[Preprint] Gu, Z., Jamison, K., Sabuncu, M., Kuceyeski A. Modulating human brain responses via optimal natural image selection and synthetic image generation. arXiv. 2023.
[Preprint] Singleton SP, Timmermann C, Luppi AI, Eckernäs E, Roseman L, Carhart-Harris RL, Kuceyeski A. Time-resolved network control analysis links reduced control energy under DMT with the serotonin 2a receptor, signal diversity, and subjective experience. bioRxiv. 2023.
Bukhari, H., Su, C., Dhamala, E., Gu, Z., Jamison, K., & Kuceyeski, A. Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score. Human Brain Mapping, 44( 9), 3541– 3554. 2023.
Tozlu C, Card S, Jamison K, Gauthier SA, Kuceyeski A. Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity. Network Neuroscience. 2023.
Singelton P, Wang J, Mithoefer M, Hanlon C, George M, Mithoefer A, Mithoefer O, Coker A, Yazar-Klosinski B, Emerson A, Doblin R, Kuceyeski A. Evidence for altered neural activity patterns after MDMA-assisted therapy in adults with chronic and severe post-traumatic stress disorder: a pilot study. Frontiers in Psychiatry. 2023. Vol 13, p 3012.
Gu Z, Jamison K, Sabuncu M, Kuceyeski A. Personalized visual encoding model construction with small data. Communications Biology. 2022. Vol 5: 1382.
Olafson E, Russello G, Jamison K, Liu H, Wang D, Bruss J, Boes A, Kuceyeski A. Frontoparietal network activation is associated with motor recovery in ischemic stroke patients. Communications Biology. 2022. 5(1):993. PMID: 36131012.
Singleton P, Luppi AI, Harris RLC, Cruzat J, Roseman L, Deco G, Kringelbach ML, Stamatakis EA, Kuceyeski A. Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain's control energy landscape. Nature Communications. 2022. Oct 3;13(1):5812. doi: 10.1038/s41467-022-33578-1. PMID: 36192411.
Ngo G, Khosla M, Jamison K, Kuceyeski A, Sabuncu M. Predicting individual task contrasts from resting-state functional connectivity using a surface-based convolutional network. NeuroImage. 2022. 248:118849. PMID: 34965456.
Kang Y, Jamison J, Wen K, Jaywant A, Dams-O’Connor K, Schiff N, Kuceyeski A, Shah S. Longitudinal alterations in GABAA receptor availability following traumatic brain injury. Brain Communications. 2022. 4(4): fcac159. PMID: 35794871.
Gu Z, Jamison K, Khosla M, St-Yves G, Naselaris T, Kay K, Sabuncu M, Kuceyeski A. NeuroGen: Synthesizing images for discovery neuroscience. NeuroImage. 2022. Vol 247:118812. PMID: 34936922.
Dhamala E, Jamison K, Jaywant A, Kuceyeski A. Shared functional connections within and between cortical networks predict individual cognitive abilities in adult males and females. Human Brain Mapping. 2022. 43(3): 1087-1102. PMID: 34811849.
Tozlu C, Jamison Keith, Gauthier S, Kuceyeski A. Dynamic functional connectivity better predicts disability than structural and static functional connectivity in people with multiple sclerosis. Frontiers in Neuroscience: Brain Imaging Methods. 2021. 15:763966. PMID: 34966255.
Olafson E, Jamison K, Sweeney E, Liu H, Wang D, Bruss J, Boes A, Kuceyeski A. Functional connectome reorganization relates to post-stroke motor recovery and structural and functional disconnection. NeuroImage. 2021. Vol 245; 118642. PMID: 34637901.
Tozlu C, Jamison Keith, Gu Z, Gauthier S, Kuceyeski A. Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups. NeuroImage: Clinical. 2021. Vol 32:102827. PMID: 34601310.
Tozlu C, Jamison Keith, Nguyen T, Zinger N, Kaunzner U, Pandya S, Wang Y, Gauthier S, Kuceyeski A. Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis. Brain and Behavior. 2021. Vol 11(10): e2353. PMID: 34601310.
Gu Z, Jamison KW, Sabuncu MR, Kuceyeski A. Regional structural-functional connectome coupling is heritable and associated with age, sex and 2 cognition in adults. Nature Communications. 2021. Vol 12, p 4894.
Dhamala E, Jamison K, Jaywant, A, Dennis, S, Kuceyeski A. Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults. Human Brain Mapping. 2021. Vol 42: p 3102– 3118.
Khosla M, Ngo GH, Jamison K, Kuceyeski A, Sabuncu MR. Cortical response to naturalistic stimuli is largely predictable with deep neural networks. Science Advances. 2021; Vol 7(22):eabe7547. doi:10.1126/sciadv.abe7547
Cha J, Speaker S, Hu B, Altinay M, Koirala P, Karne H, Spielberg J, Kuceyeski A, Dhamala E, Anand A. Neuroimaging correlates of emotional response-inhibition discriminate between young depressed adults with and without sub-threshold bipolar symptoms (Emotional Response-inhibition in Young Depressed Adults). J Affect Disord. 2021 Feb 15;281:303-311.
Sweeney EM, Nguyen TD, Kuceyeski A, Ryan SM, Zhang S, Zexter L, Wang Y, Gauthier SA. Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging. Neuroimage. 2021. 225, p 117451.
Shah SA, Lowder RJ, Kuceyeski A. Quantitative multimodal imaging in traumatic brain injuries producing impaired cognition. Curr Opin Neurol. 2020; 33(6), p 691-698.
Kang Y, Rúa SMH, Kaunzner UW, Perumal J, Nealon N, Qu W, Kothari PJ, Vartanian T, Kuceyeski A, Gauthier SA. A Multi-Ligand Imaging Study Exploring GABAergic Receptor Expression and Inflammation in Multiple Sclerosis. Mol Imaging Biol. 2020; 22(6), p1600-1608.
Dhamala E, Jamison KW, Sabuncu MR, Kuceyeski A. Sex classification using long-range temporal dependence of resting-state functional MRI. Human Brain Mapping. 2020; 41(13), p 3567-3579.
Tozlu C, Edwards D, Boes A, Labar D, Tsagaris KZ, Silverstein J, Lane HP, Sabuncu MR, Liu C, Kuceyeski A. Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke. Neurorehabilitation and Neural Repair. 2020; 34(4), p 428-439.
2019 and before
Kuceyeski A, Jamison KW, Owen JP, Raj A, Mukherjee P. Longitudinal increases in structural connectome segregation and functional connectome integration are associated with better recovery after mild TBI. Human Brain Mapping. 2019; 40, p 4441-4456.
Khosla M, Jamison KW, Kuceyeski A, Sabuncu MR. Detecting abnormalities in resting-state dynamics: An unsupervised learning approach. [preprint]. doi: 1908.06168. Posted on arXiv August 16, 2019.
Khosla M, Jamison KW, Kuceyeski A, Sabuncu MR. Ensemble learning with 3D convolutional neural networks for connectome-based prediction. NeuroImage. 2019; 199, p 651-662.
Khosla M, Jamison KW, Ngo GH, Kuceyeski A, Sabuncu MR. Machine learning in resting-state fMRI analysis. Magnetic Resonance Imaging, 2019; 64.
Respino M, Jaywant A, Kuceyeski A, Victoria LW, Hoptman MJ, Scult MA, Sankin L, Pimontel M, Liston C, Belvederi Murri M, Alexopoulos GS, Gunning FM. The impact of white matter hyperintensities on the structural connectome in late-life depression: Relationship to executive functions. Neuroimage Clin. 2019; 23, p 101852.
Sun Y, Li Y, Kuceyeski A, Basu S. Large spectral density matrix estimation by thresholding. [preprint]. doi: 1812:00532. Posted on arXiv December 3, 2018.
Kang Y, Schlyer D, Kaunzner UW, Kuceyeski A, Kothari PJ, Gauthier SA. Comparison of two different methods of image analysis for the assessment of microglial activation in patients with multiple sclerosis using (R)-[N-methyl-carbon-11]PK11195. PLoS One. 2018;13(8): e0201289.
Pandya S, Kuceyeski A, Raj A. The Brain's Structural Connectome Mediates the Relationship between Regional Neuroimaging Biomarkers in Alzheimer's Disease. J Alzheimers Dis. 2017;55(4):1639-1657.