Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models

Advances in functional and structural MR image analysis and implementation as FSL

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630

Robust Locally Weighted Regression and Smoothing Scatterplots

Cleveland, W. S. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10481038 · Cited by 11,594

Cortical Surface-Based Analysis

Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910

FreeSurfer

Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526

Whole Brain Segmentation

Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610

Nonparametric permutation tests for functional neuroimaging: A primer with examples

Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256

Cortical Surface-Based Analysis

Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147

Brain development during childhood and adolescence: a longitudinal MRI study

Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., Paus, T., Evans, A. C., & Rapoport, J. L. (1999) · Nature Neuroscience · doi:10.1038/13158 · Cited by 5,552

Introduction to Linear Regression Analysis.

Assunção, R., Sampson, P. D., Montgomery, D. C., & Peck, E. A. (1993) · Journal of the American Statistical Association · doi:10.2307/2290746 · Cited by 5,389

Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade

Jack, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C., & Trojanowski, J. Q. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(09)70299-6 · Cited by 4,806

Developmental trajectories of subcortical structures in relation to dimensional schizotypy expression along adolescence

Derome, M., Zöller, D., Modinos, G., Schaer, M., D, S. E. M., & Debbané, M. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2020.02.005 · Cited by 16

Modeling sparse longitudinal data in early neurodevelopment

Chen, Y., Dubey, P., Müller, H., Bruchhage, M. M. K., Wang, J.-L., & Deoni, S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118079 · Cited by 15

Short-term high-fat diet alters the mouse brain magnetic resonance imaging parameters consistently with neuroinflammation on males and metabolic rearrangements on females. A pre-clinical study with an optimized selection of linear mixed-effects models

Campillo, B. W., Galguera, D., Cerdán, S., López‐Larrubia, P., & Lizarbe, B. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1025108 · Cited by 15

Serum neurofilament light concentrations are associated with cortical thinning in anorexia nervosa

Hellerhoff, I., Bernardoni, F., Bahnsen, K., King, J. A., Doose, A., Pauligk, S., Tam, F. I., Mannigel, M., Gramatke, K., Roessner, V., et al. (2023) · Psychological Medicine · doi:10.1017/s0033291723000387 · Cited by 17

Altered Contralateral Sensorimotor System Organization after Experimental Hemispherectomy: A Structural and Functional Connectivity Study

Otte, W. M., van der Marel, K., van Meer, M. P. A., van Rijen, P. C., Gosselaar, P. H., Braun, K. P. J., & Dijkhuizen, R. M. (2015) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2015.101 · Cited by 15

Postischemic fish oil treatment restores long-term retrograde memory and dendritic density: An analysis of the time window of efficacy

Bacarin, C. C., Godinho, J., de Oliveira, R. M. W., Matsushita, M., Gohara, A. K., Cardozo‐Filho, L., de Lima, J. C., Previdelli, I. T. S., de Melo, S. R., Ribeiro, M. H. D. M., et al. (2016) · Behavioural Brain Research · doi:10.1016/j.bbr.2016.05.047 · Cited by 15

Synergistic Effects of APOE and CLU May Increase the Risk of Alzheimer’s Disease: Acceleration of Atrophy in the Volumes and Shapes of the Hippocampus and Amygdala

An, N., Fu, Y., Shi, J., Guo, H., Yang, Z., Li, Y., Li, S., Wang, Y., Yao, Z.-J., & Hu, B. (2021) · Journal of Alzheimer's Disease · doi:10.3233/jad-201162 · Cited by 13

A Longitudinal Electromyography Study of Complex Movements in Poststroke Therapy. 1: Heterogeneous Changes Despite Consistent Improvements in Clinical Assessments

Hesam‐Shariati, N., Trinh, T., Thompson-Butel, A. G., Shiner, C. T., & McNulty, P. A. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00340 · Cited by 15

Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference

Cespedes, M. I., Fripp, J., McGree, J. M., Drovandi, C., Mengersen, K. L., & Doecke, J. D. (2017) · BMJ Open · doi:10.1136/bmjopen-2016-012174 · Cited by 16

Prospective Study Examining the Effects of Extreme Drinking on Brain Structure in Emerging Adults

Hua, J. P. Y., Sher, K. J., Boness, C. L., Trela, C. J., McDowell, Y. E., Merrill, A. M., Piasecki, T. M., & Kerns, J. G. (2020) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14446 · Cited by 20

Longitudinal analysis of the ABCD® study

Hawes, S. W., Littlefield, A. K., Lopez, D. A., Sher, K. J., Thompson, E. L., González, R., Aguinaldo, L. D., Adams, A. R., Bayat, M., Byrd, A. L., et al. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101518 · Cited by 16

Systematic review of analytical methods applied to longitudinal studies of malaria

Stanley, C. C., Kazembe, L. N., Mukaka, M., Otwombe, K. N., Buchwald, A. G., Hudgens, M. G., Mathanga, D. P., Laufer, M. K., & Chirwa, T. F. (2019) · Malaria Journal · doi:10.1186/s12936-019-2885-9 · Cited by 13

Quality of Life Is Independently Associated With Neurocognitive Function in Patients With Brain Tumors: Analysis of a Prospective Clinical Trial

Salans, M. A., Tibbs, M. D., Huynh‐Le, M., Yip, A. T., Tringale, K. R., Karunamuni, R. A., Xu, R., Reyes, A., Macari, A. C., Pan‐Weisz, T. M., et al. (2021) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2021.05.134 · Cited by 13

Exploration of gray matter correlates of cognitive training benefit in adolescents with chronic traumatic brain injury

Catharine, V. L., Verhelst, H., Deschepper, E., Vingerhoets, G., Deblaere, K., & Caeyenberghs, K. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101827 · Cited by 15

Longitudinal cortical thinning progression differs across multiple sclerosis phenotypes and is clinically relevant: A multicentre study

de la Cruz, M. H., Valsasina, P., Gobbi, C., Gallo, A., Zecca, C., Bisecco, A., Tedeschi, G., Filippi, M., & Rocca, M. A. (2020) · Multiple Sclerosis Journal · doi:10.1177/1352458520940548 · Cited by 13

Trajectories of brain volumes in young children are associated with maternal education

Zhu, C., Chen, Y., Müller, H., Wang, J., O’Muircheartaigh, J., Bruchhage, M. M. K., & Deoni, S. (2023) · Human Brain Mapping · doi:10.1002/hbm.26271 · Cited by 10

Cerebral glucose changes after chemotherapy and their relation to long-term cognitive complaints and fatigue

Schroyen, G., Schramm, G., Weehaeghe, D. V., Leenaerts, N., Casteele, T. V., Blommaert, J., Koole, M. J. B., Smeets, A., Laere, K. V., Sunaert, S. G., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1021615 · Cited by 12

Association of Short-Term Exposure to PM2.5 with Blood Lipids and the Modification Effects of Insulin Resistance: A Panel Study in Wuhan

Sun, J., Peng, S., Li, Z., Liu, F., Wu, C., Lu, Y., & Xiang, H. (2022) · Toxics · doi:10.3390/toxics10110663 · Cited by 12

Acute effects of ambient nitrogen dioxide exposure on serum biomarkers of nervous system damage in healthy older adults

Song, J., Qu, R., Sun, B., Wang, Y., Chen, R., Kan, H., An, Z., Wu, H., Li, J., Jiang, J., et al. (2022) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2022.114423 · Cited by 12

Discriminatory experiences predict neuroanatomical changes and anxiety among healthy individuals and those at clinical high risk for psychosis

Collins, M. A., Chung, Y., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Seidman, L. J., et al. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102757 · Cited by 12

Realistic simulation of virtual multi-scale, multi-modal patient trajectories using Bayesian networks and sparse auto-encoders

Sood, M., Sahay, A., Karki, R., Emon, M. A., Vrooman, H. A., Hofmann‐Apitius, M., & Fröhlich, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-67398-4 · Cited by 12

Eye movement patterns in complex tasks: Characteristics of ambient and focal processing

Guo, Y., Helmert, J., Graupner, S., & Pannasch, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277099 · Cited by 12

A study of the longitudinal changes in multiple cerebrospinal fluid and volumetric magnetic resonance imaging biomarkers on converter and non‐converter Alzheimer's disease subjects with consideration for their amyloid beta status

Morar, U., Izquierdo, W., Martin, H., Forouzannezhad, P., Zarafshan, E., Unger, E., Bursac, Z., Cabrerizo, M., Barreto, A. B., Vaillancourt, D. E., et al. (2022) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12258 · Cited by 11

Identification of Genetic Risk Factors Based on Disease Progression Derived From Longitudinal Brain Imaging Phenotypes

Du, L., Zhao, Y., Zhang, J., Shang, M., Zhang, J., & Han, J. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3325380 · Cited by 11

Sex-Related Differences in White Matter Asymmetry and Its Implications for Verbal Working Memory in Psychosis High-Risk State

Steinmann, S., Lyall, A. E., Langhein, M., Nägele, F. L., Rauh, J., Karayumak, S. C., Zhang, F., Mußmann, M., Billah, T., Makris, N., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.686967 · Cited by 11

Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer’s disease

Pérez‐Millan, A., Contador, J., Tudela, R., Niñerola‐Baizán, A., Setoaín, X., Lladó, A., Sánchez‐Valle, R., & Sala‐Llonch, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-18129-4 · Cited by 10

Computational approaches to predicting treatment response to obesity using neuroimaging

Kozarzewski, L., Maurer, L., Mähler, A., Spranger, J., & Weygandt, M. (2021) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-021-09701-w · Cited by 9

Amyloid beta–positive subjects exhibit longitudinal network-specific reductions in spontaneous brain activity

Avants, B., Hutchison, R. M., Mikulskis, A., Salinas-Valenzuela, C., Hargreaves, R., Beaver, J. D., & Chiao, P. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.10.002 · Cited by 11

Two Neuroanatomical Signatures in Schizophrenia: Expression Strengths Over the First 2 Years of Treatment and Their Relationships to Neurodevelopmental Compromise and Antipsychotic Treatment

du Plessis, S., Chand, G. B., Erus, G., Phahladira, L., Luckhoff, H. K., Smit, R., Asmal, L., Wolf, D. H., Davatzikos, C. A., & Emsley, R. A. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbad040 · Cited by 13

Structural alterations after repetitive transcranial magnetic stimulation in depression and the link to neurotransmitter profiles

Ping, L., Chu, Z., Zhou, B., Sun, D., Chu, J., Xu, J., Li, Z., Zhang, D., & Cheng, Y. (2025) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2025.104445 · Cited by 12

Longitudinal characterization of neuroanatomical changes in the Fischer 344 rat brain during normal aging and between sexes

Fowler, C., Goerzen, D., Madularu, D., Devenyi, G. A., Chakravarty, M. M., & Near, J. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.10.003 · Cited by 9

Categorical predictive and disease progression modeling in the early stage of Alzheimer’s disease

Platero, C. A. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109581 · Cited by 8

Statistical considerations for assessing cognition and neuropathology associations in preclinical Alzheimer's disease

Malek‐Ahmadi, M. H., Mufson, E. J., Perez, S. E., & Chen, K. (2017) · Biostatistics & Epidemiology · doi:10.1080/24709360.2017.1342186 · Cited by 9

Identifying neurodevelopmental anomalies of white matter microstructure associated with high risk for psychosis in 22q11.2DS

Bagautdinova, J., Padula, M. C., Zöller, D., Sandini, C., Schneider, M., Schaer, M., & Eliez, S. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01090-z · Cited by 9

Subclinical cardiovascular outcomes of acute exposure to fine particulate matter and its constituents: A glutathione S-transferase polymorphism-based longitudinal study

Song, J., An, Z., Zhu, J., Li, J., Qu, R., Tian, G., Gui, W., Zhang, Y., Li, H., Jiang, J., et al. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.157469 · Cited by 8

Effects of Ambient Air Pollution on Brain Cortical Thickness and Subcortical Volume: A Longitudinal Neuroimaging Study

Ko, J., Sohn, J., Noh, Y., Koh, S. B., Lee, S., Kim, S., Cho, J., & Kim, C. (2024) · Neuroepidemiology · doi:10.1159/000539467 · Cited by 8

Revealing heterogeneity in mild cognitive impairment based on individualized structural covariance network

Wei, X., Xiong, R., Xu, P., Zhang, T., Zhang, J., Jin, Z., & Li, L. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01752-4 · Cited by 13

Longitudinal volumetric changes in amygdala subregions in frontotemporal dementia

Huang, M., Landín-Romero, R., Matis, S., Dalton, M. A., & Piguet, O. (2024) · Journal of Neurology · doi:10.1007/s00415-023-12172-5 · Cited by 9

Kernel machine regression in neuroimaging genetics

Ge, T., Smoller, J. W., & Sabuncu, M. R. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804076-8.00002-5 · Cited by 8

Permutation-based inference for spatially localized signals in longitudinal MRI data

Park, J. Y., & Fiecas, M. J. A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118312 · Cited by 8

Longitudinal changes in blood-based biomarkers in chronic moderate to severe traumatic brain injury: preliminary findings

Caroline Schnakers, A., Divine, J., Johnson, M. A., Lutkenhoff, E. S., Monti, M. M., Keil, K., Guthrie, J., Pouratian, N., Patterson, D. P., Jensen, G., et al. (2021) · Brain Injury · doi:10.1080/02699052.2020.1858345 · Cited by 8

A longitudinal study of lateral ventricle volumes in deficit and non-deficit schizophrenia

Hepdurgun, C., Karakoc, G., Polat, I., Özalay, Ö., Eroglu-Koc, S., Erdoğan, Y., Kitiş, Ö., & Gönül, A. S. (2021) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2021.111311 · Cited by 8

Longitudinal Patterns of Cortical Atrophy on MRI in Patients With Alzheimer Disease With and Without Lewy Body Pathology

Constant, A. B., Basavaraju, R., France, J., Honig, L. S., Marder, K. S., Provenzano, F. A., & Initiative, A. D. N. (2022) · Neurology · doi:10.1212/wnl.0000000000200947 · Cited by 8

BLMM: Parallelised computing for big linear mixed models

Maullin-Sapey, T., & Nichols, T. E. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119729 · Cited by 8

Three-year changes of cortical 18F-FDG in amnestic vs. non-amnestic sporadic early-onset Alzheimer’s disease

Vanhoutte, M., Semah, F., Leclerc, X., Sillaire, A. R., Jaillard, A., Kuchcinski, G., Delbeuck, X., Fahmi, R., Pasquier, F., & Lopes, R. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04519-w · Cited by 8

Apolipoprotein E ε4 accelerates the longitudinal cerebral atrophy in open access series of imaging studies-3 elders without dementia at enrollment

Huang, Y., Shan, Y., Qin, W., & Zhao, G. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1158579 · Cited by 9

Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain

Kim, H., Karaman, B. K., Zhao, Q., Wang, A. Q., Sabuncu, M. R., Initiative, A. D. N., Weiner, M. W., Aisen, P. S. S., Petersen, R. C., Jack, C. R., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2411492122 · Cited by 8

Formation of parametric images using mixed‐effects models: a feasibility study

Huang, H., Shih, Y., & Lin, C. (2015) · NMR in Biomedicine · doi:10.1002/nbm.3453 · Cited by 7

Aging-Dependent Genetic Effects Associated to ADHD Predict Longitudinal Changes of Ventricular Volumes in Adulthood

Vilor‐Tejedor, N., Ikram, M. A., Roshchupkin, G. V., Vinke, E. J., Vernooij, M. W., & Adams, H. H. H. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00574 · Cited by 6

Cognitive Decline Related to Diet Pattern and Nutritional Adequacy in Alzheimer’s Disease Using Surface-Based Morphometry

Hsiao, H., Ma, M.-C., Chang, H.-I., Lin, C.-H., Hsu, S., Huang, S., Lee, C., Huang, C., & Chang, C.-C. (2022) · Nutrients · doi:10.3390/nu14245300 · Cited by 7

Effect of serum concentrations of IL-6 and TNF-α on brain structure in anorexia nervosa: a combined cross-sectional and longitudinal study

Bernardoni, F., Tam, F. I., Poitz, D. M., Hellerhoff, I., Arold, D., Geisler, D., Lemme, F., Keeler, J. L., Weidner, K., Pariante, C. M., et al. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01836-z · Cited by 7

Multilevel simultaneous equation model: A novel specification and estimation approach

Hernández-Sanjaime, R., González, M., & López-Espín, J. J. (2019) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2019.112378 · Cited by 7

How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases

Müller, H., Behler, A., Landwehrmeyer, G. B., Huppertz, H., & Kassubek, J. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.682812 · Cited by 8

Cortical Structure in Pre-Readers at Cognitive Risk for Dyslexia: Baseline Differences and Response to Intervention

Economou, M., Bempt, F. V., Herck, S. V., Glatz, T., Wouters, J. M., Ghesquière, P., Vanderauwera, J., & Vandermosten, M. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00122 · Cited by 6

Systematic assessment of perinatal and socio-demographic factors associated with IQ from childhood to adult life following very preterm birth

Kroll, J., Karolis, V. R., Brittain, P. J., Tseng, C.-E. J., Froudist‐Walsh, S., Murray, R. M., & Nosarti, C. (2019) · Intelligence · doi:10.1016/j.intell.2019.101401 · Cited by 6

Sexual and spatio-temporal variation of Lake Erie Walleye growth and maturity: A consequence of multiple impacting factors

Ma, Q., Jiao, Y., Zhou, C., & Ren, Y. (2020) · Aquaculture and Fisheries · doi:10.1016/j.aaf.2020.06.010 · Cited by 6

Radiomics of PET Using Neural Networks for Prediction of Alzheimer's Disease Diagnosis

Zhang, Y., Steingrimsson, J. A., Ambekar, A., Oh, H., Eloyan, A., & Initiative, A. D. N. (2025) · Statistics in Medicine · doi:10.1002/sim.70128 · Cited by 5

Changes in leukoencephalopathy and serum neurofilament after (neo)adjuvant chemotherapy for breast cancer

Schroyen, G., Sleurs, C., Ottenbourgs, T., Leenaerts, N., Nevelsteen, I., Melis, M., Smeets, A., Deprez, S., & Sunaert, S. G. (2023) · Translational Oncology · doi:10.1016/j.tranon.2023.101769 · Cited by 5

Microstructural Cerebellar Injury Independently Associated With Processing Speed in Adult Patients With Primary Brain Tumors: Implications for Cognitive Preservation

Salans, M. A., Karunamuni, R. A., Unnikrishnan, S., Qian, A. S., Connor, M. J., Jr., Gudipati, S., Yip, A. T., Huynh‐Le, M., Tibbs, M. D., Reyes, A., et al. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.06.013 · Cited by 5

Functional Nonlinear Mixed Effects Models for Longitudinal Image Data

Luo, X., Zhu, L., Kong, L., & Hongtu Zhu, B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_63 · Cited by 5

Making use of longitudinal information in pattern recognition

Aksman, L., Lythgoe, D. J., Williams, S., Jokisch, M., Mönninghoff, C., Streffer, J. R., Jöckel, K., Weimar, C., & Marquand, A. F. (2016) · Human Brain Mapping · doi:10.1002/hbm.23317 · Cited by 5

Limitations of clinical trial sample size estimate by subtraction of two measurements

Chen, K., Guo, X., Pan, R., Xiong, C., Harvey, D. J., Chen, Y., Yao, L., Su, Y., Reiman, E. M., & Initiative, A. D. N. (2021) · Statistics in Medicine · doi:10.1002/sim.9244 · Cited by 5

Parsimonious model for mass-univariate vertexwise analysis

Couvy‐Duchesne, B., Zhang, F., Kemper, K. E., Sidorenko, J., Wray, N. R., Visscher, P. M., Colliot, O., & Yang, J. (2022) · Journal of medical imaging · doi:10.1117/1.jmi.9.5.052404 · Cited by 5

Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model

Kim, S., Kim, S.-W., Noh, Y., Lee, P. H., Na, D. L., Seo, S. W., & Seong, J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.869387 · Cited by 5

Impact of gradient scheme and non‐linear shimming on out‐of‐voxel echo artifacts in edited MRS

Song, Y., Zöllner, H. J., Hui, S. C. N., Hupfeld, K. E., Oeltzschner, G., & Edden, R. A. E. (2022) · NMR in Biomedicine · doi:10.1002/nbm.4839 · Cited by 5

Computing Brain Change over Time

Ridgway, G. R., Leung, K. K., & Ashburner, J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00313-4 · Cited by 5

Evaluation of partial volume correction and analysis of longitudinal [18F]GTP1 tau PET imaging in Alzheimer's disease using linear mixed-effects models

Bohorquez, S. M. S., Baker, S. L., Manser, P. T., Tonietto, M., Galli, C., Wildsmith, K. R., Zou, Y., Kerchner, G. A., Weimer, R. M., & Teng, E. (2024) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2024.1355402 · Cited by 4

Associations of CSF BACE1 with amyloid pathology, neurodegeneration, and cognition in Alzheimer’s disease

Gao, F., Zhang, M., Wang, Q., Ni, M., Liu, C., Deng, K., Xie, Q., Wang, S., Shi, J., & Shen, Y. (2024) · Acta Neuropathologica · doi:10.1007/s00401-024-02750-w · Cited by 4

Non-linear associations of serum lipid levels with cognitive decline: Findings from the ELSA-Brasil cohort

Ferreira, N. V., Bittencourt, M. S., Generoso, G., Gonçalves, N. G., Barreto, S. M., Giatti, L., Santos, R. D., Lotufo, P. A., Benseñor, I. M., & Suemoto, C. K. (2025) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2025.119159 · Cited by 5

Similar cortical morphometry trajectories from 5 to 9 years in children with perinatal HIV who started treatment before age 2 years and uninfected controls

Nwosu, E. C., Holmes, M., Cotton, M. F., Dobbels, E., Little, F., Laughton, B., van der Kouwe, A. J. W., Robertson, F. C., & Meintjes, E. M. (2023) · BMC Neuroscience · doi:10.1186/s12868-023-00783-7 · Cited by 4

Improved longitudinal data analysis for cross-over design settings, with a piecewise linear mixed-effects model

Mwangi, M., Verbeke, G. N., Njagi, E. N., Mwalili, S. M., Ivanova, A., Bukania, Z., & Molenberghs, G. (2021) · Communications in Statistics Case Studies Data Analysis and Applications · doi:10.1080/23737484.2021.1959468 · Cited by 4

Letter to the Editor response: Nygaard et al .: Table 1.

Towfic, F., Kusko, R. L., & Zeskind, B. J. (2016) · Biostatistics · doi:10.1093/biostatistics/kxw031 · Cited by 4

Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm

Albright, J., & Initiative, A. D. N. (2019) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1016/j.trci.2019.07.001 · Cited by 104

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Wu, D., Yu, Y., Wang, H., Zhang, J., You, J., Kai, Y., Zhao, Y., Wu, Y., Wang, K., & Tian, Y. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.01.066 · Cited by 3

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Ku, D., Mao, L., Nikolova, S., Dumkrieger, G. M., Ross, K., Huentelman, M. J., Anderson, T. R., Porreca, F., Navratilova, E., Starling, A. J., et al. (2025) · Cephalalgia · doi:10.1177/03331024251345160 · Cited by 3

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Basodi, S., Raja, R., Gazula, H., Romero, J. T., Panta, S. R., Maullin-Sapey, T., Nichols, T. E., & Calhoun, V. D. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09657-7 · Cited by 3

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Qiu, J., Zou, T., Wang, D., Luo, H.-R., Yu, H., Ling, L., & Yin, W. (2025) · Neurocritical Care · doi:10.1007/s12028-025-02339-5 · Cited by 3

Accurate non-iterative modelling and inference of longitudinal neuroimaging data

Guillaume, B. (2015) · doi:10.26481/dis.20150930bg · Cited by 3

A Bayesian hierarchical approach to jointly model structural biomarkers and covariance networks

Cespedes, M. I., McGree, J. M., Drovandi, C., Mengersen, K. L., Reid, L. B., Doecke, J. D., & Fripp, J. (2017) · QUT ePrints (Queensland University of Technology) · Cited by 3

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Lin, W., Donohue, M., Insel, P. S., Schwartzman, A., & Thompson, W. K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.10.434854 · Cited by 3

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Fjell, A. M., Sørensen, Ø., Wang, Y., Amlien, I. K., Baaré, W. F. C., David Bartrés-faz, A., Bertram, L., Boraxbekk, C., Brandmaier, A. M., Demuth, I., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.15.480501 · Cited by 3

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Edde, M., Houde, F., Theaud, G., Dumont, M., Gilbert, G., Houde, J., Maltais, L., Théberge, A., Doumbia, M., Beaudoin, A., et al. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103529 · Cited by 2

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Zhang, R., Chen, L., Oliver, L. D., Voineskos, A. N., & Park, J. Y. (2024) · Human Brain Mapping · doi:10.1002/hbm.26692 · Cited by 2

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Aubert-Broche, B., Fonov, V., Weier, K., Narayanan, S., Arnold, D. L., Banwell, B., & Collins, D. L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-14905-9_4 · Cited by 2

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Nassar, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-56404-z · Cited by 2

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Platero, C. A., Tohka, J., & Strange, B. A. (2023) · Current Alzheimer Research · doi:10.2174/0115672050295317240223162312 · Cited by 2

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An, G. Z., Xie, Y., Benzinger, T. L. S., Gordon, B. A., & Sotiras, A. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01850-3 · Cited by 2

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Chen, Y., An, H., Shen, D. G., Hongtu Zhu, B., & Lin, W. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6868092 · Cited by 2

EEG Source Reconstruction in Male Nonsmokers after Nicotine Administration during the Resting State

Ranzi, P., Thiel, C. M., & Herrmann, C. S. (2016) · Neuropsychobiology · doi:10.1159/000445481 · Cited by 2

Correlation and coherence analysis between sea surface temperature (SST) and surface wind in the Equatorial Western Sumatra Waters

Hatmaja, R. B., Wisha, U. J., Radjawane, I. M., & Tanto, T. A. (2019) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/303/1/012003 · Cited by 2

Repeatability and reproducibility of multiparametric magnetic resonance imaging of the liver

Wilman, H., Jacobs, J., Newbould, R. D., Halberstadt, M., Gyngell, M., Herlihy, A., Fernandes, C., Kelly, C., Maguire, M., Jayaratne, N., et al. (2018) · Journal of Hepatology · doi:10.1016/s0168-8278(18)31381-3 · Cited by 2

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Sabuncu, M. R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_49 · Cited by 2

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Hernández-Sanjaime, R., González, M., Peñalver, A., & López-Espín, J. J. (2021) · Entropy · doi:10.3390/e23040384 · Cited by 2

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Valiquette, V., Guma, E., Cupo, L., Gallino, D., Anastassiadis, C., Snook, E., Devenyi, G. A., & Chakravarty, M. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.09.506402 · Cited by 2

Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial

Mwangi, M., Molenberghs, G., Njagi, E. N., Mwalili, S. M., Braekers, R., Flórez, A. J., Gachau, S., Bukania, Z., & Verbeke, G. N. (2024) · Biometrical Journal · doi:10.1002/bimj.202200333 · Cited by 1

Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data

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Analysis of longitudinal diffusion weighted imaging data

Masaki, F. (2014) · eScholarship (California Digital Library)

LSTGEE: longitudinal analysis of neuroimaging data

Li, Y., Hongtu Zhu, B., Chen, Y., An, H., Gilmore, J. H., Lin, W., & Shen, D. G. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.812432 · Cited by 3

Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer’s Disease Neuroimaging Initiative-Based Evaluation Study

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