Florbetapir PET, FDG PET, and MRI in Down syndrome individuals with and without Alzheimer's dementia.

Alzheimer’s & Dementia - (2015) 1-11 1 2 3 4 5 6 7 8 9 a,b,c, b,d,e f b,d 10Q18 a,b b,d a,b b,d 11 12 b,d b,d b,d b,d a,b 13 a,b a,b b,c,d,g 14 15 a Banner Sun Health Research Institute 16Q1 b Arizona Alzheimer’s Consortium 17 c University of Arizona 18 d Banner Alzheimer’s Institute 19 e Arizona State University 20 f SRI International 21 g Translational Genomics Research Institute 22 23 24 25 Abstract Because Down syndrome (DS) is associated with amyloid b (Ab) deposition, we characterized 26 imaging measurements of regional fibrillar Ab burden, cerebral metabolic rate for glucose (rCMRgl), 27 gray matter volumes (rGMVs), and age associations in 5 DS with dementia (DS/AD1), 12 DS 28 without dementia (DS/AD2), and 9 normal controls (NCs). There were significant group differences 29 in mean standard uptake value ratios (SUVRs) for florbetapir with DS/AD1 having the highest, fol30 lowed by DS/AD2, followed by NC. For [18F]-fluorodeoxyglucose positron emission tomography, 31 posterior cingulate rCMRgl in DS/AD1 was significantly reduced compared with DS/AD2 and NC. 32 For vMRI, hippocampal volumes were significantly reduced for the DS/AD1 compared with 33Q2 DS/AD2 and NC. Age-related SUVR increases and rCMRgl reductions were greater in DS partic34 35 ipants than in NCs. DS is associated with fibrillar Ab, rCMRgl, and rGMValterations in the dementia 36 stage and before the presence of clinical decline. This study provides a foundation for the studies 37 needed to inform treatment and prevention in DS. 38 Ó 2015 The Alzheimer’s Association. Published by Elsevier Inc. All rights reserved. 39 40 41 Keywords: Down syndrome; Florbetapir; FDG-PET; PET; Dementia; Alzheimer’s disease; Imaging 42 43 44 45 with a significant increase in the risk of Alzheimer’s disease 1. Introduction 46 (AD) [1]. This increased AD risk is generally believed to be 47 Down syndrome (DS) is characterized by partial to com48 because of trisomy of the amyloid precursor protein (APP) plete trisomy of chromosome 21, developmental delay, and 49 gene, which resides on the distal arm of chromosome 21. other developmental abnormalities. DS is also associated 50 Presumably, the possession of an extra copy of the APP 51 gene induces APP overexpression, accounting for the virtu52 ally universal presence of fibrillar amyloid b (Ab) peptide 53 Conflicts of Interest: All authors declare that they have no material con54 neuropathology in autopsied DS patients over the age of flicts of interest with respect to the present study, as defined by the ICMJE 35 years [2,3]. guidelines. AD dementia occurs in approximately 10% to 25% of *Corresponding author. Tel.: 11-623-832-6500; Fax: 11-623-832persons with DS in their 40s, 20% to 50% of those in 6504. their 50s, and 60% to 75% of those over the age of 60 years E-mail address: [email protected]

Florbetapir PET, FDG PET, and MRI in Down syndrome individuals with and without Alzheimer’s dementia

*, Kewei Chen , Joseph Rogers , Adam S. Fleisher , Marwan N. Sabbagh Carolyn Liebsack , Dan Bandy , Christine Belden , Hillary Protas , Pradeep Thiyyagura , Xiaofen Liu , Auttawut Roontiva , Ji Luo , Sandra Jacobson , Michael Malek-Ahmadi , Jessica Powell , Eric M. Reiman

http://dx.doi.org/10.1016/j.jalz.2015.01.006 1552-5260/Ó 2015 The Alzheimer’s Association. Published by Elsevier Inc. All rights reserved. FLA 5.2.0 DTD JALZ1964_proof 4 April 2015 5:39 pm ce

55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109

2 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164Q3 165 Q4 166 167 168 169 170 171 172 173 174 175 176

M.N. Sabbagh et al. / Alzheimer’s & Dementia - (2015) 1-11

[2–4]. Due especially to improvements in the treatment of DS-related cardiac anomalies, more persons with DS are living to older ages [5]. Given their increased risk for AD, there is a critical and largely unmet need to characterize the clinical and biomarker changes associated with the preclinical and clinical stages of AD in DS individuals. To date, the best established brain imaging methods for the preclinical and clinical evaluation of AD include positron emission tomography (PET) measurements of fibrillar Ab burden, reductions in regional PET measurements of the cerebral metabolic rate for glucose (CMRgl), and magnetic resonance imaging (MRI) measurements of regional and whole-brain volumes. These imaging techniques have been used successfully in a small number of studies to detect and track characteristic brain alterations in DS [6–17]. To our knowledge, although, no reported studies have yet characterized all three measurements in the assessment of DS patients, and only one has reported findings in DS individuals with (DS/AD1) and without AD dementia (DS/AD2) [6]. The primary objective in this small cross-sectional study was to use PET measurements of fibrillar Ab burden, PET measurements of regional CMRgl, and MRI measurements of hippocampal and regional gray matter volumes to identify age-related brain imaging alterations associated with preclinical and clinical AD dementia in individuals with DS. Clinical, functional, and neuropsychological findings provided secondary outcomes of interest. These findings could establish a cohort for the longitudinal research of AD biomarker assessments in DS and help set the stage for the evaluation of interventions to treat and prevent AD dementia in this at-risk population.

2. Methods 2.1. Subjects Five DS participants with the clinical diagnosis of Alzheimer’s dementia (DS/AD1), 12 DS participants without the clinical diagnosis of Alzheimer’s dementia (DS/AD2), and 9 normal controls (NCs) were enrolled in the study (Table 1). Participants and/or their caregivers/legal guardians provided informed consent, and the participants were studied under protocols approved by our organization’s Institutional Review Board. DS/AD1 participants met DSM-IV and NIA-Alzheimer’s Association diagnostic criteria for AD dementia by informant report of progressive cognitive decline with clear historical evidence of functional decline from premorbid abilities [18]. DS/AD2 participants had neither evidence of progressive cognitive nor functional decline. “Although the standard diagnostic criteria from the DSMIV are not modified specifically for individuals with intellectual disability, it is stated within these criteria that change from a previous level of function is required for a diagnosis. Specifically, “The cognitive deficits in Criteria A1 and A2

(memory, language, executive functioning, etc.) each cause significant impairment in social or occupational functioning and represent a significant decline from a previous level of functioning”. The study lead here is a neurologist with a large number of patients in his practice and is familiar with premorbid deficits in DS (on average). We were careful to query parents/caregivers about noted changes in their daily life, much like is emphasized in a number of dementia screeners for ID currently in development (NTG screener from the National Task Group on Intellectual Disabilities and Dementia Practices—not available at the start of this study).” The DS/AD1 and DS/AD2 subjects had no history of epilepsy or seizures and their neurological examinations were nonfocal. NC participants were cognitively normal, without trisomy 21, and matched for age to the DS/AD2 group. All DS subjects were confirmed by chromosome testing to have trisomy 21. Exclusion criteria included a lifetime history of another neurodegenerative disease, vascular dementia, or psychiatric disorders. Except as noted later, a medical history, physical examination, clinical evaluation, functional and neuropsychological testing, chromosome testing, and brain imaging were performed in all subjects. One DS/AD2 participant withdrew consent after florbetapir PET scan and was not subsequently assessed by MRI. An additional DS/AD1 subject, who was severely impaired and agitated during imaging procedures, was excluded from the study due to extremely poor PET and MRI imaging quality. All other participants were evaluated with both florbetapir PET and MRI. 2.2. Clinical, functional and neuropsychological tests All participants completed the Dementia Questionnaire for People with Learning Disabilities (DLD) [19], the Mini-Mental State Examination (MMSE) [20], the Brief Praxis Test [21], the severe impairment battery (SIB) [22], and the Vineland Adaptive Behavior Scale, second edition (see Table 1) [23]. Premorbid IQ estimation was not available from records, nor was it assessed as part of the study. 2.3. [18F] Florbetapir PET acquisition and preprocessing Florbetapir PET imaging was used to assess mean cortical-to-pontine florbetapir standard uptake value ratios (SUVRs) as the primary outcome measure of fibrillar Ab burden. As in our previous florbetapir study [7], participants underwent a 10-minute emission scan 50 minutes after intravenous injection of 10 mCi (370 MBq) of [18F] florbetapir. Scans were performed on a Siemens Biograph XVI HiRez PET/CT scanner. The images were reconstructed using an iterative reconstruction algorithm (4 iterations, 16 subsets), with a 5-mm full-width at half-maximum Gaussian filter, and were corrected for radiation attenuation and scatter. If significant patient motion was observed, another 10-minute scan was acquired. SPM8 (http://www.fil.ion. ucl.ac.uk/spm/) was used to linearly and nonlinearly deform

FLA 5.2.0 DTD JALZ1964_proof 4 April 2015 5:39 pm ce

177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243

M.N. Sabbagh et al. / Alzheimer’s & Dementia - (2015) 1-11 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310

3

Table 1 Study subject demographic characteristics, clinical and functional ratings, and neuropsychological test scores

N Age Gender (M/F) MMSE SIB BPT DLD cognitive DLD social Vineland—receptive Vineland—expressive Vineland—written Vineland—personal Vineland—domestic vineland—community Vineland—interpersonal relationships Vineland—play and leisure time Vineland—coping skills

Q16

NC

DS/AD2

DS/AD1

Total

P-value

9 36 6 10 6/3 29 6 2 99 6 1 80 6 0 060 262 16 6 0 16 6 0 16 6 0 16 6 0 16 6 0 17 6 0 16 6 0 16 6 0 16 6 1

10 36 6 12 4/6 15 6 6z 87 6 9z 70 6 5z 2 6 3z 263 9 6 4z 9 6 3z 6 6 2z 8 6 2z 9 6 3z 7 6 3z 10 6 2z 10 6 1z 10 6 5z

5 50 6 6z,x 2/3 11 6 5z 75 6 26z 62 6 17z 5 6 1z 765 4 6 4z 10 6 6z 5 6 5z 8 6 6z 8 6 4z 4 6 3z,x 10 6 1z 8 6 3z 10 6 2z

24 39 6 12 12/12 20 6 9 90 6 14 73 6 10 263 463 11 6 5 11 6 5 10 6 6 11 6 5 12 6 4 10 6 6 12 6 3 12 6 4 12 6 4

– .036 .37*, .58y ,.001 ,.001 ,.001 .004 .06 .001 .001 ,.001 ,.001 ,.001 ,.001 ,.001 ,.001 ,.001

Abbreviations: M/F, male/female; NC, normal control; DS/AD1 and DS/AD2, Down syndrome with and without Alzheimer’s disease; MMSE, Mini-Mental State Examination; SIB, severe impairment battery; DLD, Dementia Questionnaire for People with Learning Disabilities. Mean 6 SD raw scores are reported for the MMSE, SIB, BPT, and DLD; scaled scores are reported for all Vineland subtests. *Fisher exact test P-value for NC and DS/AD2. y Fisher exact test P-value for NC and DS/AD1. Post hoc group-wise comparisons. z Significantly different from NC, P , .05. x Significantly different from DS/AD2, P , .05.

each subject’s florbetapir PET image into Montreal Neurological Institute (MNI) standard coordinate space with cubic voxel size of 2 ! 2 ! 2 mm and to smooth with 5-mm fullwidth-at-half-maximum Gaussian kernel. Mean cortical-to-pontine SUVRs were computed using measurements from a pontine region of interest (ROI) using six automatically defined bilateral (frontal, temporal, parietal, anterior cingulate, posterior cingulate [PC], and precuneus) ROIs. Because of increased cerebellar uptake in the clinical stages of autosomal dominant AD [24], another form of AD that, like DS, is associated with genetically driven increases in Ab production, pons was used as the reference region. SUVRs were typically less than 1.0 because of the relatively high nonspecific binding in pons. In post hoc analyses, SUVRs were characterized in each of the six cortical, thalamic, and striatum ROIs, and patterns of SUVR increases were characterized using the automated brain mapping routine in SPM8.

target region such as PC ROI over the counts from the whole brain. In this our rCMRgl is SUVR with whole brain as the reference region. Six 5-minute emission frames were acquired on the same PET/CT system 30 minutes after the intravenous administration of 5 mCi of [18F] FDG. FDG PET images were reconstructed, filtered, and corrected for radiation attenuation and scatter using the same procedures used for reconstruction of florbetapir PET images. The six emission frames were aligned and averaged. As with the florbetapir data, SPM8 was used to linearly and nonlinearly deform each subject’s averaged FDG PET image into MNI standard coordinate space, to normalize individual images for the variation in whole-brain PET counts using proportionate scaling (i.e., our unitless CMRgl or SUVR) and to smooth with 5-mm full-width-at-halfmaximum Gaussian kernel. In post hoc analyses, rCMRgl patterns were compared using the automated brain mapping routine in SPM8 [26].

2.4. [18F]-Fluorodeoxyglucose PET acquisition and preprocessing

2.5. MRI data acquisition and preprocessing

[18F]-Fluorodeoxyglucose (FDG)-PET was used to compare regional cerebral glucose metabolism across groups. Because reduced PC CMRgl is a relatively early indicator of AD in patients affected by and at risk for late-onset and autosomal dominant AD dementia [25], data from an automatically defined PC ROI were used as the primary rCMRgl outcome measure. Please note the CMRgl (or rCMRgl) we referred to in this study was a unitless semiquantitative ratio of the PET counts from

FreeSurfer estimated hippocampal volume (relative to intracranial volume, a unitless measure) was the primary MRI outcome measure. MRI was performed using a 1.5-T Signa system (General Electric) and a T1-weighted volumetric pulse sequence (IRSPGR; repetition time 5 33 ms; Q5 echo time 5 5 ms; a 5 30 ; number of excitations 5 1; field of view 5 24 cm; 256 ! 192 imaging matrix; slice thickness 5 1.5 mm; 124 slices; scan time 5 13:36 minutes). A T2-weighted FLAIR image was used to exclude the pres- Q6 ence of strokes and edema and a long TE gradient


311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377

4

echo acquisition was used for the assessment of microhemorrhages. Hippocampal-to-intracranial volume ratios were characterized from bilateral ROIs in each participant’s T1-weighted MRI using the FreeSurfer 5.1 software package (http://surfer.nmr.mgh.harvard.edu) [27–29]. All images were visually inspected to verify ROI characterization. Voxel-wise measurements of regional gray matter volume (rGMV, corrected for total intracranial volume) were determined using the voxel-based morphometry routine and Diffeomorphic Anatomical Registration using Exponential Lie Algebra protocol in SPM8 [30–32]. The gray matter partitions generated by this process in the MNI template space preserve the total amount of tissue from the native space images. This preservation is via voxel-by-voxel multiplication of the determinant of the Jacobian matrix for the nonlinear deformation only. The gray matter map for each subject was smoothed with an 8-mm full-width-at-halfmaximum Gaussian kernel. In post hoc analyses, rGMV patterns were compared using the automated brain mapping routine in SPM8. 2.6. Statistical analysis To compare DS/AD1, DS/AD2, and NC groups, we assessed primary (florbetapir PET, FDG PET, and volumetric MRI) and secondary (clinical examination and functional and neuropsychological testing) outcomes using one-way analyses of variance (ANOVAs). Where dictated by a priori hypotheses, significant outcomes were additionally assessed by between-group t-tests. Each primary outcome measure also was evaluated for its association with age. Here, DS groups were pooled to provide a more complete span of

ages, and a general linear model was used to determine whether the slope for a particular variable (plotted against age) deviated significantly from zero. Age-related differences between the DS group as a whole and the NC group were evaluated by comparing the age-associated slopes (age by group interaction) for each variable between groups, the same way as applied in a previous study [33]. Alpha 0.05 was used for all primary measures. For the post hoc brain mapping analyses, P .001, uncorrected for multiple comparisons, was used to provide information about the pattern of between-group differences in each brain imaging measurement. Summary data are given throughout as mean 6 standard deviation.

3. Results Participants’ demographic characteristics and clinical, functional, and neuropsychological test scores are shown in Table 1. The AD/DS1 group was significantly older than the DS/AD2 and NC groups. As predicted, the DS groups performed significantly less well than the NC group, and the DS/AD1 group performed significantly less well than the DS/AD2 group, on most of the clinical and neuropsychological tests. Fig. 1A shows comparisons on the three primary outcome measures. There were significant group differences in mean cortical-to-pontine SUVRs, with DS/AD1 (1.14 6 0.13) followed by DS/AD2 (0.94 6 0.07) followed by NC (0.80 6 0.03) (linear trend ANOVA, P , 1.0e26). PC-towhole-brain CMRgl also differed significantly between the groups, with DS/AD1 (1.32 6 0.20), DS/AD2 (1.54 6 0.18), and NC (1.76 6 0.16) demonstrating the

web 4C=FPO

378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444


Fig. 1. Mean cortical florbetapir standard uptake value ratio (SUVR), posterior cingulate cerebral metabolic rate for glucose (CMRgl), and hippocampal gray matter volumes. (A) Greater florbetapir mean cortical-to-pons SUVR values for amyloid deposition (left); lower posterior cingulate CMRgl values (middle); and hippocampus gray matter volume (right) in Down syndrome patients with and without Alzheimer’s disease (DS/AD1 and DS/AD2), and in normal control (NC). (B) Florbetapir mean cortical-to-pons SUVR values (left) and posterior cingulate CMRgl (middle), but not the hippocampus volume (right), had significantly greater association with age in DS participants (DS and DS/AD combined) than in NC. FLA 5.2.0 DTD JALZ1964_proof 4 April 2015 5:39 pm ce

445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511


lowest-to-highest readings, respectively (linear trend ANOVA, P , .001). Hippocampal GMVs, however, did not exhibit significant group differences, with similar mean values for DS/AD1 (0.005 6 0.0007), DS/AD2 (0.0052 6 0.0006), and NC (0.0049 6 0.00033) (linear trend ANOVA, P 5 .12). Associations between age and the increase in mean cortical SUVR and decline in PC CMRgl were significantly greater in the overall DS group than in the NC group (P 5.015 and P 5.011, respectively), whereas the associations between age and hippocampal GMV decline were not (P 5 .85) (Fig. 1B). In post hoc ROI analyses, there were significant between-group florbetapir SUVR differences in the frontal (P 5 1e25), temporal (P 5 1e26), parietal (P 5 8e26), anterior cingulate (P 5 1e26), PC (P 5 5e26), precuneus (P 5 2e26), and striatum (P 5 6e26) ROIs, and a nonsignificant trend in the thalamic ROI (P 5 .054).

The statistical brain maps in Figs. 2–4 show locations with significantly greater SUVRs, lower regional CMRgl, and lower regional GMV in (a) the DS/AD1 compared with NC group, (b) the DS/AD1 compared with DS/AD group, and (c) the DS/AD2 compared with NC group, respectively (P .001 in all comparisons). The DS groups had significantly higher florbetapir SUVRs than the NC group bilaterally in the vicinity of posterior and anterior cingulate, precuneus, parietal, temporal, frontal, and striatal regions (Fig. 2A, row A and C), similar to patterns in the clinical and preclinical stages of late-onset AD in published reports [24,34]. These results were still apparent after controlling for age. The magnitude of increase was significantly greater in DS/AD1 (Fig. 2A, row B). The DS/AD1 group was distinguished from the DS/ AD2 and NC groups by significantly lower CMRgl

web 4C=FPO

512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578

5

Fig. 2. Statistical brain maps showing significantly greater cerebral-to-pontine florbetapir standard uptake value ratios (SUVRs) in (A) the Down syndrome with Alzheimer’s disease (DS/AD1) versus normal control (NC) group, (B) the DS/AD1 versus Down syndrome without AD (DS/AD2) group, and (C) the DS/ AD2 versus NC group. Significant central metabolic rate for glucose (CMRgl) reductions (P .001, uncorrected for multiple comparisons) are projected onto the lateral and medial surfaces of the brain. FLA 5.2.0 DTD JALZ1964_proof 4 April 2015 5:39 pm ce

579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645

646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712


web 4C=FPO

6

Fig. 3. Statistical brain maps showing significantly lower central metabolic rate for glucose (CMRgl) in (A) the Down syndrome with (DS/AD1) versus normal control (NC) group, (B) the DS/AD1 versus Down syndrome without (DS/AD2) group, and (C) the DS/AD2 versus NC group. Significant SUVR increases (P .001, uncorrected for multiple comparisons) are projected onto the lateral and medial surfaces of the brain.

bilaterally in the vicinity of PC, lateral parietal, and temporal and frontal regions, which have been previously shown to be preferentially affected in the clinical and preclinical stages of late-onset AD (Fig. 3A, Row A and C) [35,36]. The DS/ AD1 and DS/AD2 groups were also distinguished from the NC group by significantly lower CMRgl in the vicinity of additional medial frontal regions. The DS/AD1 group had significant regional gray matter reduction compared with NC (Fig. 4A, Row A) and to DS/ AD2 groups (Fig. 4A, Row B). The most prominent reductions were in bilateral basal, medial, and prefrontal lobes,

bilateral temporal lobes, lateral parietal cortex, and precuneus. However, lateral parietal cortex and precuneus changes in the DS/AD1 versus NC comparison became nonsignificant after accounting for age effects. DS/AD2 subjects also had lower volumetric MRI measures than NC subjects in the medial, prefrontal, and occipital lobes (Fig. 4A, Row C). The DS/AD1 group was distinguished from the DS/AD2 and NC groups by significantly lower GMV bilaterally in the vicinity of PC, parietal, temporal, and frontal regions, which are preferentially affected by AD [37]. The DS/AD1 and DS/


713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779

780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846

7

web 4C=FPO


Fig. 4. Statistical brain maps showing significantly lower gray matter volume (GMV) in (A) the Down syndrome with (DS/AD1) versus normal control (NC) group, (B) the DS/AD1 versus Down syndrome without (DS/AD2) group, and (C) the DS/AD2 versus NC group. Significant GMV reductions (P .001, uncorrected for multiple comparisons) are projected onto the lateral and medial surfaces of the brain.

AD2 groups were also distinguished from the NC group by significantly lower GMV in the vicinity of additional medial frontal regions, which we again postulate to reflect differences in brain development. In addition to the voxel-wise correlation with cog scores, we also examined such correlations for the primary image outcomes (AV45, mean cortical SUVR; FDG, posterior cingulate; MRI, hippocampus volume): The correlation is significant for florbetapir SUVR, for FDG-PET CMRgl, but not for hippocampus volume. Cognitive measures did not differ significantly between DSAD1 and DSAD2. Table 2 summarizes the correlations between cognitive measures and imaging parameters. The SIB appears to correlate better with imaging modalities than other cognitive measures.

4. Discussion This brain imaging study provides new information about the fibrillar Ab, CMRgl, and GMV alterations associated with the clinical and preclinical stages of AD in participants with DS. DS subjects had florbetapir PET evidence of fibrillar Ab burden in the symptomatic dementia stage. Furthermore, fibrillar Ab was detected in DS subjects without evidence of cognitive decline (DS/AD2) as early as 35 years of age (see Fig. 1). Increases in florbetapir uptake were associated with dementia status and older age. More specifically, florbetapir uptake strongly increases with and correlates with age. DS participants with AD dementia also had lower CMRgl and GMV in brain regions known


847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913


8 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980

Table 2 Cognitive measures correlating with imaging parameters

AV45 MC pons FDG precuneus pons FDG precuneus global MRI hippocampus volume

P R P R P R P R

Q17

dmr_soc_sum

dmr_cog_sum

sib_tot

vin_comm_dom_ss

vin_gross

.005 .556 .021 2.477 .010 2.528 .330 2.213

.002 .604 .058 2.402 .045 2.422 .841 .044

1.2e24 2.706 1.3e24 .714 4e24 .676 .438 .170

.003 2.586 .010 .537 .110 .351 .898 .029

.001 2.616 .002 .605 .006 .554 .275 .238

Abbreviations: FDG, [18F]-fluorodeoxyglucose; MRI, magnetic resonance imaging.

to be preferentially affected by late-onset AD. Finally, DS participants with and without dementia had lower CMRgl and GMV than NC subjects in additional frontal regions. We believe these latter alterations may reflect differences in brain development. In this study, we used pons as the reference region for the examination of amyloid deposits measured by florbetapir. Such practice was based on its previous use in the autosomal dominant AD subjects [24]. We felt that given the similarity between PS1 and DS as amyloid overproducing conditions, this methodology was appropriate to use. In addition, neuropathologic studies have suggested the presence of cerebellar amyloid deposition in the clinical stages of early onset amyloid b (Ab) overproducing syndromes, including autosomal dominant AD mutation carriers and patients with DS. Interestingly, our results showed the greater cerebellar SUVR in DS/AD1 than in DS/AD2 or in NC group, and in DS/AD2 than in NC. Rather than that, the overall amyloid pattern findings are similar using either Pons or cerebellum as the reference region. Cognitive measures did not differ significantly between DSAD1 and DSAD2. This is likely because of smaller sample size and the possibility that the tests were not as sensitive as the neuroimaging based biomarkers. Our study was not designed to examine the difference between DSAD1/2 subjects. Rather one of our focuses was to examine the earliest changes we can detect in DS subjects compared with the NC (the age trajectory of various biomarkers). A small number of published studies have used PET to characterize fibrillar Ab burden in persons with DS [6–9]. For instance, Handen et al. used Pittsburgh Compound B (PiB) PET to study seven 20- to 44-year-old DS/AD2 participants and found evidence of fibrillar Ab burden in the two individuals who were at least 38 years of age [9]. Landt et al. used PiB PET to study 5 DS/AD1 participants, 4 DS/ AD2 participants, and 14 NCs and found evidence of fibrillar Ab burden after the age of 45 years [6]. Both PiB PET studies found preferential evidence of fibrillar Ab deposition in the striatum, similar to that found in some PiB studies of autosomal dominant AD mutation carriers. Nelson et al. used fluoroethyl-methylamino-naphthyl-ethylidene malononitrile PET to investigate fibrillar and tau burden in

19 (mean age 36.7 years old) DS/AD2 participants and reported increased SUVRs in brain regions similar to that observed in late onset AD [8]. We previously used florbetapir PET to characterize fibrillar Ab burden in an end-of-life DS/AD1 participant [7]. Here, we found a regional pattern of fibrillar Ab deposition similar to that observed in lateonset AD[7]. In our florbetapir PET study of PSEN1 E280 A mutation carriers, we also found a regional pattern of fibrillar Ab deposition similar to that observed in lateonset AD [24,38]. We subsequently confirmed the magnitude and pattern of fibrillar Ab deposition at autopsy [7]. We interpret our florbetapir PET data to preferentially affect the same brain regions that might be affected in lateonset AD. Although there was a close correlation between antemortem PET and postmortem histopathologic measurements of Ab plaques in this DS participant with AD dementia, florbetapir PET measurements may be less sensitive to the detection of the primarily diffuse Ab plaques that have been reported in virtually all DS/AD2 individuals who expired by the age of 35 years [39]. Previously published FDG PET studies have reported a pattern of CMRgl reductions in DS adults with and without AD dementia similar to that observed in late-onset AD [40], consistent with the findings reported in our study. In addition, Haier et al. reported increased inferior and medial temporal CMRgl [11,12] in middle-aged DS/AD2 participants, raising the possibility of compensatory increases in local neuronal activity in preclinical stages of AD. Although we are not aware of previously published volumetric MRI studies in DS/AD1 individuals, previously published volumetric MRI studies have reported alterations in GMV and white matter volume alterations in DS/AD2 individuals and their associations with older age [14–17]. For example, Teipel et al. [15] found significant age-related CMV reductions in precuneus, lateral parietal, and temporal, frontal, occipital, and parahippocampal regions in these individuals. Our findings are generally consistent with the GMV findings in those reports and extend them to individuals with DS/AD1. In addition to progressive CMRgl and GMV reductions in brain regions known to be preferentially affected by AD, the DS/AD1 and DS/AD2 groups had lower CMRgl and GMV


981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047

M.N. Sabbagh et al. / Alzheimer’s & Dementia - (2015) 1-11 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 Q7 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100Q8 1101 1102 Q9 1103 1104Q10 1105 1106 1107 1108 Q11 1109 1110Q12 1111 1112 1113 1114

than NCs in additional medial and basal frontal regions. Others have identified a greater occipital atrophy and relative sparing of both medial and lateral temporal regions on MRI and FDG. Although we cannot exclude the possibility that these changes are related to preclinical AD processes that do not continue to progress with age or to clinical severity after middle age, we postulate that these changes might reflect DS-related alterations in brain development. Additional studies are needed to (a) further characterize and confirm our fibrillar Ab, CMRgl, and brain tissue volume findings in a larger number of participants; (b) control for the effects of APOE genotypes and premormid IQs; and (c) clarify the extent to which the brain imaging alterations predict subsequent clinical decline. Additional studies also are needed to track these changes over time and provide sample size estimates for the evaluation of preclinical and clinical AD treatments using these brain imaging endpoints in proof-of-concept trials. Additional studies in DS individuals also are needed to characterize the most sensitive indicators of cognitive and functional decline in the preclinical and clinical stages of AD and provide sample size estimates for the evaluation of treatments using these endpoints in license-enabling trials. DS represents the largest population of individuals at risk for early onset Ab pathology, far exceeding the number of patients who carry autosomal dominant AD mutations. With the growing number of DS individuals living to older ages, there is an urgent need to find effective clinical and preclinical AD treatments in this vulnerable population [41–44].

5. Conclusions DS is associated with characteristic fibrillar Ab, regional CMRgl, and regional GMV alterations in the symptomatic dementia stage and before the onset of cognitive decline. Amyloid signal strongly increases with age in DS. This brain imaging study provides a foundation for the longitudinal studies needed to inform AD treatment and prevention trials in this vulnerable population.

Acknowledgments Supported by the Banner Sun Health Research Institute, the Arizona Alzheimer’s Research Consortium ADHS12010553, NIA 5P30AG019610-12, Avid Radiopharmaceuticals, and the Banner Alzheimer’s Institute. Partly supported by grants from the National Institute on Aging (R01AG031581), the National Institute of Mental Health (R01MH057899), the state of Arizona, and contributions from the Banner Alzheimer’s Foundation. The imaging was supported by the AARC ADHS 12-10553 grant. Avid radiopharmaceuticals provided the florbetapir. The other grants supported the time and effort of the investigators on this project.

9

1115 The authors are also grateful for the assistance of One Step 1116 Beyond, a program for adults with DS, which promoted 1117 awareness of our study in the DS community, and the Molly 1118 Lawson Foundation, which provided funding for transporta- Q13 1119 tion of participants. The authors also thank Vivek Devadas 1120 1121 and Robert Bauer III for their technical supports. 1122 Responsibility for Manuscript: Marwan Sabbagh, MD, had 1123 full access to all the data in the study and takes responsibility 1124 for the integrity of the data and the accuracy of the data anal1125 ysis. 1126 1127 Author Contributions and E-Mail Addresses: 1128 Marwan Sabbagh, MD (marwan.sabbagh@bannerhealth. 1129 com)—Conception and design of study, acquisition and 1130 interpretation of data, drafting of article, and final approval 1131 of the version to be published. 1132 1133 Kewei Chen, PhD ([email protected])—Anal1134 ysis and interpretation of data, revision of article, and final 1135 approval of the version to be published. 1136 Joseph Rogers, PhD ([email protected])—Subject 1137 recruitment, interpretation of data, revision of article, and 1138 1139 final approval of the version to be published. 1140 Adam S. Fleisher, MD (adam.fleisher@bannerhealth. 1141 com)—Design of study, acquisition and interpretation of 1142 data, revision of article, and final approval of the version 1143 to be published. 1144 1145 Carolyn Liebsack, RN (Carolyn.liebsack@bannerhealth. 1146 com)—Coordinated the study. 1147 Dan Bandy, MS ([email protected])—Oversaw 1148 all imaging MRI and PET done to ensure technical quality. 1149 Christine Belden, PsyD (Christine.Belden@bannerhealth. 1150 1151 com) administered cognitive measures and selected the 1152 cognitive instruments. 1153 Pradeep Thiyyagura, MS (Pradeep.thiyyagura@ 1154 bannerhealth.com) provided image analysis and Xiaofen 1155 Liu, MS ([email protected]) provided image 1156 1157 analysis. 1158 Auttawut Roontiva, MS (Auttawut.roontiva@bannerhealth. 1159 com) provided image analysis and Ji Luo, MS (ji.luo@ 1160 bannerhealth.com) provided image analysis. 1161 Hillary Protas, PhD ([email protected]) pro1162 1163 vided image analysis. 1164 Sandra Jacobson, MD (Sandra.jacobson@bannerhealth. 1165 com) evaluated subjects. 1166 Michael Malek-Ahmadi, MSPH (michael.ahmadi@ 1167 bannerhealth.com) performed cognitive assessment and sta1168 1169 tistical analysis. 1170 Jessica Powell PsyD ([email protected]) 1171 performed cognitive assessments. 1172 Eric M. Reiman, MD ([email protected])— 1173 Design of study, interpretation of data, revision of article, 1174 1175 and a final approval of the version to be published. 1176 Conflicts of Interest: All authors declare that they have no 1177 material conflicts of interest with respect to the present 1178 study, as defined by the ICMJE guidelines. 1179


1180 1181


10 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248

References

RESEARCH IN CONTEXT

1. Systematic review: Alzheimer’s disease (AD) dementia occurs in approximately 10% to 25% of persons with Down syndrome (DS) in their 40s, 20% to 50% of those in their 50s, and 60% to 75% of those over the age of 60 years. Given their increased risk for AD, there is a critical and largely unmet need to characterize the clinical and biomarker changes associated with the preclinical and clinical stages of AD in DS individuals. A small number of published studies have used positron emission tomography (PET) to characterize fibrillar amyloid beta (Ab) burden in persons with DS. Handen et al. used Pittsburgh compound B (PiB) PET to study seven 20- to 44-year-old Down syndrome without AD (DS/AD2) participants and found evidence of fibrillar Ab burden in the two individuals who were at least 38 years of age. Landt et al. used PiB PET to study 5 Down syndrome with AD (DS/AD+) participants, 4 DS/AD2 participants, and 14 normal controls and found evidence of fibrillar Ab burden after the age of 45 years. 2. Interpretation: Our brain imaging study provides new information about the fibrillar Ab, cerebral metabolic rate for glucose (CMRgl), and gray matter volume (GMV) alterations associated with the clinical and preclinical stages of AD in participants with DS. DS subjects had florbetapir PET evidence of fibrillar Ab burden in the symptomatic dementia stage. Furthermore, fibrillar Ab was detected in DS subjects without evidence of cognitive decline (DS/AD2) as early as 35 years of age (see Fig. 1). Increases in florbetapir uptake were associated with dementia status and older age. More specifically, florbetapir uptake strongly increases with and correlates with age. DS participants with AD dementia also had lower CMRgl and GMV in brain regions known to be preferentially affected by late-onset AD Finally, DS participants with and without dementia had lower CMRgl and GMV than NC subjects in additional frontal regions. We believe these latter alterations may reflect differences in brain development. 3. Future directions: The goal is to assess imaging characteristics in DS with and without dementia to determine when the changes occur. Do they proceed or coincide with cognitive decline? DS is associated with characteristic fibrillar Ab, regional CMRgl, and regional GMV alterations in the symptomatic dementia stage and before the onset of cognitive decline. Amyloid signal strongly increases with age in DS. Our brain imaging study provides a foundation for the longitudinal studies needed to inform AD treatment and prevention trials in this vulnerable population.

[1] Davidson MA. Primary care for children and adolescents with Down syndrome. Pediatr Clin North Am 2008;55:1099–111. [2] Mann DM, Esiri MM. The pattern of acquisition of plaques and tangles in the brains of participants under 50 years of age with Down’s syndrome. J Neurol Sci 1989;89:169–79. [3] Wisniewski KE, Dalton AJ, McLachlan C, Wen GY, Wisniewski HM. Alzheimer’s disease in Down’s syndrome: clinicopathologic studies. Neurology 1985;35:957–61. [4] Zigman WB, Lott IT. Alzheimer’s disease in Down syndrome: neurobiology and risk. Ment Retard Dev Disabil Res Rev 2007;13:237–46. [5] Bittles AH, Bower C, Hussain R, Glasson EJ. The four ages of Down syndrome. Eur J Public Health 2007;17:221–5. [6] Landt J, D’Abrera JC, Holland AJ, Aigbirhio FI, Fryer TD, Canales R, et al. Using positron emission tomography and carbon 11-labeled Pittsburgh compound B to image brain fibrillar beta-amyloid in adults with Down syndrome: safety, acceptability, and feasibility. Arch Neurol 2011;68:890–6. [7] Sabbagh MN, Fleisher A, Chen K, Rogers J, Berk C, Reiman E, et al. Positron emission tomography and neuropathologic estimates of fibrillar amyloid-beta in a patient with Down syndrome and Alzheimer’s disease. Arch Neurol 2011;68:1461–6. [8] Nelson LD, Siddarth P, Kepe V, Scheibel KE, Huang SC, Barrio JR, et al. Positron emission tomography of brain b-amyloid and t levels in adults with Down syndrome. Arch Neurol 2011;68:768–74. [9] Handen BL, Cohen AD, Channamalappa U, Bulova P, Cannon SA, Cohen WI, et al. Imaging brain amyloid in nondemented young adults with Down syndrome using Pittsburgh compound B. Alzheimers Dement 2012;8:496–501. [10] Pietrini P, Dani A, Furey ML, Alexander GE, Freo U, Grady CL, et al. Low glucose metabolism during brain stimulation in older Down’s syndrome subjects at risk for Alzheimer’s disease prior to dementia. Am J Psychiatry 1997;154:1063–9. [11] Haier RJ, Head K, Head E, Lott IT. Neuroimaging of individuals with Down’s syndrome at-risk for dementia: evidence for possible compensatory events. Neuroimage 2008;39:1324–32. [12] Head E, Lott IT, Patterson D, Doran E, Haier RJ. Possible compensatory events in adult Down syndrome brain prior to the development of Alzheimer disease neuropathology: targets for nonpharmacological intervention. J Alzheimers Dis 2007;11:61–76. [13] Dani A, Pietrini P, Furey ML, McIntosh AR, Grady CL, Horwitz B, et al. Brain cognition and metabolism in Down syndrome adults in association with development of dementia. Neuroreport 1996; 7:2933–6. [14] Teipel SJ, Schapiro MB, Alexander GE, Krasuski JS, Horwitz B, Hoehne C, et al. Relation of corpus callosum and hippocampal size to age in nondemented adults with Down’s syndrome. Am J Psychiatry 2003;160:1870–8. [15] Teipel SJ, Alexander GE, Schapiro MB, M€oller HJ, Rapoport SI, Hampel H. Age-related cortical grey matter reductions in nondemented Down’s syndrome adults determined by MRI with voxelbased morphometry. Brain 2004;127(Pt 4):811–24. [16] Krasuski JS, Alexander GE, Horwitz B, Rapoport SI, Schapiro MB. Relation of medial temporal lobe volumes to age and memory function in nondemented adults with Down’s syndrome: implications for the prodromal phase of Alzheimer’s disease. Am J Psychiatry 2002; 159:74–81. [17] White NS, Alkire MT, Haier RJ. A voxel-based morphometric study of nondemented adults with Down syndrome. Neuroimage 2003; 20:393–403. [18] McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on AgingAlzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011;7:263–9.


1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315

M.N. Sabbagh et al. / Alzheimer’s & Dementia - (2015) 1-11 1316 1317 1318 1319 1320 1321 1322Q14 1323 1324 1325Q15 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382

[19] Evenhuis HM, Kengen MMF, Eurlings HAL. Screening instrument for diagnosis of dementia in people with learning disabilities (2007). London: UK adaptation, Pearson Assessment; 2007. [20] Folstein MF, Folstein SE, McHugh PR. Mini-Mental State. J Psychiatr Res 1975;12:189–98. [21] Dalton A, Fedor B. Dyspraxia scale for adults with Down syndrome. NYS Institute for Basic Research in Developmental Disabilities; 1997. [22] McGonigle, KL, Swihart, AA, Boller, F. (1993). The severe impairment battery. Harcourt assessment 1993 London J. Saxton, K.L. McGonigle, A.A. Swihart, F. Boller. [23] Sparrow SS, Cicchetti DV, Balla DA. Vineland-II: Vineland Adaptive Behavior Scales. 2nd ed. Oxford: Pearson; 2007. [24] Fleisher AS, Chen K, Quiroz YT, Jakimovich LJ, Gomez MG, Langois CM, et al. Florbetapir PET analysis of amyloid-b deposition in the presenilin 1 E280A autosomal dominant Alzheimer’s disease kindred: a cross-sectional study. Lancet Neurol 2012;11:1057–65. [25] Caselli RJ. Reiman EM Characterizing the preclinical stages of Alzheimer’s disease and the prospect of presymptomatic intervention. J Alzheimers Dis 2013;33(Suppl 1):S405–16. [26] Protas HD, Chen K, Langbaum JB, Fleisher AS, Alexander GE, Lee W, et al. Posterior cingulate glucose metabolism, hippocampal glucose metabolism, and hippocampal volume in cognitively normal, late-middle-aged persons at 3 levels of genetic risk for Alzheimer disease. JAMA Neurol 2013;70:320–5. [27] Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33:341–55. [28] Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 2008;27:685–91. [29] Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998;17:87–97. [30] Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage 2000;11(6 Pt 1):805–21. [31] Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage 2007;38:95–113. [32] Ashburner J. Computational anatomy with the SPM software. Magn Reson Imaging 2009;27:1163–74. ^ [33] Lane RD, McRae K, Reiman EM, Chen K, Ahern GL, Thayer JF. A Neural correlates of heart rate variability during emotion. Neuroimage 2009;44:214–23.

11

[34] Rodrigue KM, Kennedy KM, Devous MD, Rieck JR, Hebrank AC, Diaz-Arrastia R, et al. b-Amyloid burden in healthy aging: regional distribution and cognitive consequences. Neurology 2012; 78:387–95. [35] Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer’s disease treatment studies. Am J Psychiatry 2002;159:738–45. [36] Reiman EM, Chen K, Alexander GE, Caselli RJ, Bandy D, Osborne D, et al. Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer’s dementia. Proc Natl Acad Sci U S A 2004; 101:284–9. [37] Fjell AM, Walhovd KB, Fennema-Notestine C, McEvoy LK, Hagler DJ, Holland D, et al. One-year brain atrophy evident in healthy aging. J Neurosci 2009;29:15223–31. [38] Klunk WE, Price JC, Mathis CA, Tsopelas ND, Lopresti BJ, Ziolko SK, et al. Amyloid deposition begins in the striatum of presenilin-1 mutation carriers from two unrelated pedigrees. J Neurosci 2007;27:6174–84. [39] Lemere CA, Blusztajn JK, Yamaguchi H, Wisniewski T, Saido TC, Selkoe DJ. Sequence of deposition of heterogeneous amyloid beta-peptides and APO E in Down syndrome: implications for initial events in amyloid plaque formation. Neurobiol Dis 1996;3:16–32. [40] Azari NP, Pettigrew KD, Pietrini P, Horwitz B, Schapiro MB. Detection of an Alzheimer disease pattern of cerebral metabolism in Down syndrome. Dementia 1994;5:69–78. [41] Kishnani PS, Sommer BR, Handen BL, Seltzer B, Capone GT, Spiridigliozzi GA, et al. The efficacy, safety, and tolerability of donepezil for the treatment of young adults with Down syndrome. Am J Med Genet A 2009;149A:1641–54. [42] Kishnani PS, Heller JH, Spiridigliozzi GA, Lott I, Escobar L, Richardson S, et al. Donepezil for treatment of cognitive dysfunction in children with Down syndrome aged 10–17. Am J Med Genet A 2010;152A:3028–35. [43] Lott IT, Doran E, Nguyen VQ, Tournay A, Head E, Gillen DL. Down syndrome and dementia: a randomized, controlled trial of antioxidant supplementation. Am J Med Genet A 2011;155A:1939–48. [44] Kondoh T, Kanno A, Itoh H, Nakashima M, Honda R, Kojima M, et al. Donepezil significantly improves abilities in daily lives of female Down syndrome participants with severe cognitive impairment: a 24-week randomized, double-blind, placebo-controlled trial. Int J Psychiatry Med 2011;41:71–89.


1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443

Brain fluorodeoxyglucose (FDG) PET in dementia.

MRI in patients with multiple myeloma.

MRI radiotracer beyond ¹⁸F-FDG.

Posterior cortical dementia with alexia: neurobehavioural, MRI, and PET findings.

Brain FDG PET and the diagnosis of dementia.

Association between clinical measures and florbetapir F18 PET neuroimaging in mild or moderate Alzheimer's disease dementia.

MRI.

CT?

CT in colorectal cancer staging and restaging: a pilot study.

A comparison of FDG PET and IQNB SPECT in normal subjects and in patients with dementia.

MRI.

Automated identification of dementia using FDG-PET imaging.

CT.

CT.

CT in patients with head and neck cancer.

MRI.

MRI Imaging.

FDG-PET and amyloid-PET imaging: the diverging paths.

CT in evaluating and predicting pathological response to preoperative neoadjuvant therapy in patients with rectal cancer?

CT in patients with head and neck cancer.

CT and pelvic MRI.

MRI Perform Equally Well in Cancer: Evidence from Studies on More Than 2,300 Patients.

CT and pelvic MRI.

CT findings with MRI and CT correlation.