Note: This copy is for your personal non-commercial use only. To order presentation-ready copies for distribution to your colleagues or clients, contact us at www.rsna.org/rsnarights.

Original Research  n  Neuroradiology

White Matter Degeneration in Atypical Alzheimer Disease1

Francesca Caso, MD Federica Agosta, MD, PhD Daniele Mattavelli, MD Raffaella Migliaccio, MD, PhD Elisa Canu, PhD Giuseppe Magnani, MD Alessandra Marcone, MD Massimiliano Copetti, PhD Monica Falautano, MD Giancarlo Comi, MD Andrea Falini, MD Massimo Filippi, MD

Purpose:

To assess white matter (WM) tract damage in patients with atypical Alzheimer disease (AD), including early-onset AD (EOAD), logopenic variant of primary progressive aphasia (lvPPA), and posterior cortical atrophy (PCA), by using diffusion-tensor magnetic resonance (MR) imaging and to identify similarities and differences across the AD spectrum.

Materials and Methods:

This study was approved by the local ethical committees on human studies, and written informed consent from all subjects was obtained prior to enrollment. WM tract damage and cortical atrophy were assessed by using diffusion-tensor MR imaging and voxel-based morphometry, respectively, in 28 patients with EOAD, 12 patients with lvPPA, and 13 patients with PCA relative to age- and sexmatched healthy subjects. Conjunction and interaction analyses were used to define overlapping and syndromespecific patterns of brain damage.

Results:

Patients with EOAD, lvPPA, and PCA shared a common pattern of WM damage that involved the body of the corpus callosum, fornix, and main anterior-posterior pathways (P , .05). They also shared cortical atrophy of the left temporoparietal regions and precuneus (P , .05, family-wise error corrected). Patients with EOAD also had specific damage to the genu and splenium of the corpus callosum and parahippocampal tract bilaterally (P , .05). In all patients with AD, particularly in the two focal forms (lvPPA and PCA), WM damage was more severe and widely distributed than expected on the basis of cortical atrophy.

Conclusion:

In atypical AD clinical phenotypes, the distribution of WM damage exceeds cortical atrophy and may reflect the pathologic dissemination through structural connections from atrophic to unaffected cortical regions. WM degeneration may be an early marker of AD pathologic changes in EOAD and focal AD forms.

1

 From the Neuroimaging Research Unit (F.C., F.A., D.M., E.C., M. Filippi), Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience (G.M., M. Falautano, G.C., M. Filippi), Department of Clinical Neurosciences (A.M.), and Department of Neuroradiology and CERMAC (A.F.), San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Via Olgettina 60, 20132 Milan, Italy; INSERM, U1127, Institut du Cerveau et de la Moelle Epinière (ICM), Hôpital de la Pitié-Salpêtrière, Paris, France (R.M.); Department of Neurology, Institut de la Mémoire et de la Maladie d’Alzheimer, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris, France (R.M.); and Biostatistics Unit, IRCCSOspedale Casa Sollievo della Sofferenza, San Giovanni Rotondo, Foggia, Italy (M.C.). Received December 1, 2014; revision requested January 22, 2015; revision received February 4; accepted February 22; final version accepted February 26. Supported in part by the Italian Ministry of Health (grant GR-2010-2303035). Address correspondence to M. Filippi (e-mail: [email protected]).

 RSNA, 2015

q

Online supplemental material is available for this article.

 RSNA, 2015

q

Radiology: Volume 000: Number 0—   2015  n  radiology.rsna.org

1

NEURORADIOLOGY: White Matter Degeneration in Atypical Alzheimer Disease

A

lzheimer disease (AD) is not a unitary clinical syndrome. Patients with early-onset AD (EOAD; ,65 years of age) can present with multidomain cognitive impairment at onset (1,2). This cognitive picture is very different from the typical profile of patients with late-onset AD (65 years of age), with progressive memory deficit as the central feature (2,3). In keeping

Advances in Knowledge nn Atypical Alzheimer disease (AD) clinical phenotypes, such as early-onset AD (EOAD), logopenic variant of primary progressive aphasia (lvPPA), and posterior cortical atrophy (PCA), shared a common pattern of white matter (WM) damage that involved the body of the corpus callosum, fornix, and main anterior-posterior pathways (P , .05) and cortical atrophy of the left temporoparietal regions and precuneus (P , .05). nn The cognitive profile of each group of patients with AD highly reflected the distribution of cortical atrophy and the patterns of WM tract damage—indeed, patients with EOAD showed severe and multidomain cognitive impairment with prominent involvement of the verbal memory (100% of patients) and visuospatial memory (96% of patients), visuospatial abilities (96% of patients), and executive functions (83% of patients); in addition to the typical language deficit profile, most patients with lvPPA also had verbal memory (87% of patients) and visuospatial (50% of patients) impairment, and PCA showed prominent deficits of visuospatial abilities, visuospatial memory, and praxis (100% of patients). nn In all patients with AD, particularly in the two focal forms (lvPPA and PCA), WM damage was more severe and widely distributed than expected on the basis of cortical atrophy. 2

Caso et al

with the multidomain cognitive deficits, EOAD is associated with a widespread pattern of cortical atrophy, centered around the parietal and lateral temporal lobes, with a relative sparing of the medial temporal structures, which are the most severely damaged regions in typical late-onset AD (4–6). AD can also manifest as atypical, relatively focal clinical syndromes, more frequently associated with EOAD—that is, as posterior cortical atrophy (PCA) (7,8) and logopenic variant of primary progressive aphasia (lvPPA) (9). PCA demonstrates visual and visuospatial impairment with less prominent memory loss (7,8). Over time, patients with PCA can develop visual agnosia, topographical difficulty, optic ataxia, simultanagnosia, ocular apraxia (Balint syndrome), alexia, acalculia, rightleft confusion, agraphia (Gerstmann syndrome), and, later, more generalized dementia. PCA is associated with main posterior temporal and occipitoparietal brain damage (6) and with an underlying AD disease process (8). Patients with lvPPA present with language deficits characterized by slow rate of speech, with long word-finding pauses. Grammar and articulation are usually preserved in lvPPA, although phonological paraphasias could be present. Repetition and comprehension are impaired for sentences but preserved for single words, and naming is moderately affected (9). In patients with lvPPA, cortical atrophy is usually centered around the left posterior temporoparietal region (6,9), and the most common underlying disease process is AD (10,11). The factors that drive AD clinicoanatomic heterogeneity are not well understood. One possible mechanism

Implication for Patient Care nn Diffusion tensor MR imaging has the potential to demonstrate early WM damage in patients with atypical forms of AD; thus, it might be a useful tool in clinical trials to evaluate pathologic AD progression before it becomes clinically evident.

that could explain the involvement of different brain regions in AD variants is the spread of disease via distinct brain networks, such as amyloid b, and tau aggregates may start to accumulate in some vulnerable networks and then propagate transneuronally through white matter (WM) connections, gradually reaching unaffected regions from affected ones (12–14). According to this hypothesis, typical AD seems to target and spread through the default mode network (12,15). What happens in atypical AD, like EOAD and focal variants, is still unclear. Investigators in recent neuroimaging studies have suggested that atypical AD forms may reflect a different pathologic dissemination through specific interconnected neural networks (16,17). Diffusion-tensor (DT) magnetic resonance (MR) imaging is used to measure the effect of tissue microstructure on the random translational motion of water molecules in biologic tissues, and it is highly sensitive to WM microstructural damage. Despite the growing number of studies that show that severe microstructural abnormalities occur

Published online before print 10.1148/radiol.2015142766  Content codes: Radiology 2015; 000:1–11 Abbreviations: AD = Alzheimer disease CSF = cerebrospinal fluid DT = diffusion tensor EOAD = early-onset AD FA = fractional anisotropy lvPPA = logopenic variant of primary progressive aphasia PCA = posterior cortical atrophy WM = white matter Author contributions: Guarantor of integrity of entire study, M. Filippi; study concepts/study design or data acquisition or data analysis/ interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, F.C., F.A., D.M., R.M., E.C., G.M., M.C., M. Falautano, A.F.; clinical studies, R.M., G.M., A.M., M. Falautano; experimental studies, F.C., F.A., R.M., M. Falautano, A.F.; statistical analysis, R.M., E.C., M.C., A.F.; and manuscript editing, F.C., F.A., D.M., R.M., E.C., G.M., M.C., G.C., A.F., M. Filippi Conflicts of interest are listed at the end of this article.

radiology.rsna.org  n Radiology: Volume 000: Number 0—   2015

NEURORADIOLOGY: White Matter Degeneration in Atypical Alzheimer Disease

along WM tracts in late-onset AD besides the known loss of neurons in the gray matter, to date, a few DT MR imaging studies have demonstrated the WM network involvement in EOAD (4,18), lvPPA (19–21), and PCA (22–24). To our knowledge, no study has been conducted to compare the patterns of WM damage in EOAD, lvPPA, and PCA. The aim of the present study was to explore the distribution of WM tract damage in EOAD, lvPPA, and PCA by using DT MR imaging and to identify similarities and differences across the AD spectrum, also in relation to the patterns of cortical atrophy. We hypothesized that all forms would show preferential WM damage of the structural connections linking the key nodes of the default mode network, in addition to the involvement of distinct syndrome-specific pathways in each phenotype. We also predicted that in all AD clinical phenotypes, WM damage would not mirror cortical atrophy, thus reflecting the pathologic dissemination from atrophic to unaffected cortical regions.

Materials and Methods This study was approved by the local ethical committee on human studies, and written informed consent from all subjects was obtained prior to enrollment. Subjects were recruited from September 2011 to March 2014.

Subjects Patients were recruited at the San Raffaele Scientific Institute. We included patients with a clinical diagnosis of EOAD, lvPPA, or PCA. Clinical diagnoses were based on patients’ history, neurologic examination, and neuropsychological testing. The following diagnostic criteria were applied: the criteria and symptom onset data prior to age 65 years for EOAD as developed by McKhann et al (25), the PCA criteria of McMonagle et al (26) as modified by Alladi et al (27), and the lvPPA criteria of Gorno-Tempini et al (28). Clinical assessment was performed by experienced neurologists (G.M. and A.M., with 30 years of experience in

clinical neurology) who were blinded to MR imaging results. Recent international criteria suggest that including biomarkers in the diagnostic work-up is likely to enhance the pathophysiological specificity of the diagnosis of AD dementia (25,28). For this reason, cerebrospinal fluid (CSF) samples were collected via lumbar puncture in the morning, and amyloid b 1–42, total tau values, and phosphorylated tau values were determined by using the Inno-Bia AlzBio3 kit (Fujirebio, Pomezia, Rome). AD-like CSF biomarkers were considered to be low amyloid b values and high tau values (25). In the case of unavailable CSF biomarkers, we included only patients with typical patterns of hypometabolism and/or perfusion at fluorodeoxyglucose positron emission tomography (PET) or single photon emission computed tomography (SPECT) scanning. Exclusion criteria were a family history suggestive of an autosomal dominant disease; significant medical illnesses or substance abuse that could interfere with cognitive functioning; any other major systemic, psychiatric, or neurologic illnesses; and other causes of focal or diffuse brain damage, including extensive cerebrovascular disorders at routine MR imaging. A total of 57 patients (32 with EOAD, 12 with lvPPA, and 13 with PCA) were screened. Two patients with EOAD were excluded owing to extensive cerebrovascular disorder, and two patients with EOAD were not able to undergo a complete MR imaging examination. CSF samples were available from 21 patients with EOAD, nine patients with lvPPA, and 10 patients with PCA, and AD-like CSF biomarkers were found in all cases. The remaining seven patients with EOAD, three patients with lvPPA, and three patients with PCA showed typical patterns of hypometabolism and/or perfusion on fluorodeoxyglucose PET or SPECT images. Fifty-three patients (28 with EOAD, 12 with lvPPA, and 13 with PCA) were included in the study (Table 1). For each patient variant, a group of age- and sex-matched healthy control subjects was selected (24 control subjects for EOAD, 20 control

Radiology: Volume 000: Number 0—   2015  n  radiology.rsna.org

Caso et al

subjects for lvPPA, and 20 control subjects for PCA) (Table 1). Two healthy control subjects were recruited among patients’ spouses, and the others were recruited by word of mouth. Healthy control subjects underwent a multidimensional assessment, including neurologic assessment and a brief cognitive evaluation with mini–mental state examination. All healthy control subjects had mini–mental state examination scores of at least 28 of 30.

Cognitive Assessment An extensive neuropsychological assessment was performed by an experienced neuropsychologist who was blinded to MR imaging findings (M. Falautano., with 20 years of experience in clinical neuropsychology). The cognitive protocol has been described previously (18) (Table 2). To make results comparable between different groups of patients, raw cognitive test scores were transformed into Z scores, and the mean Z score for each cognitive domain was calculated (Table 2). Furthermore, patients with lvPPA performed a comprehensive language and speech battery (Table E1 [online]), and patients with PCA underwent an additional evaluation aimed at investigating dorsal and ventral visual symptoms (22) (E.C., with 11 years of experience in clinical neuropsychology). For details, see Appendix E1 (online). MR Imaging Study By using a 3.0-T MR imaging unit, threedimensional T1-weighted fast field-echo and DT MR imaging sequences were performed in all individuals. MR image analysis was performed by a welltrained observer, who was blinded to the individuals’ identity (F.C., with 3 years of experience in neuroimaging). DT MR image analysis was performed by using tract-based spatial statistics implemented in the Functional MR Imaging of the Brain, or FMRIB, software library (FMRIB Analysis Group, Oxford, England), or FSL, and cortical atrophy was assessed by using voxel-based morphometry in Statistical Parametric Mapping 8 (SPM8; www.fil.ion.ucl.ac.uk/ 3

4

1.2 6 0.6 (0.5–3) 5.7 6 2.6 (2.5–12)

315 6 121 (98–571)

… …

…

619 6 515  (227–2072) 99 6 64 (46–268) …

…

…

… …

…

…

.55 ,.01

… .47

P Value*

…

…

…

… …

…

20 66.0 6 5.3  (59–75) 13 (65) 14.4 6 4.0  (8–22) …

697 6 83  (313–1064) 114 6 33 (64–154)

389 6 138  (195–627)

0.7 6 0.4 (0–1) 3.9 6 2.0 (2–7)

3.2 6 2.1 (0.6–8)

62.6 6 8.2 (46–77)

12 66.0 6 9.1  (48–80) 7 (58) 12.2 6 4.1 (5–18)

Patients with lvPPA

…

…

…

… …

…

20 61.1 6 2.7  (58–67) 13 (65) 15.9 6 4.7  (8–24) …

P values refer to the Mann-Whitney U test or the Fisher exact test (for patient sex only) between patient groups.

Numbers in parentheses are percentages.

†

‡

510 6 383  (166–1475) 79 6 34 (38–148)

329 6 169  (150–582)

1.1 6 0.4 (0.5–2) 4.9 6 2.6 (3–9.5)

13 61.8 6 5.9  (55–77) 11 (85) 9.5 6 3.2  (5–13) 57.9 6 6.7  (51–76) 3.9 6 2.3 (1–11)

Patients with PCA

…

…

…

… …

…

…

.22 ,.01

… .9

P Value*

Healthy Control Subjects vs Patients with PCA Healthy Control Subjects

* P values refer to the Mann-Whitney U test or the Fisher exact test (for patient sex only) between each patient group vs age-matched healthy control subjects.

…

…

…

… …

…

…

.71 .19

… .74

P Value*

Healthy Control Subjects vs Patients with lvPPA Healthy Control Subjects

Note.—Unless indicated otherwise, values are means 6 standard deviations. Numbers in parentheses are the range. NS = not significant.

…

…

3.76 2 (0.6–8)

…

56.6 6 6.7 (48–65)

14 (50) 9.2 6 4.5 (3–18)

Disease duration (y from onset to MR imaging) Clinical Dementia Rating Clinical Dementia Rating Sum of Boxes CSF amyloid b protein (ng/L) (normal value . 500) CSF t-tau (ng/L) (normal value, 0–500) CSF p-tau (ng/L) (normal value, 0–61)

Age at onset (y)

No. of women‡ Education (y)

28 60.3 6 4.5 (49–68)

24 59.8 6 1.9  (57–64) 14 (58) 15.3 6 5.0  (5–24) …

Patients with EOAD

No. of patients Age at MR imaging (y)

Parameter

Healthy Control Subjects

Healthy Control Subjects vs Patients with EOAD

Demographic, Clinical, and CSF Parameters of Patients with EOAD, lvPPA, and PCA and Age-matched Healthy Control Subjects

Table 1

.05 for lvPPA vs PCA

NS

NS

.03 for EOAD vs lvPPA .04 for EOAD vs lvPPA

.01 for EOAD vs lvPPA,   .05 for PCA vs lvPPA NS

.03 for EOAD vs PCA NS

… .03 for EOAD vs lvPPA

P Value for Comparisons between Groups†

NEURORADIOLOGY: White Matter Degeneration in Atypical Alzheimer Disease Caso et al

radiology.rsna.org  n Radiology: Volume 000: Number 0—   2015

NEURORADIOLOGY: White Matter Degeneration in Atypical Alzheimer Disease

Caso et al

Table 2 Neuropsychological Features of Patients with EOAD, lvPPA, and PCA Parameter

EOAD

lvPPA

19.8 6 4.1 (85.7)

22.1 6 5.7 (54.5)

PCA

P Value*

Mini–mental state examination   (cutoff = 24) Digit span forward (cutoff = 3.75) Rey list immediate recall (cutoff = 28.53) Rey list delayed recall (cutoff = 4.69) Prose memory (cutoff = 8) Verbal memory domain   Spatial span (cutoff = 3.75)   Rey figure recall (cutoff = 9.47) Visuospatial memory domain†

15.9 6 5.3 (92.3)

4.42 6 0.95 (23.1) 22.0 6 10.08 (71.4) 1.29 6 1.70 (100) 2.24 6 2.23 (95.2) 22.0 6 0.7 2.53 6 1.18 (82.3) 1.92 6 2.46 (96) 22.4 6 0.8 (24.3; 20.8)

3.80 6 1.66 (40) 27.33 6 15.53 (33.3) 6.67 6 3.21 (33.3) 3.38 6 2.05 (87.5) 21.8 6 0.9 3.00 6 1.33 (50) 8.15 6 3.76 (50) 21.7 6 1.1 (23.9; 20.7)

  Rey figure copy (cutoff = 28.88)   Clock-drawing test (cutoff , 8) Visuospatial domain†

10.52 6 9.31 (95.8) 2.00 6 3.24 (94.4) 25.6 6 2.4 (29.3; 20.7)

24.67 6 5.3 (77.8) 5.50 6 3.34 (37.5) 22.0 6 1.7 (25.7; 0.0)

0.446 0.77 (100) 0.13 6 0.35 (100) 27.0 6 1.0 (28.4; 25.7)

  Phonemic fluency (cutoff = 17)   Semantic fluency (cutoff = 25) Fluency domain†   Raven Colored Progressive Matrices   (cutoff = 18)   Attentive matrices (cutoff = 31)   Digit span backward (cutoff = 3.29)   Trail Making Test (B-A) (cutoff = 186) Executive domain†

16.54 6 9.50 (53.8) 19.50 6 9.77 (65.4) 21.8 6 0.9 (23.4; 0.5) 13.466 7.29 (60.9)

13.27 6 7.90 (45.4) 19.64 6 10.92 (54.5) 21.6 6 1.0 (23.0; 1.0) 21.75 6 8.70 (33.3)

17.69 6 10.69 (38.5) 16.316 8.02 (76.9) 21.6 6 0.9 (22.6; 0.3) 3.67 6 4.59 (100)

27.46 6 11.93 (69.2) 2.67 6 0.82 (83.3) 282.75 6 266.62 (37.5) 22.9 6 1.3 (25.4; 20.4)

31.20 6 9.35 (60) 2.00 6 0 (100) 156.0 6 60.37 (0) 21.5 6 1.3 (23.7; 0.6)

8.29 6 7.80 (100) 2.33 6 0.58 (66.7) … 23.7 6 1.4 (25.5; 21.3)

  Right ideomotor apraxia (cutoff = 13)   Left ideomotor apraxia (cutoff = 13) Praxic domain†

12.60 6 5.12 (60) 12.60 6 5.14 (60) 217.3 6 12.1 (236.6; 0.3)

14.88 6 6.13 (25) 13.63 6 5.93 (37.5) 213.46 14.2 (242.5; 0.3)

4.50 6 3.08 (100) 3.83 6 3.76 (100) 237.4 6 7.7 (247.3; 229.4)

4.38 6 1.12 (38.5) 23.00 6 4.18 (80) 2.60 6 1.82 (80) 2.28 6 2.27 (88.9) 21.8 6 0.6 0.86 6 1.07 (100) … 24.9 6 1.4 (26.1; 23.4)

.01 for PCA vs lvPPA … … … … NS … … ,.001 for PCA vs EOAD,   ,.001 for PCA vs lvPPA … … ,.001 for EOAD vs lvPPA,   ,.001 for PCA vs lvPPA … … NS … … … … .02 for EOAD vs lvPPA,   .001 for PCA vs lvPPA … … .01 for PCA vs EOAD,   .003 for PCA vs lvPPA

Note.—Unless indicated otherwise, values are means 6 standard deviations, with the percentage of patients with abnormal test scores compared with the normative data of reference in parentheses. NS = not significant. * Mini–mental state examination and Z scores were compared between groups by using the analysis of variance model, followed by pairwise comparisons with Bonferroni correction. †

Numbers in parentheses are ranges.

spm/software/spm8) (18). For details, see Appendix E1 (online).

Statistical Analysis Statistical analysis was conducted by M.C., with 6 years of experience in biostatistics. WM damage.—DT MR imaging voxelwise statistical analysis was performed to compare mean diffusivity, fractional anisotropy (FA), axial diffusivity, and radial diffusivity between groups by using a permutation-based inference tool for nonparametric statistical thresholding (“randomise,” part of FSL) (29). The number of permutations was set at 5000 (29). Analyses were adjusted for

individual age. The resulting statistical maps were thresholded at a P value less than .05, family-wise error corrected for multiple comparisons at the cluster level by using the threshold-free cluster enhancement option (30). Two WM atlases within FSL (http://fsl.fmrib.ox.ac.uk/fsl/data/ atlas-descriptions.html), the Johns Hopkins University WM tractography atlas (http://fsl.fmrib.ox.ac.uk/fsl/ data/atlas-descriptions.html), and the ICBM DTI WM labels atlas (http:// www.loni.usc.edu/ICBM/Downloads/ Downloads_DTI-81.shtml) were used to guide the identification of WM tracts of interest: the body, genu, and splenium

Radiology: Volume 000: Number 0—   2015  n  radiology.rsna.org

of the corpus callosum; fornix; cingulum; parahippocampal tract; external capsule; anterior inferior fronto-occipital fasciculus and uncinate fasciculus; posterior part of the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus; superior longitudinal fasciculus; and medial parietal WM. WM tracts of interest were overlaid onto the mean FA image of each individual and masked with the WM skeleton. For each subject, mean FA and mean diffusivity values were derived for each WM tract bilaterally. By using SAS release 9.1 software (SAS Institute, Cary, NC), normal distribution assumption was checked by means of Q-Q plot and 5

NEURORADIOLOGY: White Matter Degeneration in Atypical Alzheimer Disease

Shapiro-Wilk and Kolmogorov-Smirnov tests. FA and mean diffusivity values were compared between groups by using analysis of covariance models, followed by pairwise comparisons (SAS release 9.1; SAS Institute). A conjunction analysis was performed to identify the overlapping components of the WM damage among AD syndromes, and interaction analyses were conducted to define the patterns of WM damage specific to each AD variant (SAS release 9.1; SAS Institute). To account for the two different DT MR imaging sequences, we used a linear mixed-effects model with random intercepts for sequence covariates. All analyses were adjusted for age. The analyses were repeated by also adjusting for years of education. Cortical atrophy.—In SPM8, linear contrasts were performed to identify patterns of cortical atrophy in patients with each syndrome versus age-matched healthy control subjects. A conjunction analysis was conducted to identify regions of common atrophy across the AD variants. To investigate regions of atrophy specific to each AD syndrome compared with the others, the relevant linear contrast (eg, less in patients with PCA than in healthy control subjects) was inclusively masked with the appropriate contrast between that syndrome and the other two (eg, less in patients with PCA than in patients with EOAD and lvPPA), as described previously (6). All analyses were adjusted for total intracranial volume and age, and results were tested at a P value less than .05, family-wise error corrected for multiple comparisons. The analyses were repeated by also adjusting for years of education.

Results Cognitive Findings Patients with EOAD showed severe and multidomain cognitive impairment with prominent involvement of the verbal and visuospatial memory, visuospatial abilities, and executive functions (Table 2). In addition to the typical language deficit profile (Table E1 [online]), most 6

Caso et al

patients with lvPPA also presented with verbal memory and visuospatial impairment. PCA showed prominent deficits of visuospatial abilities, visuospatial memory, and praxis. Patients with lvPPA were the least cognitively impaired group, compared with the others (Table 2). Patients with PCA performed worse than those with lvPPA and EOAD in praxis and visuospatial memory domains and performed lower than those with lvPPA in visuospatial and executive tasks (Table 2). Patients with EOAD scored lower on visuospatial and executive tests relative to patients with lvPPA (Table 2). Table E1 (online) shows the language test scores obtained in patients with lvPPA and highlights deficits in sentence repetition, naming, and syntactic comprehension, whereas object knowledge and single-word comprehension were relatively spared. Almost all patients with PCA showed ventral symptoms, such as visual agnosia (92%), prosopagnosia (69%), or alexia (85%). Among ventral symptoms, simultagnosia was detected in 15% of patients. A minority of patients with PCA also showed dorsal signs, such as optic ataxia (38%), oculomotor apraxia (31%), and neglect (23%). Simultagnosia (often resulting from both ventral and dorsal stream impairment) was detected in 15% of patients. Gerstmann syndrome signs were common in the PCA group, including left-right disorientation (31% of patients), dysgraphia (77%), calculation difficulties (54%), and finger agnosia (85%).

WM Damage Patients with each syndrome versus matched healthy control subjects.— Patients with EOAD compared with control subjects showed a distributed and symmetric pattern of FA decrease and mean diffusivity, axial diffusivity, and radial diffusivity increases in the corpus callosum and most association WM tracts, including the fornix, cingulum, parahippocampal tract, superior longitudinal fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and uncinate fasciculus (P , .05, family-wise error corrected;

Figs 1, 2; Figs E1, E2 [online]). Patients with EOAD relative to healthy control subjects showed DT MR imaging alterations in the brainstem, involving the cerebral peduncles and middle and inferior cerebellar peduncles bilaterally (P , .05, family-wise error corrected; Figs E1, E2 [online]). Compared with control subjects, patients with lvPPA showed a leftlateralized FA decrease in the corpus callosum, cingulum, fornix, parahippocampal tract, and dorsal frontoparietal pathways, including the superior longitudinal fasciculus (P , .05, family-wise error corrected; Figs 1, 2; Figs E1, E2 [online]). In the right hemisphere, patients with lvPPA showed only a small area of FA decrease in the frontal WM (P , .05, family-wise error corrected; Fig 1). The pattern of increased mean diffusivity, axial diffusivity, and radial diffusivity was more distributed and extended to the left external and internal capsules and the same regions of the right hemisphere (P , .05, familywise error corrected; Fig 2; Figs E1, E2 [online]). Compared with control subjects, patients with PCA showed a distributed and symmetrical pattern of FA decrease, as well as axial diffusivity and radial diffusivity increases in the corpus callosum, parahippocampal tracts, and posterior part of the cingulum, superior longitudinal fasciculus, inferior longitudinal fasciculus, and inferior frontooccipital fasciculus (P , .05, familywise error corrected; Fig 1; Figs E1, E2 [online]). The pattern of increased mean diffusivity extended to the frontal regions, although a decreasing gradient from posterior to anterior was present (P , .05, family-wise error corrected; Fig 2). Overlapping and specific patterns of WM damage across the AD variants.— Figures 1 and 2 show the overlapping patterns of FA decrease and mean diffusivity increase among AD variants in the left corpus callosum, frontoparietal, and parietotemporal connections. Tables E2 and E3 (online) show the mean FA and mean diffusivity values of the main WM tracts in patients and control subjects. The conjunction

radiology.rsna.org  n Radiology: Volume 000: Number 0—   2015

NEURORADIOLOGY: White Matter Degeneration in Atypical Alzheimer Disease

Caso et al

Figure 1

Figure 1:  Axial (left six columns) and sagittal (right column) MR images of FA results in patients with AD variants relative to matched healthy control subjects. Patients with A, EOAD, B, lvPPA, and C, PCA have decreased FA compared with that in age-matched healthy control subjects. D, The overlapping pattern of decreased FA among the AD variants is shown. Voxelwise group differences are shown in red. Results are overlaid on the axial and sagittal sections of the Montreal Neurologic Institute standard brain in neurologic convention (the left side appears on the left) and displayed at a P value less than .05, family-wise error corrected for multiple comparisons. The WM skeleton is green. L = left, R = right.

analysis demonstrated that patients with EOAD, lvPPA, and PCA shared DT MR imaging abnormalities of the body of the corpus callosum, fornix, right cingulum, left superior longitudinal fasciculus, and external capsule bilaterally (P , .05). In addition to this common pattern, the interaction analysis showed that patients with EOAD experienced additional damage to the genu and splenium of the corpus callosum and parahippocampal tract bilaterally (P , .05; Tables E2 and E3 [online]). There were no additional regions of damage specific to patients with lvPPA or PCA relative to the other two groups. All the results described herein did not change when years of education were added as covariates in the statistical design.

Cortical Atrophy Voxel-based morphometry results of patient groups relative to healthy control subjects are shown in Figure E3 (online) (P , .05, family-wise error corrected). The severity and regional distribution of cortical atrophy in each AD variant were consistent with those reported previously (4–6,18). Briefly, patients with EOAD showed a widespread pattern of gray matter atrophy, involving mainly the medial and lateral parietal, temporal, and occipital lobes, extending bilaterally to the frontal regions. Patients with lvPPA showed a focal, left-lateralized atrophy of the temporoparietal junction and hippocampus. In PCA, cortical atrophy was localized in the bilateral occipital lobes;

Radiology: Volume 000: Number 0—   2015  n  radiology.rsna.org

temporal regions, including the hippocampus; and medial and lateral parietal cortices, with smaller regions of atrophy in the frontal cortex bilaterally. Overlapping and specific patterns of gray matter atrophy in AD variants were in line with our previous report (6) (Fig 3). Patients with EOAD, lvPPA, and PCA shared left-sided areas of cortical atrophy, including the precuneus, inferior parietal regions, and lateral temporal cortex (P , .05, family-wise error corrected). Cortical atrophy specific to EOAD included the right hippocampus, parahippocampal cortex, and precuneus (P , .05, family-wise error corrected). The lvPPAspecific cortical atrophy occurred in the left middle temporal gyrus (P , 7

NEURORADIOLOGY: White Matter Degeneration in Atypical Alzheimer Disease

Caso et al

Figure 2

Figure 2:  Axial (left six columns) and sagittal (right column) MR images of mean diffusivity results in patients with AD variants relative to matched healthy control subjects. Increased mean diffusivity is shown in patients with A, EOAD, B, lvPPA, and C, PCA compared with agematched healthy control subjects. D, The overlapping pattern of increased mean diffusivity among the AD variants is shown. Voxelwise group differences are shown in blue. Results are overlaid on the sagittal and axial sections of the Montreal Neurologic Institute standard brain in neurologic convention (the left side appears on the left) and displayed at a P value less than .05, family-wise error corrected for multiple comparisons. The WM skeleton is green. L = left, R = right.

.05, family-wise error corrected). Cortical atrophy specific to PCA included the inferior and middle occipital gyri, fusiform and lingual gyri bilaterally, left calcarine cortex, and small regions in the right lateral temporal and parietal cortices (P , .05, family-wise error corrected). All the results described herein did not change when years of education were added as covariates in the statistical design.

Discussion EOAD, lvPPA, and PCA offer an ideal framework for exploring AD pathologic changes in terms of altered brain cortical and WM networks. In this study, we used DT MR imaging to compare 8

the patterns of WM tract damage in these patients. We also explored the distribution of cortical atrophy by using voxel-based morphometry. We found that all patients shared a common pattern of WM damage involving the corpus callosum and the main anterior-posterior pathways. When we looked at the commonly altered cortical regions, we found that the three groups shared a pattern of atrophy, including the left temporoparietal regions and precuneus, in agreement with previous studies (6,31). More importantly, we demonstrated that the microstructural WM damage is more severe and more widely distributed than expected on the basis of cortical atrophy in all clinical phenotypes studied.

The cognitive profile of each group of patients highly reflects the distribution of cortical atrophy and the patterns of WM tract damage. In EOAD, brain damage was severe and diffuse, in keeping with the multidomain cognitive dysfunction. Compared with lvPPA and PCA and consistent with the anterior brain damage, patients with EOAD were more impaired in executive tasks consistently with their anterior brain damage and had greater memory deficits in agreement with their more severe involvement of the medial temporal and parietal cortices, splenium of the corpus callosum, and parahippocampal tracts. Patients with PCA, who are characterized by poor performance in visuospatial tests,

radiology.rsna.org  n Radiology: Volume 000: Number 0—   2015

NEURORADIOLOGY: White Matter Degeneration in Atypical Alzheimer Disease

Caso et al

Figure 3

Figure 3:  MR images of A, common and B, syndrome-specific patterns of cortical atrophy across the AD variants. A, Voxel-based morphometry results show the common pattern of cortical atrophy in patients with EOAD, lvPPA, and PCA, displayed in yellow to red. B, Voxel-based morphometry results show regions of specific cortical atrophy in each AD group: The EOAD-specific pattern is displayed in red, the lvPPA-specific pattern is displayed in blue, and the PCA-specific pattern is displayed in green. Results are overlaid on the coronal (left two columns), axial (middle two columns), and sagittal (right two columns) sections of the Montreal Neurologic Institute standard brain in neurologic convention (the right side appears on the right) and displayed at a P value less than .05, family-wise error corrected for multiple comparisons. GM = gray matter, L = left, R = right.

showed a severe and diffuse pattern of cortical atrophy, but this was more centered around bilateral occipitoparietal regions. We also found that fibers connecting the occipital cortices with temporal and frontal regions, particularly on the right side, were those with more severe DT MR imaging abnormalities in patients with PCA. Patients with lvPPA showed highly asymmetric brain damage on the left side of the brain to posterior temporal and parietal regions and superior longitudinal fasciculus and inferior fronto-occipital fasciculus and uncinate involvement, in agreement with their typical language deficits. The most important finding of the present study is that WM damage was more severe and more widely distributed than expected on the basis of cortical atrophy and the cognitive profiles, particularly in the atypical AD forms in which cortical atrophy remains

localized and the clinical symptoms relatively focal. Together with the few previous studies in which WM damage was assessed in separate series of EOAD (4,18), PCA (22–24), and lvPPA (19– 21), our findings indicate that DT MR imaging may be able to demonstrate subtle abnormalities along WM pathways that link atrophic regions with still unaffected gray matter regions. More importantly, we found that DT MR imaging has the potential to allow assessment of the extensive disorganization of brain networks in focal AD, even before overt cognitive deficits become apparent. To date, the causes of WM degeneration in AD are still unknown. In mild cognitive impairment and healthy subjects, WM damage can be detected even before the development of cortical atrophy and overt dementia (32,33). Converging data support the notion

Radiology: Volume 000: Number 0—   2015  n  radiology.rsna.org

that WM damage has a central role in how the disease strikes and progresses. A series of recent studies has provided evidence for prion-like mechanisms of pathologic transmission of amyloid b and tau aggregates in AD from neuron to neuron along WM connections (14). We recently proposed (34) that microglia activation in the presence of amyloid b in excess produces neurotoxic and oligodendrotoxic oligomers that, through WM tract damage, spread disease to neighboring and connected areas. Within this framework, the WM damage we observed in patients with EOAD, lvPPA, and PCA may be interpreted as a result of the disease spreading through structural connections. The discrepancy we found between cortical atrophy and WM damage, together with the anatomic distribution of our findings, may suggest that the disease has targeted specific peripheral networks 9

NEURORADIOLOGY: White Matter Degeneration in Atypical Alzheimer Disease

(memory, visual, language) at onset and then converged to medial and dorsal frontoparietal regions—that is, the key hubs of the default mode network. The spread would occur through the corpus callosum and the main long-range WM fibers between the posterior and anterior brain regions, as suggested by our DT MR imaging findings. This model is also consistent with recent resting-state functional MR imaging findings (17). However, in the absence of longitudinal data to show otherwise, another interpretation might be that the primary lesion of AD is in the WM and that the cortical atrophy results from secondary amyloid and tau involvement of the various cortical areas, as hypothesized previously (35). The main caveat of this study is its cross-sectional nature. Another limitation is the relatively small number of patients included, which, however, is in line with previous studies (6,20) and reflects the rarity of atypical AD forms. In addition, language and high visual functioning tests were performed only in patients with lvPPA and PCA, respectively. Future studies that include larger patient populations will need to be conducted to investigate the relationship between brain damage and specific cognitive deficits. In conclusion, our study indicates that WM degeneration may be an early marker of AD pathologic changes in EOAD and atypical AD forms, preceding gray matter atrophy. Our findings also suggest that the clinical heterogeneity of AD may be related to the fact that the disease process starts from different medial temporal or lateral neocortical hubs and then eventually progresses along the same WM network to converge to a similar pattern of involvement, matching the brain areas that are included in the default mode network. Longitudinal studies of patients with EOAD, lvPPA, and PCA in the early and/or prodromic stages of disease, by using more sophisticated statistical models such as generalized linear mixed models, are now warranted to confirm our model and elucidate the direction of the disease spreading in AD. 10

Caso et al

Disclosures of Conflicts of Interest: F.C. disclosed no relevant relationships. F.A. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author received payment from Excemed and Biogen Idec. Other relationships: disclosed no relevant relationships. D.M. disclosed no relevant relationships. R.M. disclosed no relevant relationships. E.C. disclosed no relevant relationships. G.M. disclosed no relevant relationships. A.M. disclosed no relevant relationships. M.C. disclosed no relevant relationships. M. Falautano disclosed no relevant relationships. G.C. disclosed no relevant relationships. G.C. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author received payment from Novartis, Teva, Sanofi, Genzyme, Merck, Biogen, Bayer, Excemed, Serono Symposia International Foundation, Almirall, Chugai, and Receptos. Other relationships: disclosed no relevant relationships. A.F. disclosed no relevant relationships. M. Filippi Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author received payment from Bayer Schering Pharma, Biogen Idec, Merck Serono, and Teva Pharmaceutical Industries for consulting and/or speaking; institution received grants from Bayer Schering Pharma, Biogen Idec, Merck Serono, and Teva Pharmaceutical Industries. Other relationships: disclosed no relevant relationships.

References 1. Grady CL, Haxby JV, Horwitz B, Berg G, Rapoport SI. Neuropsychological and cerebral metabolic function in early vs late onset dementia of the Alzheimer type. Neuropsychologia 1987;25(5):807–816. 2. Smits LL, Pijnenburg YA, Koedam EL, et al. Early onset Alzheimer’s disease is associated with a distinct neuropsychological profile. J Alzheimers Dis 2012;30(1):101–108. 3. Mendez MF. Early-onset Alzheimer’s disease: nonamnestic subtypes and type 2 AD. Arch Med Res 2012;43(8):677–685. 4. Canu E, Frisoni GB, Agosta F, Pievani M, Bonetti M, Filippi M. Early and late onset Alzheimer’s disease patients have distinct patterns of white matter damage. Neurobiol Aging 2012;33(6):1023–1033. 5. Frisoni GB, Pievani M, Testa C, et al. The topography of grey matter involvement in early and late onset Alzheimer’s disease. Brain 2007;130(Pt 3):720–730. 6. Migliaccio R, Agosta F, Rascovsky K, et al. Clinical syndromes associated with posterior atrophy: early age at onset AD spectrum. Neurology 2009;73(19):1571–1578. 7. Mendez MF, Ghajarania M, Perryman KM. Posterior cortical atrophy: clinical characteristics and differences compared to Al-

zheimer’s disease. Dement Geriatr Cogn Disord 2002;14(1):33–40. 8. Tang-Wai DF, Graff-Radford NR, Boeve BF, et al. Clinical, genetic, and neuropathologic characteristics of posterior cortical atrophy. Neurology 2004;63(7):1168–1174. 9. Gorno-Tempini ML, Brambati SM, Ginex V, et al. The logopenic/phonological variant of primary progressive aphasia. Neurology 2008;71(16):1227–1234. 10. Mesulam MM, Weintraub S, Rogalski EJ, Wieneke C, Geula C, Bigio EH. Asymmetry and heterogeneity of Alzheimer’s and frontotemporal pathology in primary progressive aphasia. Brain 2014;137(Pt 4):1176–1192. 11. Rabinovici GD, Jagust WJ, Furst AJ, et al. Abeta amyloid and glucose metabolism in three variants of primary progressive aphasia. Ann Neurol 2008;64(4):388–401. 12. Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target large-scale human brain networks. Neuron 2009;62(1):42–52. 13. Soto C. Transmissible proteins: expanding the prion heresy. Cell 2012;149(5):968–977. 14. Clavaguera F, Lavenir I, Falcon B, Frank S, Goedert M, Tolnay M. “Prion-like” templated misfolding in tauopathies. Brain Pathol 2013;23(3):342–349. 15. Buckner RL, Sepulcre J, Talukdar T, et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 2009;29(6):1860–1873. 16. Lehmann M, Ghosh PM, Madison C, et al. Diverging patterns of amyloid deposition and hypometabolism in clinical variants of probable Alzheimer’s disease. Brain 2013;136 (Pt 3):844–858. 17. Lehmann M, Madison CM, Ghosh PM, et al. Intrinsic connectivity networks in healthy subjects explain clinical variability in Alzheimer’s disease. Proc Natl Acad Sci U S A 2013;110(28):11606–11611. 18. Canu E, Agosta F, Spinelli EG, et al. White matter microstructural damage in Alzheimer’s disease at different ages of onset. Neurobiol Aging 2013;34(10):2331–2340. 19. Agosta F, Scola E, Canu E, et al. White matter damage in frontotemporal lobar degeneration spectrum. Cereb Cortex 2012;22(12): 2705–2714. 20. 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):3011–3029. 21. Mahoney CJ, Malone IB, Ridgway GR, et al. White matter tract signatures of the progres-

radiology.rsna.org  n Radiology: Volume 000: Number 0—   2015

NEURORADIOLOGY: White Matter Degeneration in Atypical Alzheimer Disease

sive aphasias. Neurobiol Aging 2013;34(6): 1687–1699. 22. Migliaccio R, Agosta F, Scola E, et al. Ventral and dorsal visual streams in posterior cortical atrophy: a DT MRI study. Neurobiol Aging 2012;33(11):2572–2584.

26. McMonagle P, Deering F, Berliner Y, Kertesz A. The cognitive profile of posterior cortical atrophy. Neurology 2006;66(3):331–338.

matter atrophy in Alzheimer’s disease variants. Alzheimers Dement 2012;8(5 Suppl): S78–S87, e1–e2.

27. Alladi S, Xuereb J, Bak T, et al. Focal cortical presentations of Alzheimer’s disease. Brain 2007;130(Pt 10):2636–2645.

32. Agosta F, Pievani M, Sala S, et al. White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology 2011;258(3):853–863.

23. Migliaccio R, Agosta F, Toba MN, et al. Brain networks in posterior cortical atrophy: a single case tractography study and literature review. Cortex 2012;48(10):1298–1309.

28. Gorno-Tempini ML, Hillis AE, Weintraub S, et al. Classification of primary progressive aphasia and its variants. Neurology 2011;76(11): 1006–1014.

24. Cerami C, Crespi C, Della Rosa PA, et al. Brain changes within the visuo-spatial attentional network in posterior cortical atrophy. J Alzheimers Dis 2015;43(2):385–395.

29. Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 2002;15(1):1–25.

25. McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011;7(3):263–269.

Caso et al

30. Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 2009;44(1):83–98. 31. Migliaccio R, Agosta F, Possin KL, Rabinovici GD, Miller BL, Gorno-Tempini ML. White

Radiology: Volume 000: Number 0—   2015  n  radiology.rsna.org

33. Maier-Hein KH, Westin CF, Shenton ME, et al. Widespread white matter degeneration preceding the onset of dementia. Alzheimers Dement 2014 Jul 14. [Epub ahead of print] 34. Agosta F, Dalla Libera D, Spinelli EG, et al. Myeloid microvesicles in cerebrospinal fluid are associated with myelin damage and neuronal loss in mild cognitive impairment and Alzheimer disease. Ann Neurol 2014;76(6): 813–825. 35. Bartzokis G. Alzheimer’s disease as homeostatic responses to age-related myelin breakdown. Neurobiol Aging 2011;32(8):1341– 1371.

11