Quantitative analysis of spiny neurons in the adult human caudate nucleus: can it confirm the current qualitative cell classification?

Comparison of Resting-State Functional Connectivity Between Generalized Anxiety Disorder and Social Anxiety Disorder: Differences in the Nucleus Accumbens and Thalamus Network

Background: Generalized anxiety disorder (GAD) and social anxiety disorder (SAD) are distinguished by whether anxiety is limited to social situations. However, reports on the differences in brain functional networks between GAD and SAD are few. Our objective is to understand the pathogenesis of GAD and SAD by examining the differences in resting brain function between patients with GAD and SAD and healthy controls (HCs). Methods: This study included 21 patients with SAD, 17 patients with GAD, and 30 HCs. Participants underwent psychological assessments and resting-state functional magnetic resonance imaging. Whole-brain analyses were performed to compare resting-state functional connectivity (rsFC) among the groups. In addition, logistic regression analysis was conducted on the rsFC to identify significant differences between GAD and SAD. Results: Patients with SAD and GAD had significantly higher rsFC between the bilateral postcentral gyri and bilateral amygdalae/thalami than HCs. Compared with patients with SAD, those with GAD had significantly higher rsFC between the right nucleus accumbens and bilateral thalami and between the left nucleus accumbens and right thalamus. rsFC between the left nucleus accumbens and right thalamus positively correlated with state anxiety in patients with SAD and GAD, respectively. In addition, logistic regression analysis revealed that the right nucleus accumbens and the right thalamus connectivity could distinguish SAD from GAD. Conclusions: GAD and SAD were distinguished by the right nucleus accumbens and the right thalamus connectivity. Our findings offer insights into the disease-specific neural basis of SAD and GAD. Clinical Trial Registration Number: UMIN000024087. Impact Statement This study is the first to identify a resting state functional connectivity that distinguishes social anxiety disorder (SAD) from generalized anxiety disorder (GAD) and to clarify a common connectivity in both disorders. We found that the connectivity between the right nucleus accumbens and the right thalamus differentiated SAD from GAD. Furthermore, these rsFC differences suggest an underlying basis for fear overgeneralization. Our findings shed light on the pathophysiology of these conditions and could be used as a basis for further studies to improve outcomes for such patients.

The Morphology of Brain Neurons: The Box-Counting Method in the Quantitative Analysis of 2D Images

This chapter begins by showing the difference between fractal geometry and fractal analysis. The text shows the difference between mathematical and natural fractals and how they are best defined by explaining the concept of fractal analysis. Furthermore, the text presents the most famous technique of fractal analysis: the box-counting method. Defining this method and showing the methodology that leads to the precise value of the fractal (i.e., the box) dimension is done by demonstrating the images of human dentate neurons. A more detailed explanation of the methodology was presented in the previous version of this chapter.This version promotes the notion of monofractal analysis and shows how three types of the same neuronal images can quantify four image properties. The results showed that monofractal parameters successfully quantified four image properties in three nuclei of the cerebellum. Finally, the author discusses the results of this chapter and previously published conclusions. The results show how the monofractal parameters discriminate images of neurons from the three nuclei of the human cerebrum. These outcomes are discussed along with the results of previous studies.

Quantitative analysis of the Golgi impregnated human (neo)striatal neurons: Observation of the morphological characteristics followed by an emphasis on the functional diversity of cells

BACKGROUND

The (neo)striatum is the major input structure of the basal nuclei, which is involved in the execution of voluntary movements, but also in controlling the processes that lead to the movement, such as motivation and cognition. The striatum provides its function through an interaction between projection neurons and interneurons. The aim of this study was to quantify the morphological properties of neurons in the precommissural putamen and precommissural caudate nucleus head and to evaluate whether there is a difference in cell morphology between different cell groups within one part and between the same cell groups within different parts of the striatum.

METHODS

A total of 652 neuronal images of human striatum were observed. The features of the neuronal morphology (soma size, dendritic field size, shape of neuronal image, dendritic curviness, dendritic branching complexity) were observed by determining appropriate parameters of digital images of neurons.

RESULTS

According to the presence of spines on the soma and/or dendrites, neurons were qualitatively classified into 446 spiny and 206 aspiny cells. The analysis of the distribution of the dendritic field area shows that spiny and aspiny neurons from both parts of the neostriatum can be decomposed into two distributions, which means that they can be classified into subgroups. A quantitative analysis of the spiny/aspiny neurons in the human putamen or caudate nucleus head has shown that there is a statistically significant difference between them. By comparing the morphology of neurons of the same group between different parts of the human neostriatum (putamen and caudate nucleus), it was also determined that there is a statistically significant difference.

CONCLUSION

Since the morphology and function of neurons are in close correlation, it can be assumed that different groups of neurons in the human striatum might support functional diversity of the studied area.

Classification by morphology of multipolar neurons of the human principal olivary nucleus

The principal olivary nucleus is the largest part of the inferior olivary complex and is involved in the spatial and temporal organization of movement and motor learning. Nearly all neurons in this nucleus is multipolar along with having a highly complex dendritic tree and significant asymmetry in shape. In this study, we updated the current classification scheme, examined morphological differences between the proposed groups, and investigated age-related morphological changes. Histological preparations were digitized by a light microscope and a sample of 259 images of neurons was analyzed by 17 computationally generated parameters of morphology. These were reduced to the four variables of principal component analysis and the sample was classified by k-means method of clustering into three clusters. The differences between clusters were documented and for medium-sized neurons the relationship between four morphological parameters and age were investigated. Finally, for two of the age groups the changes in the morphology were explored. This study includes a detailed and robust classification of the PON neurons and the findings improve upon past qualitative work.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PURPOSE

Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis.

METHOD

In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods.

RESULTS

All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (S_COR), contrary to SWI images which showed different values of two parameters (angular second moment-S_ASM and contrast-S_CON). Only three fractal features (binary box dimension-D_bin, normalized box dimension-D_norm and lacunarity-λ) measured on T2W and D_norm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (S_IDM) on T2W and S_IDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images.

CONCLUSIONS

Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.