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Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls

Open AccessPublished:August 05, 2022DOI:https://doi.org/10.1016/j.yebeh.2022.108858

      Highlights

      • Structural correlates of functional seizures exist, but are inconsistent.
      • We examined MRI morphometry in 90 FS versus 576 psychiatric- and other- controls.
      • Differences were in superior temporal and occipital cortex, and cerebellum.
      • Magnetic resonance imaging quality was lower in FS, but this did not account for differences.
      • Further comparison to other relevant populations is needed.

      Abstract

      Purpose

      Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations.

      Methods

      Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive–compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners.

      Results

      After Bonferroni–Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm3, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS.

      Conclusions

      These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.

      Graphical abstract

      Keywords

      1. Introduction

      Individuals with functional seizures (FS) experience episodes of involuntary, neurological symptoms that are considered physical manifestations of acute or chronic psychological distress and can resemble epileptic seizures (ES) behaviorally, but are not caused by epileptiform neural activity [
      • Dickinson P.
      • Looper K.J.
      Psychogenic nonepileptic seizures: a current overview.
      ]. FS also are known as psychogenic nonepileptic seizures (PNES) or dissociative seizures [
      • Kerr W.T.
      • Stern J.M.
      We need a functioning name for PNES: Consider dissociative seizures.
      ,
      • Tolchin B.
      • Perez D.L.
      • Szaflarski J.P.
      • Baslet G.
      • Doss J.
      • Buchhalter J.
      • et al.
      What's in a name?.
      ,
      • Asadi-Pooya A.A.
      • Brigo F.
      • Mildon B.
      • Nicholson T.R.
      Terminology for psychogenic nonepileptic seizures: Making the case for “functional seizures”.
      ]. While there are distinct ictal behaviors in FS as compared to ES, patient- and witness-reporting of these behaviors can be inaccurate, and non-expert healthcare providers may struggle to differentiate the two [
      • Kerr W.T.
      • Janio E.A.
      • Chau A.M.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • et al.
      Objective score from initial interview identifies patients with probable dissociative seizures.
      ,
      • Kerr W.T.
      • Zhang X.
      • Janio E.A.
      • Karimi A.H.
      • Allas C.H.
      • Dubey I.
      • et al.
      Reliability of additional reported seizure manifestations to identify dissociative seizures.
      ,
      • Kerr W.T.
      • Chau A.M.
      • Janio E.A.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • et al.
      Reliability of reported peri-ictal behavior to identify psychogenic nonepileptic seizures.
      ,
      • Tatum W.O.
      • Hirsch L.J.
      • Gelfand M.A.
      • Acton E.K.
      • LaFrance Jr, W.C.
      • Duckrow R.B.
      • et al.
      Assessment of the predictive value of outpatient smartphone videos for diagnosis of epileptic seizures.
      ,
      • Birca V.
      • Keezer M.R.
      • Chamelian L.
      • Lortie A.
      • Nguyen D.K.
      Recognition of psychogenic versus epileptic seizures based on videos.
      ]. Patients with FS frequently are misdiagnosed with epilepsy and treated ineffectively with antiseizure medications with potential adverse effects [
      • Kerr W.T.
      • Janio E.A.
      • Le J.M.
      • Hori J.M.
      • Patel A.B.
      • Gallardo N.L.
      • et al.
      Diagnostic delay in psychogenic seizures and the association with anti-seizure medication trials.
      ]. Long delays (median 3 years, average 8 years post-onset) can ensue until an accurate diagnosis is established through video-electroencephalographic monitoring (VEM) [
      • Reuber M.
      • Fernández G.
      • Bauer J.
      • Helmstaedter C.
      • Elger C.E.
      Diagnostic delay in psychogenic nonepileptic seizures.
      ,
      • Kerr W.T.
      • Zhang X.
      • Hill C.E.
      • Janio E.A.
      • Chau A.M.
      • Braesch C.T.
      • et al.
      Factors associated with delay to video-EEG in dissociative seizures.
      ,
      • Goldstein L.H.
      • Robinson E.J.
      • Mellers J.D.C.
      • Stone J.
      • Carson A.
      • Chalder T.
      • et al.
      Psychological and demographic characteristics of 368 patients with dissociative seizures: data from the CODES cohort.
      ]. In the interim, patients may have high healthcare utilization and poor quality of life, similar to or worse than patients with medication-resistant epilepsy [
      • Stephen C.D.
      • Fung V.
      • Lungu C.I.
      • Espay A.J.
      Assessment of emergency department and inpatient use and costs in adult and pediatric functional neurological disorders.
      ,
      • Libbon R.
      • Gadbaw J.
      • Watson M.
      • Rothberg B.
      • Sillau S.
      • Heru A.
      • et al.
      The feasibility of a multidisciplinary group therapy clinic for the treatment of nonepileptic seizures.
      ,
      • Seneviratne U.
      • Low Z.M.
      • Low Z.X.
      • Hehir A.
      • Paramaswaran S.
      • Foong M.
      • et al.
      Medical health care utilization cost of patients presenting with psychogenic nonepileptic seizures.
      ,
      • Perez D.L.
      • LaFrance W.C.
      Nonepileptic seizures: an updated review.
      ,
      • Ahmedani B.K.
      • Osborne J.
      • Nerenz D.R.
      • Haque S.
      • Pietrantoni L.
      • Mahone D.
      • et al.
      Diagnosis, costs, and utilization for psychogenic non-epileptic seizures in a US health care setting.
      ,
      • Pick S.
      • Anderson D.G.
      • Asadi-Pooya A.A.
      • Aybek S.
      • Baslet G.
      • Bloem B.R.
      • et al.
      Outcome measurement in functional neurological disorder: a systematic review and recommendations.
      ,
      • Goldstein L.H.
      • Robinson E.J.
      • Mellers J.D.C.
      • Stone J.
      • Carson A.
      • Reuber M.
      • et al.
      Cognitive behavioural therapy for adults with dissociative seizures (CODES): a pragmatic, multicentre, randomised controlled trial.
      ,
      • Boesten N.
      • Myers L.
      • Wijnen B.
      Quality of life and psychological dysfunction in traumatized and nontraumatized patients with psychogenic nonepileptic seizures (PNES).
      ,
      • Salinsky M.
      • Rutecki P.
      • Parko K.
      • Goy E.
      • Storzbach D.
      • Markwardt S.
      • et al.
      Health-related quality of life in Veterans with epileptic and psychogenic nonepileptic seizures.
      ,
      • Rawlings G.H.
      • Brown I.
      • Reuber M.
      Predictors of health-related quality of life in patients with epilepsy and psychogenic nonepileptic seizures.
      ,
      • Kerr W.T.
      • Zhang X.
      • Hill C.E.
      • Janio E.A.
      • Chau A.M.
      • Braesch C.T.
      • et al.
      Epilepsy, dissociative seizures, and mixed: Associations with time to video-EEG.
      ]. Earlier diagnosis was associated with improved seizure control, quality of life, and substantial reductions in healthcare utilization [
      • Walczak T.S.
      • Papacostas S.
      • Williams D.T.
      • Scheuer M.L.
      • Lebowitz N.
      • Notarfrancesco A.
      Outcome after diagnosis of psychogenic nonepileptic seizures.
      ]. While multiple validated clinical scores have been developed recently, early identification is limited by the lack of reliable quantitative biomarkers both for diagnosis and as objective markers for response to treatment [
      • Kerr W.T.
      • Janio E.A.
      • Chau A.M.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • et al.
      Objective score from initial interview identifies patients with probable dissociative seizures.
      ,
      • Lenio S.
      • Kerr W.T.
      • Watson M.
      • Baker S.
      • Bush C.
      • Rajic A.
      • et al.
      Validation of a predictive calculator to distinguish between patients presenting with dissociative versus epileptic seizures.
      ,
      • Trainor D.
      • Foster E.
      • Rychkova M.
      • Lloyd M.
      • Leong M.
      • Wang A.D.
      • et al.
      Development and validation of a screening questionnaire for psychogenic nonepileptic seizures.
      ,
      • Janocko N.J.
      • Jing J.
      • Fan Z.
      • Teagarden D.L.
      • Villarreal H.K.
      • Morton M.L.
      • et al.
      DDESVSFS: A simple, rapid and comprehensive screening tool for the differential diagnosis of epileptic seizures Vs functional seizures.
      ,
      • Wardrope A.
      • Jamnadas-Khoda J.
      • Broadhurst M.
      • Grünewald R.A.
      • Heaton T.J.
      • Howell S.J.
      • et al.
      Machine learning as a diagnostic decision aid for patients with transient loss of consciousness.
      ,
      • Chen M.
      • Jamnadas-Khoda J.
      • Broadhurst M.
      • Wall M.
      • Grünewald R.
      • Howell S.J.L.
      • Koepp M.
      • et al.
      Value of witness observations in the differential diagnosis of transient loss of consciousness.
      ].
      There is growing evidence that quantitative neuroimaging can demonstrate differences in brain structure as well as structural and functional connectivity associated with FS and, more broadly, with the category of functional neurological disorder (FND) [
      • Pick S.
      • Anderson D.G.
      • Asadi-Pooya A.A.
      • Aybek S.
      • Baslet G.
      • Bloem B.R.
      • et al.
      Outcome measurement in functional neurological disorder: a systematic review and recommendations.
      ,
      • Sun K.
      • Ren Z.
      • Yang D.
      • Wang X.
      • Yu T.
      • Ni D.
      • et al.
      Voxel-based morphometric MRI post-processing and PET/MRI co-registration reveal subtle abnormalities in cingulate epilepsy.
      ,
      • Kerr W.T.
      • Lee J.K.
      • Karimi A.H.
      • Tatekawa H.
      • Hickman L.B.
      • Connerney M.
      • et al.
      A minority of patients with functional seizures have abnormalities on neuroimaging.
      ,
      • Perez D.L.
      • Nicholson T.R.
      • Asadi-Pooya A.A.
      • Bègue I.
      • Butler M.
      • Carson A.J.
      • et al.
      Neuroimaging in functional neurological disorder: state of the field and research agenda.
      ,
      • Tatekawa H.
      • Kerr W.T.
      • Savic I.
      • Engel Jr, J.
      • Salamon N.
      Reduced left amygdala volume in patients with dissociative seizures (psychogenic nonepileptic seizures).
      ,
      • Goodman A.M.
      • Allendorfer J.B.
      • Blum A.S.
      • Bolding M.S.
      • Correia S.
      • Ver Hoef L.W.
      • et al.
      White matter and neurite morphology differ in psychogenic nonepileptic seizures.
      ,
      • Asadi-Pooya A.A.
      • Homayoun M.
      Structural brain abnormalities in patients with psychogenic nonepileptic seizures.
      ,
      • McSweeney M.
      • Reuber M.
      • Levita L.
      Neuroimaging studies in patients with psychogenic non-epileptic seizures: A systematic meta-review.
      ,
      • Bolen R.D.
      • Koontz E.H.
      • Pritchard 3rd, P.B.
      Prevalence and distribution of MRI abnormalities in patients with psychogenic nonepileptic events.
      ,
      • Johnstone B.
      • Velakoulis D.
      • Yuan C.Y.
      • Ang A.
      • Steward C.
      • Desmond P.
      • et al.
      Early childhood trauma and hippocampal volumes in patients with epileptic and psychogenic seizures.
      ,
      • van der Kruijs S.J.
      • Bodde N.M.
      • Vaessen M.J.
      • Lazeron R.H.
      • Vonck K.
      • Boon P.
      • et al.
      Functional connectivity of dissociation in patients with psychogenic non-epileptic seizures.
      ,
      • Foroughi A.A.
      • Nazeri M.
      • Asadi-Pooya A.A.
      Brain connectivity abnormalities in patients with functional (psychogenic nonepileptic) seizures: A systematic review.
      ,
      • Kola S.
      • LaFaver K.
      Functional movement disorder and functional seizures: What have we learned from different subtypes of functional neurological disorders?.
      ]. Broadly, FND has been associated with above-normal connectivity between affective brain areas and higher order motor control networks, as well as with below-normal connectivity and dysfunction of executive control areas [
      • Pick S.
      • et al.
      Emotional processing in functional neurological disorder: a review, biopsychosocial model and research agenda.
      ]. This can be described in terms of alterations in connectivity between the limbic network (hippocampus, parahippocampus, orbitofrontal cortex, sub- and peri-genual anterior cingulate cortex), salience network (insula, amygdala, hypothalamus, periacqueductal gray), dorsal and ventral control networks (superior and inferior parietal lobules, inferior frontal gyrus, frontal eye fields), cognitive control networks (supplementary motor area, dorsolateral prefrontal cortex), and sensorimotor control and feedback (sensorimotor cortex, temporoparietal junction, cerebellum) [
      • Drane D.L.
      • Fani N.
      • Hallett M.
      • Khalsa S.S.
      • Perez D.L.
      • Roberts N.A.
      A framework for understanding the pathophysiology of functional neurological disorder.
      ]. These findings have raised interest for treatment of FND using neurostimulation in the right temporo-parietal junction—which is involved in self-agency perception and dissociation [
      • Peterson K.T.
      • Kosior R.
      • Meek B.P.
      • Ng M.
      • Perez D.L.
      • Modirrousta M.
      Right temporoparietal junction transcranial magnetic stimulation in the treatment of psychogenic nonepileptic seizures: A case series.
      ,
      • Maurer C.W.
      • LaFaver K.
      • Ameli R.
      • Epstein S.A.
      • Hallett M.
      • Horovitz S.G.
      Impaired self-agency in functional movement disorders: A resting-state fMRI study.
      ].
      Findings from neuroimaging studies of FS have been inconsistent and reflect a broad range of brain regions potentially involved in FS, indicating the need for further study to better understand the brain correlates of disease. To illustrate this inconsistency, some older literature described abnormalities primarily in the right, nondominant hemisphere; whereas a recent evaluation highlighted changes in left-sided hubs of the default mode network [
      • Zelinski L.
      • Diez I.
      • Perez D.L.
      • Kotz S.A.
      • Wellmer J.
      • Schlegel U.
      • et al.
      Cortical thickness in default mode network hubs correlates with clinical features of dissociative seizures.
      ,
      • Devinsky O.
      • Mesad S.
      • Alper K.
      Nondominant hemisphere lesions and conversion nonepileptic seizures.
      ,
      • Reuber M.
      • Aybek S.
      • Carson A.
      • Edwards M.J.
      • Goldstein L.H.
      • Hallett M.
      • et al.
      Evidence of brain abnormality in patients with psychogenic nonepileptic seizures.
      ].
      Additionally, quantitative neuroimaging studies of FS have focused on research-quality neuroimages and compared patients with FS to patients with epilepsy, seizure-naïve controls, or traumatic brain injury (TBI) [
      • Goodman A.M.
      • Allendorfer J.B.
      • Blum A.S.
      • Bolding M.S.
      • Correia S.
      • Ver Hoef L.W.
      • et al.
      White matter and neurite morphology differ in psychogenic nonepileptic seizures.
      ]. Given the high prevalence of neuropsychiatric comorbidities in FS [
      • Kerr W.T.
      • Janio E.A.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • Patel A.B.
      • et al.
      Identifying psychogenic seizures through comorbidities and medication history.
      ], comparisons of FS to seizure-naïve patients with relevant neuropsychiatric disease are needed to distinguish effects attributable to FS from those due to the underlying neuropsychiatric disease. While it has been common to control for depression and anxiety severity or relevant psychological history within patients with FND [
      • Johnstone B.
      • Velakoulis D.
      • Yuan C.Y.
      • Ang A.
      • Steward C.
      • Desmond P.
      • et al.
      Early childhood trauma and hippocampal volumes in patients with epileptic and psychogenic seizures.
      ,
      • Begue I.
      • Adams C.
      • Stone J.
      • Perez D.L.
      Structural alterations in functional neurological disorder and related conditions: a software and hardware problem?.
      ,
      • Labate A.
      • Cerasa A.
      • Mula M.
      • Mumoli L.
      • Gioia M.C.
      • Aguglia U.
      • et al.
      Neuroanatomic correlates of psychogenic nonepileptic seizures: a cortical thickness and VBM study.
      ,
      • Kozlowska K.
      • Griffiths K.R.
      • Foster S.L.
      • Linton J.
      • Williams L.M.
      • Korgaonkar M.S.
      Grey matter abnormalities in children and adolescents with functional neurological symptom disorder.
      ,
      • Perez D.L.
      • Williams B.
      • Matin N.
      • Mello J.
      • Dickerson B.C.
      • LaFrance Jr, W.C.
      • et al.
      Anterior hippocampal grey matter predicts mental health outcome in functional neurological disorders: an exploratory pilot study.
      ,
      • Perez D.L.
      • Williams B.
      • Matin N.
      • LaFrance Jr, W.C.
      • Costumero-Ramos V.
      • Fricchione G.L.
      • et al.
      Corticolimbic structural alterations linked to health status and trait anxiety in functional neurological disorder.
      ,
      • Maurer C.W.
      • LaFaver K.
      • Limachia G.S.
      • Capitan G.
      • Ameli R.
      • Sinclair S.
      • et al.
      Gray matter differences in patients with functional movement disorders.
      ,
      • Espay A.J.
      • Maloney T.
      • Vannest J.
      • Norris M.M.
      • Eliassen J.C.
      • Neefus E.
      • et al.
      Impaired emotion processing in functional (psychogenic) tremor: A functional magnetic resonance imaging study.
      ,
      • Perez D.L.
      • Matin N.
      • Williams B.
      • Tanev K.
      • Makris N.
      • LaFrance Jr, W.C.
      • et al.
      Cortical thickness alterations linked to somatoform and psychological dissociation in functional neurological disorders.
      ,
      • McSweeney M.
      • Reuber M.
      • Hoggard N.
      • Levita L.
      Cortical thickness and gyrification patterns in patients with psychogenic non-epileptic seizures.
      ,
      • Vasta R.
      • Cerasa A.
      • Sarica A.
      • Bartolini E.
      • Martino I.
      • Mari F.
      • et al.
      The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures.
      ,
      • Lee S.
      • Allendorfer J.B.
      • Gaston T.E.
      • Griffis J.C.
      • Hernando K.A.
      • Knowlton R.C.
      • et al.
      White matter diffusion abnormalities in patients with psychogenic non-epileptic seizures.
      ,
      • Tomic A.
      • Agosta F.
      • Sarasso E.
      • Petrovic I.
      • Basaia S.
      • Pesic D.
      • et al.
      Are there two different forms of functional dystonia? A multimodal brain structural MRI study.
      ], the literature on direct comparison of patients with FS to patients with neuropsychiatric disorders is sparse. While some studies compare neurologically or psychologically similar conditions [
      • Goodman A.M.
      • Allendorfer J.B.
      • Blum A.S.
      • Bolding M.S.
      • Correia S.
      • Ver Hoef L.W.
      • et al.
      White matter and neurite morphology differ in psychogenic nonepileptic seizures.
      ,
      • Devinsky O.
      • Mesad S.
      • Alper K.
      Nondominant hemisphere lesions and conversion nonepileptic seizures.
      ,
      • Espay A.J.
      • Maloney T.
      • Vannest J.
      • Norris M.M.
      • Eliassen J.C.
      • Neefus E.
      • et al.
      Impaired emotion processing in functional (psychogenic) tremor: A functional magnetic resonance imaging study.
      ,
      • Sharma A.A.
      • Goodman A.M.
      • Allendorfer J.B.
      • Philip N.S.
      • Correia S.
      • LaFrance Jr, W.C.
      • et al.
      Regional brain atrophy and aberrant cortical folding relate to anxiety and depression in patients with traumatic brain injury and psychogenic nonepileptic seizures.
      ,
      • Riederer F.
      • Landmann G.
      • Gantenbein A.R.
      • Stockinger L.
      • Egloff N.
      • Sprott H.
      • et al.
      Nondermatomal somatosensory deficits in chronic pain are associated with cerebral grey matter changes.
      ,
      • Schrag A.E.
      • Mehta A.R.
      • Bhatia K.P.
      • Brown R.J.
      • Frackowiak R.S.
      • Trimble M.R.
      • et al.
      The functional neuroimaging correlates of psychogenic versus organic dystonia.
      ,
      • Balachandran N.
      • Goodman A.M.
      • Allendorfer J.B.
      • Martin A.N.
      • Tocco K.
      • Vogel V.
      • et al.
      Relationship between neural responses to stress and mental health symptoms in psychogenic nonepileptic seizures after traumatic brain injury.
      ,
      • Diez I.
      • Larson A.G.
      • Nakhate V.
      • Dunn E.C.
      • Fricchione G.L.
      • Nicholson T.R.
      • et al.
      Early-life trauma endophenotypes and brain circuit-gene expression relationships in functional neurological (conversion) disorder.
      ], it is rare to include a full factorial design of healthy controls, neuropsychiatric controls, and patients with FND with and without neuropsychiatric conditions (exceptions: [
      • Espay A.J.
      • Maloney T.
      • Vannest J.
      • Norris M.M.
      • Eliassen J.C.
      • Neefus E.
      • et al.
      Impaired emotion processing in functional (psychogenic) tremor: A functional magnetic resonance imaging study.
      ,
      • Espay A.J.
      • Maloney T.
      • Vannest J.
      • Norris M.M.
      • Eliassen J.C.
      • Neefus E.
      • et al.
      Dysfunction in emotion processing underlies functional (psychogenic) dystonia.
      ,
      • Szaflarski J.P.
      • Allendorfer J.B.
      • Nenert R.
      • LaFrance Jr, W.C.
      • Barkan H.I.
      • DeWolfe J.
      • et al.
      Facial emotion processing in patients with seizure disorders.
      ]). Such full factorial designs allow statistical analyses to separate unique associations with FND from associations with non-FND neuropsychiatric conditions [
      • Sanbonmatsu D.M.
      • Cooley E.H.
      • Butner J.E.
      The impact of complexity on methods and findings in psychological science.
      ].
      In this study, we evaluated whether quantitative morphometric features of magnetic resonance imaging (MRI) could identify structural correlates of FS using clinical MRIs. The specificity of these potential associations was assessed by comparing patients with FS to seizure-naïve patients with and without common comorbidities of FS including depression and anxiety [
      • Kerr W.T.
      • Janio E.A.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • Patel A.B.
      • et al.
      Identifying psychogenic seizures through comorbidities and medication history.
      ]. After this study, these morphometric correlates of FS could serve a biomarkers evaluated in the treatment of FS, as well as diagnostic biomarkers could address this delay to diagnosis when compared to patients with epilepsy. The brain sites of diagnostic and treatment biomarkers additionally could serve as targets for novel interventions.

      2. Methods

      2.1 Patient population

      Our patient sample included consecutive patients with FS admitted to the UCLA adult VEM unit from January 2006 to December 2020. We excluded patients with comorbid functional and epileptic seizures, epileptic seizures, and patients for whom there were insufficient typical events during VEM to characterize all seizure types in each patient. Diagnoses of FS met the International League Against Epilepsy (ILAE) criteria for “documented” [
      • LaFrance Jr, W.C.
      • Baker G.A.
      • Duncan R.
      • Goldstein L.H.
      • Reuber M.
      Minimum requirements for the diagnosis of psychogenic nonepileptic seizures: a staged approach: a report from the International League Against Epilepsy Nonepileptic Seizures Task Force.
      ] and were based on expert clinical opinion based on the available clinical history, physical exam, VEM, structural MRI, and 18FDG-PET. Clinical details of patients in this dataset have been published elsewhere [
      • Kerr W.T.
      • Janio E.A.
      • Chau A.M.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • et al.
      Objective score from initial interview identifies patients with probable dissociative seizures.
      ,
      • Kerr W.T.
      • Zhang X.
      • Janio E.A.
      • Karimi A.H.
      • Allas C.H.
      • Dubey I.
      • et al.
      Reliability of additional reported seizure manifestations to identify dissociative seizures.
      ,
      • Kerr W.T.
      • Chau A.M.
      • Janio E.A.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • et al.
      Reliability of reported peri-ictal behavior to identify psychogenic nonepileptic seizures.
      ,
      • Kerr W.T.
      • Janio E.A.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • Patel A.B.
      • et al.
      An objective score to identify psychogenic seizures based on age of onset and history.
      ,
      • Kerr W.T.
      • Janio E.A.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • Patel A.B.
      • et al.
      Diagnostic implications of review-of-systems questionnaires to differentiate epileptic seizures from psychogenic seizures.
      ,
      • Kerr W.T.
      • Janio E.A.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • Patel A.B.
      • et al.
      Identifying psychogenic seizures through comorbidities and medication history.
      ,
      • Kerr W.T.
      • Hwang E.S.
      • Raman K.R.
      • Barritt S.E.
      • Patel A.B.
      • Le J.M.
      • et al.
      Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation.
      ,
      • Kerr W.T.
      • Anderson A.
      • Lau E.P.
      • Cho A.Y.
      • Xia H.
      • Bramen J.
      • et al.
      Automated diagnosis of epilepsy using EEG power spectrum.
      ]. Patients with FS frequently have multiple medical and psychiatric comorbidities and over 80% were on or had been taking antiseizure medications [
      • Kerr W.T.
      • Janio E.A.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • Patel A.B.
      • et al.
      Identifying psychogenic seizures through comorbidities and medication history.
      ]. In our sample, 22% had depression, of which 40% had treatment-resistant depression with 25% on polytherapy for depression and another 15% who were failed by prior antidepressants; 19% had anxiety; and 22% had other psychiatric comorbidities. Therefore, an analysis limited to patients without significant comorbidities, without psychotropic medications, or without antiseizure medications would not describe the typical patient with this condition.
      Comorbid psychiatric history was obtained either through chart review before May of 2015 or prospective interview after that time, as described elsewhere [
      • Kerr W.T.
      • Janio E.A.
      • Chau A.M.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • et al.
      Objective score from initial interview identifies patients with probable dissociative seizures.
      ]. In brief, chart review utilized the first comprehensive neurological note describing the seizures and comorbidities. Prospective interview was performed by a trained pre-doctoral student, medical student, or neurology resident and inquired about comorbid psychiatric disorders including but not limited to depression, anxiety, post-traumatic stress disorder (PTSD), bipolar, and schizophrenia. These diagnoses were not confirmed by formal psychiatric interview or further characterized by standardized questionnaires. Due to the relative infrequency of psychiatric conditions other than depression, anxiety, and PTSD; the other psychiatric conditions were summarized by a miscellaneous category and counted within a “number of psychiatric conditions” category. This time period of January 2006 to December 2020 was based on data availability from our prior work including clinical information, EEG, and FDG-PET, with a stopping point based on when this analysis started [
      • Kerr W.T.
      • Janio E.A.
      • Chau A.M.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • et al.
      Objective score from initial interview identifies patients with probable dissociative seizures.
      ,
      • Kerr W.T.
      • Lee J.K.
      • Karimi A.H.
      • Tatekawa H.
      • Hickman L.B.
      • Connerney M.
      • et al.
      A minority of patients with functional seizures have abnormalities on neuroimaging.
      ].
      We compiled as large a comparison group of seizure-naïve patients as possible to account for potential confounding factors including age, sex, comorbidities, and MRI scanner. In this setting, this approach of maximizing the size of the comparison groups and adjusting for confounds using a multivariable model was felt to have superior statistical power to the alternative approach of one-to-one propensity matching due to the increased sample size allowing for smaller variance in the comparison populations (n = 576 versus 90), as well as being able to separate associations of individual confounding factors (e.g., depression and female sex) based on patterns in the comparison samples [
      • Elze M.C.
      • Gregson J.
      • Baber U.
      • Williamson E.
      • Sartori S.
      • Mehran R.
      • et al.
      Comparison of Propensity Score methods and covariate adjustment: evaluation in 4 cardiovascular studies.
      ,
      • Brazauskas R.
      • Logan B.R.
      Observational studies: Matching or regression?.
      ].
      Seizure-naïve controls were recruited from multiple sources. We searched the electronic health record (EHR) for all radiological images from January 2006 to December 2020 within the UCLA Picture Archive and Communication System (PACS) with combinations of the following queries: “3D Quantitative, BrainLab, NeuroReader, and thin section.” We had exclusionary terms including “post-surgical, post-operative, craniotomy, status post, resection, infiltrative, tectal, lobulated, multilobulated, WHO, grade, glioma, glial, necrotic, progression, increasing, worsening, catheter, motion, heterogeneous, compensated, expansile, hypoenhancing, irregularity, macroadenoma, and nodule.” We manually reviewed all written reports and selected patients for whom the reading radiologist’s opinion was essentially a normal study, allowing for incidental findings including meningiomas that were less than 1 mm in longest diameter and not adjacent to tissue or pineal cysts that were less than 1 mm in longest diameter. All postoperative studies were excluded.
      Information about the indication for MRI and confounding factors was acquired through chart review of data around the time of imaging by evaluating the medication list, the highest quality history and physical or consult note, anesthesia pre-procedure note if available, and available vital signs in the electronic health record or recorded on the Digital Imaging and Communications in Medicine (DICOM) header for the MRI (meta-data). The quality of the documentation of psychiatric conditions mirrored the retrospective chart review data from the patients admitted for VEM. This generally did not include a formal psychiatric interview or questionnaires delineating the severity of the condition. Most often, the psychiatric condition was documented by a non-psychiatric provider without further specification, and these diagnoses were further confirmed by the prescription of psychoactive medications with limited range of indication (e.g., taking buspirone for anxiety). We decided that documentation of a prescription for a psychoactive medication with limited range of indication was insufficient to indicate a diagnosis (e.g., olanzapine did not indicate schizophrenia).
      For each patient with FS, we reviewed the radiologist’s report from the first, highest quality MRI of the brain acquired at UCLA. If the patient had an epilepsy-protocol or other equivalent high-quality volumetric protocol (e.g., neurosurgery pre-operative navigation called BrainLab) that was acquired after an initial lower quality scan, we only report the results of the higher quality scan. If, however, no higher quality scan was available, we report the results of the first available MRI that met our quality and protocol standards.
      We also included MRIs obtained from research participants to improve our comparison with relevant psychiatric comorbidities and because clinical databases were biased to include more older patients. Based on their participation in prior research with two co-authors, Drs. Katherine Narr and Randall Espinoza, we included pretreatment scans before ketamine, electroconvulsive therapy, or sleep deprivation therapy from a set of participants with treatment-resistant depression [
      • Tsolaki E.
      • Narr K.L.
      • Espinoza R.
      • Wade B.
      • Hellemann G.
      • Kubicki A.
      • et al.
      Subcallosal cingulate structural connectivity differs in responders and nonresponders to electroconvulsive therapy.
      ,
      • Vasavada M.M.
      • et al.
      Effects of serial ketamine infusions on corticolimbic functional connectivity in major depression.
      ,
      • Wade B.S.C.
      • Hellemann G.
      • Espinoza R.T.
      • Woods R.P.
      • Joshi S.H.
      • Redlich R.
      • et al.
      Depressive symptom dimensions in treatment-resistant major depression and their modulation with electroconvulsive therapy.
      ,
      • Leaver A.M.
      • Vasavada M.
      • Kubicki A.
      • Wade B.
      • Loureiro J.
      • Hellemann G.
      • et al.
      Hippocampal subregions and networks linked with antidepressant response to electroconvulsive therapy.
      ,
      • Sahib A.K.
      • Loureiro J.R.
      • Vasavada M.
      • Anderson C.
      • Kubicki A.
      • Wade B.
      • et al.
      Modulation of the functional connectome in major depressive disorder by ketamine therapy.
      ,
      • Loureiro J.R.A.
      • Leaver A.
      • Vasavada M.
      • Sahib A.K.
      • Kubicki A.
      • Joshi S.
      • et al.
      Modulation of amygdala reactivity following rapidly acting interventions for major depression.
      ,
      • Sahib A.K.
      • Loureiro J.R.
      • Vasavada M.M.
      • Kubicki A.
      • Wade B.
      • Joshi S.H.
      • et al.
      Modulation of inhibitory control networks relate to clinical response following ketamine therapy in major depression.
      ,
      • Sun H.
      • Jiang R.
      • Qi S.
      • Narr K.L.
      • Wade B.S.
      • Upston J.
      • et al.
      Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.
      ,
      • Kubicki A.
      • Leaver A.M.
      • Vasavada M.
      • Njau S.
      • Wade B.
      • Joshi S.H.
      • et al.
      Variations in hippocampal white matter diffusivity differentiate response to electroconvulsive therapy in major depression.
      ,
      • Wade B.S.C.
      • Sui J.
      • Njau S.
      • Leaver A.M.
      • Vasvada M.
      • Gutman B.A.
      • et al.
      Data-driven cluster selection for subcortical shape and cortical thickness predicts recovery from depressive symptoms.
      ,
      • Wade B.S.
      • Joshi S.H.
      • Njau S.
      • Leaver A.M.
      • Vasavada M.
      • Woods R.P.
      • et al.
      Effect of electroconvulsive therapy on striatal morphometry in major depressive disorder.
      ,
      • Leaver A.M.
      • Espinoza R.
      • Pirnia T.
      • Joshi S.H.
      • Woods R.P.
      • Narr K.L.
      Modulation of intrinsic brain activity by electroconvulsive therapy in major depression.
      ,
      • Pirnia T.
      • Joshi S.H.
      • Leaver A.M.
      • Vasavada M.
      • Njau S.
      • Woods R.P.
      • et al.
      Electroconvulsive therapy and structural neuroplasticity in neocortical, limbic and paralimbic cortex.
      ,
      • Vasavada M.M.
      • Leaver A.M.
      • Espinoza R.T.
      • Joshi S.H.
      • Njau S.N.
      • Woods R.P.
      • et al.
      Structural connectivity and response to ketamine therapy in major depression: A preliminary study.
      ]. These research participants were selected to not have other comorbid psychiatric conditions. In addition, we used epilepsy-naïve healthy controls and patients with obsessive–convulsive disorder (OCD) that participated in research with Drs. Jamie Feusner and Joseph O’Neill [
      • Moody T.D.
      • Morfini F.
      • Cheng G.
      • Sheen C.L.
      • Kerr W.T.
      • Strober M.
      • et al.
      Brain activation and connectivity in anorexia nervosa and body dysmorphic disorder when viewing bodies: relationships to clinical symptoms and perception of appearance.
      ,
      • Rangaprakash D.
      • Bohon C.
      • Lawrence K.E.
      • Moody T.
      • Morfini F.
      • Khalsa S.S.
      • et al.
      Aberrant dynamic connectivity for fear processing in anorexia nervosa and body dysmorphic disorder.
      ,
      • Feusner J.D.
      • Lidström A.
      • Moody T.D.
      • Dhejne C.
      • Bookheimer S.Y.
      • Savic I.
      Intrinsic network connectivity and own body perception in gender dysphoria.
      ,
      • Armstrong C.C.
      • Moody T.D.
      • Feusner J.D.
      • McCracken J.T.
      • Chang S.
      • Levitt J.G.
      • et al.
      Graph-theoretical analysis of resting-state fMRI in pediatric obsessive-compulsive disorder.
      ,
      • Khalsa S.S.
      • Kumar R.
      • Patel V.
      • Strober M.
      • Feusner J.D.
      Mammillary body volume abnormalities in anorexia nervosa.
      ,
      • O'Neill J.
      • Feusner J.D.
      Cognitive-behavioral therapy for obsessive-compulsive disorder: access to treatment, prediction of long-term outcome with neuroimaging.
      ,
      • Feusner J.D.
      • Moody T.
      • Lai T.M.
      • Sheen C.
      • Khalsa S.
      • Brown J.
      • et al.
      Brain connectivity and prediction of relapse after cognitive-behavioral therapy in obsessive-compulsive disorder.
      ,
      • Moody T.D.
      • Sasaki M.A.
      • Bohon C.
      • Strober M.A.
      • Bookheimer S.Y.
      • Sheen C.L.
      • et al.
      Functional connectivity for face processing in individuals with body dysmorphic disorder and anorexia nervosa.
      ]. The OCD sample allowed for other comorbid anxiety disorders (e.g., social anxiety) but did not allow for bipolar or schizophrenia. In some of these research studies, the epilepsy-naïve controls were compared to other psychiatric populations who were not included in this study including anorexia and body dysmorphic disorder. While these epilepsy-naïve controls were participants in research studies and not patients of the healthcare system for other reasons, we use the term patients to refer to all individuals included in this study for clarity.
      All patients with FS consented for the use of their records in research, the research participants consented for the studies they participated in as well as future studies, and patients identified through PACS were included with a waiver of consent. The UCLA Institutional Review Board approved this study. This work is consistent with the Declaration of Helsinki. De-identified raw data and code for this study is available on Mendeley Data.

      2.2 Selection of MRIs for morphological analysis

      From the pool of available patients with MRIs, we selected MRIs with T1-volumetric sequences with the following minimum standards: 1 mm or smaller isometric volumetric pixels (voxels), zero inter-slice distance (gap), axial orientation, and field of view covering the entire brain. Based on MRI manufacturer, these included both magnetization-prepared rapid gradient echo (MPRAGE) and spoiled gradient-recalled (SPGR) sequences with TI, TE, and TR within manufacturer defined ranges. A board-certified radiologist (HT) and neurology resident (WTK) viewed each volumetric image and excluded images of poor quality due to motion or patients with unreported abnormalities that would influence volumetric analysis (e.g., arachnoid cysts, prior craniotomies). For patients with FS, we included images with mild abnormalities (e.g., white matter hyperintensities, hippocampal sclerosis, lacunar stroke) and excluded all postoperative images and images with gross structural abnormalities (e.g., encephalomalacia). We allowed mild abnormalities because such abnormalities may be related to FS in ways yet unknown. A substantial minority of patients with FS have radiologically apparent abnormalities, so exclusion of these patients could cause our sample to be less representative of the broader population of patients with FS [
      • Kerr W.T.
      • Lee J.K.
      • Karimi A.H.
      • Tatekawa H.
      • Hickman L.B.
      • Connerney M.
      • et al.
      A minority of patients with functional seizures have abnormalities on neuroimaging.
      ,
      • Asadi-Pooya A.A.
      • Homayoun M.
      Structural brain abnormalities in patients with psychogenic nonepileptic seizures.
      ]. Alternatively, abnormalities in FS may be reported at higher rates due to framing bias [
      • Kerr W.T.
      • Lee J.K.
      • Karimi A.H.
      • Tatekawa H.
      • Hickman L.B.
      • Connerney M.
      • et al.
      A minority of patients with functional seizures have abnormalities on neuroimaging.
      ]. In particular, the high prevalence of temporal lobe epilepsy and additional dedicated sequences focused on temporal lobe morphology in the “epilepsy protocol” MRI can lead to high detection rates for putative hippocampal sclerosis and atrophy [
      • Kerr W.T.
      • Lee J.K.
      • Karimi A.H.
      • Tatekawa H.
      • Hickman L.B.
      • Connerney M.
      • et al.
      A minority of patients with functional seizures have abnormalities on neuroimaging.
      ]. This analysis could delineate if these radiologically reported mesial temporal lobe changes reflected either subjective interpretation or quantitative morphometric differences.
      We processed all images with FreeSurfer version 6 (Boston, Massachusetts, USA) on two iMacs. We excluded any images with gross errors in registration or that FreeSurfer was unable to process. No manual modification of registrations or segmentations were performed. This lack of manual manipulation was consistent with the Enhanced NeuroImaging Genetics by Meta-Analysis (ENIGMA) mega-analysis recommendations suggesting that manual modification had minimal impact on conclusions despite large time investment [
      • Zugman A.
      • Harrewijn A.
      • Cardinale E.M.
      • Zwiebel H.
      • Freitag G.F.
      • Werwath K.E.
      • et al.
      Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group.
      ]. For all images that FreeSurfer processed, we also used MRIQC to quantify image quality [
      • Esteban O.
      • Birman D.
      • Schaer M.
      • Koyejo O.O.
      • Poldrack R.A.
      • Gorgolewski K.J.
      MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites.
      ]. These quality metrics included full width half max (FWHM) as a measure of blurring, signal-to-noise ratio, and metrics to estimate the magnitude of movement artifacts. We analyzed all metrics reported by FreeSurfer using the Desikan-Killiany Atlas including cortical and subcortical volumes, cortical surface area and thickness, with the exception of those for the optic chiasm [
      • Desikan R.S.
      • Ségonne F.
      • Fischl B.
      • Quinn B.T.
      • Dickerson B.C.
      • Blacker D.
      • et al.
      An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.
      ]. The FSBrain package for R was used for visualization of results [
      • Schaefer T.
      • Ecker C.
      fsbrain: an R package for visualization of structural neuroimaging data.
      ].

      2.3 Statistical analysis

      Linear mixed-effects multivariable regression models evaluated the association of FS with morphological differences on MRI and accounted for the impact of multiple potentially confounding factors [
      • Yu Z.
      • Guindani M.
      • Grieco S.F.
      • Chen L.
      • Holmes T.C.
      • Xu X.
      Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research.
      ]. In a mixed-effect model, the fixed effects that account for patient-specific or other well-sampled factors of high importance that may influence both the average and variability. For example, fixed effects are used to account for the age-associated atrophy and morphological associations of depression. These fixed effects were supplemented by random effects that accounted shared sources of variation (e.g., effects of MRI scanner) analogous to paired, repeated measure, or longitudinal statistics. For example, in multisite studies, differences in MRI scanner and patient population can cause data from the same site to be more related than data from different sites.
      Our approach utilized heterogenous quality MRIs as compared to a homogenous dataset including controlled and selected research quality MRIs only. This heterogeneity of patients and MRI acquisitions was expected to introduce both increased variance in each estimate, as well as potential systematic biases. To adequately account for this heterogeneity of patients and MRIs, we included as many seizure-naïve participants as possible and controlled for MRI-scanner and patient-specific factors. This analysis protocol was designed to mirror the ENIGMA consortium’s mega-analysis approach where we combined data across MRI-scanners instead of across geographical sites [
      • Zugman A.
      • Harrewijn A.
      • Cardinale E.M.
      • Zwiebel H.
      • Freitag G.F.
      • Werwath K.E.
      • et al.
      Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group.
      ,
      • Boedhoe P.S.W.
      • Heymans M.W.
      • Schmaal L.
      • Abe Y.
      • Alonso P.
      • Ameis S.H.
      • et al.
      An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group.
      ]. We performed a separate linear mixed-effects model on each morphometric parameter summarized over regions-of-interest (ROIs) and accounted for multiple testing using Bonferroni–Holm correction for 201 morphometric-ROI pairs [
      • Holm S.
      A simple sequentially rejective multiple test procedure.
      ]. This linear mixed-effects model approach to data harmonization has yielded results highly similar those of other procedures such as batch adjustment (ComBat) [
      • Fortin J.P.
      • Cullen N.
      • Sheline Y.I.
      • Taylor W.D.
      • Aselcioglu I.
      • Cook P.A.
      • et al.
      Harmonization of cortical thickness measurements across scanners and sites.
      ,
      • Radua J.
      • Vieta E.
      • Shinohara R.
      • Kochunov P.
      • Quidé Y.
      • Green M.J.
      • et al.
      Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA.
      ]. We chose this ROI-based approach to reduce multiple testing, as compared to evaluating morphological features at each of more than 100,000 FreeSurfer cortical and subcortical vertices with cluster-based or Gaussian random field statistics. When modeling volumes, surface areas, or thicknesses, we included a fixed effects term to control for total intracranial volume, average surface area within the hemisphere, and average thickness within the hemisphere, respectively [
      • Zugman A.
      • Harrewijn A.
      • Cardinale E.M.
      • Zwiebel H.
      • Freitag G.F.
      • Werwath K.E.
      • et al.
      Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group.
      ].
      We used fixed effects to control for the impact of magnet strength (1.5 Tesla vs 3 Tesla) and random effects to control for MRI scanner model (e.g. Prisma, Skyra, Magnetom, TrioTim). Scanner models with less than 20 MRIs were grouped into an “other” class. Due to the large variety of specific scanners even within the same model and limited statistical power, we did not model the impact of individual scanners or head coils. The research scans were acquired on scanners with the same model and managed by common staff with similar maintenance protocols as the (geographically proximal) clinical scanners. All voxels were isometric and, given limited variability (0.8–1.0 mm3), we did not control for voxel size.
      We used fixed-effects to control for numerous patient-specific factors based on chart review or, for patients with FS or research participants, standardized interview. We evaluated the potential influence of patient age; the square of age; female gender; and the number of prior or current antiseizure medications, antidepressants, or antipsychotics. When birth date and scan date were available, we calculated the exact age rounded to the closest day. Otherwise, we used reported chronological age rounded down to the last year. We also evaluated the influence of diagnoses including depression, treatment-resistant depression, anxiety disorders, OCD, and a miscellaneous category of other psychiatric illnesses (e.g., bipolar and schizophrenia) [
      • Gaynes B.N.
      • Lux L.
      • Gartlehner G.
      • Asher G.
      • Forman-Hoffman V.
      • Green J.
      • et al.
      Defining treatment-resistant depression.
      ]. There were insufficient patients with formal documented diagnoses of post-traumatic stress disorder to control for that reliably (n = 29 across the whole dataset). For patients with FS, we also evaluated the associations with the log of disease duration. We log transformed disease duration due to substantial prior evidence that disease duration was exponentially distributed [
      • Kerr W.T.
      • Zhang X.
      • Hill C.E.
      • Janio E.A.
      • Chau A.M.
      • Braesch C.T.
      • et al.
      Factors associated with delay to video-EEG in dissociative seizures.
      ].

      3. Results

      3.1 Patient information

      The patient availability and selection flowsheet is displayed in Fig. 1. We included 90 of 446 (20%) unique patients with FS who underwent VEM during the analysis period. Of the 446, 106 (24%) patients had MRIs available that met our quality standard, and 90 MRIs were of sufficient quality to be analyzed by FreeSurfer. From the VEM database, only one patient with physiologic seizure-like events had an MRI that was of sufficient quality to be analyzed by FreeSurfer. The PACS search queries identified roughly 2500 potential MRIs, of which 401 (16%) met our criteria for lack of significant structural abnormalities, of which 209 (8%) were sufficient for FreeSurfer. The research databases allowed for the inclusion of 156 epilepsy-naïve patients without any comorbidities, 170 participants with treatment-resistant depression, and 41 participants with OCD and other psychiatric comorbidities. In combination, our database included 90 patients with FS and 576 seizure-naïve controls. Table 1 summarizes the demographics. When we matched patients with FS to patients without FS using a propensity score matching algorithm based on age and sex, the mean age difference of pairs was less than 2 years. Supplementary Table 1 summarizes the distribution of number of MRIs obtained on each scanner by diagnostic group. The reason for MRI in clinical controls was heterogeneous and included (in decreasing order of prevalence) preoperative evaluation of semicircular canal dehiscence; recurrent dizziness; vertigo; syncope; headache; idiopathic intracranial hypertension; transient, subacute, or chronic memory loss; trigeminal neuralgia; acoustic neuroma; staging of extracranial cancer; other neurological symptoms on examination (essential tremor, dysdiadokinesis); rule out multiple sclerosis; spinal cord disease; and concern for intracranial hypotension.
      Figure thumbnail gr1
      Fig. 1STROBE patient availability and selection flow sheet. Abbreviations: Physiologic Seizure-Like Events (PSLE), number of patients (n), University of California Los Angeles (UCLA), Magnetic Resonance Images (MRI), Electroencephalograph (EEG).
      Table 1Demographics and comorbidity by group. The “Not FS” group includes all patients without functional seizures (FS). The “All” groups include all patients with the psychiatric diagnosis, with and without FS. The number of 1.5 T MRIs in patients with FS and without FS was similar (43 vs 55). Abbreviations: interquartile range (IQR), number of patients (N), percent (%), female (F).
      GroupAge (Years)GenderTeslaDepressionAnxiety
      MedianIQR% FN F% 3 TN 3 T%N%N
      FS3424–448375524722201917
      Not FS3222–44613449052139225951
      All Depression3728–4760146952331002451333
      All Anxiety3729–4660418155493310068
      All Other Psych3324–416817842148124010
      OCD3223–414920100412410229
      3 T MRI3122–4363353100568412331055
      1.5 T MRI3926–4669440019121711

      3.2 Morphometric analysis

      MRI quality metrics were lower in patients with FS compared to patients without FS (Supplemental Table 2). Surface Holes reflect poor registration of the cortical surface and were larger in FS compared to depression and other controls, with the exception that research participants with OCD had similar area of Surface Holes. The relative Surface Hole surface area associated with FS in the mixed-effects model is illustrated in Fig. 2A. The three-dimensional FWHM is a measure of blurring of volumetric pixels that can be caused by a variety of sources of noise. The FWHM was significantly higher in patients with FS compared to patients without FS, but the magnitude of this difference was small. This difference was similar to or smaller than the increased FWHM from images obtained at 3 T compared to 1.5 T. Fig. 2B illustrates the uncorrected three-dimensional average of voxel Full Width at Half Maximum (FWHM) calculated by MRIQC for patients with FS, depression, anxiety, and all participants without FS; as well as comparing images obtained with a 1.5 or 3 Tesla MRI.
      Figure thumbnail gr2
      Fig. 2MRI quality metrics. (A) FreeSurfer Surface Holes (square millimeters) represent the surface area of vertexes unable to be clearly registered to a region of interest. Units of the mixed-effects model associated with each condition relative to the average of controls (C) without any psychiatric or seizure conditions. Significant differences (Bonferroni–Holm corrected p < 0.05) indicated by *. (B) MRIQC Average Full Width Half Max (FWHM) is the three-dimensional average of the blurring of volumetric pixels in millimeters, without correction for confounding factors. Not FS indicates all participants without FS (n = 576). The “All” indication reflects with each condition, with and without comorbid FS. Abbreviations: Not significant with p > 0.1 (NS), Functional Seizures (FS), obsessive compulsive disorder (OCD), depression (Dep), anxiety (Anx), other psychiatric conditions (Psy), Tesla (T).
      Fig. 3 illustrates the relative morphometric values for the ROIs with significant associations with FS in the mixed effects multivariable models as compared to seizure-naïve patients with depression, anxiety, OCD, or controls with none of those conditions (Bonferroni–Holm corrected p < 0.05). These models controlled for age, age2, sex, magnetic strength, depression, anxiety, OCD, scanner model, and hemispheric morphometric value. To clarify the unique portion attributed to each condition, these relative values display the residuals of the mixed-effects models, plus the fixed effect term for each condition, with the average of the seizure- and psychiatric-naïve controls set at zero. These violin plots display the mean difference compared to the observed value of each individual data point and a smoothed version of a box plot to illustrate the shape of the distribution of the data. Violin plots allow the reader to evaluate for non-linearity of residuals, the contribution of outliers, and the degree to which individual measurements may identify each condition [
      • Thrun M.C.
      • Gehlert T.
      • Ultsch A.
      Analyzing the fine structure of distributions.
      ].
      Figure thumbnail gr3
      Fig. 3FreeSurfer regions of interest with significant Bonferroni–Holm corrected p-values on mixed-effects regression controlling for confounding factors (p < 0.05). Cortical thickness units in millimeters. Volume units in cubic millimeters. All units set to zero for the average control (C) without seizures or psychiatric conditions. Significant differences indicated by *. All other differences were not significant after Bonferroni–Holm correction (p > 0.05). Abbreviations: Left (L), Right (R), Functional Seizures (FS), obsessive compulsive disorder (OCD).
      Fig. 4 illustrates the effect size of FS on cortical thickness in each region of interest. Supplemental Fig. 1 illustrates the effect size of FS on cortical thickness for the regions of interest with a significant association after Bonferroni–Holm correction.
      Figure thumbnail gr4
      Fig. 4The linear mixed-effects model effect size (mean divided by standard error) of functional seizures (FS) on cortical thickness in each region of interest on a cortical surface map. After Bonferroni–Holm correction, only bilateral superior temporal thinning and left lateral occipital thickening were significant.
      The observed significant associations were as follows. The right superior temporal cortex was 0.071 mm thinner in FS (SE 0.013 mm, uncorrected p < 3 × 10−7, corrected p < 6 × 10−5) with significant confounds of average right hemispheric thickness (uncorrected p < 9 × 10−107) and age (uncorrected p = 0.04). The left superior temporal cortex was 0.053 mm thinner in FS (SE 0.013 mm, uncorrected p < 7 × 10−5, corrected p = 0.014), with significant thickening of 0.035 mm in OCD (uncorrected p = 0.031) and average left hemispheric thickness (p < 9 × 10−110). The left lateral occipital cortex was 0.052 mm thicker in FS (SE 0.012, uncorrected p < 2 × 10−5, corrected p = 0.0035) with significant confound of left hemispheric average thickness (uncorrected p < 5 × 10−76) and 0.019 mm increased thickness in 3 T scanners (uncorrected p = 0.027). The left cerebellum white matter volume was 1085 mm3 larger in FS (SE 259, uncorrected p < 4 × 10−5, corrected p = 0.0065) and 628 mm3 smaller in depression (uncorrected p = 0.00027), with a trend toward 530 mm3 smaller in OCD (uncorrected p = 0.09), with significant confound of 414 mm3 larger volume in women (uncorrected p = 0.04), 796 mm3 larger in 3 Tesla scanners (p < 10−5), and a positive association with left hemispheric volume (p < 4 × 10−22). The surface area of the FreeSurfer Surface Holes was 20 mm2 higher in FS (SE 5, uncorrected p < 9 × 10−5, corrected p = 0.016), 24 mm2 higher in OCD (uncorrected p = 0.0001), 24 mm2 lower in depression (uncorrected p < 5 × 10−15), with significant confound of 11 mm2 lower in women (uncorrected p = 0.0003). There was no significant association between Surface Holes’ surface area and scanner strength (p = 0.46) or total brain surface area (p = 0.94). For patients with FS and mild radiological abnormalities, there was no significant change in MRI quality (Supplemental Fig. 2). When a fixed term was added to account for mild radiologically reported abnormalities in patients with FS (n = 29) in each of these significant areas, the uncorrected p-value for this possible confound was not significant (right superior temporal p = 0.32, left superior temporal p = 0.22, left lateral occipital p = 0.89, left cerebellum p = 0.95).
      After correction for multiple testing, there were no significant differences observed in the amygdala or hippocampus. The left amygdala volume was non-significantly 11 mm3 lower in FS (SE 25, uncorrected p = 0.65) and significantly 75 mm3 smaller in depression (uncorrected p < 9 × 10−6). The right amygdala volume was non-significantly 62 mm3 larger in FS (SE 25, uncorrected p = 0.01, corrected p 1) and significantly 165 mm3 smaller in depression (uncorrected p < 8 × 10−22). The left hippocampus volume was non-significantly 17 mm3 larger in FS (SE 45, uncorrected p = 0.71) and significantly 192 mm3 smaller in depression (p < 4 × 10−10). The right hippocampus volume was non-significantly 13 mm3 larger in FS (SE 48, uncorrected p = 0.77) and significantly 238 mm3 smaller in depression (p < 3 × 10−13).
      When controlling for other psychiatric comorbidities, duration of FS disorder, and medication effects, the fixed effects of the association with FS were no longer significant after Bonferroni–Holm correction (full results in Supplemental Table 2). In that model, the only ROI with a fixed effect of the association with the duration of FS disorder after Bonferroni–Holm correction was the left lateral orbitofrontal thickness (0.0053 mm thinning per log-year, standard error (SE) 0.0013 mm, corrected p = 0.0026; Supplemental Table 3). Additionally, there were significantly more left, right, and total Surface Holes with longer duration of FS disorder (Total Surface Holes 1.85 mm2 more per log-year, SE 0.42 mm2, corrected p = 0.0019).

      4. Discussion

      To our knowledge, this morphometric study examined the largest number of MRIs from patients with FS and comparison subjects with and without other psychiatric disorders. The direct comparison and control for the common comorbidities of depression and anxiety addresses a key limitation of prior work in neuroimaging of FS [
      • Perez D.L.
      • Nicholson T.R.
      • Asadi-Pooya A.A.
      • Bègue I.
      • Butler M.
      • Carson A.J.
      • et al.
      Neuroimaging in functional neurological disorder: state of the field and research agenda.
      ]. The main findings associated with FS included bilateral superior temporal cortical thinning, left lateral occipital cortical thickening, and left cerebellar white matter volume increase. These results suggest that, from clinical-quality MRIs, there were quantitative morphometric features associated with patients with FS that were distinct from those associated with depression, anxiety, OCD, and controls.
      In the context of prior morphometric literature, these associations seemed pathophysiologically plausible. This builds on the growing body of literature that there may be structural associations of FND [
      • Pick S.
      • et al.
      Emotional processing in functional neurological disorder: a review, biopsychosocial model and research agenda.
      ]. However, as suggested by the overlapping violin plots (Fig. 3), these group-level associations likely were not reliable enough to serve as individual-level diagnostic biomarkers of FS. Some findings in the present report consist of between-group differences that, while statistically significant, were smaller in magnitude than effects seen in some studies of epilepsy (e.g., cortical thickness differences of 0.05 mm) [
      • Tatekawa H.
      • Kerr W.T.
      • Savic I.
      • Engel Jr, J.
      • Salamon N.
      Reduced left amygdala volume in patients with dissociative seizures (psychogenic nonepileptic seizures).
      ,
      • Whelan C.D.
      • Altmann A.
      • Botía J.A.
      • Jahanshad N.
      • Hibar D.P.
      • Absil J.
      • et al.
      Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study.
      ,
      • Hatton S.N.
      • Huynh K.H.
      • Bonilha L.
      • Abela E.
      • Alhusaini S.
      • Altmann A.
      • et al.
      White matter abnormalities across different epilepsy syndromes in adults: an ENIGMA-Epilepsy study.
      ,
      • Lariviere S.
      • Rodríguez-Cruces R.
      • Royer J.
      • Caligiuri M.E.
      • Gambardella A.
      • Concha L.
      • et al.
      Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study.
      ], and were much too modest to be applied as clinical biomarkers. This implies the need for further studies, perhaps combining multiple modalities of neuroimaging and other objective data, to develop clinically meaningful objective biomarkers of FS on the individual-level. Thereby, the present morphometric MRI results may contribute in that they have identified brain regions in which to seek potentially larger effects with other imaging techniques, such as diffusion tractography, functional connectivity, neurite morphology, positron emission tomography, and others [
      • Goodman A.M.
      • Allendorfer J.B.
      • Blum A.S.
      • Bolding M.S.
      • Correia S.
      • Ver Hoef L.W.
      • et al.
      White matter and neurite morphology differ in psychogenic nonepileptic seizures.
      ,
      • Foroughi A.A.
      • Nazeri M.
      • Asadi-Pooya A.A.
      Brain connectivity abnormalities in patients with functional (psychogenic nonepileptic) seizures: A systematic review.
      ,
      • Sharma A.A.
      • Goodman A.M.
      • Allendorfer J.B.
      • Philip N.S.
      • Correia S.
      • LaFrance Jr, W.C.
      • et al.
      Regional brain atrophy and aberrant cortical folding relate to anxiety and depression in patients with traumatic brain injury and psychogenic nonepileptic seizures.
      ,
      • Amiri S.
      • Mirbagheri M.M.
      • Asadi-Pooya A.A.
      • Badragheh F.
      • Ajam Zibadi H.
      • Arbabi M.
      Brain functional connectivity in individuals with psychogenic nonepileptic seizures (PNES): An application of graph theory.
      ,
      • Chakraborty A.R.
      • Almeida N.C.
      • Prather K.Y.
      • O'Neal C.M.
      • Wells A.A.
      • Chen S.
      • et al.
      Resting-state functional magnetic resonance imaging with independent component analysis for presurgical seizure onset zone localization: A systematic review and meta-analysis.
      ,
      • Arthuis M.
      • Micoulaud-Franchi J.A.
      • Bartolomei F.
      • McGonigal A.
      • Guedj E.
      Resting cortical PET metabolic changes in psychogenic non-epileptic seizures (PNES).
      ].
      In radiological interpretations of neuroimages from patients with FS, the temporal lobe broadly was the location of the most common abnormalities, including in this dataset, but this may be clouded by framing bias due to the frequency of temporal lobe epilepsy [
      • Kerr W.T.
      • Lee J.K.
      • Karimi A.H.
      • Tatekawa H.
      • Hickman L.B.
      • Connerney M.
      • et al.
      A minority of patients with functional seizures have abnormalities on neuroimaging.
      ,
      • Asadi-Pooya A.A.
      • Homayoun M.
      Structural brain abnormalities in patients with psychogenic nonepileptic seizures.
      ,
      • Bolen R.D.
      • Koontz E.H.
      • Pritchard 3rd, P.B.
      Prevalence and distribution of MRI abnormalities in patients with psychogenic nonepileptic events.
      ,
      • Reuber M.
      • Aybek S.
      • Carson A.
      • Edwards M.J.
      • Goldstein L.H.
      • Hallett M.
      • et al.
      Evidence of brain abnormality in patients with psychogenic nonepileptic seizures.
      ]. While the associations with temporal and lateral occipital changes were seen in one prior study [
      • McSweeney M.
      • Reuber M.
      • Hoggard N.
      • Levita L.
      Cortical thickness and gyrification patterns in patients with psychogenic non-epileptic seizures.
      ], the current quantitative neuroimaging findings were inconsistent both with our previous results and among other studies [
      • Goodman A.M.
      • Allendorfer J.B.
      • Blum A.S.
      • Bolding M.S.
      • Correia S.
      • Ver Hoef L.W.
      • et al.
      White matter and neurite morphology differ in psychogenic nonepileptic seizures.
      ,
      • McSweeney M.
      • Reuber M.
      • Levita L.
      Neuroimaging studies in patients with psychogenic non-epileptic seizures: A systematic meta-review.
      ,
      • Zelinski L.
      • Diez I.
      • Perez D.L.
      • Kotz S.A.
      • Wellmer J.
      • Schlegel U.
      • et al.
      Cortical thickness in default mode network hubs correlates with clinical features of dissociative seizures.
      ,
      • Labate A.
      • Cerasa A.
      • Mula M.
      • Mumoli L.
      • Gioia M.C.
      • Aguglia U.
      • et al.
      Neuroanatomic correlates of psychogenic nonepileptic seizures: a cortical thickness and VBM study.
      ,
      • Lee S.
      • Allendorfer J.B.
      • Gaston T.E.
      • Griffis J.C.
      • Hernando K.A.
      • Knowlton R.C.
      • et al.
      White matter diffusion abnormalities in patients with psychogenic non-epileptic seizures.
      ,
      • Ristic A.J.
      • Daković M.
      • Kerr M.
      • Kovačević M.
      • Parojčić A.
      • Sokić D.
      Cortical thickness, surface area and folding in patients with psychogenic nonepileptic seizures.
      ,
      • Ding J.
      • An D.
      • Liao W.
      • Wu G.
      • Xu Q.
      • Zhou D.
      Abnormal functional connectivity density in psychogenic non-epileptic seizures.
      ,
      • Ding J.R.
      • An D.
      • Liao W.
      • Li J.
      • Wu G.R.
      • Xu Q.
      • et al.
      Altered functional and structural connectivity networks in psychogenic non-epileptic seizures.
      ,
      • Sone D.
      • Sato N.
      • Ota M.
      • Kimura Y.
      • Matsuda H.
      Widely impaired white matter integrity and altered structural brain networks in psychogenic non-epileptic seizures.
      ,
      • Hernando K.A.
      • Szaflarski J.P.
      • Ver Hoef L.W.
      • Lee S.
      • Allendorfer J.B.
      Uncinate fasciculus connectivity in patients with psychogenic nonepileptic seizures: A preliminary diffusion tensor tractography study.
      ]. In specific, we did not replicate the amygdala and hippocampal findings that we reported on a smaller dataset included in this evaluation [
      • Tatekawa H.
      • Kerr W.T.
      • Savic I.
      • Engel Jr, J.
      • Salamon N.
      Reduced left amygdala volume in patients with dissociative seizures (psychogenic nonepileptic seizures).
      ]. This smaller dataset may have been confounded by insufficient control for comorbid depression, as the data here replicated a significant association of amygdala and hippocampal volume loss in depression [
      • Ho T.C.
      • Gutman B.
      • Pozzi E.
      • Grabe H.J.
      • Hosten N.
      • Wittfeld K.
      • et al.
      Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group.
      ]. This heterogeneity of results across studies highlights the heterogeneity of patients with FS, the limited sample sizes, and the need for larger, likely multisite, collaborations including disease-relevant comparison groups [
      • Perez D.L.
      • Nicholson T.R.
      • Asadi-Pooya A.A.
      • Bègue I.
      • Butler M.
      • Carson A.J.
      • et al.
      Neuroimaging in functional neurological disorder: state of the field and research agenda.
      ]. Therefore, while it appears that there are structural correlates of FS, we are cautious in our interpretation of the specific associations.
      Taking into account this caution, the association with bilateral superior temporal cortical thinning supports the psychopathological hypothesis of FS involving reduced awareness or conscious control of one’s actions and emotions, especially in severe depression, schizophrenia, or survivors of significant trauma [
      • Molenberghs P.
      • Brander C.
      • Mattingley J.B.
      • Cunnington R.
      The role of the superior temporal sulcus and the mirror neuron system in imitation.
      ,
      • Liu P.H.
      • Li Y.
      • Zhang A.X.
      • Sun N.
      • Li G.Z.
      • Chen X.
      • et al.
      Brain structural alterations in MDD patients with gastrointestinal symptoms: Evidence from the REST-meta-MDD project.
      ,
      • Zhuo C.
      • et al.
      Differences in functional connectivity density among subtypes of schizophrenic auditory hallucination.
      ,
      • Aguilar E.J.
      • Sanjuan J.
      • García-Martí G.
      • Lull J.J.
      • Robles M.
      MR and genetics in schizophrenia: focus on auditory hallucinations.
      ,
      • Lin X.
      • Zhuo C.
      • Li G.
      • Li J.
      • Gao X.
      • Chen C.
      • Jiang D.
      Functional brain alterations in auditory hallucination subtypes in individuals with auditory hallucinations without the diagnosis of specific neurological diseases and mental disorders at the current stage.
      ,
      • Zhao X.
      • Xi Q.
      • Wang P.
      • Li C.
      • He H.
      Altered activity and functional connectivity of superior temporal gyri in anxiety disorders: a functional magnetic resonance imaging study.
      ,
      • Shi L.J.
      • Zhou H.Y.
      • Wang Y.
      • Shen Y.M.
      • Fang Y.M.
      • He Y.Q.
      • et al.
      Altered empathy-related resting-state functional connectivity in adolescents with early-onset schizophrenia and autism spectrum disorders.
      ,
      • Mackes N.K.
      • Golm D.
      • O'Daly O.G.
      • Sarkar S.
      • Sonuga-Barke E.J.S.
      • Fairchild G.
      • et al.
      Tracking emotions in the brain - Revisiting the Empathic Accuracy Task.
      ,
      • Mitchell R.L.
      • Elliott R.
      • Barry M.
      • Cruttenden A.
      • Woodruff P.W.
      Neural response to emotional prosody in schizophrenia and in bipolar affective disorder.
      ,
      • Liu Y.
      • Liu G.
      • Wei D.
      • Li Q.
      • Yuan G.
      • Wu S.
      • et al.
      Effects of musical tempo on musicians' and non-musicians' emotional experience when listening to music.
      ,
      • Lu S.
      • Gao W.
      • Wei Z.
      • Wang D.
      • Hu S.
      • Huang M.
      • et al.
      Intrinsic brain abnormalities in young healthy adults with childhood trauma: A resting-state functional magnetic resonance imaging study of regional homogeneity and functional connectivity.
      ,
      • Mollica R.F.
      • Lyoo I.K.
      • Chernoff M.C.
      • Bui H.X.
      • Lavelle J.
      • Yoon S.J.
      • et al.
      Brain structural abnormalities and mental health sequelae in South Vietnamese ex-political detainees who survived traumatic head injury and torture.
      ,
      • Liao J.
      • Yan H.
      • Liu Q.
      • Yan J.
      • Zhang L.
      • Jiang S.
      • et al.
      Reduced paralimbic system gray matter volume in schizophrenia: Correlations with clinical variables, symptomatology and cognitive function.
      ,
      • Anderson J.E.
      • Wible C.G.
      • McCarley R.W.
      • Jakab M.
      • Kasai K.
      • Shenton M.E.
      An MRI study of temporal lobe abnormalities and negative symptoms in chronic schizophrenia.
      ,
      • Narr K.L.
      • Hageman N.
      • Woods R.P.
      • Hamilton L.S.
      • Clark K.
      • Phillips O.
      • et al.
      Mean diffusivity: a biomarker for CSF-related disease and genetic liability effects in schizophrenia.
      ,
      • Wu C.
      • Zheng H.
      • Wu H.
      • Tang Y.
      • Li F.
      • Wang D.
      Age-related brain morphological alteration of medication-naive boys with high functioning autism.
      ,
      • Geuze E.
      • Westenberg H.G.
      • Heinecke A.
      • de Kloet C.S.
      • Goebel R.
      • Vermetten E.
      Thinner prefrontal cortex in veterans with posttraumatic stress disorder.
      ,
      • Kambe T.
      • Yasuda A.
      • Kinoshita S.
      • Shigeta M.
      • Kinoshita T.
      Severity of depressive symptoms and volume of superior temporal gyrus in people who visit a memory clinic unaccompanied.
      ,
      • Matsumoto H.
      • Simmons A.
      • Williams S.
      • Hadjulis M.
      • Pipe R.
      • Murray R.
      • et al.
      Superior temporal gyrus abnormalities in early-onset schizophrenia: similarities and differences with adult-onset schizophrenia.
      ]. This concept of impaired self-agency has been described well in functional movement disorders [
      • Maurer C.W.
      • LaFaver K.
      • Ameli R.
      • Epstein S.A.
      • Hallett M.
      • Horovitz S.G.
      Impaired self-agency in functional movement disorders: A resting-state fMRI study.
      ,
      • Edwards M.J.
      • Moretto G.
      • Schwingenschuh P.
      • Katschnig P.
      • Bhatia K.P.
      • Haggard P.
      Abnormal sense of intention preceding voluntary movement in patients with psychogenic tremor.
      ,
      • Parees I.
      • Brown H.
      • Nuruki A.
      • Adams R.A.
      • Davare M.
      • Bhatia K.P.
      • et al.
      Loss of sensory attenuation in patients with functional (psychogenic) movement disorders.
      ,
      • Kranick S.M.
      • Moore J.W.
      • Yusuf N.
      • Martinez V.T.
      • LaFaver K.
      • Edwards M.J.
      • et al.
      Action-effect binding is decreased in motor conversion disorder: implications for sense of agency.
      ]. In a study of motor imitation and mirror neurons, the superior temporal sulcus was involved in differentiation of one’s own movements as compared to others [
      • Molenberghs P.
      • Brander C.
      • Mattingley J.B.
      • Cunnington R.
      The role of the superior temporal sulcus and the mirror neuron system in imitation.
      ]. While the difference between self and others can include other cortical areas [
      • Beeney J.E.
      • Hallquist M.N.
      • Ellison W.D.
      • Levy K.N.
      Self-other disturbance in borderline personality disorder: Neural, self-report, and performance-based evidence.
      ,
      • Ohata R.
      • Asai T.
      • Kadota H.
      • Shigemasu H.
      • Ogawa K.
      • Imamizu H.
      Sense of agency beyond sensorimotor process: decoding self-other action attribution in the human brain.
      ,
      • Ebisch S.J.
      • Mantini D.
      • Northoff G.
      • Salone A.
      • De Berardis D.
      • Ferri F.
      • et al.
      Altered brain long-range functional interactions underlying the link between aberrant self-experience and self-other relationship in first-episode schizophrenia.
      ], this concept of decreased awareness of one’s own movements supports the notion that FS are movements that the patient experiences as involuntary, or not consciously controlled. If FS are thought of as positive symptoms of psychological distress (i.e., marked by the presence of a behavior or experience rather than the absence of one), other positive psychiatric symptoms have been associated with bilateral superior temporal thinning including functional gastrointestinal symptoms seen in patients with depression [
      • Liu P.H.
      • Li Y.
      • Zhang A.X.
      • Sun N.
      • Li G.Z.
      • Chen X.
      • et al.
      Brain structural alterations in MDD patients with gastrointestinal symptoms: Evidence from the REST-meta-MDD project.
      ], as well as auditory hallucinations and other positive psychotic symptoms in schizophrenia [
      • Zhuo C.
      • et al.
      Differences in functional connectivity density among subtypes of schizophrenic auditory hallucination.
      ,
      • Aguilar E.J.
      • Sanjuan J.
      • García-Martí G.
      • Lull J.J.
      • Robles M.
      MR and genetics in schizophrenia: focus on auditory hallucinations.
      ,
      • Lin X.
      • Zhuo C.
      • Li G.
      • Li J.
      • Gao X.
      • Chen C.
      • Jiang D.
      Functional brain alterations in auditory hallucination subtypes in individuals with auditory hallucinations without the diagnosis of specific neurological diseases and mental disorders at the current stage.
      ]. However, we did not formally evaluate this by grouping patients with FS based on ictal behavior subtype based on negative (e.g., akinetic) or positive (e.g., hypermotor) symptoms [
      • Asadi-Pooya A.A.
      Semiological classification of psychogenic nonepileptic seizures: A systematic review and a new proposal.
      ].
      Additionally, reduced awareness of one’s own emotions has been seen in patients with FS [
      • Jalilianhasanpour R.
      • Williams B.
      • Gilman I.
      • Burke M.J.
      • Glass S.
      • Fricchione G.L.
      • et al.
      Resilience linked to personality dimensions, alexithymia and affective symptoms in motor functional neurological disorders.
      ,
      • Kaplan M.J.
      • Dwivedi A.K.
      • Privitera M.D.
      • Isaacs K.
      • Hughes C.
      • Bowman M.
      Comparisons of childhood trauma, alexithymia, and defensive styles in patients with psychogenic non-epileptic seizures vs. epilepsy: Implications for the etiology of conversion disorder.
      ,
      • Myers L.
      • Fleming M.
      • Lancman M.
      • Perrine K.
      • Lancman M.
      Stress coping strategies in patients with psychogenic non-epileptic seizures and how they relate to trauma symptoms, alexithymia, anger and mood.
      ,
      • Tojek T.M.
      • Lumley M.
      • Barkley G.
      • Mahr G.
      • Thomas A.
      Stress and other psychosocial characteristics of patients with psychogenic nonepileptic seizures.
      ]. Similarly, patients with bilateral thinning in superior temporal cortex had reduced cognitive awareness of empathy and emotional content of language and musical stimuli [
      • Zhao X.
      • Xi Q.
      • Wang P.
      • Li C.
      • He H.
      Altered activity and functional connectivity of superior temporal gyri in anxiety disorders: a functional magnetic resonance imaging study.
      ,
      • Shi L.J.
      • Zhou H.Y.
      • Wang Y.
      • Shen Y.M.
      • Fang Y.M.
      • He Y.Q.
      • et al.
      Altered empathy-related resting-state functional connectivity in adolescents with early-onset schizophrenia and autism spectrum disorders.
      ,
      • Mackes N.K.
      • Golm D.
      • O'Daly O.G.
      • Sarkar S.
      • Sonuga-Barke E.J.S.
      • Fairchild G.
      • et al.
      Tracking emotions in the brain - Revisiting the Empathic Accuracy Task.
      ,
      • Mitchell R.L.
      • Elliott R.
      • Barry M.
      • Cruttenden A.
      • Woodruff P.W.
      Neural response to emotional prosody in schizophrenia and in bipolar affective disorder.
      ,
      • Liu Y.
      • Liu G.
      • Wei D.
      • Li Q.
      • Yuan G.
      • Wu S.
      • et al.
      Effects of musical tempo on musicians' and non-musicians' emotional experience when listening to music.
      ]. More broadly, superior temporal cortical thinning has been a marker of prior trauma as well as increased severity of psychiatric symptoms in depression, schizophrenia, bipolar disorder, PTSD, and high functioning autism [
      • Lu S.
      • Gao W.
      • Wei Z.
      • Wang D.
      • Hu S.
      • Huang M.
      • et al.
      Intrinsic brain abnormalities in young healthy adults with childhood trauma: A resting-state functional magnetic resonance imaging study of regional homogeneity and functional connectivity.
      ,
      • Mollica R.F.
      • Lyoo I.K.
      • Chernoff M.C.
      • Bui H.X.
      • Lavelle J.
      • Yoon S.J.
      • et al.
      Brain structural abnormalities and mental health sequelae in South Vietnamese ex-political detainees who survived traumatic head injury and torture.
      ,
      • Liao J.
      • Yan H.
      • Liu Q.
      • Yan J.
      • Zhang L.
      • Jiang S.
      • et al.
      Reduced paralimbic system gray matter volume in schizophrenia: Correlations with clinical variables, symptomatology and cognitive function.
      ,
      • Anderson J.E.
      • Wible C.G.
      • McCarley R.W.
      • Jakab M.
      • Kasai K.
      • Shenton M.E.
      An MRI study of temporal lobe abnormalities and negative symptoms in chronic schizophrenia.
      ,
      • Narr K.L.
      • Hageman N.
      • Woods R.P.
      • Hamilton L.S.
      • Clark K.
      • Phillips O.
      • et al.
      Mean diffusivity: a biomarker for CSF-related disease and genetic liability effects in schizophrenia.
      ,
      • Wu C.
      • Zheng H.
      • Wu H.
      • Tang Y.
      • Li F.
      • Wang D.
      Age-related brain morphological alteration of medication-naive boys with high functioning autism.
      ,
      • Geuze E.
      • Westenberg H.G.
      • Heinecke A.
      • de Kloet C.S.
      • Goebel R.
      • Vermetten E.
      Thinner prefrontal cortex in veterans with posttraumatic stress disorder.
      ,
      • Kambe T.
      • Yasuda A.
      • Kinoshita S.
      • Shigeta M.
      • Kinoshita T.
      Severity of depressive symptoms and volume of superior temporal gyrus in people who visit a memory clinic unaccompanied.
      ,
      • Matsumoto H.
      • Simmons A.
      • Williams S.
      • Hadjulis M.
      • Pipe R.
      • Murray R.
      • et al.
      Superior temporal gyrus abnormalities in early-onset schizophrenia: similarities and differences with adult-onset schizophrenia.
      ]. While we included an explicit comparison to patients with treatment-resistant depression with active symptoms, these associations emphasize the need for direct comparisons of patients with FS to patients with schizophrenia, bipolar disorder, high-functioning autism, and significant trauma.
      In previous studies, suicidal ideation and attempts also were associated with this area as well as lateral occipital cortical thickening that we also observed [
      • Chen C.F.
      • Chen W.N.
      • Zhang B.
      Functional alterations of the suicidal brain: a coordinate-based meta-analysis of functional imaging studies.
      ,
      • Kang S.G.
      • Cho S.E.
      • Na K.S.
      • Lee J.S.
      • Joo S.W.
      • Cho S.J.
      • et al.
      Differences in brain surface area and cortical volume between suicide attempters and non-attempters with major depressive disorder.
      ]. This also may correspond to Yakovlevian torque of increased occipital asymmetry in patients with depression, bipolar, and schizophrenia; although we did not compute asymmetry indices [
      • Kong X.Z.
      • Mathias S.R.
      • Guadalupe T.
      • ENIGMA Laterality Working Group
      • Glahn D.C.
      • Franke B.
      • et al.
      Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium.
      ,
      • Maller J.J.
      • Anderson R.J.
      • Thomson R.H.
      • Daskalakis Z.J.
      • Rosenfeld J.V.
      • Fitzgerald P.B.
      Occipital bending in schizophrenia.
      ,
      • Maller J.J.
      • Anderson R.
      • Thomson R.H.
      • Rosenfeld J.V.
      • Daskalakis Z.J.
      • Fitzgerald P.B.
      Occipital bending (Yakovlevian torque) in bipolar depression.
      ,
      • Maller J.J.
      • Thomson R.H.
      • Rosenfeld J.V.
      • Anderson R.
      • Daskalakis Z.J.
      • Fitzgerald P.B.
      Occipital bending in depression.
      ]. This combination of superior temporal and lateral occipital changes has rarely been reported in the literature, but has been reported in depression associated with subcortical vascular mild cognitive impairment [
      • Wang J.
      • Lyu H.
      • Chen J.
      • Lin S.
      • Zheng H.
      • Li J.
      • et al.
      Cortical alterations are associated with depression in subcortical vascular mild cognitive impairment revealed by surface-based morphometry.
      ]. In this study, we did not observe lateral occipital cortical thickness changes associated with depression.

      4.1 Limitations

      The purpose of this study was to evaluate if structural correlates of FS could be identified on clinically obtained neuroimages. Accordingly, there were inherent limitations in the quality of clinically obtained images and selection bias. While we aimed to include the full diversity of patients with FS, only 20% (90/446) met our MRI quality criteria. Among other potential selection biases, these patients may have been selected for MRI because their FS were more epilepsy-like FS, thereby warranting a sufficient quality MRI. Additionally, health insurance had to approve an MRI at a Comprehensive Epilepsy Center, as compared to a satellite, which may select for patients with more comprehensive health insurance coverage. While we attempted to exclude all controls that had FS or other FNDs, the reason for MRI rarely indicated potential neurological symptoms without other explanation that could be functional (e.g., rule out multiple sclerosis, recurrent dizziness).
      Further, the 20% who were included had significantly lower MRI quality metrics than the comparison populations whose images also were obtained in a clinical setting, including but not limited to three-dimensional FWHM (Fig. 3); each MRIQC type of signal-to-noise ratio metric except coefficient of joint variation (p = 0.19), information theory measures, and voxel-based morphometric measures [
      • Esteban O.
      • Birman D.
      • Schaer M.
      • Koyejo O.O.
      • Poldrack R.A.
      • Gorgolewski K.J.
      MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites.
      ]. Similar to the finding that MRI quality was lower in patients with attention deficit hyperactivity disorder (ADHD) [
      • Colby J.B.
      • Rudie J.D.
      • Brown J.A.
      • Douglas P.K.
      • Cohen M.S.
      • Shehzad Z.
      Insights into multimodal imaging classification of ADHD.
      ], there may be inherent features of patients with FS that contribute to lower MRI quality. This can include pain and other physical discomfort from lying supine in the context of common comorbidities of low back and other chronic pain [
      • Lenio S.
      • Kerr W.T.
      • Watson M.
      • Baker S.
      • Bush C.
      • Rajic A.
      • et al.
      Validation of a predictive calculator to distinguish between patients presenting with dissociative versus epileptic seizures.
      ,
      • Kerr W.T.
      • Janio E.A.
      • Braesch C.T.
      • Le J.M.
      • Hori J.M.
      • Patel A.B.
      • et al.
      Identifying psychogenic seizures through comorbidities and medication history.
      ], motion artifact from claustrophobia and other physical manifestations of anxiety, or increased registration errors incurred by mild neuroimaging abnormalities allowed for patients with FS but led to exclusion of seizure-naïve controls. However, our significant findings in FS were not reproduced in the research participants with OCD that had similar quality measured by Surface Holes in FreeSurfer (Fig. 2). Research participants with OCD were included to enable comparison of the image quality of the clinical MRIs obtained retrospectively in our patients with FS to MRIs acquired prospectively at a higher quality research standard. As a group, patients with OCD tend to produce relatively few motion and other patient-related artifacts, rending them apt for such comparisons [
      • Parkes L.
      • Fulcher B.
      • Yücel M.
      • Fornito A.
      An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI.
      ]. When comparing MRI morphometric findings between our FS and OCD cohorts outside of quality control, the reader should bear the aforementioned between-group differences in acquisition and study design in mind. Additionally, exclusion of the worst quality images using a criterion of average 3-directional Full-Width-Half-Maximum (FWHM) greater than 5 did not substantially change our results (analysis not shown). However, lower level degradations in quality may, in part, account for our observed differences. Despite these caveats, one of our goals was to evaluate if objective structural associations were visible on clinical quality MRIs, therefore utilization of typical clinical data was necessary.
      In addition to these considerations, we highlight that the differences that we observed represent cross-sectional associations with FS and provide limited evidence regarding causation. Additionally, the absolute magnitude of the cortical thickness differences was small relative to voxel size. While these levels of precision were possible due to averaging across an ROI and were greater than re-measurement studies using FreeSurfer [
      • Kharabian Masouleh S.
      • Eickhoff S.B.
      • Zeighami Y.
      • Lewis L.B.
      • Dahnke R.
      • Gaser C.
      • et al.
      Influence of processing pipeline on cortical thickness measurement.
      ], these differences are best interpreted as population-level average differences as compared to individual-level changes that may be diagnostic in isolation. Due to the complexity of accounting for developmental changes in the MRI, our sample also includes adult patients and excluded patients younger than 18, therefore our results may not generalize to pediatric FS. Lastly, the characteristics of patients with FS diagnosed using VEM at a Comprehensive Epilepsy Center may differ from those diagnosed at other centers or without VEM [
      • Dickinson P.
      • Looper K.J.
      Psychogenic nonepileptic seizures: a current overview.
      ,
      • Chen D.K.
      • Sharma E.
      • LaFrance Jr., W.C.
      Psychogenic non-epileptic seizures.
      ,
      • Hingray C.
      • El-Hage W.
      • Duncan R.
      • Gigineishvili D.
      • Kanemoto K.
      • LaFrance Jr, W.C.
      • et al.
      Access to diagnostic and therapeutic facilities for psychogenic nonepileptic seizures: An international survey by the ILAE PNES Task Force-2nd Revision.
      ].

      4.2 Future directions

      While this dataset included more rigorous comparison to relevant comparison populations for FS including depression, anxiety, and OCD; increased sampling of other relevant populations is necessary to identify the extent to which our findings are specific to FS (e.g., bipolar disorder and schizophrenia). In addition, the psychiatric information we had was not confirmed by formal psychiatric assessment, which has been associated with a 44% rate of misdiagnosis [
      • AlSalem M.
      • AlHarbi M.A.
      • Badeghiesh A.
      • Tourian L.
      Accuracy of initial psychiatric diagnoses given by nonpsychiatric physicians: A retrospective chart review.
      ]. This level of misdiagnosis may vary between seizure-naïve controls and patients with FS. There also was insufficient power to reliably account for the influence of psychoactive medications including antidepressants, antipsychotics, benzodiazepines, and antiseizure medications, all of which have been associated with morphological changes on MRI [
      • Han K.M.
      • Kim D.
      • Sim Y.
      • Kang J.
      • Kim A.
      • Won E.
      • et al.
      Alterations in the brainstem volume of patients with major depressive disorder and their relationship with antidepressant treatment.
      ,
      • Espinoza Oyarce D.A.
      • Burns R.
      • Butterworth P.
      • Cherbuin N.
      Volumetric brain differences in clinical depression in association with anxiety: a systematic review with meta-analysis.
      ,
      • Hu N.
      • Sun H.
      • Fu G.
      • Zhang W.
      • Xiao Y.
      • Zhang L.
      • et al.
      Anatomic abnormalities of hippocampal subfields in never-treated and antipsychotic-treated patients with long-term schizophrenia.
      ,
      • Pardoe H.R.
      • Berg A.T.
      • Jackson G.D.
      Sodium valproate use is associated with reduced parietal lobe thickness and brain volume.
      ]. Addressing these limitations would require large scale prospective collection of research quality MRIs and formal psychiatric interviews in patients with FS, MRI-negative epilepsy, epilepsy with similar abnormalities to those seen in FS, depression, anxiety, panic disorder, PTSD, bipolar, schizophrenia, and other seizure- and psychiatrically naïve controls [
      • Perez D.L.
      • Nicholson T.R.
      • Asadi-Pooya A.A.
      • Bègue I.
      • Butler M.
      • Carson A.J.
      • et al.
      Neuroimaging in functional neurological disorder: state of the field and research agenda.
      ]. While the quality of our data matched what would be available during an outpatient neurology evaluation, this limited quantity of quality comparison data from seizure-naïve neuropsychiatrically similar controls and patients with epilepsy has been a common limitation of prior morphometric work in FS that the field is working to address [
      • Perez D.L.
      • Nicholson T.R.
      • Asadi-Pooya A.A.
      • Bègue I.
      • Butler M.
      • Carson A.J.
      • et al.
      Neuroimaging in functional neurological disorder: state of the field and research agenda.
      ].

      5. Conclusion

      These MRI morphometric differences associated with FS provide further evidence that there are quantitative structural correlates of FS. The superior temporal cortical thinning appeared mechanistically plausible as a correlate of dissociation from conscious awareness of one’s emotions, physical sensations, and physical actions, as has been seen in seizure-naïve participants, schizophrenia, and bipolar disorder. Further detailed comparison with epilepsy and other seizure-naïve populations is needed to evaluate if these findings were unique to FS or are markers of other comorbidities.

      Acknowledgements

      We thank Ronald Ly, Benjamin S. Wade, Cole Anderson, Ashish Shahib; and Behzad S. Khorashad for providing access to MRI and confounding information from research subjects; and for assistance with visualization of results. This work was supported by National Institutes of Health [grant numbers: R25NS065723 , R25NS089450 , U01MH110008 , R01MH085900 , R01NS033310 , P20NS080181 , T32GM08042 , T90DA022768 , R90DA022768 , R90DA023422 , U24NS107158 ], the Muriel Harris Chair of Geriatric Psychiatry , and the William M. Keck Foundation .

      Conflicts of Interest & Ethical Publication

      Drs. Engel, Stern, Kerr, Espinoza, Salamon, Beimer, Stacey, and Kerr have clinical responsibilities that include the diagnosis and treatment of patients with FS. Drs. Engel, Stern, and Kerr have received speaking fees and honoraria for articles on this topic. The remaining authors have no declared conflicts of interest. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

      Appendix A. Supplementary data

      The following are the Supplementary data to this article:

      References

        • Dickinson P.
        • Looper K.J.
        Psychogenic nonepileptic seizures: a current overview.
        Epilepsia. 2012; 53: 1679-1689
        • Kerr W.T.
        • Stern J.M.
        We need a functioning name for PNES: Consider dissociative seizures.
        Epilepsy Behav. 2020; 105: 107002
        • Tolchin B.
        • Perez D.L.
        • Szaflarski J.P.
        • Baslet G.
        • Doss J.
        • Buchhalter J.
        • et al.
        What's in a name?.
        Epilepsy Behav. 2020; 112: 107364
        • Asadi-Pooya A.A.
        • Brigo F.
        • Mildon B.
        • Nicholson T.R.
        Terminology for psychogenic nonepileptic seizures: Making the case for “functional seizures”.
        Epilepsy Behav. 2020; 104: 106895
        • Kerr W.T.
        • Janio E.A.
        • Chau A.M.
        • Braesch C.T.
        • Le J.M.
        • Hori J.M.
        • et al.
        Objective score from initial interview identifies patients with probable dissociative seizures.
        Epilepsy Behav. 2020; 2020: 107525
        • Kerr W.T.
        • Zhang X.
        • Janio E.A.
        • Karimi A.H.
        • Allas C.H.
        • Dubey I.
        • et al.
        Reliability of additional reported seizure manifestations to identify dissociative seizures.
        Epilepsy Behav. 2020; 115: 107696
        • Kerr W.T.
        • Chau A.M.
        • Janio E.A.
        • Braesch C.T.
        • Le J.M.
        • Hori J.M.
        • et al.
        Reliability of reported peri-ictal behavior to identify psychogenic nonepileptic seizures.
        Seizure. 2019; 67: 45-51
        • Tatum W.O.
        • Hirsch L.J.
        • Gelfand M.A.
        • Acton E.K.
        • LaFrance Jr, W.C.
        • Duckrow R.B.
        • et al.
        Assessment of the predictive value of outpatient smartphone videos for diagnosis of epileptic seizures.
        JAMA Neurol. 2020; 77: 593-600
        • Birca V.
        • Keezer M.R.
        • Chamelian L.
        • Lortie A.
        • Nguyen D.K.
        Recognition of psychogenic versus epileptic seizures based on videos.
        Can J Neurol Sci. 2021; : 1-9
        • Kerr W.T.
        • Janio E.A.
        • Le J.M.
        • Hori J.M.
        • Patel A.B.
        • Gallardo N.L.
        • et al.
        Diagnostic delay in psychogenic seizures and the association with anti-seizure medication trials.
        Seizure. 2016; 40: 123-126
        • Reuber M.
        • Fernández G.
        • Bauer J.
        • Helmstaedter C.
        • Elger C.E.
        Diagnostic delay in psychogenic nonepileptic seizures.
        Neurology. 2002; 58: 493-495
        • Kerr W.T.
        • Zhang X.
        • Hill C.E.
        • Janio E.A.
        • Chau A.M.
        • Braesch C.T.
        • et al.
        Factors associated with delay to video-EEG in dissociative seizures.
        Seizure. 2021; 86: 155-160
        • Goldstein L.H.
        • Robinson E.J.
        • Mellers J.D.C.
        • Stone J.
        • Carson A.
        • Chalder T.
        • et al.
        Psychological and demographic characteristics of 368 patients with dissociative seizures: data from the CODES cohort.
        Psychol Med. 2020; 51: 1-13
        • Stephen C.D.
        • Fung V.
        • Lungu C.I.
        • Espay A.J.
        Assessment of emergency department and inpatient use and costs in adult and pediatric functional neurological disorders.
        JAMA Neurol. 2020; 78: 88-101
        • Libbon R.
        • Gadbaw J.
        • Watson M.
        • Rothberg B.
        • Sillau S.
        • Heru A.
        • et al.
        The feasibility of a multidisciplinary group therapy clinic for the treatment of nonepileptic seizures.
        Epilepsy Behav. 2019; 98: 117-123
        • Seneviratne U.
        • Low Z.M.
        • Low Z.X.
        • Hehir A.
        • Paramaswaran S.
        • Foong M.
        • et al.
        Medical health care utilization cost of patients presenting with psychogenic nonepileptic seizures.
        Epilepsia. 2019; 60: 349-357
        • Perez D.L.
        • LaFrance W.C.
        Nonepileptic seizures: an updated review.
        CNS Spectr. 2016; 21: 239-246
        • Ahmedani B.K.
        • Osborne J.
        • Nerenz D.R.
        • Haque S.
        • Pietrantoni L.
        • Mahone D.
        • et al.
        Diagnosis, costs, and utilization for psychogenic non-epileptic seizures in a US health care setting.
        Psychosomatics. 2013; 54: 28-34
        • Pick S.
        • Anderson D.G.
        • Asadi-Pooya A.A.
        • Aybek S.
        • Baslet G.
        • Bloem B.R.
        • et al.
        Outcome measurement in functional neurological disorder: a systematic review and recommendations.
        J Neurol Neurosurg Psychiatry. 2020; 91: 638-649
        • Goldstein L.H.
        • Robinson E.J.
        • Mellers J.D.C.
        • Stone J.
        • Carson A.
        • Reuber M.
        • et al.
        Cognitive behavioural therapy for adults with dissociative seizures (CODES): a pragmatic, multicentre, randomised controlled trial.
        Lancet Psychiatry. 2020; 7: 491-505
        • Boesten N.
        • Myers L.
        • Wijnen B.
        Quality of life and psychological dysfunction in traumatized and nontraumatized patients with psychogenic nonepileptic seizures (PNES).
        Epilepsy Behav. 2019; 92: 341-344
        • Salinsky M.
        • Rutecki P.
        • Parko K.
        • Goy E.
        • Storzbach D.
        • Markwardt S.
        • et al.
        Health-related quality of life in Veterans with epileptic and psychogenic nonepileptic seizures.
        Epilepsy Behav. 2019; 94: 72-77
        • Rawlings G.H.
        • Brown I.
        • Reuber M.
        Predictors of health-related quality of life in patients with epilepsy and psychogenic nonepileptic seizures.
        Epilepsy Behav. 2017; 68: 153-158
        • Kerr W.T.
        • Zhang X.
        • Hill C.E.
        • Janio E.A.
        • Chau A.M.
        • Braesch C.T.
        • et al.
        Epilepsy, dissociative seizures, and mixed: Associations with time to video-EEG.
        Seizure. 2021; 86: 116-122
        • Walczak T.S.
        • Papacostas S.
        • Williams D.T.
        • Scheuer M.L.
        • Lebowitz N.
        • Notarfrancesco A.
        Outcome after diagnosis of psychogenic nonepileptic seizures.
        Epilepsia. 1995; 36: 1131-1137
        • Lenio S.
        • Kerr W.T.
        • Watson M.
        • Baker S.
        • Bush C.
        • Rajic A.
        • et al.
        Validation of a predictive calculator to distinguish between patients presenting with dissociative versus epileptic seizures.
        Epilepsy Behav. 2021; 116107767
        • Trainor D.
        • Foster E.
        • Rychkova M.
        • Lloyd M.
        • Leong M.
        • Wang A.D.
        • et al.
        Development and validation of a screening questionnaire for psychogenic nonepileptic seizures.
        Epilepsy Behav. 2020; 112107482
        • Janocko N.J.
        • Jing J.
        • Fan Z.
        • Teagarden D.L.
        • Villarreal H.K.
        • Morton M.L.
        • et al.
        DDESVSFS: A simple, rapid and comprehensive screening tool for the differential diagnosis of epileptic seizures Vs functional seizures.
        Epilepsy Res. 2021; 171106563
        • Wardrope A.
        • Jamnadas-Khoda J.
        • Broadhurst M.
        • Grünewald R.A.
        • Heaton T.J.
        • Howell S.J.
        • et al.
        Machine learning as a diagnostic decision aid for patients with transient loss of consciousness.
        Neurol Clin Pract. 2019; 10: 96-105
        • Chen M.
        • Jamnadas-Khoda J.
        • Broadhurst M.
        • Wall M.
        • Grünewald R.
        • Howell S.J.L.
        • Koepp M.
        • et al.
        Value of witness observations in the differential diagnosis of transient loss of consciousness.
        Neurology. 2019; 92: e895-e904
        • Sun K.
        • Ren Z.
        • Yang D.
        • Wang X.
        • Yu T.
        • Ni D.
        • et al.
        Voxel-based morphometric MRI post-processing and PET/MRI co-registration reveal subtle abnormalities in cingulate epilepsy.
        Epilepsy Res. 2021; 171106568
        • Kerr W.T.
        • Lee J.K.
        • Karimi A.H.
        • Tatekawa H.
        • Hickman L.B.
        • Connerney M.
        • et al.
        A minority of patients with functional seizures have abnormalities on neuroimaging.
        J Neurol Sci. 2021; 427117548
        • Perez D.L.
        • Nicholson T.R.
        • Asadi-Pooya A.A.
        • Bègue I.
        • Butler M.
        • Carson A.J.
        • et al.
        Neuroimaging in functional neurological disorder: state of the field and research agenda.
        Neuroimage Clin. 2021; 30102623
        • Tatekawa H.
        • Kerr W.T.
        • Savic I.
        • Engel Jr, J.
        • Salamon N.
        Reduced left amygdala volume in patients with dissociative seizures (psychogenic nonepileptic seizures).
        Seizure. 2020; 75: 43-48
        • Goodman A.M.
        • Allendorfer J.B.
        • Blum A.S.
        • Bolding M.S.
        • Correia S.
        • Ver Hoef L.W.
        • et al.
        White matter and neurite morphology differ in psychogenic nonepileptic seizures.
        Ann Clin Transl Neurol. 2020; 7: 1973-1984
        • Asadi-Pooya A.A.
        • Homayoun M.
        Structural brain abnormalities in patients with psychogenic nonepileptic seizures.
        Neurol Sci. 2020; 41: 555-559
        • McSweeney M.
        • Reuber M.
        • Levita L.
        Neuroimaging studies in patients with psychogenic non-epileptic seizures: A systematic meta-review.
        Neuroimage Clin. 2017; 16: 210-221
        • Bolen R.D.
        • Koontz E.H.
        • Pritchard 3rd, P.B.
        Prevalence and distribution of MRI abnormalities in patients with psychogenic nonepileptic events.
        Epilepsy Behav. 2016; 59: 73-76
        • Johnstone B.
        • Velakoulis D.
        • Yuan C.Y.
        • Ang A.
        • Steward C.
        • Desmond P.
        • et al.
        Early childhood trauma and hippocampal volumes in patients with epileptic and psychogenic seizures.
        Epilepsy Behav. 2016; 64: 180-185
        • van der Kruijs S.J.
        • Bodde N.M.
        • Vaessen M.J.
        • Lazeron R.H.
        • Vonck K.
        • Boon P.
        • et al.
        Functional connectivity of dissociation in patients with psychogenic non-epileptic seizures.
        J Neurol Neurosurg Psychiatry. 2012; 83: 239-247
        • Foroughi A.A.
        • Nazeri M.
        • Asadi-Pooya A.A.
        Brain connectivity abnormalities in patients with functional (psychogenic nonepileptic) seizures: A systematic review.
        Seizure. 2020; 81: 269-275
        • Kola S.
        • LaFaver K.
        Functional movement disorder and functional seizures: What have we learned from different subtypes of functional neurological disorders?.
        Epilepsy Behav Rep. 2022; 18: 100510
        • Pick S.
        • et al.
        Emotional processing in functional neurological disorder: a review, biopsychosocial model and research agenda.
        J Neurol Neurosurg Psychiatry. 2019; 90: 704-711
        • Drane D.L.
        • Fani N.
        • Hallett M.
        • Khalsa S.S.
        • Perez D.L.
        • Roberts N.A.
        A framework for understanding the pathophysiology of functional neurological disorder.
        CNS Spectr. 2020; : 1-7
        • Peterson K.T.
        • Kosior R.
        • Meek B.P.
        • Ng M.
        • Perez D.L.
        • Modirrousta M.
        Right temporoparietal junction transcranial magnetic stimulation in the treatment of psychogenic nonepileptic seizures: A case series.
        Psychosomatics. 2018; 59: 601-606
        • Maurer C.W.
        • LaFaver K.
        • Ameli R.
        • Epstein S.A.
        • Hallett M.
        • Horovitz S.G.
        Impaired self-agency in functional movement disorders: A resting-state fMRI study.
        Neurology. 2016; 87: 564-570
        • Zelinski L.
        • Diez I.
        • Perez D.L.
        • Kotz S.A.
        • Wellmer J.
        • Schlegel U.
        • et al.
        Cortical thickness in default mode network hubs correlates with clinical features of dissociative seizures.
        Epilepsy Behav. 2022; 128: 108605
        • Devinsky O.
        • Mesad S.
        • Alper K.
        Nondominant hemisphere lesions and conversion nonepileptic seizures.
        J Neuropsychiatry Clin Neurosci. 2001; 13: 367-373
        • Reuber M.
        • Aybek S.
        • Carson A.
        • Edwards M.J.
        • Goldstein L.H.
        • Hallett M.
        • et al.
        Evidence of brain abnormality in patients with psychogenic nonepileptic seizures.
        Epilepsy Behav. 2002; 3: 249-254
        • Kerr W.T.
        • Janio E.A.
        • Braesch C.T.
        • Le J.M.
        • Hori J.M.
        • Patel A.B.
        • et al.
        Identifying psychogenic seizures through comorbidities and medication history.
        Epilepsia. 2017; 58: 1852-1860
        • Begue I.
        • Adams C.
        • Stone J.
        • Perez D.L.
        Structural alterations in functional neurological disorder and related conditions: a software and hardware problem?.
        Neuroimage Clin. 2019; 22: 101798
        • Labate A.
        • Cerasa A.
        • Mula M.
        • Mumoli L.
        • Gioia M.C.
        • Aguglia U.
        • et al.
        Neuroanatomic correlates of psychogenic nonepileptic seizures: a cortical thickness and VBM study.
        Epilepsia. 2012; 53: 377-385
        • Kozlowska K.
        • Griffiths K.R.
        • Foster S.L.
        • Linton J.
        • Williams L.M.
        • Korgaonkar M.S.
        Grey matter abnormalities in children and adolescents with functional neurological symptom disorder.
        Neuroimage Clin. 2017; 15: 306-314
        • Perez D.L.
        • Williams B.
        • Matin N.
        • Mello J.
        • Dickerson B.C.
        • LaFrance Jr, W.C.
        • et al.
        Anterior hippocampal grey matter predicts mental health outcome in functional neurological disorders: an exploratory pilot study.
        J Neurol Neurosurg Psychiatry. 2018; 89: 1221-1224
        • Perez D.L.
        • Williams B.
        • Matin N.
        • LaFrance Jr, W.C.
        • Costumero-Ramos V.
        • Fricchione G.L.
        • et al.
        Corticolimbic structural alterations linked to health status and trait anxiety in functional neurological disorder.
        J Neurol Neurosurg Psychiatry. 2017; 88: 1052-1059
        • Maurer C.W.
        • LaFaver K.
        • Limachia G.S.
        • Capitan G.
        • Ameli R.
        • Sinclair S.
        • et al.
        Gray matter differences in patients with functional movement disorders.
        Neurology. 2018; 91: e1870-e1879
        • Espay A.J.
        • Maloney T.
        • Vannest J.
        • Norris M.M.
        • Eliassen J.C.
        • Neefus E.
        • et al.
        Impaired emotion processing in functional (psychogenic) tremor: A functional magnetic resonance imaging study.
        Neuroimage Clin. 2018; 17: 179-187
        • Perez D.L.
        • Matin N.
        • Williams B.
        • Tanev K.
        • Makris N.
        • LaFrance Jr, W.C.
        • et al.
        Cortical thickness alterations linked to somatoform and psychological dissociation in functional neurological disorders.
        Hum Brain Mapp. 2018; 39: 428-439
        • McSweeney M.
        • Reuber M.
        • Hoggard N.
        • Levita L.
        Cortical thickness and gyrification patterns in patients with psychogenic non-epileptic seizures.
        Neurosci Lett. 2018; 678: 124-130
        • Vasta R.
        • Cerasa A.
        • Sarica A.
        • Bartolini E.
        • Martino I.
        • Mari F.
        • et al.
        The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures.
        Epilepsy Behav. 2018; 87: 167-172
        • Lee S.
        • Allendorfer J.B.
        • Gaston T.E.
        • Griffis J.C.
        • Hernando K.A.
        • Knowlton R.C.
        • et al.
        White matter diffusion abnormalities in patients with psychogenic non-epileptic seizures.
        Brain Res. 2015; 1620: 169-176
        • Tomic A.
        • Agosta F.
        • Sarasso E.
        • Petrovic I.
        • Basaia S.
        • Pesic D.
        • et al.
        Are there two different forms of functional dystonia? A multimodal brain structural MRI study.
        Mol Psychiatry. 2020; 25: 3350-3359
        • Sharma A.A.
        • Goodman A.M.
        • Allendorfer J.B.
        • Philip N.S.
        • Correia S.
        • LaFrance Jr, W.C.
        • et al.
        Regional brain atrophy and aberrant cortical folding relate to anxiety and depression in patients with traumatic brain injury and psychogenic nonepileptic seizures.
        Epilepsia. 2022; 63: 222-236
        • Riederer F.
        • Landmann G.
        • Gantenbein A.R.
        • Stockinger L.
        • Egloff N.
        • Sprott H.
        • et al.
        Nondermatomal somatosensory deficits in chronic pain are associated with cerebral grey matter changes.
        World J Biol Psychiatry. 2017; 18: 227-238
        • Schrag A.E.
        • Mehta A.R.
        • Bhatia K.P.
        • Brown R.J.
        • Frackowiak R.S.
        • Trimble M.R.
        • et al.
        The functional neuroimaging correlates of psychogenic versus organic dystonia.
        Brain. 2013; 136: 770-781
        • Balachandran N.
        • Goodman A.M.
        • Allendorfer J.B.
        • Martin A.N.
        • Tocco K.
        • Vogel V.
        • et al.
        Relationship between neural responses to stress and mental health symptoms in psychogenic nonepileptic seizures after traumatic brain injury.
        Epilepsia. 2021; 62: 107-119
        • Diez I.
        • Larson A.G.
        • Nakhate V.
        • Dunn E.C.
        • Fricchione G.L.
        • Nicholson T.R.
        • et al.
        Early-life trauma endophenotypes and brain circuit-gene expression relationships in functional neurological (conversion) disorder.
        Mol Psychiatry. 2021; 26: 3817-3828
        • Espay A.J.
        • Maloney T.
        • Vannest J.
        • Norris M.M.
        • Eliassen J.C.
        • Neefus E.
        • et al.
        Dysfunction in emotion processing underlies functional (psychogenic) dystonia.
        Mov Disord. 2018; 33: 136-145
        • Szaflarski J.P.
        • Allendorfer J.B.
        • Nenert R.
        • LaFrance Jr, W.C.
        • Barkan H.I.
        • DeWolfe J.
        • et al.
        Facial emotion processing in patients with seizure disorders.
        Epilepsy Behav. 2018; 79: 193-204
        • Sanbonmatsu D.M.
        • Cooley E.H.
        • Butner J.E.
        The impact of complexity on methods and findings in psychological science.
        Front Psychol. 2020; 11: 580111
        • LaFrance Jr, W.C.
        • Baker G.A.
        • Duncan R.
        • Goldstein L.H.
        • Reuber M.
        Minimum requirements for the diagnosis of psychogenic nonepileptic seizures: a staged approach: a report from the International League Against Epilepsy Nonepileptic Seizures Task Force.
        Epilepsia. 2013; 54: 2005-2018
        • Kerr W.T.
        • Janio E.A.
        • Braesch C.T.
        • Le J.M.
        • Hori J.M.
        • Patel A.B.
        • et al.
        An objective score to identify psychogenic seizures based on age of onset and history.
        Epilepsy Behav. 2018; 80: 75-83
        • Kerr W.T.
        • Janio E.A.
        • Braesch C.T.
        • Le J.M.
        • Hori J.M.
        • Patel A.B.
        • et al.
        Diagnostic implications of review-of-systems questionnaires to differentiate epileptic seizures from psychogenic seizures.
        Epilepsy Behav. 2017; 69: 69-74
        • Kerr W.T.
        • Hwang E.S.
        • Raman K.R.
        • Barritt S.E.
        • Patel A.B.
        • Le J.M.
        • et al.
        Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation.
        Int Workshop Pattern Recognit Neuroimaging. 2014; : 1-4
        • Kerr W.T.
        • Anderson A.
        • Lau E.P.
        • Cho A.Y.
        • Xia H.
        • Bramen J.
        • et al.
        Automated diagnosis of epilepsy using EEG power spectrum.
        Epilepsia. 2012; 53: e189-e192
        • Elze M.C.
        • Gregson J.
        • Baber U.
        • Williamson E.
        • Sartori S.
        • Mehran R.
        • et al.
        Comparison of Propensity Score methods and covariate adjustment: evaluation in 4 cardiovascular studies.
        J Am Coll Cardiol. 2017; 69: 345-357
        • Brazauskas R.
        • Logan B.R.
        Observational studies: Matching or regression?.
        Biol Blood Marrow Transplant. 2016; 22: 557-563
        • Tsolaki E.
        • Narr K.L.
        • Espinoza R.
        • Wade B.
        • Hellemann G.
        • Kubicki A.
        • et al.
        Subcallosal cingulate structural connectivity differs in responders and nonresponders to electroconvulsive therapy.
        Biol Psychiatry Cogn Neurosci Neuroimaging. 2021; 6: 10-19
        • Vasavada M.M.
        • et al.
        Effects of serial ketamine infusions on corticolimbic functional connectivity in major depression.
        Biol Psychiatry Cogn Neurosci Neuroimaging. 2021; 6: 735-744
        • Wade B.S.C.
        • Hellemann G.
        • Espinoza R.T.
        • Woods R.P.
        • Joshi S.H.
        • Redlich R.
        • et al.
        Depressive symptom dimensions in treatment-resistant major depression and their modulation with electroconvulsive therapy.
        J ECT. 2020; 36: 123-129
        • Leaver A.M.
        • Vasavada M.
        • Kubicki A.
        • Wade B.
        • Loureiro J.
        • Hellemann G.
        • et al.
        Hippocampal subregions and networks linked with antidepressant response to electroconvulsive therapy.
        Mol Psychiatry. 2020; 26: 4288-4299
        • Sahib A.K.
        • Loureiro J.R.
        • Vasavada M.
        • Anderson C.
        • Kubicki A.
        • Wade B.
        • et al.
        Modulation of the functional connectome in major depressive disorder by ketamine therapy.
        Psychol Med. 2020; : 1-10
        • Loureiro J.R.A.
        • Leaver A.
        • Vasavada M.
        • Sahib A.K.
        • Kubicki A.
        • Joshi S.
        • et al.
        Modulation of amygdala reactivity following rapidly acting interventions for major depression.
        Hum Brain Mapp. 2020; 41: 1699-1710
        • Sahib A.K.
        • Loureiro J.R.
        • Vasavada M.M.
        • Kubicki A.
        • Wade B.
        • Joshi S.H.
        • et al.
        Modulation of inhibitory control networks relate to clinical response following ketamine therapy in major depression.
        Transl Psychiatry. 2020; 10: 260
        • Sun H.
        • Jiang R.
        • Qi S.
        • Narr K.L.
        • Wade B.S.
        • Upston J.
        • et al.
        Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.
        Neuroimage Clin. 2020; 26: 102080
        • Kubicki A.
        • Leaver A.M.
        • Vasavada M.
        • Njau S.
        • Wade B.
        • Joshi S.H.
        • et al.
        Variations in hippocampal white matter diffusivity differentiate response to electroconvulsive therapy in major depression.
        Biol Psychiatry Cogn Neurosci Neuroimaging. 2019; 4: 300-309
        • Wade B.S.C.
        • Sui J.
        • Njau S.
        • Leaver A.M.
        • Vasvada M.
        • Gutman B.A.
        • et al.
        Data-driven cluster selection for subcortical shape and cortical thickness predicts recovery from depressive symptoms.
        Proc IEEE Int Symp Biomed Imaging. 2017; 2017: 502-506
        • Wade B.S.
        • Joshi S.H.
        • Njau S.
        • Leaver A.M.
        • Vasavada M.
        • Woods R.P.
        • et al.
        Effect of electroconvulsive therapy on striatal morphometry in major depressive disorder.
        Neuropsychopharmacology. 2016; 41: 2481-2491
        • Leaver A.M.
        • Espinoza R.
        • Pirnia T.
        • Joshi S.H.
        • Woods R.P.
        • Narr K.L.
        Modulation of intrinsic brain activity by electroconvulsive therapy in major depression.
        Biol Psychiatry Cogn Neurosci Neuroimaging. 2016; 1: 77-86
        • Pirnia T.
        • Joshi S.H.
        • Leaver A.M.
        • Vasavada M.
        • Njau S.
        • Woods R.P.
        • et al.
        Electroconvulsive therapy and structural neuroplasticity in neocortical, limbic and paralimbic cortex.
        Transl Psychiatry. 2016; 6: e832
        • Vasavada M.M.
        • Leaver A.M.
        • Espinoza R.T.
        • Joshi S.H.
        • Njau S.N.
        • Woods R.P.
        • et al.
        Structural connectivity and response to ketamine therapy in major depression: A preliminary study.
        J Affect Disord. 2016; 190: 836-841
        • Moody T.D.
        • Morfini F.
        • Cheng G.
        • Sheen C.L.
        • Kerr W.T.
        • Strober M.
        • et al.
        Brain activation and connectivity in anorexia nervosa and body dysmorphic disorder when viewing bodies: relationships to clinical symptoms and perception of appearance.
        Brain Imaging Behav. 2021; 15: 1235-1252
        • Rangaprakash D.
        • Bohon C.
        • Lawrence K.E.
        • Moody T.
        • Morfini F.
        • Khalsa S.S.
        • et al.
        Aberrant dynamic connectivity for fear processing in anorexia nervosa and body dysmorphic disorder.
        Front Psychiatry. 2018; 9: 273
        • Feusner J.D.
        • Lidström A.
        • Moody T.D.
        • Dhejne C.
        • Bookheimer S.Y.
        • Savic I.
        Intrinsic network connectivity and own body perception in gender dysphoria.
        Brain Imaging Behav. 2017; 11: 964-976
        • Armstrong C.C.
        • Moody T.D.
        • Feusner J.D.
        • McCracken J.T.
        • Chang S.
        • Levitt J.G.
        • et al.
        Graph-theoretical analysis of resting-state fMRI in pediatric obsessive-compulsive disorder.
        J Affect Disord. 2016; 193: 175-184
        • Khalsa S.S.
        • Kumar R.
        • Patel V.
        • Strober M.
        • Feusner J.D.
        Mammillary body volume abnormalities in anorexia nervosa.
        Int J Eat Disord. 2016; 49: 920-929