Professor Derek Jones

2021

• Barakovic, M.et al. 2021. Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation. NeuroImage 227, article number: 117617. (10.1016/j.neuroimage.2020.117617)
• de Almeida Martins, J. P.et al. 2021. Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain. Human Brain Mapping 42(2), pp. 310-328. (10.1002/hbm.25224)
• Guo, F.et al. 2021. The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion MRI data. Human Brain Mapping 42(2), pp. 367-383. (10.1002/hbm.25228)
• Koller, K.et al. 2021. MICRA: Microstructural Image Compilation with Repeated Acquisitions. NeuroImage 225, article number: 117406. (10.1016/j.neuroimage.2020.117406)
• Afzali, M.et al. 2021. The sensitivity of diffusion MRI to microstructural properties and experimental factors. Journal of Neuroscience Methods 347, article number: 108951. (10.1016/j.jneumeth.2020.108951)

2020

• Aja-Fernández, S., Tristán-Vega, A. and Jones, D. K. 2020. Apparent propagator anisotropy from single-shell diffusion MRI acquisitions. Magnetic Resonance in Medicine (10.1002/mrm.28620)
• Ning, L.et al. 2020. Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: algorithms and result. NeuroImage 221, article number: 117128. (10.1016/j.neuroimage.2020.117128)
• Casella, C.et al. 2020. Drumming motor sequence training induces apparent myelin remodelling in Huntington’s disease: a longitudinal diffusion MRI and quantitative magnetization transfer study. Journal of Huntington's Disease 9(3), pp. 303-320. (10.3233/JHD-200424)
• Afzali, M., Aja-Fernandez, S. and Jones, D. K. 2020. Direction-averaged diffusion-weighted MRI signal using different axisymmetric B-tensor encoding schemes. Magnetic Resonance in Medicine 84(3), pp. 1579-1591. (10.1002/mrm.28191)
• Casella, C.et al. 2020. A critical review of white matter changes in Huntington’s disease. Movement Disorders 35(8), pp. 1302-1311. (10.1002/mds.28109)
• Kleban, E.et al. 2020. Strong diffusion gradients allow the separation of intra- and extra-axonal gradient-echo signals in the human brain. NeuroImage 217, article number: 116793. (10.1016/j.neuroimage.2020.116793)
• Genc, S.et al. 2020. Impact of b-value on estimates of apparent fibre density. Human Brain Mapping 41(10), pp. 2583-2595. (10.1002/hbm.24964)
• Yeh, C.et al. 2020. Mapping structural connectivity using diffusion MRI : challenges and opportunities. Journal of Magnetic Resonance Imaging (10.1002/jmri.27188)
• Tax, C.et al. 2020. The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain. NeuroImage 210, article number: 116534. (10.1016/j.neuroimage.2020.116534)
• Lipp, I.et al. 2020. Tractography in the presence of multiple sclerosis lesions. NeuroImage 209, article number: 116471. (10.1016/j.neuroimage.2019.116471)
• Postans, M.et al. 2020. Uncovering a role for the dorsal hippocampal commissure in recognition memory. Cerebral Cortex 30(3), pp. 1001-1015. (10.1093/cercor/bhz143)
• Poudel, G. R.et al. 2020. Network diffusion modeling predicts neurodegeneration in traumatic brain injury. Annals of Clinical and Translational Neurology 7(3), pp. 270-279. (10.1002/acn3.50984)
• Martins, J. P. d. A.et al. 2020. Transferring principles of solid-state and Laplace NMR to the field of in vivo brain MRI. Magnetic Resonance 1, pp. 27-43. (10.5194/mr-1-27-2020)
• Rudrapatna, U.et al. 2020. A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra-strong-gradient MRI scanner. Magnetic Resonance in Medicine 85(2), pp. 1104-1113. (10.1002/mrm.28464)
• Veraart, J.et al. 2020. Noninvasive quantification of axon radii using diffusion MRI. eLife 9, article number: e49855. (10.7554/eLife.49855)
• Chamberland, M.et al. 2020. Beyond lesion-load: tractometry-based metrics for characterizing white matter lesions within fibre pathways. Presented at: MICCAI 2020 International Workshop on Computational Diffusion MRI (CDMRI 2020), Virtual, 8 October 2020.
• Casella, C.et al. 2020. Drumming motor sequence training induces myelin remodelling in Huntington?s disease: a longitudinal diffusion MRI and quantitative magnetization transfer study. BioRxiv
• Dimitriadis, S. I.et al. 2020. Global brain flexibility during working memory is reduced in a high genetic risk group for schizophrenia. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
• Harrison, J. R.et al. 2020. Imaging Alzheimer's genetic risk using Diffusion MRI: a systematic review. NeuroImage: Clinical 27, article number: 102359. (10.1016/j.nicl.2020.102359)
• Lipp, I.et al. 2020. Predictors of training-related improvement in visuomotor performance in patients with multiple sclerosis: a behavioural and MRI study. Multiple Sclerosis (10.1177/1352458520943788)
• Chamberland, M.et al. 2020. Tractometry-based anomaly detection for single-subject white matter analysis. Presented at: Medical Imaging with Deep Learning (MIDL 2020), Montréal, Canada, 6-9 July 2020.

2019

• Drakesmith, M.et al. 2019. Estimating axon conduction velocity in vivo from microstructural MRI. NeuroImage 203, article number: 116186. (10.1016/j.neuroimage.2019.116186)
• Chamberland, M.et al. 2019. Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain. NeuroImage 200, pp. 89-100. (10.1016/j.neuroimage.2019.06.020)
• Messaritaki, E., Dimitriadis, S. I. and Jones, D. K. 2019. Optimization of graph construction can significantly increase the power of structural brain network studies. NeuroImage 199, pp. 495-511. (10.1016/j.neuroimage.2019.05.052)
• Lipp, I.et al. 2019. Comparing MRI metrics to quantify white matter microstructural damage in multiple sclerosis. Human Brain Mapping 40(10), pp. 2917-2932. (10.1002/hbm.24568)
• Tax, C. M. W.et al. 2019. Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms. NeuroImage 195, pp. 285-299. (10.1016/j.neuroimage.2019.01.077)
• Afzali Deligani, M.et al. 2019. Comparison of different tensor encoding combinations in microstructural parameter estimation. Presented at: IEEE International Symposium on Biomedical Imaging, Venice, Italy, 8-11 Apr 20192019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE pp. 1471-1474., (10.1109/ISBI.2019.8759100)
• Chamberland, M.et al. 2019. Obtaining representative core streamlines for white matter tractometry of the human brain. Presented at: International MICCAI Workshop, Granada, Spain, Sep 2018 Presented at Bonet-Carne, E. et al. eds.Computational Diffusion MRI. Mathematics and Visualization Cham: Springer pp. 359-366., (10.1007/978-3-030-05831-9_28)
• Ning, L.et al. 2019. Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): Progress and results. Presented at: MICCAI 2018, Granada, Spain, 16-20 September 2018 Presented at Bonet-Carne, E. et al. eds.Computational Diffusion MRI, Vol. 1. Mathematics and Visualization Cham: Springer pp. 217-224., (10.1007/978-3-030-05831-9_18)
• Bourbon Teles, J.et al. 2019. Myelin breakdown in human Huntington's disease: multi-modal evidence from diffusion MRI and quantitative magnetization transfer. Neuroscience 403, pp. 79-92. (10.1016/j.neuroscience.2017.05.042)
• Metzler-Baddeley, C.et al. 2019. Sex-specific effects of central adiposity and inflammatory markers on limbic microstructure. NeuroImage 189, pp. 793-803. (10.1016/j.neuroimage.2019.02.007)
• Imms, P.et al. 2019. The structural connectome in traumatic brain injury: A meta-analysis of graph metrics. Neuroscience and Biobehavioral Reviews 99, pp. 128-137. (10.1016/j.neubiorev.2019.01.002)
• Lancaster, T. M.et al. 2019. Structural and functional neuroimaging of polygenic risk for schizophrenia: a recall-by-genotype-based approach. Schizophrenia Bulletin 45(2), pp. 405-414. (10.1093/schbul/sby037)
• David, S.et al. 2019. The Superoanterior Fasciculus (SAF): a novel white matter pathway in the human brain?. Frontiers in Neuroanatomy 13, pp. -., article number: 24. (10.3389/fnana.2019.00024)
• Drakesmith, M.et al. 2019. Genetic risk for schizophrenia and developmental delay is associated with shape and microstructure of midline white-matter structures. Translational Psychiatry 9(1), article number: 102. (10.1038/s41398-019-0440-7)
• Metzler-Baddeley, C.et al. 2019. Fornix white matter glia damage causes hippocampal gray matter damage during age-dependent limbic decline. Scientific Reports 9, article number: 1060. (10.1038/s41598-018-37658-5)
• Fonville, L.et al. 2019. MRI indices of cortical development in young people with psychotic experiences: influence of genetic risk and persistence of symptoms. Schizophrenia Bulletin 45(1), pp. 169-179., article number: sbx195. (10.1093/schbul/sbx195)
• Hyde, C.et al. 2019. White matter organization in developmental coordination disorder: a pilot study exploring the added value of constrained spherical deconvolution. NeuroImage: Clinical 21, article number: 101625. (10.1016/j.nicl.2018.101625)

2018

• Chamberland, M., Tax, C. M. W. and Jones, D. K. 2018. Meyer's loop tractography for image-guided surgery depends on imaging protocol and hardware. NeuroImage: Clinical 20, pp. 458-465. (10.1016/j.nicl.2018.08.021)
• Messaritaki, E.et al. 2018. Improving the predictions of computational models of convection-enhanced drug delivery by accounting for diffusion non-Gaussianity. Frontiers in Neurology 9, article number: 1092. (10.3389/fneur.2018.01092)
• Jones, D. K.et al. 2018. Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI. NeuroImage 182, pp. 8-38. (10.1016/j.neuroimage.2018.05.047)
• Molina-Romero, M.et al. 2018. A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure characterization. Magnetic Resonance in Medicine 80(5), pp. 2155-2172. (10.1002/mrm.27181)
• Caeyenberghs, K.et al. 2018. Evidence for training-dependent structural neuroplasticity in brain-injured patients: A critical review. Neurorehabilitation and Neural Repair 32(2), pp. 99-114. (10.1177/1545968317753076)

2017

• Chamberland, M.et al. 2017. Interactive computation and visualization of structural connectomes in real-time. Presented at: CNI 2017: International Workshop on Connectomics in Neuroimaging, Quebec City, QC, Canada, 14 September 2017 Presented at Wu, G. et al. eds.Connectomics in NeuroImaging, Vol. 10511. Lecture Notes in Computer Science Cham, Switzerland: Springer Verlag pp. 35-41., (10.1007/978-3-319-67159-8_5)
• Metzler-Baddeley, C.et al. 2017. Dynamics of white matter plasticity underlying working memory training: Multi-modal evidence from diffusion MRI and relaxometry. Journal of Cognitive Neuroscience 29(9), pp. 1509-1520. (10.1162/jocn_a_01127)
• Dimitriadis, S.et al. 2017. Improving the reliability of network metrics in structural brain networks by integrating different network weighting strategies into a single graph. Frontiers in Neuroscience 11, article number: 694. (10.3389/fnins.2017.00694)

2016

• Christiansen, K.et al. 2016. Topographic separation of fornical fibers associated with the anterior and posterior hippocampus in the human brain: an MRI-diffusion study. Brain and Behavior 7(1) (10.1002/brb3.604)
• Hunt, B. A. E.et al. 2016. Relationships between cortical myeloarchitecture and electrophysiological networks. Proceedings of the National Academy of Sciences 113(47), pp. 13510-13515. (10.1073/pnas.1608587113)
• Rok, B.et al. 2016. Global efficiency of structural networks mediates cognitive control in Mild Cognitive Impairment. Frontiers in Aging Neuroscience 8, article number: 292. (10.3389/fnagi.2016.00292)
• De Santis, S.et al. 2016. T1 relaxometry of crossing fibres in the human brain. NeuroImage 141, pp. 133-142. (10.1016/j.neuroimage.2016.07.037)
• Gómez, P. A.et al. 2016. Simultaneous parameter mapping, modality synthesis, and anatomical labeling of the brain with MR fingerprinting. Presented at: MICCAI 2016: nternational Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece, 17-21 October 2016 Presented at Ourselin, S. et al. eds.Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III, Vol. 9902. Lecture Notes in Computer Science Cham: Springer pp. 579-586., (10.1007/978-3-319-46726-9_67)
• Drakesmith, M.et al. 2016. Volumetric, relaxometric and diffusometric correlates of psychotic experiences in a non-clinical sample of young adults. NeuroImage: Clinical 12, pp. 550-558. (10.1016/j.nicl.2016.09.002)
• Steventon, J.et al. 2016. Longitudinal in vivo MRI in a Huntington's disease mouse model: global atrophy in the absence of white matter microstructural damage. Scientific Reports 6, article number: 32423. (10.1038/srep32423)
• Ecker, C.et al. 2016. Relationship Between Cortical Gyrification, White Matter Connectivity, and Autism Spectrum Disorder. Cerebral Cortex 26(7), pp. 3297-3309. (10.1093/cercor/bhw098)
• Keedwell, P. A.et al. 2016. Subgenual cingulum microstructure supports control of emotional conflict. Cerebral Cortex 26(6), pp. 2850-2862. (10.1093/cercor/bhw030)
• Metzler-Baddeley, C.et al. 2016. Longitudinal data on cortical thickness before and after working memory training. Data in Brief 7, pp. 1143-1147. (10.1016/j.dib.2016.03.090)
• Steventon, J.et al. 2016. Robust MR-based approaches to quantifying white matter structure and structure/function alterations in Huntington's disease. Journal of Neuroscience Methods 265, pp. 2-12. (10.1016/j.jneumeth.2015.08.027)
• Drakesmith, M.et al. 2016. Mediation of psychosis risk factors by white-matter microstructure in young adults with psychotic experiences. JAMA Psychiatry 73(4), pp. 396-406. (10.1001/jamapsychiatry.2015.3375)
• Metzler-Baddeley, C.et al. 2016. Task complexity and location specific changes of cortical thickness in executive and salience networks after working memory training. NeuroImage 130, pp. 48-62. (10.1016/j.neuroimage.2016.01.007)
• Caeyenberghs, K.et al. 2016. Dynamics of the human structural connectome underlying working memory training. Journal of Neuroscience 36(14), pp. 4056-4066. (10.1523/JNEUROSCI.1973-15.2016)
• Dimitriadis, S.et al. 2016. A prolonged maturational time course in brain development for cortical processing of temporal modulations. Clinical Neurophysiology 127(2), pp. 994-998. (10.1016/j.clinph.2015.09.001)
• De Santis, S., Jones, D. K. and Roebroeck, A. 2016. Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter. NeuroImage 130, pp. 91-103. (10.1016/j.neuroimage.2016.01.047)
• Bracht, T. K.et al. 2016. Myelination of the right parahippocampal cingulum is associated with physical activity in young healthy adults. Brain Structure and Function 221(9), pp. 4537-48. (10.1007/s00429-016-1183-3)
• Rees, D. A.et al. 2016. White matter microstructure and cognitive function in young women with polycystic ovary syndrome. Journal of Clinical Endocrinology & Metabolism 101(1), pp. 314-323. (10.1210/jc.2015-2318)

2015

• De Santis, S.et al. 2015. Resolving relaxometry and diffusion properties within the same voxel in the presence of crossing fibres by combining inversion recovery and diffusion-weighted acquisitions. Magnetic Resonance in Medicine 75(1), pp. 372-380. (10.1002/mrm.25644)
• Drakesmith, M.et al. 2015. Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data. NeuroImage 118, pp. 313-333. (10.1016/j.neuroimage.2015.05.011)
• Hodgetts, C. J.et al. 2015. Dissociable roles of the inferior longitudinal fasciculus and fornix in face and place perception. eLife 4, article number: e07902. (10.7554/eLife.07902)
• Caseras, X.et al. 2015. Emotion regulation deficits in euthymic bipolar I versus bipolar II disorder: a functional and diffusion-tensor imaging study. Bipolar Disorders 17(5), pp. 461-470. (10.1111/bdi.12292)
• Steventon, J.et al. 2015. In Vivo MRI evidence that neuropathology is attenuated by cognitive enrichment in the Yac128 Huntington's Disease mouse model. Journal of Huntington's Disease 4(2), pp. 149-160. (10.3233/JHD-150147)
• Drakesmith, M.et al. 2015. Schizophrenia-like topological changes in the structural connectome of individuals with subclinical psychotic experiences. Human Brain Mapping 36(7), pp. 2629-2643. (10.1002/hbm.22796)
• Bracht, T.et al. 2015. Hedonic tone is associated with left supero-lateral medial forebrain bundle microstructure. Psychological Medicine 45(4), pp. 865-874. (10.1017/S0033291714001949)
• Dutt, A.et al. 2015. Exploring neural dysfunction in 'clinical high risk' for psychosis: A quantitative review of fMRI studies. Journal of Psychiatric Research 61, pp. 122-134. (10.1016/j.jpsychires.2014.08.018)
• Cooper, M., Thapar, A. and Jones, D. K. 2015. ADHD severity is associated with white matter microstructure in the subgenual cingulum. NeuroImage: Clinical 7, pp. 653-660. (10.1016/j.nicl.2015.02.012)
• Ray, N. J.et al. 2015. Cholinergic basal forebrain structure influences the reconfiguration of white matter connections to support residual memory in mild cognitive impairment. Journal of Neuroscience 35(2), pp. 739-747. (10.1523/JNEUROSCI.3617-14.2015)
• Bracht, T.et al. 2015. Limbic white matter microstructure plasticity reflects recovery from depression. Journal of Affective Disorders 170, pp. 143-149. (10.1016/j.jad.2014.08.031)
• Caeyenberghs, K.et al. 2015. Hyperconnectivity in juvenile myoclonic epilepsy: A network analysis. NeuroImage: Clinical 7, pp. 98-104. (10.1016/j.nicl.2014.11.018)

2014

• Cooper, M., Thapar, A. and Jones, D. 2014. Erratum to: White matter microstructure predicts autistic traits in Attention-Deficit/Hyperactivity Disorder. Journal of Autism and Developmental Disorders 44(11), pp. 2755. (10.1007/s10803-014-2162-2)
• Cooper, M., Thapar, A. and Jones, D. K. 2014. White matter microstructure predicts autistic traits in attention-deficit/hyperactivity disorder. Journal of Autism and Developmental Disorders 44(11), pp. 2742-2754. (10.1007/s10803-014-2131-9)
• Powell, R.et al. 2014. Hyperconnectivity in JME-a network analysis. Journal of Neurology, Neurosurgery & Psychiatry 85(10), pp. A2., article number: e4. (10.1136/jnnp-2014-309236.5)
• Postans, M.et al. 2014. Individual variation in fornix microstructure and macrostructure is related to visual discrimination accuracy for scenes but not faces. Journal of Neuroscience 34(36), pp. 12121-12126. (10.1523/JNEUROSCI.0026-14.2014)
• Chantiluke, K.et al. 2014. Disorder-specific functional abnormalities during temporal discounting in youth with Attention Deficit Hyperactivity Disorder (ADHD), Autism and comorbid ADHD and Autism. Psychiatry Research: Neuroimaging 223(2), pp. 113. (10.1016/j.pscychresns.2014.04.006)
• Berlot, R.et al. 2014. CSF contamination contributes to apparent microstructural alterations in mild cognitive impairment. NeuroImage 92, pp. 27-35. (10.1016/j.neuroimage.2014.01.031)
• De Santis, S.et al. 2014. Why diffusion tensor MRI does well only some of the time: Variance and covariance of white matter tissue microstructure attributes in the living human brain. NeuroImage 89, pp. 35-44. (10.1016/j.neuroimage.2013.12.003)
• De Santis, S.et al. 2014. Improved precision in CHARMED assessment of white matter through sampling scheme optimization and model parsimony testing. Magnetic Resonance in Medicine 71(2), pp. 661-671. (10.1002/mrm.24717)
• Metzler-Baddeley, C.et al. 2014. Improved executive function and callosal white matter microstructure after rhythmn exercise in Huntington's disease. Journal of Huntington's Disease 3(3), pp. 273-283. (10.3233/JHD-140113)

2013

• Jones, D. K. and Linden, D. E. J. 2013. Abstracts of Presentations at the International Conference on Basic and Clinical Multimodal Imaging (BaCI), a Joint Conference of the International Society for Neuroimaging in Psychiatry (ISNIP), the International Society for Functional Source Imaging (ISFSI), the International Society for Bioelectromagnetism (ISBEM), the International Society for Brain Electromagnetic Topography (ISBET), and the EEG and Clinical Neuroscience Society (ECNS), in Geneva, Switzerland, September 5-8, 2013. Clinical EEG and Neuroscience 44(4), pp. E1. (10.1177/1550059413507209)
• Assaf, Y.et al. 2013. The CONNECT project: Combining macro- and micro-structure. NeuroImage 80, pp. 273-282. (10.1016/j.neuroimage.2013.05.055)
• De Santis, S., Evans, C. J. and Jones, D. K. 2013. RAPID: A routine assurance pipeline for imaging of diffusion. Magnetic Resonance in Medicine 70(2), pp. 490-496. (10.1002/mrm.24465)
• Metzler-Baddeley, C.et al. 2013. Individual differences in fornix microstructure and body mass index. PLoS ONE 8(3), article number: e59849. (10.1371/journal.pone.0059849)
• Jones, D. K.et al. 2013. Distinct subdivisions of the cingulum bundle revealed by diffusion MRI fibre tracking: Implications for europsychological investigations.. Neuropsychologia 51(1), pp. 67-78. (10.1016/j.neuropsychologia.2012.11.018)
• Parker, G.et al. 2013. A pitfall in the reconstruction of fibre ODFs using spherical deconvolution of diffusion MRI data. NeuroImage 65, pp. 433-448. (10.1016/j.neuroimage.2012.10.022)
• De Santis, S.et al. 2013. The CONNECT brain atlas of white matter microstructure [Conference Material]. Proceedings of the International Society for Magnetic Resonance Imaging 21, pp. 843-843.
• Jeurissen, B.et al. 2013. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Human Brain Mapping 34(11), pp. 2747-2766. (10.1002/hbm.22099)

2012

• Metzler-Baddeley, C.et al. 2012. Cingulum microstructure predicts cognitive control in older age and mild cognitive impairment. Journal of Neuroscience 32(49), pp. 17612-17619. (10.1523/JNEUROSCI.3299-12.2012)
• Metzler-Baddeley, C.et al. 2012. Temporal association tracts and the breakdown of episodic memory in mild cognitive impairment. Neurology 79(23), pp. 2233-2240. (10.1212/WNL.0b013e31827689e8)
• Keedwell, P. A.et al. 2012. Cingulum white matter in young women at risk of depression: The effect of family history and anhedonia. Biological Psychiatry 72(4), pp. 296-302. (10.1016/j.biopsych.2012.01.022)
• Anderson, E. J.et al. 2012. Cortical network for gaze control in humans revealed using multimodal MRI. Cerebral Cortex 22(4), pp. 765-775. (10.1093/cercor/bhr110)
• Metzler-Baddeley, C.et al. 2012. How and how not to correct for CSF-contamination in diffusion MRI. Neuroimage 59(2), pp. 1394-1403. (10.1016/j.neuroimage.2011.08.043)
• Richmond, S.et al. 2012. Detailing patient specific modeling to aid clinical decision-making. Lecture Notes in Computational Vision and Biomechanics 5, pp. 105-131. (10.1007/978-94-007-4552-0_5)
• Ben-Amitay, S., Jones, D. K. and Assaf, Y. 2012. Motion correction and registration of high b-value diffusion weighted images. Magnetic Resonance in Medicine 67(6), pp. 1694-1702. (10.1002/mrm.23186)
• Vos, S. B.et al. 2012. The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain. NeuroImage 59(3), pp. 2208-2216. (10.1016/j.neuroimage.2011.09.086)
• Sundram, F.et al. 2012. White matter microstructural abnormalities in the frontal lobe of adults with antisocial personality disorder. Cortex 48(2), pp. 216-229. (10.1016/j.cortex.2011.06.005)
• Ecker, C.et al. 2012. Brain Anatomy and Its Relationship to Behavior in Adults With Autism Spectrum Disorder: A Multicenter Magnetic Resonance Imaging Study. Archives of General Psychiatry 69(2), pp. 195-209. (10.1001/archgenpsychiatry.2011.1251)
• De Santis, S., Assaf, Y. and Jones, D. K. 2012. Using the biophysical CHARMED model to elucidate the underpinnings of contrast in diffusional kurtosis analysis of diffusion-weighted MRI. Magnetic Resonance Materials in Physics, Biology and Medicine 25(4), pp. 267-276. (10.1007/s10334-011-0292-5)

2011

• Metzler-Baddeley, C.et al. 2011. Frontotemporal connections in episodic memory and aging: A diffusion MRI tractography study. The Journal of Neuroscience 31(37), pp. 13236-13245. (10.1523/JNEUROSCI.2317-11.2011)
• Metzler-Baddeley, C.et al. 2011. The fornix in ageing and memory decline: a diffusion MRI tractography study. Journal of Neurology 258(S1), pp. 18-18. (10.1007/s00415-011-6026-9)
• Edden, R. A. E. and Jones, D. K. 2011. Spatial and orientational heterogeneity in the statistical sensitivity of skeleton-based analyses of diffusion tensor MR imaging data. Journal of Neuroscience Methods 201(1), pp. 213-219. (10.1016/j.jneumeth.2011.07.025)
• Jeurissen, B.et al. 2011. Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Human Brain Mapping 32(3), pp. 461-479. (10.1002/hbm.21032)
• Vos, S. B.et al. 2011. Partial volume effect as a hidden covariate in DTI analyses. NeuroImage 55(4), pp. 1566-1576. (10.1016/j.neuroimage.2011.01.048)
• Lombardo, M. V.et al. 2011. Specialization of right temporo-parietal junction for mentalizing and its relation to social impairments in autism. NeuroImage 56(3), pp. 1832-1838. (10.1016/j.neuroimage.2011.02.067)
• Blain, C. R. V.et al. 2011. Differential corticospinal tract degeneration in homozygous 'D90A' SOD-1 ALS and sporadic ALS. Journal of Neurology, Neurosurgery & Psychiatry 82(8), pp. 843-849. (10.1136/jnnp.2010.236018)
• Lai, M.et al. 2011. A Behavioral Comparison of Male and Female Adults with High Functioning Autism Spectrum Conditions. PLoS ONE 6(6), article number: e20835. (10.1371/journal.pone.0020835)
• Jones, D. K. and Leemans, A. 2011. Diffusion tensor imaging. In: Modo, M. and Bulte, J. W. M. eds. Magnetic Resonance Neuroimaging: Methods and Protocols., Vol. 711. Methods in Molecular Biology Vol. 2. Springer, pp. 127-144., (10.1007/978-1-61737-992-5_6)
• Jones, D. K. ed. 2011. Diffusion MRI: theory, methods and applications. Oxford: Oxford University Press.

2010

• Muthukumaraswamy, S. D.et al. 2010. Visual gamma oscillations and evoked responses: Variability, repeatability and structural MRI correlates. NeuroImage 49(4), pp. 3349-3357. (10.1016/j.neuroimage.2009.11.045)
• Bloemen, O. J. N.et al. 2010. White matter integrity in Asperger syndrome: a preliminary diffusion tensor magnetic resonance imaging study in adults. Autism Research 3(5), pp. 203-213. (10.1002/aur.146)
• Jones, D. K. and Cercignani, M. 2010. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR in Biomedicine 23(7), pp. 803-820. (10.1002/nbm.1543)
• Sundram, F.et al. 2010. White matter microstructure in 22q11 deletion syndrome: a pilot diffusion tensor imaging and voxel-based morphometry study of children and adolescents. Journal of Neurodevelopmental Disorders 2(2), pp. 77-92. (10.1007/s11689-010-9043-6)
• Jones, D. K. 2010. Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI. Imaging in Medicine 2(3), pp. 341-355. (10.2217/iim.10.21)
• Lombardo, M. V.et al. 2010. Shared Neural Circuits for Mentalizing about the Self and Others. Journal of Cognitive Neuroscience 22(7), pp. 1623-1635. (10.1162/jocn.2009.21287)
• Lombardo, M. V.et al. 2010. Atypical neural self-representation in autism. Brain 133(2), pp. 611-624. (10.1093/brain/awp306)
• Jones, D. K. 2010. Precision and Accuracy in Diffusion Tensor Magnetic Resonance Imaging. Topics in Magnetic Resonance Imaging 21(2), pp. 87-99. (10.1097/RMR.0b013e31821e56ac)
• Ecker, C.et al. 2010. Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach. NeuroImage 49(1), pp. 44-56. (10.1016/j.neuroimage.2009.08.024)

2009

• Muthukumaraswamy, S. D.et al. 2009. Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans. Proceedings of the National Academy of Sciences of the United States of America 106(20), pp. 8356-8361. (10.1073/pnas.0900728106)
• Leemans, A. and Jones, D. K. 2009. The B-matrix must be rotated when correcting for subject motion in DTI data. Magnetic Resonance in Medicine 61(6), pp. 1336-1349. (10.1002/mrm.21890)
• Kanaan, R. A. A.et al. 2009. Microstructural Organization of Cerebellar Tracts in Schizophrenia. Biological Psychiatry 66(11), pp. 1067-1069. (10.1016/j.biopsych.2009.07.028)
• Kontis, D.et al. 2009. Diffusion tensor MRI of the corpus callosum and cognitive function in adults born preterm. NeuroReport 20(4), pp. 424-428. (10.1097/WNR.0b013e328325a8f9)
• Jones, D. K. 2009. Gaussian modeling of the diffusion signal. In: Johansen-Berg, H. and Behrens, T. E. J. eds. Diffusion MRI: From Quantitative Measurement to In vivo Neuroanatomy. Amsterdam: Elsevier, pp. 37-64., (10.1016/B978-0-12-374709-9.00003-1)
• Jones, D. K. 2009. Fundamentals of diffusion MR imaging. In: Gillard, J. H., Waldman, A. D. and Barker, P. . B. eds. Clinical MR Neuroimaging: Physiological and Functional Techniques. 2nd ed.. Cambridge: Cambridge University Press, pp. 44-67.

2008

• Deoni, S. C. L.et al. 2008. Gleaning Multicomponent T-1 and T-2 Information From Steady-State Imaging Data. Magnetic Resonance in Medicine 60(6), pp. 1372-1387. (10.1002/mrm.21704)
• Jones, D. K. 2008. Studying connections in the living human brain with diffusion MRI. Cortex 44(8), pp. 936-952. (10.1016/j.cortex.2008.05.002)
• Jones, D. K. 2008. Tractography gone wild: Probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI. IEEE Transactions on Medical Imaging 27(9), pp. 1268-1274. (10.1109/TMI.2008.922191)
• Catani, M.et al. 2008. Altered cerebellar feedback projections in Asperger syndrome. NeuroImage 41(4), pp. 1184-1191. (10.1016/j.neuroimage.2008.03.041)
• Deoni, S. C. L.et al. 2008. Standardized Structural Magnetic Resonance Imaging in Multicenter Studies using Quantitative T1 and T2 Imaging at 1.5T. NeuroImage 40(2), pp. 662-671. (10.1016/j.neuroimage.2007.11.052)

2007

• Shergill, S. S.et al. 2007. A diffusion tensor imaging study of fasciculi in schizophrenia. American Journal of Psychiatry 164(3), pp. 467-473. (10.1176/appi.ajp.164.3.467)
• Deoni, S. C. L., Rutt, B. K. and Jones, D. K. 2007. Investigating the effect of exchange and multicomponentT1 relaxation on the short repetition time spoiled steady-state signal and the DESPOT1T1 quantification method. Journal of Magnetic Resonance Imaging 25(3), pp. 570-578. (10.1002/jmri.20836)
• Blain, C. R. V.et al. 2007. A longitudinal study of diffusion tensor MRI in ALS. Amyotrophic Lateral Sclerosis 8(6), pp. 348-355. (10.1080/17482960701548139)
• Catani, M.et al. 2007. Symmetries in human brain language pathways correlate with verbal recall. Proceedings of the National Academy of Sciences of the United States of America 104(43), pp. 17163-17168. (10.1073/pnas.0702116104)
• Freidlin, R. Z.et al. 2007. Parsimonious model selection for tissue segmentation and classification: study on simulated and experimental DTI data. IEEE Transactions on Medical Imaging 26(11), pp. 1576-1584. (10.1109/TMI.2007.907294)

2006

• O'Gorman, R. L. and Jones, D. K. 2006. Just how much data need to be collected for reliable bootstrap DT-MRI?. Magnetic Resonance in Medicine 56(4), pp. 884-890. (10.1002/mrm.21014)
• Jones, D. K.et al. 2006. Age effects on diffusion tensor magnetic resonance imaging tractography measures of frontal cortex connections in schizophrenia. Human Brain Mapping 27(3), pp. 230-238. (10.1002/hbm.20179)
• Kanaan, R. A.et al. 2006. Tract-specific anisotropy measurements in diffusion tensor imaging. Psychiatry Research: Neuroimaging 146(1), pp. 73-82. (10.1016/j.pscychresns.2005.11.002)
• Blain, C. R. V.et al. 2006. Measuring brain stem and cerebellar damage in parkinsonian syndromes using diffusion tensor MRI. Neurology 67(12), pp. 2199-2205. (10.1212/01.wnl.0000249307.59950.f8)
• Turner, M. R.et al. 2006. Neuroimaging in amyotrophic lateral sclerosis. In: Brown, R. H. J., Swash, M. and Pasinelli, P. eds. Amyotrophic Lateral Sclerosis (2nd ed.). Abingdon: Informa Healthcare, pp. 45-68.

2005

• Jones, D. K.et al. 2005. A diffusion tensor magnetic resonance imaging study of frontal cortex connections in very-late-onset schizophrenia-like psychosis. American Journal of Geriatric Psychiatry 13(12), pp. 1092-1099. (10.1176/appi.ajgp.13.12.1092)
• Jones, D. K.et al. 2005. The Effect of Filter Size on the Outcome of VBM Analyses of DT-MRI Data. NeuroImage 26(2), pp. 546-554. (10.1016/j.neuroimage.2005.02.013)
• Jones, D. K.et al. 2005. PASTA: Pointwise assessment of streamline tractography attributes. Magnetic Resonance in Medicine 53(6), pp. 1462-1467. (10.1002/mrm.20484)
• Jones, D. K. and Pierpaoli, C. 2005. Confidence mapping in diffusion tensor magnetic resonance imaging tractography using a bootstrap approach. Magnetic Resonance in Medicine 53(5), pp. 1143-1149. (10.1002/mrm.20466)
• Chang, L., Jones, D. K. and Pierpaoli, C. 2005. RESTORE: Robust estimation of tensors by outlier rejection. Magnetic Resonance in Medicine 53(5), pp. 1088-1095. (10.1002/mrm.20426)
• Catani, M., Jones, D. K. and Ffytche, D. H. 2005. Perisylvian language networks of the human brain. Annals of Neurology 57(1), pp. 8-16. (10.1002/ana.20319)
• Daurignac, E.et al. 2005. Applications of Morphometric and Diffusion Tensor Magnetic Resonance Imaging to the Study of Brain Abnormalities in the Alcoholism Spectrum. Alcoholism: Clinical & Experimental Research 29(1), pp. 159-166. (10.1097/01.ALC.0000150891.72900.62)
• Jones, D. K.et al. 2005. A diffusion tensor magnetic resonance imaging study of frontal cortex connections in very late-onset schizophrenia-like psychosis. American Journal of Geriatric Psychiatry 13(12), pp. 1092-1099.

2004

• Jones, D. K. 2004. Fundamentals of diffusion MR imaging. In: Gillard, J. H., Waldman, A. D. and Barker, P. B. eds. Clinical MR Neuroimaging: Diffusion, Perfusion and Spectroscopy. Cambridge: Cambridge University Press, pp. 54-85., (10.1017/CBO9780511544958.006)
• Jones, D. K. and Basser, P. J. 2004. "Squashing peanuts and smashing pumpkins": How noise distorts diffusion-weighted MR data. Magnetic Resonance in Medicine 52(5), pp. 979-993. (10.1002/mrm.20283)
• Jones, D. K. 2004. The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study. Magnetic Resonance in Medicine 51(4), pp. 807-815. (10.1002/mrm.20033)
• O'Sullivan, M.et al. 2004. Diffusion tensor MRI correlates with executive dysfunction in patients with ischaemic leukoaraiosis. Journal of Neurology, Neurosurgery and Psychiatry 75(3), pp. 441-447. (10.1136/jnnp.2003.014910)

2003

• Catani, M.et al. 2003. Occipito-temporal connections in the human brain. Brain 126(9), pp. 2093-2107. (10.1093/brain/awg203)
• Jones, D. K. 2003. Determining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI. Magnetic Resonance in Medicine 49(1), pp. 7-12. (10.1002/mrm.10331)

2002

• Horsfield, M. A. and Jones, D. K. 2002. Applications of diffusion-weighted and diffusion tensor MRI to white matter diseases - a review. NMR in Biomedicine 15(7-8), pp. 570-577. (10.1002/nbm.787)
• Basser, P. J. and Jones, D. K. 2002. Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. NMR in Biomedicine 15(7-8), pp. 456-467. (10.1002/nbm.783)
• Jones, D. K.et al. 2002. Spatial normalisation and averaging of diffusion tensor MRI data sets. NeuroImage 17(2), pp. 592-617. (10.1016/S1053-8119(02)91148-1)
• Catani, M.et al. 2002. Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage 17(1), pp. 77-94. (10.1006/nimg.2002.1136)
• Jones, D. K.et al. 2002. Isotropic resolution diffusion tensor imaging with whole brain acquisition in a clinically acceptable time. Human Brain Mapping 15(4), pp. 216-230. (10.1002/hbm.10018)
• Taber, K. H.et al. 2002. The future for diffusion tensor imaging in neuropsychiatry. The Journal of Neuropsychiatry & Clinical Neurosciences 14(1), pp. 1-5.

2001

• O'Sullivan, M.et al. 2001. Normal-appearing white matter in ischemic leukoaraiosis: A diffusion tensor MRI study. Neurology 57(12), pp. 2307-2310. (10.1212/WNL.57.12.2307)
• Rose, S. E.et al. 2001. MRI based diffusion and perfusion predictive model to estimate stroke evolution. Magnetic Resonance Imaging 19(8), pp. 1043-1053. (10.1016/S0730-725X(01)00435-0)
• O'Sullivan, M.et al. 2001. Evidence for cortical "disconnection" as a mechanism of age-related cognitive decline. Neurology 57(4), pp. 632-638. (10.1212/WNL.57.4.632)

2000

• Jones, D. K.et al. 2000. Cluster analysis of diffusion tensor magnetic resonance images in human head injury. Neurosurgery 47(2), pp. 306-314.

1999

• Wilcock, D. J.et al. 1999. Echoplanar MRI in patients with an acute stroke syndrome. British Journal of Radiology 72(861), pp. 914-921.
• Ellis, C. M.et al. 1999. Diffusion tensor MRI assesses corticospinal tract damage in ALS. Neurology 53(5), pp. 1051-1058. (10.1212/WNL.53.5.1051)
• Jones, D. K., Horsfield, M. A. and Simmons, A. 1999. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magnetic Resonance in Medicine 42(3), pp. 515-525. (10.1002/(SICI)1522-2594(199909)42:3<515::AID-MRM14>3.0.CO;2-Q)
• Jones, D. K.et al. 1999. Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI. Magnetic Resonance in Medicine 42(1), pp. 37-41. (10.1002/(SICI)1522-2594(199907)42:1<37::AID-MRM7>3.0.CO;2-O)
• Jones, D. K.et al. 1999. Characterization of white matter damage in ischemic leukoaraiosis with diffusion tensor MRI. Stroke 30(2), pp. 393-397. (10.1161/01.STR.30.2.393)
• Simmons, A.et al. 1999. Application of diffusion tensor MRI to neurological segmentation. International Journal of Imaging Systems and Technology 10(3), pp. 273-286. (10.1002/(SICI)1098-1098(1999)10:3<273::AID-IMA8>3.0.CO;2-E)

1998

• Horsfield, M. A.et al. 1998. Diffusion magnetic resonance imaging in multiple sclerosis. Journal of Neurology, Neurosurgery and Psychiatry 64(S), pp. S80-S84.