The process carried out in this paper has two phases: first, cluster selection in FA volumes, and, second, a correlation analysis between image clusters and blood biomarkers. For cluster selection, we apply Tract-Based Spatial Statistics (TBSS) (Smith et al., 2006; Bach et al., 2014) to find significant WM tract differences between AD and LOBD patients. We expect TBSS to provide tract specific effects, improving the anatomical finding reported by our previous approaches (Graña et al., 2011; Besga et al., 2012, 2016). TBSS identifies WM tract voxel clusters with significant difference in FA between AD and LOBD on the mean FA skeleton. To enhance localization power we carry out an ANOVA F-test. The correlation analysis is carried out by computing Pearson's correlation coffiencients of the FA values at voxel sites and blood biomarker values across all subjects. Machine learning is not used because of the small sample size that makes cross-validation results very unstable and not significant.

The ethics committee of the Alava University Hospital, Spain, approved this study. All patients gave their written consent to participate in the study, which was conducted according to the provisions of the Helsinki declaration. After written informed consent was obtained, venous blood samples (10 mL) were collected from the volunteers, after which all the mood scales and cognitive tests were performed. The study has been registered as an observation trial¹ in the ISRCTN registry.

The blood plasma biomarkers selected for analysis include:

Neurotrophins: nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF).

Their measurements are described in Besga et al. (2015), except for PGE2 and PGJ2 which were measured by enzyme immunoassay (EIA) using reagents in kit form (Prostaglandin E2 EIA Kit-Monoclonal; Cayman Chemical Europe, Tallinn, Estonia and 15-deoxy-Δ12,14- Prostaglandin J2 ELISA Kit DRG Diagnostics, Marburg, Germany). Samples were measured following manufacturer's instructions.

Diffusion-Weighted Imaging (DWI) uses MRI acquisition sequences computing signal differences along several gradient directions in order to obtain a signal that measures water diffusion. Diffusion Tensor Imaging (DTI) is a compact representation by means of 3 × 3 matrix of water diffusion in each spatial direction at each voxel (Basser et al., 1994; Pierpaoli et al., 1996). Specifically, in this paper we will work on FA values, which are computed as follows:

where {λ₁, λ₂, λ₃} are the diffusion tensor eigenvalues at the voxel site j. The specific parameters of the data capture on a 1.5 Tesla scanner (Magnetom Avanto, Siemens), data preprocessing, computing FA, and registration have been given in Besga et al. (2016). We use the FSL software suite (http://www.fmrib.ox.ac.uk/fsl/) to carry out DWI preprocessing, DTI estimation (Behrens et al., 2003), image registration (Andersson et al., 2007a,b), and TBSS described below. We did not perform spatial smoothing. The pre-processing consists in the removal of non-brain voxels using the brain extraction tool (BET) from FSL, the correction of eddy currents artifacts, and the rigid registration of the gradient images to cope with motion of the subject. On the spatially aligned DWI we estimate the diffusion tensor at each voxel, and the FA values. The FA volumes are then spatially normalized by non-linear registration to the FMRIB58_FA template provided with FSL standards. We have not computed a template from the actual FA dataset because the population is small and very heterogenous so the resulting mean FA template is quite noisy and blurry. Besides we need to register the data to the MNI152 space in order to report atlas based localizations, which is already done in the template provided by FSL. We do not carry out any intensity normalization on the FA images.

We apply TBSS (Smith et al., 2006; Bach et al., 2014), a module of FSL (Smith et al., 2004)² to detect differences in white matter tracts between HC, AD, and LOBD subjects. The specific TBSS procedure applied is as follows: (1) We warp the FA volumes according to the registration carried out before, so we have all aligned in the common space. (2) We compute the mean FA image and extract the common skeleton from it by morphological image processing. This skeleton is assumed to represent the centerline of the WM tracts in all the FA volumes. Each subject's aligned FA data is then projected onto this skeleton. This projection is achieved by assigning the closest local maximum of FA value in the orthogonal direction to the skeleton. (3) For each possible contrast (i.e., HC > AD, AD > LOBD, HC > LOBD, AD > HC, LOBD > AD, LOBD > HC, and ANOVA F-test over all pairwise contrasts) we compute a permutation test applying the randomize tool of FSL with 50,000 permutations and threshold free cluster enhancement (TFCE)(Smith and Nichols, 2009) skeleton cluster selection over the contrast statistics. The advantage of TFCE over other methods is that it combines the spatial and statistics value optimally achieving high significance, and its parameters are already optimized by an automated procedure.

The correlation analysis is applied to the clusters selected by each binary contrast masked by the F-test cluster detection. The resulting clusters are much smaller than the binary contrast detection but more specific. Considering independently each voxel site j of the selected clusters, we build a vector v_j composed of the FA intensities at the j-th voxel site across all the subjects. We compute Pearson's correlation coefficient between this vector and the value of the blood biomarker for this subject, denoted y_i in the following, obtaining the correlation values at each voxel site. Pearson's correlation (Pearson, 1895; Kendall and Stuart, 1973) at the j-th voxel site is computed as follows:

where v_ij is the value of FA at the j-th voxel site in thei-th subject, and y_i is the blood plasma biomarker value of that i-th subject. We compute correlation values for each subpopulation (i.e., AD and LOBD) independently (following a reviewer recommendation), retaining voxels significantly correlated (p < 0.01) for examination.

Locations reported by atlasquery tool from FSL using the JHU White-Matter Tractography Atlas are collected for each contrast after the permutation test, and each correlation analysis between detected FA skeleton clusters and blood biomarkers. The report produced by the atlasquery tool are the probability of a cluster voxel to belong to a tract, therefore the size of the cluster falling in a tract is computed as product of atlasquery probabilities and the total detection volume.

We focus the discussion on the tracts where the F-test finds the greatest clusters for the LOBD > AD contrast, which has quantitatively greater effects than the AD > LOBD contrast. The size of cluster localizations suggest greater axonal degradation in AD than in LOBD in pathways that serve to integrate cognitive and social structures, including tracts that mediate connectivity to frontal and temporal lobes.

In particular, significant difference s found in ILF indicate degradation of the fronto-temporal-occipital circuit which is very important for social and emotional processing, leading to behavioral deterioration, which has been assessed as the main discriminant between AD and LOBD (Besga et al., 2015). The ILF tract connects the occipital cortex and temporal lobes including the superior, middle and anterior lobes, mediating the connectivity between three regions: the superior temporal sulcus, the fusiform face area, and the amygdala. Therefore, ILF degradation has impact on the processes of detecting biological motion and eye gaze (Pelphrey and Carter, 2008), as well as facial information processing with social significance, i.e., face identification and facial expression interpretation (Adolphs et al., 1999). We found also significant differences in the cingulate gyrus C_CG, which is part of the cingulate cortex lying above the corpus callosum, and part of the limbic system in charge of processing emotional contents. Together with the previously described impairments of ILF pathway, disruption of C_CG impedes the structural connectivity of an extended circuit that involves frontal, temporal and occipital regions. This circuit controls socio-emotional processing, so its degradation leads to greater behavioral an d emotional impairments of AD than LOBD.

The IFOF connects the occipital, posterior temporal, and the orbito-frontal areas (Ashtari, 2012). Simultaneously degraded axonal integrity of left UF, IFOF, and ATR has significant impact on the semantic processing (Han et al., 2013) during specific cognitive tasks related to object recognition. This concurrent effect can explain the increased cognitive impairment of the AD relative to LOBD (Besga et al., 2015), though we have not carried out correlation study of image data with cognitive neuropsychological tests results. The FMi collects most of the clusters detected, so its relative degradation in AD compared to LOBD is a salient biomarker. The FMi connects the lateral and medial surfaces of the frontal lobes crossing the midline via the genu of the corpus callosum. Damage of the FMi detected by decreasing FA in DTI imaging has been associated with fatigue and depression in multiple sclerosis (Gobbi et al., 2014). Degradation of FMiin mild cognitive impairment (MCI) and AD relative to HC was found in a cohort study using multiple diffussion measures (Alves et al., 2013).

Peripheral biomarkers of inflammation, oxidative stress, and neurotrophins have been related to clinical symptoms, cognitive decline and illness severity in BD (Barbosa et al., 2012; Martinez-Cengotitabengoa et al., 2014) as well as in AD (Berridge, 2013). It has been suggested that inflammation and oxidative stress do not cause AD or LOBD by themselves, but that they reinforce interactions among factors related to these complex neuropsychiatric disorders during brain aging (Forcada et al., 2015), leading to a misbalance between protective and degenerative factors, which predisposes the brain to neurodegenerative diseases (Lewandowski et al., 2011). On the other hand, negative correlation of inflammation biomarkers, i.e., TNFα, with FA in the body and isthmus of the corpus callosum has been also found in healthy aging subjects by a TBSS analysis (Arfanakis et al., 2013), showing that systemic inflammation is not necessarily associated with cognitive decline. There are also reports of a significant decrease in BDNF and IL-6 in BD patients at a later stage compared to its early stage, while, inversely, TNFα has a significant increase at the later stage of BD (Kauer-Sant'Anna M, 2009; Grande et al., 2014), suggesting that the inflammation lies in the pathogenesis of BD.

Brain injuries promote the up-regulation of proinflamatory prostaglandins PGE2 (Ahmad et al., 2006), hence blocking the corresponding receptor has been proposed as a target of treatment for stroke and other traumatic brain injuries. Cyclopentenone Prostaglandin PGJ2 is a recently discovered prostaglandin, which has anti-inflammatory functions (Scher and Pillinger, 2005; Zhao et al., 2006), such as the inhibition of a gene in T cells, therefore positive correlation with FA voxels is consistently related to axonal integrity in this area. TNFα is a cytokine involved in systemic inflammation and acute phase reaction, whose role is the regulation of immune cells, inducing inflammation and other effects, such as apoptotic cell death.

The role of NGF as a therapeutic tool for AD has received a lot of attention in the last years (Xu et al., 2016), with strong consideration of the impairment of NGF pathway as cause of AD via the accumulation of amyloid plaques. Clinical trials have been carried out^3,4 studying the effect of NGF gene therapy (Tuszynski et al., 2005). Postmortem analysis showed that the NGF treatment induced response in degenerating neurons exhibited trophic response without adverse pathological effects (Tuszynski et al., 2015).

In our study, regarding inflammation biomarkers in Table 1, we find no effect of the protective PGJ2, and almost no effect of TNFα, while there PGE2 has both positive and negative correlation with LOBD image data, so no conclusion can be given from them. However, though sparsely distributed in different clusters, we find positive correlation of IL1β and IL6 with LOBD and negative with AD, hence these blood biomarkers are a clear indication of greater inflammation in LOBD pathogenesis.

Regarding oxidative stress biomarkers, we found no differential effect of NO2, because both populations showed the same sizes of negative correlation clusters, but MDA shows positive correlation with LOBD hinting to an added pathogenesis factor. This result is also in agreement with our previous findings using eigenanatomy (Besga et al., 2016). Notice in Figure 3 that IL1β, IL6, and MAD affects mostly the IFOF as a cause for behavior impairment.

Regarding neurotrophic factors, we found a big effect of NGF which correlates negatively with AD imaging data, i.e., with the degradation of synaptic integrity in the located tract. We had also a small positive correlation with LOBD that reinforces the value of NGF as a differential diagnostic biomarker between AD and LOBD. This result is in complete agreement with recent AD therapeutic research lines (Tuszynski et al., 2005, 2015; Xu et al., 2016). Notice from Figure 3 that most of the correlation effects of NGF are located in FMi, suggesting a role in cognitive decline.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.