All,
Our lab is working with longitudinal (two time points) data from a cohort of around 130 individuals. We are attempting to make comparisons between time points 1 and 2 on measures like total brain volume, ventricular volume, ventricle-brain-ratio, and cortical thickness. However, we have run into an issue--we are seeing net increases in cortical thickness and total brain volume over time (which seems biologically implausible, given our age range of ~30-60 yrs old). We think it may have something to do with the fact that the T1w data from each time point was acquired with slightly different parameters and on different scanners, possibly leading to a rounding error in quantification of volumes/thickness. Timepoint 1 data are 1.0mm isotropic and were acquired on a 3T Siemens Tim Trio with 12ch headcoil. Timepoint 2 data are 0.8mm isotropic and were acquired on the same scanner, but which went through an upgrade to a Prisma Fit between timepoints, using a 32ch coil.
Currently we have been comparing data from the cross sectional stream. We would like to use the longitudinal stream if that would improve results, but we saw this post that cautioned against it: https://www.mail-archive.c om/freesurfer@nmr.mgh.harvard.edu/msg52992.html
What would be your recommendation for comparing this data longitudinally? Any thoughts on why we are seeing net increases in volume/thickness, and how to avoid that? One idea we had was perhaps degrading each image by rigid co-registration and then bringing each image into the halfway space between the two (as FSL's SIENA does).
Thanks,
James
All,
Just wanted to follow up on this message. Does anyone have recommendations for the best way to compare this data longitudinally?
Thanks very much,
James
On Thu, Mar 29, 2018 at 1:19 PM, James Gullickson jgullick@umn.edu wrote:
All,
Our lab is working with longitudinal (two time points) data from a cohort of around 130 individuals. We are attempting to make comparisons between time points 1 and 2 on measures like total brain volume, ventricular volume, ventricle-brain-ratio, and cortical thickness. However, we have run into an issue--we are seeing net increases in cortical thickness and total brain volume over time (which seems biologically implausible, given our age range of ~30-60 yrs old). We think it may have something to do with the fact that the T1w data from each time point was acquired with slightly different parameters and on different scanners, possibly leading to a rounding error in quantification of volumes/thickness. Timepoint 1 data are 1.0mm isotropic and were acquired on a 3T Siemens Tim Trio with 12ch headcoil. Timepoint 2 data are 0.8mm isotropic and were acquired on the same scanner, but which went through an upgrade to a Prisma Fit between timepoints, using a 32ch coil.
Currently we have been comparing data from the cross sectional stream. We would like to use the longitudinal stream if that would improve results, but we saw this post that cautioned against it: https://www.mail-archive.c om/freesurfer@nmr.mgh.harvard.edu/msg52992.html
What would be your recommendation for comparing this data longitudinally? Any thoughts on why we are seeing net increases in volume/thickness, and how to avoid that? One idea we had was perhaps degrading each image by rigid co-registration and then bringing each image into the halfway space between the two (as FSL's SIENA does).
Thanks,
James
Here are some statistics I generated for one of my subjects processed through the longitudinal stream with T0 as the initial scan and T1 18 mos later. (not sure if the inline tables will be properly formatted so I attached text files of them).
Unfortunately the only initial scan I had was a 5mm resolution for T0 versus 1mm for T1. I understand FS recommends a resolution not exceeding 1.5mm, but we gave it a try anyway to see if there was anything useful. My expectation was that the stats would be off by a consistent ratio due to the different resolutions, however I was surprised by the variability.
In particular, as James found, for some ROIs there are net increases in cortical thickness and brain volume over time.
Is this simply a factor that the algorithms are confused by the different image resolutions and therefore no possible longitudinal study can reliably be presumed in this circumstance?
Should we expect similar anomalies in cross-sectional studies, such as if my subjects have 1mm resolutions and a collaborating institution has 0.8mm subjects?
Cheers,
-Matt
Aseg Stats Measure:volume T0 T1 Base T0.long.base T1.long.base Left-Lateral-Ventricle 9,455.2 13,085.8 12,344.2 10,928.1 13,268.0 Left-Inf-Lat-Vent 61.8 174.1 171.7 90.3 303.3 Left-Cerebellum-White-Matter 26,892.5 18,563.1 17,066.4 23,905.6 17,597.8 Left-Cerebellum-Cortex 57,390.8 66,458.7 64,631.3 60,374.1 65,898.6 Left-Thalamus-Proper 10,757.3 9,094.2 9,918.5 10,350.8 9,588.3 Left-Caudate 3,619.4 3,673.8 3,608.8 3,588.7 3,872.7 Left-Putamen 5,463.7 5,439.2 5,171.0 5,602.0 5,721.7 Left-Pallidum 2,380.9 2,208.6 1,897.1 2,270.8 2,147.9 3rd-Ventricle 1,181.8 1,174.6 1,328.7 1,432.2 1,416.9 4th-Ventricle 1,209.1 1,602.0 1,547.7 1,322.4 1,981.1 Brain-Stem 25,154.2 26,141.3 25,890.7 25,988.7 25,954.6 Left-Hippocampus 4,205.3 4,335.6 4,378.0 4,478.2 4,407.2 Left-Amygdala 1,488.1 1,725.9 1,588.6 1,531.3 1,638.0 CSF 1,440.9 1,321.0 1,551.0 1,910.1 1,394.7 Left-Accumbens-area 295.0 273.2 327.7 274.9 380.0 Left-VentralDC 5,086.1 4,935.5 5,429.6 5,106.9 5,041.7 Left-vessel - 16.6 7.7 - 48.5 Left-choroid-plexus 221.6 419.4 271.7 429.5 734.2 Right-Lateral-Ventricle 6,465.1 9,581.5 8,695.5 6,893.5 9,713.9 Right-Inf-Lat-Vent 360.0 333.7 408.5 400.5 518.0 Right-Cerebellum-White-Matter 21,673.2 16,458.6 14,923.8 21,877.6 15,850.3 Right-Cerebellum-Cortex 57,831.7 68,348.9 65,845.5 60,300.5 68,194.1 Right-Thalamus-Proper 9,219.0 9,128.7 9,229.9 9,560.8 9,177.7 Right-Caudate 3,727.7 3,729.9 3,221.4 3,590.4 3,915.1 Right-Putamen 5,401.9 5,515.4 5,088.3 5,410.8 5,894.9 Right-Pallidum 2,470.8 2,259.8 1,965.1 2,211.6 1,999.4 Right-Hippocampus 4,071.4 4,189.8 4,256.0 4,259.8 4,026.3 Right-Amygdala 1,591.1 1,982.6 1,836.8 1,733.3 1,950.0 Right-Accumbens-area 522.2 605.3 594.3 545.3 599.0 Right-VentralDC 4,692.4 4,600.8 4,948.9 4,844.5 4,824.4 Right-vessel - 15.0 - - 59.3 Right-choroid-plexus 353.5 590.1 506.4 956.9 1,234.4 5th-Ventricle - - - 6.4 2.5 WM-hypointensities 99,547.8 930.7 59,648.6 25,495.7 35,013.7 Left-WM-hypointensities - - - - - Right-WM-hypointensities - - - - - non-WM-hypointensities - - - 1.0 6.8 Left-non-WM-hypointensities - - - - - Right-non-WM-hypointensities - - - - - Optic-Chiasm 322.9 238.7 300.8 318.2 293.4 CC_Posterior 1,296.8 1,302.2 1,297.9 1,297.3 1,368.4 CC_Mid_Posterior 729.7 759.3 731.0 636.1 660.5 CC_Central 570.7 608.2 565.6 558.5 642.8 CC_Mid_Anterior 607.7 589.4 597.4 598.1 616.3 CC_Anterior 1,274.7 1,081.6 1,170.6 1,030.2 945.3 BrainSegVol 1,549,180.0 1,365,340.0 1,492,000.0 1,336,450.0 1,432,910.0 BrainSegVolNotVent 1,530,890.0 1,338,040.0 1,466,940.0 1,314,050.0 1,402,270.0 BrainSegVolNotVentSurf 1,548,790.0 1,337,320.0 1,467,090.0 1,312,310.0 1,402,540.0 lhCortexVol 135,943.0 264,232.0 154,220.0 248,780.0 195,707.0 rhCortexVol 132,570.0 262,630.0 154,444.0 256,386.0 196,353.0 CortexVol 268,513.0 526,862.0 308,664.0 505,166.0 392,060.0 lhCerebralWhiteMatterVol 535,470.0 289,459.0 473,714.0 295,008.0 389,857.0 rhCerebralWhiteMatterVol 510,884.0 284,097.0 454,917.0 276,325.0 384,043.0 CerebralWhiteMatterVol 1,046,350.0 573,556.0 928,631.0 571,334.0 773,900.0 SubCortGrayVol 67,106.0 65,140.0 65,461.0 67,627.0 66,411.0 TotalGrayVol 455,328.0 729,645.0 508,170.0 696,747.0 596,129.0 SupraTentorialVol 1,412,720.0 1,193,890.0 1,330,690.0 1,168,890.0 1,265,300.0 SupraTentorialVolNotVent 1,397,630.0 1,170,460.0 1,309,630.0 1,150,610.0 1,239,500.0 SupraTentorialVolNotVentVox 1,363,830.0 1,166,040.0 1,302,340.0 1,145,530.0 1,231,860.0 MaskVol 1,902,380.0 1,917,320.0 1,989,220.0 1,923,720.0 1,980,280.0 BrainSegVol-to-eTIV 0.9 0.8 0.9 0.8 0.8 MaskVol-to-eTIV 1.1 1.1 1.1 1.1 1.1 lhSurfaceHoles 61.0 24.0 156.0 - - rhSurfaceHoles 70.0 21.0 130.0 - - SurfaceHoles 131.0 45.0 286.0 - - EstimatedTotalIntraCranialVol 1,659,240.0 1,720,440.0 1,741,620.0 1,741,620.0 1,741,620.0
aparc.stata rh.aparc.thickness T0 T1 Base T0.long.base T1.long.base rh_bankssts_thickness 1.662 2.613 1.480 2.961 1.944 rh_caudalanteriorcingulate_thickness 1.229 2.324 2.184 2.925 2.371 rh_caudalmiddlefrontal_thickness 1.822 2.477 1.596 3.027 1.827 rh_cuneus_thickness 2.168 1.824 1.375 2.999 1.482 rh_entorhinal_thickness 2.491 3.513 2.677 3.608 3.546 rh_fusiform_thickness 1.699 2.676 1.750 2.947 2.573 rh_inferiorparietal_thickness 2.125 2.605 1.766 3.192 2.419
rh_inferiortemporal_thickness 1.815 2.989 2.458 3.438 3.125
rh_isthmuscingulate_thickness 1.768 2.414 1.832 3.164 1.967
rh_lateraloccipital_thickness 2.226 2.195 1.931 3.549 1.888
rh_lateralorbitofrontal_thickness 1.589 2.583 1.822 2.936 2.501 rh_lingual_thickness 1.774 2.001 1.644 3.205 2.229 rh_medialorbitofrontal_thickness 2.082 2.513 1.797 3.064 2.469 rh_middletemporal_thickness 2.216 3.015 2.539 3.415 3.067
rh_parahippocampal_thickness 2.604 2.517 2.032 2.937 2.268
rh_paracentral_thickness 1.969 2.599 1.517 2.982 1.524
rh_parsopercularis_thickness 1.449 2.428 1.507 2.884 2.007
rh_parsorbitalis_thickness 1.797 2.669 1.971 3.448 2.599
rh_parstriangularis_thickness 1.856 2.460 1.777 3.249 2.278
rh_pericalcarine_thickness 1.895 1.616 1.335 3.209 1.388
rh_postcentral_thickness 1.884 2.146 1.590 3.063 1.464
rh_posteriorcingulate_thickness 1.606 2.355 1.746 2.931 2.528 rh_precentral_thickness 1.873 2.580 1.473 3.156 1.447 rh_precuneus_thickness 1.808 2.598 1.447 3.044 2.029 rh_rostralanteriorcingulate_thickness 1.113 2.557 2.154 3.081 2.491 rh_rostralmiddlefrontal_thickness 1.888 2.380 1.826 3.029 2.268 rh_superiorfrontal_thickness 2.112 2.777 1.721 3.206 2.107
rh_superiorparietal_thickness 1.862 2.300 1.500 2.854 1.641
rh_superiortemporal_thickness 2.034 2.749 2.150 3.188 2.446
rh_supramarginal_thickness 1.742 2.721 1.611 3.188 2.006
rh_frontalpole_thickness 2.592 2.509 2.510 3.844 2.725
rh_temporalpole_thickness 1.797 3.706 2.278 2.757 3.316
rh_transversetemporal_thickness 1.512 2.540 1.758 3.041 1.630 rh_insula_thickness 1.727 2.975 1.941 3.160 2.598 rh_MeanThickness_thickness 1.922 2.517 1.792 3.143 2.167
BrainSegVolNotVent 1,530,890.000 1,338,040.000 1,466,940.000 1,314,050.000 1,402,270.000 eTIV 1,659,240.000 1,720,440.000 1,741,620.000 1,741,620.000 1,741,620.000
On Thu, Apr 5, 2018 at 3:29 PM, James Gullickson jgullick@umn.edu wrote:
All,
Just wanted to follow up on this message. Does anyone have recommendations for the best way to compare this data longitudinally?
Thanks very much,
James
On Thu, Mar 29, 2018 at 1:19 PM, James Gullickson jgullick@umn.edu wrote:
All,
Our lab is working with longitudinal (two time points) data from a cohort of around 130 individuals. We are attempting to make comparisons between time points 1 and 2 on measures like total brain volume, ventricular volume, ventricle-brain-ratio, and cortical thickness. However, we have run into an issue--we are seeing net increases in cortical thickness and total brain volume over time (which seems biologically implausible, given our age range of ~30-60 yrs old). We think it may have something to do with the fact that the T1w data from each time point was acquired with slightly different parameters and on different scanners, possibly leading to a rounding error in quantification of volumes/thickness. Timepoint 1 data are 1.0mm isotropic and were acquired on a 3T Siemens Tim Trio with 12ch headcoil. Timepoint 2 data are 0.8mm isotropic and were acquired on the same scanner, but which went through an upgrade to a Prisma Fit between timepoints, using a 32ch coil.
Currently we have been comparing data from the cross sectional stream. We would like to use the longitudinal stream if that would improve results, but we saw this post that cautioned against it: https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg52992.html
What would be your recommendation for comparing this data longitudinally? Any thoughts on why we are seeing net increases in volume/thickness, and how to avoid that? One idea we had was perhaps degrading each image by rigid co-registration and then bringing each image into the halfway space between the two (as FSL's SIENA does).
Thanks,
James
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Hi,
never switch hardware or protocols in a longitudinal study. This is a good example for what happens: you will find effects that are caused by different acquisition rather than anatomical differences. It will be impossible to disentangle true change from scanner/acquisition induced changes, especially with only a few time points and no or little subjects scanned on both scanners or with both protocols.
Also 5mm resolution is too low for freesurfer and anything can come out of that. It won’t be reliable.
Best, Martin
On 6. Apr 2018, at 21:21, Matthew Grecsek matt@grecsek.com wrote:
Here are some statistics I generated for one of my subjects processed through the longitudinal stream with T0 as the initial scan and T1 18 mos later. (not sure if the inline tables will be properly formatted so I attached text files of them).
Unfortunately the only initial scan I had was a 5mm resolution for T0 versus 1mm for T1. I understand FS recommends a resolution not exceeding 1.5mm, but we gave it a try anyway to see if there was anything useful. My expectation was that the stats would be off by a consistent ratio due to the different resolutions, however I was surprised by the variability.
In particular, as James found, for some ROIs there are net increases in cortical thickness and brain volume over time.
Is this simply a factor that the algorithms are confused by the different image resolutions and therefore no possible longitudinal study can reliably be presumed in this circumstance?
Should we expect similar anomalies in cross-sectional studies, such as if my subjects have 1mm resolutions and a collaborating institution has 0.8mm subjects?
Cheers,
-Matt
Aseg Stats Measure:volume T0 T1 Base T0.long.base T1.long.base Left-Lateral-Ventricle 9,455.2 13,085.8 12,344.2 10,928.1 13,268.0 Left-Inf-Lat-Vent 61.8 174.1 171.7 90.3 303.3 Left-Cerebellum-White-Matter 26,892.5 18,563.1 17,066.4 23,905.6 17,597.8 Left-Cerebellum-Cortex 57,390.8 66,458.7 64,631.3 60,374.1 65,898.6 Left-Thalamus-Proper 10,757.3 9,094.2 9,918.5 10,350.8 9,588.3 Left-Caudate 3,619.4 3,673.8 3,608.8 3,588.7 3,872.7 Left-Putamen 5,463.7 5,439.2 5,171.0 5,602.0 5,721.7 Left-Pallidum 2,380.9 2,208.6 1,897.1 2,270.8 2,147.9 3rd-Ventricle 1,181.8 1,174.6 1,328.7 1,432.2 1,416.9 4th-Ventricle 1,209.1 1,602.0 1,547.7 1,322.4 1,981.1 Brain-Stem 25,154.2 26,141.3 25,890.7 25,988.7 25,954.6 Left-Hippocampus 4,205.3 4,335.6 4,378.0 4,478.2 4,407.2 Left-Amygdala 1,488.1 1,725.9 1,588.6 1,531.3 1,638.0 CSF 1,440.9 1,321.0 1,551.0 1,910.1 1,394.7 Left-Accumbens-area 295.0 273.2 327.7 274.9 380.0 Left-VentralDC 5,086.1 4,935.5 5,429.6 5,106.9 5,041.7 Left-vessel - 16.6 7.7 - 48.5 Left-choroid-plexus 221.6 419.4 271.7 429.5 734.2 Right-Lateral-Ventricle 6,465.1 9,581.5 8,695.5 6,893.5 9,713.9 Right-Inf-Lat-Vent 360.0 333.7 408.5 400.5 518.0 Right-Cerebellum-White-Matter 21,673.2 16,458.6 14,923.8 21,877.6 15,850.3 Right-Cerebellum-Cortex 57,831.7 68,348.9 65,845.5 60,300.5 68,194.1 Right-Thalamus-Proper 9,219.0 9,128.7 9,229.9 9,560.8 9,177.7 Right-Caudate 3,727.7 3,729.9 3,221.4 3,590.4 3,915.1 Right-Putamen 5,401.9 5,515.4 5,088.3 5,410.8 5,894.9 Right-Pallidum 2,470.8 2,259.8 1,965.1 2,211.6 1,999.4 Right-Hippocampus 4,071.4 4,189.8 4,256.0 4,259.8 4,026.3 Right-Amygdala 1,591.1 1,982.6 1,836.8 1,733.3 1,950.0 Right-Accumbens-area 522.2 605.3 594.3 545.3 599.0 Right-VentralDC 4,692.4 4,600.8 4,948.9 4,844.5 4,824.4 Right-vessel - 15.0 - - 59.3 Right-choroid-plexus 353.5 590.1 506.4 956.9 1,234.4 5th-Ventricle - - - 6.4 2.5 WM-hypointensities 99,547.8 930.7 59,648.6 25,495.7 35,013.7 Left-WM-hypointensities - - - - - Right-WM-hypointensities - - - - - non-WM-hypointensities - - - 1.0 6.8 Left-non-WM-hypointensities - - - - - Right-non-WM-hypointensities - - - - - Optic-Chiasm 322.9 238.7 300.8 318.2 293.4 CC_Posterior 1,296.8 1,302.2 1,297.9 1,297.3 1,368.4 CC_Mid_Posterior 729.7 759.3 731.0 636.1 660.5 CC_Central 570.7 608.2 565.6 558.5 642.8 CC_Mid_Anterior 607.7 589.4 597.4 598.1 616.3 CC_Anterior 1,274.7 1,081.6 1,170.6 1,030.2 945.3 BrainSegVol 1,549,180.0 1,365,340.0 1,492,000.0 1,336,450.0 1,432,910.0 BrainSegVolNotVent 1,530,890.0 1,338,040.0 1,466,940.0 1,314,050.0 1,402,270.0 BrainSegVolNotVentSurf 1,548,790.0 1,337,320.0 1,467,090.0 1,312,310.0 1,402,540.0 lhCortexVol 135,943.0 264,232.0 154,220.0 248,780.0 195,707.0 rhCortexVol 132,570.0 262,630.0 154,444.0 256,386.0 196,353.0 CortexVol 268,513.0 526,862.0 308,664.0 505,166.0 392,060.0 lhCerebralWhiteMatterVol 535,470.0 289,459.0 473,714.0 295,008.0 389,857.0 rhCerebralWhiteMatterVol 510,884.0 284,097.0 454,917.0 276,325.0 384,043.0 CerebralWhiteMatterVol 1,046,350.0 573,556.0 928,631.0 571,334.0 773,900.0 SubCortGrayVol 67,106.0 65,140.0 65,461.0 67,627.0 66,411.0 TotalGrayVol 455,328.0 729,645.0 508,170.0 696,747.0 596,129.0 SupraTentorialVol 1,412,720.0 1,193,890.0 1,330,690.0 1,168,890.0 1,265,300.0 SupraTentorialVolNotVent 1,397,630.0 1,170,460.0 1,309,630.0 1,150,610.0 1,239,500.0 SupraTentorialVolNotVentVox 1,363,830.0 1,166,040.0 1,302,340.0 1,145,530.0 1,231,860.0 MaskVol 1,902,380.0 1,917,320.0 1,989,220.0 1,923,720.0 1,980,280.0 BrainSegVol-to-eTIV 0.9 0.8 0.9 0.8 0.8 MaskVol-to-eTIV 1.1 1.1 1.1 1.1 1.1 lhSurfaceHoles 61.0 24.0 156.0 - - rhSurfaceHoles 70.0 21.0 130.0 - - SurfaceHoles 131.0 45.0 286.0 - - EstimatedTotalIntraCranialVol 1,659,240.0 1,720,440.0 1,741,620.0 1,741,620.0 1,741,620.0
aparc.stata rh.aparc.thickness T0 T1 Base T0.long.base T1.long.base rh_bankssts_thickness 1.662 2.613 1.480 2.961 1.944 rh_caudalanteriorcingulate_thickness 1.229 2.324 2.184 2.925 2.371 rh_caudalmiddlefrontal_thickness 1.822 2.477 1.596 3.027 1.827 rh_cuneus_thickness 2.168 1.824 1.375 2.999 1.482 rh_entorhinal_thickness 2.491 3.513 2.677 3.608 3.546 rh_fusiform_thickness 1.699 2.676 1.750 2.947 2.573 rh_inferiorparietal_thickness 2.125 2.605 1.766 3.192 2.419 rh_inferiortemporal_thickness 1.815 2.989 2.458 3.438 3.125 rh_isthmuscingulate_thickness 1.768 2.414 1.832 3.164 1.967 rh_lateraloccipital_thickness 2.226 2.195 1.931 3.549 1.888 rh_lateralorbitofrontal_thickness 1.589 2.583 1.822 2.936 2.501 rh_lingual_thickness 1.774 2.001 1.644 3.205 2.229 rh_medialorbitofrontal_thickness 2.082 2.513 1.797 3.064 2.469 rh_middletemporal_thickness 2.216 3.015 2.539 3.415 3.067 rh_parahippocampal_thickness 2.604 2.517 2.032 2.937 2.268 rh_paracentral_thickness 1.969 2.599 1.517 2.982 1.524 rh_parsopercularis_thickness 1.449 2.428 1.507 2.884 2.007 rh_parsorbitalis_thickness 1.797 2.669 1.971 3.448 2.599 rh_parstriangularis_thickness 1.856 2.460 1.777 3.249 2.278 rh_pericalcarine_thickness 1.895 1.616 1.335 3.209 1.388 rh_postcentral_thickness 1.884 2.146 1.590 3.063 1.464 rh_posteriorcingulate_thickness 1.606 2.355 1.746 2.931 2.528 rh_precentral_thickness 1.873 2.580 1.473 3.156 1.447 rh_precuneus_thickness 1.808 2.598 1.447 3.044 2.029 rh_rostralanteriorcingulate_thickness 1.113 2.557 2.154 3.081 2.491 rh_rostralmiddlefrontal_thickness 1.888 2.380 1.826 3.029 2.268 rh_superiorfrontal_thickness 2.112 2.777 1.721 3.206 2.107 rh_superiorparietal_thickness 1.862 2.300 1.500 2.854 1.641 rh_superiortemporal_thickness 2.034 2.749 2.150 3.188 2.446 rh_supramarginal_thickness 1.742 2.721 1.611 3.188 2.006 rh_frontalpole_thickness 2.592 2.509 2.510 3.844 2.725 rh_temporalpole_thickness 1.797 3.706 2.278 2.757 3.316 rh_transversetemporal_thickness 1.512 2.540 1.758 3.041 1.630 rh_insula_thickness 1.727 2.975 1.941 3.160 2.598 rh_MeanThickness_thickness 1.922 2.517 1.792 3.143 2.167 BrainSegVolNotVent 1,530,890.000 1,338,040.000 1,466,940.000 1,314,050.000 1,402,270.000 eTIV 1,659,240.000 1,720,440.000 1,741,620.000 1,741,620.000 1,741,620.000
On Thu, Apr 5, 2018 at 3:29 PM, James Gullickson <jgullick@umn.edu mailto:jgullick@umn.edu> wrote: All,
Just wanted to follow up on this message. Does anyone have recommendations for the best way to compare this data longitudinally?
Thanks very much,
James
On Thu, Mar 29, 2018 at 1:19 PM, James Gullickson <jgullick@umn.edu mailto:jgullick@umn.edu> wrote: All,
Our lab is working with longitudinal (two time points) data from a cohort of around 130 individuals. We are attempting to make comparisons between time points 1 and 2 on measures like total brain volume, ventricular volume, ventricle-brain-ratio, and cortical thickness. However, we have run into an issue--we are seeing net increases in cortical thickness and total brain volume over time (which seems biologically implausible, given our age range of ~30-60 yrs old). We think it may have something to do with the fact that the T1w data from each time point was acquired with slightly different parameters and on different scanners, possibly leading to a rounding error in quantification of volumes/thickness. Timepoint 1 data are 1.0mm isotropic and were acquired on a 3T Siemens Tim Trio with 12ch headcoil. Timepoint 2 data are 0.8mm isotropic and were acquired on the same scanner, but which went through an upgrade to a Prisma Fit between timepoints, using a 32ch coil.
Currently we have been comparing data from the cross sectional stream. We would like to use the longitudinal stream if that would improve results, but we saw this post that cautioned against it: https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg52992.html https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg52992.html
What would be your recommendation for comparing this data longitudinally? Any thoughts on why we are seeing net increases in volume/thickness, and how to avoid that? One idea we had was perhaps degrading each image by rigid co-registration and then bringing each image into the halfway space between the two (as FSL's SIENA does).
Thanks,
James
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Martin,
Thanks for the feedback. Given our data set (1mm^3 timepoint1 and 0.8mm^3 timepoint 2), what would be the best way to salvage this data and look for longitudinal changes? Would it be possible to upsample/downsample the images so that they are the same resolution (i.e. using mriconvert)? Also, would a normalized measure like ventricle-brain-ratio theoretically be resistant to these scanner/acquisition induced biases?
Thanks,
James
On Mon, Apr 16, 2018 at 5:15 AM, Martin Reuter mreuter@nmr.mgh.harvard.edu wrote:
Hi,
never switch hardware or protocols in a longitudinal study. This is a good example for what happens: you will find effects that are caused by different acquisition rather than anatomical differences. It will be impossible to disentangle true change from scanner/acquisition induced changes, especially with only a few time points and no or little subjects scanned on both scanners or with both protocols.
Also 5mm resolution is too low for freesurfer and anything can come out of that. It won’t be reliable.
Best, Martin
On 6. Apr 2018, at 21:21, Matthew Grecsek matt@grecsek.com wrote:
Here are some statistics I generated for one of my subjects processed through the longitudinal stream with T0 as the initial scan and T1 18 mos later. (not sure if the inline tables will be properly formatted so I attached text files of them).
Unfortunately the only initial scan I had was a 5mm resolution for T0 versus 1mm for T1. I understand FS recommends a resolution not exceeding 1.5mm, but we gave it a try anyway to see if there was anything useful. My expectation was that the stats would be off by a consistent ratio due to the different resolutions, however I was surprised by the variability.
In particular, as James found, for some ROIs there are net increases in cortical thickness and brain volume over time.
Is this simply a factor that the algorithms are confused by the different image resolutions and therefore no possible longitudinal study can reliably be presumed in this circumstance?
Should we expect similar anomalies in cross-sectional studies, such as if my subjects have 1mm resolutions and a collaborating institution has 0.8mm subjects?
Cheers,
-Matt
Aseg Stats Measure:volume T0 T1 Base T0.long.base T1.long.base Left-Lateral-Ventricle 9,455.2 13,085.8 12,344.2 10,928.1 13,268.0 Left-Inf-Lat-Vent 61.8 174.1 171.7 90.3 303.3 Left-Cerebellum-White-Matter 26,892.5 18,563.1 17,066.4 23,905.6 17,597.8 Left-Cerebellum-Cortex 57,390.8 66,458.7 64,631.3 60,374.1 65,898.6 Left-Thalamus-Proper 10,757.3 9,094.2 9,918.5 10,350.8 9,588.3 Left-Caudate 3,619.4 3,673.8 3,608.8 3,588.7 3,872.7 Left-Putamen 5,463.7 5,439.2 5,171.0 5,602.0 5,721.7 Left-Pallidum 2,380.9 2,208.6 1,897.1 2,270.8 2,147.9 3rd-Ventricle 1,181.8 1,174.6 1,328.7 1,432.2 1,416.9 4th-Ventricle 1,209.1 1,602.0 1,547.7 1,322.4 1,981.1 Brain-Stem 25,154.2 26,141.3 25,890.7 25,988.7 25,954.6 Left-Hippocampus 4,205.3 4,335.6 4,378.0 4,478.2 4,407.2 Left-Amygdala 1,488.1 1,725.9 1,588.6 1,531.3 1,638.0 CSF 1,440.9 1,321.0 1,551.0 1,910.1 1,394.7 Left-Accumbens-area 295.0 273.2 327.7 274.9 380.0 Left-VentralDC 5,086.1 4,935.5 5,429.6 5,106.9 5,041.7 Left-vessel - 16.6 7.7 - 48.5 Left-choroid-plexus 221.6 419.4 271.7 429.5 734.2 Right-Lateral-Ventricle 6,465.1 9,581.5 8,695.5 6,893.5 9,713.9 Right-Inf-Lat-Vent 360.0 333.7 408.5 400.5 518.0 Right-Cerebellum-White-Matter 21,673.2 16,458.6 14,923.8 21,877.6 15,850.3 Right-Cerebellum-Cortex 57,831.7 68,348.9 65,845.5 60,300.5 68,194.1 Right-Thalamus-Proper 9,219.0 9,128.7 9,229.9 9,560.8 9,177.7 Right-Caudate 3,727.7 3,729.9 3,221.4 3,590.4 3,915.1 Right-Putamen 5,401.9 5,515.4 5,088.3 5,410.8 5,894.9 Right-Pallidum 2,470.8 2,259.8 1,965.1 2,211.6 1,999.4 Right-Hippocampus 4,071.4 4,189.8 4,256.0 4,259.8 4,026.3 Right-Amygdala 1,591.1 1,982.6 1,836.8 1,733.3 1,950.0 Right-Accumbens-area 522.2 605.3 594.3 545.3 599.0 Right-VentralDC 4,692.4 4,600.8 4,948.9 4,844.5 4,824.4 Right-vessel - 15.0
- 59.3Right-choroid-plexus 353.5 590.1 506.4 956.9 1,234.4 5th-Ventricle - - - 6.4 2.5 WM-hypointensities 99,547.8 930.7 59,648.6 25,495.7 35,013.7 Left-WM-hypointensities -
Right-WM-hypointensities -
non-WM-hypointensities -
- 1.06.8 Left-non-WM-hypointensities -
Right-non-WM-hypointensities -
Optic-Chiasm 322.9 238.7 300.8 318.2 293.4 CC_Posterior 1,296.8 1,302.2
...
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Hi James,
it will be impossible to distinguish changes caused by the different acquisition (resolution) vs real changes. The Ratio should be more stable, but that is only a guess. Could be that Ventricle boundary is not affected by resolution changes as much as GM boundary.
Best, Martin
On 4. May 2018, at 12:23, James Gullickson jgullick@umn.edu wrote:
Martin,
Thanks for the feedback. Given our data set (1mm^3 timepoint1 and 0.8mm^3 timepoint 2), what would be the best way to salvage this data and look for longitudinal changes? Would it be possible to upsample/downsample the images so that they are the same resolution (i.e. using mriconvert)? Also, would a normalized measure like ventricle-brain-ratio theoretically be resistant to these scanner/acquisition induced biases?
Thanks,
James
On Mon, Apr 16, 2018 at 5:15 AM, Martin Reuter <mreuter@nmr.mgh.harvard.edu mailto:mreuter@nmr.mgh.harvard.edu> wrote: Hi,
never switch hardware or protocols in a longitudinal study. This is a good example for what happens: you will find effects that are caused by different acquisition rather than anatomical differences. It will be impossible to disentangle true change from scanner/acquisition induced changes, especially with only a few time points and no or little subjects scanned on both scanners or with both protocols.
Also 5mm resolution is too low for freesurfer and anything can come out of that. It won’t be reliable.
Best, Martin
On 6. Apr 2018, at 21:21, Matthew Grecsek <matt@grecsek.com mailto:matt@grecsek.com> wrote:
Here are some statistics I generated for one of my subjects processed through the longitudinal stream with T0 as the initial scan and T1 18 mos later. (not sure if the inline tables will be properly formatted so I attached text files of them).
Unfortunately the only initial scan I had was a 5mm resolution for T0 versus 1mm for T1. I understand FS recommends a resolution not exceeding 1.5mm, but we gave it a try anyway to see if there was anything useful. My expectation was that the stats would be off by a consistent ratio due to the different resolutions, however I was surprised by the variability.
In particular, as James found, for some ROIs there are net increases in cortical thickness and brain volume over time.
Is this simply a factor that the algorithms are confused by the different image resolutions and therefore no possible longitudinal study can reliably be presumed in this circumstance?
Should we expect similar anomalies in cross-sectional studies, such as if my subjects have 1mm resolutions and a collaborating institution has 0.8mm subjects?
Cheers,
-Matt
Aseg Stats Measure:volume T0 T1 Base T0.long.base T1.long.base Left-Lateral-Ventricle 9,455.2 13,085.8 12,344.2 10,928.1 13,268.0 Left-Inf-Lat-Vent 61.8 174.1 171.7 90.3 303.3 Left-Cerebellum-White-Matter 26,892.5 18,563.1 17,066.4 23,905.6 17,597.8 Left-Cerebellum-Cortex 57,390.8 66,458.7 64,631.3 60,374.1 65,898.6 Left-Thalamus-Proper 10,757.3 9,094.2 9,918.5 10,350.8 9,588.3 Left-Caudate 3,619.4 3,673.8 3,608.8 3,588.7 3,872.7 Left-Putamen 5,463.7 5,439.2 5,171.0 5,602.0 5,721.7 Left-Pallidum 2,380.9 2,208.6 1,897.1 2,270.8 2,147.9 3rd-Ventricle 1,181.8 1,174.6 1,328.7 1,432.2 1,416.9 4th-Ventricle 1,209.1 1,602.0 1,547.7 1,322.4 1,981.1 Brain-Stem 25,154.2 26,141.3 25,890.7 25,988.7 25,954.6 Left-Hippocampus 4,205.3 4,335.6 4,378.0 4,478.2 4,407.2 Left-Amygdala 1,488.1 1,725.9 1,588.6 1,531.3 1,638.0 CSF 1,440.9 1,321.0 1,551.0 1,910.1 1,394.7 Left-Accumbens-area 295.0 273.2 327.7 274.9 380.0 Left-VentralDC 5,086.1 4,935.5 5,429.6 5,106.9 5,041.7 Left-vessel - 16.6 7.7 - 48.5 Left-choroid-plexus 221.6 419.4 271.7 429.5 734.2 Right-Lateral-Ventricle 6,465.1 9,581.5 8,695.5 6,893.5 9,713.9 Right-Inf-Lat-Vent 360.0 333.7 408.5 400.5 518.0 Right-Cerebellum-White-Matter 21,673.2 16,458.6 14,923.8 21,877.6 15,850.3 Right-Cerebellum-Cortex 57,831.7 68,348.9 65,845.5 60,300.5 68,194.1 Right-Thalamus-Proper 9,219.0 9,128.7 9,229.9 9,560.8 9,177.7 Right-Caudate 3,727.7 3,729.9 3,221.4 3,590.4 3,915.1 Right-Putamen 5,401.9 5,515.4 5,088.3 5,410.8 5,894.9 Right-Pallidum 2,470.8 2,259.8 1,965.1 2,211.6 1,999.4 Right-Hippocampus 4,071.4 4,189.8 4,256.0 4,259.8 4,026.3 Right-Amygdala 1,591.1 1,982.6 1,836.8 1,733.3 1,950.0 Right-Accumbens-area 522.2 605.3 594.3 545.3 599.0 Right-VentralDC 4,692.4 4,600.8 4,948.9 4,844.5 4,824.4 Right-vessel - 15.0 - - 59.3 Right-choroid-plexus 353.5 590.1 506.4 956.9 1,234.4 5th-Ventricle - - - 6.4 2.5 WM-hypointensities 99,547.8 930.7 59,648.6 25,495.7 35,013.7 Left-WM-hypointensities - - - - - Right-WM-hypointensities - - - - - non-WM-hypointensities - - - 1.0 6.8 Left-non-WM-hypointensities - - - - - Right-non-WM-hypointensities - - - - - Optic-Chiasm 322.9 238.7 300.8 318.2 293.4 CC_Posterior 1,296.8 1,302.2
...
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Matthew Grecsek