Statistical shape analysis of corpus callosum in functional neurological disorder

Introduction

Functional neurological disorder (FND) is the presence of neurological symptoms in the absence of a neurological disease and the dysfunction defined for its diagnosis should not be associated with any medical disorder (American Psychiatric Association 2013; Aybek et al. 2015). Diagnostic criteria include the presence of one or more changes in volitional, motor, or sensory functions that may explain this appearance, and discordance and inconsistency between neurological or general medical conditions (American Psychiatric Association 2013). Although the findings reported in structural and functional neuroimaging studies in the literature do not adequately explain the pathophysiology of FND, some studies suggest and support the existence of important findings accompanying FND symptoms. These findings have led to an emphasis on disorders associated with FND symptoms that can be identified by neuroimaging and neurophysiological measurements (Aybek et al. 2015; Byrd et al. 1990; Vuilleumier et al. 2001; Atmaca et al. 2006; Giedd et al. 2012; Baumann and Mattingley 2012; Atmaca et al. 2016; Baykara et al. 2021; Baykara and Baykara 2021). Neurological diseases can cause degenerative atrophic changes in any part of the brain and cause texture, size and shape changes especially in these regions (plasticity). It is possible that these changes alter the size and shape by causing changes in the structure of the corpus callosum (CC), which is the major white matter structure connecting the brain hemispheres and containing the most nerve fiber pathways. Essentially, the main function of the CC is to connect regions of the cerebral cortex to achieve interhemispheric integration. It also acts as a conduit to transmit sensory information (McLeod et al. 1987; Byrd et al. 1990). It is known that structural changes in the CC are associated with neuropsychiatric symptoms such as epilepsy, Asperger’s syndrome, learning disabilities, behavioral problems, depression, adjustment disorder, schizophrenia, alexithymia, delusions, hallucinations and conversion disorder (Bhatia et al. 2016; Randall 1983).
In the last two decades, statistical shape analysis (SSA) has been widely used in medicine to examine various structures of interest to determine morphometric features of abnormalities of an organ associated with a particular condition or disease. Offering many different methods for measuring anatomical brain structures, SSA has become a growing interest, particularly in neuroimaging. These studies often rely on measurements of volume and area, which are quantitative features that can predict the progression of disease-induced atrophy or dilation. However, structural changes occurring in certain locations may not be adequately reflected in these volume and area measurements, because it is possible for two different individuals to have the same organs in the same volume and area dimensions, but with differences in shape. Today, with technical developments, imaging views and shapes of organs or tissue structures are widely used as photographic input data. With these data, geometric shape changes in any organ or textural structure can be statistically analyzed using certain signs. SSA is a modern geometric-morphometric analysis method that can be used to evaluate the effects of demographic factors, environmental influences or diseases on growth and allometry and compares body shapes with specific anatomical features using static landmarks. This method is a form of analysis that gives more objective results with multivariate and integrated data on morphological shapes compared to analyses using classical linear measurements. SSA is increasingly used in medicine to examine various structures to identify morphometric abnormalities associated with a particular condition or disease that can aid diagnosis and treatment (Kaya et al. 2019; Styner et al. 2004; Ozdemir et al. 2007; He et al. 2010; Casanova et al. 2011; Joshi et al. 2013; Jiang et al. 2018; Sigirli et al. 2020; Huang and Tang 2021).
Studies in the literature have shown that abnormalities in the CC are associated with FND symptoms (David et al. 1993; Baykara et al. 2021). Based on this information, the aim of this study is to evaluate the CC with SSA using T1-weighted sagittal MR images of patients with FND and compare them with healthy controls.

Material and methods

Local Ethics Committee approval was obtained (date: 03/28/2013, session: 05, decision: 02) and all participants signed an informed consent form.

Study population

Twenty female patients who were diagnosed with FND according to the Diagnostic and Statistical Manual of Mental Disorders-5 (American Psychiatric Association and American Psychiatric Association DSM-5 Task Force, 2013) criteria in the psychiatry outpatient clinic and whose first symptom was psychogenic non-epileptic seizures were evaluated. The diagnosis of the patients was confirmed as a result of a psychiatric evaluation by a psychiatrist with 15 years of experience. Patients had been using selective serotonin reuptake inhibitors or tricyclic antidepressants for at least the past four months, and their doses were stabilized two months before the study. Inclusion criteria were 18-65 years of age, no other psychiatric diagnosis, no mental retardation, no neurological or physiological disease, no history of alcohol or substance use in the last 6 months, and no contraindications for magnetic resonance imaging (MRI) examination. These criteria were applied using the hospital information system and information obtained from patient statements. As the control group, 20 age and gender equivalent healthy individuals who met the study criteria and did not have a psychiatric diagnosis were selected.

Imaging process

A 1.5T General Electric Signa Excite scanner (GE, Milwaukee, Wisconsin, USA) with an 8-channel HR brain coil was used for MRI. High resolution structural T1-weighted sagittal 3D fast spin echo MRI images were obtained (TR = 2000 ms, TE = 15.6 ms, TI = 700 ms, FOV = 240 mm, echo spacing = 15.6 ms, 8 echoes, resolution = 0.9375 × 0.9375 × 1.328 mm, 128 contiguous slices). The antero-posterior commissure line and interhemispheric fissure were used to align the brains of all individuals in a standard position in MRI examinations (Fig. 1).

Analysis of images

Obtaining two-dimensional landmarks
Mid-sagittal T1-weighted two-dimensional digital MRI images of each individual, most clearly showing the cerebral aqueduct, CC, and superior colliculus, were selected. The corpus callosum was marked (Fig. 1) with TpsDig2 version 2.32 software on each selected image using standardized anatomical landmarks (Bookstein et al. 2001; Ozdemir et al. 2007; Sigirli et al. 2012) and data were collected (Rohlf 2015).

Statistical deformation analysis

The mean of ‘Procrustes’ landmark points was calculated and shape deformations were evaluated using thin plate spline (TPS) analysis with Past version 4.07b (Hammer et al. 2001). Areas that show the greatest expansion or contraction as a result of this analysis are marked using different colors to indicate deformations. The homo-geneity of the variance-covariance matrices was examined using the Box-M test (Hammer et al. 2001; Dryden and Mardia 2016). Because of the non-homogeneous matrices, the James Fᴊ test based on a resampling procedure was used to compare the shapes of the CC between the control and FND groups (Dryden and Mardia 2016; R Core Team 2021). In addition, the root mean square of Kendall’s Riemann distance rho was compared with the mean shape to obtain overall shape variability measures for the controls and FND groups. Allometric evaluation was performed using multivariate regression analysis of centroid sizes (CS, the square root of the sum of the square of the Euclidean distances from each sign to the center) and tangent coordinates. Model significance was assessed using the Wilks’ lambda test. Assessment of model fit was performed based on the mean square error (MSE) and the coefficient of determination (R2) (He et al. 2010; Kaya et al. 2019; Sigirli et al. 2020).

Landmark reliability

In this study, a single rater manually defined all landmarks. Intra-rater reliability was not calculated, as the high rater reliability of landmark selection was demonstrated by previous studies (Kaya et al. 2019; Sigirli et al. 2020).

Statistical analysis

Shapes version 1.2.6 package with the R version 4.1.1 and PAST version 4.07b were used for the statistical shape analysis (Hammer et al. 2001; Dryden and Mardia 2016). Data were expressed as mean ± standard deviation. According to normal distribution analysis (one sample Kolmogorov-Smirnov test) of variables, Student’s t test was used to compare the groups for other data. All statistical analyses were made with IBM SPSS Statistics (SPSS for Windows version 25, IBM Corporation, Armonk, New York, USA). A p value of < 0.05 was considered statistically significant.

Results

All patients and control subjects were female. The mean age was 32.50 ±10.47 years in the patient group and 38.45 ±10.65 years in the control group, and there was no significant difference between the groups according to age (p = 0.083). There was no significant difference between the CC area in the mid-sagittal images of the patients diagnosed with FND and controls (6.01 ±0.84 cm2, 5.64 ±0.69 cm2 respectively; p = 0.133).
As the Box-M test identifies inhomogeneous matrices (F = –0.70919, p < 0.001), the James Fᴊ test was applied and CC shapes of FND patients were found to be significantly different from controls (T2 = 182.1054, p < 0.001) (Fig. 2). The root mean square of Kendall’s Riemann distance (rho) to main shape was 0.08552594 for controls, 0.08202397 for FNDs, and 0.05922028 for all (Fig. 3).
The effect of size-dependent shape changes and deformations in the mean shape on graphs (shrinkage) was demonstrated and compared between controls and FND patients using TPS (Fig. 4). Maximum deformation was observed at marked points (landmarks) in the posterior region of the CC (especially landmarks 5, 4, 15 and 3, 8, 6, 11, 10, 1, 9, 2, 12, 13, 16, 14, and 7 in descending order) (Table 1).
The cubic growth model was chosen as the optimal model for CC and it was observed that CS for CCs decreased with increasing disease duration (Fig. 5).
The multivariate regression test was used for the relationship between size and shape in the evaluation of allometry and a statistically significant model (p = 6.41E-40, R2 = 0.08868, MSE = 0.0002344 and Wilks’  = 3.69E-09) was obtained for the CC.

Discussion

Until recently, for nearly two centuries, the diagnosis of functional neurological disorder was based on the inconsistency of its sensory, motor, neurovegetative symptoms with any known neurological disease. While it is thought that no anatomical or functional pathology was detected in the brains of these patients with neuroimaging, the latest study findings point to structural and functional changes in the regions of the brain associated with symptoms (Reynolds 2012; Baykara et al. 2021; Baykara and Baykara 2021; Byrd et al. 1990; Vuilleumier et al. 2001; Atmaca et al. 2006; Voon et al. 2011; Baumann and Mattingley 2012; Giedd et al. 2012; Aybek et al. 2014; Aybek et al. 2015; Atmaca et al. 2016; Hassa et al. 2017).
The CC provides integration between the hemispheres by connecting the relevant cerebral cortex regions in the brain (McLeod et al. 1987; Byrd et al. 1990). Disruption of this connection causes decreased transfer of information from one hemisphere to the other, impaired harmony between right and left visual fields, alexithymia, which is common in FND patients, delusions and hallucinations. In addition, the presence of schizophrenia, Asperger’s syndrome, personality disorder, depressive symptoms and conversion symptoms in CC agenesis that is different from the others has also been reported (David et al. 1993; Demartini et al. 2014). Furthermore, the association of white matter, including CC, with psychogenic conditions has been investigated in many studies, and empirical evidence from neuroimaging studies suggests that the CC plays an important role in mood states (Wu et al. 1993; Huber et al. 2002; Lyoo et al. 2002; Thomas et al. 2002; Nery-Fernandes et al. 2012; Lent and Schmidt 1993; Schutter and Harmon-Jones 2013), but structural neuroimaging fails to extract information about its functionality.
Statistical shape analysis has received more attention recently for its potential to demonstrate morphological changes, but relatively few medical studies have analyzed the shape of the CC to investigate the etiology of psychiatric disorders (Kaya et al. 2019; Sigirli et al. 2020; Huang and Tang 2021).
In the present study, when examined with SSA, the CC shape of the patients was different from that of healthy controls; however, no significant difference was found between FND patients and controls in terms of CC areas in cross-sectional images.
As the assessment of general form differentiation using neuroanatomical landmarks was considered most appropriate, analyses were performed using the traditional landmark-based TPS method to examine the shape abnormalities of CC in FND in this study. To our knowledge, this study was the first to use landmark-based SSA methods to analyze CC abnormalities in FND, and local shape comparison had not been performed before.
Some studies using shape analysis have reported significant findings about various CC subdivisions, such as in Alzheimer’s disease, where the CC is globally atrophic and this is more prominent in the posterior segment, and the lower border is longer than the upper margin; in autism, there are differences in the body segment of CC; and in multiple sclerosis, there are differences in body and anterior segments of CC. Regional distribution differences of CC injury should result from variable regional fiber amounts associated with different neuroanatomical regions (He et al. 2010; Sigirli et al. 2012; Sigirli et al. 2020; Jiang et al. 2018; Walterfang et al. 2014).
In this study, it was determined that the maximum deformation was located in the posterior region (isthmus and splenium), inferior edge of the body and anterior region (genu) of the CC. The first two CC regions connect temporal (especially superior), occipital, parietal and motor activation cortical areas (landmarks = 11, 5, 4, 3, 2, 16 and 10). They are also predominantly areas of tactile, auditory, and peripheral and central visual stimulation. The genu connects prefrontal and cingulate areas, which are associated with cognitive information (landmarks 15 and 1) (Friedrich et al. 2020; Fabri and Polonara 2013; Chao et al. 2009; Hofer and Frahm 2006).

Limitations

The present study had certain limitations. Since there were no previous studies that analyzed CC with SSA in patients with FND, we were not able to compare the study findings. The fact that all our patients were female prevented the generalization of the study findings to both sexes. The small sample size of patients and controls was another limitation of the study. Although morphological evaluation with 3D data may seem difficult now, it is certain that it will give results that are more accurate in the morphological analysis of organs in spatial dimensions. In addition, study participants had been taking antidepressant medications, specifically selective serotonin reuptake inhibitors, for the past few months. Although it has been shown in animal models that the physiology of serotonin in CC is impaired by antidepressant drugs (Reyes-Haro et al. 2003), the effects of antidepressants on CC have not been well studied and further studies are needed in this regard.

Conclusions

In this study, SSA and CC analysis using MRI images revealed significant differences between patients with FND and healthy controls. The study findings highlighted the abnormal distribution of white matter in the CC and the variable subregional nature of the CC in FND patients. Future studies with larger samples may contribute to further elucidation of these findings. This study may help future studies in terms of FND etiology, diagnosis and treatment options.

Disclosure

The authors declare no conflict of interest.

References