Multimodal Resting‐State fMRI Reveals Subtype‐Specific Network‐Level Functional Differences in Frontotemporal Dementia
Bibliographic record
Abstract
BACKGROUND: Frontotemporal dementia (FTD) is a major cause of early-onset dementia, including behavioral variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA), and nonfluent variant PPA (nfvPPA). Each subtype is linked to distinct but overlapping disruptions in brain networks. Resting-state functional MRI (rs-fMRI) offers a non-invasive way to assess intrinsic brain activity and may help identify subtype-specific functional biomarkers (1). While functional connectivity (FC) has been widely studied, other rs-fMRI metrics: Amplitude of Low-Frequency Fluctuations (ALFF), fractional ALFF (fALFF), and Regional Homogeneity (ReHo), remain underexplored. These metrics capture complementary aspects of brain function, and their combined use may improve early and accurate differentiation of FTD subtypes. METHOD: We analyzed T1-weighted and rs-fMRI data from 98 FTD patients:41 bvFTD (mean age 60.6 ± 6.5 years; 27 males, 14 females), 31 svPPA (63.2 ± 6.4 years; 19 males, 12 females), and 26 nfvPPA (68.0 ± 7.7 years; 12 males, 14 females), along with 88 controls (62.8 ± 7.6 years; 38 males, 50 females), obtained from the NIFD/FTLDNI database via the IDA platform (2). Preprocessing was performed using fMRIPrep (3), followed by XCP-D (4). ALFF, fALFF, ReHo, and FC metrics. All measures were averaged within Gordon atlas regions (5). Two-sample t-tests with Bonferroni correction were used for group comparisons (FWE p < 0.05; FC p < 0.01), and results were mapped to functional networks. RESULT: Significant differences were observed between FTD subtypes and controls (Figure 1), bvFTD showed the most widespread ALFF alterations (p = 0.020), especially in frontal and salience regions. svPPA exhibited localized ALFF changes (p ≈ 0.020). fALFF differences were most notable in nfvPPA (p = 0.0186). ReHo alterations were strongest in svPPA (p = 0.0076), followed by nfvPPA (p = 0.017) and bvFTD (p = 0.027). ReHo and fALFF distinguished svPPA from nfvPPA (Figure 2). Only bvFTD showed significant FC disruptions (29 edges, FWE p < 0.05)(Figure 3). CONCLUSION: Multimodal rs-fMRI revealed subtype-specific alterations in FTD. bvFTD showed widespread disruptions, while svPPA and nfvPPA had more localized changes in attention and default networks. Reference: 1. Canu et al., Mol Psychiatry, 2022. 2. https://ida.loni.usc.edu/login.jsp 3. Esteban et al., Nat Methods, 2019. 4. Mehta et al., Imaging Neurosci, 2024. 5. Gordon et al., Cereb Cortex, 2016.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".