Dependent Personality Disorder: An Approach Based on fMRI

Research Article

Austin J Clin Neurol. 2023; 10(1): 1161.

Dependent Personality Disorder: An Approach Based on fMRI

Waqas Tariq1; Waqar Mahmood Dar2; Abid Ali3; Ali Haider4*; Umair Waqas2; Rehan Afsar2; Muhammad Arslan5

1Lecturer, Department of Computer Science, Gift University Gujranwala, Pakistan

2Lecturer, Department of Allied Health Sciences, The University of Chenab, Gujrat, Pakistan

3Associate Professor, Department of Allied Health Sciences, The University of Chenab, Gujrat, Pakistan

4BMLS Scholar, Department of Allied Health Sciences, The University of Lahore, Gujrat Campus, Pakistan

5Nawaz Sharif Medical College, Gujrat, Pakistan

*Corresponding author: Ali Haider BMLS Scholar, Department of Allied Health Sciences, The University of Lahore, Gujrat Campus, Pakistan. Tel: +923304627172 Email: [email protected]

Received: April 20, 2023 Accepted: May 30, 2023 Published: June 06, 2023

Abstract

Background: Dependent Personality Disorder (DPD) is one of the most frequent diagnosed personality disorders. People with DPD become over-dependent on other people and unable to make even everyday decisions.

Objective: To detect the neuron network and activation in Brodmann areas (BA 10, BA 11, & BA 47) of the human brain by using Functional Magnetic Resonance Imaging (fMRI) and to identify voxels used in decision making process.

Material and Methods: It is an experimental study conducted on a single subject under a controlled observed environment using Functional Magnetic Resonance Imaging (fMRI). The respondent selected was performing a task based on two visual conditions in which two different types of images (easy and hard birds) were displayed to the respondent. The subject was asked for identification of the particular bird image to visualize voxels used in decision making process. Three Brodmann areas (BA 10, BA 11, & BA 47) from prefrontal cortex were selected as a region of interest. The current study analyzed activated brain regions during making ordinary decisions related to two visual conditions. A multivariate technique Multilayer Perceptron (MLP) neural network was applied for the categorization of two ordinary decisions made by respondent while performing visual task.

Results: The experimental results show the high classification accuracy 92.9% for ordinary decisions between two visual conditions. Sensitivity and specificity of MLP neural network is represented by ROC curve. The MLP neural network technique also detected some important voxels of Brodmann areas 10, 11 and 47 in prefrontal cortex of brain, which engaged in primary role of ordinary decision making process. The current study tracked down the area of that particular prevailing voxels in prefrontal cortex of brain in context of MNI coordinates.

Conclusion: In conclusion, the MLP neural network proficiently classified the voxels which were activated in decision making and provided high classification rates. It also identified significant voxels helping a person in ordinary decision making. The locations of that particular voxels were found in context of MNI coordinates. This study can be helpful in the treatment of patients with DPD by combination of medication and psychotherapy.

Advances in Knowledge: This paper explains precise location of dominant voxels discovered in decision making process which can be used in treatment methods among dependent personality disorder patients. At present, the treatment options are limited, and this paper will give psychiatrists and psychologists the knowledge they need to advance their combination therapies.

Keywords: Dependent personality disorder; Functional magnetic resonance imaging; Brodmann areas; Computational neural networks

Introduction

Functional magnetic resonance imaging is an exceptional tool for diagnosing mental disorders [1]. Functional MRI has low temporal resolution but excellent spatial resolution and fewer radiations are reasons that use of fMRI has increased intuitively [1]. The fMRI machine identifies blood oxygenation level dependent effect for functional images of brain [2]. Functional magnetic resonance imaging provides the view of how brain works in different cognitive tasks. Functional MRI was extensively used to study treatment effects of mental disorders [3]. In task-based fMRI experiments different brain regions interacting each other called Default Mode Network (DMN) [4,5].

The incidence of personality disorders is 10% to 20% in general population and its symptoms appear within some years [6]. Personality disorder leads to significant personality instabilities if untreated [6]. Mental Disorder appears mostly at young age. If a mental disorder is diagnosed and treated timely, it can improve cognitive ability [7]. Dependent personality disorder is one of most common disorder in human beings. Extreme care primes a person to dependent personality disorder patient. DPD affects the way people think and act [8]. People with DPD have a devastating need to have others for taking care of them and often depend on people close to them for their needs [8]. They even rely on others for making everyday decisions like what to wear. The important characteristics of DPD are lack of self-assurance, depending on others and sidestepping from taking responsibilities. There are different approaches for the treatment of depended individuals but some of them have resulted [9]. Self-efficacy of DPD patients can be improved by cognitive behavioral therapy [5].

The current experimental study used event-related fMRI data in which respondent was performing a task based on two visual conditions. Two different types of images (easy and hard birds) was displayed to the respondent and asked for identification of the particular bird in the image. The objective of the current study was to propose a novel approach for DPD patients by classification and identification of voxels which is used in ordinary decision making. The selected three Brodmann areas (BA 10, BA 11, & BA 47) from prefrontal cortex as our region of interest were selected, because the decision making is one of function of these Brodmann areas [10]. MLP neural network was applied for categorization of two ordinary decisions made by respondent while performing visual task and for identification of most significant voxels which helps an individual in making decision [11].

Material and Methodology

It is an experimental study conducted on a single subject under a controlled observed environment using Functional Magnetic Resonance Imaging (fMRI). The respondent selected was performing a task based on two visual conditions in which two different types of images (easy and hard birds) were displayed to the respondent. The subject was asked for identification of the particular bird image to visualize voxels used in decision making process. Three Brodmann areas (BA 10, BA 11, & BA 47) from prefrontal cortex were selected as a region of interest. The current study analyzed activated brain regions during making ordinary decisions related to two visual conditions. A multivariate technique Multilayer Perceptron (MLP) neural network was applied for the categorization of two ordinary decisions made by respondent while performing visual task. ANN stirred by incredible functionality of human brain [12]. ANN is a multivariate interdependence technique and used in extraction of patterns and finding of trend [9]. ANN also used in finding causal relationship [12]. There are different types of ANN which used in machine learning and artificial intelligence [13].

Architecture of MLP Neural Network

One of most common type of ANN is MLP neural network. Among feed-forward and feedback structures, MLP is based on feed-forward structure in which data circulates one way [14]. MLP consist of input layer, hidden layer and output layer [15]. Input layer collects data from outward source and transfer to next layer (hidden layer), then hidden layer converts the data into some other form which is useable by output layer [16]. A weight is assigned to every input. Greater weight of input represents greater importance of it [16]. The structure of MLP neural network is shown in (Figure 1).