<p>Widely Impaired White Matter Integrity and Altered Structural Brain Networks in Psychogenic Non-Epileptic Seizures</p>

Abstract

The underlying neural correlates of psychogenic non-epileptic seizures (PNES) are still unknown and their identification would be helpful for clinicians and patients. This study aimed to reveal details of white matter microstructure and alterations in brain structural networks in patients with PNES by using diffusion tensor imaging (DTI) and graph theoretical connectivity analysis. Methods: Seventeen patients with PNES and 26 age-and sex-matched healthy controls were enrolled. All participants underwent DTI on a 3.0-T MRI scanner, and fractional anisotropy (FA) and mean diffusivity (MD) maps were compared by tract-based spatial statistics. Additionally, the structural networks derived from DTI data were analyzed using graph theory and two different parcellation schemes. Results: Patients with PNES showed widespread decreases in FA and increases in MD, particularly in the deep white matter. In addition, graph theoretical analysis revealed impaired brain networks in PNES, including increased path length, decreased network efficiency, altered nodal topology, and reduced regional connectivity in the right posterior areas. Conclusion: We found widely impaired white matter integrity and impaired brain structural networks in Japanese patients with PNES. These findings contribute to the accumulation of evidence on PNES and may improve understanding of this condition.

Disrupted White Matter Integrity and Structural Brain Networks in Temporal Lobe Epilepsy With and Without Interictal Psychosis

“…The pipelines and settings for network analysis were the same as in our previous study (19). From the DTI data, the PANDA toolbox constructed individuals' structural white matter networks using the number of fibers as deterministic, weighted edges.…”

White matter and neurite morphology differ in psychogenic nonepileptic seizures

“…DMRI is typically used to assess the integrity of the white matter (WM) pathways via diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), and deterministic tractography metrics, including fiber bundle density and length. To our knowledge, only four preliminary PNES studies have demonstrated standard DTI measure alterations within sensorimotor, default‐mode, attention, and emotion regulation functional networks 13–16 . More specifically, PNES have been linked to decreased FA and asymmetry of fiber bundle indices within the uncinate fasciculus pathway (i.e., emotion regulation), 13,14 widespread decreases in FA and increases in MD, 16 and reduced small‐worldness (i.e., shortest mean path‐length) among attention, sensorimotor, subcortical, and default‐mode networks 15 .…”

“…To our knowledge, only four preliminary PNES studies have demonstrated standard DTI measure alterations within sensorimotor, default-mode, attention, and emotion regulation functional networks. [13][14][15][16] More specifically, PNES have been linked to decreased FA and asymmetry of fiber bundle indices within the uncinate fasciculus pathway (i.e., emotion regulation), 13,14 widespread decreases in FA and increases in MD, 16 and reduced small-worldness (i.e., shortest mean path-length) among attention, sensorimotor, subcortical, and default-mode networks. 15 Likewise, other FNSD (i.e., functional dystonia), have been linked to global WM disconnection affecting main sensorimotor and emotional control circuits, 17 whereas FA decreases within stria terminalis/fornix, medial forebrain bundle, extreme capsule, uncinate fasciculus, cingulum bundle, corpus callosum, and striatal-postcentral gyrus projections have been linked to mixed FNSDs.…”

Altered Structural Brain Networks Related to Adrenergic/Muscarinic Receptor Autoantibodies in Chronic Fatigue Syndrome

“…9 This method has been applied in various neurodegenerative and neuropsychiatric diseases, but these MRI protocols are not feasible in routine clinical practice due to the long acquisition time required. [10][11][12][13] In addition, these studies were restricted to a group-level analysis. To resolve these problems, Tijms et al proposed single-subject brain networks based on the analysis of the similarities of gray matter features, using conventional 3D-T1 weighted images (T1WI) which are routinely obtained in clinical practice.…”

“…A graph theoretical analysis is a mathematical method that has been useful for analyses of brain connectivity networks with the use of functional MRI and diffusion tensor imaging (DTI) 9 . This method has been applied in various neurodegenerative and neuropsychiatric diseases, but these MRI protocols are not feasible in routine clinical practice due to the long acquisition time required 10–13 . In addition, these studies were restricted to a group‐level analysis.…”