Harvard Catalyst Profiles

Contact, publication, and social network information about Harvard faculty and fellows.

Charles R.G. Guttmann, M.D.

Co-Author

This page shows the publications co-authored by Charles Guttmann and Howard Weiner.
Connection Strength

3.228
  1. History of fatigue in multiple sclerosis is associated with grey matter atrophy. Sci Rep. 2019 10 14; 9(1):14781.
    View in: PubMed
    Score: 0.210
  2. Microstructural fronto-striatal and temporo-insular alterations are associated with fatigue in patients with multiple sclerosis independent of white matter lesion load and depression. Mult Scler. 2020 11; 26(13):1708-1718.
    View in: PubMed
    Score: 0.207
  3. Dual-Sensitivity Multiple Sclerosis Lesion and CSF Segmentation for Multichannel 3T Brain MRI. J Neuroimaging. 2018 01; 28(1):36-47.
    View in: PubMed
    Score: 0.185
  4. The relationship between normal cerebral perfusion patterns and white matter lesion distribution in 1,249 patients with multiple sclerosis. J Neuroimaging. 2012 Apr; 22(2):129-36.
    View in: PubMed
    Score: 0.116
  5. One year activity on subtraction MRI predicts subsequent 4 year activity and progression in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2011 Oct; 82(10):1125-31.
    View in: PubMed
    Score: 0.116
  6. HLA (A-B-C and -DRB1) alleles and brain MRI changes in multiple sclerosis: a longitudinal study. Genes Immun. 2011 Apr; 12(3):183-90.
    View in: PubMed
    Score: 0.114
  7. Seasonal prevalence of MS disease activity. Neurology. 2010 Aug 31; 75(9):799-806.
    View in: PubMed
    Score: 0.111
  8. Regional white matter atrophy--based classification of multiple sclerosis in cross-sectional and longitudinal data. AJNR Am J Neuroradiol. 2009 Oct; 30(9):1731-9.
    View in: PubMed
    Score: 0.104
  9. Incidence and factors associated with treatment failure in the CLIMB multiple sclerosis cohort study. J Neurol Sci. 2009 Sep 15; 284(1-2):116-9.
    View in: PubMed
    Score: 0.102
  10. Rate of brain atrophy in benign vs early multiple sclerosis. Arch Neurol. 2009 Feb; 66(2):234-7.
    View in: PubMed
    Score: 0.100
  11. Predicting clinical progression in multiple sclerosis with the magnetic resonance disease severity scale. Arch Neurol. 2008 Nov; 65(11):1449-53.
    View in: PubMed
    Score: 0.098
  12. Medulla oblongata volume: a biomarker of spinal cord damage and disability in multiple sclerosis. AJNR Am J Neuroradiol. 2008 Sep; 29(8):1465-70.
    View in: PubMed
    Score: 0.096
  13. MR imaging intensity modeling of damage and repair in multiple sclerosis: relationship of short-term lesion recovery to progression and disability. AJNR Am J Neuroradiol. 2007 Nov-Dec; 28(10):1956-63.
    View in: PubMed
    Score: 0.092
  14. Cognitive dysfunction in patients with clinically isolated syndromes or newly diagnosed multiple sclerosis. Mult Scler. 2007 Sep; 13(8):1004-10.
    View in: PubMed
    Score: 0.090
  15. Time-series modeling of multiple sclerosis disease activity: a promising window on disease progression and repair potential? Neurotherapeutics. 2007 Jul; 4(3):485-98.
    View in: PubMed
    Score: 0.090
  16. Predicting short-term disability in multiple sclerosis. Neurology. 2007 Jun 12; 68(24):2059-65.
    View in: PubMed
    Score: 0.089
  17. Magnetic resonance imaging surrogates of multiple sclerosis pathology and their relationship to central nervous system atrophy. J Neuroimaging. 2004 Jul; 14(3 Suppl):46S-53S.
    View in: PubMed
    Score: 0.073
  18. MRI contrast uptake in new lesions in relapsing-remitting MS followed at weekly intervals. Neurology. 2003 Feb 25; 60(4):640-6.
    View in: PubMed
    Score: 0.066
  19. Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy. J Magn Reson Imaging. 2002 Feb; 15(2):203-9.
    View in: PubMed
    Score: 0.062
  20. MRI Lesion State Modulates the Relationship Between Serum Neurofilament Light and Age in Multiple Sclerosis. J Neuroimaging. 2021 03; 31(2):388-393.
    View in: PubMed
    Score: 0.057
  21. Regional magnetic resonance imaging lesion burden and cognitive function in multiple sclerosis: a longitudinal study. Arch Neurol. 2001 Jan; 58(1):115-21.
    View in: PubMed
    Score: 0.057
  22. Changes in activated T cells in the blood correlate with disease activity in multiple sclerosis. Arch Neurol. 2000 Aug; 57(8):1183-9.
    View in: PubMed
    Score: 0.055
  23. Temporal association of sNfL and gad-enhancing lesions in multiple sclerosis. Ann Clin Transl Neurol. 2020 06; 7(6):945-955.
    View in: PubMed
    Score: 0.055
  24. Serial magnetic resonance imaging in multiple sclerosis: correlation with attacks, disability, and disease stage. J Neuroimmunol. 2000 May 01; 104(2):164-73.
    View in: PubMed
    Score: 0.054
  25. Changes in serum levels of ICAM and TNF-R correlate with disease activity in multiple sclerosis. Neurology. 1999 Sep 11; 53(4):758-64.
    View in: PubMed
    Score: 0.052
  26. Quantitative follow-up of patients with multiple sclerosis using MRI: reproducibility. J Magn Reson Imaging. 1999 Apr; 9(4):509-18.
    View in: PubMed
    Score: 0.051
  27. Serial neuropsychological assessment and magnetic resonance imaging analysis in multiple sclerosis. Arch Neurol. 1997 Aug; 54(8):1018-25.
    View in: PubMed
    Score: 0.045
  28. The effect of alcohol and red wine consumption on clinical and MRI outcomes in multiple sclerosis. Mult Scler Relat Disord. 2017 Oct; 17:47-53.
    View in: PubMed
    Score: 0.045
  29. Exploration of machine learning techniques in predicting multiple sclerosis disease course. PLoS One. 2017; 12(4):e0174866.
    View in: PubMed
    Score: 0.044
  30. Characterizing Clinical and MRI Dissociation in Patients with Multiple Sclerosis. J Neuroimaging. 2017 09; 27(5):481-485.
    View in: PubMed
    Score: 0.044
  31. Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: Comparison of linear mixed-effect models. Neuroimage Clin. 2015; 8:606-10.
    View in: PubMed
    Score: 0.039
  32. Using multiple imputation to efficiently correct cerebral MRI whole brain lesion and atrophy data in patients with multiple sclerosis. Neuroimage. 2015 Oct 01; 119:81-8.
    View in: PubMed
    Score: 0.039
  33. Longitudinal MRI in multiple sclerosis: correlation between disability and lesion burden. Neurology. 1994 Nov; 44(11):2120-4.
    View in: PubMed
    Score: 0.037
  34. An expanded composite scale of MRI-defined disease severity in multiple sclerosis: MRDSS2. Neuroreport. 2014 Oct 01; 25(14):1156-61.
    View in: PubMed
    Score: 0.037
  35. Clinical relevance and functional consequences of the TNFRSF1A multiple sclerosis locus. Neurology. 2013 Nov 26; 81(22):1891-9.
    View in: PubMed
    Score: 0.035
  36. The impact of lesion in-painting and registration methods on voxel-based morphometry in detecting regional cerebral gray matter atrophy in multiple sclerosis. AJNR Am J Neuroradiol. 2012 Sep; 33(8):1579-85.
    View in: PubMed
    Score: 0.031
  37. Magnetic resonance disease severity scale (MRDSS) for patients with multiple sclerosis: a longitudinal study. J Neurol Sci. 2012 Apr 15; 315(1-2):49-54.
    View in: PubMed
    Score: 0.031
  38. Brain MRI lesion load at 1.5T and 3T versus clinical status in multiple sclerosis. J Neuroimaging. 2011 Apr; 21(2):e50-6.
    View in: PubMed
    Score: 0.029
  39. A putative Alzheimer's disease risk allele in PCK1 influences brain atrophy in multiple sclerosis. PLoS One. 2010 Nov 30; 5(11):e14169.
    View in: PubMed
    Score: 0.028
  40. A randomized controlled double-masked trial of albuterol add-on therapy in patients with multiple sclerosis. Arch Neurol. 2010 Sep; 67(9):1055-61.
    View in: PubMed
    Score: 0.028
  41. HLA B*44: protective effects in MS susceptibility and MRI outcome measures. Neurology. 2010 Aug 17; 75(7):634-40.
    View in: PubMed
    Score: 0.028
  42. Smoking and disease progression in multiple sclerosis. Arch Neurol. 2009 Jul; 66(7):858-64.
    View in: PubMed
    Score: 0.026
  43. 3 T MRI relaxometry detects T2 prolongation in the cerebral normal-appearing white matter in multiple sclerosis. Neuroimage. 2009 Jul 01; 46(3):633-41.
    View in: PubMed
    Score: 0.025
  44. Deep gray matter involvement on brain MRI scans is associated with clinical progression in multiple sclerosis. J Neuroimaging. 2009 Jan; 19(1):3-8.
    View in: PubMed
    Score: 0.025
  45. CTLA4Ig treatment in patients with multiple sclerosis: an open-label, phase 1 clinical trial. Neurology. 2008 Sep 16; 71(12):917-24.
    View in: PubMed
    Score: 0.024
  46. Estimating Time to Event From Longitudinal Categorical Data: An Analysis of Multiple Sclerosis Progression. J Am Stat Assoc. 2007 12; 102(480):1254-1266.
    View in: PubMed
    Score: 0.023
  47. Serial blood T cell repertoire alterations in multiple sclerosis patients; correlation with clinical and MRI parameters. J Neuroimmunol. 2006 Aug; 177(1-2):151-60.
    View in: PubMed
    Score: 0.021
  48. Kinin B1 receptor expression on multiple sclerosis mononuclear cells: correlation with magnetic resonance imaging T2-weighted lesion volume and clinical disability. Arch Neurol. 2005 May; 62(5):795-800.
    View in: PubMed
    Score: 0.019
  49. Quantitative follow-up of patients with multiple sclerosis using MRI: technical aspects. J Magn Reson Imaging. 1999 Apr; 9(4):519-30.
    View in: PubMed
    Score: 0.013
  50. Predictive value of gadolinium-enhanced magnetic resonance imaging for relapse rate and changes in disability or impairment in multiple sclerosis: a meta-analysis. Gadolinium MRI Meta-analysis Group. Lancet. 1999 Mar 20; 353(9157):964-9.
    View in: PubMed
    Score: 0.013
Connection Strength
The connection strength for co-authors is the sum of the scores for each of their shared publications.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.