Magnetic resonance imaging histogram analysis of corpus callosum in a functional neurological disorder

Biomarkers and Rehabilitation for Functional Neurological Disorder

Functional neurological disorder, or FND, is widely misunderstood, particularly when considering recent research indicating that the illness has numerous biological markers in addition to its psychiatric disorder associations. Nonetheless, the long-held view that FND is a mental illness without a biological basis, or even a contrived (malingered) illness, remains pervasive both in current medical care and general society. This is because FND involves intermittent disability that rapidly and involuntarily alternates with improved neurological control. This has in turn caused shaming, perceived low self-efficacy, and social isolation for the patients. Until now, biomarker reviews for FND tended not to examine the features that are shared with canonical neurological disorders. This review, in contrast, examines current research on FND biomarkers, and in particular their overlap with canonical neurological disorders, along with the encouraging outcomes for numerous physical rehabilitation trials for FND. These findings support the perspective endorsed here that FND is unquestionably a neurological disorder that is also associated with many biological markers that lie outside of the central nervous system. These results suggest that FND entails multiple biological abnormalities that are widely distributed in the body. General healthcare providers would benefit their care for their patients through their improved understanding of the illness and recourses for support and treatment that are provided in this review.

Difference in Laterality of the Dorsal Striatum in Schizoaffective Disorder

BACKGROUND

Recent research has demonstrated that the dorsal striatum is directly associated with the integration of cognitive, sensory-motor, and motivational/emotional data. Disruptions in the corticostriatal circuit have been implicated in the pathophysiology of psychosis. The dorsal striatum was reported to show lateralized pathology in psychotic disorders. In this study, we aimed to analyze the laterality of the dorsal striatum with texture analysis of T2-weighted magnetic resonance imaging (MRI) images from schizoaffective disorder (SAD) patients.

METHODS

Twenty SAD patients, met the inclusion criteria and had available cranial MRI data were assigned as the patient group. Twenty healthy individuals were determined as the control group. Texture analysis values were obtained from striatum region of interests (ROI) generated from T2-weighted MRI images. Data are presented as mean and standard deviation. The suitability of the data for normal distribution was analyzed with the Kolmogorov-Smirnov test. Analysis of variance (ANOVA) test (Post Hoc TUKEY) was employed to compare the group data based on test findings.

RESULTS

There was no significant difference between the groups in terms of gender and age. There were differences in the values of texture analysis parameters of both caudate and putamen nuclei in comparison to controls. We identified differences in the left dorsal striatum nuclei in SAD. The differences in the putamen were more and more pronounced than in the caudate.

CONCLUSIONS

Texture analyses suggest that the left dorsal striatum nuclei may be different in SAD patients. Further studies are needed to determine the pathophysiology of SAD and how it may affect disease treatment.

Is the Caudate, Putamen, and Globus Pallidus the Delusional Disorder's Trio? A Texture Analysis Study

BACKGROUND

The neurobiological basis of delusional disorder is less explored through neuroimaging techniques than in other psychotic disorders. This study aims to provide information about the neural origins of delusional disorder (DD) by examining the neuroanatomical features of some basal nuclei with magnetic resonance imaging (MRI) texture analysis.

MATERIALS AND METHODS

Twenty DD patients and 20 healthy individuals were included in the study. Globus pallidus, putamen, and caudate nuclei were selected individually with a region of interest (ROI) on the axial MRI images. The entire texture analysis algorithm applied to all selected ROIs was done with an in-house software. Nuclei on both sides were taken as separate samples.

RESULTS

There were no significant differences between groups in terms of age and gender. The average "mean, median and maximum" values of all three nuclei were decreased in DD patients. The small putamen area and the differences detected in different tissue parameters for all three nuclei in delusional disorder patients indicate that they differ in delusional disorder from normal controls (p < 0.05).

CONCLUSION

The differences detected in the texture parameters for all three nuclei indicate that there is something different in the DD from in the normal controls. Neuroimaging studies with larger samples and different techniques in the future may shed light on the etiology of delusional disorder.

Statistical shape analysis of corpus callosum in delusional disorder

Neurobiological foundations of delusional disorder (DD) have been studied less with neuroimaging techniques when compared to other psychotic disorders. The present study aimed to delineate the neural substrates of DD by investigating neuroanatomical characteristics of the corpus callosum (CC) with statistical shape analysis (SSA) conducted on magnetic resonance images (MRI). Twenty (female:male=1:1) DSM-5 DD patients and 20 age- and gender-matched healthy individuals were included. High-resolution 3D T1 Turbo Field Echo MRI images were scanned with a 1.5 T MR device. The landmarks that were selected to determine the shape differences in CC were identified based on previous studies. Furthermore, constructed landmarks were determined and employed to better assess regional shape differences. There was no significant difference in the CC area in the mid-sagittal images between the DD patients and controls. However, DD patients exhibited a pattern of structural CC changes in various regions. The study findings emphasizes the variable subregional nature of CC in DD patients. Future SSA studies with larger samples could shed further light on DD etiology, diagnosis, classification and treatment options.

Evaluation of the corpus callosum using magnetic resonance imaging histogram analysis in autism spectrum disorder

BACKGROUND

Histogram analysis is a texture analysis method that can be used in medical images. Quantitative values of the intensity of images can be obtained with histogram analysis. It aimed to evaluate corpus callosum in magnetic resonance images (MRIs) using histogram analysis of pediatric patients with autism spectrum disorder (ASD) to compare them with healthy controls.

METHODS

This study included 29 children with ASD and 29 healthy children with normal brain MRI. High-resolution three-dimensional turbo field echo images were obtained with a 1.5 T scanner device for brain magnetic resonance imaging. On the corpus callosum in the sagittal T1-weighted images obtained, mean gray level density (mean), the standard deviation, median, minimum, maximum, entropy, variance, skewness, kurtosis, uniformity, size % L, size % M, size % U, and percentile parameters were measured.

RESULTS

In ASD patients, mean, standard deviation, maximum, median, variance, entropy, 25%, 75%, 90%, 97%, and 99% values were found to be lower than the control group, and size % U value was higher. In addition, the corpus callosum area was significantly lower in the ASD compared to the controls.

CONCLUSION

According to our study, corpus callosum of patients with ASD showed differences compared to healthy controls by histogram analysis, even though they were seen as normal in brain MRI. We think that histogram analysis can be used to evaluate possibly affected areas of brain in ASD patients.

Meme manyetik rezonans görüntülemede malign ve benign lezyonların ayrımında histogram analizi: ön çalışma

Purpose: The present study assesses whether malignant and benign lesions can be distinguished through histogram analysis of non-fat-suppressed T1-weighted and fat-suppressed T2-weighted breast magnetic resonance images (MRIs). Materials and Methods: MRIs of 20 malignant and 20 benign breast lesions were reviewed retrospectively by histogram analysis performed using Osirix V.4.9 software. The regions of interest (ROIs) were drawn manually to include almost the entire lesion, and values from these ROIs were used to calculate gray-level intensity mean, standard deviation, entropy, uniformity, skewness, kurtosis, and percentile values. Results: In non-fat-suppressed T1-weighted images, the minimum, 1st, 3rd, 5th, 10th and 25th percentile values were significantly lower in the malignant lesions than in the benign lesions. The minimum value had sensitivity of 70% and specificity of 63.2%. On the fat-suppressed T2-weighted images, skewness was significantly higher while uniformity was significantly lower in malignant lesions than benign lesions. Skewness had 68.4% sensitivity and 60% specificity, and uniformity had 65% sensitivity and 68.4% specificity. Conclusion: The results of this study demonstrated that histogram analysis of non-fat-suppressed T1-weighted and fat-suppressed T2-weighted images can be used to differentiate malignant and benign lesions in breast MRI.

Could computed tomography histogram analysis add value to the diagnosis of cholesteatoma?

BACKGROUND

To investigate the potential role of computed tomography (CT) histogram analysis in differentiating cholesteatoma (CHS) and non-cholesteatoma (NCHS).

METHODS

We evaluated 77 temporal bone CT images (from November 2016 to February 2020) that were obtained preoperatively (mean age, 37.10±17.27 years in CHS; 36.72±16.08 years in NCHS group). Histogram analyses of the resulting XML files were conducted using the R Project 3.3.2 program. ROC analysis was used to find threshold values, and the diagnostic efficiency of these values in differentiating CHS-NCHS was determined.

RESULTS

The CT images of 41 CHS (53.25%) and 36 NHCS cases (46.75%) were evaluated. There was a statistically significant difference between the CHS and NCHS group in terms of the mean, maximum, and median values (p = 0.036, p = 0.006, p = 0.043). When examining the ROC curve obtained from the mean of these parameters, area under the curve (AUC) is determined as 0.638, and when the threshold value is selected as 42.55, the mean value was determined to have a sensitivity of 86.50% and specificity of 56.10% in differentiating CHS-NCHS.

Texture analysis of dorsal striatum in functional neurological (conversion) disorder

In this study, it was aimed to evaluate the dorsal striatum nuclei of patients diagnosed with Functional Neurological Disorder by texture analysis method from magnetic resonance imaging images and to compare them with healthy controls. Study groups consisted of 20 female patients and 20 healthy women. The brains of patients and controls were scanned for high-resolution images with a 1.5T scanner using the sagittal plane and 3D spiral fast spin echo sequence. Using the texture analysis method, mean, standard deviation, minimum, maximum, median, variance, entropy, size %L, size %U, size %M, kurtosis, skewness and homogeneity values of the dorsal striatum nuclei were calculated from the images. The data were compared with comparison tests according to Kolmogorov-Smirnov test results. There was no statistically significant difference between paired regions in terms of texture analysis findings in the cross-sectional images of the participants. In patients, mean, standard deviation, minimum, maximum, median, variance and entropy values for the putamen nucleus, and mean, standard deviation, minimum, maximum, median, variance, entropy and kurtosis values for the caudate nucleus were found significantly higher than controls. Additional receiver operating characteristic curve and logistic regression analyzes were performed. The implications of the results of the study are that there are significant microstructural changes in the dorsal striatum nuclei of patients and their reflection on brain images. Texture analysis is a useful technique to show tissue changes in the dorsal striatum of patients using images. It is highly recommended to use texture analysis to identify and evaluate potentially affected areas of the brain in new studies.