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        White Matter Damage in the Semantic Variant of Primary Progressive Aphasia
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        White Matter Damage in the Semantic Variant of Primary Progressive Aphasia
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Background: The semantic variant of primary progressive aphasia (svPPA) is a form of dementia, mainly featuring language impairment, for which the extent of white matter (WM) damage is less described than its associated grey matter (GM) atrophy. Our study aimed to characterise the extent of this damage using a sensitive and unbiased approach. Methods: We conducted a between-group study comparing 10 patients with a clinical diagnosis of svPPA, recruited between 2011 and 2014 at a tertiary reference centre, with 9 cognitively healthy, age-matched controls. From diffusion tensor imaging (DTI) data, we extracted fractional anisotropy (FA) values using a tract-based spatial statistics approach. We further obtained GM volumetric data using the Freesurfer automated segmentation tool. We compared both groups using non-parametric Wilcoxon rank-sum tests, correcting for multiple comparisons. Results: Demographic data showed that patients and controls were comparable. As expected, clinical data showed lower results in svPPA than controls on cognitive screening tests. Tractography showed impaired diffusion in svPPA patients, with FA mostly decreased in the longitudinal, uncinate, cingulum and external capsule fasciculi. Volumetric data show significant atrophy in svPPA patients, mostly in the left entorhinal, amygdala, inferior temporal, middle temporal, superior temporal and temporal pole cortices, and bilateral fusiform gyri. Conclusions: This syndrome appears to be associated not only with GM but also significant WM degeneration. Thus, DTI could play a role in the differential diagnosis of atypical dementia by specifying WM damage specific to svPPA.



Primary progressive aphasia1 is a neurodegenerative disorder whose prominent feature is language impairment,2 as opposed to early episodic memory or motor deficits that are predominant in other dementias. The semantic variant of primary progressive aphasia (svPPA), also called semantic dementia, is one of its three variants, alongside progressive non-fluent aphasia and logopenic progressive aphasia. These aphasias are forms or phenotypes of frontotemporal lobar degeneration,3, 4 a family of diseases that represent 10% of all dementias in adults <65 years old.5, 6 It has been often reported that logopenic PPA is most often Alzheimer’s disease, and not a frontotemporal dementia per se.79

Clinically, features of svPPA include impairment in confrontation naming and single-word comprehension and may include surface dyslexia or dysgraphia, while repetition and speech production is preserved.10 Pathologically, svPPA appears to be associated with ubiquitin and TDP-43.4, 1113 Anatomically, changes in cortical grey matter (GM) have been well studied in svPPA, to the extent that imaging can be used as a supportive criterion for the diagnosis. These findings include the presence, on magnetic resonance imaging (MRI), of cortical atrophy located predominantly in the anterior temporal lobes, bilaterally but more severely in the left hemisphere, especially in its ventral and lateral regions.1419 In studies involving presymptomatic subjects, focusing on correlating genetics and imaging, researchers have found that GRN mutations are associated with GM changes in the striatum, insula and with early temporal and parietal atrophy, while MAPT mutations are associated mainly with temporal atrophy, particularly in its anteromedial portion.2023

Comparatively, the extent of white matter (WM) damage in svPPA has been much less studied, in part due to the difficulty in recruiting large patient groups. Only a few reports2427 have addressed this topic (largest N = 10). They all made use of diffusion tensor imaging (DTI), a technique that allows the study of WM fibre tracts in MRI, and from which one can extract maps of fractional anisotropy (FA), a scalar value indicating the directionality of diffusion in axons, itself a proxy of microstructural damage in the brain.28 These studies reported lower FA mostly lateralised in the left hemisphere29 and affecting the postero-inferior longitudinal, uncinate and posterior cingulate fascicule,2427 in opposition to non-fluent primary progressive aphasia that appears to affect the left intrafrontal and frontostriatal fascicule.30

Furthermore, all of these cited WM studies but one used a region-of-interest rather than a tract-based statistical (TBSS)31 approach to analyse FA maps. The latter is a whole-brain skeleton-based technique that is more sensitive than the ROI voxel-based technique, as demonstrated in a simulation study;32 and hence that should provide a better resolution of WM damage in the brains of svPPA patients.


We designed this study to increase our current knowledge of concurrent WM and GM damage in svPPA in a different study group. Our objectives were to localise and quantify cerebral GM and especially WM differences in patients with svPPA, compared with cognitively healthy controls, through automated cerebral segmentation and tract-based statistics of fractional anisotropy.



The ethics committee of the Institut universitaire en santé mentale de Québec approved the study (project #300-2012). Informed and free consent was obtained from each participant.

Study Design

We used a between-group experimental design, in which we compared patients with a diagnosis of svPPA with cognitively and neurologically healthy, age-matched control subjects. All participants were native French speakers from Quebec, Canada.

Setting and Participants

Patients – Ten patients who fulfilled current svPPA clinical criteria1 and were being followed at Clinique Interdisciplinaire de Mémoire (CHU de Québec, Quebec City, Canada) between August 2013 and August 2014 were included in this study. These patients all had a diagnosis of svPPA confirmed by a neurologist, without family histories indicative of a possible genetic risk. Additionally, a comprehensive neuropsychological battery was administered to all participants. The most salient results of this battery included:

  • Mild to moderate word production and anomia difficulties measured with a picture-naming task (Boston Naming Test)33 and phonological, semantic and free fluency tasks (MEC Protocol);34

  • Impaired verbal and non-verbal single-word comprehension, assessed with visual and oral word–picture matching tasks (BECLA battery)35 and semantic association of pictures (pyramids and palm trees test);36

  • Spared repetition (TEFREP)37 and absence of apraxia of speech (PENO).38

Some of the patients also had impaired object recognition (object decision and object matching subtests, BORB battery)39 with or without surface dyslexia (regular and irregular word and pseudoword reading test),40 while others presented with surface dyslexia with unimpaired object recognition. They all were free of noticeable deficit on tests tapping other cognitive domains such as non-verbal episodic memory, visuospatial abilities (Rey-Osterrieth Complex Figure Test)41 and working memory (forward and backward digit SPANs Wechsler Memory Scale IV).42

Controls – Cognitively healthy controls were recruited among patients’ proxies and members of the community and were matched by age, gender, education and manual dominance to the svPPA patients. They were screened via telephone for inclusion criteria by means of a structured questionnaire. Then they went through the same neuropsychological battery as did the patients.

Neither patients nor controls had histories of either moderate or severe traumatic brain injury, cerebrovascular disease or intracranial surgery, encephalitis or meningitis. They had no history of a significant psychiatric syndrome, alcoholism or drug addiction or unstable medical or metabolic condition (e.g., uncontrolled diabetes, vitamin B12 or folic acid deficiency, hypothyroidism), nor any history of learning or reading difficulties. They were also screened for MRI compatibility by means of another questionnaire.

Data Sources and Measures

Above and beyond the tests previously mentioned, the demographic and clinical data included the presence or absence of semantic dementia, age, gender, education (in years), manual dominance, and global cognition assessed via the Montreal Cognitive Assessment Test (MoCA)43 and Mini-Mental State Examination (MMSE).44 Since both groups of participants were matched by age, gender, education and manual dominance, we did not make further sensitivity analyses. It would have been unlikely that the 10:9 ratio of patients and controls affected the volumetric or FA data.

MRIs were acquired within 4 weeks of the clinical assessment using the Canadian Dementia Imaging Protocol45 ( on a 3-T magnetic resonance scanner (Philipps Medical Systems, Best, The Netherlands) at the IRM Québec radiology clinic in Quebec City. Specifically, the protocol included:

  • An isotropic, 1 mm3 3D T1-weighted sequence (TR = 7.3 ms; TE = 3.3 ms; flip angle = 9 degrees); and

  • A 60-direction, 2 mm3 isotropic diffusion imaging sequence (b-value = 1000).

For cerebral WM data, fractional anisotropy maps and tract-based statistics were obtained following these steps. First, we converted our native data from DICOM files to the NIFTI format using MRIcron ( Then, we used FMRIB’s Diffusion Toolbox46 to create an FA image from the DTI data of each subject. This included eddy current correction, brain masking and diffusion tensor model fitting. Afterwards, we used the FMRIB Software Library 5.0.647 to perform voxel-wise analyses, following the TBSS approach.31 There were five steps to this process: (1) pre-processing; (2) nonlinear alignment of all FA images to a standard-space image (FMRIB58_FA); (3) creation of the mean FA image and generation of its skeleton; (4) projection of every pre-aligned FA image on the study-specific template skeleton; and (5) generation of voxel-wise statistics with the Randomise tool, using the Threshold-Free Cluster Enhancement. The final output was a brain map in which we could identify voxels that were statistically different in FA between patients and controls. We used the Atlasquery tool to localise these voxels and provide numerical FA values for each subject in each of the 48 regions of interest (ROIs) identified in the ICBM-DTI-81 WM labels atlas of the Laboratory of Brain Anatomical MRI at Johns Hopkins University.48

For GM analysis, we used an automated segmentation tool (FreeSurfer)49, 50 to segment the 3D T1-weighted image for all subjects, producing surfaces (mm), thicknesses (mm2) and volumes (mm3) for the whole brain and 44 different cortical and subcortical structures according to their atlases (DKT40 and ASEG51).

Statistical Methods

For all demographic, clinical and imaging data, we opted for non-parametric Wilcoxon rank-sum test to compare cases with controls, given the size of our sample and the robustness of the test. We used a significance level of p-value = 0.05, correcting for multiple comparisons using the false discovery rate (FDR) method.


This study follows the recommendation of the STROBE Statement.52


Demographic and Clinical Data

Nineteen participants (10 svPPA and 9 controls) were recruited in this study. Every subject that was screened happened to be eligible for the study. Analysis of demographic data showed no significant difference between groups in age, gender, education or manual dominance. Clinical data showed significantly lower results for svPPA patients at both MoCA and MMSE tests, which was expected (see Table 1).

Table 1: Demographic and clinical data for svPPA patients and controls

svPPA = semantic variant of primary progressive aphasia; MoCA = Montreal Cognitive Assessment; MMSE = Mini-Mental State Examination.

* Statistically significant.

Results are presented as median (mean ± standard deviation).

WM Analysis

Volumetric data showed that both groups had similar total WM volumes. Qualitative assessment of diffusion data demonstrated multiple WM regions where svPPA patients had significantly lower FA values, slightly more severely in the left hemisphere and temporal lobe. Statistical analysis of numerical FA values resulted in 16 out of 48 tract regions with significantly lower FA values in svPPA patients than in healthy controls, after FDR correction. The most significantly impaired regions were the right hippocampus and uncinate fasciculus tract regions, and the left external capsule and superior longitudinal fasciculus (see Table 2 and Figure 1).

Figure 1: WM maps: statistically significant differences in fractional anisotropy between svPPA patients and controls. svPPA, semantic variant of primary progressive aphasia.

Table 2: WM study: fractional anisotropy data for svPPA patients and controls

FDR = false discovery rate; svPPA = semantic variant of primary progressive aphasia; WM = white matter.

* p < 0.05 (false discovery rate corrected).

Results are presented as median (mean ± standard deviation).

GM Analysis

Volumetric data showed that both groups had indistinguishable total intracranial capacity and total brain volumes. However, our group of svPPA patients had smaller subcortical and left hemispheric cortical GM volumes. When looking at specific ROIs, volumes were consistently lower in svPPA patients than in healthy controls, reaching significance in 22 of the 33 studied regions, after FDR correction. The most significant differences were noted in the left amygdala, entorhinal, fusiform, inferior temporal, middle temporal and superior temporal gyri, as well as in the temporal poles and right fusiform GM (see Table 3 and Figure 2).

Figure 2: GM images: examples of atrophy in a svPPA patient compared with a cognitively healthy control. svPPA, semantic variant of primary progressive aphasia.

Table 3: GM study: volumetric data for svPPA cases and controls

GM = grey matter; svPPA = semantic variant of primary progressive aphasia; Bankssts = banks of the superior temporal sulcus; WM = white matter.

* p < 0.05 (false discovery rate corrected).

Results are presented as median (mean ± standard deviation). Units are mm3.


The semantic variant of primary progressive aphasia is a neurodegenerative disorder with language impairment as its hallmark. Previous literature reported cortical GM atrophy predominantly in the anterior temporal lobes, bilaterally but markedly in the left hemisphere. As for WM damage, literature is scant and based on a small number of studies and patients. Only a few reports have described abnormal diffusion, in one or more of the left postero-inferior longitudinal, uncinate, posterior cingulate fasciculi. Of these studies, only one used a more sensible tract-based approach instead of the ROI approach. We designed this study principally to address the knowledge gap we observed concerning WM damage in svPPA, in addition to validating the characterisation of GM atrophy. Our ultimate goal is to use such information to improve diagnosis, as we know that an early diagnosis might lead to earlier care and, in turn, decreased social difficulty.10

Our first aim was to characterise cerebral WM lesions in our cohort of svPPA patients, compared with cognitively healthy, age-matched control subjects. Using a tract-based whole-brain approach, we found FA differences between our two groups, including statistically lower FA in the right hippocampus and uncinate fasciculus tract regions, as well as the left external capsule and superior longitudinal fasciculus tracts. Our second aim was to characterise cerebral GM lesions in these same patients. Using volumetric tools, we found significant atrophy differences between patients and controls. Notably, larger GM atrophy in svPPA patients was found in the left amygdala, in the left entorhinal, fusiform, inferior temporal, middle temporal and superior temporal gyri, left temporal pole and right fusiform gyrus.


Our WM results showed a significant diminution of FA in different tracts, which is known to demonstrate microstructural damages in these fasciculi. In accordance with some of the few reports available, we found impairment of the longitudinal fascicule and some of the uncinate fascicule;24, 26, 27, 53 however, we also found that the damage was more extensive and less clearly lateralised than previously thought, including more extensive than GM atrophy. The latter may constitute an interesting line of inquiry as to the longitudinal progression of the disease.

Our GM results showed a lateralisation of atrophy related to svPPA, as it was more severe in the left hemisphere. We also found significant atrophy in the temporal lobes. These results are in line with those previously described in literature.40 This is also in accordance with fundamental neuroimaging studies on the cerebral regions associated with language,40 which have shown that the left anterior temporal lobe is part of the semantic brain network, whereas other functions like syntax would be processed more in the posterior temporal or frontal lobes.


Two different hypotheses have been proposed to explain the pattern of WM damage.24, 5459 The first one posits an extrinsic cause. Following GM atrophy by neuronal loss, a process of Wallerian degeneration or axonal degeneration begins, hence causing WM loss. Considering the severity of GM damage that we also observed in our cohort, this hypothesis would explain why not only tracts close to the left temporal lobe, but also other connecting tracts that are located more distantly, are impaired. The second hypothesis is an intrinsic direct axonal pathology.60 This explanation puts forward that intrinsic brain networks propagate WM disease, either by means of a process of neuroinflammation or by a cascade of misfolded proteins (prion-like),61 an hypothesis put forward recently in a case of a novel prion protein variant in a patient with svPPA.62

Since our study is purely descriptive and cross-sectional, we cannot support firmly either hypothesis; however, the significantly large extent of WM atrophy would tend to support the intrinsic theory. Longitudinal studies are needed to understand the evolution of GM and WM damage in the brain, and hence confirm or infirm either hypothesis.

Strengths and Limitations

Compared with similar studies in the literature, and given the prevalence of svPPA in the population, the number of patients that we were able to recruit is a strength insofar as it allows us to compare directly with other studies of similar, or smaller, sizes, even though it still implied the use of non-parametric tests, with their inevitably lower statistical power. Furthermore, our inclusion and exclusion criteria prevented confounding factors from altering the internal validity of our results, while maximising participant recruitment. Finally, the whole-brain TBSS approach has many advantages such as correcting for misalignment, registration and smoothing, and removing the need to determine a priori ROI for the analysis, which would imply the use of a yet-incomplete knowledge of connectomics.

In contrast, there were some limitations arising from these same measurement techniques. The first comes from the use of DTI, as it is known that the diffusion tensor model has limited sensitivity to crossing fibres. Future studies could use high-angular-resolution diffusion imaging63 to alleviate this problem. The second limitation relating to the technique comes from the choice of FA as our sole metric to evaluate WM microstructural changes. The use of apparent diffusion coefficient, or axial, radial and mean diffusivity, could have improved the sensitivity of our study. It is indeed known that FA is a scalar value, and that different combinations of diffusion tensor eigenvalues that have changed concurrently could generate identical FA values.54 However, FA is a known, oft-reported metric that captures the essential changes related to WM integrity; employing other techniques could only uncover further areas or a deeper level of WM damages. Equally, TBSS will also be subject to reduced sensibility whenever fibres cross, for similar reasons as mentioned previously. Finally, the atlas used to extract numerical values remains composed of rather large structures that may end up masking small, sub-regional effects. The Human Connectome Project64 aims to improve our knowledge of WM tracts and is currently addressing this issue.

Our results in a French-speaking population are comparable with previous studies conducted on native speakers of other languages, mostly English. This means that, in terms of external validity, language does not seem to be a concern. This could lead to multicentric studies, without language barriers, involving a higher number of subjects in future studies, especially those on a longitudinal basis and those correlating clinical, anatomical and pathological data. As well, to better understand the notions of cerebral lateralisation and brain plasticity, it would also be of interest to include, as much as available, left-handed patients to assess if findings differ for this group.


In summary, our study confirms known GM atrophy in svPPA patients, as well as validates WM damage using a more sensitive and unbiased approach than previously reported. MRI and DTI appear to have the potential to occupy an important place in the initial investigation of dementia and could eventually be very useful in the differential diagnosis of atypical dementia, to characterise, as precisely as possible, the cerebral damage in one patient and to link it to a specific disease through its imaging signature.


All authors thank participants in the study for their dedication. MAW gratefully acknowledges financial support from the Réseau Québécois de Recherche sur le Vieillissement et Fonds de recherche du Québec – Société et culture # 168556). SD is a research scholar at Fonds de recherche du Québec – Santé.


The authors have no conflicts of interest to declare.

Authors’ Contributions

LOB: Study conception and design, analysis and interpretation of data, manuscript drafting and approval for publication.

MAW: Contribution to conception and design of study, interpretation of data, critical revision of manuscript for important intellectual content, and approval for publication.

RL: Patient recruitment, interpretation of data, critical revision of manuscript for important intellectual content, and approval for publication.

SD: Contribution to conception and design of study, interpretation of data, critical revision of manuscript for important intellectual content, and approval for publication.

All authors agree to be accountable for all aspects of the work.


1.Gorno-Tempini, ML, Hillis, AE, Weintraub, S, et al. Classification of primary progressive aphasia and its variants. Neurology. 2011;00(11):1006–14.
2.Mesulam, MM. Primary progressive aphasia. Ann Neurol. 2001;49(4):425–32.
3.Riedl, L, Mackenzie, IR, Förstl, H, et al. Frontotemporal lobar degeneration: current perspectives. Neuropsychiatr Dis Treat. 2014;10:297310.
4.Mesulam, M, Wicklund, A, Johnson, N, et al. Alzheimer and frontotemporal pathology in subsets of primary progressive aphasia. Ann Neurol. 2008;63(6):709–19.
5.Fiest, KM, Jetté, N, Roberts, JI, et al. The prevalence and incidence of dementia: a systematic review and meta-analysis. Can J Neurol Sci. 2016;43 Suppl 1:S3–50.
6.Hogan, DB, Jetté, N, Fiest, KM, et al. The prevalence and incidence of frontotemporal dementia: a systematic review. Can J Neurol Sci. 2016;43 Suppl 1:S96–109.
7.Ahmed, S, de Jager, CA, Haigh, AM, et al. Logopenic aphasia in Alzheimer’s disease: clinical variant or clinical feature? J Neurol Neurosurg Psychiatry. 2012;83(11):1056–62.
8.Bergeron, D, Gorno-Tempini, ML, Rabinovici, GD, et al. Prevalence of amyloid-beta pathology in distinct variants of primary progressive aphasia. Ann Neurol. 2018;84(5):729–40.
9.Teichmann, M, Kas, A, Boutet, C, et al. Deciphering logopenic primary progressive aphasia: a clinical, imaging and biomarker investigation. Brain. 2013;136(Pt 11):3474–88.
10.Routhier, S, Gravel-Laflamme, K, Macoir, J. Non-pharmacological therapies for language deficits in the agrammatic and logopenic variants of primary progressive aphasia: a literature review. Geriatr Psychol Neuropsychiatr Vieil. 2013;11(1):8797.
11.Hodges, JR, Davies, RR, Xuereb, JH, et al. Clinicopathological correlates in frontotemporal dementia. Ann Neurol. 2004;56(3):399406.
12.Grossman, M, Wood, EM, Moore, P, et al. TDP-43 pathologic lesions and clinical phenotype in frontotemporal lobar degeneration with ubiquitin-positive inclusions. Arch Neurol. 2007;64(10):1449–54.
13.Nestor, PJ, Balan, K, Cheow, HK, et al. Nuclear imaging can predict pathologic diagnosis in progressive nonfluent aphasia. Neurology. 2007;68(3):238–9.
14.Gorno-Tempini, ML, Dronkers, NF, Rankin, KP, et al. Cognition and anatomy in three variants of primary progressive aphasia. Ann Neurol. 2004;55(3):335–46.
15.Mummery, CJ, Patterson, K, Price, CJ, et al. A voxel-based morphometry study of semantic dementia: relationship between temporal lobe atrophy and semantic memory. Ann Neurol. 2000;47(1):3645.
16.Mesulam, M, Wieneke, C, Rogalski, E, et al. Quantitative template for subtyping primary progressive aphasia. Arch Neurol. 2009;66(12):1545–51.
17.Galton, CJ, Patterson, K, Graham, K, et al. Differing patterns of temporal atrophy in Alzheimer’s disease and semantic dementia. Neurology. 2001;57(2):216–25.
18.Rosen, HJ, Kramer, JH, Gorno-Tempini, ML, et al. Patterns of cerebral atrophy in primary progressive aphasia. Am J Geriatr Psychiatry. 2002;10(1):8997.
19.Collins, JA, Montal, V, Hochberg, D, et al. Focal temporal pole atrophy and network degeneration in semantic variant primary progressive aphasia. Brain. 2017;140(2):457–71.
20.Rohrer, JD, Nicholas, JM, Cash, DM, et al. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis. Lancet Neurol. 2015;14(3):253–62.
21.Rohrer, JD, Warren, JD, Fox, NC, et al. Presymptomatic studies in genetic frontotemporal dementia. Rev Neurol (Paris). 2013;169(10):820–4.
22.Rohrer, JD, Warren, JD. Phenotypic signatures of genetic frontotemporal dementia. Curr Opin Neurol. 2011;24(6):542–9.
23.Whitwell, JL, Weigand, SD, Boeve, BF, et al. Neuroimaging signatures of frontotemporal dementia genetics: C9ORF72, tau, progranulin and sporadics. Brain. 2012;135(Pt 3):794806.
24.Galantucci, S, Tartaglia, MC, Wilson, SM, et al. White matter damage in primary progressive aphasias: a diffusion tensor tractography study. Brain. 2011;134(Pt 10):3011–29.
25.Whitwell, JL, Avula, R, Senjem, ML, et al. Gray and white matter water diffusion in the syndromic variants of frontotemporal dementia. Neurology. 2010;74(16):1279–87.
26.Agosta, F, Scola, E, Canu, E, et al. White matter damage in frontotemporal lobar degeneration spectrum. Cereb Cortex. 2012;22(12):2705–14.
27.Mahoney, CJ, Malone, IB, Ridgway, GR, et al. White matter tract signatures of the progressive aphasias. Neurobiol Aging. 2013;34(6):1687–99.
28.Basser, PJ, Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996;111(3):209–19.
29.Vandenberghe, R. Classification of the primary progressive aphasias: principles and review of progress since 2011. Alzheimers Res Ther. 2016;8(1):16.
30.Mandelli, ML, Caverzasi, E, Binney, RJ, et al. Frontal white matter tracts sustaining speech production in primary progressive aphasia.J Neurosci. 2014;34(29):9754–67.
31.Smith, SM, Jenkinson, M, Johansen-Berg, H, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31(4):1487–505.
32.Ball, G, Boardman, JP, Arichi, T, et al. Testing the sensitivity of Tract-Based Spatial Statistics to simulated treatment effects in preterm neonates. PLoS One. 2013;8(7):e67706.
33.Kaplan, E, Goodglass, H, Weintraub, S. Boston naming test. Philadelphia: Lea & Febiger; 1983.
34.Joanette, Y, Ska, B, Coté, H. Protocole Montréal d’Evaluation de la Communication MEC. Isbergues, France: Ortho Editions; 2004.
35.Macoir, J, Gauthier, C, Jean, C, et al. BECLA, a new assessment battery for acquired deficits of language: normative data from Quebec-French healthy younger and older adults. J Neurol Sci. 2016;361:220–8.
36.Howard, D, Patterson, K. Pyramids and palm trees: a test of semantic access from pictures and words. Bury St. Edmunds, England: Thames Valley Test Company; 1992.
37.Bourgeois-Marcotte, J, Wilson, MA, Forest, M. TEFREP: Épreuve de répétition de phrases en franco-québécois. Développement, validation et normalisation [TEFREP: sentence repetition test in French-Canadian. Development, validation and norms]. Can J Aging. 2015;34(3):16.
38.Joanette, Y, Ska, B, Belleville, S. Évaluation neuropsychologique dans la démence de type Alzheimer: Un compromis optimal. L’Année Gérontologique. 1995;3:6983.
39.Riddoch, MJ, Humphreys, GW. BORB: Birmingham object recognition battery. Hove: Erlbaum; 1993.
40.Wilson, MA, Joubert, S, Ferré, P, et al. The role of the left anterior temporal lobe in exception word reading: reconciling patient and neuroimaging findings. Neuroimage. 2012;60(4):2000–7.
41.Meyers, J, Lange, D. The complex figure: a recognition subtest. Clin Neuropsychol. 1994;8:153–66.
42.Wechsler, D. Wechsler memory scale, 4th ed. San Antonio, TX: The Psychological Corporation; 2009.
43.Nasreddine, ZS, Phillips, NA, Bédirian, V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.J Am Geriatr Soc. 2005;53(4):695–9.
44.Folstein, MF, Folstein, SE, McHugh, PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98.
45.Duchesne, S, Chouinard, I, Potvin, O, et al. The Canadian dementia imaging protocol: harmonizing national cohorts. J Magn Reson Imaging. 2019;49(2):456–65.
46.Smith, SM, Jenkinson, M, Woolrich, MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 Suppl 1:S208–19.
47.Woolrich, MW, Jbabdi, S, Patenaude, B, et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage. 2009;45(1 Suppl):S173–86.
48.Oishi, K, Faria, A, Jiang, H, et al. Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer’s disease participants. Neuroimage. 2009;46(2):486–99.
49.Dale, AM, Fischl, B, Sereno, MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9(2):179–94.
50.Desikan, RS, Ségonne, F, Fischl, B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006. 31(3):968–80.
51.Fischl, B,Salat, DH, Busa, E, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33(3):341–55.
52.von Elm, E, Altman, DG, Egger, M, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7.
53.Agosta, F, Galantucci, S, Canu, E, et al. Disruption of structural connectivity along the dorsal and ventral language pathways in patients with nonfluent and semantic variant primary progressive aphasia: a DT MRI study and a literature review. Brain Lang. 2013;127(2):157–66.
54.Aghakhanyan, G, Aghakhanyan, G, Martinuzzi, A, et al. Brain white matter involvement in hereditary spastic paraplegias: analysis with multiple diffusion tensor indices. AJNR Am J Neuroradiol. 2014;35(8):1533–8.
55.Worker, A, Blain, C, Jarosz, J, et al. Diffusion tensor imaging of Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy: a tract-based spatial statistics study. PLoS One. 2014;9(11):e112638.
56.Bejanin, A, Joie, R, Landeau, B, et al. Distinct interplay between atrophy and hypometabolism in Alzheimer’s versus semantic dementia. Cereb Cortex. 2018;29(5):1889–99.
57.Bejanin, A, Desgranges, B, La Joie, R, et al. Distinct white matter injury associated with medial temporal lobe atrophy in Alzheimer’s versus semantic dementia. Hum Brain Mapp. 2017;38(4):1791–800.
58.Bejanin, A, Chételat, G, Laisney, M, et al. Distinct neural substrates of affective and cognitive theory of mind impairment in semantic dementia. Soc Neurosci. 2017;12(3):287302.
59.Powers, JP, McMillan, CT, Brun, CC, et al. White matter disease correlates with lexical retrieval deficits in primary progressive aphasia. Front Neurol. 2013;4:212.
60.Dagher, A, Nagano-Saito, A. Functional and anatomical magnetic resonance imaging in Parkinson’s disease. Mol Imaging Biol. 2007;9(4):234–42.
61.Heneka, MT, Carson, MJ, El Khoury, J, et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 2015;14(4):388405.
62.Kenny, J, Woollacott, I, Koriath, C, et al. A novel prion protein variant in a patient with semantic dementia. J Neurol Neurosurg Psychiatry. 2017;88(10):890–2.
63.Descoteaux, M, Angelino, E, Fitzgibbons, S, et al. Apparent diffusion coefficients from high angular resolution diffusion imaging: estimation and applications. Magn Reson Med. 2006;56(2): 395410.
64.Van Essen, DC, Ugurbil, K, Auerbach, E, et al. The human connectome project: a data acquisition perspective. Neuroimage. 2012;62(4):2222–31.