Ned H. Kalin

YNIMG Journal 2022 Journal Article

Longitudinal assessment of early-life white matter development with quantitative relaxometry in nonhuman primates

• Jason F. Moody
• Nakul Aggarwal
• Douglas C. Dean
• Do P.M. Tromp
• Steve R. Kecskemeti
• Jonathan A. Oler
• Ned H. Kalin
• Andrew L. Alexander

Alterations in white matter (WM) development are associated with many neuropsychiatric and neurodevelopmental disorders. Most MRI studies examining WM development employ diffusion tensor imaging (DTI), which relies on estimating diffusion patterns of water molecules as a reflection of WM microstructure. Quantitative relaxometry, an alternative method for characterizing WM microstructural changes, is based on molecular interactions associated with the magnetic relaxation of protons. In a longitudinal study of 34 infant non-human primates (NHP) (Macaca mulatta) across the first year of life, we implement a novel, high-resolution, T1-weighted MPnRAGE sequence to examine WM trajectories of the longitudinal relaxation rate (qR1) in relation to DTI metrics and gestational age at scan. To the best of our knowledge, this is the first study to assess developmental WM trajectories in NHPs using quantitative relaxometry and the first to directly compare DTI and relaxometry metrics during infancy. We demonstrate that qR1 exhibits robust logarithmic growth, unfolding in a posterior-anterior and medial-lateral fashion, similar to DTI metrics. On a within-subject level, DTI metrics and qR1 are highly correlated, but are largely unrelated on a between-subject level. Unlike DTI metrics, gestational age at birth (time in utero) is a strong predictor of early postnatal qR1 levels. Whereas individual differences in DTI metrics are maintained across the first year of life, this is not the case for qR1. These results point to the similarities and differences in using quantitative relaxometry and DTI in developmental studies, providing a basis for future studies to characterize the unique processes that these measures reflect at the cellular and molecular level.

YNIMG Journal 2021 Journal Article

Spatiotemporal dynamics of nonhuman primate white matter development during the first year of life

• Nakul Aggarwal
• Jason F. Moody
• Douglas C. Dean
• Do P.M. Tromp
• Steve R. Kecskemeti
• Jonathan A. Oler
• Andy L. Alexander
• Ned H. Kalin

White matter (WM) development early in life is a critical component of brain development that facilitates the coordinated function of neuronal pathways. Additionally, alterations in WM have been implicated in various neurodevelopmental disorders, including psychiatric disorders. Because of the need to understand WM development in the weeks immediately following birth, we characterized changes in WM microstructure throughout the postnatal macaque brain during the first year of life. This is a period in primates during which genetic, developmental, and environmental factors may have long-lasting impacts on WM microstructure. Studies in nonhuman primates (NHPs) are particularly valuable as a model for understanding human brain development because of their evolutionary relatedness to humans. Here, 34 rhesus monkeys (23 females, 11 males) were imaged longitudinally at 3, 7, 13, 25, and 53 weeks of age with T1-weighted (MPnRAGE) and diffusion tensor imaging (DTI). With linear mixed-effects (LME) modeling, we demonstrated robust logarithmic growth in FA, MD, and RD trajectories extracted from 18 WM tracts across the brain. Estimated rate of change curves for FA, MD, and RD exhibited an initial 10-week period of exceedingly rapid WM development, followed by a precipitous decline in growth rates. K-means clustering of raw DTI trajectories and rank ordering of LME model parameters revealed distinct posterior-to-anterior and medial-to-lateral gradients in WM maturation. Finally, we found that individual differences in WM microstructure assessed at 3 weeks of age were significantly related to those at 1 year of age. This study provides a quantitative characterization of very early WM growth in NHPs and lays the foundation for future work focused on the impact of alterations in early WM developmental trajectories in relation to human psychopathology.

YNIMG Journal 2021 Journal Article

U-net model for brain extraction: Trained on humans for transfer to non-human primates

• Xindi Wang
• Xin-Hui Li
• Jae Wook Cho
• Brian E. Russ
• Nanditha Rajamani
• Alisa Omelchenko
• Lei Ai
• Annachiara Korchmaros

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

YNIMG Journal 2018 Journal Article

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates

• Gengyan Zhao
• Fang Liu
• Jonathan A. Oler
• Mary E. Meyerand
• Ned H. Kalin
• Rasmus M. Birn

Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0. 985 and a mean average symmetric surface distance of 0. 220 mm. A better performance against all the compared methods was verified by statistical tests (all p-values < 10−4, two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0. 116 across all the 100 subjects. The behavior of the uncertainty was also studied, which shows the uncertainty increases as the training set size decreases, the number of inconsistent labels in the training set increases, or the inconsistency between the training set and the testing set increases.

YNIMG Journal 2012 Journal Article

A diffusion tensor brain template for Rhesus Macaques

• Nagesh Adluru
• Hui Zhang
• Andrew S. Fox
• Steven E. Shelton
• Chad M. Ennis
• Anne M. Bartosic
• Jonathan A. Oler
• Do P.M. Tromp

Diffusion tensor imaging (DTI) is a powerful and noninvasive imaging method for characterizing tissue microstructure and white matter organization in the brain. While it has been applied extensively in research studies of the human brain, DTI studies of non-human primates have been performed only recently. The growing application of DTI in rhesus monkey studies would significantly benefit from a standardized framework to compare findings across different studies. A very common strategy for image analysis is to spatially normalize (co-register) the individual scans to a representative template space. This paper presents the development of a DTI brain template, UWRMAC-DTI271, for adolescent Rhesus Macaque (Macaca mulatta) monkeys. The template was generated from 271 rhesus monkeys, collected as part of a unique brain imaging genetics study. It is the largest number of animals ever used to generate a computational brain template, which enables the generation of a template that has high image quality and accounts for variability in the species. The quality of the template is further ensured with the use of DTI-TK, a well-tested and high-performance DTI spatial normalization method in human studies. We demonstrated its efficacy in monkey studies for the first time by comparing it to other commonly used scalar-methods for DTI normalization. It is anticipated that this template will play an important role in facilitating cross-site voxelwise DTI analyses in Rhesus Macaques. Such analyses are crucial in investigating the role of white matter structure in brain function, development, and other psychopathological disorders for which there are well-validated non-human primate models.

YNIMG Journal 2012 Journal Article

Evidence for coordinated functional activity within the extended amygdala of non-human and human primates

• Jonathan A. Oler
• Rasmus M. Birn
• Rémi Patriat
• Andrew S. Fox
• Steven E. Shelton
• Cory A. Burghy
• Diane E. Stodola
• Marilyn J. Essex

Neuroanatomists posit that the central nucleus of the amygdala (Ce) and bed nucleus of the stria terminalis (BST) comprise two major nodes of a macrostructural forebrain entity termed the extended amygdala. The extended amygdala is thought to play a critical role in adaptive motivational behavior and is implicated in the pathophysiology of maladaptive fear and anxiety. Resting functional connectivity of the Ce was examined in 107 young anesthetized rhesus monkeys and 105 young humans using standard resting-state functional magnetic resonance imaging (fMRI) methods to assess temporal correlations across the brain. The data expand the neuroanatomical concept of the extended amygdala by finding, in both species, highly significant functional coupling between the Ce and the BST. These results support the use of in vivo functional imaging methods in nonhuman and human primates to probe the functional anatomy of major brain networks such as the extended amygdala.

YNIMG Journal 2005 Journal Article

Stability of amygdala BOLD response to fearful faces over multiple scan sessions

• Tom Johnstone
• Leah H. Somerville
• Andrew L. Alexander
• Terrence R. Oakes
• Richard J. Davidson
• Ned H. Kalin
• Paul J. Whalen

We used fMRI to examine amygdala activation in response to fearful facial expressions, measured over multiple scanning sessions. 15 human subjects underwent three scanning sessions, at 0, 2 and 8 weeks. During each session, functional brain images centered about the amygdala were acquired continuously while participants were shown alternating blocks of fearful, neutral and happy facial expressions. Intraclass correlation coefficients calculated across the sessions indicated stability of response in left amygdala to fearful faces (as a change from baseline), but considerably less left amygdala stability in responses to neutral expressions and for fear versus neutral contrasts. The results demonstrate that the measurement of fMRI BOLD responses in amygdala to fearful facial expressions might be usefully employed as an index of amygdala reactivity over extended periods. While signal change to fearful facial expressions appears robust, the experimental design employed here has yielded variable responsivity within baseline or comparison conditions. Future studies might manipulate the experimental design to either amplify or attenuate this variability, according to the goals of the research.

YNIMG Journal 2004 Journal Article

Amygdalar interhemispheric functional connectivity differs between the non-depressed and depressed human brain

• William Irwin
• Michael J. Anderle
• Heather C. Abercrombie
• Stacey M. Schaefer
• Ned H. Kalin
• Richard J. Davidson

The amygdalae are important, if not critical, brain regions for many affective, attentional and memorial processes, and dysfunction of the amygdalae has been a consistent finding in the study of clinical depression. Theoretical models of the functional neuroanatomy of both normal and psychopathological affective processes which posit cortical hemispheric specialization of functions have been supported by both lesion and functional neuroimaging studies in humans. Results from human neuroimaging studies in support of amygdalar hemispheric specialization are inconsistent. However, recent results from human lesion studies are consistent with hemispheric specialization. An important, yet largely ignored, feature of the amygdalae in the primate brain—derived from both neuroanatomical and electrophysiological data—is that there are virtually no direct interhemispheric connections via the anterior commissure (AC). This feature stands in stark contrast to that of the rodent brain wherein virtually all amygdalar nuclei have direct interhemispheric connections. We propose this feature of the primate brain, in particular the human brain, is a result of influences from frontocortical hemispheric specialization which have developed over the course of primate brain evolution. Results consistent with this notion were obtained by examining the nature of human amygdalar interhemispheric connectivity using both functional magnetic resonance imaging (FMRI) and positron emission tomography (PET). We found modest evidence of amygdalar interhemispheric functional connectivity in the non-depressed brain, whereas there was strong evidence of functional connectivity in the depressed brain. We interpret and discuss the nature of this connectivity in the depressed brain in the context of dysfunctional frontocortical–amygdalar interactions which accompany clinical depression.

YNIMG Journal 1998 Journal Article

Ratings of Affective Stimulus Characteristics and Measures of Affective Reactivity Predict MR Signal Change in the Human Amygdala

• William Irwin
• Bryan J. Mock
• Steven K. Sutton
• Michael J. Anderle
• Ned H. Kalin
• James A. Sorenson
• Patrick A. Turski
• Richard J. Davidson