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Anand A. Joshi

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9 papers

1 author row

YNIMG Journal 2026 Journal Article

A hierarchical brain MRI atlas of the coppery titi monkey (Plecturocebus cupreus)

Alita Jesal D Almeida
Brad A. Hobson
Anelise Caceres
Sarah Tam
John P. Paulus
Claudia Manca
Anand A. Joshi
Sara M. Freeman

INTRODUCTION: The coppery titi monkey (Plecturocebus cupreus) is an essential nonhuman primate model for social neuroscience, yet neuroimaging studies have been severely constrained by the paucity of standardized atlases. We address this gap by introducing the first MRI-based atlas package for the titi monkey brain that includes a single-subject atlas (UC Davis Titi monkey Neuroimaging Atlas (UCD-TiNA)), alongside a population atlas (UCD-TiNA_group) and a manually-segmented atlas compilation (UCD-TiNA_mac). METHODS: C]GR103545 PET and MRI study (N = 42 monkeys) to quantify regional kappa opioid binding. RESULTS: C]GR103545 binding potential was consistent with published patterns. Quantitative PET analyses showed <1% median error and a high correlation (Spearman r = 0.99) between the warped and manually-segmented labels. DISCUSSION: This work delivers the first in vivo atlas package to enable standardized, reproducible and cross-modal analyses of titi monkey neuroimaging data. By providing a common anatomical reference, this atlas package should facilitate rigorous and harmonized data processing, supporting high-throughput and longitudinal investigations in social neurobiology and informing translational research on social behavior.

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YNIMG Journal 2026 Journal Article

Optimizing electrode placement and information capacity for local field potentials in cortex

Jace A. Willis
Christopher E. Wright
Ruoqian Zhu
Yilan Ruan
Joshua Stallings
Amada M. Abrego
Takfarinas Medani
Promit Moitra

Recent neurosurgery advancements include improved stereotactic targeting and increased density and specificity of electrophysiological evaluation. This study introduces a subject-specific, in silico modeling tool for optimizing electrode placement and maximizing coverage with a variety of devices. The basis for optimization is the Shannon-Hartley information capacity of field potentials derived from dipolar sources. The approach integrates subject-specific MRI data with finite element modeling (FEM) used to simulate the sensitivity of subdural and intracortical devices. Sensitivity maps, or lead fields, from these models enable the comparison of different electrode placements, contact sizes, contact configurations, and substrate properties, which are often overlooked factors. One key tool is a genetic algorithm that optimizes electrode placement by maximizing information capacity. Another is a sparse sensor method, Sparse Electrode Placement for Input Optimization (SEPIO), that selects the best sensor subsets for accurate source classification. We demonstrate several use cases for clinicians, engineers, and researchers. Overall, these open-source tools provide a quantitative framework to select devices from a neurosurgical armament and to optimize device and contact placement. Using these tools may help refine electrode coverage with low channel count devices while minimizing the burden of invasive surgery. The study demonstrates that optimized electrode placement significantly improves the information capacity and signal quality of local field potential (LFP) recordings. The tools developed offer a valuable approach for refining neurosurgical techniques and enhancing the design of neural implants.

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YNIMG Journal 2021 Journal Article

Robust brain network identification from multi-subject asynchronous fMRI data

Jian Li
Jessica L. Wisnowski
Anand A. Joshi
Richard M. Leahy

We describe a novel method for robust identification of common brain networks and their corresponding temporal dynamics across subjects from asynchronous functional MRI (fMRI) using tensor decomposition. We first temporally align asynchronous fMRI data using the orthogonal BrainSync transform, allowing us to study common brain networks across sessions and subjects. We then map the synchronized fMRI data into a 3D tensor (vertices × time × subject/session). Finally, we apply Nesterov-accelerated adaptive moment estimation (Nadam) within a scalable and robust sequential Canonical Polyadic (CP) decomposition framework to identify a low rank tensor approximation to the data. As a result of CP tensor decomposition, we successfully identified twelve known brain networks with their corresponding temporal dynamics from 40 subjects using the Human Connectome Project's language task fMRI data without any prior information regarding the specific task designs. Seven of these networks show distinct subjects' responses to the language task with differing temporal dynamics; two show sub-components of the default mode network that exhibit deactivation during the tasks; the remaining three components reflect non-task-related activities. We compare results to those found using group independent component analysis (ICA) and canonical ICA. Bootstrap analysis demonstrates increased robustness of networks found using the CP tensor approach relative to ICA-based methods.

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YNIMG Journal 2018 Journal Article

Are you thinking what I'm thinking? Synchronization of resting fMRI time-series across subjects

Anand A. Joshi
Minqi Chong
Jian Li
Soyoung Choi
Richard M. Leahy

We describe BrainSync, an orthogonal transform that allows direct comparison of resting fMRI (rfMRI) time-series across subjects. For this purpose, we exploit the geometry of the rfMRI signal space to propose a novel orthogonal transformation that synchronizes rfMRI time-series across sessions and subjects. When synchronized, rfMRI signals become approximately equal at homologous locations across subjects. The method is based on the observation that rfMRI data exhibit similar connectivity patterns across subjects, as reflected in the pairwise correlations between different brain regions. We show that if the data for two subjects have similar correlation patterns then their time courses can be approximately synchronized by an orthogonal transformation. This transform is unique, invertible, efficient to compute, and preserves the connectivity structure of the original data for all subjects. Analogously to image registration, where we spatially align structural brain images, this temporal synchronization of brain signals across a population, or within-subject across sessions, facilitates cross-sectional and longitudinal studies of rfMRI data. The utility of the BrainSync transform is illustrated through demonstrative simulations and applications including quantification of rfMRI variability across subjects and sessions, cortical functional parcellation across a population, timing recovery in task fMRI data, comparison of task and resting state data, and an application to complex naturalistic stimuli for annotation prediction.

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YNICL Journal 2017 Journal Article

Hemoglobin and mean platelet volume predicts diffuse T1-MRI white matter volume decrease in sickle cell disease patients

Soyoung Choi
Adam M. Bush
Matthew T. Borzage
Anand A. Joshi
William J. Mack
Thomas D. Coates
Richard M. Leahy
John C. Wood

Sickle cell disease (SCD) is a life-threatening genetic condition. Patients suffer from chronic systemic and cerebral vascular disease that leads to early and cumulative neurological damage. Few studies have quantified the effects of this disease on brain morphometry and even fewer efforts have been devoted to older patients despite the progressive nature of the disease. This study quantifies global and regional brain volumes in adolescent and young adult patients with SCD and racially matched controls with the aim of distinguishing between age related changes associated with normal brain maturation and damage from sickle cell disease. T1 weighted images were acquired on 33 clinically asymptomatic SCD patients (age=21. 3±7. 8; F=18, M=15) and 32 racially matched control subjects (age=24. 4±7. 5; F=22, M=10). Exclusion criteria included pregnancy, previous overt stroke, acute chest, or pain crisis hospitalization within one month. All brain volume comparisons were corrected for age and sex. Globally, grey matter volume was not different but white matter volume was 8. 1% lower (p=0. 0056) in the right hemisphere and 6. 8% (p=0. 0068) in the left hemisphere in SCD patients compared with controls. Multivariate analysis retained hemoglobin (β=0. 33; p=0. 0036), sex (β=0. 35; p=0. 0017) and mean platelet volume (β=0. 27; p=0. 016) as significant factors in the final prediction model for white matter volume for a combined r2 of 0. 37 (p<0. 0001). Lower white matter volume was confined to phylogenetically younger brain regions in the anterior and middle cerebral artery distributions. Our findings suggest that there are diffuse white matter abnormalities in SCD patients, especially in the frontal, parietal and temporal lobes, that are associated with low hemoglobin levels and mean platelet volume. The pattern of brain loss suggests chronic microvascular insufficiency and tissue hypoxia as the causal mechanism. However, longitudinal studies of global and regional brain morphometry can help us give further insights on the pathophysiology of SCD in the brain.

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YNIMG Journal 2015 Journal Article

Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization

Chitresh Bhushan
Justin P. Haldar
Soyoung Choi
Anand A. Joshi
David W. Shattuck
Richard M. Leahy

Diffusion MRI provides quantitative information about microstructural properties which can be useful in neuroimaging studies of the human brain. Echo planar imaging (EPI) sequences, which are frequently used for acquisition of diffusion images, are sensitive to inhomogeneities in the primary magnetic (B0) field that cause localized distortions in the reconstructed images. We describe and evaluate a new method for correction of susceptibility-induced distortion in diffusion images in the absence of an accurate B0 fieldmap. In our method, the distortion field is estimated using a constrained non-rigid registration between an undistorted T1-weighted anatomical image and one of the distorted EPI images from diffusion acquisition. Our registration framework is based on a new approach, INVERSION (Inverse contrast Normalization for VERy Simple registratION), which exploits the inverted contrast relationship between T1- and T2-weighted brain images to define a simple and robust similarity measure. We also describe how INVERSION can be used for rigid alignment of diffusion images and T1-weighted anatomical images. Our approach is evaluated with multiple in vivo datasets acquired with different acquisition parameters. Compared to other methods, INVERSION shows robust and consistent performance in rigid registration and shows improved alignment of diffusion and anatomical images relative to normalized mutual information for non-rigid distortion correction.

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YNIMG Journal 2013 Journal Article

Canonical granger causality between regions of interest

Syed Ashrafulla
Justin P. Haldar
Anand A. Joshi
Richard M. Leahy

Estimating and modeling functional connectivity in the brain is a challenging problem with potential applications in the understanding of brain organization and various neurological and neuropsychological conditions. An important objective in connectivity analysis is to determine the connections between regions of interest in the brain. However, traditional functional connectivity analyses have frequently focused on modeling interactions between time series recordings at individual sensors, voxels, or vertices despite the fact that a single region of interest will often include multiple such recordings. In this paper, we present a novel measure of interaction between regions of interest rather than individual signals. The proposed measure, termed canonical Granger causality, combines ideas from canonical correlation and Granger causality analysis to yield a measure that reflects directed causality between two regions of interest. In particular, canonical Granger causality uses optimized linear combinations of signals from each region of interest to enable accurate causality measurements from substantially less data compared to alternative multivariate methods that have previously been proposed for this scenario. The optimized linear combinations are obtained using a variation of a technique developed for optimization on the Stiefel manifold. We demonstrate the advantages of canonical Granger causality in comparison to alternative causality measures for a range of different simulated datasets. We also apply the proposed measure to local field potential data recorded in a macaque brain during a visuomotor task. Results demonstrate that canonical Granger causality can be used to identify causal relationships between striate and prestriate cortexes in cases where standard Granger causality is unable to identify statistically significant interactions.

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YNIMG Journal 2011 Journal Article

Resting-state fMRI can reliably map neural networks in children

Moriah E. Thomason
Emily L. Dennis
Anand A. Joshi
Shantanu H. Joshi
Ivo D. Dinov
Catie Chang
Melissa L. Henry
Rebecca F. Johnson

Resting-state MRI (rs-fMRI) is a powerful procedure for studying whole-brain neural connectivity. In this study we provide the first empirical evidence of the longitudinal reliability of rs-fMRI in children. We compared rest–retest measurements across spatial, temporal and frequency domains for each of six cognitive and sensorimotor intrinsic connectivity networks (ICNs) both within and between scan sessions. Using Kendall'sW, concordance of spatial maps ranged from. 60 to. 86 across networks, for various derived measures. The Pearson correlation coefficient for temporal coherence between networks across all Time 1–Time 2 (T1/T2) z-converted measures was. 66 (p <. 001). There were no differences between T1/T2 measurements in low-frequency power of the ICNs. For the visual network, within-session T1 correlated with the T2 low-frequency power, across participants. These measures from resting-state data in children were consistent across multiple domains (spatial, temporal, and frequency). Resting-state connectivity is therefore a reliable method for assessing large-scale brain networks in children.

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YNIMG Journal 2010 Journal Article

Sulcal set optimization for cortical surface registration

Anand A. Joshi
Dimitrios Pantazis
Quanzheng Li
Hanna Damasio
David W. Shattuck
Arthur W. Toga
Richard M. Leahy

Flat mapping based cortical surface registration constrained by manually traced sulcal curves has been widely used for inter subject comparisons of neuroanatomical data. Even for an experienced neuroanatomist, manual sulcal tracing can be quite time consuming, with the cost increasing with the number of sulcal curves used for registration. We present a method for estimation of an optimal subset of size N C from N possible candidate sulcal curves that minimizes a mean squared error metric over all combinations of N C curves. The resulting procedure allows us to estimate a subset with a reduced number of curves to be traced as part of the registration procedure leading to optimal use of manual labeling effort for registration. To minimize the error metric we analyze the correlation structure of the errors in the sulcal curves by modeling them as a multivariate Gaussian distribution. For a given subset of sulci used as constraints in surface registration, the proposed model estimates registration error based on the correlation structure of the sulcal errors. The optimal subset of constraint curves consists of the N C sulci that jointly minimize the estimated error variance for the subset of unconstrained curves conditioned on the N C constraint curves. The optimal subsets of sulci are presented and the estimated and actual registration errors for these subsets are computed.

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