Tracking functional and structural brain plasticity in patients with visual snow

Poster No:

W392 

Submission Type:

Abstract Submission 

Authors:

Lars Michels1, Njoud Aldusary1, Patrick Freund2, Marco Piccirelli1, Arwa Baeshen1, Jamuna Alghamdi3, Bujar Saliju4, Shila Pazahr1, Reza Mazloum1, Fahad Alshehri1, Klara Landau4, Spyros Kollias1

Institutions:

1Department of Neuroradiology, University Hospital of Zürich, Zurich, Switzerland, 2Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland, 3King Abdulaziz University, Jeddah, Saudi Arabia, 4Department of Ophthalmology, University Hospital of Zürich, Zurich, Switzerland

Introduction:

Visual snow is a distressing life-impacting condition with persistent visual phenomena such as the continuous perception of innumerable flickering dots, often leading to reading problems (Schankin, Maniyar et al. 2014). Visual snow results in multiple unnecessary examinations and treatment attempts. Neuronally, visual snow patients show hypermetabolism in visual processing areas, resulting in altered neuronal excitability (Schankin et al. 2014, Puledda et al. 2018). Based on this observation, we hypothesize to see disease-depended increases in functional connectivity and grey matter in brain regions associated with visual perception

Methods:

We recruited 10 patients with visual snow and 10 age- and sex-matched healthy controls. Functional magnetic resonance imaging (fMRI) was applied to examine spontaneous resting-state signal fluctuations by functional connectivity (rsFC). Volume changes were assessed by means of voxel-based morphometry and cortical thickness as well as by the delineation of the size of the lateral geniculate nucleus (LGN). Finally, we assessed associations between MRI and clinical parameters

Results:

Patients with visual snow were found to have significant (p < 0.05, corrected) hyperconnectivity of visuo-temporal brain regions compared to healthy controls. Grey matter volume increases (right: p = 0.026, left: p = 0.020, corrected) and greater cortical thickness (right: p = 0.038, left: p = 0.032; corrected) were found in the lingual gyrus bilaterally in patients. Visual snow patients had a lower mean LGN volume (p = 0.045). Symptom duration positively correlated both to rsFC strength of hyperconnected brain regions (p = 0.032) as well as to grey matter volume of the right lingual gyrus (p = 0.015).
Supporting Image: Figure1.JPG
   ·Figure 1
 

Conclusions:

Using a multimodal imaging approach, we demonstrate that visual snow is associated with abnormal excitability of brain regions involved in visual processing; its magnitude being associated with disease duration. This suggests that both functional and structural plasticity contribute to evolving impairments in visual snow patients. These in-vivo neuroimaging biomarkers hold potential to predict individual outcome and to track the effects of therapeutic intervention

Disorders of the Nervous System:

Disorders of the Nervous System Other 2

Imaging Methods:

BOLD fMRI 1
Multi-Modal Imaging

Modeling and Analysis Methods:

fMRI Connectivity and Network Modeling

Neuroanatomy:

Cortical Anatomy and Brain Mapping

Keywords:

ADULTS
FUNCTIONAL MRI
MRI
Perception
STRUCTURAL MRI
Thalamus
Vision
Other - visual snow

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.

No

Please indicate below if your study was a "resting state" or "task-activation” study.

Resting state

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI
Structural MRI

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

SPM

Provide references using author date format

Puledda, F. et al. (2018). 'Visual snow syndrome: what we know so far', Current Opinion in Neurology 31(1): 52-58.
Schankin, C. et al. (2014). ''Visual snow' - a disorder distinct from persistent migraine aura', Brain 137(Pt 5): 1419-1428.