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52. The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

Author

Liew, Sook-Lei, primary, Zavaliangos-Petropulu, Artemis, additional, Jahanshad, Neda, additional, Lang, Catherine E., additional, Hayward, Kathryn S., additional, Lohse, Keith, additional, Juliano, Julia M., additional, Assogna, Francesca, additional, Baugh, Lee A., additional, Bhattacharya, Anup K., additional, Bigjahan, Bavrina, additional, Borich, Michael R., additional, Boyd, Lara A., additional, Brodtmann, Amy, additional, Buetefisch, Cathrin M., additional, Byblow, Winston D., additional, Cassidy, Jessica M., additional, Conforto, Adriana B., additional, Craddock, Cameron, additional, Dimyan, Michael A., additional, Dula, Adrienne N., additional, Ermer, Elsa, additional, Etherton, Mark R., additional, Fercho, Kelene A., additional, Gregory, Chris M., additional, Hadidchi, Sharam, additional, Holguin, Jess Anthony, additional, Hwang, Darryl H., additional, Jung, Simon, additional, Kautz, Steven A., additional, Khlif, Mohamed Salah, additional, Khoshab, Nima, additional, Kim, Bokkyu, additional, Kim, Hosung, additional, Kuceyeski, Amy, additional, Lotze, Martin, additional, MacIntosh, Bradley J., additional, Margetis, John L., additional, Mohamed, Feroze B., additional, Piras, Fabrizio, additional, Ramos-Murguialday, Ander, additional, Richard, Genevieve, additional, Roberts, Pamela, additional, Robertson, Andrew D., additional, Rondina, Jane M., additional, Rost, Natalia S., additional, Sanossian, Nerses, additional, Schweighofer, Nicolas, additional, Shiroishi, Mark S., additional, Soekadar, Surjo R., additional, Spalletta, Gianfranco, additional, Stinear, Cathy M., additional, Suri, Anisha, additional, Tang, Wai Kwong W., additional, Thielman, Gregory T., additional, Vecchio, Daniela, additional, Villringer, Arno, additional, Ward, Nick, additional, Werden, Emilio, additional, Westlye, Lars T., additional, Winstein, Carolee, additional, Wittenberg, George Frederick, additional, Wong, Kristin A., additional, Yu, Chunshui, additional, Cramer, Steven C., additional, and Thompson, Paul, additional

Published
2020
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53. Smaller spared subcortical nuclei are associated with worse post-stroke sensorimotor outcomes in 28 cohorts worldwide.

Author

Sook-Lei Liew, Zavaliangos-Petropulu, Artemis, Schweighofer, Nicolas, Jahanshad, Neda, Lang, Catherine E., Lohse, Keith R., Banaj, Nerisa, Barisano, Giuseppe, Baugh, Lee A., Bhattacharya, Anup K., Bigjahan, Bavrina, Borich, Michael R., Boyd, Lara A., Brodtmann, Amy, Buetefisch, Cathrin M., Byblow, Winston D., Cassidy, Jessica M., Charalambou, Charalambos C., Ciullo, Valentina, and Conforto, Adriana B.

Published
2021
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54. ENIGMA Military Brain Injury: Altered Subcortical Volume in Chronic TBI Revealed by Mega-Analysis (P2.9-061)

Author

Dennis, Emily, primary, Wilde, Elisabeth, additional, Zavaliangos-Petropulu, Artemis, additional, Newsome, Mary, additional, Scheibel, Randall, additional, Troyanskaya, Maya, additional, Velez, Carmen, additional, Wade, Benjamin, additional, Drennon, Ann Marie, additional, York, Gerald, additional, Bigler, Erin, additional, Abildskov, Tracy, additional, Taylor, Brian, additional, Jaramillo, Carlos, additional, Eapen, Blessen, additional, Belanger, Heather, additional, Morey, Rajendra, additional, Haswell, Courtney, additional, Bouchard, Heather, additional, Adamson, Maheen, additional, Kang, Xiaojian, additional, Koerte, Inga, additional, Shenton, Martha, additional, Levin, Harvey, additional, Hinds, Sidney, additional, Walker, William, additional, Thompson, Paul, additional, and Tate, David, additional

Published
2019
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55. Multi-Shell Diffusion MRI Measures of Brain Aging: A Preliminary Comparison From ADNI3

Author

Nir, Talia M., primary, Thomopoulos, Sophia I., additional, Villalon-Reina, Julio E., additional, Zavaliangos-Petropulu, Artemis, additional, Dennis, Emily L., additional, Reid, Robert I., additional, Bernstein, Matt A., additional, Borowski, Bret, additional, Jack, Clifford R., additional, Weiner, Michael W., additional, Jahanshad, Neda, additional, and Thompson, Paul M., additional

Published
2019
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56. Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3

Author

Zavaliangos-Petropulu, Artemis, primary, Nir, Talia M., additional, Thomopoulos, Sophia I., additional, Reid, Robert I., additional, Bernstein, Matt A., additional, Borowski, Bret, additional, Jack Jr., Clifford R., additional, Weiner, Michael W., additional, Jahanshad, Neda, additional, and Thompson, Paul M., additional

Published
2019
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59. A White Matter Connection of Schizophrenia and Alzheimer's Disease.

Author

Kochunov, Peter, Zavaliangos-Petropulu, Artemis, Jahanshad, Neda, Thompson, Paul M, Ryan, Meghann C, Chiappelli, Joshua, Chen, Shuo, Du, Xiaoming, Hatch, Kathryn, Adhikari, Bhim, Sampath, Hemalatha, Hare, Stephanie, Kvarta, Mark, Goldwaser, Eric, Yang, Fude, Olvera, Rene L, Fox, Peter T, Curran, Joanne E, Blangero, John, and Glahn, David C

Subjects
ALZHEIMER'S disease, COGNITION disorders, DEMENTIA, SCHIZOPHRENIA, WHITE matter (Nerve tissue), COGNITIVE aging
Abstract

Schizophrenia (SZ) is a severe psychiatric illness associated with an elevated risk for developing Alzheimer's disease (AD). Both SZ and AD have white matter abnormalities and cognitive deficits as core disease features. We hypothesized that aging in SZ patients may be associated with the development of cerebral white matter deficit patterns similar to those observed in AD. We identified and replicated aging-related increases in the similarity between white matter deficit patterns in patients with SZ and AD. The white matter "regional vulnerability index" (RVI) for AD was significantly higher in SZ patients compared with healthy controls in both the independent discovery (Cohen's d = 0.44, P = 1·10–5, N = 173 patients/230 control) and replication (Cohen's d = 0.78, P = 9·10–7, N = 122 patients/64 controls) samples. The degree of overlap with the AD deficit pattern was significantly correlated with age in patients (r =.21 and.29, P <.01 in discovery and replication cohorts, respectively) but not in controls. Elevated RVI-AD was significantly associated with cognitive measures in both SZ and AD. Disease and cognitive specificities were also tested in patients with mild cognitive impairment and showed intermediate overlap. SZ and AD have diverse etiologies and clinical courses; our findings suggest that white matter deficits may represent a key intersecting point for these 2 otherwise distinct diseases. Identifying mechanisms underlying this white matter deficit pattern may yield preventative and treatment targets for cognitive deficits in both SZ and AD patients. [ABSTRACT FROM AUTHOR]

Published
2021
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62. Do Candidate Genes Affect the Brain’s White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans

Author

Jahanshad, Neda, primary, Ganjgahi, Habib, additional, Bralten, Janita, additional, den Braber, Anouk, additional, Faskowitz, Joshua, additional, Knodt, Annchen R, additional, Lemaitre, Hervé, additional, Nir, Talia M, additional, Patel, Binish, additional, Richie, Stuart, additional, Sprooten, Emma, additional, Hoogman, Martine, additional, van Hulzen, Kimm, additional, Zavaliangos-Petropulu, Artemis, additional, Zwiers, Marcel P, additional, Almasy, Laura, additional, Bastin, Mark E, additional, Bernstein, Matt A, additional, Blangero, John, additional, Curran, Joanne, additional, Deary, Ian J, additional, de Zubicary, Greig I, additional, Duggirala, Ravi, additional, Fisher, Simon E, additional, Franke, Barbara, additional, Fox, Peter, additional, Goldman, David, additional, Haberg, Asta K, additional, Hariri, Ahmad, additional, Hong, L Elliot, additional, Huentelman, Matt, additional, Martin, Nicholas G, additional, Martinot, Jean-Luc, additional, McIntosh, Andrew, additional, McMahon, Katie L, additional, Medland, Sarah E, additional, Mitchell, Braxton D, additional, Muñoz Maniega, Susana, additional, Olvera, Rene L, additional, Oosterlaan, Jaap, additional, Peterson, Charles, additional, Royle, Natalie, additional, Saykin, Andrew J, additional, Schumann, Gunter, additional, Starr, John, additional, Stein, Elliot A, additional, Sussmann, Jessika, additional, Valdés Hernández, Maria del C., additional, van’t Ent, Dennis, additional, Wardlaw, Joanna M, additional, Weiner, Michael W, additional, Williamson, Douglas E, additional, Winkler, Anderson M, additional, Wright, Margaret J, additional, Yang, Yihong, additional, Thompson, Paul M, additional, Glahn, David C, additional, Nichols, Thomas E, additional, and Kochunov, Peter, additional

Published
2017
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63. Variable clustering reveals associations between subcortical brain volume and cognitive changes in pediatric traumatic brain injury

Author

Zavaliangos-Petropulu, Artemis, additional, Dennis, Emily L., additional, Ver Steeg, Greg, additional, Babikian, Talin, additional, Mink, Richard, additional, Babbitt, Christopher, additional, Johnson, Jeffrey, additional, Giza, Christopher C., additional, Asarnow, Robert F., additional, and Thompson, Paul M., additional

Published
2017
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69. Association of brain age, lesion volume, and functional outcome in patients with stroke

Author

Sook-Lei Liew, Nicolas Schweighofer, James H. Cole, Artemis Zavaliangos-Petropulu, Bethany P. Lo, Laura K.M. Han, Tim Hahn, Lianne Schmaal, Miranda R. Donnelly, Jessica N. Jeong, Zhizhuo Wang, Aisha Abdullah, Jun H. Kim, Alexandre Hutton, Giuseppe Barisano, Michael R. Borich, Lara A. Boyd, Amy Brodtmann, Cathrin M. Buetefisch, Winston D. Byblow, Jessica M. Cassidy, Charalambos C. Charalambous, Valentina Ciullo, Adriana Bastos Conforto, Rosalia Dacosta-Aguayo, Julie A. DiCarlo, Martin Domin, Adrienne N. Dula, Natalia Egorova-Brumley, Wuwei Feng, Fatemeh Geranmayeh, Chris M. Gregory, Colleen A. Hanlon, Kathryn Hayward, Jess A. Holguin, Brenton Hordacre, Neda Jahanshad, Steven A. Kautz, Mohamed Salah Khlif, Hosung Kim, Amy Kuceyeski, David J. Lin, Jingchun Liu, Martin Lotze, Bradley J. MacIntosh, John L. Margetis, Maria Mataro, Feroze B. Mohamed, Emily R. Olafson, Gilsoon Park, Fabrizio Piras, Kate P. Revill, Pamela Roberts, Andrew D. Robertson, Nerses Sanossian, Heidi M. Schambra, Na Jin Seo, Surjo R. Soekadar, Gianfranco Spalletta, Cathy M. Stinear, Myriam Taga, Wai Kwong Tang, Greg T. Thielman, Daniela Vecchio, Nick S. Ward, Lars T. Westlye, Carolee J. Winstein, George F. Wittenberg, Steven L. Wolf, Kristin A. Wong, Chunshui Yu, Steven C. Cramer, Paul M. Thompson, Liew, Sook Lei, Schweighofer, Nicolas, Cole, James H, Zavaliangos-Petropulu, Artemis, Hordacre, Brenton, and Thompson, Paul M

Subjects
brain health, Neurology (clinical), stroke, functional outcome, brain age
Abstract

Background and ObjectivesFunctional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good vs poor outcomes.MethodsWe conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a 3-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good vs poor outcomes in patients with matched lesion damage.ResultsWe examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (β = 0.21; 95% CI 0.04–0.38,p= 0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (β = −0.28; 95% CI −0.41 to −0.15,p< 0.001) and across multiple domains of function (β = −0.14; 95% CI −0.22 to −0.06,p< 0.001). Brain age mediated 15% of the impact of lesion damage on sensorimotor performance (95% CI 3%–58%,p= 0.01). Greater brain resilience explained why people have better outcomes, given matched lesion damage (odds ratio 1.04, 95% CI 1.01–1.08,p= 0.004).DiscussionWe provide evidence that younger brain age is associated with superior poststroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets.

Published
2023

70. A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

Author

Sook-Lei Liew, Bethany P. Lo, Miranda R. Donnelly, Artemis Zavaliangos-Petropulu, Jessica N. Jeong, Giuseppe Barisano, Alexandre Hutton, Julia P. Simon, Julia M. Juliano, Anisha Suri, Zhizhuo Wang, Aisha Abdullah, Jun Kim, Tyler Ard, Nerisa Banaj, Michael R. Borich, Lara A. Boyd, Amy Brodtmann, Cathrin M. Buetefisch, Lei Cao, Jessica M. Cassidy, Valentina Ciullo, Adriana B. Conforto, Steven C. Cramer, Rosalia Dacosta-Aguayo, Ezequiel de la Rosa, Martin Domin, Adrienne N. Dula, Wuwei Feng, Alexandre R. Franco, Fatemeh Geranmayeh, Alexandre Gramfort, Chris M. Gregory, Colleen A. Hanlon, Brenton G. Hordacre, Steven A. Kautz, Mohamed Salah Khlif, Hosung Kim, Jan S. Kirschke, Jingchun Liu, Martin Lotze, Bradley J. MacIntosh, Maria Mataró, Feroze B. Mohamed, Jan E. Nordvik, Gilsoon Park, Amy Pienta, Fabrizio Piras, Shane M. Redman, Kate P. Revill, Mauricio Reyes, Andrew D. Robertson, Na Jin Seo, Surjo R. Soekadar, Gianfranco Spalletta, Alison Sweet, Maria Telenczuk, Gregory Thielman, Lars T. Westlye, Carolee J. Winstein, George F. Wittenberg, Kristin A. Wong, Chunshui Yu, University of Southern California (USC), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Clinical and Behavioral Neurology - Neuroscienze e riabilitazione, IRCCS Fondazione Santa Lucia [Roma], Emory University School of Medicine, Emory University [Atlanta, GA], University of British Columbia [Vancouver], University of Melbourne, Child Mind Institute, Department Biostatistics University of North Carolina, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Universidade de São Paulo = University of São Paulo (USP), University of California (UC), University of Barcelona, Technische Universität München = Technical University of Munich (TUM), Universität Greifswald - University of Greifswald, University of Texas at Austin [Austin], Duke University [Durham], Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, New York University School of Medicine (NYU Grossman School of Medicine), Imperial College London, Modèles et inférence pour les données de Neuroimagerie (MIND), IFR49 - Neurospin - CEA, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Medical University of South Carolina [Charleston] (MUSC), Wake Forest School of Medicine [Winston-Salem], Wake Forest Baptist Medical Center, University of South Australia [Adelaide], The Florey Institute of Neuroscience and Mental Health, Tianjin University (TJU), University of Toronto, Universitat de Barcelona (UB), Oslo Metropolitan University (OsloMet), University of Michigan [Ann Arbor], University of Michigan System, University of Bern, University of Waterloo [Waterloo], Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Oslo University Hospital [Oslo], Supported by The European Research Council under the European Union’s Horizon 2020 research and Innovation program (ERC StG, Grant 802998)., Liew, Sook-Lei, Lo, Bethany P, Donnelly, Miranda R, Zavaliangos-Petropulu, Artemis, Hordacre, Brenton G, and Winstein, Carolee J

Subjects
accurate image processing, Statistics and Probability, Image Processing, 610 Medicine & health, Neuroimaging, Bioengineering, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], Library and Information Sciences, Education, Computer-Assisted, Image Processing, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, stroke rehabilitation, [STAT.AP]Statistics [stat]/Applications [stat.AP], segmented lesion, Neurosciences, Brain, ATLAS, Magnetic Resonance Imaging, Computer Science Applications, Stroke, Networking and Information Technology R&D (NITRD), 570 Life sciences, biology, Statistics, Probability and Uncertainty, Algorithms, Information Systems
Abstract

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

Published
2022

71. Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis

Author

Artemis Zavaliangos‐Petropulu, Bethany Lo, Miranda R. Donnelly, Nicolas Schweighofer, Keith Lohse, Neda Jahanshad, Giuseppe Barisano, Nerisa Banaj, Michael R. Borich, Lara A. Boyd, Cathrin M. Buetefisch, Winston D. Byblow, Jessica M. Cassidy, Charalambos C. Charalambous, Adriana B. Conforto, Julie A. DiCarlo, Adrienne N. Dula, Natalia Egorova‐Brumley, Mark R. Etherton, Wuwei Feng, Kelene A. Fercho, Fatemeh Geranmayeh, Colleen A. Hanlon, Kathryn S. Hayward, Brenton Hordacre, Steven A. Kautz, Mohamed Salah Khlif, Hosung Kim, Amy Kuceyeski, David J. Lin, Jingchun Liu, Martin Lotze, Bradley J. MacIntosh, John L. Margetis, Feroze B. Mohamed, Fabrizio Piras, Ander Ramos‐Murguialday, Kate P. Revill, Pamela S. Roberts, Andrew D. Robertson, Heidi M. Schambra, Na Jin Seo, Mark S. Shiroishi, Cathy M. Stinear, Surjo R. Soekadar, Gianfranco Spalletta, Myriam Taga, Wai Kwong Tang, Gregory T. Thielman, Daniela Vecchio, Nick S. Ward, Lars T. Westlye, Emilio Werden, Carolee Winstein, George F. Wittenberg, Steven L. Wolf, Kristin A. Wong, Chunshui Yu, Amy Brodtmann, Steven C. Cramer, Paul M. Thompson, Sook‐Lei Liew, Zavaliangos Petropulu, Artemis, Lo, Bethany, Donnelly, Miranda R, Schweighofer, Nicolas, Hordacre, Brenton, and Liew, Sook-Lei

Subjects
Male, Stroke, Upper Extremity, Cross-Sectional Studies, Quality of Life, Stroke Rehabilitation, Humans, Sensorimotor impairment, Female, Recovery of Function, Cardiology and Cardiovascular Medicine, Hippocampus
Abstract

Background. Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper‐limb sensorimotor impairment. We investigated associations between non‐lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results. Cross‐sectional T1‐weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta‐Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA‐UE (Fugl‐Meyer Assessment of Upper Extremity). Robust mixed‐effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni‐corrected, P

Published
2022

72. Modulation of the Association Between Corticospinal Tract Damage and Outcome After Stroke by White Matter Hyperintensities.

Author

Ferris JK, Lo BP, Barisano G, Brodtmann A, Buetefisch CM, Conforto AB, Donnelly MR, Egorova-Brumley N, Hayward KS, Khlif MS, Revill KP, Zavaliangos-Petropulu A, Boyd L, and Liew SL

Subjects
Humans, Male, Female, Aged, Middle Aged, Magnetic Resonance Imaging, Recovery of Function physiology, Aged, 80 and over, Pyramidal Tracts diagnostic imaging, Pyramidal Tracts pathology, White Matter diagnostic imaging, White Matter pathology, Stroke diagnostic imaging, Stroke pathology, Stroke complications, Stroke physiopathology
Abstract

Background and Objectives: Motor outcomes after stroke relate to corticospinal tract (CST) damage. The brain leverages surviving neural pathways to compensate for CST damage and mediate motor recovery. Thus, concurrent age-related damage from white matter hyperintensities (WMHs) might affect neurologic capacity for recovery after CST injury. The role of WMHs in post-stroke motor outcomes is unclear. In this study, we evaluated whether WMHs modulate the relationship between CST damage and post-stroke motor outcomes., Methods: We used data from the multisite ENIGMA Stroke Recovery Working Group with T1 and T2/fluid-attenuated inversion recovery imaging. CST damage was indexed with weighted CST lesion load (CST-LL). WMH volumes were extracted with Freesurfer's SAMSEG. Mixed-effects beta-regression models were fit to test the impact of CST-LL, WMH volume, and their interaction on motor impairment, controlling for age, days after stroke, and stroke volume., Results: A total of 223 individuals were included. WMH volume related to motor impairment above and beyond CST-LL (β = 0.178, 95% CI 0.025-0.331, p = 0.022). Relationships varied by WMH severity (mild vs moderate-severe). In individuals with mild WMHs, motor impairment related to CST-LL (β = 0.888, 95% CI 0.604-1.172, p < 0.001) with a CST-LL × WMH interaction (β = -0.211, 95% CI -0.340 to -0.026, p = 0.026). In individuals with moderate-severe WMHs, motor impairment related to WMH volume (β = 0.299, 95% CI 0.008-0.590, p = 0.044), but did not significantly relate to CST-LL or a CST-LL × WMH interaction., Discussion: WMHs relate to motor outcomes after stroke and modify relationships between motor impairment and CST damage. WMH-related damage may be under-recognized in stroke research as a factor contributing to variability in motor outcomes. Our findings emphasize the importance of brain structural reserve in motor outcomes after brain injury.

Published
2024
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73. Ketamine treatment modulates habenular and nucleus accumbens static and dynamic functional connectivity in major depression.

Author

Taraku B, Loureiro JR, Sahib AK, Zavaliangos-Petropulu A, Al-Sharif N, Leaver A, Wade B, Joshi S, Woods RP, Espinoza R, and Narr KL

Abstract

Dysfunctional reward processing in major depressive disorder (MDD) involves functional circuitry of the habenula (Hb) and nucleus accumbens (NAc). Ketamine elicits rapid antidepressant and alleviates anhedonia in MDD. To clarify how ketamine perturbs reward circuitry in MDD, we examined how serial ketamine infusions (SKI) modulate static and dynamic functional connectivity (FC) in Hb and NAc networks. MDD participants (n=58, mean age=40.7 years, female=28) received four ketamine infusions (0.5mg/kg) 2-3 times weekly. Resting-state fMRI scans and clinical assessments were collected at baseline and 24 hours post-SKI completion. Static FC (sFC) and dynamic FC variability (dFCv) were calculated from left and right Hb and NAc seeds to all other brain regions. Paired t-tests examined changes in FC pre-to-post SKI, and correlations were used to determine relationships between FC changes with mood and anhedonia. Following SKI, significant increases in left Hb-bilateral visual cortex FC, decreases in left Hb-left inferior parietal cortex FC, and decreases in left NAc-right cerebellum FC occurred. Decreased dFCv between left Hb and right precuneus and visual cortex, and decreased dFCv between right NAc and right visual cortex both significantly correlated with improvements in Hamilton Depression Rating Scale. Decreased FC between left Hb and bilateral visual/parietal cortices as well as increased FC between left NAc and right visual/parietal cortices both significantly correlated with improvements in anhedonia. Subanesthetic ketamine modulates functional pathways linking the Hb and NAc with visual, parietal, and cerebellar regions. Overlapping effects between Hb and NAc functional systems were associated with ketamine's therapeutic response., Competing Interests: Disclosures The authors have no conflicts of interest to report.

Published
2023
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74. White matter hyperintensities modify relationships between corticospinal tract damage and motor outcomes after stroke.

Author

Ferris JK, Lo BP, Barisano G, Brodtmann A, Buetefisch CM, Conforto AB, Donnelly MH, Egorova-Brumley N, Hayward KS, Khlif MS, Revill KP, Zavaliangos-Petropulu A, Boyd LA, and Liew SL

Abstract

Motor outcomes after stroke relate to corticospinal tract (CST) damage. Concurrent damage from white matter hyperintensities (WMHs) might impact neurological capacity for recovery after CST injury. Here, we evaluated if WMHs modulate the relationship between CST damage and post-stroke motor impairment outcome. We included 223 individuals from the ENIGMA Stroke Recovery Working Group. CST damage was indexed with weighted CST lesion load (CST-LL). Mixed effects beta-regression models were fit to test the impact of CST-LL, WMH volume, and their interaction on motor impairment. WMH volume related to motor impairment above and beyond CST-LL (β = 0.178, p = 0.022). We tested if relationships varied by WMH severity (mild vs. moderate-severe). In individuals with mild WMHs, motor impairment related to CST-LL (β = 0.888, p < 0.001) with a CST-LL x WMH interaction (β = -0.211, 0.026). In individuals with moderate-severe WMHs, motor impairment related to WMH volume (β = 0.299, p = 0.044), but did not significantly relate to CST-LL or a CST-LL x WMH interaction. WMH-related damage may be under-recognised in stroke research as a factor contributing to variability in motor outcomes. Our findings emphasize the importance of brain structural reserve in motor outcomes after brain injury., Competing Interests: Competing Interests A.C. was a consultant for Boehringer Ingelheim in 2021.

Published
2023
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75. Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging.

Author

Petrov D, Gutman BA, Yu SJ, van Erp TGM, Turner JA, Schmaal L, Veltman D, Wang L, Alpert K, Isaev D, Zavaliangos-Petropulu A, Ching CRK, Calhoun V, Glahn D, Satterthwaite TD, Andreasen OA, Borgwardt S, Howells F, Groenewold N, Voineskos A, Radua J, Potkin SG, Crespo-Facorro B, Tordesillas-Gutiérrez D, Shen L, Lebedeva I, Spalletta G, Donohoe G, Kochunov P, Rosa PGP, James A, Dannlowski U, Baune BT, Aleman A, Gotlib IH, Walter H, Walter M, Soares JC, Ehrlich S, Gur RC, Doan NT, Agartz I, Westlye LT, Harrisberger F, Riecher-Rössler A, Uhlmann A, Stein DJ, Dickie EW, Pomarol-Clotet E, Fuentes-Claramonte P, Canales-Rodríguez EJ, Salvador R, Huang AJ, Roiz-Santiañez R, Cong S, Tomyshev A, Piras F, Vecchio D, Banaj N, Ciullo V, Hong E, Busatto G, Zanetti MV, Serpa MH, Cervenka S, Kelly S, Grotegerd D, Sacchet MD, Veer IM, Li M, Wu MJ, Irungu B, Walton E, and Thompson PM

Abstract

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.

Published
2017
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76. FEATURE SELECTION IMPROVES THE ACCURACY OF CLASSIFYING ALZHEIMER DISEASE USING DIFFUSION TENSOR IMAGES.

Author

Demirhan A, Nir TM, Zavaliangos-Petropulu A, Jack CR Jr, Weiner MW, Bernstein MA, Thompson PM, and Jahanshad N

Abstract

Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.

Published
2015
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