Conference Paper
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).
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.
C. Maffei and A. Yendiki. 2019. “Using HCP data to improve diffusion tractography in routine-quality data: Application to the virtual dissection of the SLF system.” In ISMRM.
R. J. Jones, G. Grisot, J. Augustinack, D. A. Boas, B. Fischl, H. Wang, B. Bilgic, and A. Yendiki. 2019. “Validation of DSI compressed sensing reconstruction in ex vivo human brain.” In ISMRM (oral presentation).
A. Scholz, M. May, R. Etzel, M. Mahmutovic, N. Kutscha, L. L. Wald, A. Yendiki, and B. Keil. 2019. “A 48-channel ex vivo brain array coil for diffusion-weighted MRI at 3T.” In ISMRM.
J. E. Iglesias, K. Van Leemput, P. Golland, and A. Yendiki. 2019. “Joint inference on structural and diffusion MRI for sequence-adaptive Bayesian segmentation of thalamic nuclei with probabilistic atlases.” In IPMI.
G. Grisot, S. N. Haber, and A. Yendiki. 2018. “Validation of diffusion MRI models and tractography algorithms using chemical tracing.” In ISMRM (oral presentation).
Journal Article
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. 2021. “Reward-Related Neural Circuitry in Depressed and Anxious Adolescents: A Human Connectome Project.” J Am Acad Child Adolesc Psychiatry.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 cuts 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 14-17-year-old adolescents presenting with a primary depressive and/or anxiety disorder (n=129) or no lifetime history of mental disorders (n=64). Additionally, 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, only reduced nucleus accumbens volume, however, predicted depressive symptoms over time. An important next step will be to clarify why structural alterations impact 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. 2020. “Functional Alterations in Cerebellar Functional Connectivity in Anxiety Disorders.” Cerebellum.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 (R = 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.
Suzanne N Haber, Anastasia Yendiki, and Saad Jbabdi. 2020. “Four Deep Brain Stimulation Targets for Obsessive-Compulsive Disorder: Are They Different?” Biol Psychiatry.Abstract
Deep brain stimulation is a promising therapeutic approach for patients with treatment-resistant obsessive-compulsive disorder, a condition linked to abnormalities in corticobasal ganglia networks. Effective targets are placed in one of four subcortical areas with the goal of capturing prefrontal, anterior cingulate, and basal ganglia connections linked to the limbic system. These include the anterior limb of the internal capsule, the ventral striatum, the subthalamic nucleus, and a midbrain target. The goal of this review is to examine these 4 targets with respect to the similarities and differences of their connections. Following a review of the connections for each target based on anatomic studies in nonhuman primates, we examine the accuracy of diffusion magnetic resonance imaging tractography to replicate those connections in nonhuman primates, before evaluating the connections in the human brain based on diffusion magnetic resonance imaging tractography. Results demonstrate that the four targets generally involve similar connections, all of which are part of the internal capsule. Nonetheless, some connections are unique to each site. Delineating the similarities and differences across targets is a critical step for evaluating and comparing the effectiveness of each and how circuits contribute to the therapeutic outcome. It also underscores the importance that the terminology used for each target accurately reflects its position and its anatomic connections, so as to enable comparisons across clinical studies and for basic scientists to probe mechanisms underlying deep brain stimulation.
Maria Gabriela Figueiro Longo, Can Ozan Tan, Suk-Tak Chan, Jonathan Welt, Arman Avesta, Eva Ratai, Nathaniel David Mercaldo, Anastasia Yendiki, Jacqueline Namati, Isabel Chico-Calero, Blair A Parry, Lynn Drake, Rox Anderson, Terry Rauch, Ramon Diaz-Arrastia, Michael Lev, Jarone Lee, Michael Hamblin, Benjamin Vakoc, and Rajiv Gupta. 2020. “Effect of Transcranial Low-Level Light Therapy vs Sham Therapy Among Patients With Moderate Traumatic Brain Injury: A Randomized Clinical Trial.” JAMA Netw Open, 3, 9, Pp. e2017337.Abstract
Importance: Preclinical studies have shown that transcranial near-infrared low-level light therapy (LLLT) administered after traumatic brain injury (TBI) confers a neuroprotective response. Objectives: To assess the feasibility and safety of LLLT administered acutely after a moderate TBI and the neuroreactivity to LLLT through quantitative magnetic resonance imaging metrics and neurocognitive assessment. Design, Setting, and Participants: A randomized, single-center, prospective, double-blind, placebo-controlled parallel-group trial was conducted from November 27, 2015, through July 11, 2019. Participants included 68 men and women with acute, nonpenetrating, moderate TBI who were randomized to LLLT or sham treatment. Analysis of the response-evaluable population was conducted. Interventions: Transcranial LLLT was administered using a custom-built helmet starting within 72 hours after the trauma. Magnetic resonance imaging was performed in the acute (within 72 hours), early subacute (2-3 weeks), and late subacute (approximately 3 months) stages of recovery. Clinical assessments were performed concomitantly and at 6 months via the Rivermead Post-Concussion Questionnaire (RPQ), a 16-item questionnaire with each item assessed on a 5-point scale ranging from 0 (no problem) to 4 (severe problem). Main Outcomes and Measures: The number of participants to successfully and safely complete LLLT without any adverse events within the first 7 days after the therapy was the primary outcome measure. Secondary outcomes were the differential effect of LLLT on MR brain diffusion parameters and RPQ scores compared with the sham group. Results: Of the 68 patients who were randomized (33 to LLLT and 35 to sham therapy), 28 completed at least 1 LLLT session. No adverse events referable to LLLT were reported. Forty-three patients (22 men [51.2%]; mean [SD] age, 50.49 [17.44] years]) completed the study with at least 1 magnetic resonance imaging scan: 19 individuals in the LLLT group and 24 in the sham treatment group. Radial diffusivity (RD), mean diffusivity (MD), and fractional anisotropy (FA) showed significant time and treatment interaction at 3-month time point (RD: 0.013; 95% CI, 0.006 to 0.019; P < .001; MD: 0.008; 95% CI, 0.001 to 0.015; P = .03; FA: -0.018; 95% CI, -0.026 to -0.010; P < .001).The LLLT group had lower RPQ scores, but this effect did not reach statistical significance (time effect P = .39, treatment effect P = .61, and time × treatment effect P = .91). Conclusions and Relevance: In this randomized clinical trial, LLLT was feasible in all patients and did not exhibit any adverse events. Light therapy altered multiple diffusion tensor parameters in a statistically significant manner in the late subacute stage. This study provides the first human evidence to date that light therapy engages neural substrates that play a role in the pathophysiologic factors of moderate TBI and also suggests diffusion imaging as the biomarker of therapeutic response. Trial Registration: Identifier: NCT02233413.
NA Hubbard, V Siless, IR Frosch, M Goncalves, N Lo, J Wang, CCC Bauer, K Conroy, E Cosby, A Hay, R Jones, M Pinaire, F Vaz De Souza, G Vergara, S Ghosh, A Henin, DR Hirshfeld-Becker, SG Hofmann, IM Rosso, RP Auerbach, DA Pizzagalli, A Yendiki, JDE Gabrieli, and S Whitfield-Gabrieli. 2020. “Brain function and clinical characterization in the Boston adolescent neuroimaging of depression and anxiety study.” Neuroimage Clin, 27, Pp. 102240.Abstract
We present a Human Connectome Project study tailored toward adolescent anxiety and depression. This study is one of the first studies of the Connectomes Related to Human Diseases initiative and is collecting structural, functional, and diffusion-weighted brain imaging data from up to 225 adolescents (ages 14-17 years), 150 of whom are expected to have a current diagnosis of an anxiety and/or depressive disorder. Comprehensive clinical and neuropsychological evaluations and longitudinal clinical data are also being collected. This article provides an overview of task functional magnetic resonance imaging (fMRI) protocols and preliminary findings (N = 140), as well as clinical and neuropsychological characterization of adolescents. Data collection is ongoing for an additional 85 adolescents, most of whom are expected to have a diagnosis of an anxiety and/or depressive disorder. Data from the first 140 adolescents are projected for public release through the National Institutes of Health Data Archive (NDA) with the timing of this manuscript. All other data will be made publicly-available through the NDA at regularly scheduled intervals. This article is intended to serve as an introduction to this project as well as a reference for those seeking to clinical, neurocognitive, and task fMRI data from this public resource.
Viviana Siless, Nicholas A Hubbard, Robert Jones, Jonathan Wang, Nicole Lo, Clemens CC Bauer, Mathias Goncalves, Isabelle Frosch, Daniel Norton, Genesis Vergara, Kristina Conroy, Flavia Vaz De Souza, Isabelle M Rosso, Aleena Hay Wickham, Elizabeth Ann Cosby, Megan Pinaire, Dina Hirshfeld-Becker, Diego A Pizzagalli, Aude Henin, Stefan G Hofmann, Randy P Auerbach, Satrajit Ghosh, John Gabrieli, Susan Whitfield-Gabrieli, and Anastasia Yendiki. 2020. “Image acquisition and quality assurance in the Boston Adolescent Neuroimaging of Depression and Anxiety study.” Neuroimage Clin, 26, Pp. 102242.Abstract
The Connectomes Related to Human Diseases (CRHD) initiative was developed with the Human Connectome Project (HCP) to provide high-resolution, open-access, multi-modal MRI data to better understand the neural correlates of human disease. Here, we present an introduction to a CRHD project, the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, which is collecting multimodal neuroimaging, clinical, and neuropsychological data from 225 adolescents (ages 14-17), 150 of whom are expected to have a diagnosis of depression and/or anxiety. Our transdiagnostic recruitment approach samples the full spectrum of depressed/anxious symptoms and their comorbidity, consistent with NIMH Research Domain Criteria (RDoC). We focused on an age range that is critical for brain development and for the onset of mental illness. This project sought to harmonize imaging sequences, hardware, and functional tasks with other HCP studies, although some changes were made to canonical HCP methods to accommodate our study population and questions. We present a thorough overview of our imaging sequences, hardware, and scanning protocol. We detail similarities and differences between this study and other HCP studies. We evaluate structural-, diffusion-, and functional-image-quality measures that may be influenced by clinical factors (e.g., disorder, symptomatology). Signal-to-noise and motion estimates from the first 140 adolescents suggest minimal influence of clinical factors on image quality. We anticipate enrollment of an additional 85 participants, most of whom are expected to have a diagnosis of anxiety and/or depression. Clinical and neuropsychological data from the first 140 participants are currently freely available through the National Institute of Mental Health Data Archive (NDA).
Zhiqiang Sha, Amelia Versace, Kale E Edmiston, Jay Fournier, Simona Graur, Tsafrir Greenberg, João Paulo Lima Santos, Henry W Chase, Richelle S Stiffler, Lisa Bonar, Robert Hudak, Anastasia Yendiki, Benjamin D Greenberg, Steven Rasmussen, Hesheng Liu, Gregory Quirk, Suzanne Haber, and Mary L Phillips. 2020. “Functional disruption in prefrontal-striatal network in obsessive-compulsive disorder.” Psychiatry Res Neuroimaging, 300, Pp. 111081.Abstract
Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive, compulsive behaviors. While a cortico-striatal-limbic network has been implicated in the pathophysiology of OCD, the neural correlates of this network in OCD are not well understood. In this study, we examined resting state functional connectivity among regions within the cortico-striatal-limbic OCD neural network, including the rostral anterior cingulate cortex, dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, orbitofrontal cortex, ventromedial prefrontal cortex, amygdala, thalamus and caudate, in 44 OCD and 43 healthy participants. We then examined relationships between OCD neural network connectivity and OCD symptom severity in OCD participants. OCD relative to healthy participants showed significantly greater connectivity between the left caudate and bilateral dorsolateral prefrontal cortex. We also found a positive correlation between left caudate-bilateral dorsolateral prefrontal cortex connectivity and depression scores in OCD participants, such that greater positive connectivity was associated with more severe symptoms. This study makes a significant contribution to our understanding of functional networks and their relationship with depression in OCD.
Robert Jones, Giorgia Grisot, Jean Augustinack, Caroline Magnain, David A Boas, Bruce Fischl, Hui Wang, and Anastasia Yendiki. 2020. “Insight into the fundamental trade-offs of diffusion MRI from polarization-sensitive optical coherence tomography in ex vivo human brain.” Neuroimage, 214, Pp. 116704.Abstract
In the first study comparing high angular resolution diffusion MRI (dMRI) in the human brain to axonal orientation measurements from polarization-sensitive optical coherence tomography (PSOCT), we compare the accuracy of orientation estimates from various dMRI sampling schemes and reconstruction methods. We find that, if the reconstruction approach is chosen carefully, single-shell dMRI data can yield the same accuracy as multi-shell data, and only moderately lower accuracy than a full Cartesian-grid sampling scheme. Our results suggest that current dMRI reconstruction approaches do not benefit substantially from ultra-high b-values or from very large numbers of diffusion-encoding directions. We also show that accuracy remains stable across dMRI voxel sizes of 1 ​mm or smaller but degrades at 2 ​mm, particularly in areas of complex white-matter architecture. We also show that, as the spatial resolution is reduced, axonal configurations in a dMRI voxel can no longer be modeled as a small set of distinct axon populations, violating an assumption that is sometimes made by dMRI reconstruction techniques. Our findings have implications for in vivo studies and illustrate the value of PSOCT as a source of ground-truth measurements of white-matter organization that does not suffer from the distortions typical of histological techniques.
Viviana Siless, Juliet Y Davidow, Jared Nielsen, Qiuyun Fan, Trey Hedden, Marisa Hollinshead, Elizabeth Beam, Constanza M Vidal Bustamante, Megan C Garrad, Rosario Santillana, Emily E Smith, Aya Hamadeh, Jenna Snyder, Michelle K Drews, Koene RA Van Dijk, Margaret Sheridan, Leah H Somerville, and Anastasia Yendiki. 2020. “Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan.” Neuroimage, 214, Pp. 116703.Abstract
Diffusion MRI tractography produces massive sets of streamlines that need to be clustered into anatomically meaningful white-matter bundles. Conventional clustering techniques group streamlines based on their proximity in Euclidean space. We have developed AnatomiCuts, an unsupervised method for clustering tractography streamlines based on their neighboring anatomical structures, rather than their coordinates in Euclidean space. In this work, we show that the anatomical similarity metric used in AnatomiCuts can be extended to find corresponding clusters across subjects and across hemispheres, without inter-subject or inter-hemispheric registration. Our proposed approach enables group-wise tract cluster analysis, as well as studies of hemispheric asymmetry. We evaluate our approach on data from the pilot MGH-Harvard-USC Lifespan Human Connectome project, showing improved correspondence in tract clusters across 184 subjects aged 8-90. Our method shows up to 38% improvement in the overlap of corresponding clusters when comparing subjects with large age differences. The techniques presented here do not require registration to a template and can thus be applied to populations with large inter-subject variability, e.g., due to brain development, aging, or neurological disorders.
Suzanne N Haber, Wei Tang, Eun Young Choi, Anastasia Yendiki, Hesheng Liu, Saad Jbabdi, Amelia Versace, and Mary Phillips. 2020. “Circuits, Networks, and Neuropsychiatric Disease: Transitioning From Anatomy to Imaging.” Biol Psychiatry, 87, 4, Pp. 318-327.Abstract
Since the development of cellular and myelin stains, anatomy has formed the foundation for understanding circuitry in the human brain. However, recent functional and structural studies using magnetic resonance imaging have taken the lead in this endeavor. These innovative and noninvasive approaches have the advantage of studying connectivity patterns under different conditions directly in the human brain. They demonstrate dynamic and structural changes within and across networks linked to normal function and to a wide range of psychiatric illnesses. However, these indirect methods are unable to link networks to the hardwiring that underlies them. In contrast, anatomic invasive experimental studies can. Following a brief review of prefrontal cortical, anterior cingulate, and striatal connections and the different methodologies used, this article discusses how data from anatomic studies can help inform how hardwired connections are linked to the functional and structural networks identified in imaging studies.
Zhiqiang Sha, Kale E Edmiston, Amelia Versace, Jay C Fournier, Simona Graur, Tsafrir Greenberg, João Paulo Lima Santos, Henry W Chase, Richelle S Stiffler, Lisa Bonar, Robert Hudak, Anastasia Yendiki, Benjamin D Greenberg, Steven Rasmussen, Hesheng Liu, Gregory Quirk, Suzanne Haber, and Mary L Phillips. 2020. “Functional Disruption of Cerebello-thalamo-cortical Networks in Obsessive-Compulsive Disorder.” Biol Psychiatry Cogn Neurosci Neuroimaging, 5, 4, Pp. 438-447.Abstract
BACKGROUND: Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive, compulsive behaviors. Neuroimaging studies have implicated altered connectivity among the functional networks of the cerebral cortex in the pathophysiology of OCD. However, there has been no comprehensive investigation of the cross-talk between the cerebellum and functional networks in the cerebral cortex. METHODS: This functional neuroimaging study was completed by 44 adult participants with OCD and 43 healthy control participants. We performed large-scale data-driven brain network analysis to identify functional connectivity patterns using resting-state functional magnetic resonance imaging data. RESULTS: Participants with OCD showed lower functional connectivity within the somatomotor network and greater functional connectivity among the somatomotor network, cerebellum, and subcortical network (e.g., thalamus and pallidum; all p < .005). Network-based statistics analyses demonstrated one component comprising connectivity within the somatomotor network that showed lower connectivity and a second component comprising connectivity among the somatomotor network, and motor regions in particular, and the cerebellum that showed greater connectivity in participants with OCD relative to healthy control participants. In participants with OCD, abnormal connectivity across both network-based statistics-derived components positively correlated with OCD symptom severity (p = .006). CONCLUSIONS: To our knowledge, this study is the first comprehensive investigation of large-scale network alteration across the cerebral cortex, subcortical regions, and cerebellum in OCD. Our findings highlight a critical role of the cerebellum in the pathophysiology of OCD.
Lilla Zöllei, Camilo Jaimes, Elie Saliba, Ellen P Grant, and Anastasia Yendiki. 2019. “TRActs constrained by UnderLying INfant anatomy (TRACULInA): An automated probabilistic tractography tool with anatomical priors for use in the newborn brain.” Neuroimage, 199, Pp. 1-17.Abstract
The ongoing myelination of white-matter fiber bundles plays a significant role in brain development. However, reliable and consistent identification of these bundles from infant brain MRIs is often challenging due to inherently low diffusion anisotropy, as well as motion and other artifacts. In this paper we introduce a new tool for automated probabilistic tractography specifically designed for newborn infants. Our tool incorporates prior information about the anatomical neighborhood of white-matter pathways from a training data set. In our experiments, we evaluate this tool on data from both full-term and prematurely born infants and demonstrate that it can reconstruct known white-matter tracts in both groups robustly, even in the presence of differences between the training set and study subjects. Additionally, we evaluate it on a publicly available large data set of healthy term infants (UNC Early Brain Development Program). This paves the way for performing a host of sophisticated analyses in newborns that we have previously implemented for the adult brain, such as pointwise analysis along tracts and longitudinal analysis, in both health and disease.