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. Author manuscript; available in PMC: 2021 Feb 1.
Published in final edited form as: Eur J Neurol. 2019 Sep 8;27(2):235–243. doi: 10.1111/ene.14058

Table 3.

Association of PC with MRI characteristics in multiple sclerosis patients and healthy individuals.


Multiple Sclerosis RR-MS Healthy Individuals


Linear regression Multiple regression Multiple regression Linear regression
rp P rp P rp P rp P
T2-LV 0.116 0.181 / / / / 0.095 0.555
T1-LV 0.025 0.778 / / / / - -
WBV −0.265 0.002 −0.195 0.026 −0.228 0.042 −0.151 0.347
WMV −0.095 0.277 / / / / −0.194 0.225
GMV −0.330 0.0001 −0.258 0.003 −0.292 0.008 −0.096 0.551
CV −0.310 0.0003 −0.230 0.008 −0.289 0.009 −0.086 0.591
LVV 0.069 0.429 / / / / −0.039 0.810
DGMV −0.279 0.001 −0.228 0.009 −0.262 0.019 −0.191 0.232
Thalamus volume −0.291 0.001 −0.235 0.007 −0.273 0.014 −0.189 0.236

LV, lesion volume; WBV, whole brain volume; WMV, white matter volume; GMV, gray matter volume; CV, cortical volume; LVV, lateral ventricular volume; DGMV, deep grey matter volume.

Partial correlation (rp) and P-value from regression analysis are shown. Linear regression model: each MRI characteristic was used as the dependent variable while the PC levels, expressed as natural logarithm, were the predictor variable.

Multiple regression model: each MRI characteristic was used as the dependent variable while gender, age, and natural logarithm of PC as predictor variables. The first block included the forced entry of age and gender, and the second block included the stepwise entry of the natural logarithmic of PC levels.

No multiple regression equations were produced (/) in the progressive multiple sclerosis and healthy individuals groups. Healthy individuals have not T1-LV (-). Significant results are in bold.