Progression from Vegetative to Minimally Conscious State Is Associated with Changes in Brain Neural Response to Passive Tasks: A Longitudinal Single-Case Functional MRI Study
Bibliographic record
Abstract
OBJECTIVES: Functional magnetic resonance imaging (fMRI) may be adopted as a complementary tool for bedside observation in the disorders of consciousness (DOC). However, the diagnostic value of this technique is still debated because of the lack of accuracy in determining levels of consciousness within a single patient. Recently, Giacino and colleagues (2014) hypothesized that a longitudinal fMRI evaluation may provide a more informative assessment in the detection of residual awareness. The aim of this study was to measure the correspondence between clinically defined level of awareness and neural responses within a single DOC patient. METHODS: We used a follow-up fMRI design in combination with a passive speech-processing task. Patient's consciousness was measured through time by using the Coma Recovery Scale. RESULTS: The patient progressed from a vegetative state (VS) to a minimally conscious state (MCS). Patient's task-related neural responses mirrored the clinical change from a VS to an MCS. Specifically, while in an MCS, but not a VS, the patient showed a selective recruitment of the left angular gyrus when he listened to a native speech narrative, as compared to the reverse presentation of the same stimulus. Furthermore, the patient showed an increased response in the language-related brain network and a greater deactivation in the default mode network following his progression to an MCS. CONCLUSIONS: Our findings indicate that longitudinal assessment of brain responses to passive stimuli can contribute to the definition of the clinical status in individual patients with DOC and represents an adequate counterpart of the bedside assessment during the diagnostic decision-making process. (JINS, 2016, 22, 620-630).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".