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Using Graph Theory to Connect the Dots in Obsessive-Compulsive Disorder
| Content Provider | Semantic Scholar |
|---|---|
| Author | Taylor, Stephan F. |
| Copyright Year | 2014 |
| Abstract | The typical functional magnetic resonance imaging (fMRI) study seeks to map psychological faculties, such as fear or working memory, to specific structures, such as the amygdala or dorsolateral prefrontal cortex. Although specialization of brain regions is a well-established principle, many functions are distributed widely and emerge from the integrated activity of networks or highly connected subsystems of the brain. With the discovery of intrinsic (“resting state”) activity in the blood oxygenation level–dependent (BOLD) fMRI signal, an alternative approach to measuring brain function has emerged. This approach has blossomed within the new field of connectomics, which employs powerful analytic techniques and ever-expanding computing power to describe the functional and structural organization of the brain at multiple scales (1). Connectomics also provides conceptual and experimental leverage for understanding psychopathology in terms of “network-emergent” functions, which may not readily map onto the more intuitive faculty psychological categories that typically guide the design of fMRI experiments. In this issue of Biological Psychiatry, Shin et al. (2) employ one of these techniques, called graph theory, to analyze resting state fMRI scans in patients with obsessive-compulsive disorder (OCD), before and after treatment with a selective serotonin reuptake inhibitor (SSRI). Graph theory provides a means to study the characteristics of a network, or graph, defined as a set of nodes (also called “vertices”) connected by edges (Figure 1), and it has been usefully applied to phenomena as diverse as social relationships, semantics in language, and molecular interactions as well as the organization of the brain. Networks can be described mathematically based on how efficiently information can be exchanged, which nodes are highly connected “hubs,” and which nodes form clusters or “modules.” Graph theory can also provide metrics to capture global concepts about brain function, such as the balance between segregation (modules) and integration (connections between modules via hubs). The metrics provided by graph theory can seem very removed from the behavior of individual neurons, raising questions as to the significance of these abstract formulations. At this stage of research, this work is necessarily exploratory, but accumulating studies have begun to link these metrics with meaningful phenomena, such as development and behavioral performance. For example, connectivity metrics associate with differences in performance on cognitive domains and subscale performance on intelligence tests and personality traits, and the topology of connectivity is heritable. Network hubs emerge in infants in sensorimotor cortex and subsequently switch to heteromodal cortex in adults through the course of development (3). Over the last 6 years, graph theoretical analyses have been fruitfully |
| Starting Page | 593 |
| Ending Page | 594 |
| Page Count | 2 |
| File Format | PDF HTM / HTML |
| DOI | 10.1016/j.biopsych.2014.01.012 |
| PubMed reference number | 24673774 |
| Journal | Medline |
| Volume Number | 75 |
| Alternate Webpage(s) | https://api.elsevier.com/content/article/pii/S0006322314000511 |
| Alternate Webpage(s) | https://www.sciencedirect.com/science/article/pii/S0006322314000511?dgcid=api_sd_search-api-endpoint |
| Journal | Biological Psychiatry |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
