Nellie Karistianis-Georgiou

PloS one • March 18, 2025

Nellie Georgiou Karistianis, Louise Corben, Kathrin Reetz, Isaac Adanyeguh, Manuela Corti, Dinesh Deelchand, Martin Delatycki, Imis Dogan, Rebecca Evans, Jennifer Farmer, Marcondes França, William Gaetz, Ian Harding, Karen Harris, Steven Hersch, Richard Joules, James Joers, Michelle Krishnan, Michelle Lax, Eric Lock, David Lynch, Thomas Mareci, Sahan Muthuhetti Gamage, Massimo Pandolfo, Marina Papoutsi, Thiago J Rezende, Timothy P Roberts, Jens Rosenberg, Sandro Romanzetti, Jörg Schulz, Traci Schilling, Adam Schwarz, Sub Subramony, Bert Yao, Stephen Zicha, Christophe Lenglet, Pierre-gilles Henry

There are errors in the Funding statement. The correct Funding statement is as follows: This study is funded by grants from the Friedreich’s Ataxia Research Alliance (FARA) to each of the academic sites and IXICO plc with financial support from Takeda Pharmaceuticals Company Ltd, Novartis Gene Therapies, IXICO plc and PTC Therapeutics. The Friedreich Ataxia Research Alliance does not use grant numbers. Monash University is the coordinating site. The study sites and the site principal Investigators (main authors who received funding) are detailed below. Monash University (N.G.K) University of Minnesota (P.G.H and C.L) Children’s Hospital of Philadelphia (W.G) University of Florida (S.S) RWTH Aachen University (K.R) University of Campinas (M.C.F) McGill University (M.Pan) Funding bodies Friedreich’s Ataxia Research Alliance (FARA): https://www.curefa.org/IXICOplc:https://ixico.com/ Novartis Gene Therapies: https://www.novartis.com/about/innovative-medicines/novartis-pharmaceuticals Takeda Pharmaceuticals company Ltd: https://www.takeda.com/en-au/ PTC Therapeutics:https://www.ptcbio.com/ FARA plays an ongoing role in study oversight, study design, decision to publish and author J.F who is J.F. is employed by the Friedreich’s Ataxia Research Alliance (FARA) played a role in the preparation of the manuscript. Takeda Pharmaceuticals Company Ltd plays an ongoing role in study oversight, study design, decision to publish and author S.Z is an employee of Takeda Pharmaceuticals Company Ltd and played a role in preparation of the manuscript. Additionally former employees of Takeda Pharmaceuticals. A.J.S. and R.E. also contributed to the preparation of the manuscript. PTC Therapeutics plays an ongoing role in study oversight, study design, decision to publish and authors T.S. and B.Y. who are employees of PTC Therapeutics contributed to the preparation of the manuscript. Novartis Gene Therapies plays an ongoing role in study oversight, study design, decision to publish and authors M.L.K who holds shares in Novartis Gene Therapies as indicated in the conflicts of interest played a role in the preparation of the manuscript. IXICO plc plays an ongoing role in study oversight, study design, decision to publish will perform independent quality control and data analysis of brain anatomical imaging and brain diffusion imaging data for one third of TRACK-FA participants. Authors M.L, R.J and M.Pap are employees of IXICO and contributed to the preparation of the manuscript. There are errors in the Competing Interests statement. The correct Competing Interests statement is as follows: I have read the journal’s policy and the authors of this manuscript have the following competing interests: S.Z. is employed by Takeda Pharmaceutical Company Ltd and receives salary and holds stocks in the company. A.J.S. and R.E were employed by Takeda Pharmaceutical Company Ltd at the time of their contribution to the TRACK-FA project. Takeda Pharmaceutical Company Ltd remains committed to FRDA research and will help develop translational tools to monitor patient disease and share with the FRDA community. T.S. and B.Y. are both employees of PTC Therapeutics. D.L. is a grant recipient from the National Institute of Health (NIH), Muscular Dystrophy Association (MDA), Friedreich’s Ataxia Research Alliance (FARA), Reata Pharmaceuticals Inc, Retrotope Inc, Voyager Therapeutics, Novartis Gene Therapies, Audentes Therapeutics (Astellas Gene Therapies) and Minoryx Therapeutics S.L. T.P.L.R. has equity interest in PRISM Clinical Imaging and Proteus Neurodynamics and consulting/advisory board engagement with CTF MEG International Services LP, Ricoh Company Ltd, Spago Nanomedical AB, Avexis (Novartis Gene Therapies) and Acadia Pharmaceuticals Inc. P.G.H. is a grant recipient from the Friedreich’s Ataxia Research Alliance (FARA), GoFAR, Ataxia UK, the Bob Allison Ataxia Research Centre, and the National Institute of Health (NIH). CMRR is supported by NIH grants P41EB027061 and P30NS076408. P.G.H. reports grants from Minoryx Therapeutics for activities outside this study. M.Pap. and R.J. and M.L. are employed by IXICO plc, ML is a shareholder for IXICO plc. M.C.F. is a grant recipient from PTC Therapeutics and has taken part in advisory board for PTC Therapeutics and Avexis (Novartis Gene Therapies). T.J.R.R. is a grant recipient from the Friedreich’s Ataxia Research Alliance (FARA). C.L. is a research grant recipient from the Friedreich’s Ataxia Research Alliance (FARA), GoFAR, Ataxia UK, the Bob Allison Ataxia Research Center, and National Institute of Health (NIH) grants P41. EB027061 and P30 NS076408. C.L. reports research grants from Minoryx Therapeutics and Biogen Inc. for activities outside this study. S.S. is a broad member of the Research Advisory Board for National Ataxia Foundation (USA), a research grant recipient from the Friedreich’s Ataxia Research Alliance (FARA), Wyck Foundation, National Ataxia Foundation, Muscular Dystrophy Association (MDA), National Institute of Health (NIH), FDA and receives industry support from Reata Pharmaceutical Inc, Retrotope Inc, PTC Therapeutics, Biohaven Pharmaceuticals, Avidity Biosciences Inc, and Strides Pharma Science Limited. M.L.K. holds shares in Novartis Gene Therapies. K.R. has received grants from the German Federal Ministry of Education and Research (BMBF 01GQ1402, 01DN18022), the German Research Foundation (IRTG 2150, ZUK32/1), Alzheimer Forschung Initiative e.V. (AFI 13812, NL-18002CB) and honoraria for presentations or advisory boards from Biogen and Roche. J.F. is employed by the Friedreich’s Ataxia Research Alliance (FARA) and receives a salary from this institution. L.C. is a research grant recipient from the Friedreich Ataxia Research Alliance (FARA), Ataxia UK, Medical Research Future Fund and is funded by a Medical Research Futures Fund Next Generation Career Development Fellowship. M.B.D. is a research grant recipient from the Friedreich Ataxia Research Alliance (FARA), Medical Research Future Fund and National Health and Medical Research Council. J.B.S. receives grants related to this work from the German Research Foundation (DFG), the German Federal Ministry of Education and Research (BMBF), EuroAtaxia, Voyager Therapeutics, and the Christina Foundation. M.Pan. is a Scientific Advisory Board member of the Friedreich’s Ataxia Research Alliance (FARA), a Board member for the ARSACS Association (Canada), a research grant recipient from FARA, and has consulting/advisory board engagement with Aavanti Bio, Design Therapeutics, Larimar, Minoryx, UCB. M.C. is a co-Founder and Member of the Board of Directors at AavantiBio; is a consultant for Reata Pharmaceutical and AavantiBio; is a member of the Charcot Marie Tooth (CMT) DSMB; is a research grant recipient from the Friedreich’s Ataxia Research Alliance (FARA), Muscular Dystrophy Association (MDA), GOFAR, Duchenne UK foundations and National Institute of Health (NIH)

Radiology. Artificial Intelligence • August 06, 2025

Diogo Shiraishi, Susmita Saha, Isaac Adanyeguh, Sirio Cocozza, Louise Corben, Andreas Deistung, Martin Delatycki, Imis Dogan, William Gaetz, Nellie Georgiou Karistianis, Simon Graf, Marina Grisoli, Pierre-gilles Henry, Gustavo Jarola, James Joers, Christian Langkammer, Christophe Lenglet, Jiakun Li, Camila Lobo, Eric Lock, David Lynch, Thomas Mareci, Alberto R Martinez, Serena Monti, Anna Nigri, Massimo Pandolfo, Kathrin Reetz, Timothy Roberts, Sandro Romanzetti, David Rudko, Alessandra Scaravilli, Jörg Schulz, S Subramony, Dagmar Timmann, Marcondes França, Ian Harding, Thiago J Rezende

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a dentate nucleus (DN) segmentation tool using deep learning (DL) applied to brain MRI-based quantitative susceptibility mapping (QSM) images. Materials and Methods Brain QSM images from healthy controls and individuals with cerebellar ataxia or multiple sclerosis were collected from nine different datasets (2016-2023) worldwide for this retrospective study (ClinicalTrials.gov Identifier: NCT04349514). Manual delineation of the DN was performed by experienced raters. Automated segmentation performance was evaluated against manual reference segmentations following training with several DL architectures. A two-step approach was used, consisting of a localization model followed by DN segmentation. Performance metrics included intraclass correlation coefficient (ICC), Dice score, and Pearson correlation coefficient. Results The training and testing datasets comprised 328 individuals (age range, 11-64 years; 171 female), including 141 healthy individuals and 187 with cerebellar ataxia or multiple sclerosis. The manual tracing protocol produced reference standards with high intrarater (average ICC 0.91) and interrater reliability (average ICC 0.78). Initial DL architecture exploration indicated that the nnU-Net framework performed best. The two-step localization plus segmentation pipeline achieved a Dice score of 0.90 ± 0.03 and 0.89 ± 0.04 for left and right DN segmentation, respectively. In external testing, the proposed algorithm outperformed the current leading automated tool (mean Dice scores for left and right DN: 0.86 ± 0.04 vs 0.57 ± 0.22, P < .001; 0.84 ± 0.07 vs 0.58 ± 0.24, P < .001). The model demonstrated generalizability across datasets unseen during the training step, with automated segmentations showing high correlation with manual annotations (left DN: r = 0.74; P < .001; right DN: r = 0.48; P = .03). Conclusion The proposed model accurately and efficiently segmented the DN from brain QSM images. The model is publicly available (https://github.com/art2mri/DentateSeg). ©RSNA, 2025.

Journal Of Neurology • September 04, 2024

Katharine Huynh, Sharna Jamadar, Amit Lampit, M Siddiqui, Julie Stout, Nellie Georgiou Karistianis

Background: Huntington's disease (HD) is a rare neurodegenerative disease that causes progressive cognitive, physical, and psychiatric symptoms. Computerised cognitive training (CCT) is a novel intervention that aims to improve and maintain cognitive functions through repeated practice. The effects of CCT have yet to be established in HD. This randomised pilot trial examined the feasibility of a large scale trial to assess efficacy of multidomain CCT in pre-manifest and early-stage HD. Methods: 28 participants were randomised to either at-home CCT (2 × 60 min sessions per week for 12 weeks; n = 13) or lifestyle education through monthly newsletters (n = 15). Participants completed cognitive tasks and questionnaires at baseline and follow up, either in person (n = 18) or via video teleconferencing (n = 10). Results: All participants were retained at follow up, and adherence to CCT ranged from 96 to 100%, with 11/13 participants completing all sessions. Preliminary analyses showed evidence of a large effect of CCT on task switching and response inhibition, compared to lifestyle education. There was no evidence of specific benefit to other cognitive domains (processing speed, basic and divided attention, working memory), or psychosocial functions (subjective cognition, mood, health-related quality of life). Conclusions: Whilst retention and adherence rates were high, recruitment rates were low, suggesting that a large scale trial may be feasible with some modifications to increase recruitment rates, such as by reducing time burden associated with the study, and using a multi-site trial design. Potential effects on cognitive functioning warrant further investigation. Background: The trial was prospectively registered on the Australian New Zealand Clinical Trials Registry (ACTRN12622000908730).

Scientific Reports • October 16, 2024

Susmita Saha, Louise Corben, Louisa Selvadurai, Ian Harding, Nellie Georgiou Karistianis

Friedreich ataxia (FRDA) is a rare, inherited progressive movement disorder for which there is currently no cure. The field urgently requires more sensitive, objective, and clinically relevant biomarkers to enhance the evaluation of treatment efficacy in clinical trials and to speed up the process of drug discovery. This study pioneers the development of clinically relevant, multidomain, fully objective composite biomarkers of disease severity and progression, using multimodal neuroimaging and background data (i.e., demographic, disease history, genetics). Data from 31 individuals with FRDA and 31 controls from a longitudinal multimodal natural history study IMAGE-FRDA, were included. Using an elasticnet predictive machine learning (ML) regression model, we derived a weighted combination of background, structural MRI, diffusion MRI, and quantitative susceptibility imaging (QSM) measures that predicted Friedreich ataxia rating scale (FARS) with high accuracy (R2 = 0.79, root mean square error (RMSE) = 13.19). This composite also exhibited strong sensitivity to disease progression over two years (Cohen's d = 1.12), outperforming the sensitivity of the FARS score alone (d = 0.88). The approach was validated using the Scale for the assessment and rating of ataxia (SARA), demonstrating the potential and robustness of ML-derived composites to surpass individual biomarkers and act as complementary or surrogate markers of disease severity and progression. However, further validation, refinement, and the integration of additional data modalities will open up new opportunities for translating these biomarkers into clinical practice and clinical trials for FRDA, as well as other rare neurodegenerative diseases.

Annals Of Neurology • October 22, 2024

Nellie Georgiou Karistianis, Louise Corben, Eric Lock, Helena Bujalka, Isaac Adanyeguh, Manuela Corti, Dinesh Deelchand, Martin Delatycki, Imis Dogan, Jennifer Farmer, Marcondes França, Anthony Gabay, William Gaetz, Ian Harding, James Joers, Michelle Lax, Jiakun Li, David Lynch, Thomas Mareci, Alberto R Martinez, Massimo Pandolfo, Marina Papoutsi, Richard Parker, Kathrin Reetz, Thiago J Rezende, Timothy Roberts, Sandro Romanzetti, David Rudko, Susmita Saha, Jörg Schulz, Sub Subramony, Veena Supramaniam, Christophe Lenglet, Pierre-gilles Henry

Objective: We aimed to quantify differences in the brain and spinal cord between Friedreich ataxia and controls, stratified by age and disease stage, including for the first time in young children. Methods: TRACK-FA is the largest prospective, longitudinal, multi-modal neuroimaging study in Friedreich ataxia to date. We assessed individuals with Friedreich ataxia and controls, 5 to 42 years, at 7 sites across 4 continents. The 17 imaging primary outcome measures (POMs) were selected from metrics that showed a significant longitudinal change in previous small-scale studies. These included brain and spinal cord morphometry (structural magnetic resonance imaging [MRI]) and microstructure (diffusion MRI); brain iron levels (quantitative susceptibility mapping); and spinal cord biochemistry (magnetic resonance spectroscopy). This study is registered with ClinicalTrials.gov (NCT04349514). Results: Between February 2021 and August 2023, we assessed 169 individuals with Friedreich ataxia and 95 controls. Compared to controls, individuals with Friedreich ataxia had lower volume of dentate nucleus and superior cerebellar peduncles; smaller cross-sectional area of spinal cord; lower fractional anisotropy and higher diffusivity in spinal cord and superior cerebellar peduncles; and lower total N-acetyl-aspartate/myo-inositol ratio in spinal cord. Morphometric differences in spinal cord and superior cerebellar peduncles increased dramatically with age during childhood, with rapid development in controls, but not in Friedreich ataxia. Many imaging POMs showed significant associations with clinical severity. Conclusions: Our findings provide strong imaging evidence of impaired development of spinal cord and superior cerebellar peduncles during childhood in Friedreich ataxia and open the way for the use of neuroimaging biomarkers in clinical trials. ANN NEUROL 2025;98:386-397.