Patients With Pain Disorder Show Gray-Matter Loss in Pain-Processing Structures: A Voxel-Based Morphometric Study
Bibliographic record
Abstract
Objective: To investigate whether the functional changes in pain disorder might be reflected by structural brain changes. Pain disorder assessed with the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria is characterized by persistent and distressing chronic pain at one or more body sites which cannot be fully explained by a physiological process or somatic disorder. Psychological factors are thought to play a major role. Recent neuroimaging studies evidenced altered pain processing in patients suffering from this disorder. Methods: Fourteen right-handed women fulfilling the DSM-IV criteria for pain disorder and 25 healthy age-matched women were investigated with magnetic resonance imaging. In the voxel-based morphometry analysis, we compared both groups for changes of gray-matter density. We included age and Beck Depression Inventory scores as nuisance variables to minimize possible confounding effects of age or depressive comorbidity. Results: In the patient group, we found significant gray-matter decreases in the prefrontal, cingulate, and insular cortex. These regions are known to be critically involved in the modulation of subjective pain experiences. Conclusions: In the context of similar results in patients with other functional pain syndromes, such as fibromyalgia and chronic back pain, we suggest that structural changes in fronto-limbic brain circuits represent not only an objective marker of these pain syndromes but also constitute a critical pathophysiological element. These findings represent a further proof of the important role of central changes in pain disorder. DSM-IV = Diagnostic and Statistical Manual of Mental Disorders, 4th Edition; ICD-10 = International Statistical Classification of Diseases and Related Health Problems, 10th Revision; VBM = voxel-based morphometry; fMRI = functional magnetic resonance imaging; GM = gray matter; WM = white matter; CSF = cerebrospinal fluid; BDI = Beck Depression Inventory; MNI = standardized reference space defined by the Montreal Neurological Institute; SOMS = screening for somatoform symptoms; PPS = Pain Perception Scale; SCID = Structured Clinical Interview for DSM-IV disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".