1M).

Assessments of Visual and Auditory Function

Composite sensory scores were significantly higher in aged monkeys as compared to adults, where higher values indicate poorer sensory function (t-test, p = 0.0019, t = -4.16; Figure 2A).

Auditory brainstem response: ABR thresholds to click stimuli were not significantly different between adult and aged monkeys (t-test, p = 0.13, t = -1.67; Figure 2D). Additionally, pure-tone average thresholds were not different between age groups (t-test, p = 0.078, t = -1.99). In the temporal-ABR analysis, wave IV latencies increased when stimuli were presented at 50 Hz as compared to the 20 Hz stimulus condition (ANOVA, F(1,11) = 17.49, p = 0.0007; Figure 2F). Aged monkeys had significantly higher latencies in the 50 Hz stimulus condition compared to adult monkeys, but not in the 20 Hz stimulus condition (t-test, 20 Hz: p = 0.094, t = -1.83; 50 Hz: p = 0.04, t = -2.35; Figure 2F). The latency difference between the 50 Hz and 20 Hz stimulus conditions was significantly higher for aged monkeys than for adults (t-test, p = 0.0012, t = -4.68; Figure 2G).

Visual evoked potential: VEP P75 latencies from stimulus onset were not different between adult and aged monkeys in either the 1 Hz or 2 Hz stimulus conditions (ANOVA, Frequency: F(1,11) = 0.68, p

= 0.42; Figure 2I). VEP P75 latency differences between 1 Hz and 2 Hz stimulus presentation were not significantly different between age groups, though there was a trend towards greater latency differences in aged animals (t-test, p = 0.077, t = -1.99; Figure 2J).

Relationship between Cognitive and Sensory Function

Composite cognitive scores were significantly correlated with sensory z-scores of ABR wave IV latency differences, derived from the temporal-ABR analysis, (robust regression, p = 0.027, r = -0.72, t =

-2.70; Figure 3A), but were not correlated with the sensory z-scores of the ABR threshold or VEP latency differences (robust regression, ABR threshold: p = 0.61, r = -0.39, t = -0.52; temporal-VEP: p = 0.72, r = - 21

0.20, t = -0.38; Figure 3A). Due to the specificity of the relationship between cognitive function and

ABR wave IV latency differences, only correlations between cognitive tasks and wave IV latency differences are described below.

ABR wave IV latency differences were positively correlated with the learning trials in the object reversal learning task, where a higher learning trials indicates worse performance (robust regression, p =

0.037, r = 0.67, t = 2.50; Figure 3B). Difference scores in the object-based reward devaluation task did not significantly correlate with ABR wave IV latency differences (robust regression, p = 0.24, r = -0.49, t

= -1.30; Figure 3C). ABR wave IV latency differences were positively correlated with the number of errors to criterion in the 92% similarity condition of the visual discrimination task (robust regression, p =

0.032, r = 0.87, t = 3.23; Figure 3D). Average performance across all delays on the DNMS task negatively correlated with ABR wave IV latency differences (robust regression, p = 0.00056, r = -0.64, t

= -5.52; Figure 3E). Average performance across all delay conditions on the DR task did not correlate with ABR wave IV latency differences (robust regression, p = 0.99, r = 0.0089, t = -0.0021; Figure 3F).

Fractional anisotropy indices of interhemispheric and thalamocortical white matter tracts connecting sensory regions and relationships with sensory function

Auditory Cortex-Associated Corpus Callosal Fibers: Fractional anisotropy measures for these callosal fibers were significantly lower in aged monkeys as compared to adult monkeys (t-test, p =

0.0121, t = 3.13; Figure 4A). These fractional anisotropy estimates were significantly negatively correlated with ABR thresholds to click stimuli (robust regression, p = 0.037, r = -0.67, t = -2.45; Figure

4B), but did not with temporal-ABR wave IV latency differences (robust regression, p = 0.13, r = -0.57, t

= -1.70; Figure 4C). However, there was a clear negative trend between the ABR wave IV latency differences and the fractional anisotropy measures for this tract. Normalized fractional anisotropy estimates for this tract were not correlated to VEP P75 waveform latency differences (robust regression, p

= 0.44, r = -0.099, t = -0.82). 22

Acoustic Radiation: Normalized fractional anisotropy measures from the acoustic radiations were not different between adult and aged monkeys (ANOVA, F(1,10) = 0.01, p = 0.91; Figure 4D), and there was no effect of hemisphere (ANOVA, F(1,10) = 0.05, p = 0.83). Neither right nor left hemisphere acoustic radiation fractional anisotropy measures were significantly associated with ABR wave IV latency differences (robust regression, left hemisphere p = 0.76, r = 0.82, t = 0.31; right hemisphere: p = 0.58; r =

0.19; t = 0.56; Figure 4E). Similarly, neither left nor right hemisphere acoustic radiation fractional anisotropy measures correlated with ABR thresholds (robust regression, left hemisphere: p = 0.74, r = -

0.11, t = -0.35; right hemisphere: p = 0.82, r = -0.06, t = -0.24; Figure 4F). Left and right hemisphere acoustic radiation fractional anisotropy measures did not correlate with VEP P75 waveform latency differences (robust regression, left hemisphere: p = 0.91, r = 0.004, t = -0.12; right hemisphere: p = 0.64, r

= 0.19, t = 0.49).

Posterior Forceps: Estimates of posterior forceps fractional anisotropy were not significantly different between aged and adult animals (t-test, p = 0.13, t = 1.64; Figure 5A); however, there was a trend towards decreased fractional anisotropy measures for aged monkeys. The fractional anisotropy measures of this tract were significantly negatively correlated with VEP P75 waveform latency differences

(robust regression, p = 0.029, r = -0.61, t = -2.66; Figure 5B). Normalized fractional anisotropy estimates for this tract were not correlated to ABR thresholds or temporal-ABR wave IV latency differences (robust regression, ABR threshold: p = 0.22, r = -0.43, t = -1.31; temporal-ABR: p = 0.50, r = -0.28, t = -0.71).

Optic Radiation: Estimates of optic radiation fractional anisotropy were significantly lower in aged animals compared to adults (ANOVA, F(1,10) = 11.2, p = 0.0036, Figure 5C), although there was no hemispheric difference (ANOVA, F(1,10) = 0.37, p = 0.55). Posthoc tests confirmed that normalized fractional anisotropy measures from the optic radiations were significantly lower in aged animals in the left hemisphere (t-test, p = 0.019, t = 2.85), and a similar trend was seen in the right (t-test, p = 0.097, t =

1.8). Right hemisphere fractional anisotropy measures were significantly associated with VEP P75 latency differences (robust regression, p = 0.01, r = -0.60, t = -3.35; Figure 5D). Left hemisphere fractional 23

anisotropy estimates were not significantly correlated with VEP P75 latency differences (robust regression, p = 0.58, r = -0.17, t = -0.58; Figure 5I). VEP P75 latencies were not significantly associated with neither left nor right hemisphere fractional anisotropy estimates (robust regression, left hemisphere: p = 0.97, r =

-0.03, t = -0.04; right hemisphere, p = 0.54, r = 0.24, t = 0.63 Figure 5E). Neither left nor right hemisphere fractional anisotropy indices correlated to ABR thresholds (robust regression, left hemisphere: p = 0.37, r = -0.33, t = -0.94; right hemisphere: p = 0.12, r = -0.52, t = -1.74) or ABR wave

IV latency differences (robust regression, left hemisphere: p = 0.056, r = -0.63, t = -1.96; right hemisphere: p = 0.61, r = -0.20, t = -0.54).

Superior Thalamic Radiation: Estimates of superior thalamic radiation fractional anisotropy were not statistically different between adult and aged monkeys (ANOVA, F(1,10) = 0.35, p = 0.56; Figure 6).

Right hemisphere fractional anisotropy measures were significantly lower than left hemisphere estimates

(ANOVA; F(1,10) = 0.4.42, p = 0.049). The fractional anisotropy measures of this radiation did not correlate with VEP P75 latency differences (robust regression, left hemisphere: p = 0.20, r = -0.48, t = -

1.39; right hemisphere: p = 0.40, r = -0.32, t = -0.88), ABR thresholds (robust regression, left hemisphere: p = 0.73, r = -0.11, t = -0.35; right hemisphere: p = 0.91, r = 0.071, t = 0.12), or ABR wave IV latency differences (robust regression, left hemisphere: p = 0.20, r = -0.28, t = -1.40; right hemisphere: p = 0.16, r

= -0.50, t = -1.55).

Fractional anisotropy of sensory white matter tracts and relationship to cognitive function

The fractional anisotropy measures of the auditory-cortex associated corpus callosal fibers significantly correlated with performance on the object-based reward devaluation task (robust regression, p = 0.014, r = 0.83, t = 3.42; Figure 7A). However, the fractional anisotropy estimates of this tract did not correlate with performance on the object-free version of the reward devaluation task (robust regression, p

= 0.24, r = -0.48, t = -1.29). The fractional anisotropy measures from this tract also showed a strong negative trend with the numbers of errors to criterion on the visual discrimination task in the 92% 24

stimulus condition, although this trend did not reach statistical significance (robust regression, p = 0.052, r = -0.78, t = -2.54; Figure 7B).

Fractional anisotropy estimates from the left hemisphere acoustic radiation were significantly correlated with estimated learning trials on the object discrimination task (robust regression, p < 0.001, r

= - 0.89, t = -5.1; Figure 7C). Right hemisphere fractional anisotropy measures were not significantly associated with estimated learning trials on the object discrimination task (robust regression, p = 0.26, r =

- 0.41, t = -1.2; Figure 7C). Fractional anisotropy measures from the acoustic radiations were not correlated with the number of errors to criterion on the visual discrimination task (robust regression, left hemisphere: p = 0.55, r = 0.29, t = 0.64; right hemisphere: p = 0.74, r = -0.15, t = -0.35; Figure 7D).

Right hemisphere optic radiation fractional anisotropy estimates were significantly associated with the estimated learning trials on the object discrimination task (robust regression, p = 0.01, r = - 0.81, t = -3.30; Figure 7E). Left hemisphere fractional anisotropy estimates showed a similar trend that did not reach statistical significance (robust regression, p = 0.092, r = - 0.51, t = -1.91; Figure 7E). Fractional anisotropy measures from the optic radiations were not correlated with the number of errors to criterion on the visual discrimination task (robust regression, left hemisphere: p = 0.11, r = -0.68, t = -1.96; right hemisphere: p = 0.77, r = -0.16, t = -0.31; Figure 7F).

Animals with higher normalized fractional anisotropy estimates of the posterior forceps performed better on the spatial short-term memory task and required less learning trials on the object discrimination task, though neither of these trends reached statistical significance (robust regression, DR: p = 0.11, r = 0.56 t = 1.80, Figure 7G; OD: p = 0.12, r = -0.55 t = -1.76, Figure 7H).

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Discussion The present study used the same cohort of aged and adult monkeys as in Gray, 2019. Thus, the cognitive and sensory data for these animals have already been discussed in detail. Briefly, we found that, compared to adult animals, the aged monkeys showed poorer cognitive and sensory function. Importantly, although aged animals had poorer global cognitive function, only certain cognitive tasks showed a decline in performance with age. Specifically, aged animals were impaired in the object-based reward devaluation task, reversal learning task, and in discriminating objects with a high degree of feature overlap (visual discrimination). These findings are consistent with studies in both nonhuman primates and humans that have shown that selective cognitive domains are affected by age, whereas others are more spared (e.g.

Burke et al., 2011; Hara et al., 2012; Holden and Gilbert, 2012; Makris et al., 2007; Peters et al., 1996;

Voineskos et al., 2012). Similarly, only specific sensory functions showed significant declines in these aged animals. Aged animals had significantly longer ABR wave IV latency differences, suggesting that the ability to process temporally complex auditory stimuli decreases with age, while ABR thresholds and measures of temporal visual processing (VEP P75 latency differences) were not different between age groups. This finding indicates that temporal auditory processing may be affected prior to, or more robustly than auditory acuity during the aging process in these animals.

Additionally, composite cognitive scores were significantly correlated with temporal-ABR measures, but not threshold-ABR or temporal-VEP measures. Thus, higher cognitive abilities were associated with the ability to process complex auditory stimuli in these animals, regardless of auditory acuity or visual system function. Specifically, temporal auditory processing abilities correlated with performance on tasks that are known to depend on the integrity of structures in the medial temporal lobe, particularly concurrent reversal learning, object recognition memory, and visual discrimination, but did show these associations with tasks that rely on frontal and occipital lobe structures. Gray, 2019 concluded that the proximity of the auditory processing centers in the dorsal and lateral superior temporal gyrus and these cognitive temporal lobe regions may in part explain their association, as these regions could be 26

similarly affected by age-related risk factors. In addition, Gray, 2019 found through tractography analyses that the white matter connectivity of two medial temporal lobe tracts, the fimbria-fornix and hippocampal commissure, were significantly associated with visual discrimination abilities and temporal auditory processing. Gray, 2019 proposed several hypotheses as to why brain functions driven by brain structures within the temporal lobe become functionally associated as animals age. Briefly, shared neurovascular characteristics, the extent of neuronal connectivity between the temporal lobe structures, and lobe-specific embryological origins could all lead to susceptibility to specific age-related risk factors that selectively affect this structure.

The present study is an extension of Gray, 2019 aimed at examining the microstructural integrity of auditory and visual system white matter tracts in age. Additionally, a second goal of this study was to observe whether various sensory and cognitive functions were associated with the connectivity of this white matter. We found that the fractional anisotropy (FA) of sensory-associated white matter did decline with age in certain tracts. In particular, the tracts that showed significant age-associated reductions in FA were the commissural fibers connecting the auditory and visual cortices and the optic thalamocortical radiation. The connectivity of each sensory white matter tract correlated with specific aspects of sensory function, matching to the known function of the region connected by the tract. The connectivity of white matter in the visual system was significantly associated with temporal visual processing abilities, but not with similar estimates of auditory function. Similarly, the connectivity of commissural auditory fibers was significantly associated with measures of auditory function, but not visual function. In addition, visual system white matter connectivity was associated with simple object discrimination abilities and visuospatial short-term memory, while auditory system white matter connectivity was associated with simple object discrimination and visual discrimination abilities, along with performance on an object- based reward devaluation task.

27

Specificity of relationship between sensory white matter connectivity and sensory function

One important finding in this study was that higher white matter microstructural integrity of the sensory tracts was associated specifically with better sensory function within the modality associated with the regions connected by the white matter tracts analyzed. In particular, animals with higher white matter connectivity in the callosal auditory tract had lower auditory thresholds and a trend towards better temporal auditory processing abilities. Additionally, monkeys with higher connectivity of the posterior forceps, connecting the visual cortices in either hemisphere through the splenium, had better temporal visual processing abilities as measured by VEP P75 latency differences. Similarly, animals with greater connectivity in the optic thalamocortical radiation also had better visual processing abilities. Critically, the connectivity of the two auditory-associated white matter tracts were not associated with measures of temporal visual processing, nor did the connectivity of the two visual-associated white matter tracts correlate with measures of auditory function. This indicates that individual differences in sensory white matter composition impact sensory function specifically within a modality. Moreover, the superior thalamic radiation white matter connectivity did not differ between aged and adult animals nor did it correlate with any measures of auditory or visual function, further supporting the modality-specificity in these relationships.

A powerful aspect of the present study design was that two different estimates of auditory function were collected alongside estimates of structural connectivity from two hierarchically-distinct acoustic white matter tracts. This allowed for an analysis of how the structural composition at distinct levels of the auditory system map onto individual differences in different aspects of hearing function.

Callosal auditory fibers had significantly lower white matter connectivity in the aged animals as compared to the adult animals, while the connectivity of the acoustic thalamocortical radiation did not differ between adult and aged animals. This observation indicates that different levels of the auditory system experience the impact of normative aging at least partially independently of one another.

Intriguingly, unlike the callosal auditory fibers, the connectivity of the acoustic thalamocortical radiation 28

did not correlate with any measures of auditory function. Presently, it is unclear what exact functions the auditory-associated interhemispheric white matter and the acoustic thalamocortical radiation serve. Our data suggest that temporal auditory processing may rely more on interhemispheric communication between the auditory cortices than thalamocortical connectivity. In support of this hypothesis, one DTI study has shown that stronger anatomical connections between the superior temporal lobes resulted in better transfer of auditory information between hemispheres during a dichotic listening task and improved speech perception (Westerhausen et al., 2009). Furthermore, surgical resection of the posterior corpus callosum produces bilateral deficits in auditory temporal pattern detection (Davies, 2016). The temporal processing of complex auditory stimuli is important for many aspects of acoustic processing, including speech comprehension, and has been shown to decline in elderly listeners (Fitzgibbons and Gordon-

Salant, 1996; Füllgrabe et al., 2015; Gordon-Salant and Fitzgibbons, 1993; Wingfield et al., 2006). Thus, the present data suggest that age-related declines in auditory temporal processing could in part be due to a decline in auditory-associated interhemispheric white matter microstructure that decreases the integration of acoustic information. In addition, these data suggest that the acoustic thalamocortical radiation does not play a prominent role in higher information processing and might instead primarily relay acoustic information between subcortical auditory regions and the primary auditory cortex. This conclusion is supported by limited evidence from functional lesions to the acoustic radiation that result in auditory detection deficits in the absence of peripheral pathology, a disorder known as

(Aralasmak et al., 2006).

Threshold-ABR measures are thought to provide an estimate of cochlear health since it has been shown that ABR thresholds in nonhuman primates correlate to various pathologies in the cochlea (Engle et al., 2013). Thus, the observed association between the connectivity of callosal auditory fibers and ABR thresholds was surprising. One possibility is that structural changes in the auditory may occur alongside changes in auditory acuity with age, either from a more general impact of aging on numerous levels of the auditory system or in response to changes in auditory sensitivity due to cochlear damage. A 29

previous study using the same monkeys used here showed significant relationships between the auditory thresholds and the connectivity of cognitive white matter associated with hippocampal and frontal cortex connectivity (Gray, 2019). This provides additional support for the idea that general age-related risk factors that impact peripheral and central brain structures together in part drive the relatively general associations observed between ABR thresholds and white matter composition across the brain. The quality of incoming auditory stimuli could affect higher level auditory processing, a function that potentially relies on the microstructure of the callosal auditory fibers, resulting in the observed covariation between the connectivity of this tract and declining auditory acuity with age. Under this hypothesis, the acoustic thalamocortical radiation may not be as related to the quality of auditory stimuli since we did not observe any structural changes in this tract with age nor any relationship between the connectivity of this tract and any measure of auditory function.

In the visual system, the posterior forceps showed a trend towards lower white matter connectivity with age. Likewise, optic radiation connectivity in the left hemisphere was significantly reduced with age, while the right hemisphere showed a similar trend. The connectivity of both of these tracts was associated with temporal visual processing abilities. In human studies, temporal visual processing abilities and temporal resolution have been shown to decline with age, where the elderly are especially disadvantaged when information is relatively rapidly presented to the subject (for review see

Scialfa, 2002). This is thought to result from a reduction in the number of photons that can reach the retina due to an increase in lens opacity and age-related changes in the magnocellular pathway, which respond to temporal features and contribute to temporal resolution (Schiller and Logothetis, 1990; Scialfa,

2002). Other studies have proposed that age-related differences in temporal processing speed, specifically for moving objects, emerge at the level of the visual cortex (e.g. Mendelson and Wells, 2002). Our results also implicate commissural visual tracts in playing a role in temporal processing, similar to the case with the auditory-associated interhemispheric tract in this study. Unlike the acoustic thalamocortical radiation, however, higher optic thalamocortical radiation connectivity was associated with better temporal 30

processing. This pattern of results suggests that interactions between the visual thalamus (lateral geniculate nucleus) and cortices plays a more important role in temporal processing within the visual domain than interactions between the auditory thalamus (medial geniculate nucleus) and cortices have on temporal processing in the auditory domain. One explanation for this difference is that the visual system has a vastly different structure than the auditory system, where the flow of visual information runs through fewer processing centers after leaving the retina compared to acoustic information after it leaves the cochlea (Felix II et al., 2018; Martin and Solomon, 2011). Additionally, the visual system contains distinct functionally segregated neuronal pathways, specifically the magnocellular and parvocellular pathways, that are maintained from the retina to the cortex, while the auditory system appears to integrate acoustic information at more subcortical levels (Felix II et al., 2018; Liu et al., 2006). These differences in system connectivity and number of processing centers could help explain the distinction between the visual and auditory thalamocortical radiations’ roles in temporal processing of sensory information within each modality. As age-related changes in the visual peripheral system cannot account for the selective declines observed in perceptual processing (Andersen, 2012), we propose that structural changes in the central visual white matter may play a prominent role in the degradation of the processing of complex visual stimuli with age.

Sensory white matter connectivity is associated with specific cognitive functions

Previously, our group has shown that the microstructural condition of white matter associated with medial temporal lobe connectivity is significantly related to estimates of auditory system information processing (Gray, 2019). This observation suggested that temporal lobe-associated sensory and cognitive function become associated across an animal’s lifespan due to regionally selective structural covariations.

In this study, we sought to add to these findings by evaluating how the connectivity of white matter tracts associated with multiple sensory systems relate to cognitive functions in the same monkeys.

In the visual system, the monkeys with higher white matter connectivity in the optic radiation and posterior forceps learned a simple object discrimination task more quickly than animals with lower 31

connectivity in these fiber tracts. Discrimination and recognition of objects with non-overlapping features requires several brain regions in the ventral visual processing stream, including the striate (V1), prestriate

(V2), and the inferotemporal cortices (Efremova and Inui, 2014; Mishkin et al., 1983). It is thought, for example, that the prestriate cortex encodes basic visual features that are then integrated in the higher- order inferotemporal region to form representations of the visual stimuli (Mishkin 1982; Mishkin et al.,

1983). Importantly, the optic radiation and posterior forceps both innervate the striate and prestriate cortex (Arrigo et al., 2016). Thus, one interpretation of these data is that the structure of visual system white matter innervating the occipital lobe influences visual processing capacities enough to impact object discrimination abilities. In support of this idea, higher connectivity in both visual-associated white matter tracts was also associated with better visual processing in these same monkeys, as discussed above.

Additionally, animals with higher connectivity of the posterior forceps tended to have better visuospatial short-term memory. This relationship has been observed in at least one DTI study in human participants where the white matter connectivity of the splenium, through which the posterior forceps traverse, was correlated with visuospatial memory assessed by a paired-association learning task (Davis et al., 2009). Like in the ventral visual stream, visual information destined for the dorsal visual stream also passes through the striate and prestriate cortex before eventually reaching the posterior parietal cortex

(Mishkin et al., 1983). Lesions and functional imaging experiments in both humans and macaques indicate that the dorsal stream of visual processing is responsible for encoding the visual location of objects, among other functions. In fact, one fMRI study has shown that the occipitoparietal cortex is activated during a visuospatial paired-association learning task (Gould et al., 2003). From the dorsal occipitoparietal pathway, visuospatial information is then sent to the dorsolateral prefrontal cortex through long-range connections (Cavada and Goldman-Rakic, 1989; Courtney et al., 1996), likely giving rise to the dependency on this area for proper visuospatial short-term memory function (Goldman and

Rosvold, 1970). Therefore, the current observations may suggest that the posterior forceps plays a role in 32

integrating spatial information from both visual fields during visuospatial short-term memory tasks before sending this information through the dorsal visual processing stream to the dorsolateral prefrontal cortex.

In the auditory system, higher acoustic radiation white matter connectivity was also significantly associated with better object discrimination learning. Unlike the case with visual system white matter, these associations likely do not reflect functional dependencies between auditory system structure and object discrimination abilities. Rather, this association may reflect age-associated structural changes that impact auditory and visual processing centers in the temporal lobe together to create covariations between structure and function between modalities. For example, multiple lesion studies have shown that the inferotemporal cortex is necessary for learning the object discrimination task, possibly by supporting the associative stages of visual discrimination learning (e.g. Cowey and Gross, 1970; Dean, 1976; Mishkin,

1982; Phillips et al., 1988), and this brain region sits just ventral to the superior temporal gyrus, where the bulk of auditory processing in the macaque forebrain occurs (Kaas and Hackett, 2000; Recanzone and

Sutter, 2008). This idea is supported by previous observations in these same animals that the connectivity of white matter specifically within the medial temporal lobe, but not the frontal cortex, is significantly associated with auditory processing abilities (Gray, 2019).

While the connectivity of the acoustic radiation was associated with object discrimination abilities, the connectivity of white matter in the callosal auditory fibers was not. Instead, these measures of auditory system interhemispheric connectivity were associated both with visual discrimination of objects with feature overlap and performance on an object-based reward devaluation task. Previously, visual discrimination abilities were shown to relate to better auditory information processing in these monkeys (Gray, 2019). Remarkably, this study also demonstrated that monkeys with greater structural integrity in hippocampus-associated white matter performed better on visual discrimination tasks and have higher auditory processing capacities than animals with lower connectivity. The present observations suggest that in addition to medial temporal lobe-associated white matter connectivity, the connectivity of interhemispheric white matter connecting the superior temporal gyri of either hemisphere 33

also becomes associated with visual discrimination abilities as macaques age. Together these observations provide additional evidence that temporal lobe-dependent functions become functionally coupled with age, possibly due to these structures being affected by similar age-related risk factors.

One surprising finding was that monkeys with higher white matter connectivity of the callosal auditory fibers also performed better on an object-based reward devaluation task. This finding is interesting because substantial evidence suggests that this type of reward devaluation task is highly dependent on orbitofrontal cortex interactions with the amygdala (Burke et al., 2014; Baxter et al., 2000;

Rudebeck et al., 2013). An important consideration is that the foundation of this task is an object discrimination problem, where each object in the presented pairs is associated with a specific food.

Accordingly, some representation of those objects must exist along the ventral visual pathway, and the inferotemporal cortex is a strong candidate region since this area of the brain is known to be necessary for representing object associations with rewards (Cowey and Gross, 1970). Thus, one interpretation of these findings is that object-based reward devaluation abilities may become associated with callosal auditory fiber connectivity for similar reasons that visual discrimination abilities do – that is, the relative anatomical proximity of the inferotemporal cortex to auditory processing centers along the superior temporal gyrus could cause these regions to become similarly impacted by brain aging risk factors. In support of this idea, we did not observe an association between callosal auditory fiber connectivity and performance on a version of the reward devaluation task that did not use objects to represent reward value.

Conclusion

To our knowledge, this is the first report of associations between the connectivity of specific white matter tracts connecting sensory cortical regions and both sensory and cognitive function in the context of normative aging. Our results indicate that different sensory white matter tracts appear to independently experience changes in connectivity with age. Furthermore, the connectivity of these sensory-associated white matter tracts correlates to sensory functions within the modality associated with the regions 34

connected by these tracts. For the auditory system, the age-related decline in white matter connectivity is specific to commissural fibers, which may play a prominent role in the decline of temporal auditory processing abilities in older people. Additionally, while the visual system white matter connectivity was associated with cognitive tasks that rely on visual regions, the auditory system white matter connectivity was associated with cognitive functions that rely on temporal lobe regions in proximity to auditory processing centers. Thus, the present data support a shared age-related structural and functional decline in the integrity of temporal lobe structures, providing further evidence for an anatomical basis based on regional proximity to account for the observed relationship between temporal auditory processing abilities and temporal lobe-dependent cognitive functions. Understanding functional covariations between modalities in older individuals will provide novel insights into how the aging brain compensates for decreased functional integrity in certain systems and the potential to improve age-related cognitive decline by increasing the quality of sensory input through sensory aids.

Conflict of Interest Statement

No conflicts.

Acknowledgements

We are grateful to Mike Valdez for design and fabrication of the MRI-compatible nonhuman primate stereotactic frame, Scott Squire for assistance in MRI, Kojo Plange for behavioral training, and Luann

Snyder and Michelle Albert for administrative assistance.

This work was supported by NIH grant AG050548, NIH grant 5F31 AG055263-02, the Evelyn F.

McKnight Brain Institute and the Arizona Alzheimer's Consortium-State of Arizona DHS.

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References 1. Allen A.R. and Starr A. Auditory brain stem potentials in monkey (M. mulatta) and man. Electroencelphalogr Clin Neurophysiol. 1978; 45: 53-63. 2. Andersen G.J. Aging and Vision: Changes in Function and Performance from Optics to Perception. Wiley Interdiscip Rev Cogn Sci. 2012; 3(3): 403-410. 3. Andersson J.L.R. and Sotiropoulos S.N. Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes. NeuroImage. 2015; 122: 166-176. 4. Aralasmak A., et al. Association, commissural, and projection pathways and their functional deficit reported in literature. J Comput Assist Tomogr. 2006; 30(5): 695-715. 5. Arrigo A., et al. New Insights in the Optic Radiations Connectivity in the . Invest Ophthalmol Vis Sci. 2016; 57(1): 1-5. 6. Baltes P.B. and Lindenberger U. Emergence of a powerful connection between sensory and cognitive functions across the adult life span: a new window to the study of cognitive aging? Psychol Aging. 1994; 12: 12–21. 7. Bartzokis G., et al. Heterogeneous age-related breakdown of white matter structural integrity: implications for cortical “disconnection” in aging and Alzheimer’s disease. Neurobiol Aging. 2004; 25(7): 843-51. 8. Baxter M.G., et al. Control of response selection by reinforcer value requires interaction of amygdala and orbital prefrontal cortex. J Neurosci. 2000; 20(11): 4311-9. 9. Bourne R.R.A., et al. Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis. Lancet Glob Health. 2017; 5(9): e888-e897. 10. Brown R.L. and Barrett A.E. Visual Impairment and Quality of Life Among Older Adults: An Examination of Explanations for the Relationship. J Gerontol B Psychol Sci Soc Sci. 2011; 66(3): 364-73. 11. Burke S.N., et al. Age-Associated Deficits in Pattern Separation Functions of the Perirhinal Cortex: A Cross-species Consensus. Behav Neurosci. 2011; 125(6): 836-847. 12. Burke S.N., et al. Orbitofrontal Cortex Volume in Area 11/13 Predicts Reward Devaluation, But Not Reversal Learning Performance, in Young and Aged Monkeys. J. Neurosci. 2014; 34: 9905– 9916. 13. Castiglione A., et al. Aging, Cognitive Decline and Hearing Loss: Effects of Auditory Rehabilitation and Training with Hearing Aids and Cochlear Implants on Cognitive Function and Depression among Older Adults. Audiol Neurootol 2016; 21 Suppl 1:21-28. 14. Cavada C. and Goldman-Rakic P.S. Posterior Parietal Cortex in Rhesus Monkey: 111. Evidence for Segregated Corticocortical Networks Linking Sensory and Limbic Areas With the Frontal Lobe. J Comp Neurol. 1989; 287(4): 422-45. 15. Comrie A.E., et al. Different macaque models of cognitive aging exhibit task-dependent behavioral disparities. Behav. Brain Res. 2018; 344: 110–119. 16. Courtney S.M., et al. Object and Spatial Visual Working Memory Activate Separate Neural Systems in Human Cortex. Cereb Cortex. 1996; 6(1): 39-49. 17. Cowey A. and Gross C.G. Effects of foveal prestriate and inferotemporal lesions on visual discrimination by rhesus monkeys. Exp Brain Res. 1970; 11(2): 128-44. 18. Davies R.A. Audiometry and other hearing tests. Handb Clin Neurol. 2016; 137: 157-76. 19. Davis S.W., et al. Assessing the effects of age on long white matter tracts using diffusion tensor tractography. Neuroimage. 2009; 46(2): 530–541. 36

20. Dean P. Effects of inferotemporal lesions on the behavior of monkeys. Psych Bull. 1976; 83(1): 41-71. 21. Efremova N.A. and Inui T. An inferior temporal cortex model for object recognition and classification. Sci and Tech Infor Processing. 2014; 41(6): 362-369. 22. Engle J.R., et al. Age-Related Hearing Loss in Rhesus Monkeys Is Correlated with Cochlear Histopathologies. PLOS ONE. 2013; 8: e55092. 23. Felix R.A. 2nd, et al. Subcortical pathways: Towards a better understanding of auditory disorders. Hear Res. 2018; 362: 48-60. 24. Fitzgibbons P.J. and Gordon-Salant S. Auditory temporal processing in elderly listeners. J Am Acad Audiol. 1996; 7(3): 183-9. 25. Fowler C.G., et al. Auditory function in rhesus monkeys: Effects of aging and caloric restriction in the Wisconsin monkeys five years later. Hear Res. 2010; 261: 75-81. 26. Füllgrabe C., et al. Age-group differences in speech identification despite matched audiometrically normal hearing: contributions from auditory temporal processing and cognition. Front Aging Neurosci. 2015; 6: 347. 27. Goldman P.S. and Rosvold H.E. Localization of function within the dorsolateral prefrontal cortex of the rhesus monkey. Exp Neurol. 1970; 27(2): 291-304. 28. Gordon-Salant S. and Fitzgibbons P.J. Temporal factors and speech recognition performance in young and elderly listeners. J Speech Hear Res. 1993; 36(6): 1276-85. 29. Gould R.L., et al. FMRI BOLD response to increasing task difficulty during successful paired associates learning. Neuroimage. 2003; 20(2): 1006-19. 30. Gray D.T. Examining the relationship between auditory function and cognitive decline in aging macaque monkeys. The University of Arizona, Proquest Dissertations Publishing. 13425664. 2019. 31. Gray D.T., et al. Attentional updating and monitoring and affective shifting are impacted independently by aging in macaque monkeys. Behav. Brain Res. 2017; 322: Part B, 329–338. 32. Gray D.T., et al. Tract-specific white matter correlates of age-related reward devaluation deficits in macaque monkeys. J Neuroimaging Psychiatry Neurol. 2018; 3(2): 13–26. 33. Greve D.N. and Fischl B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage. 2009; 48: 63-72. 34. Hara Y., et al. Neuronal and morphological bases of cognitive decline in aged rhesus monkeys. Age (Dordr). 2012; 34(5): 1051-73. 35. Holden H.M. and Gilbert P.E. Less efficient pattern separation may contribute to age-related spatial memory deficits. Front Aging Neurosci. 2012; 4: 9. 36. Humes L.E., et al. Are age-related changes in cognitive function driven by age-related changes in sensory processing? Atten Percept Psychophys. 2013; 75(3): 508-24. 37. Izquierdo and Jentsch. Reversal learning as a measure of impulsive and compulsive behavior in addictions. Psychopharmacology (Berl). 2012; 219(2): 607–620. 38. Jayakody D.M.P., et al. Impact of Cochlear Implantation on Cognitive Functions of Older Adults: Pilot Test Results. Otol Neurotol. 2017; 38(8): e289-e295. 39. Jayakody D.M.P., et al. Impact of Aging on the Auditory System and Related Cognitive Functions: A Narrative Review. Front Neurosci. 2018a; 12: 125. 40. Jayakody D.M.P., et al. Association between speech and high-frequency hearing loss and depression, anxiety and stress in older adults. Maturitas. 2018b; 110: 86-91. 41. Kaas J.H. and Hackett T.A. Subdivisions of auditory cortex and processing streams in primates. Proc Natl Acad Sci U S A. 2000; 97(22): 11793–11799. 37

42. Kennedy K.M. and Raz N. Aging White Matter and Cognition: Differential Effects of Regional Variations in Diffusion Properties on Memory, Executive Functions, and Speed. Neuropsychologia. 2009; 47(3): 916-27. 43. Kohama S.G., et al. Age-related changes in human and non-human primate white matter: from myelination disturbances to cognitive decline. Age (Dordr). 2012; 34(5): 1093-110. 44. Li K.Z. and Lindenberger U. Relations between aging sensory/sensorimotor and cognitive functions. Neurosci Biobehav Rev. 2002; 26(7): 777-83. 45. Lin F.R., et al. Hearing Loss and Incident Dementia. Arch Neurol. 2011; 68(2): 214-220. 46. Lindenberger U. and Baltes P.B. Sensory functioning and intelligence in old age: a strong connection. Psychol Aging. 1994; 9(3): 339-55. 47. Liu C.S., et al. Magnocellular and parvocellular visual pathways have different blood oxygen level-dependent signal time courses in human primary visual cortex. AJNR Am J Neuroradiol. 2006; 27(8): 1628-34. 48. Madden D.J., et al. Cerebral White Matter Integrity and Cognitive Aging: Contributions from Diffusion Tensor Imaging. Neuropsychol Rev. 2009; 19(4): 415-35. 49. Madden D.J., et al. Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. Biochim Biophys Acta. 2012; 1822(3): 386-400. 50. Makris N., et al. Frontal connections and cognitive changes in normal aging rhesus monkeys: A DTI study. Neurobiol Aging. 2007; 28(10): 1556-67. 51. Manjón J.V., et al. Diffusion Weighted Image Denoising Using Overcomplete Local PCA. PLoS One. 2013; 8(9): e73021. 52. Martin P.R. and Solomon S.G. Information processing in the primate visual system. J Physiol. 2011; 589(Pt 1): 29–31. 53. Mehraei G., et al. Auditory Brainstem Response Latency in Noise as a Marker of Cochlear Synaptopathy. J Neurosci. 2016; 36(13): 3755–3764. 54. Mehraei G., et al. Auditory brainstem response latency in forward masking, a marker of sensory deficits in listeners with normal hearing thresholds. Hear Res. 2017; 346: 34–44. 55. Mendelson J.R. and Wells E.F. Age-related changes in the visual cortex. Vision Res. 2002; 42(6): 695-703. 56. Mishkin M. A memory system in the monkey. Philos Trans R Soc Lond B Biol Sci. 1982; 298(1089): 83-95. 57. Mishkin M., et al. Object vision and spatial vision:two cortical pathways. Trends in Neurosci. 1983; 6: 414-417. 58. Nagahara A.H., et al. Age-Related Cognitive Deficits In Rhesus Monkeys Mirror Human Deficits on an Automated Test Battery. Neurobiol Aging. 2010; 31(6): 1020-31. 59. Ng C.W., et al. Age-related changes of auditory brainstem responses in nonhuman primates. J. Neurophysiol. 2015; 114: 455–467. 60. Ota M., et al. Age-related degeneration of corpus callosum measured with diffusion tensor imaging. Neuroimage. 2006; 31(4): 1445-52. 61. Ozdamar O. and Kraus N. Auditory middle-latency responses in humans. Audiol Off Organ Int Soc Audiol. 1983; 22: 34-49. 62. Peters A., et al. Neurobiological bases of age-related cognitive decline in the rhesus monkey. J Neuropathol Exp Neurol. 1996; 55(8): 861-74. 63. Pfefferbaum A., et al. Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging. Neuroimage. 2005; 26(3): 891-9. 64. Phillips R.R., et al. Dissociation of the effects of inferior temporal and limbic lesions on object discrimination learning with 24-h intertrial intervals. Behav Brain Res. 1988; 27(2): 99-107. 38

65. Pierpaoli C., et al. TORTOISE: an integrated software package for processing of diffusion MRI data. In ISMRM 18th Annual Meeting, Stockholm, Sweden. 2010; 1597. 66. Rasmussen C.E. and Williams C.K.I. Gaussian processes for machine learning. Cambridge, MA, USA: MIT Press. 2006. 67. Recanzone G.H. and Sutter M.L. The biological basis of audition. Annu Rev Psychol. 2008; 59: 119-42. 68. Roberts K.L. and Allen H.A. Perception and Cognition in the Ageing Brain: A Brief Review of the Short- and Long-Term Links between Perceptual and Cognitive Decline. Front Aging Neurosci. 2016; 8: 39. 69. Rudebeck P.H., et al. Effects of amygdala lesions on reward-value coding in orbital and medial prefrontal cortex. Neuron. 2013; 80(6): 1519-1531. 70. Salami A., et al. Age-related white matter microstructural differences partly mediate age-related decline in processing speed but not cognition. Biochim Biophys Acta. 2012; 1822(3): 408-15. 71. Sanguebuche T.R., et al. Speech-evoked Brainstem Auditory Responses and Auditory Processing Skills: A Correlation in Adults with Hearing Loss. Int Arch Otorhinolaryngol. 2018; 22: 38–44. 72. Schiller P.H. and Logothetis N.K. The color-opponent and broad-band channels of the primate visual system. Trends Neurosci. 1990; 13(10): 392-8. 73. Scialfa C.T. The role of sensory factors in cognitive aging research. Can J Exp Psychol. 2002; 56(3): 153-63. 74. Simic G., et al. Volume and number of neurons of the human hippocampal formation in normal aging and Alzheimer’s disease. J Comp Neurol. 1997; 379(4): 482-494. 75. Sonnet M.H., et al. Cognitive Abilities and Quality of Life After Cochlear Implantation in the Elderly. Otol Neurotol 2017; 38(8): e296-e301. 76. Tustison N. and Gee J. N4ITK: Nick’s N3 ITK implementation for MRI bias field correction. Insight J. 2009. 77. Tustison N.J., et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010; 29: 1310-1320. 78. Voineskos A.N., et al. Age-related decline in white matter tract integrity and cognitive performance: a DTI tractography and structural equation modeling study. Neurobiol Aging. 2012; 33(1): 21-34. 79. West M.J., et al. Preserved hippocampal cell number in aged monkeys with recognition memory deficits. Soc for Neurosci Abstr. 1993; 19: 599. 80. Westernhausen R., et al. Functional relevance of interindividual differences in temporal lobe callosal pathways: a DTI tractography study. Cereb Cortex. 2009; 19(6): 1322-9. 81. Wingfield A., et al. Sensory and Cognitive Constraints in Comprehension of Spoken Language in Adult Aging. Seminars in Hearing. 2006; 27(4): 273-283. 82. Zahr N.M., et al. Problem solving, working memory, and motor correlates of association and commissural fiber bundles in normal aging: a quantitative fiber tracking study. Neuroimage. 2009; 44(3): 1050-62.

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Figure 1. Description of cognitive tasks and performance. All of these data have been published in Gray, 2019. A) Boxplot of composite cognitive score of adult and aged monkeys. B) Schematic diagram of reversal learning task paradigm. C) Boxplots of estimated learning trials for the object discrimination component and the reversal learning component of the task for adult and aged monkeys. Boxes denote the middle 50% of the data, and horizontal lines indicate the median of each distribution. Filled circle represent an individual monkey, with adult animals represented by black circles and aged animals with grey. These data have been published elsewhere (Gray et al., 2017). D) Schematic diagram of the visual discrimination task using objects formed with LEGOs. The two images are examples of the 86% similarity condition. E) Boxplots of the number of errors to reach criterion on the visual discrimination task for the similarity conditions of 60%, 71%, 86%, and 92% for adult and aged monkeys. These data have been published in Burke et al., 2011. F) Schematic diagram of the reward devaluation task with objects. G) Boxplots of the difference scores of adult and aged animals in the object-free and object-based versions of the reward devaluation task. These data have been published in Burke et al., 2014. H) Schematic diagram of the object recognition memory task. I) Boxplots of the trials to reach performance criterion on the object recognition memory task on the 10 second delay condition for adult and aged animals. J) Boxplots of each monkey’s performance averaged across the 15, 30, 60, 120, and 600 second delays. These data have been published elsewhere (Comrie et al., 2018). K) Schematic diagram of the delayed response task. L) Boxplots of the trials to reach performance criterion on the delayed response task on the 1 second delay condition for adult and aged animals. M) Boxplots of each monkey’s performance averaged across the 5, 10, 15, 30, and 60 second delays. These data have been published elsewhere (Comrie et al., 2018). * = p < 0.05

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Figure 2. Electrophysiological measures of auditory and visual function. All of these data have been published in Gray, 2019. A) Boxplots of the composite sensory scores of adult and aged animals. Boxes denote the middle 50% of the data, and horizontal lines indicate the median of each distribution. Filled circle represent an individual monkey, with adult animals represented by black circles and aged animals with grey. B) Representative example of an auditory brainstem response (ABR) recording used in the threshold ABR analysis from an adult monkey. C) Representative example of a threshold ABR recording from an aged monkey. D) Boxplots of the ABR thresholds to click stimuli from adult and aged monkeys. E) Representative example of the auditory brainstem response (ABR) recording from an adult and aged monkey used in the temporal ABR analysis. The black traces are derived from a recording in which acoustic stimuli were presented at a rate of 20 Hz, whereas grey traces are derived from recording in which the acoustic stimuli were presented at 50 Hz. Scale bar represents 1 µV and sound presentation occurred at time 0. F) Boxplots of the temporal ABR latencies of the 20 Hz and 50 Hz stimulus conditions from adult and aged animals. G) Boxplots for the wave IV latency differences between the 50 Hz and 20 Hz stimulus conditions from adult and aged animals. H) Representative example of a visual evoked potential (VEP) recording from an adult and aged monkey. The black traces are derived from recordings in which visual stimuli were presented at a rate of 1 Hz, whereas grey traces are derived from recordings in which the stimuli were presented at 2 Hz. In both plots the P75 component is labelled. Scale bar is 5 µV, and stimulus presentation occurred at time 0. I) Boxplots of the VEP P75 latencies for the 1 Hz and 2 Hz stimulus conditions from adult and aged monkeys. J) Boxplots of the VEP P75 latency differences between the 2 Hz and 1 Hz stimulus conditions from adult and aged animals. ** = p < 0.01

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Figure 3. Relationship between cognitive and sensory function. All of these data have been published in Gray, 2019. A) Scatter plot of ABR wave IV latency differences (temporal ABR), ABR thresholds (threshold ABR), and VEP P75 latency differences (temporal VEP) plotted against composite cognitive scores. All data were z- score normalized. In all plots, solid trend lines represent a significant relationship as tested with a robust regression analysis, and dotted trend lines represent non-significant relationships using the same statistical test. A significant relationship between ABR wave IV latency differences and composite cognitive scores was observed. B) Scatter plot of temporal ABR wave IV latency differences and the learning trials from the reversal learning task. C) Scatter plot of temporal ABR wave IV latency differences and the difference score from the reward devaluation task. D) Scatter plot of temporal ABR wave IV latency differences and number of errors to criterion on the visual discrimination task for the 92% similarity condition. E) Scatter plot of temporal ABR wave IV latency differences and performance on the delayed nonmatching-to-sample task. F) Scatter plot of ABR wave IV latency differences and performance on the spatial delayed response task.

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Figure 4. Auditory-associated white matter tracts and relationships with sensory measures. A) Boxplots of auditory cortex-associated corpus callosal fibers normalized fractional anisotropy estimates for each individual monkey separated by age. Boxes represent the middle 50% of the data, and horizontal lines mark the median of each distribution. Each filled circle indicates an individual monkey, with black circles representing adult animals and grey representing the aged. Asterisk (*) denotes p < 0.05. Also included is a representative probability map of the auditory cortex-associated corpus callosal fibers, derived from probabilistic tractography in FSL overlaid on a T1-weighted MRI. B) Scatter plot of auditory cortex-associated corpus callosal fibers fractional anisotropy estimates and ABR click thresholds. In all scatterplots solid trend lines represent significant relationships as tested with a robust regression analysis, and dotted trend lines represent non-significant relationships. C) Scatter plot of auditory cortex-associated corpus callosal fibers fractional anisotropy estimates and ABR wave IV latency differences. D) Boxplots of acoustic radiation normalized fractional anisotropy estimates for each individual monkey separated by left and right hemisphere. Also included is a representative probability map of the right hemisphere acoustic radiation, derived from probabilistic tractography in FSL overlaid on a T1- weighted MRI. E) Scatter plot of acoustic radiation fractional anisotropy estimates and ABR wave IV latency differences. Gray lines represent left hemispheric measures while black lines represent right hemispheric measures. F) Scatter plot of acoustic radiation fractional anisotropy and ABR thresholds. Gray lines represent left hemispheric measures while black lines represent right hemispheric measures.

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Figure 5. Visual-associated white matter tracts and relationships with sensory measures. A) Boxplots of posterior forceps normalized fractional anisotropy estimates for each individual monkey separated by age. Boxes represent the middle 50% of the data, and horizontal lines mark the median of each distribution. Each filled circle indicates an individual monkey, with black circles representing adult animals and grey representing the aged. Also included is a representative probability map of the posterior forceps, derived from probabilistic tractography in FSL overlaid on a T1-weighted MRI. B) Scatter plot of posterior forceps fractional anisotropy estimates and VEP P75 latency differences. In all scatterplots solid trend lines represent significant relationships as tested with a robust regression analysis, and dotted trend lines represent non-significant relationships. C) Boxplots of optic radiation normalized fractional anisotropy estimates for each individual monkey separated by left and right hemisphere. Also included is a representative probability map of the right hemisphere optic radiation, derived from probabilistic tractography in FSL overlaid on a T1-weighted MRI. Asterisk (*) denotes p < 0.05. D) Scatter plot of optic radiation fractional anisotropy estimates and VEP P75 latency differences. Gray lines represent left hemispheric measures while black lines represent right hemispheric measures. E) Scatter plot of optic radiation fractional anisotropy estimates and VEP P75 latencies. Gray lines represent left hemispheric measures while black lines represent right hemispheric measures.

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Figure 6. Superior thalamic radiation fractional anisotropy estimates. Boxplots of superior thalamic radiation normalized fractional anisotropy estimates for each individual monkey separated by left and right hemisphere. Boxes represent the middle 50% of the data, and horizontal lines mark the median of each distribution. Each filled circle indicates an individual monkey, with black circles representing adult animals and grey representing the aged. Also included is a representative probability map of the right hemisphere optic radiation, derived from probabilistic tractography in FSL overlaid on a T1-weighted MRI.

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Figure 7. Sensory-associated white matter tracts and relationships with cognitive tasks. A) Relationship between auditory cortex-associated corpus callosal fibers normalized fractional anisotropy measures and the difference score for the object-based reward devaluation task. In all scatterplots solid trend lines represent significant relationships as tested with a robust regression analysis, and dotted trend lines represent non-significant relationships. B) Relationship between auditory cortex-associated corpus callosal fibers normalized fractional anisotropy estimates and the number of errors to criterion on the 92% similarity visual discrimination task. C) Relationship between acoustic radiation normalized fractional anisotropy measures and the estimated learning trial of the object discrimination task. Gray lines represent left hemispheric measures while black lines represent right hemispheric measures. D) Relationship between acoustic radiation normalized fractional anisotropy estimates and the number of errors to criterion on the 92% similarity visual discrimination task. E) Relationship between optic radiation normalized fractional anisotropy measures and the estimated learning trial of the object discrimination task. Gray lines represent left hemispheric measures while black lines represent right hemispheric measures. F) Relationship between optic radiation normalized fractional anisotropy estimates and the number of errors to criterion on the 92% similarity visual discrimination task. G) Relationship between posterior forceps normalized fractional anisotropy measures and the average performance on the delayed response task for all delays. H) Relationship between posterior forceps normalized fractional anisotropy estimates and the estimated learning trial of the object discrimination task.