Task-fMRI Group and Functional Connectivity Analysis of the Brain During Faradarmani Consciousness Field Connection
Bibliographic record
Abstract
Taheri Consciousness (T-Consciousness) was introduced and defined by Mohammad Ali Taheri as one of the constituent components of the Cosmos in addition to matter and energy, from which Taheri Consciousness Fields (TCFs) are derived. TCFs are not matter or energy, but they can be proven by scientific experiments. The effect of Faradarmani CF, as one TCFs, was examined in this study. Faradarmangar is a certified and trained individual who has been entrusted with the TCFs. Task fMRI has played a critical role in recognizing the specific functions of the different regions of the human brain during various cognitive activities. This study aimed to investigate the group analysis and functional connectivity in the Faradarmangars’ brains during Faradarmani CF (FCF) connection. Using task functional MRI (task-fMRI), we attempted the identification of different activated and deactivated brain regions during the TCFs connection. Clusters that showed significant differences in peak intensity between the task and rest groups were selected as seeds for seed-voxel analysis. Connectivity of group differences in functional connectivity analysis was determined following each activation and deactivation network. In this study, we report the fMRI-based representation of the FCF connection at the human brain level. The group analysis of the FCF connection task revealed activation of the frontal lobe (BA6/BA10/BA11). Moreover, seed-based functional connectivity analysis showed decreased connectivity within activated clusters and posterior Cingulate Gyrus (BA31). Moreover, we observed increased connectivity within deactivated clusters and the frontal lobe (BA11/BA47) during the FCF connection. Activation clusters, as well as the increased and decreased connectivity between different regions of the brain during the FCF connection, firstly, validates the significant effect of FCF and, secondly, indicates a distinctive pattern of connection with this non material and non-energetic field in the brain.
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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) | 0.001 | 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".