Brain connectivity abnormalities in patients with functional (psychogenic nonepileptic) seizures: A systematic review

Abstract

The aim of the current endeavor was to systematically review the existing evidence on brain connectivity abnormalities in patients with functional seizures (FS). Methods: This systematic review was prepared according to the instructions of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. MEDLINE (accessed from PubMed) and Scopus from inception to April 4, 2020 were systematically searched. The following search strategy was implemented and these key words (in the title/abstract) were used: "connectivity" OR "network" AND "psychogenic" OR "dissociative" OR "nonepileptic". Results: Through the search strategy, we could identify eighteen articles. These studies have applied various methodologies and they could identify a variety of brain connectivity abnormalities in people with FS. However, none of these studies provided a high level of evidence. They were all small studies (none had a sample size of more than 21 patients). In addition, most of the studies did not match their cases and their controls with respect to the psychiatric comorbidities and other significant confounders. Conclusion: Abnormal functional connectivity between emotion processing areas of the brain with regions involved in executive control and cognitive performance, and the functional connections of the anterior cingulate cortex are of major interest and may be involved in the pathophysiology of FS. Pursuing the concept of brain connectivity abnormalities in patients with FS and comparing the findings with well-matched controls in welldesigned studies may result in a breakthrough in identifying the exact neurobiological origin of FS.

Neuroimaging in Functional Neurological Disorder: State of the Field and Research Agenda

“…Across functional and structural neuroimaging studies, evidence supports modeling FND as a multi-network brain disorder implicating alterations within and across limbic/salience, self-agency/multimodal integration, attentional, and sensorimotor circuits ( Baizabal-Carvallo et al, 2019 , Bègue et al, 2019 , Drane et al, 2020 , Foroughi et al, 2020 , Szaflarski and LaFrance, 2018 , Voon et al, 2016 ). Studies where patients engage in affectively-valenced tasks while being scanned have generally characterized increased limbic/paralimbic activity in patients with FND compared to controls ( Pick et al, 2019 ).…”

“…To analyze BOLD signal in the resting-state, some FND researchers have studied the temporal dynamics of the time series (e.g., amplitude of low frequency fluctuations (ALFF)) ( Li et al, 2015a , Yang et al, 2020 ). However, rsfcMRI approaches interrogating brain networks in FND cohorts are more popular ( Chen et al, 2020 , Foroughi et al, 2020 ) (see Table 2 ). rsfcMRI analyses examine the dependency between the time series of different brain regions to compute a similarity measure between them.…”

Electroencephalogram Connectivity for the Diagnosis of Psychogenic Non-epileptic Seizures

“…Previous researchers have used EEG connectivity to investigate the underlying pathology of PNES and most found a difference between PNES and epilepsy or healthy controls [20], [21], [22]. Notably, a systematic review of EEG connectivity in PNES found that abnormal connectivity may be involved in the pathophysiology of PNES [7]. Investigators have also used machine learning to explore the diagnostic abilities of EEG connectivity [8], [9], [10], [11].…”

“…These factors can impact the brain networks [18], [19]. Furthermore, the systematic review [7] concluded that subjects should also be comorbidity-matched, since disorders such as depression are prevalent in PNES [17] and can affect the brain networks. However, comorbidity-matching was not done by any study, the current one included.…”

“…A potential solution to reduce these problems is machine learning applied to interictal EEG, as it removes the requirement for video recording a seizure to assist a specialist. EEG connectivity has been a point of interest for researchers as a means for improving understanding of PNES [7]. Machine learning techniques have been used to analyse EEG connectivity in PNES patients in previous studies.…”