Brain network dynamics in people with visual snow syndrome

Abstract

Visual snow syndrome (VSS) is a neurological disorder characterized by a range of continuous visual disturbances. Little is known about the functional pathological mechanisms underlying VSS and their effect on brain network topology, studied using high-resolution resting-state (RS) 7 T MRI. Forty VSS patients and 60 healthy controls underwent RS MRI. Functional connectivity matrices were calculated, and global efficiency (network integration), modularity (network segregation), local efficiency (LE, connectedness neighbors) and eigenvector centrality (significance node in network) were derived using a dynamic approach (temporal fluctuations during acquisition). Network measures were compared between groups, with regions of significant difference correlated with known aberrant ocular motor VSS metrics (shortened latencies and higher number of inhibitory errors) in VSS patients. Lastly, nodal co-modularity, a binary measure of node pairs belonging to the same module, was studied. VSS patients had lower modularity, supramarginal centrality and LE dynamics of multiple (sub)cortical regions, centered around occipital and parietal lobules. In VSS patients, lateral occipital cortex LE dynamics correlated positively with shortened prosaccade latencies (p = .041, r = .353). In VSS patients, occipital, parietal, and motor nodes belonged more often to the same module and demonstrated lower nodal co-modularity with temporal and frontal regions. This study revealed reduced dynamic variation in modularity and local efficiency strength in the VSS brain, suggesting that brain network dynamics are less variable in terms of segregation and local clustering. Further investigation of these changes could inform our understanding of the pathogenesis of the disorder and potentially lead to treatment strategies.

Keywords: 7 T MRI; network topology; ocular motor behavior; resting-state functional MRI; ultra-high field MRI; visual snow; visual snow syndrome.

FIGURE 1

Overview network analyses pipeline. (a) High‐resolution structural images were used for cortical and subcortical segmentation analyses using FreeSurfer. (b) Preprocessed fMRI signals were extracted from regions of interest and connectivity between node pairs was calculated using correlation coefficients, (c) resulting in a connectivity matrix. (d) A dynamic approach was used, and correlations were performed in sliding windows of 16 s (20 volumes) across the acquisition (shift length of 5 volumes), resulting in 76 windows. (e) Network metrics were calculated for each window and dynamics defined as the coefficient of variation over time.

FIGURE 2

Reduced network dynamics in VSS patients. Resting‐state network dynamics was significantly reduced in VSS patients. (a) Cortical regions are colored with the effect size. (b) Areas of significant differences are colored with p values (−Log10). Significant differences were observed in occipital, parietal, temporal regions, with primary visual processing areas showing the strongest effect.

FIGURE 3

Conodal modularity. Conodal modularity matrices for controls (left) and VSS patients (right). The color of each node pair indicates the proportion of subjects where node pairs were members of the same module. The most noticeable difference between groups is for nodal co‐modularity in occipital, parietal and primary motor areas. Nodal co‐modularity is highest in these regions, suggesting that these nodes belong more often to the same module in VSS patients. However, the opposite was seen between nodes in these networks and frontal and cingulate areas, suggesting these node pairs belong less often to the same module. DGM, deep gray matter; S1, primary somatosensory cortex; M1, primary motor cortex