Diffusion Tensor Imaging detects age-related white matter change over a two-year
follow-up which is associated with working memory decline
R.A. Charlton PhD1, F. Schiavone MSc1, T.R. Barrick PhD1, R.G. Morris PhD2, and H.S.
Markus DM FRCP1.
1 Clinical Neuroscience, St George’s University of London, UK.
2 Department of Psychology, King’s College, Institute of Psychiatry, University of London,
Corresponding Author: Rebecca Charlton
St George’s University of London,
London, SW17 0RE
Tel: ++ 44 (0) 20 8725 5385
Fax: ++ 44 (0) 20 8725 2950
Keywords: cognitive decline, diffusion tensor imaging, longitudinal, normal aging, working
Word count: 2,688
Background: Diffusion tensor imaging (DTI) is a sensitive method for detecting white matter
damage, and in cross-sectional studies DTI measures correlate with age-related cognitive
decline. However, there is little data on whether DTI can detect age-related changes over
short time-periods, and whether such change correlates with cognitive function.
Methods: In a community sample of 84 middle-aged and elderly adults MRI and cognitive
testing was performed at baseline and after two-years. Changes in DTI white matter
histograms, white matter hyperintensity (WMH) volume and brain volume were determined.
Change over time in performance on tests of executive function, working memory and
information processing speed were also assessed.
Results: Significant change in all MRI measures was detected. For cognition, change was
detected for working memory and this correlated with change in DTI only. In a stepwise
regression, with change in working memory as the dependent variable, a DTI histogram
measure explained 10.8% of the variance in working memory. Change in WMH or brain
volume did not contribute to the model.
Conclusions: DTI is sensitive to age-related change in white matter ultrastructure and appears
useful for monitoring age-related white matter change even over short time periods.
There is increasing evidence that white matter damage plays a prominent role in age-related
cognitive decline. Post-mortem studies show evidence of white matter damage including loss
of myelinated white matter fibres 1, age-related activation of microglia 2 and microglial cell
inclusions believed to represent phagocytosis of myelin 3. MRI white matter hyperintensities
(WMH), best seen on T2-weighted and FLAIR sequences, become more common with age 4,
and correlate with cognitive impairment 5;6. Correlations are strongest with those cognitive
domains, such as executive function, believed to be more sensitive to changes in white matter
integrity and deterioration in cortical-subcortical connectivity 7. However, correlations
between WMH and cognition are only moderate and some studies have failed to find any
Diffusion tensor imaging (DTI) is a MRI technique that may measure white matter functional
integrity more directly. Even where white matter appears normal on T2-weighted or FLAIR
images, DTI values have indicated reduced white matter integrity 9, suggesting that subtle
damage is present even in regions outside WMH. Histopathology studies have also
demonstrated the presence of white matter damage outside WMH in Alzheimer’s disease 10.
Moreover, cross sectional studies in normal aging 11 and cerebral small vessel disease (SVD)
12;13 have shown that DTI measures correlate more strongly with cognition (and particularly
executive function) than WMH volume . In these SVD studies WMH no longer remained
independently related to cognition once DTI was entered in multivariate analysis 12;13.
DTI provides quantitative measures which are suitable for incorporation into large-scale
clinical trials. It may provide a useful surrogate marker to evaluate the effect of therapeutic
interventions on age-related cognitive decline. Before adopting DTI as a surrogate marker it is
first necessary to show that changes in DTI parameters can be detected over short time
periods, of similar duration to those used in clinical trials. Secondly it is necessary to show
that it is more sensitive to change than other conventional MRI markers such as T2-WMH,
and than repeated neuropsychological testing which is currently the usual method of
assessment. Thirdly change in DTI should correlate with change in clinical parameters,
including cognition. Currently there are no published longitudinal ageing studies using this
technique which can answer these questions. Therefore we performed serial DTI imaging at
baseline and two-year follow-up in a community population of middle-aged and elderly
individuals to determine whether changes in DTI parameters could be detected over this time
period. Cognitive testing was also performed at both time-points.
The population sample of 106 people (55 males, 51 females; age range 50-90 years; mean
69) was recruited via a local family practice by random sampling as part of the prospective
GENIE study; this sample has been described in detail previously 14. Briefly, participants had
no contraindications to MRI, and no prior psychiatric or neurological disorder, and had
English as their first language. Out of this sample 84 participants re-attended at follow-up (48
males, 36 females; mean age [range] 71 [55-91]). Of those who did not attend follow-up; 3
were deceased, 2 declined to participate due to ill-health, 1 had moved away, 16 chose not to
provide a reason for withdrawal (an option required by our ethical approval). Of the 84
follow-ups, 82 completed all cognitive assessments, 81 attended for MRI, with 2 having MRI
only. Five did not complete the MRI and had missing data; 3 had poor quality or missing
baseline DTI and were therefore not included in longitudinal analysis. In summary,
longitudinal data was available for: cognition =82, brain volume =80, WMH =79, DTI =73
(69 people completed the study without any missing variables).
Participants underwent a battery of standardized neuropsychological tests. Mean scores were
calculated for executive function, working memory and information processing speed, using
methods as described previously 14. Briefly executive function comprised: the Stroop test total
correct 15, letter fluency (FAS) total correct, category fluency (animals and boys names) total
correct, and the following sub-tests from the Delis-Kaplan Executive Function System (D-
KEFS) 16: the Trails test - number-letter switching minus motor speed, and Towers total
achievement score. Working memory comprised the digit span backwards and letter-number
sequencing subtests from the Wechsler Memory Scale III (WMS-III) 17. Information
processing speed included sub-test A (total completed) from the Attention, Memory and
Information Processing Battery (AMIPB) 18, digit symbol number completed from Wechsler
Adult Intelligence Scale Revised (WAIS-R) 19, and the time to complete the grooved
pegboard with the dominant hand 20.
At baseline, raw scores for each test were transformed into z-scores (using means and
standard deviations for the whole group); follow-up data was transformed into z-scores using
the means and standard deviations from baseline data. Z-scores were re-coded so a high score
reflected good performance. Mean scores were calculated from the z-scores for each ability at
baseline and follow-up.
Magnetic Resonance Imaging
All MRI was performed on a General Electric 1.5T(22 mT/m) Signa scanner. Whole brain
DTI (acquisition matrix=96x96, FOV=240mm x 240mm; TE=80ms; TR=7s, maximum b-
value=1000 s mm-2) was acquired in two interleaved series of 4 repeats, each containing
twenty-five 2.8mm slices, with a gap of 2.8mm, providing contiguous whole brain coverage.
Images were acquired with no diffusion weighting (b= 0 s mm-2) and in 6 directions and the
negative of those 6 to eliminate diffusion-imaging gradient cross terms. Also acquired were:
FLAIR (slices=28; thickness=5mm; acquisition matrix=256x256, FOV=240mm x 240mm;
TE=120ms; TR=9000ms); and T1-weighted whole brain volume scan (slices=96;
thickness=1.5mm; acquisition matrix=256x256x92, FOV=240mm x 240mm; TE=3ms;
TR=17ms; Flip Angle=10º).
All data were analyzed on an independent workstation (Sun Blade 1500; Sun Microsystems,
Mountain View, CA).
DTI: The analysis derived measures of mean diffusivity (MD) and fractional anisotropy (FA),
two main descriptors of white matter ultrastructural integrity. Images were realigned to
remove linear eddy current distortions using automated image registration (AIR) software
21;22. Diffusion tensor elements were computed at each voxel 23 and diagonalized to determine
eigenvalues and eigenvectors, from which MD and FA values were generated.
For baseline data, images were segmented (using the EPI-B0 [b=0 s mm-2 images]) into grey
matter, white matter, and CSF concentration maps in SPM2
(http://www.fil.ion.ucl.ac.uk/spm/), incorporating a correction for image intensity non-
uniformity 24. For each participant, hard segmentations of each tissue type (grey, white, CSF)
were generated to include all voxels whose probability was greater than the combined
probability of the remaining tissue types. Each segmentation was checked to assure accuracy.
To ensure DTI histogram measurements for both time-points were taken from the same brain
region, follow-up EPI-B0 images were co-registered to the baseline EPI-B0 images using an
affine transformation in SPM2. This realigned data was used to generate follow-up
histograms using the segmentation computed from baseline data.
DTI Histograms: Histograms were calculated for MD and FA maps of all brain white matter,
using the segmentation described above. MD histograms bin width was set to 4x10-5, between
0.0 and 0.004 (4x10-3). FA histograms bin width was 0.01, between 0.0 and 1.0. To correct for
individual differences in brain volume each histogram was normalized over the number of
voxels in the segmented images. Histograms do not necessarily demonstrate Gaussian profiles
in MD and FA data (see Figure 1), so mean and standard deviation measures may be less
relevant than other descriptors of the histogram, such as median, peak height frequency or
peak height intensity, skewness (i.e. how asymmetric is the histogram?) and kurtosis (i.e. how
peaked is the histogram?) which were used in the analysis. Distributions with kurtosis of less
than zero are referred to as having negative kurtosis or being platykurtic (i.e. having a flatter
peak, and longer, fatter tails than the normal distribution) and those with a kurtosis of greater
than zero are referred to as having positive kurtosis or being leptokurtic (i.e. distributions with
narrower, well-defined peaks and fatter, shorter tails than the normal distribution). It is also
helpful to consider the histogram parameters in terms of their difference in symmetry from the
normal distribution, termed skewness. Here zero indicates a symmetrical distribution, whereas
a distribution with larger tails to the left or right is referred to as possessing negative or
positive skewness, respectively. Furthermore, a greater skewness magnitude indicates greater
asymmetry of the histogram distribution. Figure 1 clearly shows the MD distribution to be
positively skewed and leptokurtic, whereas the FA distribution is positively skewed and less
leptokurtic than the MD distribution. Furthermore, there is an interaction between the
measured histogram parameters, for example, the MD distribution, which is highly leptokurtic
has a greater peak height frequency than the FA distribution, while the MD distribution also
has a greater positive skew (i.e. it is more asymmetrical) than the FA.
In order to assess the reproducibility of DTI measurements, scans were repeated within a
week in 8 healthy volunteers. Test-retest errors for DTI histogram measures indicated good
reproducibility. For example, normalized peak height frequency and median are given below,
and other parameters demonstrated similar error levels. Test-retest errors: MD normalized
peak height frequency (mean = 4 x 10 -6, standard deviation (SD) = .00003), MD median
(mean = 4 x 10 -6, SD = .00002), FA normalized peak height frequency (mean = -.003, SD =
.009), FA median (mean = .004, SD = .015).
White Matter Hyperintensities (WMH): FLAIR images were loaded into dispunc (David
Plummer, University College London, UK), and WMH were outlined using the contour
function. All areas of increased signal were included unless: i) the area was less than 10
pixels, ii) the area was a narrow band (less than one pixel wide) along the ventricles, iii)
hyperintensities were due to the presence of blood vessels. Baseline and follow-up images
were blinded for date of acquisition, and WMH on both scans were outlined concurrently.
Lacunar infarcts were counted on T1 images by an experienced observer. Although lacunar
infarcts were rare (4 in 1 subject, 2 in 2 subjects, and 1 in 8 subjects, with none in the
remainder), where WMH were present adjacent to a lacunar infarct, the lacunar infarct
volume was excluded from the WMH volume.
Brain Volume Measurements: These were performed using SIENAX and SIENA software
(http://www.fmrib.ox.ac.uk/fsl/siena/index.html) 25. Using SIENAX, T1-weighted volume
images were used to calculate baseline normalized brain volume, controlling for individual
differences in the skull size. Percentage change in brain volume between the two time-points
was automatically calculated using SIENA.
Analysis was performed on the participants who attended at both baseline and follow-up.
Measures were normally distributed at baseline, follow-up, and on the measure of change for
all cognitive data, DTI parameters and brain volume. WMH volumes were not normally
distributed at baseline or follow-up, and were therefore log transformed. The change
measurement for WMH volume was normally distributed.
Paired sample t-tests assessed the difference between baseline and follow-up measures.
Change between the time-points was calculated as a ratio using the following equation
b S ; follow-up=
f S ):
This equation was used to take into account baseline levels of functioning, and reduces
distortion due to scalar differences in change. In order to investigate whether changes in
imaging measures predicted change in cognitive decline, only variables that demonstrated
significant change over the two-year delay were included in subsequent analysis. Pearson’s
correlations were used to assess the relationships between change in imaging and change in
cognitive variables. Stepwise regression analyses were performed with change in imaging
measures as independent variables, and change in cognitive abilities as the dependent
There were no differences between the individuals who did and did not attend for follow-up
in baseline measures of age, blood pressure, WMH-volume, DTI parameters, or information
processing speed. The drop-out group had smaller baseline normalized brain volume (t= -
2.518, p= .013) and lower baseline executive function (t= -2.70, p= .008) and working
memory (t= -2.00, p= .048) scores. Demographic details, baseline scores and follow-up scores
are shown in table 1.
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See Table 1 on next page
Table 1: Cognitive ability scores and MRI measures for baseline and 2 year follow-up data.
Mean (and standard deviations)
Paired sample t-test
of data at baseline
Executive Function .076 (.684) .119 (.697) t= -.979, p= .330
Working Memory .082 (.844) -.073 (.810)
t= -2.57, p= .012
Information Processing Speed .062 (.757) .010 (.467) t= .640, p= .524
Normalized Whole Brain Volume
1589.11 (75.16) 1569.71 (81.71)
t = 4.38, p < .001
WMH (in mm3)
t = -5.03, p < .001
MD - Normalized peak height
.19547 (.03144) .19110 (.03426)
t = -9.83, p < .001
MD - Peak height intensity .00074 (.00004) .00075 (.00004)
t = 2.55, p = .013
MD – Skewness 5.07 (.989) 4.36 (.821)
t = -.486, p < .001
MD – Kurtosis 34.33 (15.574) 28.04 (10.776)
t = -3.49, p = .001
MD – Median .00080 (.00004) .00082 (.00004)
t = 5.73, p < .001
FA - Normalized peak height
.03112 (.00386) .03342 (.00437)
t = 3.17, p = .002
FA - Peak height intensity .22288 (.04020) .21493 (.03485)
t = -5.70, p < .001
FA – Skewness .811 (.153) .849 (.159)
t = 11.70, p < .001
FA – Kurtosis .570 (.417) .659 (.436)
t = 6.51, p < .001
FA – Median .282 (.028) .275 (.025)
t = -7.66, p < .001
All DTI measures changed significantly between the two time-points with an increase in
median MD and a decrease in median FA (table 1 for data and t-tests). Figure 1 shows the
baseline and follow-up MD and FA histograms for the two time-points. Change in the MD
distribution between time-points was characterized by a significant increase in median MD
and a significant decrease in positive kurtosis. These parameter changes caused the
distribution to be significantly more symmetrical (i.e. have smaller positive skew) and
significantly less peaked (i.e. have a decreased normalized peak height frequency). Change in
the FA distribution between time-points was characterized by a significant decrease in median
FA and a significant increase in positive kurtosis. In turn these parameter changes caused the
distribution to be more significantly asymmetrical (i.e. have greater positive skew) and
significantly more peaked (i.e. have an increased normalized peak height frequency). Figure 2
illustrates the change in MD and FA normalized peak height frequency between baseline and
WMH, expressed as a percentage of brain volume, increased from 7.21% (SD 9.79) at
baseline to 9.00% (SD 12.39) at follow-up (t = -5.03, p< .001), see table 1. Normalized whole
brain volume decreased from 1589.63 cm3 (SD 76.76) at baseline to 1569.71 cm3 (SD 80.71)
at follow-up (t = 4.38, p< .001), a mean reduction of 0.87% (SD 0.993), approximately 0.44%
per year, see table 1.
There were no differences between the mean z-score for the two time-points for executive
function (mean= .042, SD= .393; t= -.979, p= .330) or information processing speed (mean= -
.054, SD= .759; t= .640, p= .524). In contrast working memory scores had declined at follow-
up compared to baseline (mean= -.155, SD= .547; t= -2.57, p= .012). See table 1.
Change Measures and Age
Correlations were performed to assess whether changes in working memory or imaging
measures were associated with increasing age. Age did not correlate significantly with change
in working memory (r = .034, p = .763). There were no significant correlations between age at
baseline and WMH (r = -.056, p = .624) or normalized brain volume (r = -.205, p = .070).
There were no significant correlations between FA histogram measures and age, and only one
significant correlation with change in the MD histogram measures and age, with MD
normalized peak height frequency (r = .244, p = .037).
Correlations between change in MRI parameters and change in working memory
The association between imaging and longitudinal cognitive data was only considered for
working memory, the measure that showed significant change at follow-up.
The working memory change ratio correlated with change in MD normalized peak height
frequency, MD skewness, and MD kurtosis. There were no correlations with change in FA
parameters (table 2). There were no correlations between either change in WMH (r= .098, p=
.396) or normalized brain volume (r= -.075, p= .517) and change in working memory.
To check that age-effects were not influencing the results the correlations between change in
working memory and change in the imaging measures were repeated, controlling for age at
baseline. The results remained largely unchanged with the working memory change ratio
remaining significantly correlated with change in MD normalized peak height frequency (r = -
.346, p = .004), MD skewness (r = -.270, p = .026), and MD kurtosis (r = -.306, p = .011), see
table 2. Secondary analyses were performed to assess the relationship between change in
imaging measures and change in both executive function and information processing speed.
No significant correlations were observed in these analyses.
Table 2: Correlations for associations between the change ratio for working memory and
change in imaging measures, and partial correlations controlling for age. Results for a number
of different parameters derived from the DTI histogram are shown.
Correlations Partial correlations
controlling for age
Normalized whole brain
WMH volume r= .098
MD FA MD FA
Normalized peak height
r = -.329
p = .005
r = .077
p = .523
r = -.346
p = .004
r = .085
p = .490
Peak height intensity r = -.125
p = .298
r = -.183
p = .126
r = -.118
p = .336
r = -.180
p = .142
Skewness r = -.252
p = .034
r = .019
p = .872
r = -.270
p = .026
r = .019
p = .879
Kurtosis r = -.292
p = .013
r = -.034
p = .777
r = -.306
p = .011
r = -.036
p = .771
Median r = .025
p = .839
r = -.118
p = .327
r = .035
p = .775
r = -.113
p = .359
Additional partial correlations were performed to investigate whether the relationship between
change in DTI parameters and change in working memory was independent of change in the
volume of WMH. The pattern of correlations remained the same: change in working memory
correlated significantly with change in MD normalized peak height frequency (r = -.320, p =
.007), MD skewness (r = -.251, p = .038), and MD kurtosis (r = -.294, p = .014).
Multivariate analysis of all MRI and working memory data
A stepwise regression analysis was computed with change in working memory as the
dependent variable. MD normalized peak height frequency was selected to represent the DTI
histogram as it demonstrated the most robust correlation. Independent variables were change
in MD normalized peak height frequency, WMH, and normalized brain volume. Only MD
normalized peak height frequency remained in the model (beta = .329, p= .005), explaining
10.8% of the variance in working memory (F(1,68) = 8.27, p= .005).
Correlations between baseline MRI parameters and change in working memory
No significant correlations were observed between baseline imaging measures and change in
Previous cross sectional studies have shown that DTI parameters correlate strongly with both
age and aspects of cognition including executive function and working memory 14;26;27.
Furthermore they have shown that correlations of DTI with cognition are generally stronger
than between WMH and cognition 12;13. The current study, describing longitudinal data from
the GENIE cohort, extends these findings and demonstrates that DTI is sensitive to short term
age-related white matter change.
WMH have been widely used as a MRI marker of white matter damage. However, high
signal merely reflects increased tissue water content 28, and not necessarily tissue destruction
and white matter tract disruption. DTI would be expected to provide a better measure of white
matter tract disruption. Cross sectional studies support this hypothesis. In both sporadic 12;13
and hereditary 29 white matter ischaemia due to SVD, DTI parameters correlate more strongly
than WMH with cognition. This suggests that DTI may be a sensitive measure to monitor
changes in age-related white matter damage, and also a potential surrogate marker to evaluate
new therapies. However, prior to this it is essential to determine, in prospective studies,
whether DTI can detect change over short time-periods and whether this change correlates
with changes in cognition.
Over the two year follow-up we found highly significant changes in all descriptors of the MD
and FA histogram distributions. In particular, median MD increased and the histogram
distribution became more symmetrical and less peaked across the time-points, whilst the
median FA decreased and histogram became more asymmetrical and more peaked. In terms
of structural integrity changes the median changes indicate a reduction in overall white matter
integrity. Interpretation of changes in skewness, kurtosis and peak height frequency are more
complicated and are explained in the following section. In particular, changes in kurtosis are
caused by both global and local MD and FA values changes between time points. Global
changes identified as increased median MD and decreased median FA cause the skewness to
decrease for MD and increase for FA. Furthermore, new or expanding regions of WMH will
cause large local increases in MD and decreases in FA. These regions will cause MD voxels
included in the histogram body at the initial time point to be found in the tail at time point 2
(thus increasing positive skewness), and high FA voxels initially found in the histogram tail to
be located within the body at time point 2 (thus decreasing the positive skewness). It is
evident that for MD these effects work against each other, but the finding of a significant
decrease in positive kurtosis implies that the global structural integrity changes identified by
MD are more prevalent than the effect of emerging WMH regions in this case. For FA the
local and global structural integrity changes work together to increase positive skewness,
possibly leading to the finding of greater significant change in skewness for FA than MD in
the histogram data. To understand changes in kurtosis and peak height frequency the changes
in homogeneity of the MD and FA maps between time points should be considered. Initially
MD maps are highly homogeneous whereas FA maps are highly heterogeneous. Changes in
structural integrity such as increases in MD and decreases in FA will manifest differently at
each white matter voxel, however, changes will act to increase the heterogeneity of the MD
maps (therefore causing decreased MD kurtosis and peak height frequency) and increase the
homogeneity of the FA maps (therefore causing increased FA kurtosis and peak height
There were also significant changes in WMH and brain volume. In contrast to the significant
changes in MRI, and particularly the DTI parameters, there was much less detectable change
in cognitive parameters. Working memory was the only cognitive domain to demonstrate
significant decline. No significant changes were observed in executive function or
information processing speed. This demonstrates that DTI is sensitive to change over this
short time period whilst this is not the case for the cognitive tests conventionally used. It is
possible that at least part of the lack of change in executive function and processing speed
occurs because deterioration of abilities is cancelled out by practice effects, although it is
possible that these abilities are stable over this period.
We looked for correlations between changes in MRI parameters with the cognitive domain in
which change could be detected, namely working memory. Significant correlations were
observed between the change in DTI histogram parameters and the change in working
memory only, although they were small or moderate in size. There were no correlations
between change in working memory and change in WMH or brain atrophy. This also supports
the potential of DTI as a surrogate marker of age-related cognitive decline. However, the
correlations were not consistent across different aspects of the MD histogram, and we were
unable to detect correlations with FA parameters. This is likely to reflect the small cognitive
decline seen over the two year follow-up. The reason for observed associations between
changes in working memory and MD, where FA does not demonstrate this relationship are
likely due to the nature of these measures. FA is non-linear and heterogeneous across the
brain. Areas with high FA that lose structural integrity will demonstrate large amounts of
change, but only in local regions. In contrast, MD tends to be more homogenous so changes
may be more “stable” across the brain, and are unlikely to demonstrate large regional
variations. This may explain why MD demonstrates stronger associations with working
memory abilities, and suggests that the distributed white matter networks required for
cognitive function are globally more sensitive to distributed changes in MD than FA.
Although change in MD histogram measures correlated with change in working memory, no
associations were observed between baseline imaging and change in working memory. This
either suggests that the baseline white matter pathophysiology is not strongly predictive of
further physiological change that would be coupled with working memory decline or that the
measurements are not sufficiently sensitive to detect this association. Longer term follow-up
is required to explore these associations further.
WMH has previously been suggested as a surrogate marker to evaluate treatments for age-
related cognitive decline 33;34. The changes in WMH volume in our cohort were within the
range reported previously 33;34. The rate of brain atrophy in our study (-.44% annually) is also
similar to that previously reported with previous estimates varying between -0.32% and -
0.45% in middle age and elderly populations 35-37.
In this study we used an automated histogram analysis, which reduces inter-individual user
error, and may be suitable for larger multicentre studies using DTI as a surrogate marker. The
associations with age and cognition using histogram and region of interest approaches are
similar 14. A number of different parameters can be derived from the histogram. All are
closely related but some may offer particular advantages. To explore this we described a
number of different parameters and assessed their pattern of change with age and their
sensitivity to change. Median MD increased and the MD histogram distribution became more
symmetrical and less peaked across the time-points, whilst median FA decreased and the FA
histogram became more asymmetrical and more peaked. Median changes indicate a reduction
in overall white matter integrity, whereas changes in kurtosis indicate that voxel increases in
MD are more variable than decreases in FA. These findings indicate that although there are
consistent changes in structural integrity of white matter, the changes in MD are more
variable than FA throughout the cerebral white matter. We found the parameter which
correlated strongest with cognition was normalized peak height frequency. The same
parameter correlates strongly with cognition in SVD 13.
An advantage of this study is that it represents a normal community population with
individuals being randomly selected. A proportion of patients did not attend follow-up
examination and these tended to have worse cognitive profiles and lower measures of brain
integrity at baseline. It is possible that these individuals had different rates of brain or
cognitive changes and this would lead to two potentially outcomes. If these individuals were
less likely to deteriorate then those who remained in the study would be more likely to
deteriorate and hence increase the correlation between brain and cognitive changes.
Conversely, if the participants who “dropped-out” would have deteriorated more, then the
remaining sample would demonstrate less decline and correlations would be reduced. On
balance the latter possibility is perhaps more likely, in view of the lower baseline values for
brain imaging measures and poorer cognitive performance in the drop-out group. Despite this
potentially reduced sensitivity, significant results are detected in this sample.
In summary, in this longitudinal study of normal aging we demonstrated that DTI is sensitive
to white matter damage and can reliably detect change even over a two year period. Despite
the highly significant change in DTI parameters there was little detectable change in
cognition, with a significant change only in working memory and no detectable change in
executive function or information processing speed. A significant correlation was detected
between change in DTI and change in working memory performance. Our data suggest DTI
may be a promising marker to assess age related white matter change. Further studies are
required with longer follow-up and larger sample size to show whether changes in DTI
robustly correlate with decline in different cognitive domains thought to depend on white
Acknowledgements: We are grateful to the staff of Church Lane Surgery, Merton Park,
London for assistance with patient recruitment.
Conflict of interest: None declared.
Funding: The GENIE study was funded by a UK charity Research into Ageing program
grant. Francesca Schiavone is funded by an MRC studentship.
The Corresponding Author has the right to grant on behalf of all authors and does grant on
behalf of all authors, an exclusive licence (or non-exclusive for government employees) on a
worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if
accepted) to be published in Journal of Neurology, Neurosurgery & Psychiatry and any other
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Figure 1: Whole-brain white matter mean MD and FA histograms for baseline and follow-up
Figure 2: Changes of MD and FA normalized peak height frequency with time. Each line
represents an individual participant.
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