Used for fine-tuning training suitable for pathological cases

[xingzouxiaoli]

Hello, I am trying to use the Krakencoder model, but the correlation coefficient after prediction is not very good. I plan to fine-tune the model with my own data to better adapt to my patients, but the problem I currently face is that because the number of my subjects is very small, only 105, I cannot perform PCA256. Could you tell me if there are any other solutions? I only got to this step

流程.txt

[kjamison]

What are you finding as far as correlation coefficients for your initial "adaptmode meanfit+meanshift" outputs? If you run the following it will produce heatmaps that show several output metrics:

python run_model.py --inputdata
    "[FCcorr_fs86_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_fs86_hpf.mat" \
    "[FCcorr_fs86_hpfgsr]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_fs86_hpfgsr.mat" \
    "[FCpcorr_fs86_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCpcorr_fs86_hpf.mat" \
    "[FCcorr_shen268_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_shen268_hpf.mat" \
    "[FCcorr_shen268_hpfgsr]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_shen268_hpfgsr.mat" \
    "[FCpcorr_shen268_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCpcorr_shen268_hpf.mat" \
    "[FCcorr_coco439_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_coco439_hpf.mat" \
    "[FCcorr_coco439_hpfgsr]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_coco439_hpfgsr.mat" \
    "[FCpcorr_coco439_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCpcorr_coco439_hpf.mat" \
    "[SCsdstream_fs86_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCsdstream_fs86_volnorm.mat" \
    "[SCifod2act_fs86_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCifod2act_fs86_volnorm.mat" \
    "[SCsdstream_shen268_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCsdstream_shen268_volnorm.mat" \
    "[SCifod2act_shen268_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCifod2act_shen268_volnorm.mat" \
    "[SCsdstream_coco439_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCsdstream_coco439_volnorm.mat" \
    "[SCifod2act_coco439_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCifod2act_coco439_volnorm.mat" \
    --adaptmode meanfit+meanshift \
    --checkpoint kraken_chkpt_SCFC_fs86+shen268+coco439_pc256_225paths_latent128_20240413_ep002000.pt \
    --inputxform kraken_ioxfm_SCFC_fs86_pc256_710train.npy \
        kraken_ioxfm_SCFC_shen268_pc256_710train.npy \
        kraken_ioxfm_SCFC_coco439_pc256_710train.npy \
    --fusion --inputname all --outputname all \
    --heatmap sample_heatmap.png \
    --heatmapcsv sample_heatmap.csv \
    --heatmapmetrics top1acc avgcorr avgrank avgcorr_resid \
    --heatmap_colormap magma2

If you can show me what those heatmaps look like, I can help assess whether it is running as expected.

The specific problem you are encountering with re-training is actually a bug on our end. My documentation suggests "pca50" is valid but it actually requires "pc50". I just pushed a commit to accept "pca50" as well, but if you change it to --transformation pc50, it should run. That said, for so few subjects, I might not expect fine-tuning to be appropriate. You would generally want to separate your data into training and testing, so your evaluation would be on an even smaller N. By default, run_training.py uses --trainvalsplitfrac .8 --valsplitfrac .1 which for your data would mean 84 subjects are "train+val" (.8*105), 75 are in the "train" set (84*(1-.1)), 8 are in the "val" set (84*.1) used for estimating performance during training, and 21 are in the completely ignored "test" set (which you could use for held-out testing after tuning the model). 75 subjects is a small N for training, 8 is a VERY small N for any of the correlation or identifiability validation metrics, and 21 is still a very small N as well for any testing.

In our own work, when we have applied the krakencoder model to smaller pathological datasets, for instance 100 patients with MS as mentioned in the supp fig 1 in our paper's supplement, we stuck with the run_model.py --adaptmode meanfit+meanshift approach. The variance explained is indeed lower than other larger, cleaner datasets, but along with the expected domain/acquisition-related shift, the SC and FC data themselves are lower resolution and somewhat noisier in clinical acquisitions.

[xingzouxiaoli]

thank you very much ！Doctor By the way，the follows will be heatmap.

sample_heatmap.csv

你发现初始“适应模式均值拟合+均值移”输出的相关系数如何？如果你运行以下程序，它会生成热力图，显示多个输出指标：

python run_model.py --inputdata
    "[FCcorr_fs86_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_fs86_hpf.mat" \
    "[FCcorr_fs86_hpfgsr]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_fs86_hpfgsr.mat" \
    "[FCpcorr_fs86_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCpcorr_fs86_hpf.mat" \
    "[FCcorr_shen268_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_shen268_hpf.mat" \
    "[FCcorr_shen268_hpfgsr]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_shen268_hpfgsr.mat" \
    "[FCpcorr_shen268_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCpcorr_shen268_hpf.mat" \
    "[FCcorr_coco439_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_coco439_hpf.mat" \
    "[FCcorr_coco439_hpfgsr]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCcorr_coco439_hpfgsr.mat" \
    "[FCpcorr_coco439_hpf]=E:\DATA\Kraken_Splits\1_Train_Set_85\FCpcorr_coco439_hpf.mat" \
    "[SCsdstream_fs86_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCsdstream_fs86_volnorm.mat" \
    "[SCifod2act_fs86_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCifod2act_fs86_volnorm.mat" \
    "[SCsdstream_shen268_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCsdstream_shen268_volnorm.mat" \
    "[SCifod2act_shen268_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCifod2act_shen268_volnorm.mat" \
    "[SCsdstream_coco439_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCsdstream_coco439_volnorm.mat" \
    "[SCifod2act_coco439_volnorm]=E:\DATA\Kraken_Splits\1_Train_Set_85\SCifod2act_coco439_volnorm.mat" \
    --adaptmode meanfit+meanshift \
    --checkpoint kraken_chkpt_SCFC_fs86+shen268+coco439_pc256_225paths_latent128_20240413_ep002000.pt \
    --inputxform kraken_ioxfm_SCFC_fs86_pc256_710train.npy \
        kraken_ioxfm_SCFC_shen268_pc256_710train.npy \
        kraken_ioxfm_SCFC_coco439_pc256_710train.npy \
    --fusion --inputname all --outputname all \
    --heatmap sample_heatmap.png \
    --heatmapcsv sample_heatmap.csv \
    --heatmapmetrics top1acc avgcorr avgrank avgcorr_resid \
    --heatmap_colormap magma2

如果你能给我看看那些热力图长什么样，我可以帮你评估它是否正常运行。

你遇到的重新培训问题其实是我们这边的一个bug。我的文档显示“pca50”是有效的，但实际上需要“pc50”。我刚也推送了一个提交，接受“pca50”，但如果你改成 ，它应该能运行。不过，对于这么少的科目，我可能不指望微调是合适的。你通常会想把数据分成训练和测试，所以你的评估会在更小的N上。默认情况下，run_training.py使用的是84个受试者是“训练+评估”（0.8105），75个处于“训练”集合（84（1-0.1）），8个属于用于估计训练表现的“val”集合（84*.1），21个属于完全被忽略的“测试”集合（你可以用来做模型调整后的长期测试）。75个受试者对于训练来说是个小N，8对于任何相关性或可识别性验证指标来说是非常小的N，21对于任何测试来说也是一个非常小的N。--transformation pc50``--trainvalsplitfrac .8 --valsplitfrac .1

在我们自己的工作中，当我们将krakencoder模型应用于较小的病理数据集时，例如我们论文补充图1中提到的100名多发性硬化症患者，我们坚持采用了这一方法。所解释的方差确实低于其他更大、更清晰的数据集，但随着预期的领域/获取相关变化，SC和FC数据本身在临床采集中分辨率较低且噪声稍大。run_model.py --adaptmode meanfit+meanshift

[xingzouxiaoli]

thank you very much ！Doctor By the way，the follows will be heatmap.

sample_heatmap.csv

[kjamison]

If I concentrate on the avgrank (bottom left heatmap), the within-FC predictions are mostly within reason, but the FCcorr vs FCpcorr (partial corr) are lower than I would expect. And the major disagreement between SCsdstream and SCifod2act is suspicious.

There could be a mismatch between the processing approaches. Can you provide more information on the acquistion and processing? For FC, how many timepoints per subject, what TR, and what if any nuisance regression and temporal filtering was done (and what tools did you use for that)? For SC, what was the spatial resolution, b-shells, and directions per shell? And how did you run tractography to get SCsdstream and SCifod2act?

[xingzouxiaoli]

Regarding the low correlation for certain regions, I think you are referring to coco439corr. Actually, my original data had a resolution of 2 mm. When I used the 1 mm atlas coco439, many brain regions were lost, so I resliced them to 1 mm. Therefore, I believe the low correlation should be due to this reason. As for the low pcorr, I think it may be due to a minor mathematical calculation error.

Moreover, all my data come from four sites. For the ABIDE dataset, subjects with both DTI and fMRI data are extremely rare. Therefore, I will attach a table of TR and time points. Additionally, all my fMRI data were processed using DPABI, with cerebrospinal fluid signals fully regressed out. For SC, we made some new attempts. For dtSC, I used DSI Studio for fiber tractography. For ptSC, I used the classic software FSL for computation. Due to the absence of some specific files, the preprocessing was somewhat challenging, resulting in some oddities in the registered images. As a result, the final ptSC outcomes might be somewhat unusual, which is an issue I cannot be certain about.