MRI biomarkers for memory-related impairment in amyotrophic lateral sclerosis: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder associated with cognitive and behavioral impairments and motor symptoms. Magnetic resonance imaging (MRI) biomarkers have been investigated as potential tools for detecting and monitoring memory-related impairment in ALS. Our objective was to examine the importance of identifying MRI biomarkers for memory-related impairment in ALS, motor neuron disease (MND), and ALS frontotemporal dementia (FTD) (ALS-FTD) patients. METHODS: PubMed and Scopus databases were searched. Keywords covering magnetic resonance imaging, ALS, MND, and memory impairments were searched. There were a total of 25 studies included in our work here. RESULTS: The structural MRI (sMRI) studies reported gray matter (GM) atrophy in the regions associated with memory processing, such as the hippocampus and parahippocampal gyrus (PhG), in ALS patients. The diffusion tensor imaging (DTI) studies showed white matter (WM) alterations in the corticospinal tract (CST) and other tracts that are related to motor and extra-motor functions, and these alterations were associated with memory and executive function impairments in ALS. The functional MRI (fMRI) studies also demonstrated an altered activation in the prefrontal cortex, limbic system, and other brain regions involved in memory and emotional processing in ALS patients. CONCLUSION: MRI biomarkers show promise in uncovering the neural mechanisms of memory-related impairment in ALS. Nonetheless, addressing challenges such as sample sizes, imaging protocols, and longitudinal studies is crucial for future research. Ultimately, MRI biomarkers have the potential to be a tool for detecting and monitoring memory-related impairments in ALS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".