“RESTING STATE FUNCTIONAL MRI IN PATIENTS WITH OBSESSIVE COMPULSIVE DISORDER TO DETECT THE AREAS

(1)

A DISSERTATION ON

“RESTING STATE FUNCTIONAL MRI IN PATIENTS WITH OBSESSIVE COMPULSIVE DISORDER TO DETECT THE AREAS

OF ACTIVATION”

Submitted to

THE TAMIL NADU DR.M.G.R.MEDICAL UNIVERISTY CHENNAI

In Partial Fulfillment of the Regulations For the Award of the degree

M.D. DEGREE BRANCH VIII RADIODIAGNOSIS

STANLEY MEDICAL COLLEGE, CHENNAI.

MAY -2020

(2)

CERTIFICATE

This is to certify that the dissertation titled “RESTING STATE FUNCTIONAL MRI IN PATIENTS WITH OBSESSIVE COMPULSIVE DISORDER TO DETECT THE AREAS OF ACTIVATION” submitted by Dr.SEBASTIAN ANTONY X , appearing for M.D.RADIODIAGNOSIS degree examination in April 2020, is a bonafide record of work done by him under my guidance and supervision in partial fulfillment of requirements of The Tamilnadu Dr. M.G.R Medical University, Chennai. I forward this to The Tamilnadu Dr. M.G.R Medical University, Chennai.

PROF. DR. R. SHANTHI MALAR, M.D.,D.A., Dean,

Stanley Medical College, Chennai – 600 001.

.

2

(3)

CERTIFICATE

PROF.DR.C.AMARNATH,MDRD, Professor,

Head of the Department, Department of Radio Diagnosis,

Stanley Medical College, Chennai – 600 001.

(4)

CERTIFICATE

PROF.DR.P.CHIRTRARASAN, MDRD Guide,

Professor,

Department of Radio Diagnosis, Stanley Medical College,

Chennai – 600 001.

(5)

DECLARATION

I, Dr. SEBASTIAN ANTONY. X, Registration No:201718207 certainly declare that this dissertation titled, “RESTING STATE FUNCTIONAL MRI IN PATIENTS WITH OBSESSIVE COMPULSIVE DISORDER TO DETECT THE AREAS OF ACTIVATION”, represent a genuine work of mine done at the Department of Radio Diagnosis, Stanley Medical College, under the supervision of the PROF.DR.P.CHIRTRARASAN , MDRD, Professor, Department of Radio Diagnosis, Stanley Medical College, Chennai – 600 001.

I, also affirm that this bonafide work or part of this work was not submitted by me or any others for any award, degree or diploma to any other university board, neither in India or abroad. This is submitted to The Tamil Nadu Dr.MGR Medical University, Chennai in partial fulfilment of the rules and regulation for the award of Master of Radiodiagnosis Branch VIII.

Dr. SEBASTIAN ANTONY X

Date : Registration No:201718207

Place: Chennai

3

(6)

ACKNOWLEDGEMENT

I would like to express my deep sense of gratitude to the Dean, PROF. SHANTHI MALAR, M.D., Stanley Medical College, Chennai, and PROF.C.AMARNATH, MDRD, Professor, Head of the Department, Department of Radio Diagnosis, Stanley Medical College, Chennai, for allowing me to undertake this study on “RESTING STATE FUNCTIONAL MRI IN PATIENTS WITH OBSESSIVE COMPULSIVE DISORDER TO DETECT THE AREAS OF ACTIVATION”.

I was able to carry out my study to my fullest satisfaction, thanks to guidance, encouragement, motivation and constant supervisionextended to me, by my beloved Head of the Department PROF.C.AMARNATH, MDRD, Hence my profuse thanks are due for him.

I would like to express my deep gratitude and respect to my guide PROF. DR. P. CHIRTRARASAN, MDRD, whose advice and insight was invaluable to me. This work would not have been possible without h guidance, support and encouragement.

I am also extremely indebted to Professor. Dr. Suhasini.B, MDRD., our former Associate Professor for her valuable suggestions, personal attention and guidance especially at the inception of the study.

My sincerethanks to Professor Dr. G. Sathyan, MDRD forhis practical comments and constructive cricticism during my study and I also wish to thank Prof. Dr. C.Nellaiappan, MDRD for his valuable support through out the study.

(7)

I am bound by ties of gratitude to my respected Associate Professors, Dr. Chezhian.J , Dr. Sudhakar , and Assistant Professors , Dr. Balaji.A, Dr. Komalavalli, Dr. Sivakumar.K, Dr. Priya.M , Dr. Sakthivel Raja.G, in general, for placing and guiding me on the right track from the very beginning of my career in Radiodiagnosis till this day.

I sincerely thank Prof. Dr Alexander Gnanadurai., Department of Psychiatry and other Professors, Associate Professors, Post-Graduates and staff nurses of Department of Psychiatry for helping me and giving me permission to take to conduct this study in Psychiatric patients.

I also thank my past and Present fellow Postgraduates who helped me in carrying out my work and preparing this dissertation. I thank all the

Radiology Technicians, Staff Nurses and all the Paramedical Staff Members in my Department, for their fullest co-operation. I thank my statistician who rendered his valuable timely help in completing this study.

I thank my lovable Parents and My Brothers for their constant and persistent support for my studies and in all my endeavours. My heartfelt thanks to my wife, for her endless support, continued and unfailing love, which helped me to overcome the difficulties encountered in the pursuit of this degree.

I would be failing in my duty if I don’t place on record my sincere thanks to those patients and their relatives who inspite of their sufferings extended their fullest co-operation to the study.

Dr.SEBASTIAN ANTONY.X

(8)

INTRODUCTION

Obsessive Compulsive Disorder (OCD) is disorder characterized by the presence of intrusive unwanted thoughts (obsessions), which the individual tries to neutralize by repetitive behaviours (compulsions).Two psychological mechanisms seems to be associated with OCD- an emotional mechanism characterized by intense anxiety associated with intrusive thoughts & a cognitive mechanism exemplified by executive deficits.¹

Obsessive–Compulsive Disorder (OCD) is associated with a wide array of brain changes, including altered neurotransmitter systems, anatomy, and functional organization, correlating with deficits in cognition, emotion, and behavior. Patients suffering from Obsessive–Compulsive Disorder are unable to shift their attention from internally generated intrusive thoughts, which they perceive as their own and real. Even though Obsessive–Compulsive Disorder patients recognize that their obsessions are irrational, they are still compelled to engage in ritual and/or repetitive behaviors.

Many of these Obsessive–Compulsive Disorder manifestations have been linked to problems in cognitive control.The central executive network includes key regions, such as the dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), and the posterior parietal cortex². It is of central importance for cognitive control, including response inhibition, planning, and set shifting, which are impaired in Obsessive–Compulsive Disorder patients.

(10)

A more thorough understanding of the central executive network and its disease-related plasticity might help us better understand the neurobiological mechanisms of Obsessive–Compulsive Disorder. The brain regions within the central executive network are tightly functionally connected and essential for cognitive control.

Interestingly, these regions appear to exhibit altered structural and functional connectivity in Obsessive–Compulsive Disorder. A wide group of studies ranging from task based functional MRIs to meta-analysis of voxel based morphometry studies has been done so far. These studies found that Obsessive–

Compulsive Disorder patients had gray matter abnormalities in the central executive network, including the DLPFC and OFC³.

Additionally, altered activity in a top-down resting-state control network, including the lateral frontal and cingulate cortices had been observed in Obsessive–Compulsive Disorder. Additional information found was that Obsessive–Compulsive Disorder patients showed increased inward connectivity between brain regions (i.e., DLPFC and OFC) within the executive network.

The blood-oxygen-level dependent activation signal observed using resting-state magnetic resonance imaging (rs-fMRI) indirectly reflects the spontaneous, or intrinsic neuronal activity in the brain at rest. Independent component analysis (ICA) is a data-driven technique that can extract intrinsic patterns of coherent neuronal activities without a prior assumption and can identify several resting-state functional networks.

(11)

Regional homogeneity (ReHo) is a method for measuring local brain synchronization of spontaneous activity within neighboring voxels. Abnormal ReHo values may be related to a disequilibrium of baseline activity in a brain region. The seed-based functional connectivity approach, meanwhile, is the most widely used technique for exploring inter-regional synchronized activities between remote brain regions.

The role of resting state functional MRI in Obsessive Compulsive Disorder has various applications. The main applications of resting state functionial MRI in Obsessive Compulsive Disorder are to detect specific changes in brain in Obsessive–Compulsive Disorder compared to other psychiatric illness and to detect specific changes between different types of Obsessive–Compulsive Disorder ( eg. OCD with tics & without tics).

The other uses of resting state funstional MRI are to measure the cerebral activities which may correspond to treatment( before & after treatment),to detect the effects of various treatments (eg. Psychotherapy, Pharmacotherapy) and to detect activities during symptoms & control states and to detect overlapping of brain changes in Obsessive–Compulsive Disorder patients and their first degree relatives thereby establishing the genetic risk⁴.

In this study, we analyse the functional connectivity to investigate changes in spontaneous neural activities within the central executive network in Obsessive–Compulsive Disorder patients at rest.

(12)

Our hypotheses were that Obsessive–Compulsive Disorder patients would exhibit altered intra and inter-regional functional connectivity within the central executive network and these changes would be associated with the clinical symptoms of Obsessive–Compulsive Disorder. This would help us in establishing the pathophysiology of OCD.

(13)

AIM AND OBJECTIVES

The aim of my study is to detect the various networks of activation in resting state in patients of Obsessive Compulsive Disorder using rs-fMRI (resting state functional MRI) and to compare the same with normal individuals.

This would be of greater use in defining the pathophysiology of Obsessive Compulsive Disorder , the effectiveness of treatment in reducing the activity in abnormal networks and prediction of the course of the disease.

(14)

REVIEW OF LITERATURE

FUNCTIONAL MRI

BOLD (Blood Oxygen level Dependant) imaging is a FMRI method of studying the neural activity of brain indirectly by measuring the hemodynamic response related to neural stimulation⁷. The contrast agent used in this method is deoxy-Hb. The basics behind the use of deoxy-Hb being the contrast is being extensively discussed below.

THE HEMODYNAMIC RESPONSE

Figure 3.1: This figure explains how the cerebral blood flow and oxygenation are closely related to neural activity which forms the basis of functional imaging. The initial dip is due consumption of O2 from local capillaries, followed by overshoot due to increased blood flow and O2 supply following a task which is then followed by slight undershoot.

(15)

First in 1980, Roy and Sherrington demonstrated changes in blood flow and blood oxygenation in the brain are closely linked to neural activity.

When nerve cells are active they consume oxygen carried by hemoglobin in RBCs from local capillaries. The local response to this oxygen utilisation is an increase in blood flow to regions of increased neural activity, occurring after a delay of approximately 1-5 seconds. This haemodynamic response rises to a peak over 4-5 seconds, before falling back to baseline (and typically undershooting slightly).This leads to local changes in the relative concentration of oxyhemoglobin and deoxyhemoglobin and changes in local cerebral blood volume

BOLD IMAGING

Haemoglobin is diamagnetic when oxygenated but paramagnetic when deoxygenated. The magnetic resonance (MR) signal of blood is therefore slightly different depending on the level of oxygenation^5-6. These differential signals can be detected using an appropriate MR pulse sequence as blood-oxygen-level dependent (BOLD) contrast. Higher BOLD signal intensities arise from decreases in the concentration of deoxygenated hemoglobin since the blood magnetic susceptibility now more closely matches the tissue magnetic susceptibility.

By collecting data in an MRI scanner with parameters sensitive to changes in magnetic susceptibility one can assess changes in BOLD contrast.

(16)

These changes can be either positive or negative depending upon the relative changes in both cerebral blood flow (CBF) and oxygen consumption.

Increases in CBF that outstrip changes in oxygen consumption will lead to increased BOLD signal, conversely decreases in CBF that outstrip changes in oxygen consumption will cause decreased BOLD signal intensity.

While arterial blood is similar in its magnetic properties to tissue, deoxygenated blood is paramagnetic and so induces inhomogeneities within the magnetic field in tissue. These cause the MRI signal to decay faster. Signals from activated regions of cortex increase as the tissue becomes more magnetically uniform.

Figure 3.2: This flow chart explains the sequence of events occurring in a task based f-MRI and the basis of acquisition of MR signal.

(17)

THE DESIGNS

There are two types of designs used in task based Functional MRI. They are Block Design-fMRI and Event Design-fMRI. Patients and subjects are positioned in the scanner as for a conventional scan. During a typical functional imaging series, 30 images are acquired in a 90 sec run where the initial and last 10 images are baseline conditions and the middle 10 images (30 secs) are acquired during a task⁸

BLOCK DESIGN

Blocks of the stimulus or task are presented typically for 20-30 seconds, alternating with periods of rest or a “control” condition. The “control’

task is chosen carefully such that it activates all of the neural processes common to the stimulus-task with the exception of the cognitive process of interest. By subtracting the brain regions recruited during the performance of the control task from the brain regions recruited during the test condition, the areas of the brain whose activity is associated specifically with the cognitive process of interest can be identified⁹.

(18)

Figure 3.3: This figure explains the block design of task based f-MRI. Blocks of the stimulus or task are presented typically for 20-60 seconds, alternating with periods of rest or a “control” condition.

EVENT DESIGN

An alternative experimental approach is to present stimuli as isolated brief events separated in time so that the individual response to single events can be identified. The principle advantage of this “event-related” approach is that it avoids the potential confounding factors of habituation or fatigue which may arise in a block design as a consequence of the presentation of repeated identical stimuli.

(19)

Figure 3.4: This figure explains the event design of task based f-MRI.

Presentation of stimuli as isolated brief events separated in time so that the individual response to single events can be identified.

RESTING STATE FUNCTIONAL MRI-THE NEED OF THE HOUR The basic differences between the rs -fMRI and the task based fMRI is that the rs f-MRI analyses of the spontaneous BOLD signal in the absence of any explicit task or an input. The task based fMRI analyses of the spontaneous modulations in the BOLD signal in the presence of a particular activity (e.g.

finger-tapping, eye-blinking, naming, memorizing, etc). While, 60–80% of brain’s energy is consumed during resting state, task-related increase in neuronal metabolism are only less than 5%.¹⁰

During task-based activity the focus is only on a very small fraction of the brain’s overall activity. In terms of overall brain function, the resting state brain activity is far more significant than task-related activity. The signals which are discarded as noise by the task based fMRI are the signals which are utilised by

(20)

the resting state fMRI. Therefore, the resting state fMRI has an improved Signal Noise Ratio (SNR), when compared with task based technique.

In resting state fMRI, the acquired data may be used to analyse one or more functions, whereas in task based fMRI a separate task may be required to analyse each function¹¹. Paediatric patients, patients with low IQ and even patients in the vegetative and coma state are able to do rs-fMRI whereas in task based fMRI patient cooperation is crucially important.

In rs-fMRI ,due to the absence of task, we are able to avoid the task-related confusions and uncertainties faced by task based fMRI. On the contrary, repeated sessions of task-based activity to assess the disease prognosis, treatment effect etc. will result in familiarity with the task which will affect the output adversely.

The functional connectivity methods used to study resting state functional connectivity detect similarities among various regions of the brain, in line with the definition of functional connectivity. These functional connectivity methods can be broadly divided into voxel- based and node- based methods. The key aspect ,the voxel- based methods have in common with node based methods is that they all estimate a functional connectivity value for each voxel in the brain (i.e., they describe functional connectivity in terms of spatially distributed effects). The voxel- based methods estimate functional connectivity, all of these methods result in a map of the brain containing values for all voxels. This map- based output is the main difference between voxel- based and node- based methods.

(21)

VOXEL BASED METHODS

There are four main voxel- based functional connectivity analyses methods 1)Seed- based correlation analysis (SCA)

2)Independent component analysis (ICA)

3)fractional Amplitude of low- frequency fluctuations (fALFF) 4)Regional homogeneity (ReHo).

It is helpful to understand that there are no specifically “wrong” or “right”

functional connectivity methods. However, there are less appropriate and more appropriate functional connectivity analysis methods depending on dataset and research in question.¹²

1)Seed- based correlation analysis (SCA)

There are five important steps involved in running a seed-based correlation analysis on a single subject, as mentioned below¹³:

1. Defining the seed region (using anatomical or functional information).

2. Converting the seed region into a functional space, as it is quite common for the seed to be created in a different space (such as standard space).

3. Extracting the ROI time series.

4. Performing the voxel- wise correlation at every voxel relative to the seed time series to create the SCA map.

5. Fisher’s r- to- Z transformation.

(22)

Figure 3.5: This image depicts the first step in Seed- based correlation analysis (SCA), i.e defining the seed region. Here the posterior cingulate cortex is the defined seed region.

Figure 3.6: This image depicts the third step in Seed- based correlation analysis (SCA), i.e extraction of .the BOLD signal from the seed defined region.

(23)

Figure 3.7: This image depicts the role of Seed- based correlation analysis (SCA), in finding the various networks in brain. Here the identified network is Default Mode Network.

2) Independent Component Analysis (ICA)

Figure 3.8: This image depicts the working model of Independent Component Analysis (ICA). After extraction of the f-MRI data, it is separated into set of components that are each explained by their own time course and a spatial maps.

(24)

The fMRI BOLD data are represented in several rows, such that each row representing data from a 3D volume at one time point (space) and each column representing data from all time points (time) at one voxel. These voxels are all then reordered and assembled next to each other to create a long row, that depicts the entire three- dimensional brain¹⁵.

After performing ICA, this data is separated into a set of components that are each explained by their own time course and a spatial map. One time course is taken for each component (i.e., the number of columns indicates the dimensionality or model order). ¹⁴

For each of these time courses there is a spatial map, and these are indicated in the same way as the input data such that all voxels are assembled in one single long row. The number of rows and the number of columns in the spatial map matrix and the time courses respectively are the same, representing the ICA dimensionality (i.e., the number of components).

3) Fractional Amplitude of low- frequency fluctuations (fALFF)

ALLF is defined as the total power within the frequency range between 0.01 and 0.1 Hz, and is calculated individually for each voxel. It is a method to measure the amplitude of the low- frequency fluctuations (ALLF) for each voxel of the brain in the resting state thereby estimating , the neuronal component of the measured BOLD signal in that region .These low- frequency amplitude fluctuations is higher in gray matter ( highest in DMN followed by posterior cingulate and medial prefrontal cortex) than in white matter¹⁶.

(25)

Therefore, ALLF can be used a marker to spot the brain regions with altered low- frequency amplitudes in patients (for eg, in ADHD children) compared with healthy controls.

Fractional ALLF is defined as the total power in the low- frequency range (0.01– 0.1 Hz) divided by the total power across the entire (detectable) frequency range for the same voxel. It is calculated by dividing the average square root power in the low- frequency range by the average square root power across all estimated frequencies.

Both ALFF and fALLF are relatively easy and fast to calculate.

However this method does not measure functional connectivity directly, instead, results from ALFF and (f)ALFF analyses consolidate the frequency characteristics of the local BOLD data. Therefore, (f)ALFF measures are more sensitive to non- neuronal confounds.

4) Regional homogeneity (ReHo)

Regional homogeneity (ReHo) is defined as the correlation of a voxel’s time series with that of its local neighboring voxels. Therefore, the output of a ReHo analysis is a single whole brain map, in which higher values represent voxels that have strong temporal correlation with their immediate neighborhood of voxels.

The measure that is used to calculate ReHo is Kendall’s coefficient of concordance (KCC). It is usually preferable to do ReHo on the surface of the brain , than in volumetric space.The aim of ReHo is to investigate a

(26)

fundamentally different aspect of functional connectivity compared with the other methods described above and, therefore, it is a potentially interesting marker. The disadvantages of ReHo is that, it is highly sensitive to the amount of spatial smoothing. It is also confounded by non- neuronal localized fluctuations, like in other methods. ¹⁷

NODE BASED METHODS

“Nodes” are different brain regions, and “edges” are the connection strengths between the nodes. Node- based connectivity analyses are a form of graph- based connectivity modeling. A graph is the easy way to represent nodes and edges in a diagram.

Figure 3.9. This figure explains the working model of Node based methods.

The black rounded regions are “ Nodes” and the interconnecting white lines are “Edges”.

(27)

Node- based methods, typically evaluate the connections (“edges”) between a larger number of functional units (“nodes”) that are relatively spatially smaller in size. In node- based methods, group-level analyses compare the strength of connectivity in these edges across subjects.

It is possible, to use voxel- based methods in order to perform subsequent node- based analyses. For example, seed- based correlation maps can be calculated for each voxel to help with node definition.¹⁸

All node- based connectivity analyses have the following steps :

1. Defining the nodes, i.e., grouping voxels together into areas that are to be considered as functionally homogeneous regions.

2. Extracting the timeseries from each node. The timeseries represent the BOLD signal fluctuations over the course of the scan in each node.

3. Calculating the connectivity (“edges”) between all pairs of nodes using the extracted timeseries.

4. Building a connectivity matrix (also called a network matrix or adjacency matrix). For example, if the node- based analysis contains 100 regions (“nodes”

of the graph), you can build a 100 by 100 matrix that describes all possible pair- wise connections (“edges” of the graph). That is, the element on the 8th row and 17th column in the connectivity matrix describes the strength of functional connectivity between node 8 and node 17.

(28)

OBSESSIVE-COMPULSIVE DISORDER (OCD)

Obsessive-compulsive disorder (OCD) is a common, chronic, anxiety condition that can have disabling effects on both genders throughout the patient's lifespan. OCD can manifest with a wide range of clinical pictures. The disorder is among the most disabling anxiety conditions and counts for more than half of serious anxiety cases¹⁹.

The broad spectrum of Obsessive-compulsive related disorders (OCRDs) includes the somatoform disorders (for example, body dysmorphic disorder (BDD) and hypochondriasis, the impulse-control disorders (for example, trichotillomania (TTM), pathological gambling, skin picking and others) and the tic disorders (for example, Tourette's syndrome) but others, including drug induced and non-psychiatric disorders, could overlap and show similar clinical pictures.

The most common age of onset of OCD is reported to be between 22 and 35, while affected patients spend an average of 17 years before receiving a correct diagnosis and treatment, with most OCD and OCRDs often showing a waxing and waning course, frequently increasing in severity when left untreated.

Further increasing the burden of OCD is the fact that affected subjects, along with many psychiatric patients, often experience discrimination and stigmatization due to a non-medical perception of the phenomenon. Yet OCD and OCRDs represent relevant medical conditions. Findings provided by recent studies, mainly focusing on the role played by corticostriatal-thalamic pathways,

(29)

also indicate a relationship between OCD manifestations and its neurobiological basis, suggesting new therapeutic strategies.

HISTORICAL BACKGROUND AND CURRENT NOSOGRAPHY

Obsessions thoughts and compulsive urges or actions are a part of everyday life. We return to check that we locked a door and switched off the light. We cannot stop thinking about the stressful event scheduled for the next week. We refuse to eat with the spoon that dropped on the floor, even if we know the chance of contamination is remote.These events are part of the normal feedback and control loop between our thoughts and our actions, and they have

an ancestral biological survival value²⁰. It is only when obsessive thoughts become frequent or intense, or unavoidable, or when these compulsive rituals become so prominent that they interfere with an individual's functioning, that the diagnosis of OCD is made.

Obsessions and compulsions were first described in the psychiatric literature by Esquirol in 1838, and, by the end of the 19th century, they were generally regarded as manifestations of melancholy or depression. By the beginning of the 20th century, the view of obsessive-compulsive phenomena had begun to shift OCD toward a psychological explanation, Janet had already described the successful treatment of compulsive rituals with what would come to be known behavioral techniques, and with Freud's publication in 1909 of the psychoanalysis of a case of obsessional neurosis (the Rat Man), obsessive and

(30)

compulsive actions came to be seen as the results of unconscious conflicts and the isolation of thoughts and actions from their emotional components.

EPIDEMIOLOGY

The 1 month prevalence of adult OCD is about 0.6% while the DSM-5 12 month prevalence ranges from 0.6% to 1%. Regardless, the prevalence of OCD, as well OCRDs, may vary depending on the source of data and the choice of diagnostic instruments.

There seems to be a bimodal age of onset for OCD. The mean age for adult OCD occurs between age 22 and 35. In a small number of cases the onset of the disorder occurs at age of 50 or more. Usually, the earlier the age of onset, the worse the course of OCD and OCRDs; by contrast, no specific gender predominance has been reported. While economic, social and cultural effects may play a role in producing different clinical pictures of OCD, biological, immune and genetic factors and family predisposition may also contribute to the pathogenesis of the disorder²¹ .For example, streptococcal infection may be associated with an abrupt, exacerbating-remitting early onset form of OCD, which is termed pediatric autoimmune disorder associated with streptococcus (PANDAS).

OCD's burden may also vary depending on the case in question, on the course of disorder and on the fact it is almost unknown among the general population. As a consequence, many patients do not seek medical care until

(31)

(originally) milder forms of OCD and OCRDs become more distressful and possibly harder to treat²². Sometimes patients do not realize that they are affected by OCD. In some cases, the 'typically obsessive' features of intrusive, 'ego- dystonic' feelings and thoughts are absent, as in the poor-insight obsessive- compulsive disorder (PI-OCD), complicating the course and severity of the illness.

DSM-5 DIAGNOSTIC CRITERIA FOR OBSESSIVE-COMPULSIVE DISORDER⁴

A. Presence of obsessions, compulsions, or both:

Obsessions are defined by (1) and (2):

1. Recurrent and persistent thoughts, urges, or images that are experienced, at some time during the disturbance, as intrusive and unwanted, and that in most individuals cause marked anxiety or distress.

2. The individual attempts to ignore or suppress such thoughts, urges, or images, or to neutralize them with some other thought or action (i.e., by performing a compulsion).

Compulsions are defined by (1) and (2):

1. Repetitive behaviors (e.g., hand washing, ordering, checking) or mental acts (e.g., praying, counting, repeating words silently) that the individual feels driven to perform in response to an obsession or according to rules that must be applied rigidly.

(32)

2. The behaviors or mental acts are aimed at preventing or reducing anxiety or distress, or preventing some dreaded event or situation; however, these behaviors or mental acts are not connected in a realistic way with what they are designed to neutralize or prevent, or are clearly excessive.

Note: Young children may not be able to articulate the aims of these behaviors or mental acts.

B. The obsessions or compulsions are time-consuming (e.g., take more than 1 hour per day) or cause clinically significant distress or impairment in social, occupational, or other important areas of functioning.

C. The obsessive-compulsive symptoms are not attributable to the physiological effects of a substance (e.g., a drug of abuse, a medication) or another medical condition.

D. The disturbance is not better explained by the symptoms of another mental disorder (e.g., excessive worries, as in generalized anxiety disorder;

preoccupation with appearance, as in body dysmorphic disorder; difficulty discarding or parting with possessions, as in hoarding disorder; hair pulling, as in trichotillomania [hair-pulling disorder]; skin picking, as in excoriation [skin- picking] disorder; stereotypies, as in stereotypic movement disorder; ritualized eating behavior, as in eating disorders; preoccupation with substances or gambling, as in substance-related and addictive disorders; preoccupation with having an illness, as in illness anxiety disorder; sexual urges or fantasies, as in paraphilic disorders; impulses, as in disruptive, impulse-control, and conduct disorders; guilty ruminations, as in major depressive disorder; thought insertion

(33)

or delusional preoccupations, as in schizophrenia spectrum and other psychotic disorders; or repetitive patterns of behavior, as in autism spectrum disorder).

Specify if:

With good or fair insight: The individual recognizes that obsessive- compulsive disorder beliefs are definitely or probably not true or that they may or may not be true.

With poor insight: The individual thinks obsessive-compulsive disorder beliefs are probably true. With absent insight/delusional beliefs: The individual is completely convinced that obsessive-compulsive disorder beliefs are true.

Tic-related: The individual has a current or past history of a tic disorder.

Figure 3.10:This figure explains the emotional and cognitive impairments in OCD. The emotions such as disgust, shame and guilt are responsible for the Obsessions and the impairments in cognition such as inhibitory control, working memory and cognitive flexibility are responsible for Compulsions.

(34)

NEUROBIOLOGY

Neuroimaging findings indicate OCD involves subtle structural and functional abnormalities of the orbito-frontal cortex (OFC), the anterior cingulate cortex (ACC), the caudate nucleus (Cn), the amygdala nuclei (An), the accumbens nucleus (NAc), the cortical thalamic nuclei (Tn) as well the white matter (WM), the hippocampus (HP) and other regions.²³

The OFC is involved with social consciousness regarding proper behavior. Hypoactivity in this area (whether occurring spontaneously or as a result of damage from a perinatal or head injury, temporal lobe epilepsy, infection or brain tumor and other conditions) leads to coarsening of social consciousness and behaviors. This may lead to hypersexuality (paraphilic OCRDs), overeating behaviour (EDs and Prader-Willi syndrome (PWS)), personality changes and Tourette's syndrome (frequently presenting with inappropriate use of profanity) or crude jokes (Sado Masochistic Disorder (SMD)). Overactivity of the OFC may results in excessive social concern, meticulousness and 'nitpicking' habits, fastidiousness and avoidant behaviors and more.

The Caudate nucleus, along with other striatal structures, is also involved in regular repetitive behaviors . It has been hypothesized that if 'too many' messages regarding worries about 'how things should be done' reach the Caudate nucleus, they are not filtered properly and spill over into consciousness²⁴.

(35)

The anterior caudatus putamen and the Anterior Cingulate Cortex directly leading to the shell of the accumbens nucleus, the pallidus internus and the thalamus may play a specific role in impulsive-repetitive psychic manifestations of OCD and OCRDs .

The dysregulation of the posterior caudatus putamen and the dorsolateral-prefrontal cortex, the pallidus internus and the thalamus may account for the neurological symptoms such as tics, Tourette's motor abnormalities and other OCD spectrum motor issues . Both the striatal and the frontal brain areas are richly supplied with serotonergic neurons. However, even though 5-hydroxytryptamine (5- HT) is a core neurotransmitter involved in OCD and OCRD manifestations, this does not help us in identifying the exact causes of this psychopathology .²⁵

A great amount of literature evidence reports OCD and OCRD to show an inherited transmission. Some families have at least four successive generations with clear OCD cases²⁶ . Since family members could have 'learned' these behaviors from other relatives, the presence of OCD across generations alone is not sufficient to unequivocally prove inheritance. However, successive family members often have different obsessions and compulsions, suggesting that they have not 'learned' them. What appears to be inherited is the capacity to respond to common life experiences with obsessions and compulsions.

There are also studies of inheritance involving identical (homozygotic) and fraternal (heterozygotic) twins, which also provide supportive evidence for an inherited component in OCD and OCRDs²⁷. Molecular genetics studies have

(36)

begun to provide evidence that specific genes may play a role in the manifestations of OCD. Segregation analysis has examined familiar patterns of OCD transmission.

FUNCTIONAL CONNECTIVITY ANALYSIS OF VARIOUS NETWORKS IN RESTING STATE

Functional connectivity studies have reported many networks that result to be functionally connected during rest .These key networks include:

(1) The Default Mode Network (2) The Sensorimotor Network (3) The Visual Network

(4) The Executive Control Network

(5) The Lateralized Fronto-Parietal Network (6) The Auditory Network

(7) The Temporo-Parietal Network

These resting-state networks are anatomically separated, but functionally connected regions showing a high level of correlated BOLD signal activity.

These networks are consistent across various studies, despite differences in their data acquisition and analysis techniques.

(37)

DEFAULT MODE NETWORK (DMN)

This network is seen in:

(1) Precuneus/ Posterior cingulate (2) Lateral parietal cortex

(3) Mesial prefrontal cortex.

This set of regions is activated during the rest and relatively not activated during the demanding tasks. Activation of the DMN regions tend to correlate negatively with the areas that increase activity during demanding tasks²⁸.

The precuneus and posterior cingulate region appears to be more important, because it appears to be related with the other nodes of the network and acts as a mediator of intrinsic connectivity among these regions.

The activity in DMN is due to introspective mental processes, the tendency of human minds to wander, to our ability to rethink about the recent past and to imagine future events. The precuneus and the mesial frontal gyrus, were functionally connected during rest and as well as active-working memory task suggesting that the DMN is a marker of a given cognitive ability.

(38)

Figure 3.11: This figure shows a set of regions namely Posterior cingulate cortex, Precuneus, Lateral prefrontal cortex and Mesial prefrontal cortex.

These regions are functionally connected at rest and are collectively called the Default Mode Network

The strength of this connection may be of diagnostic value for different clinical conditions and diseases. Coherent BOLD signal fluctuations were seen within the DMN during reduced levels of consciousness such as light sleep, as shown by the EEG recording

By all means , we can conclude that coherent activity throughout the DMN:

(1) Emerges principally under resting conditions, but is also present during the performance of active tasks;

(2) Has been observed across different states of consciousness

(3) It is influenced by the characteristics of the task performed just before the data acquisition.

DMN

(39)

SENSORIMOTOR NETWORK (SMN)

This network includes (1) Precentral gyrus (2) Postcentral gyrus

(3) Supplementary motor area.

This network is characterized by the engagement of regions that correspond to motor as well as sensory areas based on anatomical and functional evidences.

Figure 3.12: This figure shows a set of regions namely Precentral gyrus, Post central gyrus and supplementary motor area . These regions are functionally connected at rest and are collectively called the Sensorimotor Network

SMN

(40)

The activity of the SMN in resting state shows a degree of hemispheric lateralization which correlates with the lateralization of activity in the same regions that emerges during an active finger tapping task.

The spontaneous fluctuations seen in this network are likely to reflect the neural activity which subserves active motor tasks. The reason for this is that, the regions that tend to be activated together during active tasks show correlations in the spontaneous activity, like that of a memory of previous coordinated processing²⁹.

VISUAL NETWORK (VN)

Three distinct networks have been reported in literature, during rest.

Network 1 is characterized by the activity in the striate cortex and extra-striate regions such as lingual gyrus,

Network 2 is characterized by the activity in the lateral visual areas such as the occipital pole and occipito-temporal regions

Network 3 is characterized by the activity in the striate cortex and in polar visual areas.

The choice of decomposition parameters on ICA decomposition have caused the different number of visual components observed across various studies.

The visual network number 2 has demonstrated that resting-state BOLD connections were modulated by a visual task performed before data acquisition.

(41)

This evidence supports the fact that resting-state BOLD fluctuations also have dynamic components, that are experience dependent.

Figure 3.13: This figure shows a set of regions namely Occipital pole and Occipito temporal regions . These regions are functionally connected at rest and are collectively called the Visual Network

EXECUTIVE CONTROL NETWORK (ECN)

This signal network is seen in:

(1) Medial frontal gyrus (2) Superior frontal gyrus (3) Anterior cingulate gyrus

VN

(42)

These regions, along with lateral parietal areas, are usually involved functions, such as control processes and working memory³⁰.

Studies have shown that , the intrinsic connectivity throughout this network has been correlated with the performance on the trail-making test, a neuropsychological exam which includes tapping executive functioning. The results exhibit a link between individual differences in intrinsic connectivity and the variability observed in the fundamental features of cognitive functioning.

Figure 3.14: This figure shows a set of regions namely Medial frontal gyrus , Superior frontal gyrus and Anterior cingulate gyrus . These regions are functionally connected at rest and are collectively called the Executive Contol Network.

ECN

(43)

LATERALIZED FRONTO-PARIETAL NETWORK(LFPN)

There are two strongly lateralized networks, one in the right hemisphere and the other in the left hemisphere predominanlty with a specular pattern³¹. This involves:

(1) The inferior frontal gyrus (2) The medial frontal gyrus (3) The precuneus

(4) The inferior parietal gyrus (5) The angular gyrus

As most of these resting-state areas of this network, tend to represent known functional networks, that is, these regions represent sharing of cognitive function, the main role of this network remains less clear.

The regions of this network are closely coupled in a wide range of cognitive processes, such as language , memory , attention and visual functions.

(44)

Figure 3.15(a): This figure shows a set of regions namely inferior frontal gyrus , medial frontal gyrus ,precuneus, angular gyrus and inferior parietal lobule . These regions are functionally connected at rest and are collectively called the Lateralized Fronto-Parietal Network

A recent, resting-state functional study which evaluated language and reading networks found correlations of this network to Broca’s area which included the medial frontal gyrus and the angular gyrus. The activation pattern did not overlap in entirity with this network described here, but their findings are somewhat relevant , as they showed a connectivity-behavior relationship³². In precise, the functional connectivity strength between Broca’s area and Angular gyrus during both rest and reading was had correlated with reading ability.

LFPN

(45)

Figure 3.15(b): This figure shows a set of regions namely inferior frontal gyrus , medial frontal gyrus ,precuneus, angular gyrus and inferior parietal lobule . These regions are functionally connected at rest and are collectively called the Lateralized Fronto-Parietal Network

The measurement of resting-state spontaneous fluctuations before and after the doing a sensorimotor task ,had shown that this network was modulated by motor learning. These results suggest that , the ‘memory trace’ left by a recently acquired motor activity is measurable as a functional connectivity change in the rest condition after learning.

AUDITORY NETWORK (AN)

This network involves:

(1) Superior temporal gyrus (2) Heschl’s gyrus

(3) Insula

(4) Postcentral gyrus.

LFPN

(46)

Figure 3.16: This figure shows a set of regions namely Superior temporal gyrus, Heschl’s gyrus and insula . These regions are functionally connected at rest and are collectively called the Auditory Network.

The overlapping of Task-based fMRI in the region of the superior temporal gyrus with resting state fMRI has been established. However, the connectivity was only anatomical, as the correlation between the neural activity of the resting-state network and the language task has not been evaluated.

TEMPORO-PARIETAL NETWORK (TPN)

This Network involves:

(1) Inferior frontal gyrus (2) Medial temporal gyrus (3) Superior temporal gyrus (4) Angular gyrus

AN

(47)

Figure 3.17: This figure shows a set of regions namely inferior frontal gyrus , medial temporal gyrus , superior temporal gyrus and angular gyrus . These regions are functionally connected at rest and are collectively called the Temporo Parietal Network

This network is characterized by the involvement of regions that are associated to language processing. The functional connectivity map of this network correlated with the resting-state study of regions that are involved in reading, which are identified in the posterior middle temporal gyrus and the inferior frontal gyrus.

The posterior middle temporal gyrus, which was found to be important for language comprehension exhibited a resting-state functional connectivity pattern which overlapped extensively with that of the temporo-parietal network. The patterns of resting-state functional connectivity reflect an intrinsic functional organization underlying cognitive processes which is the language processes.

TPN

(48)

THE ROLE OF RESTING STATE f-MRI IN OBSESSIVE COMPULSIVE DISORDER

Resting-state fMRI is a promising imaging technique that can be used to detect abnormalities in spontaneous neuronal activity. In recent years, most OCD studies have demonstrated resting-state functional connectivity

abnormalities within the cortical–striatal–thalamic–cortical (CSTC) circuits, including the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), thalamus, putamen and caudate nucleus.³³

Figure 3.18: This figure shows a set of regions namely Orbito Frontal Cortex, the striatum and the thalamus . These regions are functionally connected at rest in Obsessive Compulsive Disorder patients and collectively form a network called the Obrito-Frontal Striatal Network

(49)

Furthermore, the strength of the functional connectivity in the resting state between the caudate nucleus and OFC has been reported to predict clinical symptom severity in patients with OCD.The CSTC circuits have extensive connectivity to numerous cortical and subcortical regions, and dysregulation of the connectivity within CSTC circuits is thought to be associated with impaired executive performance, inability to inhibit cognition and behaviour and enhanced error monitoring processes in patients with OCD.

Despite these advances, an important question that remains unanswered is whether or not OCD-related functional changes during the resting state occur only within the CSTC circuits¹⁰. A seed-based approach is useful to detect regionally specific hypotheses of brain function, but has limited power to detect connectivity patterns not predicted a priority.

Figure 3.19: This figure shows a seed based approach which is used to identify various functionally connected regions in the brain. The seed in the Posterior cingulate cortex has connections with the superior and middle frontal gyrus.

(50)

Recently, several lines of evidence from neuroimaging studies have indicated that there are functional abnormalities in large-scale networks outside CSTC circuits, including the parietal, temporal, insular and occipital regions.

Another important question pertains to whether or not there are neuroimaging endophenotypes during the resting state in patients with OCD. Two previous structural studies demonstrated that patients with OCD and their first-degree relatives exhibited grey and white matter abnormalities in common regions, including the OFC and the striatum, suggesting that there may be structural endophenotypes representing markers of increased genetic risk for OCD.

Task-based fMRI studies have revealed abnormally reduced activation of the OFC during reversal learning not only in patients with OCD, but also in their healthy first- degree relatives. These structural and functional findings directly support the existence of underlying neuroimaging endophenotypes and provide insight into the pathophysiology of OCD¹¹.

Using resting-state fMRI, studies have demonstrated that patients with OCD have abnormal resting-state functional connectivity that is not limited to CSTC circuits and involves abnormalities in the limbic system¹². Moreover, studies have identified similar functional connectivity abnormalities within CSTC circuits in patients with OCD and their healthy first-degree relatives. Such brain-based neuroimaging endophenotypes may be helpful in the search for an underlying genetic predisposition.

(51)

MATERIALS AND METHODS

with many psychiatric patients, often experience discrimination and

stigmatization due to a non-medical perception of the phenomenon. Yet OCD and OCRDs represent relevant medical conditions. Findings provided by recent

studies, mainly focusing on the role played by corticostriatal-thalamic pathways,

STUDY DESIGN:Prospective analytical study STUDY PERIOD: 1.5 years

CALCULATION OF THE SAMPLE SIZE:

Sample size N=3.85xP(1-P)/0.0025 Where

3.85 is the confidence interval for normal distribution P is prevalence

0.0025 is the margin of error Here the prevalence is 3%

And sample size is 31

SOURCE OF PATIENTS AND RECRUITMENT:

All patients are recruited from Psychiatry Department of Government Stanley Medical College from the Jan 2018-Aug 2019.

All the patients are diagnosed under DSM-5 criteria and were registered and undergoing treatment in the Psychiatry Department. The Government Stanley Medical College Ethical Committee approved the study protocol.

All the patients and the accompanying relatives were adequately educated about the study. The patients informed consent was sought before the study.Pre-MRI institutional questionnaires were distributed to the patients, and the not falling into criteria –patients were excluded (as described in the exclusion criteria below).

(52)

PATIENT SELECTION Inclusion Criteria

OCD patients diagnosed under DSM-5 criteria who have registered and undergoing treatment in Govt. Stanley hospital

Exclusion Criteria

Patients who are contraindicated for MRI such as

• Implanted electric & electronic devices

• Heart pacemakers

• Implanted hearing aids

• Intracranial metal chips etc. are excluded

• H/O of other psychiatric or neurological illness

• H/O drug or alcohol abuse

• Serious physical illness

• Lack of insight

PATIENT PREPARATION AND POSITIONING

All our images were acquired using a 1.5 T MRI system (Siemens) with an 8-channel phased array head coil. During the MRI scans, all participants were instructed to relax with their eyes closed and lie still without moving. The resting state functional images were acquired using an echo-planar imaging (EPI) sequence.For each participant, the fMRI scanning lasted for 480 seconds, and 240 volumes were obtained.

(53)

DATA ACQUISITION

The parameters used in the acquisition of images are:

S.NO Rs- fMRI Protocol 1.5T Seimens

1 Sequence Gradient echo planar imaging

sequence

2 TR 2000

3 TE 20

4 Flip angle 90

5 Slice thickness 5

6 Image Matrix 320x320

7 Isotropic Voxel Size 3.75x3.75

8 Number of Slices 25

9 Acquisition Order Interleaved bottom top Table 4.1: This table shows the various parameters employed for the acquisition of image.

IMAGING EXAMINATION

Processing of the images and statistical analysis were performed with SPM 12 in MATLAB .Functional data were corrected for di erences in time of acquisition by sinc interpolation, realigned to the first image of every session and linearly and non-linearly normalized to the Montreal Neurological Institute reference brain images .Data were spatially smoothed with a Gaussian kernel . All data were inspected for movement artifacts. In addition, individual parameters entered analyses as covariates of no interest.

(54)

On the first level, various resting state brain activations networks were analyzed voxel wise to for both cases and controls. The Chi-square test employing a number of ratios and associations were used to demonstrate the statistical significance between the cases and controls. They include Pearson Chi- square ,Continuity Correction, Likelihood ratio, Fishers Extract Test and Linear by Linear association.

(55)

STATISTICAL ANALYSIS

The collected data from all the enrolled patients were analysed with IBM.SPSS statistics software 23.0 Version.

To describe about the data descriptive statistics, frequency analysis, percentage analysis were used for categorical variables and the mean & Standard Deviation were used for continuous variables.

To find the significant difference between the bivariate samples in the independent groups, the unpaired sample t-test was used.

To find the significance in categorical data Chi-Square test was used.

In both the above statistical tools the probability value .05 is considered as significant level.

(56)

5.1 AGE DISTRIBUTION AMONG THE STUDY POPULATION

AGE GROUP ( IN YEARS ) STUDY

POPULATION

0 TO 5 0

6 TO 10 0

11 TO 15 0

16 TO 20 9

21 TO 25 30

26 TO 30 9

31 TO 35 3

36 TO 40 4

41 TO 45 0

46 TO 50 2

51 TO 55 0

TOTAL 57

Table 5.1:Frequency table showing the age range distribution of the study population. Average age of the participants in the study for study

population = 23± 14 years.

Figure 5.1:Frequency table showing the age range distribution of the study population. Average age of the participants in the study for study

population = 23± 14 years.

0 10 20 30 40 50 60

AGE DISTRIBUTION AMONG STUDY POPULATION

(57)

5.2 AGE DISTRIBUTION AMONG STUDY POPULATION BASED ON GENDER

AGE GROUP WOMEN MEN

0 TO 5 0 0

6 TO 10 0 0

11 TO 15 0 0

16 TO 20 6 3

21 TO 25 14 16

26 TO 30 9 0

31 TO 35 1 2

36 TO 40 2 2

41 TO 45 0 0

46 TO 50 0 2

51 TO 55 0 0

TOTAL 32 25

Table 5.2:Frequency table showing the age range distribution of the study group based on gender. Average age of the participants in the study

for males = 23± 14 years and for females =22± 13 years .

Figure 5.2 :Graph showing the age range distribution of the

study group based on gender. Average age of the participants in the study

0 5 10 15 20 25 30 35

0 TO 5 6 TO 10

11 TO 15

16 TO 20

21 TO 25

26 TO 30

31 TO 35

36 TO 40

41 TO 45

46 TO 50

51 TO 55

TOTAL

AGE DISTRIBUTION AMONG STUDY POPULATION

BASED ON GENDER

WOMEN MEN

(58)

5.3 AGE DISTRIBUTION AMONG STUDY POPULATION BASED ON CASES AND CONTROLS.

Table 5.3:Frequency table showing the age range distribution of the study group based on cases and controls. Average age of the participants in the study for cases = 23± 14 years and for controls =22± 13 years

Figure 5.3:Graph showing the age range distribution of the

study group based on cases and controls. Average age of the participants in the study for cases = 23± 14 years and for controls =22± 13 years

0 2 4 6 8 10 12 14 16 18 20

0 TO 5 6 TO 10 11 TO 15 16 TO 20 21 TO 25 26 TO 30 31 TO 35 36 TO 40 41 TO 45 46 TO 50 51 TO 55

AGE DISTRIBUTION AMONG CASES AND CONTROLS

CASES CONTROLS

AGE

DISTRIBUTION CASES CONTROLS

0 TO 5 0 0

6 TO 10 0 0

11 TO 15 0 0

16 TO 20 2 7

21 TO 25 12 19

26 TO 30 9 0

31 TO 35 3 0

36 TO 40 4 0

41 TO 45 0 0

46 TO 50 2 0

51 TO 55 0 0

TOTAL 31 26

(59)

5.4 GENDER DISTRIBUTION AMONG STUDY POPULATION BASED ON CASES AND CONTROLS

Figure 5.4 (a): Gender distribution among study population based on cases and controls. 25% of the cases were males and 75% of cases were females.

60% of the controls were males and 40% of the controls were females

Figure 5.4 (b): Gender distribution among study population. 55% of the study population were males and 45% of the study population were females.

10 15

21 11

0 5 10 15 20 25

CASES CONTROLS

GENDER DISTRIBUTION AMONG CASES AND CONTROLS

FEMALE MALE

GENDER DISTRIBUTION

MALE FEMALE

GENDER CASES CONTROLS

MALE 10 15

FEMALE 21 11 TOTAL 31 26

(60)

5.5. FUNCTIONAL ACTIVITY IN DEFAULT MODE NETWORK AMONG CASES AND CONTROLS

Figure 5.5: Barchart showing the functional activity in Default Mode Network among cases and controls. 83% of the cases showed activity in this region and 92% of the controls showed activity in this region

Study population

Functional Activity Present

Functional Activity Absent

Total responses

Cases 26 5 31

Controls 24 2 26

Total 50 7 57

Table 5.5 (a): Table showing the functional activity in Default Mode Network among cases and controls. 83% of the cases showed activity in this region and 92% of the controls showed activity in this region.

0 10 20 30 40 50 60

Functional Activity Present Functional Activity Absent total responses

DEFAULT MODE NETWORK (DMN)

cases controls total

(61)

5.5 FUNCTIONAL ACTIVITY IN DEFAULTY MODE NETWORK AMONG CASES AND CONTROLS

CHI-SQUARE TESTS Value df Asymp. Sig.

(2-sided)

Exact Sig.

(2-sided)

Exact Sig.

(1-sided) Pearson

Chi-Square .934^a 1 0.334

Continuity

Correction^b 0.315 1 0.574

Likelihood

Ratio 0.969 1 0.325

Fisher's

Exact Test 0.436 0.291

Linear-by- Linear Association

0.918 1 0.338

N of Valid

Cases 57

No. of valid study - 57

a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is 3.19.

b. Computed only for a 2x2 table

Table 5.5 (b) : Chi square test showing no significant difference (P=0.334) in Functional activity concerned with Default Mode Network among the tests and controls in the study group.

“RESTING STATE FUNCTIONAL MRI IN PATIENTS WITH OBSESSIVE COMPULSIVE DISORDER TO DETECT THE AREAS

A DISSERTATION ON