Illán‑Gala et al. Alzheimer’s Alzheimer’s Research & Therapy (2022) 14:27 https://doi.org/10.1186/s13195-022-00974-0 Research & Therapy RESEARCH Open Access Cortical microstructure in primary progressive aphasia: a multicenter study Ignacio Illán‑Gala1,2,3*† , Victor Montal1,2†, Sergi Borrego‑Écija2,4, Maria Luisa Mandelli5, Neus Falgàs3,5, Ariane E. Welch5, Jordi Pegueroles1,2, Miguel Santos‑Santos1,2, Alexandre Bejanin1,2, Daniel Alcolea1,2, Oriol Dols‑Icardo1,2, Olivia Belbin1,2, Mª. Belén Sánchez‑Saudinós1, Nuria Bargalló6, Sofía González‑Ortiz7, Albert Lladó2,4, Rafael Blesa1,2, Bradford C. Dickerson8,9, Howard J. Rosen5, Bruce L. Miller5, Alberto Lleó1,2, Maria Luisa Gorno‑Tempini5, Raquel Sánchez‑Valle2,4 and Juan Fortea1,2,10* Abstract Background Cortical mean diffusivity is a novel imaging metric sensitive to early changes in neurodegenerative syndromes. Higher cortical mean diffusivity values reflect microstructural disorganization and have been proposed as a sensitive biomarker that might antedate macroscopic cortical changes. We aimed to test the hypothesis that cortical mean diffusivity is more sensitive than cortical thickness to detect cortical changes in primary progressive aphasia (PPA). Methods In this multicenter, case-control study, we recruited 120 patients with PPA (52 non-fluent, 31 semantic, and 32 logopenic variants; and 5 GRN-related PPA) as well as 89 controls from three centers. The 3-Tesla MRI protocol included structural and diffusion-weighted sequences. Disease severity was assessed with the Clinical Dementia Rat‑ ing scale. Cortical thickness and cortical mean diffusivity were computed using a surface-based approach. Results The comparison between each PPA variant and controls revealed cortical mean diffusivity increases and cor‑ tical thinning in overlapping regions, reflecting the canonical loci of neurodegeneration of each variant. Importantly, cortical mean diffusivity increases also expanded to other PPA-related areas and correlated with disease severity in all PPA groups. Cortical mean diffusivity was also increased in patients with very mild PPA when only minimal cortical thinning was observed and showed a good correlation with measures of disease severity. Conclusions Cortical mean diffusivity shows promise as a sensitive biomarker for the study of the neurodegenera‑ tion-related microstructural changes in PPA. Keywords Diffusion, Magnetic resonance, Primary progressive aphasia, Alzheimer’s disease, Frontotemporal lobar degeneration Introduction Primary progressive aphasia (PPA) encompasses different neurodegenerative syndromes characterized by promi- † Ignacio Illán-Gala and Victor Montal contributed equally to this work. nent deterioration of speech and language, and rela- *Correspondence: tive sparing of other cognitive functions [1, 2]. In 2011, Ignacio Illán‑Gala iillan@santpau.cat an international group of experts introduced a common Juan Fortea framework in which three variants of PPA were recog- jfortea@santpau.cat nized, based on specific speech and language features: Full list of author information is available at the end of the article © The Author(s) 2022, corrected publication 2023. 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The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 2 of 13 the non-fluent agrammatic variant (nfvPPA), the seman- Materials and methods tic variant (svPPA), and the logopenic (lvPPA) variant [3]. Study population Currently, there are no disease-modifying treatments Participants with PPA were recruited from two cohorts, for PPA, but many pharmacological and non-pharmaco- at three different centers: 88 at the Memory and Aging logical treatments are being developed with promising Center (MAC) of University of California, San Francisco results [4]. However, several factors hamper the design of (UCSF, CA, USA), and 72 at the Catalan Frontotemporal clinical trials in PPA. First, PPA cases are relatively rare, Dementia Initiative (CATFI; 32 at Hospital de Sant Pau limiting the recruitment of participants [5]. Second, the and 40 at the Hospital Clinic de Barcelona, Barcelona, diagnosis of PPA is difficult at very mild stages, when dis- Spain). In both cohorts, the diagnosis was made by neu- ease-modifying interventions are more likely to be effec- rologists with expertise in the evaluation of PPA [19, 20]. tive, but atrophy can be subtle [6]. Finally, the prognosis All patients underwent a complete clinical history, physi- of each PPA variant is heterogeneous and the progression cal examination, neuropsychological evaluation, genetic rate can vary substantially within a given PPA variant [7, screening for major FTLD mutations (for additional 8]. In this complex scenario, biomarkers represent pow- details, please refer to the “Genetic studies, biomarkers, erful tools for the design of clinical trials [7]. Particularly, and pathological assessment” section) and structural future clinical trials would largely benefit from instru- brain imaging [15, 21, 22]. A total of 92 age-matched ments sensitive to the earliest neurodegenerative changes healthy controls from the two cohorts were also included to increase diagnostic certainty, and optimize the meas- as imaging controls (35 UCSF and 57 CATFI). All healthy urement of treatment effects [7, 8]. controls had normal cognitive performance according to Quantitative analysis of brain structure with magnetic normative data and did not have any neurologic, psychi- resonance imaging (MRI) can detect disease-specific atric, or other major medical illnesses [20, 23]. abnormalities and enable the comparison with normal Inclusion criteria for PPA participants were (i) meeting populations and the determination of the vulnerable basic PPA criteria as defined by Mesulam [1], (ii) meeting regions in each syndrome. Previous MRI studies have international criteria for one of the three PPA variants shown that each PPA variant is characterized by a rela- [3], and (iii) 3T MRI study available for structural and tively focal onset, and spreading along highly connected cortical mean diffusivity analysis (see below for details). cerebral regions [9–12]. To date, most neuroimaging Participants fulfilling basic PPA criteria that were found studies in PPA have focused on the cortical macrostruc- to have a mutation in the GRN gene (n = 5) were classi- ture with different metrics (gray matter density in voxel- fied in a separate group because these cases are charac- based morphometry studies or cortical thickness in terized by a widespread and asymmetric pattern of gray surface-based analyses) or white matter microstructural matter loss [24, 25] and usually display clinical features properties (namely diffusion tensor imaging metrics of more than one PPA subtype [26]. Figure 1 shows a such as fractional anisotropy). However, diffusion tensor flowchart of the sample composition. A total of 252 par- imaging can also be used to measure the magnitude of ticipants with appropriate 3T structural and diffusion- diffusivity (mean diffusivity), in the cerebral cortex [13]. weighted MRI were considered for analysis. Of these, 43 Higher cortical mean diffusivity values reflect micro- (17%) participants were excluded due to quality control structural disorganization and disruption of cellular issues or processing errors. membranes and have been proposed as a sensitive bio- marker that might antedate macroscopic cortical changes Measures of disease severity and global cognitive function [13, 14]. We have previously shown that diffusion imag- At presentation, the CDR Dementia Staging Instrument ing can also be used to measure the structural organi- and Language Domains sum of boxes (CDR® plus NACC plus National Alzheimer’s Coordinating Center Behavior zation of the cerebral cortex and that such changes may reveal neurodegeneration that is not detected with corti- disease severity [27, 28]. The CDR® plus NACC FTLD-SB FTLD-SB) scores were recorded as a measure of global of cal thickness measurements in different neurodegenera- tive diseases [14–16], even in the preclinical phase [17, was designed to apply the CDR® in FTLD natural history 18]. studies. It includes cognitive/functional domains: mem- In this multicenter study, our objective was to com- ory, orientation, judgment and problem solving, commu- pare the cortical thickness and cortical mean diffusivity nity affairs, home and hobbies, personal care, behavior, changes in patients with PPA. We also aimed to correlate and language. Each category domain is rated on a 5-point these changes with clinical measures of disease sever- scale ranging from 0 (normal), 0.5 (questionably or mini- ity. We hypothesized that cortical mean diffusivity is mally impaired), 1 (mildly but definitely impaired), 2 more sensitive than cortical thickness to detect cortical (moderately impaired), to 3 (severely impaired). All rat- changes in PPA. ings are based on the semi-structured interview from Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 3 of 13 Fig. 1 Flowchart of the sample composition. Abbreviations: CATFI, Catalan Frontotemporal Dementia Initiative; FTLD, frontotemporal lobar degeneration; HC, healthy controls; lvPPA, logopenic variant of primary progressive aphasia; MRI, magnetic resonance image; nfvPPA, non-fluent/ agrammatic variant of primary progressive aphasia; PPA, primary progressive aphasia; svPPA, semantic variant of primary progressive aphasia; UCSF, University of California San Francisco both a patient and a knowledgeable informant (typically Amyloid PET and core AD biomarkers were also availa- neuropsychological assessments. The CDR® plus NACC a close family member of a patient) and do not consider ble in a subset of participants. Amyloid PET studies were read as positive or negative, as previously described [20], and were performed with two different tracers 11C-Pitts- adding the rating of each domain. The global CDR® plus FTLD-SB score (ranging from 0 to 24) is obtained by burgh compound B at UCSF [n = 37] and with 18F-Flor- NACC FTLD (ranging from 0 to 3) score was also cal- betapir at CATFI [n =4]). The core AD biomarkers were culated following recently published scoring rules [28] available for CATFI participants and were performed as (global CDR® plus NACC FTLD score of 0.5). CDR® plus to identify PPA participants at a mild stage of disease previously described [20, 31]. Neuropathologic assessments performed at UCSF or NACC FTLD-SB was available in 183 (88%) of partici- Barcelona’s Neurological Tissue Bank followed previously pants. The Mini-Mental State Examination (MMSE) was described procedures [20, 21]. Participants were classi- recorded in all centers as a general measure of cognitive fied into FTLD major molecular classes (tau, TDP-43, or impairment [29]. FUS) and subtypes [22] or AD [32]. Genetic studies, biomarkers, and pathological assessment MRI APOE was genotyped in all participants according to pre- MRI acquisition viously described methods [30]. Patients were screened All participants underwent a 3T MRI using 4 different for genetic mutations known to cause frontotemporal scanners. The acquisition parameters by the scanner can lobar degeneration and Alzheimer’s disease (AD) [15, 21]. be found in Supplementary Table 1-2. All centers had Genetic screening was conducted for mutations known a structural 3D MPRAGE T1-weighted acquisition of to cause autosomal dominant FTLD or AD (MAPT, approximately 1 × 1 × 1 mm isotropic resolution and an C9orf72, GRN, TARDBP, FUS, PSEN1, PSEN2, and APP) EPI diffusion-weighted acquisition of at least 2.7 × 2.7 × at each site, as previously described [15, 21]. 2.7 mm isotropic resolution. Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 4 of 13 Cortical thickness processing (using Freesurfer’s mri_vol2surf command). Finally, indi- Cortical thickness reconstruction was performed with vidual cortical mean diffusivity maps were normalized the Freesurfer package v6 (“FreeSurfer,” n.d.) using a pre- to an average standard surface using a spherical regis- viously described procedure [33]. All individual cortical tration, enabling an accurate inter-subject matching of reconstructions were visually inspected on a slice-by- cortical locations for the statistical analyses. Before sta- slice basis to check for accuracy of the gray/white matter tistical analyses, we applied a Gaussian kernel of 15 mm boundary segmentation. From the initial 252 participants as implemented in Freesurfer [34], to obtain equivalent with 3T MRI available from the three centers, 19 were data effective smoothing between cortical thickness and excluded due to segmentation issues. Finally, each indi- cortical mean diffusivity. vidual reconstructed brain was registered, and cortical thickness maps were morphed, to the fsaverage standard Cortical mean diffusivity harmonization between centers surface provided by Freesurfer, using a spherical regis- Because diffusion tensor imaging metrics are very tration, enabling an accurate inter-subject matching of sensitive to acquisition parameters, harmonization cortical locations for the computation of further statis- approaches are required to mitigate center-specific dif- tics. Before statistical analyses, we smoothed the cortical ferences in multicenter studies. We applied a multicenter thickness maps using a Gaussian kernel with FWHM of harmonization algorithm based on ComBat, to reduce 15mm as implemented in Freesurfer [34]. center-specific differences in cortical mean diffusivity quantifications before any statistical analysis [38]. Briefly, Cortical mean diffusivity processing ComBat uses an empirical Bayes framework to estimate We used a previously described home-made surface- the additive (mean) and multiplicative (variance) contri- based approach to process cortical diffusion MRI [14]. bution of each site, at each vertex, for a specific diffusion Recent studies have shown the potential of surface-based tensor imaging metric and corrects these effects. Impor- methods to measure microstructural changes in neurode- tantly, this approach allows the inclusion of biological generative diseases [35] and the cortical architecture [36]. information (such as clinical group or age), and it has An important advantage of these methods is the miti- been reported to preserve within-site biological variabil- gation of partial volume effects or kernel-sensitive CSF ity, thereby increasing the statistical power [38]. signal inclusion during the smoothing step [37]. Briefly, diffusion-weighted imaging data were first corrected Statistical methods for motion effects applying a rigid body transformation Data were explored for normality using the Shapiro-Wilk between the b = 0 image and the diffusion-weighted test. When necessary, variables were log-transformed acquisitions. Then, after removing non-brain tissue using using the natural log to fulfill the normal distribution the Brain Extraction Tool, diffusion tensors were fitted, assumption. Between-group differences were determined and mean diffusivity was computed using the FSL’s dti- with ANOVA or t-test for continuous variables (with fit command. We then computed the affine transforma- Bonferroni correction for multiple comparisons) and the tion between the skull-stripped b0 and the segmented chi-square for dichotomous or categorical data. We first T1-weighted volume using a boundary-based algorithm performed group comparisons for cortical thickness and as implemented in Freesurfer’s bbregister. This approach cortical mean diffusivity with a two-class general linear takes advantage of the accurate segmentation of the white model, as implemented in Freesurfer, comparing each matter surface and pial surface obtained during Freesurf- PPA variant to the control group. These analyses were er’s segmentation (cortical thickness processing section), repeated for each center independently. Moreover, we to accurately register the b0 and the T1-weighted image, participants with a global CDR® plus NACC FTLD score also performed additional analyses including only PPA maximizing the intensity gradient across gray matter and white matter between both volumes. At this point, all of 0.5 (mild PPA). Next, we performed a vertex-wise cor- the diffusion to T1 registrations were visually inspected relation analysis in each PPA variant group between the to exclude those subjects with an erroneous co-registra- CDR® plus NACC FTLD-SB (as a general measure of cortical thickness and cortical mean diffusivity and the tion. Seven participants were excluded due to diffusion- weighted imaging processing errors. Then, the mean disease severity) and the MMSE (as a general measure of diffusivity volume for each individual was sampled at the cognitive function). Specifically, a general linear model midpoint of the cortical ribbon (half the distance along was created in which cortical mean diffusivity or corti- the normal vector between the white matter surface and CDR® plus NACC FTLD-SB scores (and MMSE scores) cal thickness was included as the dependent variable, and the gray matter surface) and projected to each surface reconstruction obtained during the Freesurfer process- were independent variables. We included age, sex, hand- ing, to create a surface map of cortical mean diffusivity edness, and center as nuisance variables in the cortical Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 5 of 13 thickness analysis. In mean diffusivity analysis, only age, and controls for either cortical thickness or cortical mean sex, and handedness were included since diffusion tensor diffusivity. We then computed the difference between the imaging data were already harmonized between centers cortical thickness and cortical mean diffusivity effect size in a previous step. Only results that survived multiple maps to obtain a topographical representation of the net comparisons (family-wise error < 0.05) based on Monte effect size for each metric. Carlo simulation with 10,000 repeats as implemented in Freesurfer are presented. We used a very stringent Results threshold of α = 0.001 for the group analyses includ- Table 1 shows the demographics and clinical features of ing all PPA participants and HC, and a threshold of α = the participants in the study. Age at MRI, years of educa- 0.05 for the analyses including PPA subgroups and the tion, and disease duration at MRI were similar between correlation analyses. A full description of the multiple participants with PPA and controls and between PPA comparison’s methodology can be found in the Supple- subgroups. Sex and handedness distribution were also showed lower values of MMSE and CDR® plus NACC mentary material. similar between all groups. As expected, all PPA variants We computed the Cohen’s d effect size metric for both cortical thickness and cortical mean diffusivity, on a ver- FTLD-SB scores compared to the control group. Partici- tex-wise basis, to obtain a topographical representation pants in the lvPPA group had lower MMSE scores when of the effect size for the group comparison between par- compared to participants in the nfvPPA and PPA-GRN ticipants with PPA and controls. Effect size computation groups. APOEɛ4 carriers were overrepresented in the was restricted to cortical regions showing statistically lvPPA group (33%) compared to the nfvPPA, svPPA, and significant differences between participants with PPA GRN-PPA groups (19%, 16%, and 0%, respectively). The Table 1 Characteristics of the participants PPA HC nfvPPA svPPA lvPPA PPA-GRN All PPA HC n=52 (43%) n=31 (26%) n=32 (27%) n=5 (4%) n=120 (100%) n=89 Cohort, no. (CATFI/UCSF) 18/34 15/16 11/21 3/2 47/73† 54/35† Demographics, genetic, and clinical features Age at MRI, years 69.0 (7.3) 67.1 (7.5) 66.3 (7.9) 61.7 (3.3) 67.5 (7.5) 66.0 (6.9) Biological sex (men/women) 20/32 16/15 16/16 0/5 52/68 31/58 APOEɛ4, no. (positive/negative) 9/39 4/21 9/18 0/4 22/82 7/28 Education, years 14.2 (5.2) 14.6 (4.0) 13.1 (7.2) 12.4 (5.7) 13.9 (5.5) 14.7 (4.4) Handedness (right/left) 45/7 28/3 29/3 4/1 106/14 85/4 Disease duration at MRI, years 4.2 (2.3) 4.5 (3.2) 4.1 (1.8) 2.5 (1.6) 4.2 (2.4) - Measures of cognition and disease severity MMSE, /30 25.0 (4.6)‡ 23.9 (4.6) 20.7 (6.2)‡ 27.0 (2.6)‡ 23.7 (5.3)† 29.1 (0.9)† CDR® plus NACC FTLD-SB, /24a 3.9 (2.6)§ 6.3 (3.2)§ 5.1 (2.8) 3.8 (4.6) 4.9 (3.0)† 0.0 (0)† Global CDR® plus NACC FTLD = 0.5, no. (%) 20 (38) 6 (19) 9 (28) 1 (20) 36 (30)† 0 (0)† Biomarkers and autopsy findings Amyloid PET, no. (positive/negative) 0/21 1/6 13/0 0/0 14/27 1/1 AD biomarkers in CSF, no. (Positive/Negative)b 3/12 2/12 9/0 0/2 14/26 5/27 Pathological diagnosis, no. (FTLD/AD)c 16/0 3/0 0/8 2/0 21/8 0/0 ® Abbreviations: AD, Alzheimer’s disease; CATFI, Catalan Frontotemporal Dementia Initiative; CDR, Clinical Dementia Rating; CDR plus NACC FTLD-SB, CDR Dementia Staging Instrument plus National Alzheimer’s Coordinating Center Behavior and Language Domains sum of boxes; MRI, magnetic resonance image; MMSE, Mini- Mental State Examination; UCSF, University of California San Francisco; lvPPA, logopenic variant of primary progressive aphasia; nfvPPA, non-fluent/agrammatic variant of primary progressive aphasia; svPPA, semantic variant of primary progressive aphasia a CDR® plus NACC FTLD-SB was available in 183 (88%) of participants (94 PPA and 89 HC) b A positive Alzheimer’s disease biomarker profile was defined by an abnormal Aß1-42 to Aß1-40 ratio and a total-tau to Aß1-42 ratio c The 21 FTLD cases included 6 cases with corticobasal degeneration (all in the nfvPPA group), 5 cases with progressive supranuclear palsy (all in the nfvPPA group), 5 cases with Pick’s disease (4 in the nfvPPA group and 1 in the svPPA group), 2 cases with FTLD-TDP type A (all in the GRN-PPA group), 1 case with FTLD-TDP type B (in the svPPA group), 1 case with FTLD-TDP type C (in the svPPA group) and 1 case with an unclassifiable tauopathy (in the nfvPPA group) †Different between all PPA group and HC group (P<.05) ‡Different between lvPPA, nfvPPA, and PPA-GRN (P<.05, Bonferroni adjusted) §Different between svPPA and nfvPPA (P<.05, Bonferroni adjusted) Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 6 of 13 CDR® plus NACC FTLD-SB scores were higher in the more regions, including the frontal pole, the insula, dor- frequency of PPA participants with a global CDR® plus svPPA group when compared to the nfvPPA group. The sal anterior and pregenual cingulate cortex, posterior cingulate and precuneus, and the temporo-parietal junc- NACC FTLD score of 0.5 ranged from 19% in svPPA to tion of both hemispheres as well as a large swatch of the 38% in nfvPPA. left lateral temporal cortex (Fig. 2B). The svPPA showed As shown in Table 1, 29 (14%) of the participants with the expected pattern of cortical thinning involving the PPA underwent autopsy evaluation. All the participants lateral, ventral, and medial anterior temporal cortex and with a neuropathological diagnosis of Alzheimer’s dis- temporal pole, insula, subgenual anterior cingulate cor- ease (n = 8) had a diagnosis of lvPPA. Conversely, all par- tex, inferior parietal lobule, and caudal inferior frontal ticipants with a diagnosis of FTLD were classified in the gyrus of the left hemisphere and, to a lesser extent, the nfvPPA (n = 16), svPPA (n = 3), and GRN-PPA (n = 2) anterior temporal cortex and insula of the right hemi- groups. sphere (Fig. 2A). Cortical mean diffusivity changes were We first compared cortical thickness and cortical mean present in the same regions as thickness changes but diffusivity between PPA and controls. As shown in Fig. 2, extended beyond those to include the pregenual and dor- the nfvPPA group showed the expected pattern of corti- sal anterior cingulate cortex of both hemispheres and cal thinning in the precentral, inferior, middle, and supe- the dorsolateral prefrontal cortex and the inferior pari- rior frontal gyri as well as supplementary motor and etal lobule of the left hemisphere (Fig. 2B). Participants dorsomedial prefrontal cortex in both hemispheres, but within the lvPPA group displayed cortical thinning in the higher effect sizes were observed in the left hemisphere. expected lateral temporal and inferior parietal lobules of In this group, the cortical mean diffusivity map involved both hemispheres, with additional thinning in superior Fig. 2 Group comparison of cortical thickness and cortical mean diffusivity between participants with PPA and healthy controls. Statistically significant results between major PPA subtypes (52 nfvPPA, 31 svPPA, 32 lvPPA) and healthy controls (n = 89) for cortical thickness (A) and cortical mean diffusivity (B). Cortical thickness analyses were adjusted for age, sex, handedness, and MRI scan. Cortical mean diffusivity analyses were adjusted for age, sex, and handedness after a harmonization step. Only effect sizes (Cohen’s d) inside clusters that survived family-wise error correction (P<.001) are shown. Abbreviations: lvPPA, logopenic variant of primary progressive aphasia; nfvPPA, non-fluent/agrammatic variant of primary progressive aphasia; svPPA, semantic variant of primary progressive aphasia Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 7 of 13 parietal lobules, prefrontal cortex, and posterior cingu- participants with early-stage PPA (global CDR® plus late and precuneus cortex, with higher effect sizes in the NACC FTLD score of 0.5). As expected, we observed left hemisphere (Fig. 2A). Again, in the lvPPA variant, we the same pattern of cortical thinning and cortical mean also observed more widespread cortical mean diffusiv- diffusivity increases. However, these changes were more ity increases involving those same regions as well as the restricted to the neurodegeneration cores of each PPA medial prefrontal cortex and pregenual and dorsal ante- variant (Fig. 5). rior cingulate cortex of both hemispheres (Fig. 2B). Finally, we evaluated the capacity of cortical thickness in PPA as measured by the CDR® plus NACC FTLD-SB Thus, while cortical thickness and cortical mean diffu- and cortical mean diffusivity to reflect disease severity sivity maps showed a partial overlap in all PPA variants, relation between CDR® plus NACC FTLD-SB scores and cortical mean diffusivity changes with at least moderate scores. As shown in Fig. 6, we observed an inverse cor- effect sizes extended beyond the areas of cortical thin- ning (Fig. 3). Importantly, the effect sizes of cortical mean cortical thickness in left-lateralized prefrontal regions diffusivity in overlapping areas were higher than the in nfvPPA, widespread regions of the right frontal and effect sizes of cortical thickness with the sole exception of temporal cortex in svPPA, and no localizable effects in neurodegeneration cores for each PPA variant (namely, lvPPA. As with our other effects, we observed larger clus- fusivity and CDR® plus NACC FTLD-SB scores (Fig. 6). the left temporal pole for svPPA, premotor cortex for ters with similar localization between cortical mean dif- nfvPPA, and temporo-parietal cortex for lvPPA). Moreo- ver, we observed essentially the same patterns of corti- We observed similar results when studying the correla- cal thickness and cortical mean diffusivity changes when tion of cortical thickness and cortical mean diffusivity each cohort was analyzed separately (data not shown). with MMSE (Supplementary Fig. 1). Figure 4 shows cortical thickness and cortical mean dif- fusivity comparison between PPA participants with GRN Discussion mutation and controls. As expected, we observed corti- This large multicenter study investigated cortical mean cal thinning in the prefrontal, lateral temporal, and infe- diffusivity in PPA. Importantly, we showed that cortical rior parietal cortex. In this group, cortical thinning was mean diffusivity increases not only coincided with areas restricted to the left hemisphere. In the PPA-GRN group, that showed cortical thinning but also involved other we also observed widespread increases in cortical mean areas that typically become affected later during disease diffusivity affecting both hemispheres (Fig. 4). progression. Cortical mean diffusivity was increased in Next, we explored group differences in cortical thick- patients with very mild PPA when only minimal corti- ness and cortical mean diffusivity in the subgroup of cal thinning was observed. Finally, we also explored the Fig. 3 Net effect size. The net effect size (A) was obtained by subtracting the cortical thickness and cortical mean diffusivity effect size maps of the comparison between PPA participants and healthy controls. The red-yellow color represents cortical areas where the cortical mean diffusivity has a higher effect size than cortical thickness. The blue color represents cortical areas where the cortical thickness has a higher effect size than cortical mean diffusivity. Abbreviations: lvPPA, logopenic variant of primary progressive aphasia; nfvPPA, non-fluent/agrammatic variant of primary progressive aphasia; svPPA, semantic variant of primary progressive aphasia Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 8 of 13 Fig. 4 Group comparison of cortical thickness and cortical mean diffusivity between participants with GRN mutation and healthy controls. Statistically significant results between PPA participants with GRN mutation (n = 5) and healthy controls for cortical thickness (left) and cortical mean diffusivity (right). For these analyses, we only considered healthy controls scanned at the same MRI than PPA participants with GRN mutation (n = 38). Cortical thickness analyses were adjusted for age, sex, handedness, and MRI scan. Cortical mean diffusivity analyses were adjusted for age, sex, and handedness after a harmonization step. Only effect sizes (Cohen’s d) inside clusters that survived family-wise error correction (P<.05) are shown correlation between cortical mean diffusivity and clini- over cortical thickness, particularly in mildly sympto- cal measures of disease severity and general cognitive matic cases. function. Taken together, these findings suggest that PPA variants are characterized by a focal onset of neu- cortical mean diffusivity might be more sensitive than rodegeneration spreading along specific networks [40]. cortical thickness to detect the earliest disease-related We observed cortical thinning including the core of neu- cortical changes in PPA. rodegeneration of each PPA when comparing cortical Cortical mean diffusivity has been recently proposed thickness and cortical mean diffusivity of PPA partici- as a sensitive biomarker for the detection of the earli- pants and controls. Importantly, we also found cortical est cortical changes in sporadic Alzheimer’s disease mean diffusivity increases beyond the regions with atro- and the Amyotrophic Lateral Sclerosis-Frontotem- phy in regions that are known to become involved with poral dementia continuum [13, 14, 16–18]. This study disease progression. The effect size maps showed mod- expands these findings to PPA. We showed that cortical erate to high net effect size favoring cortical mean dif- mean diffusivity increases spread beyond the areas of fusivity in key regions for each PPA subtype except in cortical thinning in PPA. Most previous studies using the neurodegenerative core of each syndrome. Although diffusion tensor imaging in PPA patients have focused the origin of these microstructural changes is unclear, on the white matter, probably because of the techni- higher cortical mean diffusivity values have been found cal difficulties in the study of cortical microstructure. to reflect a loss of tissue integrity and breakdown of cell One study found overlapping patterns between atrophy membranes in the cortex. We also hypothesize that corti- and increases in cortical mean diffusivity in the nfvPPA cal mean diffusivity might reach floor effects earlier than and semantic dementia [39]. Our study builds on these atrophy. Hence, the very focal atrophy seen at the epi- results using a larger sample, a surface-based approach, centers in PPA (particularly in the svPPA variant) [11] is and the inclusion of all PPA variants included in the better reflected with cortical thickness, but the spread of 2011 consensus criteria. Consequently, we were able pathology to other cortical areas may be better captured to explore the added value of cortical mean diffusivity by cortical mean diffusivity. Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 9 of 13 Fig. 5 Group comparison of cortical thickness and cortical mean diffusivity between participants with mild PPA and healthy controls. Statistically significant results between participants with mild PPA (as defined by a global CDR® plus NACC FTLD score of 0.5; 20 nfvPPA, 6 svPPA, and 9 lvPPA) and healthy controls for cortical thickness (A) and cortical mean diffusivity (B). Cortical thickness analyses were adjusted for age, sex, handedness, and MRI scan. Cortical mean diffusivity analyses were adjusted for age, sex, and handedness after a harmonization step. Only effect sizes (Cohen’s d) inside clusters that survived family-wise error correction (P<.001) are shown. Abbreviations: lvPPA, logopenic variant of primary progressive aphasia; nfvPPA, non-fluent/agrammatic variant of primary progressive aphasia; svPPA, semantic variant of primary progressive aphasia als [28, 41]. Although the CDR® plus NACC FTLD-SB Furthermore, we performed an exploratory analysis validated as a tool for disease monitoring in clinical tri- in the PPA participants with GRN mutations. We classi- fied PPA participants with GRN mutations in a separate scores correlated with cortical thickness in some small group because these cases are characterized by a more frontotemporal clusters, we found a stronger and more widespread and asymmetric pattern of gray matter loss. widespread correlation with cortical mean diffusivity. In the PPA-GRN group, cortical thinning was restricted These results should be, however, considered explora- to the left hemisphere, but cortical mean diffusivity tory. More studies are needed to determine the precise extended to both hemispheres. This finding also supports relationship between cortical mean diffusivity and FTLD- the view that cortical mean diffusivity is capturing early related neurodegeneration, from the preclinical to the neurodegenerative changes antedating overt neuronal dementia stage. loss and cortical thinning. This observation in the PPA- Although clinical-pathological correlations are not per- GRN group also encourages the investigation of corti- fect, each PPA variant is associated with typical patholo- cal mean diffusivity in larger samples of FTLD mutation gies. For instance, in the absence of a GRN mutation, carriers. the nfvPPA syndrome is frequently associated with Tau The suggestion that cortical mean diffusivity may be subtypes of FTLD, while the svPPA is associated with the more sensitive than cortical thickness to detect the PPA TDP-C subtype of FTLD, and lvPPA is typically associ- tion analyses with the CDR® plus NACC FTLD-SB and cortical changes is further supported by our correla- ated with AD pathologic changes [42]. We observed simi- MMSE scores. The CDR® plus NACC FTLD-SB has been lar cortical mean diffusivity increases for each PPA group, suggesting that cortical mean diffusivity changes may be Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 10 of 13 Fig. 6 Correlation of cortical thickness and cortical mean diffusivity with the CDR® plus NACC FTLD-SB. Relationship of cortical thickness (A) and cortical mean diffusivity (B) with the CDR® plus NACC FTLD scores. The CDR® plus NACC FTLD-SB scores were negatively correlated with cortical thickness and positively correlated with cortical mean diffusivity. Cortical thickness analyses were adjusted for age, sex, handedness, and MRI scan. correction (P < .05) are shown. Abbreviations: CDR, clinical dementia rating; CDR® plus NACC FTLD-SB, CDR Dementia Staging Instrument plus Mean diffusivity analyses were adjusted for age, sex, and handedness after a harmonization step. Only clusters that survived family-wise error National Alzheimer’s Coordinating Center Behavior and Language Domains sum of boxes; lvPPA, logopenic variant of primary progressive aphasia; nfvPPA, non-fluent/agrammatic variant of primary progressive aphasia; svPPA, semantic variant of primary progressive aphasia an unspecific biomarker of neurodegeneration. How- tau. Of note, we have previously shown that amyloid dep- ever, the difference between cortical thickness and corti- osition impacts cortical mean diffusivity, even in the pre- cal mean diffusivity changes was less clear in the lvPPA clinical phase of Alzheimer’s disease [44]. Because only group. In addition, the correlation between cortical mean tau pathology (and not amyloid deposition) is closely cor- diffusivity and disease severity was also less evident in related with neurodegeneration in Alzheimer’s disease, the lvPPA group than in the nfvPPA and svPPA groups. we hypothesize that amyloid-related microstructural Two important factors should be considered when inter- changes could attenuate the association between disease preting these findings. First, the pattern of neurodegener- severity and cortical mean diffusivity in the lvPPA group ation in the lvPPA variant is more widespread and more [45]. However, additional multimodal imaging studies are heterogeneous than the pattern observed in other PPA needed to confirm this hypothesis and precise the rela- variants, as noted by previous studies [43]. Importantly, tionship between amyloid deposition, tau pathology, and a higher heterogeneity within a neurodegenerative syn- cortical microstructure in Alzheimer’s disease. In addi- drome penalizes finding statistically significant correla- tion, autopsy-proven studies should explore the added tions between general measures of disease severity and value of cortical mean diffusivity (alone or combined neuroimage measures [15]. Second, the lvPPA syndrome with white matter diffusion tensor imaging changes) to is strongly associated with Alzheimer’s disease, a disease detect specific neuropathological signatures (i.e., promi- characterized by the extraneuronal deposition of fibril- nent microstructural changes in the subcortical white lary amyloid, together with intraneuronal aggregation of matter in FTLD-Tau or widespread gray matter changes Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 11 of 13 in Alzheimer’s disease with relative sparing of subcortical Abbreviations AD Alzheimer’s disease white matter) [46–48]. CATFI Catalan Frontotemporal Dementia Initiative Our findings support the role of cortical mean diffusiv- CDR® plus CDR Clinical Dementia Rating ity as a potential neurodegeneration biomarker in PPA to NACC FTLD-SB CDR Dementia Staging Instrument plus National Alzheimer’s be used in clinical trials. Trials of drugs targeting abnor- Coordinating Center Behavior and Language Domains sum mal protein deposition need meaningful end points. His- of boxes torically, these end points included neuropsychological FDR False discovery rate FWE Family-wise error scores and functional scales. Brain imaging could provide HC Healthy control more sensitive and robust evidence of disease modifica- MMSE Mini-Mental State Examination tion [49]. Our study shows that cortical mean diffusiv- MRI Magnetic resonance image PPA Primary progressive aphasia ity is a more sensitive biomarker than cortical thickness UCSF University of California San Francisco and correlates more strongly with clinical and cognitive measures, in agreement with a previous report [49]. The Supplementary Information increased sensitivity of cortical mean diffusivity suggests The online version contains supplementary material available at https://​doi.​ it may be a sensitive tool suitable to monitor the earli- org/​10.​1186/​s13195-​022-​00974-0. est cortical changes in preclinical or mildly symptomatic phases of FTLD [50]. Additional file 1. Supplementary material. The main strengths of this study are the relatively large number of participants with PPA at a mild dis- Acknowledgements ease stage, and the surface-based analyses using a previ- The authors thank the patients and their relatives for their support for this study. ously validated technique. This surface-based approach solves some of the limitations and methodological con- Authors’ contributions cerns that have been previously reported when using a Ignacio Illan-Gala: drafting/revision of the manuscript for content, includ‑ ing medical writing for content; major role in the acquisition of data; study voxel-based approach [37]. Moreover, we enriched our concept or design; analysis or interpretation of the data. Victor Montal: description of the cortical mean diffusivity in PPA and drafting/revision of the manuscript for content, including medical writing for we were able to replicate our results in two different content; analysis or interpretation of the data. Sergi Borrego-Écija: drafting/ revision of the manuscript for content, including medical writing for content; cohorts. major role in the acquisition of the data. Maria Luisa Mandelli: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of the data; analysis or interpretation of the data. Neus Limitations Falgàs: drafting/revision of the manuscript for content, including medical This study has also some limitations. A proportion of writing for content. Ariane E. Welch: drafting/revision of the manuscript for participants with PPA were excluded due to segmenta- content, including medical writing for content; major role in the acquisition of the data Jordi Pegueroles: drafting/revision of the manuscript for content, tion or diffusion tensor imaging processing errors. Even including medical writing for content Miguel Santos-Santos: drafting/revision though this is an inherent limitation of our surface-based of the manuscript for content, including medical writing for content; major approach, future improvements in T1 MRI acquisitions role in the acquisition of the data. Alexandre Bejanin: drafting/revision of the manuscript for content, including medical writing for content. Daniel Alcolea: or the use of higher field MRIs will likely reduce the drafting/revision of the manuscript for content, including medical writing for number of subjects excluded due to segmentation errors. content; major role in the acquisition of the data. Oriol Dols-Icardo: drafting/ Also, both the PPA-GRN group and the subgroup of PPA revision of the manuscript for content, including medical writing for content. Olivia Belbin: drafting/revision of the manuscript for content, including medi‑ participants at a mild disease stage were small. Finally, cal writing for content. María Belén Sánchez-Saudinós: drafting/revision of the further longitudinal studies in presymptomatic muta- manuscript for content, including medical writing for content; major role in tion carriers should confirm that cortical mean diffusivity the acquisition of the data. Nuria Bargalló: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisi‑ changes track disease progression and antedate cortical tion of the data. Sofía González-Ortiz: drafting/revision of the manuscript for atrophy in patients with PPA. content, including medical writing for content; major role in the acquisition of the data. Albert Llado: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of the data. Conclusions Rafael Blesa: drafting/revision of the manuscript for content, including medi‑ cal writing for content; major role in the acquisition of the data. Bradford C. Cortical mean diffusivity shows promise as a sensitive Dickerson: drafting/revision of the manuscript for content, including medical biomarker for the study of the earliest neurodegenera- writing for content; major role in the acquisition of the data. Howard J. Rosen: tion-related cortical changes in PPA. Further longitudi- drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of the data; analysis or interpretation nal studies including preclinical mutation carriers are of the data. Bruce L. Miller: drafting/revision of the manuscript for content, needed to fully determine the potential utility of this including medical writing for content; major role in the acquisition of the data. biomarker to detect cortical changes antedating cerebral Alberto Lleó: drafting/revision of the manuscript for content, including medi‑ cal writing for content; major role in the acquisition of the data. Maria Luisa atrophy at the earliest stages of the disease and to track Gorno-Tempini: drafting/revision of the manuscript for content, including disease progression. medical writing for content; major role in the acquisition of the data; analysis Illán‑Gala et al. Alzheimer’s Research & Therapy (2022) 14:27 Page 12 of 13 or interpretation of the data. Raquel Sánchez-Valle: drafting/revision of the N.B. is member of the executive committee of the European Society of Radiol‑ manuscript for content, including medical writing for content; major role in ogy and reports examiner honoraria from the European Board of Neuroradiol‑ the acquisition of the data. Juan Fortea: drafting/revision of the manuscript for ogy; S.G.O. reports no disclosures; A. Llado reports no disclosures; R.B. reports content, including medical writing for content; major role in the acquisition of honoraria from Nutricia; B.C.D reports no disclosures; H.J.R. reports honoraria the data; study concept or design; analysis or interpretation of the data. The from Biogen, Wave neuroscience, and Ionis; B.L.M. reports honoraria from authors read and approved the final manuscript. Biogen, Cambridge University Press Guilford Publications, Inc. Oxford Univer‑ sity Press Taylor & Francis Group Elsevier, Inc.; B.L.M. also served as medical/ Authors’ information scientific advisor of The John Douglas French Foundation, The Larry L. Hillblom Not applicable Foundation, Association for Frontotemporal Degeneration, National Institute for Health Research Cambridge Biomedical Research Centre and its subunit, Funding the Biomedical Research Unit in Dementia, University of Washington ADRC, I.I.G. and N.F. are Atlantic Fellow for Equity in Brain Health at the Global Stanford University ADRC, Arizona Alzheimer’s Disease Center (ADC), Mas‑ Brain Health Institute (GBHI), and I.I.G. is supported with funding from GBHI, sachusetts Alzheimer Disease Research Center, The Buck Institute for Research Alzheimer’s Association, and Alzheimer’s Society (GBHI ALZ UK-21-720973). on Aging, University of Southern California (USC) PO1 Urban Air Pollution and I.I.G. is also supported by the Juan Rodés Contract (JR20/0018) from Instituto Alzheimer’s Disease: Risk, Heterogeneity and Mechanisms. A.Lleó has a patent de Salud Carlos III. V.M. is supported by a PFIS contract (FI18/00275) from the (WO2019175379 A1 Markers of synaptopathy in neurodegenerative disease); Carlos III Health Institute. S.B.E. is the recipient of Emili Letang post-residency received personal consulting fees from Biogen, Nutricia, Roche, and Fujirebio- research grant from Hospital Clínic de Barcelona. A.E.W received salary from Europe; and has served at scientific advisory boards from Fujirebio-Europe, UCSF (supported by NIH grants). A.B. is supported by a Miguel Servet I grant Nutricia, Biogen, and Roche Diagnostics and has filed a patent application of (CP20/00038) from the Carlos III Health Institute and the postdoctoral Fellow‑ synaptic markers in neurodegenerative diseases; M.G.T. reports no disclosures; ship, “Juan de la Cierva Incorporación,” from the Spanish Ministry of Science, R.S.V. has served at scientific advisory boards from Wave pharmaceuticals and Innovation, and Universities. O.D.I. is a recipient of a grant by The Association Ionis-Biogen and received personal honoraria from Janssen, Roche Diagnos‑ for Frontotemporal Degeneration (Clinical Research Postdoctoral Fellowship, tics, Neuraxpharm for educational activities; D.C.P. reports no disclosures; J.F. AFTD 2019-2021) and FUNDELA. The Catalan Frontotemporal Initiative (CATFI) has a patent (WO2019175379 A1 Markers of synaptopathy in neurodegenera‑ was funded by the Health Department of the Government of Catalonia (grant tive disease), has served at advisory boards for AC Immune, Zambon, and PERIS SLT002/16/00408 to A.Lleó and R.S.V). This work was supported by NIH Lundbeck, and received honoraria for lectures from Roche, NovoNordisk, grants AG019724, AG032306, AG045390, NS092089, AG045333, AG056749, Esteve, and Biogen. AG062422, P30AG062422, and P01AG019724. This work was also sup‑ ported by the National Institutes of Health (NIA grants 1R01AG056850-01A1; Author details 1 R21AG056974; and R01AG061566 to JF) and by “Fondo de Investigaciones Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Sanitario” (FIS), Instituto de Salud Carlos III (PI14/01126 and PI17/01019 to Pau, Biomedical Research Institute Sant Pau, Sant Antoni Maria Claret, 167, JF, PI13/01532 and PI16/01825 to RB, PI18/00335 to MCI, INT19/00016 and 08025 Barcelona, Spain. 2 Centro de Investigación Biomédica en Red de PI18/00435 to DA, PI15/01618 to R.R., PI20/00448 to R.S-V, and PI17/01896, Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain. 3 Atlantic Fel‑ PI20/01330 and AC19:00103 to A.Lleó), the CIBERNED program (Program 1, low for Equity in Brain Health at the University of California San Francisco, San Alzheimer Disease to A.Lleó and SIGNAL study,www.​signa​lstudy.​es), partly Francisco, CA 94115, USA. 4 Alzheimer’s Disease and Other Cognitive Disorders jointly funded by Fondo Europeo de Desarrollo Regional, Unión Europea, una Unit, Service of Neurology, Hospital Clínic de Barcelona, Institut d’Investigació manera de hacer Europa, the “Marató TV3” grant (20141210 to J.F., 044412 to Biomèdica August Pi i Sunyer, University of Barcelona, 08036 Barcelona, Spain. 5 R.B., and 20143810 to R. S.V), by Generalitat de Catalunya (2014SGR-0235 to Memory and Aging Center, Department of Neurology, University of California, A.Lleó, PERIS SLT006/17/125 to D.A., and SLT006/17/00119 to J. F.), by BBVA San Francisco, CA 94115, USA. 6 Radiology Department, Hospital Clinic Barce‑ foundation (grant to A. Lleó), by a grant from the Fundació Bancaria La Caixa lona and Magnetic Resonance Image Core facility, Institut d’Investigacions to R.B. and by Sage Pharma to R.S-V (HCB2020/1483). European Union’s Hori‑ Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. 7 Department zon 2020, ’MES-CoBraD’ (H2020-SC1-BHC-2018-2020/GA 965422 to JF). of Radiology, Hospital del Mar, Barcelona, Spain. 8 Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, Availability of data and materials USA. 9 Massachusetts Alzheimer’s Disease Research Center, Boston, MA, USA. 10 The datasets analyzed during the current study are available from the cor‑ Barcelona Down Medical Center. Fundació Catalana de Síndrome de Down, responding authors on reasonable request. Barcelona, Spain. Received: 29 September 2021 Accepted: 2 February 2022 Declarations Ethics approval and consent to participate Informed consent was obtained from all subjects, and local institutional review boards for human research approved the study. References 1. Mesulam MM. Primary progressive aphasia. Ann Neurol. 2001;49:425–32. Consent for publication 2. Elahi FM, Miller BL. 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