Discrimination of fatigue and depression networks in patients with multiple sclerosis


Fatigue is a disabling symptom in 65-97% of patients with multiple sclerosis (MS). Fatigue is the presenting symptom in one third of MS patients and 15-40% describe fatigue as their most severe symptom. Despite its clinical significance, the pathophysiology of fatigue is not well understood. Neural, immune, endocrine and metabolic mechanisms have all been proposed. Depression is more common in patients with MS than in the general population. Its prevalence is 10-50%. Depression substantially impairs quality of life (QOL) and has also been associated with multi-factorial origin, including structural brain changes, genetic, biochemical, immunological and psychosocial factors. The relationship between fatigue and depression is complex in MS. Previous structural and functional neuroimaging studies have associated fatigue and depression with alterations in several brain structures in MS patients. However, inconsistencies remain and not all findings were replicated between studies. In addition, most studies demonstrate a strong inter-correlation between these symptoms, and it remains unclear whether the neuroanatomical substrate for these symptoms is the same or diverges at least in part. We propose to analyze existing MRI and clinical data from the CLIMB (Comprehensive Longitudinal Investigations of MS at the Brigham and Women`s Hospital (www.climbstudy.org), n>2400). In the proposed project, we will apply advanced network analysis approaches, such as Structural Covariance Network (SCN) analysis and Diffusion Tensor (DT) tractography, in the belief that they will provide more consistent results and improve statistical power in associating structural damage to fatigue or depression.



To robustly differentiate networks associated with fatigue or depression in MS patients through the use of network analysis of structural MRI.



  • SCN analysis to investigate inter-individual macrostructural/volumetric differences in brain structures co-varying with volumes of other brain structures.
  • DT tractography to investigate the microstructural/axonal connectivity between brain regions.



  What skills do you need?

  • Programming skills in C++, Matlab, and python (javascript is a plus)
  • Image processing background. Knowledge of ITK, VTK is a plus.


  What skills you will acquire?

  • Improve presentation skills.
    • You will attend monthly, sometimes weekly, presentations from researchers of groups collaborating with us.
    • You will have to present to the group at least three times (first: project and objectives, fourth: advances, and last month: preparation for defense)
  • Team working
    • Expose your ideas within a multidisciplinary team
  • Data management
    • You will be exposed to different types of data, typically images (MRI mostly) and clinical data. You will learn the best practices to handle this information.
  • Networking
    • Being in a multidisciplinary team and linked to research groups all around the world you will enhance your networking skills.


  What you will learn?

  • Neuroscience
    • You will learn about brain morphology and physiology
  • Neuroimaging
    • You will understand the basics of MRI and how to interpret them, with emphasis on the grey matter and the white matter of the human brain.
    • You will handle structural MRIs, including T1-weighted, T2-weighted and Diffusion Tensor Images (DTI) and will understand the basic physics underneath these types of acquisitions.






  • 1249 Boylston St, Boston 02215, Massachusetts, USA



1. Induruwa, I., C.S. Constantinescu, and B. Gran, Fatigue in multiple sclerosis - a brief review. J Neurol Sci, 2012. 323(1-2): p. 9-15.

2. Dobryakova, E., et al., Neural correlates of cognitive fatigue: cortico-striatal circuitry and effort-reward imbalance. J Int Neuropsychol Soc, 2013. 19(8): p. 849-53.

3. Gobbi, C., et al., Forceps minor damage and co-occurrence of depression and fatigue in multiple sclerosis. Mult Scler, 2014.

4. Rocca, M.A., et al., Regional but not global brain damage contributes to fatigue in multiple sclerosis. Radiology, 2014. 273(2): p. 511-20.

5. Feinstein, A., et al., The link between multiple sclerosis and depression. Nat Rev Neurol, 2014. 10(9): p. 507-17.

6. Marrie, R.A., et al., Differences in the burden of psychiatric comorbidity in MS vs the general population. Neurology, 2015. 85(22): p. 1972-9.

7. Marrie, R.A., et al., The incidence and prevalence of psychiatric disorders in multiple sclerosis: a systematic review. Mult Scler, 2015. 21(3): p. 305-17.

8. Wood, B., et al., Prevalence and concurrence of anxiety, depression and fatigue over time in multiple sclerosis. Mult Scler, 2013. 19(2): p. 217-24.

See also: Project