Identifying altered brain networks and optimal stimulation loci for individualised Alzheimer’s disease treatment
Bibliographic record
Abstract
Dementia is the seventh leading cause of death worldwide, with most cases attributed to Alzheimer's disease (AD). AD is associated with cognitive decline and disrupted brain connectivity. Current treatment methods are mainly pharmacological and only address symptoms without stopping disease progression. An alternative treatment method is to modulate the structure of the neurons through neuromodulation, such as transcranial magnetic stimulation (TMS). The goal of this thesis is to identify connectivity alterations in AD and suggest stimulation loci for individualised treatments. Disrupted connections in AD can be inspected noninvasively using diffusion magnetic resonance imaging (dMRI). Traditional approaches involve analysing individual connections, which becomes inefficient in large networks. The network-based statistic (NBS) is a MATLAB toolbox that offers a more efficient approach by detecting connectivity differences at the subnetwork level. The resulting regions with the most connectivity differences are suggested as stimulation sites in neuromodulation. This study used dMRI-derived matrices from the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI-3), comprising 892 individuals divided into three groups: healthy controls, mild cognitive impairment, and AD patients. Covariates included gender, age, and Montreal Cognitive Assessment scores. NBS revealed significant alterations in the subcortex and the default mode network (DMN). The strong connections found in cognitive impairment were interpreted as compensatory, with MCI subjects exhibiting the highest DMN connectivity. In AD subjects, the connectivity in the subcortex was reduced, particularly in the hippocampus and thalamus. Only cortical sites were proposed as stimulation targets to strengthen weakened connections, or attenuate strengthened connections. Weakened subcortical regions were indirectly targeted through strongly connected cortical nodes. In contrast, the overactive regions were identified as targets for stimulation to reduce excessive activity. However, the effects of indirect stimulation are uncertain, and may lead to unintended activation of off-target areas. Future research should explore a broader range of NBS parameters, and neuropsychological test scores to validate these findings and provide a stronger basis for disease progression analysis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".