Publications

Conference Paper
C. Maffei, F. Wang, S. Haber, and A. Yendiki. 2022. “Submillimeter dMRI protocol optimization for accurate in-vivo reconstruction of deep-brain circuitry.” In ISMRM (oral presentation, summa cum laude).
R. Jones, C. Maffei, Q. Tian, S. Huang, V. Sundaresan, and A. Yendiki. 2022. “In vivo demonstration of generalized anisotropy profiles for resolving boundaries between subcortical gray and white matter.” In ISMRM (oral presentation, summa cum laude).
G. Ramos-Llordén, D. Park, C. Mirkes, C.M. Cushing, P. Weavers, H.-H. Lee, Q. Tian, A. Scholz, B. Keil, B. Bilgic, A. Yendiki, T. Witzel, and S.Y. Huang. 2022. “Distortion- and ghosting-free high b-value ex vivo human brain diffusion MRI achieved with spatiotemporal magnetic field monitoring.” In ISMRM (oral presentation).
C. Maffei, G. Girard, K. G. Schilling, D. B. Aydogan, N. Adluru, A. Zhylka, Y. Wu, M. Mancini, A. Hamamci, A. Sarica, D. Karimi, F.-C. Yeh, M.E. Yildiz, A. Gholipour, A. Quattrone, A. Quattrone, P.-T. Yap, A. de Luca, J. Pluim, A. Leemans, V. Prabhakaran, B. B. Bendlin, A. L. Alexander, B. A. Landman, E.J. Canales-Rodríguez, M. Barakovic, J. Rafael-Patino, T. Yu, G. Rensonnet, S. Schiavi, A. Daducci, M. Pizzolato, E. Fischi-Gomez, J.-P. Thiran, G. Dai, G. Grisot, N. Lazovski, S. Puch, M. Ramos, P. Rodrigues, V. Prchkovska, R. Jones, J. Lehman, S. Haber, and A. Yendiki. 2021. “New insights from the IronTract challenge: Simple post-processing enhances the accuracy of diffusion tractography.” In ISMRM (oral presentation, magna cum laude).
G. Ramos-Llordén, C. Maffei, Q. Tian, B. Bilgic, J. Augustinack, T. Witzel, B. Keil, A. Yendiki, and S. Huang. 2021. “Ex-vivo whole human brain high b-value diffusion MRI at 550 micron isotropic resolution using a 3T Connectom scanner.” In ISMRM (oral presentation).
G. Ramos-Llordén, R. A. Lobos, T. H. Kim, Q. Tian, S. Tounetki, T. Witzel, B. Keil, A. Yendiki, B. Bilgic, J. P. Haldar, and S. Huang. 2021. “Improved multi-shot EPI ghost correction for high gradient strength diffusion MRI using structured low-rank modeling k-space reconstruction.” In ISMRM.
R. Jones, C. Maffei, Q. Fan, J. Augustinack, B. Wichtmann, A. Nummenmaa, S. Huang, and A. Yendiki. 2021. “Validation of between-bundle differences and within-bundle continuity of microstructural indices in ex vivo human brain tissue.” In ISMRM.
R. Jones, Q. Tian, C. Maffei, J. Augustinack, A. Nummenmaa, S. Huang, and A. Yendiki. 2021. “Generalized anisotropy profiles distinguish cortical and subcortical structures in ex vivo diffusion MRI.” In ISMRM.
A. Yendiki, R. Jones, A. Dalca, H. Wang, and B. Fischl. 2020. “Towards taking the guesswork (and the errors) out of diffusion tractography.” In ISMRM (oral presentation).
C. Maffei, G. Girard, K. G. Schilling, N. Adluru, D. B. Aydogan, A. Hamamci, F.-C. Yeh, M. Mancini, Y. Wu, A. Sarica, A. Teillac, S. H. Baete, D. Karimi, Y.-C. Lin, F. Boada, N. Richard, B. Hiba, A. Quattrone, Y. Hong, D. Shen, P.-T. Yap, T. Boshkovski, J. S. W. Campbell, N. Stikov, G. B. Pike, B. B. Bendlin, A. L. Alexander, V. Prabhakaran, A. Anderson, B. A. Landman, E. J. Z. Canales-Rodríguez, M. Barakovic, J. Rafael-Patino, T. Yu, G. Rensonnet, S. Schiavi, A. Daducci, M. Pizzolato, E. Fischi-Gomez, J.-P. Thiran, G. Dai, G. Grisot, N. Lazovski, A. Puente, M. Rowe, I. Sanchez, V. Prchkovska, R. Jones, J. Lehman, S. Haber, and A. Yendiki. 2020. “The IronTract challenge: Validation and optimal tractography methods for the HCP diffusion acquisition scheme.” In ISMRM (oral presentation, magna cum laude).
C. H. Sudre, C. Maffei, J. Barnes, D. Thomas, D. Cash, T. Parker, C. Lane, M. Richards, G. Zhang, S. Ourselin, J. Schott, A. Yendiki, and M. J. Cardoso. 2020. “Along-tract correlation analysis of diffusion metrics and white matter lesions in a 70-year old birth cohort.” In ISMRM.
Journal Article
Gabriel Ramos-Llordén, Rodrigo A Lobos, Tae Hyung Kim, Qiyuan Tian, Thomas Witzel, Hong-Hsi Lee, Alina Scholz, Boris Keil, Anastasia Yendiki, Berkin Bilgiç, Justin P Haldar, and Susie Y Huang. 2022. “High-fidelity, high-spatial-resolution diffusion MRI of the ex vivo whole human brain at ultra-high gradient strength with structured low-rank EPI ghost correction.” NMR Biomed, Pp. e4831.Abstract
Diffusion magnetic resonance imaging (dMRI) of whole ex vivo human brain specimens enables 3D mapping of structural connectivity at the mesoscopic scale, providing detailed evaluation of fiber architecture and tissue microstructure at a spatial resolution that is difficult to access in vivo. To account for the short T2 and low diffusivity of fixed tissue, ex vivo dMRI is often acquired using strong diffusion-sensitizing gradients and multi-shot/segmented 3D echo-planar imaging (EPI) sequences to achieve high spatial resolution. However, the combination of strong diffusion-sensitizing gradients and multi-shot/segmented EPI readout can result in pronounced ghosting artifacts incurred by nonlinear spatiotemporal variations in the magnetic field produced by eddy currents. Such ghosting artifacts cannot be corrected with conventional correction solutions and pose a significant roadblock to leveraging human MRI scanners with ultra-high gradients for ex vivo whole-brain dMRI. Here, we show that ghosting correction approaches that correct for either polarity-related ghosting or shot-to-shot variations in a separate manner are suboptimal for 3D multi-shot diffusion-weighted EPI experiments in fixed human brain specimens using strong diffusion-sensitizing gradients on the 3T Connectom MRI scanner, resulting in orientationally biased dMRI estimates. We apply a recently developed advanced k-space reconstruction method based on structured low-rank matrix modeling (SLM), that handles both polarity-related ghosting and shot-to-shot variation simultaneously, to mitigate artifacts in high-angular resolution multi-shot dMRI data acquired in several fixed human brain specimens at 0.7-0.8 mm isotropic spatial resolution using b-values up to 10,000 s/mm2 and gradient strengths up to 280 mT/m. We demonstrate the improved mapping of diffusion tensor imaging and fiber orientation distribution functions in key neuroanatomical areas distributed across the whole brain using SLM-based EPI ghost correction compared to alternative techniques.
João Paulo Lima Santos, Michele Bertocci, Genna Bebko, Tina Goldstein, Tae Kim, Satish Iyengar, Lisa Bonar, Mary Kay Gill, John Merranko, Anastasia Yendiki, Boris Birmaher, Mary L Phillips, and Amelia Versace. 2022. “White Matter Correlates of Early-Onset Bipolar Illness and Predictors of One-Year Recurrence of Depression in Adults with Bipolar Disorder.” J Clin Med, 11, 12.Abstract
Diffusion Magnetic Resonance Imaging (dMRI) studies have reported abnormalities in emotion regulation circuits in BD; however, no study has examined the contribution of previous illness on these mechanisms. Using global probabilistic tractography, we aimed to identify neural correlates of previous BD illness and the extent to which these can help predict one-year recurrence of depressive episodes. dMRI data were collected in 70 adults with early-onset BD who were clinically followed for up to 18 years and 39 healthy controls. Higher number of depressive episodes during childhood/adolescence and higher percentage of time with syndromic depression during longitudinal follow-up was associated with lower fractional anisotropy (FA) in focal regions of the forceps minor (left, F = 4.4, p = 0.003; right, F = 3.1, p = 0.021) and anterior cingulum bundle (left, F = 4.7, p = 0.002; right, F = 7.0, p < 0.001). Lower FA in these regions was also associated with higher depressive and anxiety symptoms at scan. Remarkably, those having higher FA in the right cluster of the forceps minor (AOR = 0.43, p = 0.017) and in a cluster of the posterior cingulum bundle (right, AOR = 0.50, p = 0.032) were protected against the recurrence of depressive episodes. Previous depressive symptomatology may cause neurodegenerative effects in the forceps minor that are associated with worsening of BD symptomatology in subsequent years. Abnormalities in the posterior cingulum may also play a role.
Chiara Maffei, Gabriel Girard, Kurt G Schilling, Dogu Baran Aydogan, Nagesh Adluru, Andrey Zhylka, Ye Wu, Matteo Mancini, Andac Hamamci, Alessia Sarica, Achille Teillac, Steven H Baete, Davood Karimi, Fang-Cheng Yeh, Mert E Yildiz, Ali Gholipour, Yann Bihan-Poudec, Bassem Hiba, Andrea Quattrone, Aldo Quattrone, Tommy Boshkovski, Nikola Stikov, Pew-Thian Yap, Alberto De Luca, Josien Pluim, Alexander Leemans, Vivek Prabhakaran, Barbara B Bendlin, Andrew L Alexander, Bennett A Landman, Erick J Canales-Rodríguez, Muhamed Barakovic, Jonathan Rafael-Patino, Thomas Yu, Gaëtan Rensonnet, Simona Schiavi, Alessandro Daducci, Marco Pizzolato, Elda Fischi-Gomez, Jean-Philippe Thiran, George Dai, Giorgia Grisot, Nikola Lazovski, Santi Puch, Marc Ramos, Paulo Rodrigues, Vesna Prčkovska, Robert Jones, Julia Lehman, Suzanne N Haber, and Anastasia Yendiki. 2022. “Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI.” Neuroimage, 257, Pp. 119327.Abstract
Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
Arman Avesta, Anastasia Yendiki, Vincent Perlbarg, Lionel Velly, Omid Khalilzadeh, Louis Puybasset, Damien Galanaud, and Rajiv Gupta. 2022. “Synergistic Role of Quantitative Diffusion Magnetic Resonance Imaging and Structural Magnetic Resonance Imaging in Predicting Outcomes After Traumatic Brain Injury.” J Comput Assist Tomogr, 46, 2, Pp. 236-243.Abstract
OBJECTIVE: This study aimed to assess if quantitative diffusion magnetic resonance imaging analysis would improve prognostication of individual patients with severe traumatic brain injury. METHODS: We analyzed images of 30 healthy controls to extract normal fractional anisotropy ranges along 18 white-matter tracts. Then, we analyzed images of 33 patients, compared their fractional anisotropy values with normal ranges extracted from controls, and computed severity of injury to white-matter tracts. We also asked 2 neuroradiologists to rate severity of injury to different brain regions on fluid-attenuated inversion recovery and susceptibility-weighted imaging. Finally, we built 3 models: (1) fed with neuroradiologists' ratings, (2) fed with white-matter injury measures, and (3) fed with both input types. RESULTS: The 3 models respectively predicted survival at 1 year with accuracies of 70%, 73%, and 88%. The accuracy with both input types was significantly better (P < 0.05). CONCLUSIONS: Quantifying severity of injury to white-matter tracts complements qualitative imaging findings and improves outcome prediction in severe traumatic brain injury.
Anastasia Yendiki, Manisha Aggarwal, Markus Axer, Amy FD Howard, Anne-Marie Cappellen vanvan Walsum, and Suzanne N Haber. 2022. “Post mortem mapping of connectional anatomy for the validation of diffusion MRI.” Neuroimage, 256, Pp. 119146.Abstract
Diffusion MRI (dMRI) is a unique tool in the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.
Qiuyun Fan, Cornelius Eichner, Maryam Afzali, Lars Mueller, Chantal MW Tax, Mathias Davids, Mirsad Mahmutovic, Boris Keil, Berkin Bilgic, Kawin Setsompop, Hong-Hsi Lee, Qiyuan Tian, Chiara Maffei, Gabriel Ramos-Llordén, Aapo Nummenmaa, Thomas Witzel, Anastasia Yendiki, Yi-Qiao Song, Chu-Chung Huang, Ching-Po Lin, Nikolaus Weiskopf, Alfred Anwander, Derek K Jones, Bruce R Rosen, Lawrence L Wald, and Susie Y Huang. 2022. “Mapping the Human Connectome using Diffusion MRI at 300 mT/m Gradient Strength: Methodological Advances and Scientific Impact.” Neuroimage, 254, Pp. 118958.Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in Continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength dMRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for dMRI and where the field is headed in the coming years.
Chao J Liu, William Ammon, Robert J Jones, Jackson Nolan, Ruopeng Wang, Shuaibin Chang, Matthew P Frosch, Anastasia Yendiki, David A Boas, Caroline Magnain, Bruce Fischl, and Hui Wang. 2022. “Refractive-index matching enhanced polarization sensitive optical coherence tomography quantification in human brain tissue.” Biomed Opt Express, 13, 1, Pp. 358-372.Abstract
The importance of polarization-sensitive optical coherence tomography (PS-OCT) has been increasingly recognized in human brain imaging. Despite the recent progress of PS-OCT in revealing white matter architecture and orientation, quantification of fine-scale fiber tracts in the human brain cortex has been a challenging problem, due to a low birefringence in the gray matter. In this study, we investigated the effect of refractive index matching by 2,2'-thiodiethanol (TDE) immersion on the improvement of PS-OCT measurements in ex vivo human brain tissue. We show that we can obtain fiber orientation maps of U-fibers that underlie sulci, as well as cortical fibers in the gray matter, including radial fibers in gyri and distinct layers of fibers exhibiting laminar organization. Further analysis shows that index matching reduces the noise in axis orientation measurements by 56% and 39%, in white and gray matter, respectively. Index matching also enables precise measurements of apparent birefringence, which was underestimated in the white matter by 82% but overestimated in the gray matter by 16% prior to TDE immersion. Mathematical simulations show that the improvements are primarily attributed to the reduction in the tissue scattering coefficient, leading to an enhanced signal-to-noise ratio in deeper tissue regions, which could not be achieved by conventional noise reduction methods.
Randy P Auerbach, David Pagliaccio, Nicholas A Hubbard, Isabelle Frosch, Rebecca Kremens, Elizabeth Cosby, Robert Jones, Viviana Siless, Nicole Lo, Aude Henin, Stefan G Hofmann, John DE Gabrieli, Anastasia Yendiki, Susan Whitfield-Gabrieli, and Diego A Pizzagalli. 2022. “Reward-Related Neural Circuitry in Depressed and Anxious Adolescents: A Human Connectome Project.” J Am Acad Child Adolesc Psychiatry, 61, 2, Pp. 308-320.Abstract
OBJECTIVE: Although depression and anxiety often have distinct etiologies, they frequently co-occur in adolescence. Recent initiatives have underscored the importance of developing new ways of classifying mental illness based on underlying neural dimensions that cut across traditional diagnostic boundaries. Accordingly, the aim of the study was to clarify reward-related neural circuitry that may characterize depressed-anxious youth. METHOD: The Boston Adolescent Neuroimaging of Depression and Anxiety Human Connectome Project tested group differences regarding subcortical volume and nucleus accumbens activation during an incentive processing task among 14- to 17-year-old adolescents presenting with a primary depressive and/or anxiety disorder (n = 129) or no lifetime history of mental disorders (n = 64). In addition, multimodal modeling examined predictors of depression and anxiety symptom change over a 6-month follow-up period. RESULTS: Our findings highlighted considerable convergence. Relative to healthy youth, depressed-anxious adolescents exhibited reduced nucleus accumbens volume and activation following reward receipt. These findings remained when removing all medicated participants (∼59% of depressed-anxious youth). Subgroup analyses comparing anxious-only, depressed-anxious, and healthy youth also were largely consistent. Multimodal modeling showed that only structural alterations predicted depressive symptoms over time. CONCLUSION: Multimodal findings highlight alterations within nucleus accumbens structure and function that characterize depressed-anxious adolescents. In the current hypothesis-driven analyses, however, only reduced nucleus accumbens volume predicted depressive symptoms over time. An important next step will be to clarify why structural alterations have an impact on reward-related processes and associated symptoms.
Yoon Ji Lee, Xavier Guell, Nicholas A Hubbard, Viviana Siless, Isabelle R Frosch, Mathias Goncalves, Nicole Lo, Atira Nair, Satrajit S Ghosh, Stefan G Hofmann, Randy P Auerbach, Diego A Pizzagalli, Anastasia Yendiki, John DE Gabrieli, Susan Whitfield-Gabrieli, and Sheeba Arnold Anteraper. 2021. “Functional Alterations in Cerebellar Functional Connectivity in Anxiety Disorders.” Cerebellum, 20, 3, Pp. 392-401.Abstract
Adolescents with anxiety disorders exhibit excessive emotional and somatic arousal. Neuroimaging studies have shown abnormal cerebral cortical activation and connectivity in this patient population. The specific role of cerebellar output circuitry, specifically the dentate nuclei (DN), in adolescent anxiety disorders remains largely unexplored. Resting-state functional connectivity analyses have parcellated the DN, the major output nuclei of the cerebellum, into three functional territories (FTs) that include default-mode, salience-motor, and visual networks. The objective of this study was to understand whether FTs of the DN are implicated in adolescent anxiety disorders. Forty-one adolescents (mean age 15.19 ± 0.82, 26 females) with one or more anxiety disorders and 55 age- and gender-matched healthy controls completed resting-state fMRI scans and a self-report survey on anxiety symptoms. Seed-to-voxel functional connectivity analyses were performed using the FTs from DN parcellation. Brain connectivity metrics were then correlated with State-Trait Anxiety Inventory (STAI) measures within each group. Adolescents with an anxiety disorder showed significant hyperconnectivity between salience-motor DN FT and cerebral cortical salience-motor regions compared to controls. Salience-motor FT connectivity with cerebral cortical sensorimotor regions was significantly correlated with STAI-trait scores in HC (R2 = 0.41). Here, we report DN functional connectivity differences in adolescents diagnosed with anxiety, as well as in HC with variable degrees of anxiety traits. These observations highlight the relevance of DN as a potential clinical and sub-clinical marker of anxiety.

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