P3‐402: Detection of PCC functional connectivity characteristics in subcortical vascular mild cognitive impairment: A resting‐state fMRI study
Bibliographic record
Abstract
Subcortical vascular mild cognitive impairment (svMCI) is known to be a prodromal stage of subcortical vascular dementia (SVaD) (1-3) who may also developed to mixed Alzheimer's disease (AD) (4). Based on position emission tomography (PET) (5) and structural MRI (sMRI) (3) data sets, neuroimaging studies showed that functional and structural deficits in the cortical brain regions [e.g., the medial frontal cortex, the posterior cingulate cortex (PCC) and the medial temporal lobe] and the subcortical brain regions (e.g., the thalamus and the caudate) in svMCI. Increasing evidence from diffusion tensor imaging (DTI) studies (6, 7) suggested that these locally deficits might arise from disturbances of fiber connections. To explore the brain network deficits in svMCI, we employed the regions of interest (ROI) based functional connectivity (FC) method investigating FC alterations in the svMCI patients. Fifty-four right-handed participants, including 26 svMCI patients and 28 healthy controls (HC) participated in this study. The recruited patients were outpatients who were registered at the neurology department of Xuanwu Hospital, Capital Medical University, Beijing, China. All images were acquired using a 3.0 T Siemens scanner at Xuanwu Hospital, Capital Medical University. Functional MRI data was preprocessed usingSPM5 package (http://www.fil.ion.ucl.ac.uk/spm), including slice timing, head motion correction, spatial normalization (through structural images), spatial smoothing (radius = 6mm) and temporal filtering (0.01 - 0.08 Hz). The resultant functional images were subject to voxel based Functional connectivity computation using REST software (www.restfmri.net) with the PCC (0,-52,22; radius = 5mm) as the ROI. Then, a Fisher z-transform was applied to normalize the correlation coefficients. To determine between-group differences in FC, multiple linear regression analyses were separately performed with age, gender and years of education as covariates. All results were presented at the statistical threshold of P < 0.05 for individual voxel combined with P < 0.05 for spatial cluster size determined by Monte Carlo simulations (8). Compared with normal controls, widespread PCC related functional connectivity changes were found in the svMCI patients.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".