IC‐P‐064: Changes of brain network connectivity in early Alzheimer's disease: Preliminary findings applying a data‐driven approach
Bibliographic record
Abstract
Resting-state fMRI signal fluctuations have been used to study the default brain network connectivity. Such studies on AD have often applied user-biased reference functions and/or regions of interest, while emerging data-driven methods have shown promise, or even superior, performance for automatically mapping synchronous brain activations. Here, we apply an efficient self-organizing-map-clustering method to identify AD-associated changes in regional activation and global functional connectivity. Patients with mild AD (n = 6, age = 84+/-5 years, 3MS = 74+/-7) and healthy older adults (n = 17, age = 76+/-6 years, 3MS = 97+/-5) were scanned consecutively for 60 seconds during rest using a 4-Tesla Varian-Oxford human imaging system. Data were acquired using a two-shot spiral readout sequence (TR/TE = 1000/15ms, flip angle = 60°; FOV = 240×240×121 mm; matrix = 64 × 64; 22 axial slices, 5.0 mm + 0.5mmgap). Data were pre-processed and filtered for noise and low (>0.1 Hz) and high (<0.01 Hz) frequency and oscillating trends. A self-organizing-map algorithm was employed to identify characteristic temporal patterns. In an iterative correlation process, voxels were arranged in a two-dimensional lattice according to the similarity of their temporal patterns. The k-means clustering algorithm was used to partition the trained map into hard clusters. Data from each scan was examined independently three times and individual means were calculated. A similar number of clusters were observed for the AD and control groups (7.5+/-1.5 vs. 6.5+/-1.2, p > 0.05). The cluster members involved voxels located closely and those located remotely, representing the somato-motor, visio-spatial, and memory networks. However, the spatial representation of temporal patterns in AD was comparatively sparse in AD, and few voxels (p < 0.05) with greater variations in time-course were found within a cluster. In contrast, the intensity of signal fluctuations in certain cortical regions including the prefrontal lobes was greater in AD (p < 0.05). The altered spontaneous fMRI fluctuations in early AD suggest increased functional activation in certain cortical regions and decreased global functional synchronization connecting many such regions. Nevertheless, heterogeneity in spontaneous neural activity presents in both AD patients and healthy older adults.
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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 | 0.001 |
| 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) | 0.001 | 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".