Gray and white matter alterations in Obsessive-Compulsive Personality Disorder: a data fusion machine learning approach
Abstract
The field of clinical psychology and neuroscience consistently seeks to deepen its understanding of complex mental conditions. Among these, Obsessive-Compulsive Personality Disorder (OCPD) stands out as a particularly intricate mental condition. It is fundamentally characterized by an pervasive pattern of excessive perfectionism, an overwhelming preoccupation with orderliness, and an unyielding rigidity in thought and behavior. These traits frequently emerge and become firmly established during the formative years of adolescence or in early adulthood. Epidemiological data suggest that OCPD affects a notable segment of the population, with prevalence rates estimated to range from 1.9% to 7.8%.
It is crucial to distinguish OCPD from Obsessive-Compulsive Disorder (OCD), a related yet distinct condition. While both disorders share the term “obsessive-compulsive,” OCPD is differentiated by a fundamental compromise of the individual’s personality structure, marked by a deeply distorted self-representation, and an altered perception of others. Despite these evident clinical differences, and paradoxically, unlike the comparatively well-studied neural bases of OCD, the neurobiological underpinnings of OCPD remain significantly understudied. The limited research conducted thus far has primarily identified alterations in gray matter volume or density within specific brain regions, most notably the striatum and the prefrontal cortex. However, a truly comprehensive and integrated model of OCPD’s neurobiology, particularly concerning the potential contribution of white matter abnormalities, remains largely unclear and warrants further investigation.
One particularly intriguing and compelling hypothesis, which has gained traction in the study of other anxiety disorders and OCD, posits that regions belonging to the Default Mode Network (DMN) might play a significant role in OCPD as well. The DMN is a network of brain regions that are active when an individual is not focused on the outside world and the brain is at wakeful rest, such as during daydreaming or self-reflection. Given its involvement in self-referential processing and social cognition, its potential disruption in OCPD, which involves distorted self-perception and interpersonal rigidity, presents a compelling avenue for research.
Methods
To rigorously test this intriguing hypothesis and explore the neurobiological underpinnings of OCPD more comprehensively, a novel analytical approach was employed. Gray matter and white matter neuroimaging data were acquired from two distinct groups: 30 individuals who had received a formal diagnosis of OCPD, comprising 73% females with a mean age of 29.300 years, and a control group consisting of 34 non-OCPD individuals, with 82% females and a mean age of 25.599 years. For the first time in the investigation of OCPD, these multimodal imaging datasets were subjected to analysis using a sophisticated data fusion unsupervised machine learning method. This method is specifically known as Parallel Independent Component Analysis (pICA). The primary objective of employing pICA was to detect the joint contribution of both gray matter and white matter modalities to the distinctive neurobiological profile associated with an OCPD diagnosis, thereby providing a more holistic understanding than single-modality analyses.
Results
The application of the Parallel Independent Component Analysis (pICA) to the neuroimaging data yielded significant and insightful results, indicating distinct neural differences between the OCPD group and the control group. Specifically, the analysis revealed that two gray matter networks, identified as GM-05 and GM-23, along with one white matter network, designated WM-25, exhibited statistically significant differences between individuals diagnosed with OCPD and the non-OCPD control participants.
Further detailed examination of these identified networks provided crucial anatomical and functional insights. The GM-05 network was found to encompass key brain regions that are well-established components of both the Default Mode Network (DMN) and the Salience Network. The DMN is primarily associated with self-referential thought and internal processing, while the Salience Network is involved in detecting and orienting attention to important internal and external stimuli. Crucially, the activity or connectivity within this GM-05 network was also found to be significantly correlated with anxiety levels, suggesting a potential neurobiological link between these brain networks and the experience of anxiety in OCPD. The second gray matter network, GM-23, included specific portions of the cerebellum, a region traditionally associated with motor control but increasingly recognized for its roles in cognition and emotion, alongside parts of the precuneus, which is involved in self-consciousness and memory, and the fusiform gyrus, known for its role in object and face recognition. Lastly, the white matter network, WM-25, was found to comprise white matter tracts and bundles that are spatially adjacent to and connect regions belonging to the Default Mode Network. This finding is particularly significant as it suggests that the structural integrity of communication pathways within and around the DMN may be altered in OCPD, further supporting the hypothesis of DMN involvement.
Discussion
These compelling findings represent a significant advancement in our understanding of the neurobiology of Obsessive-Compulsive Personality Disorder. By employing a sophisticated data fusion approach that simultaneously considered both gray and white matter contributions, our study has shed new and illuminating light on the intricate neural underpinnings of OCPD. The identification of specific gray matter networks, notably GM-05 and GM-23, and a distinct white matter network, WM-25, that differentiate individuals with OCPD from healthy controls provides crucial anatomical and functional insights.
The most notable finding is the involvement of GM-05, a network comprising regions from both the Default Mode Network and the Salience Network. The Default Mode Network’s association with self-referential thought, introspection, and social cognition, coupled with the Salience Network’s role in detecting and integrating emotionally relevant information, aligns well with the core clinical characteristics of OCPD, such as distorted self-representation, excessive perfectionism, and rigidity in social interactions. The observed correlation between GM-05 and anxiety further underscores the clinical relevance of these neural alterations. The involvement of WM-25, comprising white matter adjacent to DMN regions, strongly suggests that not only the functional hubs of the DMN but also the structural connections supporting its activity may be compromised in OCPD. This points towards potential disruptions in the efficient communication pathways necessary for fluid self-regulation and social processing. The findings regarding GM-23, encompassing the cerebellum, precuneus, and fusiform gyrus, also contribute to a more nuanced view of OCPD neurobiology, extending beyond previously identified regions.
Collectively, these results provide strong evidence for the involvement of distributed gray and white matter networks, particularly those linked to the Default Mode Network, in the pathophysiology of OCPD. FDW028 This comprehensive neurobiological signature not only enhances our fundamental understanding of this understudied disorder but also holds promising implications for future clinical applications. By identifying specific neural markers associated with OCPD, these findings may ultimately pave the way for the development of more objective and biologically informed diagnostic tools and potentially novel therapeutic interventions tailored to the specific neural dysfunctions observed in individuals with Obsessive-Compulsive Personality Disorder. The continued exploration of these objective markers could revolutionize how OCPD is diagnosed, monitored, and treated, moving beyond purely symptom-based assessments to incorporate quantifiable neurobiological indicators.