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> 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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