Ventral Striatum

The procedure for co-registration of the MRI and PiB PET images has been described.^{18 (link)} Regions-of-interest (ROIs) were hand-drawn on a template that was a high-resolution MR image of a single elderly MCI subject.^{19 (link)} The ROIs included five primary cortical areas [i.e., anterior cingulate (ACG), frontal cortex (FRC), lateral temporal cortex (LTC), parietal cortex (PAR), precuneus cortex (PRC) which comprised the Global-5 composite region], as well as medial temporal cortex (MTC), anterior ventral striatum (AVS), occipital cortex (OCC), occipital pole (OCP), sensorimotor cortex (SMC), thalamus (THL), subcortical white matter (SWM), pons (PON), and cerebellum (CER)]. PiB retention was measured using the standardized uptake value ratio (SUVR) over the 50-70 min scan (or SUV: scaled to injected dose and body mass) that is then normalized by the SUV of the CER reference region.
All statistical analyses were performed using SAS (version 9.2; SAS Institute Inc., Cary, NC, USA). All two-sample comparisons were evaluated using a Wilcoxon nonparametric test. In settings where the sample size was below 25 in any group, exact methods were used for the computation of the significance level. For the analysis of the neuropsychological outcomes, the significance levels for the two sample comparisons were computed from a linear regression model including age, gender, and education. Each model was evaluated using regression diagnostics to identify potentially influential observations. When a Cook's D value greater than 0.2 was observed, the corresponding model was recomputed with the observation removed from the data set. These instances are denoted in the tables and text.
A two-way ANOVA model was performed with diagnosis (NC and MCI) and PiB status (PiB-negative and PiB-positive) as grouping factors to determine voxel-wise gray matter differences. The interaction effect between diagnosis and PiB status, and the main effects were examined using appropriate contrasts. Analysis of covariance was also performed in SPM8 to determine voxel-wise gray matter differences between groups: PIB-negative NC > PIB-positive MCI and PIB-negative NC > PIB-negative MCI. These models included age and gender as covariates and were applied to gray matter maps with intensity threshold masking of 0.3. Thresholds of 0, 0.2 and 0.3 were examined but the latter was chosen because this provided a good compromise between inclusion of gray matter and exclusion of background instabilities. Significance levels were set to p<0.025, FDR corrected.

Amygdala blocks were sectioned and immunostained for TDP-43 (polyclonal
antibody MC2085 that recognizes a peptide sequence in the 25-kDa C-terminal
fragment[48 (link)]) with a DAKO-Autostainer
(DAKO-Cytomaton, Carpinteria, California) and 3, 3’-diaminobenzidine as
the chromogen. Sections were lightly counterstained with Hematoxylin. Amygdala
sections were screened (by DWD), to assess for the presence of TDP-43
immunoreactive neuronal cytoplasmic inclusions, dystrophic neurites, or neuronal
intranuclear inclusions (Figure 1). We
screened the amygdala as the amygdala has been shown to be the first region
affected in AD by TDP-43 pathology[19 (link)].
Any AD case not showing TDP-43 immunoreactivity in the amygdala was considered
TDP-negative (Figure 2a), while any AD case
showing any amount of TDP-43 immunoreactivity in the amygdala was considered
TDP-positive (Figure 2b–d). Hence,
amygdaloid positivity was all that was necessary to call an individual AD case
TDP-43 positive. For TDP-positive cases, we sectioned additional paraffin blocks
of the middle frontal, superior temporal, and inferior parietal cortices,
nucleus basalis, hippocampus, midbrain and medulla using the same protocol as
described above for the amygdala. The following 14 distinct brain regions per
case were reviewed simultaneously with a multi-headed microscope (by DWD and
KAJ) for TDP-43 immunoreactivity: amygdala, entorhinal cortex, subiculum,
hippocampal dentate fascia, occipitotemporal cortex, inferior temporal cortex,
basal forebrain, insula, ventral striatum, frontal lobe, basal ganglia,
substantia nigra, midbrain tegmentum, and inferior olive. A region was
considered positive if TDP-43 immunoreactive lesions were observed at
20× magnification screening the entire region, with subsequent
confirmation at 40× magnification. The number of cases with TDP-43
immunoreactivity for each of the 14 regions is shown in Figure 3.
To ensure antibody sensitivity, we additionally screened amygdala
sections from 10% of the TDP-negative cases using a different antibody
against phosphorylated TDP-43 peptide (1:5,000 rabbit polyclonal anti-human
phosphoserine 409/410). None of the cases that had initially screened negative
with the polyclonal antibody MC2085 showed TDP-43 immunoreactivity with the
phosphorylated antibody, ensuring excellent sensitivity of MC2085.
TDP-43 burden was assessed in the hippocampal dentate fascia using the
Aperio slide scanner and a customized color deconvolution algorithm enabling the
detection of any abnormal TDP-43 (Figure
4). The dentate fascia was selected for the burden analysis since the
dentate fascia has been demonstrated to be most strongly associated with memory
loss [36 ]. TDP-43 immunostained sections
of the posterior hippocampus at the level of the lateral geniculate were scanned
at ultra-resolution on the ScanScope XT (Aperio Technologies, Vista, CA). This
instrument permits scanning of the entire slide from which large areas of
interest can be annotated using ImageScope version 11.2 (Aperio Technologies,
Vista, CA). The method greatly increases the sampling frame compared to some
other image analysis systems that are limited to the field of view of the
microscope or require image tiling [38 (link)].
The entirety of the dentate fascia was assessed to quantitatively determine
TDP-43 immunohistochemical burden. To operationalize annotation of the dentate
fascia, the ruler tool was used to measure 125µm across the granule cell
layer to molecular layer to avoid quantification variability resulting from
tissue sectioning differences. Any dust or dirt particles or tissue folds were
excluded using the negative trace tool. Annotated layers were analyzed in
Spectrum version 11.2 (Aperio Technologies, Vista, CA) using a custom-designed
color deconvolution algorithm, as previously described [22 (link)]. After applying the color deconvolution algorithm, each
high resolution image was reviewed independently by two investigators (MM
& AL) in order to ensure that only abnormal TDP-43 was being measured.
Cases where the algorithm was unable to separate abnormal TDP-43 from normal
nuclear TDP-43, were removed from further analysis of TDP-43 burden (n=20).
TDP-43 burden was then expressed as the area of immunoreactive pixels to the
total area of the annotated region.