Distinct associations of corticolimbic hypometabolism and atrophy patterns correlate with postmortem neuropathologies in cognitively impaired individuals

Distinct associations of corticolimbic hypometabolism and atrophy patterns correlate with postmortem neuropathologies in cognitively impaired individuals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinct associations of corticolimbic hypometabolism and atrophy patterns correlate with postmortem neuropathologies in cognitively impaired individuals Sophia H. Wheatley, Daniel Ferreira, Agneta Nordberg, Rosaleena Mohanty, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8910160/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Biological heterogeneity in cognitively impaired individuals has been described by distinct hypometabolic (FDG-PET) and atrophy (MRI) corticolimbic neurodegeneration patterns. However, the neuroimaging modalities can show different patterns at the individual-level. This study investigated whether postmortem neuropathologies may explain these differences. The study includes 245 individuals, 69 with cognitive impairment who underwent in vivo neuroimaging and neuropathological assessment and 176 cognitively unimpaired individuals as a reference group. Neurodegeneration patterns were identified in both in vivo FDG-PET and MRI, and their link to postmortem AD (amyloid-beta, tau) and non-AD (CAA, alpha-synuclein, TDP-43, and hippocampal sclerosis) pathologies was examined. In vivo individual-level differences of hypometabolic and atrophy neurodegeneration patterns could be associated to different AD and non-AD pathologies postmortem. The patterns significantly differed by neuropathology and neuronal loss at the regional-level but not global-level. Specifically, limbic predominant atrophy was related to distant (neocortical) amyloid, tau, and arteriolosclerosis while limbic predominant hypometabolism with local (mediotemporal) tau and arteriolosclerosis. Both limbic atrophy and hypometabolism reflected local (mediotemporal) TDP-43 and neuronal loss, with limbic hypometabolism additionally reflecting neocortical neuronal loss. Cortical predominant atrophy and hypometabolism were correlated with local (neocortical) alpha synuclein. Neurodegeneration patterns differentially reflect underlying pathologies. Specifically, limbic predominant patterns were more frequently associated with TDP-43 pathology and hippocampal sclerosis, whereas cortical predominant patterns more often reflected cerebrovascular disease and alpha-synuclein pathology. Differences between corticolimbic hypometabolic and atrophy patterns were observed only in cases with postmortem-confirmed AD pathology, suggesting that non-AD pathologies (TDP-43, hippocampal sclerosis, cerebrovascular disease, and alpha-synuclein) may differentially modify AD-related neurodegeneration. Neurobiology of Disease In vivo neuroimaging FDG PET MRI heterogeneity postmortem neuropathology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Alzheimer's disease (AD) is characterized by the presence of amyloid-beta plaques and neurofibrillary tangles (NFTs) in the brain. The regional load of these pathologies can vary 1 from the common staging schemes 2 , 3 . Amyloid-beta plaques and NFTs often lead to neuronal death or neurodegeneration, downstream markers which are not specific to AD 4 . To capture neurodegeneration in vivo , the latest diagnostic framework for AD includes unspecific neuroimaging markers such as reduced glucose metabolism from [ 18 F] fluorodeoxyglucose-PET (FDG-PET) and atrophy from magnetic resonance imaging (MRI) along with fluid-based biomarkers 5 . Neuroimaging markers in AD typically show medial-temporal atrophy 6 and temporo-parietal hypometabolism 7 . However, different neurodegeneration patterns have also been observed namely, Limbic Predominant, Cortical Predominant and Diffuse (including Typical AD) 8 , 9 . Pattern-specific demographic and clinical differences highlight the need for a move towards precision medicine 8 . Increasing evidence shows that the AD hallmarks, amyloid-beta plaques and NFTs, rarely present in isolation 5 , 10 – 13 . Concomitant pathologies include transactive response DNA binding protein of 43 kDa (TDP-43) 11 , 14 , hippocampal sclerosis 11 , 14 , arteriolosclerosis 15 , cerebral amyloid angiopathy (CAA) 16 and alpha-synuclein 14 , 17 , 18 . The number of concomitant pathologies increases with age 5 and can contribute to the neurodegenerative processes in the brain 5 . Distinct neuroimaging patterns have also been linked to these pathologies. Regional glucose metabolism patterns from FDG-PET have been shown to correlate with TDP-43 19,20 , hippocampal sclerosis 21 and aggregates composed from alpha-synuclein, Lewy bodies 22 . MRI has also shown to be useful in distinguishing between postmortem-confirmed AD and other neurodegenerative diseases such as frontotemporal lobe dementia 23 and hippocampal sclerosis 23 , 24 . A multimodal neuroimaging study showed that FDG-PET and MRI patterns in cognitively impaired individuals are not interchangeable at the individual-level 25 . Although, tau accumulation has been proposed to precede neurodegeneration 5 , differences between both load and regional distribution have also been observed between tau-PET and MRI 26 . Importantly, even among measures of neurodegeneration, differences persist. Reasons for these differences between FDG-PET and MRI AD patterns remain unclear, and whether other pathologies could partly explain the differences have not been fully investigated. To address this gap, we investigate whether the differences in topography of in vivo FDG-PET and MRI neurodegeneration patterns are linked to postmortem neuropathology in individuals with cognitive impairment. The hypothesis is that distinct combinations of AD and non-AD pathologies explain these differences – specifically that cortical neurodegeneration patterns are associated with alpha-synuclein and/or cerebrovascular pathologies, whereas limbic neurodegeneration patterns are linked to TDP-43 pathology and/or hippocampal sclerosis. Methods Participants In this study a total of 245 individuals were included, 69 individuals with cognitive impairment from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with both in vivo neuroimaging (FDG-PET and MRI) and postmortem neuropathologic assessment. Further, 176 Aβ-negative cognitively unimpaired individuals with in vivo neuroimaging (FDG-PET and MRI) were included as a reference group. Cognitively impaired individuals in ADNI were either classed as mild cognitively impaired (‘MCI’) or ‘AD’, based on clinical assessment irrespective of biomarker status. ADNI inclusion criteria were: memory complaints, MMSE scores 24–30 (MCI) or 20–26 (AD), CDR global scores 0.5 (MCI) or 0.5–1.0 (AD), and for AD participants only, NINCDS/ADRDA criteria for probable AD 27 . For determining Aβ positivity in the cognitively impaired group, florbetapir amyloid PET 28 was used with cutoff 1.11 standardised uptake ratio and if not available, cerebrospinal fluid Aβ 1–42 with cutoff ≤ 880 pg/ml was used 29 . ADNI is a longitudinal multi-centre study aimed at collecting neuroimaging, genetic, clinical, psychological and neuropathologic measures. ADNI was launched in 2003 as a public-private partnership, led by principal investigator Michael W. Weiner, MD. The detailed inclusion and exclusion criteria for the participants can be found on the ADNI website ( https://adni.loni.usc.edu/help-faqs/adni-documentation/ ). Antemortem Neuroimaging Biomarkers We included the antemortem FDG-PET and MRI scans closest to postmortem evaluation. The mean interval between FDG-PET and MRI was 48 days. Mean interval between FDG-PET scan and death was 3.6 ± 2.6 years and mean interval between MRI scan and death was 2.92 ± 2.2 years. T1-weighted magnetisation-prepared rapid gradient-echo (MPRAGE) MRI scans were collected from various sites using multiple scanners (both 1.5 and 3T scanners), following the appropriate protocol ( https://adni.loni.usc.edu/help-faqs/adni-documentation/ ). Scans were pre-processed in-house using FreeSurfer (version 6.0.0, https://freesurfer.net ) through theHiveDB database 30 . Briefly, the preprocessing pipeline involved removal of artefacts, transformation to Talairach space and segmentation of cortical and subcortical regions from the Desikan-Killiany and Fischl atlases 31 , 32 . Cortical and subcortical grey matter volumes were extracted using automated segmentation labelling respectively. Grey matter volumes were adjusted for head size using the estimated total intracranial volumes from FreeSurfer 33 , 34 . FDG-PET from multiple scanners following the appropriate protocols were acquired ( https://adni.loni.usc.edu/help-faqs/adni-documentation/ ). FDG-PET scans co-registered to the last MRI scan and regional standardised uptake ratios (SUVRs) were extracted using PETSurfer 31 , 32 . SUVRs were extracted without partial volume effect (PVE) correction and scaled to the pons. The pons was chosen because it has been deemed optimal compared to other reference regions across ageing and neurodegeneration studies using FDG-PET 35 . Post-hoc Neuroimaging-based Measures Based on prior studies assessing non-AD pathologies, two surrogate measures were calculated, namely: cingulate island sign (FreeSurfer regions: posterior cingulate, precuneus, cuneus) and inferior temporal/medial temporal lobe ratio (IMT ratio based on FreeSurfer regions including inferiortemporal, middletemporal, hippocampus, amygdala). These two measures use regional FDG-PET; higher cingulate island sign indicates higher likelihood of Dementia with Lewy bodies 36 and a higher IMT ratio is related with hippocampal sclerosis 21 . Antemortem Fluid Biomarkers Cerebrospinal fluid (CSF) biomarkers taken at the last assessment closest to the imaging data included Aβ 1–42, total tau (t-tau) and phosphorylated tau (p-tau). The cutoff of 26.64 pg/ml in CSF p-tau was used for determining tau positivity 37 , 38 . Neuropathologic Variables We included postmortem neuropathologic assessment provided by the ADNI (Version 11: 12th April 2018 & 14th November 2022). Neuropathological data included pathological counts in all individuals (n = 69), and regional neuropathologic assessment in a subsample (n = 43). All participants consented to participate in the ADNI study according to Good Clinical Practice guidelines and the Declaration of Helsinki. Additional consent for postmortem brain collection and neuropathologic assessment was attained by a trained physician at the ADNI site 39 . At the global level, AD pathology was assessed in terms of the amyloid plaque (staged A0-A3), tau tangle scores (staged B0-B3) and composite Alzheimer’s Disease Neuropathologic Change scores (not AD, low, intermediate, high) as per the National Institute on Aging–Alzheimer’s Association guidelines for the neuropathologic assessment 40 . Non-AD pathologies assessed in the current study were alpha-synucleinopathy (presence of Lewy body pathology in brainstem, limbic region, neocortex, amygdala and olfactory bulb 41 ), arteriolosclerosis (moderate-severe), cerebral amyloid angiopathy (CAA) (moderate-severe), limbic TDP-43 (presence in amygdala, hippocampus, entorhinal/inferior temporal cortex) 20 , overall TDP-43 pathology (presence in limbic regions and neocortex), and hippocampal sclerosis (presence in one or both hemispheres). At the regional level, we separately examined the same neuropathologic variables as above along with neuronal loss or gliosis using semi quantitative scores and/or regional counts in mediotemporal regions including amygdala, hippocampal subfields (CA1, dentate gyrus, parahippocampal gyrus), entorhinal cortex, as well as neocortical regions including inferior parietal lobe, superior temporal gyrus, and middle frontal gyrus. Pattern Identification and Characterization Following prior work on neuropathologically defined imaging patterns, we applied similar measures using neuroimaging data 1 , 42 . Using a categorical approach , cognitively impaired individuals were classified into distinct patterns based on the hippocampus-to-cortex ratio per modality 42 , 43 ( Suppl. Figure 1 ). Three patterns were labelled as ‘Limbic Predominant’, ‘Cortical Predominant’ and ‘Diffuse’. We chose to use the term ‘Cortical Predominant’ as it has been commonly used in the FDG-PET subtyping 9 , 44 , and parallels the term ‘Hippocampal Sparing’ in the neuropathologically-defined 1 , 43 and MRI-based subtyping 8 , 42 , 45 , 46 . Additionally, using a continuous approach, we assessed corticolimbic neurodegenerative patterns along the two previously proposed axes of ‘typicality’ and ‘severity’ 8 . Typicality was measured by hippocampus-to-cortex ratio in each modality. Severity was measured by average cortical SUVR in FDG-PET; or total grey matter volume adjusted for estimated intracranial volume in MRI. Statistical Analyses Firstly, to assess differences in our in vivo and postmortem variables between our cognitively impaired and cognitively unimpaired groups, we ran Mann-Whitney U and chi-square testing with multiple comparison adjustments using Benjamini-Hochberg false discovery rate (BH-FDR). To visualize the topographical patterns of regional neurodegeneration (hypometabolism in FDG-PET and atrophy in MRI), w-scores adjusted for age at scan, sex, education and APOE -ε4 allele carriership relative to the cognitively unimpaired reference group were calculated. These values were averaged across the cognitively impaired individuals in each pattern and reversed so that higher w-scores correspond to greater neurodegeneration. Brain maps were created using python-ggseg 47 . We assessed differences in in vivo and postmortem variables using Kruskal-Wallis and chi-square tests adjusted for multiple comparisons. BH-FDR correction was applied within variable groups (demographic, genetic, cognitive, CSF biomarker, imaging and neuropathology measures). In the categorical approach of pattern identification, the percentage overlap between the FDG-PET and MRI patterns was visualized in a confusion matrix, which quantifies agreement between patterns. To assess whether specific pathologies may explain differences between FDG-PET and MRI patterns, we compared patterns pairwise by frequency of non-AD pathologies at an individual-level for the whole sample and stratified by AD Neuropathologic Change status. In the continuous approach of pattern identification, we examined whether region-specific pathologies may better explain differences between FDG-PET and MRI patterns based on Spearman’s partial correlations between typicality/severity and neuropathologic variables in mediotemporal and neocortical regions. Models were adjusted for age at scan, the complementary axis, i.e., severity for typicality models and vice versa, and additionally field strength for MRI. Significance values were uncorrected due to the use of small sample size and use of postmortem data (gold standard) which strengthen the results found 48 – 50 . For easier interpretation of the results, the severity values were reversed in the partial correlation heatmaps, so that positive values indicate higher severity, i.e. more neurodegeneration. All analyses were performed using Python (version 3.11.5). Results Demographic, clinical, and postmortem variables of the groups used in this study are shown in Table 1 . The cognitively impaired cohort (median age at scan 79 years FDG PET and 80 years for MRI) was older than the cognitively unimpaired reference group (74 years, age at scan). Most of the individuals were males (78%), more than half were APOE- ɛ4 carriers (59%), had median Mini-Mental State Examination (MMSE) scores of 21, in vivo Aβ+ (64%) and had high/intermediate AD Neuropathologic Change (82%). In the cognitively unimpaired reference group ( in vivo only cohort), half were male (52%), few were APOE- ɛ4 carriers (19%), all were Aβ- and had median MMSE score of 29. Table 1 Demographic, clinical, and postmortem characteristics of the cohorts used in this study. Values are described either as median with standard deviations or number or percentage. The reference cohort was Aβ- cognitively unimpaired individuals without neuropathological data. All p-values are adjusted for multiple comparisons using Benjamini-Hochberg false discovery rate. Abbreviations: ADNC: Alzheimer’s Disease Neuropathologic Change, CDR: Clinical Dementia Rating, IMT ratio: inferior medial temporal ratio, MCI: Mild cognitive impairment, MMSE: Mini-Mental State examination, TDP-43: transactive response DNA binding protein 43 kDa Cognitively Impaired Cognitively Unimpaired (in vivo only) Adjusted p-values N 69 176 — Male (%) 54 (78%) 92 (52%) < 0.05 Age at death (years) 82 (7.5) — — Age at FDG-PET scan (years) 79 (7.3) 74 (7.1) < 0.05 Age at MRI scan (years) 80 (7.0) 74 (7.1) < 0.05 Field Strength (% 3T scans) 34 (49%) 133 (76%) < 0.05 APOE ε4 carriership (%) 41 (59%) 34 (19%) < 0.05 Education (years) 16.0 (2.7) 17 (2.6) 0.048 MMSE 21.0 (5.9) 29 (1.4) < 0.05 Global CDR 1.0 (0.7) 0.025 (0.16) < 0.05 PET or CSF Aβ+ (%) 44 (64%) 0 (0%) — Abeta 1–42 (pg/ml) 593.6 (343.3) 1204 (333) < 0.05 CSF t-tau (pg/ml) 303.9 (152.8) 223 (70) < 0.05 CSF p-tau (pg/ml) 29.2 (17.3) 19.8 (6.4) < 0.05 p-tau positive (%) 32 (62%) 15 (9%) < 0.05 IMT ratio 1.2 (0.1) 0.98 (0.04) < 0.05 Cingulate Island Sign ratio 0.4 (0.1) 0.44 (0.04) 0.34 TDP-43 positive (%) 35 (51%) — — ADNC: Not AD/Low, Intermediate/High 13 (18%), 56 (82%) — — Brain weight (grams) 1211.5 (131.4) — — FDG-PET and MRI patterns of neurodegeneration Brain maps were generated to visualize patterns of neurodegeneration (w-scores adjusted for age at scan, sex, education and APOE-ε4 allele carriership relative to cognitively unimpaired reference group) using FDG-PET and MRI (Fig. 1 ). Overall, each imaging modality showed the expected regional pattern of neurodegeneration: FDG-PET revealed temporo-parietal hypometabolism, while MRI demonstrated medial temporal atrophy. The overlap between the FDG-PET and MRI patterns from the categorical approach is shown in Fig. 2 . In the Limbic Predominant pattern, both modalities showed greater neurodegeneration in deeper structures (such as the hippocampus and amygdala) with relative preservation of posterior cortical regions, including the cingulate. The Diffuse pattern showed widespread hypometabolism, most pronounced in the posterior cingulate — an area commonly affected in AD. Diffuse atrophy followed a similarly distribution but with focal atrophy in the temporal lobe, hippocampus, and amygdala. The Cortical Predominant patterns reflected the greatest degree of cortical neurodegeneration. Cortical Predominant hypometabolism showed little to no involvement of deeper structures, whereas the corresponding atrophy pattern still exhibited relatively high levels of atrophy in these regions. FDG-PET and MRI pattern characteristics Differences in demographic and biological data in the FDG-PET and MRI patterns can be found in Table 2 . Due to the small sample size and irregular group sizes due to the nature of the group categorisation, only trends can be determined from these results. Limbic Predominant patterns showed the highest age at death. Limbic Predominant hypometabolism pattern was significantly older at FDG-PET scan. APOE ε4 carriership was the least frequent in Cortical Predominant patterns. Cortical Predominant patterns also had the lowest MMSE and highest Clinical Dementia Rating scores. CSF t-tau was lower in Limbic Predominant, higher in Cortical Predominant atrophy. Table 2 Demographic, clinical, and postmortem variables of the FDG-PET and MRI-based patterns. Pattern identification methods in each modality were performed on the same sample (n = 69). Variables are presented as median values with standard deviation in brackets, except for number, sex, tau positivity and pathologies which are stated with number of cases and percentage. All p-values are adjusted for multiple comparisons using Benjamini-Hochberg false discovery rate. Abbreviations: APOE: apolipoprotein E gene, CDR: Clinical Dementia Rating Scale; GMV: grey matter volume; ICV: intracranial volume; IMT ratio: inferior medial temporal ratio; MMSE: Mini Mental State examination; SUVR: standard uptake value ratio; TDP-43: transactive response DNA binding protein 43 kDa. FDG-PET Patterns MRI Patterns Limbic Predominant Hypometabolism N = 9 (13%) Diffuse Hypometabolism N = 51 (74%) Cortical Predominant Hypometabolism N = 9 (13%) Adjusted p-values Limbic Predominant Atrophy N = 8 (12%) Diffuse Atrophy N = 54 (78%) Cortical Predominant Atrophy N = 7 (10%) Adjusted p-values Male (%) 8 (89%) 40 (78%) 6 (67%) 0.52 8 (100%) 41 (76%) 5 (71%) 0.27 Age at death (years) 88.0 (6.5) 82.0 (7.6) 84.0 (6.8) 0.28 84.5 (5.3) 82.0 (6.8) 84.0 (13.4) 0.76 Age at FDG-PET scan (years) 85.0 (5.5) 76.0 (7.4) 79.0 (6.2) < 0.05 80.5 (6.1) 78.5 (6.5) 82.0 (13.5) 0.83 Age at MRI scan (years) 85.0 (5.5) 78.0 (7.2) 80.0 (6.0) 0.06 82.0 (5.2) 79.0 (6.3) 82.0 (12.8) 0.65 APOE ε4 carriership (%) 5 (56%) 32 (63%) 4 (44%) 0.57 6 (75%) 33 (61%) 2 (29%) 0.17 Education (years) 16.0 (3.6) 16.0 (2.7) 14.0 (1.7) 0.18 17.5 (2.6) 16.0 (2.5) 13.0 (4.0) 0.27 MMSE 21.0 (6.7) 22.0 (5.8) 17.0 (5.8) 0.20 21.5 (4.3) 21.0 (6.3) 18.0 (4.5) 0.60 Global CDR 1.0 (0.7) 1.0 (0.7) 2.0 (0.7) 0.20 1.0 (0.6) 1.0 (0.7) 2.0 (0.6) 0.60 Abeta 1–42 (pg/ml) 519.7 (391.7) 615.5 (352.7) 470.9 (232.0) 0.99 503.1 (463.1) 617.7 (345.2) 491.9 (149.4) 0.99 CSF p-tau (pg/ml) 29.3 (16.6) 29.2 (17.7) 29.3 (18.0) 0.99 27.7 (14.0) 29.2 (18.1) 38.6 (15.8) 0.99 CSF t-tau (pg/ml) 283.6 (156.4) 320.4 (154.3) 300.4 (166.7) 0.99 270.9 (125.8) 303.9 (159.3) 424.6 (147.0) 0.99 CSF of PET abeta positive (%) 6 (67%) 32 (63%) 6 (67%) 0.99 4 (50%) 35 (65%) 5 (71%) 0.99 CSF p-tau positive (%) 4 (44%) 24 (47%) 4 (44%) 0.99 3 (38%) 26 (48%) 3 (43%) 0.99 FDG-PET Hippocampus-to-cortex ratio 0.3 (0.0) 0.3 (0.0) 0.4 (0.1) < 0.05 0.3 (0.1) 0.3 (0.1) 0.4 (0.1) 0.07 MRI Hippocampus-to-cortex ratio 0.2 (0.0) 0.2 (0.0) 0.2 (0.0) 0.12 0.1 (0.0) 0.2 (0.0) 0.2 (0.0) < 0.05 FDG-PET Average Total Cortical SUVR 1.5 (0.1) 1.5 (0.2) 1.4 (0.1) 0.12 1.5 (0.2) 1.5 (0.2) 1.4 (0.1) 0.91 MRI Total Gray Matter Volume, ICV adjusted 523484.2 (42836.8) 533837.1 (52513.5) 502197.0 (53689.9) 0.30 550845.2 (15637.6) 528827.0 (54417.7) 502197.0 (37882.6) 0.07 FDG-PET IMT ratio 1.3 (0.1) 1.1 (0.1) 1.1 (0.1) < 0.05 1.2 (0.2) 1.2 (0.1) 1.1 (0.1) 0.12 FDG-PET Cingulate Island Sign ratio 0.5 (0.0) 0.4 (0.0) 0.5 (0.1) 0.65 0.4 (0.1) 0.4 (0.0) 0.5 (0.1) 0.68 Intermediate or high ADNC (%) 7 (78%) 42 (82%) 7 (78%) 0.99 6 (75%) 45 (83%) 5 (71%) 0.97 Arteriolosclerosis (%) 2 (22%) 13 (25%) 4 (44%) 0.81 1 (12%) 16 (30%) 2 (29%) 0.81 Cerebral amyloid angiopathy (%) 2 (22%) 24 (47%) 3 (33%) 0.77 5 (62%) 21 (39%) 3 (43%) 0.97 Lewy bodies (%) 5 (56%) 31 (61%) 5 (56%) 0.58 4 (50%) 33 (61%) 4 (57%) 0.83 Hippocampal Sclerosis (%) 3 (33%) 2 (4%) 0 (0%) < 0.05 1 (12%) 4 (7%) 0 (0%) 0.81 TDP-43 positive (%) 7 (78%) 24 (47%) 4 (44%) 0.23 7 (88%) 24 (44%) 4 (57%) 0.38 Brain weight (grams) 1278.5 (102.8) 1191.0 (134.2) 1170.0 (136.8) 0.54 1275.0 (100.1) 1200.0 (136.9) 1150.0 (99.8) 0.38 From the in-vivo neuroimaging measures, hippocampus-to-cortex ratios were significantly greatest in Cortical Predominant and lowest in Limbic Predominant patterns. However, this is expected as the pattern identification was applied using these measures. Cortical Predominant patterns had the lowest total cortical SUVR and total gray matter volumes; Limbic Predominant had the highest values. In vivo FDG-PET IMT ratios were significantly highest in Limbic Predominant hypometabolism and lowest in Cortical Predominant hypometabolism pattern. All five hippocampal sclerosis cases occurred in Limbic Predominant or Diffuse patterns. Brain weight was lower in Cortical Predominant and higher in Limbic Predominant patterns. Global neuropathological differences between FDG-PET and MRI patterns We examined the differences between in vivo FDG-PET and MRI patterns (Fig. 2 ) in relation to individual-level postmortem neuropathologic findings (Fig. 3 ). Beyond AD, we focused on three groups of pathologies: cerebrovascular disease (CAA, arteriolosclerosis), alpha-synucleinopathy (Lewy bodies), and limbic pathologies (TDP-43, hippocampal sclerosis) (Fig. 3 A) and stratified by AD Neuropathologic Change status (Fig. 3 B-C). FDG-PET and MRI patterns were different at the individual-level only in cases with intermediate/high AD Neuropathologic Change, i.e., confirmed AD neuropathology (Fig. 3 C), suggesting that non-AD pathologies may differentially modify AD-related hypometabolism and atrophy. Cerebrovascular pathologies were relatively more frequent in Cortical Predominant patterns ( CP in Fig. 3 C: Diff_CP and CP_Diff ), whereas limbic pathologies (hippocampal sclerosis, TDP-43) were more frequent in Limbic Predominant patterns ( LP in Fig. 3 C: LP_Diff ). Diffuse patterns were more likely to have three or more pathologies. Alpha-synuclein pathology were frequent across all patterns irrespective of the AD Neuropathologic Change status. Regional neuropathological differences between FDG-PET and MRI patterns Building on the group-level trends described above, we next evaluated FDG-PET and MRI patterns in relation to the continuous measures of typicality and severity 8 and investigated their associations with regional neuropathologies. Typicality characterizes the relative predominance of limbic versus cortical involvement, whereas severity indexes the extent of neurodegeneration. Across modalities, typicality and severity demonstrated distinct correlations with mediotemporal and neocortical pathologies (Fig. 4 ). Hippocampal sclerosis was excluded from analysis due to its occurrence in only five cases. Associations with typicality: Limbic predominant atrophy, but not hypometabolism, was associated with elevated amyloid, tau, and arteriolosclerosis in neocortical regions. Conversely, Limbic Predominant hypometabolism, but not atrophy, was associated with increased tau and arteriolosclerosis within mediotemporal regions. Limbic Predominant atrophy exhibited a stronger association with mediotemporal neuronal loss compared with hypometabolism, whereas Limbic Predominant hypometabolism additionally corresponded to neocortical neuronal loss. In both FDG-PET and MRI, cortical predominance was associated with increased neocortical alpha-synuclein pathology, whereas limbic predominance was associated with elevated TDP-43 pathology. Associations with severity: Greater atrophy, but not hypometabolism, was associated with increased neocortical tau burden. In contrast, greater hypometabolism, but not atrophy, was associated with heightened neocortical arteriolosclerosis. Atrophy showed a stronger association with neocortical neuronal loss than hypometabolism, while both measures demonstrated comparable associations with mediotemporal neuronal loss. In neither FDG-PET nor MRI did overall neurodegeneration severity correlate with amyloid, alpha-synuclein, or TDP-43 pathology. Significant values were not corrected for multiple testing, therefore should be interpreted as exploratory findings. All correlations (significant and non-significant) for the regional pathologies used in this study can be found in the Supplementary Material ( Suppl. Figure 2 ). Discussion Heterogeneity in neurodegeneration in terms of corticolimbic patterns is captured well by both FDG-PET and MRI. However, factors underlying the differences between FDG and MRI patterns remain partly unresolved. In this study, we demonstrate that a differential involvement of core AD and non-AD pathologies partly account for these discrepancies. Specifically, differences between patterns reflects interactions between AD pathology and cerebrovascular, limbic and alpha-synuclein pathologies. Furthermore, FDG-PET and MRI patterns show selective vulnerability to various pathologies and neuronal loss in a region-specific manner (mediotemporal versus neocortical). Topography of FDG-PET and MRI patterns showed neurodegeneration and clinical characteristics consistent with previous subtyping studies 8 , 9 , 43 , 45 , 51 – 53 . However, FDG-PET and MRI patterns did not align at an individual-level, a difference consistent with prior studies comparing FDG-PET and MRI patterns 52 as well as tau PET and MRI patterns 54 . A key finding of our study is that differences between FDG-PET and MRI patterns were observed in relation to non-AD pathologies, but only in individuals with intermediate or high AD neuropathologic change (i.e., confirmed AD neuropathology). This finding suggests that interaction of core AD and non-AD pathologies may result in differential expressions of downstream functional (hypometabolism) and structural (atrophy) patterns. While differences between hypometabolism and atrophy in AD has been shown to vary by alpha-synuclein status 55 , our study expands further by additionally accounting for cerebrovascular and limbic pathologies in different neurodegenerative patterns. In line with the revised framework for AD 5 , our findings support consideration of multiple proteinopathies in disentangling the manifestation of neurodegenerative patterns. In line with our hypotheses, cerebrovascular pathology (CAA, arteriolosclerosis) was relatively frequent in Cortical Predominant patterns and limbic pathology (hippocampal sclerosis, TDP-43) was relatively frequent in Limbic Predominant and Diffuse patterns. CAA contributes to cortical hypometabolism and cortical atrophy independently and in concomitance with AD pathology 8 , 56 – 60 . In addition, both TDP-43 pathology and hippocampal sclerosis selectively affect limbic areas, so neurodegeneration in these regions is plausible 61 – 65 . An unexpected yet interesting observation was that one individual showed limbic predominance in FDG-PET and cortical predominance in MRI – presence of both limbic (TDP-43) and cerebrovascular (CAA) pathologies as well as AD pathology could explain such a manifestation. Regarding the diffuse pattern, over 50% of the cases with Diffuse pattern showed AD biomarker positivity in vivo which reached to over 80% with confirmed AD pathology postmortem. We further observed that diffuse neurodegeneration showed combinations of all of AD, cerebrovascular, limbic and alpha-synuclein pathologies postmortem. While concomitance of pathologies has been linked to widespread neurodegeneration in this pattern in MRI 48 and our study extends a similar association to FDG-PET. FDG-PET and MRI patterns based on typicality captured regional differences in amyloid-beta, tau, alpha-synuclein, cerebrovascular, limbic pathologies and neuronal loss whereas severity mainly captured differences in tau, cerebrovascular pathology and neuronal loss. This result shows that neurodegeneration-based typicality captures phenotypic variations based on AD and non-AD pathologies and neuronal loss at a regional level. Based on typicality, FDG-PET and MRI patterns showed some shared findings in relation to mediotemporal and neocortical pathologies. Cortical predominance in both modalities was related to increased cortical alpha-synuclein. Cortical Predominant hypometabolism and atrophy is a common pattern in dementia with Lewy bodies 66 , 67 , a synucleinopathy, both with and without AD pathology 22 , 68 . Our Cortical Predominant patterns showed neurodegeneration in frontal and posterior brain regions, consistent with the fronto-occipital pattern described in dementia with Lewy bodies with highest frequency of AD pathology 69 . Limbic predominance in both modalities was related to increased mediotemporal TDP-43, which is expected and consistent with prior reports in cases with concomitant AD and TDP-43 62,70 . Notably, FDG-PET and MRI patterns also showed associations in relation to AD and cerebrovascular pathologies in the mediotemporal versus neocortical regions. Limbic Predominant atrophy was related to neocortical amyloid, tau, and arteriolosclerosis while hypometabolism was related to mediotemporal tau and arteriolosclerosis, demonstrating that similar patterns in FDG-PET and MRI can have different pathological correlates. This finding may indicate that limbic atrophy is linked to more distributed pathological processes, while limbic hypometabolism is linked to regionally proximal pathological processes. Both limbic hypometabolism and atrophy reflected mediotemporal neuronal loss while limbic hypometabolism additionally reflected more distant cortical neuronal loss, supporting the notion that hypometabolism exceeds atrophy in this pattern. Regional differences between FDG-PET and MRI have been shown to vary by the underlying pathology (AD, alpha-synuclein, TDP-43, etc.) 71 . Whether such differences occur in patterns has not been reported before. Taken together, our findings support that the relationship between hypometabolism and atrophy depends not only on regional pathologies but also on the pattern. Based on severity, both global hypometabolism and atrophy correlated with mediotemporal and neocortical neuronal loss, which indicates that the diffuse pattern corresponds to greater neurodegeneration in vivo and postmortem, in alignment with prior literature 2 , 72 – 74 . Our current study additionally demonstrates that they differ in their pathological correlates. Specifically, greater overall hypometabolism correlated with temporal and frontal arteriolosclerosis, regions which have been implicated in vascular dementia 75 . Given that hypometabolism also correlated with temporal neuronal loss, it is possible that cerebrovascular pathology may partly explain neurodegeneration captured by FDG-PET. In contrast, greater atrophy has stronger association with frontal tau and neuronal loss, corresponding to the advanced Braak stages of tau pathology 2 . These findings may suggest that severity may better capture AD-unspecific (cerebrovascular) neuronal loss in FDG-PET and AD-specific (tau) neuronal loss in MRI. We acknowledge the limitations of this study. The sample size was modest, especially within the subsample with regional neuropathology but expected for a study including postmortem data. In addition, the sample was even smaller when looking at individuals with regional pathology data, so the results need to be replicated in a larger sample. Postmortem pathologies were evaluated for positivity or presence, which may not fully capture the different stages of each pathology. Future studies should consider incorporating insights from longitudinal neuroimaging to assess whether the pathologic correlates observed in vivo can be detected even earlier. In conclusion, this study provides a head-to-head comparison of in vivo FDG-PET and MRI patterns in individuals with cognitive impairment in relation to postmortem AD and non-AD pathologies. Despite capturing corticolimbic neurodegeneration, individual-level differences were observed between FDG-PET and MRI patterns only in presence of AD pathology and explained by cerebrovascular, limbic, alpha-synuclein pathologies postmortem. As hypothesised, cortical neurodegeneration patterns were correlated with alpha-synuclein pathology and limbic neurodegeneration patterns with hippocampal sclerosis and TDP-43 pathologies. Regional pathologies suggest that FDG-PET and MRI patterns may have comparable associations with alpha-synuclein and limbic pathologies but differential associations with AD (amyloid, tau) and cerebrovascular pathologies. Declarations Conflict of Interest Disclosures: DF consults for BioArctic and has received honoraria from Esteve Pharmaceuticals S.A. Funding/Support This study was funded by the Swedish Research Council (VR) No. 2016-02282, 2021-01861, 2025-02405; the Center for Innovative Medicine (CIMED) No. FoUI-954459, FoUI-975174, FoUI-987392; the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet No. FoUI-952838, FoUI-954893; The Swedish Brain Foundation (Hjärnfonden) No. FO2022-0084, FO2024-0239; The Swedish Alzheimer's Foundation (Alzheimerfonden) No. AF-967495, AF-980387, AF-1031812; The Swedish Parkinson's foundation (Parkinsonfonden) No. 1647/25, 1557/24, 1521/23; VINNOVA 2025-03749; Olle Engkvists Foundation (Olle Engkvists Stiftelse) No. 186-0660, 224-0069; EU Innovative Health Initiative Joint Undertaking (IHI JU) AD-RIDDLE and ACCESS-AD; King Gustaf V:s and Queen Victorias Foundation; The Swedish Dementia Foundation (Demensfonden); The Strategic Research Programme in Neuroscience at Karolinska Institutet (StratNeuro); The Swedish Society for Medical Research (SSMF) PD21-0042; the Åke Wiberg Foundation; Neurofonden; Karolinska Institutet Research Grants (Foundation for Geriatric Diseases at Karolinska Institutet, Loo and Hans Osterman Foundation for Medical Research); The Lars Hierta Memorial Foundation; Gun and Bertil Stohne’s Foundation; The Foundation for Old Maids as well as Birgitta and Sten Westerberg for additional financial support. DF receives funding from the Swedish Research Council (Vetenskapsrådet, grants 2022-00916 and 2025-02984), the Center for Innovative Medicine (CIMED, grants 20200505 and FoUI-988826), the regional agreement on medical training and clinical research of Stockholm Region (ALF Medicine, grants FoUI-962240, FoUI-987534, and FoUI-1023640), the Swedish Brain Foundation (Hjärnfonden FO2021-0131, FO2022-0175, FO2023-0261, and FO2025-0214), the Swedish Alzheimer Foundation (Alzheimerfonden AF-968032, AF-980580, AF-994058, AF-1010553, and AF-1031740), the Swedish Dementia Foundation (Demensfonden), the Gamla Tjänarinnor Foundation, the Gun och Bertil Stohnes Foundation, the Åke Wiberg Foundation, the Strategic Research Programme in Neuroscience at Karolinska Institutet (StratNeuro) Bridging Grant, the Swedish Parkinson Foundation (Parkinsonfonden), the Hans-Gabriel and Alice Trolle-Wachtmeisters Foundation, the Greta and Johan Kocks Foundation, Funding for Research from Karolinska Institutet, Neurofonden, and the Foundation for Geriatric Diseases at Karolinska Institutet, contributions from private bequests and academic agreements with industry. The funding sources did not have any involvement in the study design, collection, analysis, and interpretation of data, writing of the report, and the decision to submit the article for publication . Acknowledgments Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. References Murray ME, Graff-Radford NR, Ross OA, Petersen RC, Duara R, Dickson DW (2011) Neuropathologically defined subtypes of Alzheimer's disease with distinct clinical characteristics: a retrospective study. Lancet Neurol 10(9):785–796 Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82(4):239–259 Thal DR, Rüb U, Orantes M, Braak H (2002) Phases of Aβ-deposition in the human brain and its relevance for the development of AD. Neurology 58(12):1791–1800 Jack CR Jr, Bennett DA, Blennow K et al (2018) NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimer's Dement 14(4):535–562 Jack CR Jr, Andrews JS, Beach TG et al (2024) Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimer's Dement 20(8):5143–5169 Jack CR Jr, Petersen RC, Xu YC et al (1997) Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease. Neurology 49(3):786–794 Minoshima S, Cross D, Thientunyakit T, Foster NL, Drzezga A (2022) 18F-FDG PET imaging in neurodegenerative dementing disorders: insights into subtype classification, emerging disease categories, and mixed dementia with copathologies. J Nucl Med 63(Supplement 1):2S–12S Ferreira D, Nordberg A, Westman E (2020) Biological subtypes of Alzheimer disease: A systematic review and meta-analysis. Neurology Mar 10(10):436–448. 10.1212/WNL.0000000000009058 Levin F, Ferreira D, Lange C et al (2021) Data-driven FDG-PET subtypes of Alzheimer’s disease-related neurodegeneration. Alzheimers Res Ther 13:1–14 DeTure MA, Dickson DW (2019) The neuropathological diagnosis of Alzheimer’s disease. Mol neurodegeneration 14(1):32 Kovacs GG, Milenkovic I, Wöhrer A et al (2013) Non-Alzheimer neurodegenerative pathologies and their combinations are more frequent than commonly believed in the elderly brain: a community-based autopsy series. Acta Neuropathol 126:365–384 Arvanitakis Z, Leurgans SE, Wang Z, Wilson RS, Bennett DA, Schneider JA (2011) Cerebral amyloid angiopathy pathology and cognitive domains in older persons. Ann Neurol 69(2):320–327 Rabinovici GD, Carrillo MC, Forman M et al (2017) Multiple comorbid neuropathologies in the setting of Alzheimer's disease neuropathology and implications for drug development. Alzheimer's dementia: translational Res Clin interventions 3(1):83–91 Amador-Ortiz C, Lin WL, Ahmed Z et al (2007) TDP‐43 immunoreactivity in hippocampal sclerosis and Alzheimer's disease. Annals Neurology: Official J Am Neurol Association Child Neurol Soc 61(5):435–445 Arvanitakis Z, Capuano AW, Leurgans SE, Bennett DA, Schneider JA (2016) Relation of cerebral vessel disease to Alzheimer's disease dementia and cognitive function in elderly people: a cross-sectional study. Lancet Neurol 15(9):934–943 Attems J, Jellinger KA (2014) The overlap between vascular disease and Alzheimer’s disease-lessons from pathology. BMC Med 12:1–12 Boyle PA, Yu L, Wilson RS, Leurgans SE, Schneider JA, Bennett DA (2018) Person-specific contribution of neuropathologies to cognitive loss in old age. Ann Neurol 83(1):74–83 Hamilton RL (2000) Lewy bodies in Alzheimer's disease: a neuropathological review of 145 cases using α-synuclein immunohistochemistry. Brain Pathol 10(3):378–384 Meneses A, Koga S, O’Leary J, Dickson DW, Bu G, Zhao N (2021) TDP-43 pathology in Alzheimer’s disease. Mol neurodegeneration 16:1–15 Grothe MJ, Moscoso A, Silva-Rodríguez J et al (2023) Differential diagnosis of amnestic dementia patients based on an FDG‐PET signature of autopsy‐confirmed LATE‐NC. Alzheimer's Dement 19(4):1234–1244 Botha H, Mantyh WG, Murray ME et al (2018) FDG-PET in tau-negative amnestic dementia resembles that of autopsy-proven hippocampal sclerosis. Brain 141(4):1201–1217 Silva-Rodríguez J, Labrador-Espinosa MA, Moscoso A et al (2023) Differential effects of Tau Stage, Lewy Body Pathology, and substantia Nigra degeneration on 18F-FDG PET patterns in clinical Alzheimer disease. J Nucl Med 64(2):274–280 Rabinovici G, Seeley W, Kim E et al (2008) Distinct MRI atrophy patterns in autopsy-proven Alzheimer's disease and frontotemporal lobar degeneration. Am J Alzheimer's Disease Other Dementias® 22(6):474–488 Nho K, Saykin AJ, Initiative ADN, Nelson PT (2016) Hippocampal sclerosis of aging, a common Alzheimer’s disease ‘mimic’: risk genotypes are associated with brain atrophy outside the temporal lobe. J Alzheimer’s Disease 52(1):373–383 Wheatley SH, Mohanty R, Poulakis K et al (2024) Divergent neurodegenerative patterns: Comparison of [18F] fluorodeoxyglucose-PET-and MRI-based Alzheimer’s disease subtypes. Brain Commun 6(6):fcae426 Mohanty R, Ferreira D, Nordberg A, Westman E, Initiative ADN (2023) Associations between different tau-PET patterns and longitudinal atrophy in the Alzheimer’s disease continuum: biological and methodological perspectives from disease heterogeneity. Alzheimers Res Ther 15(1):37 McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984) Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 34(7):939–939 Landau S, Thomas B, Thurfjell L et al (2014) Amyloid PET imaging in Alzheimer’s disease: a comparison of three radiotracers. Eur J Nucl Med Mol Imaging 41:1398–1407 Hansson O, Seibyl J, Stomrud E et al (2018) CSF biomarkers of Alzheimer's disease concord with amyloid-β PET and predict clinical progression: a study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimer's Dement 14(11):1470–1481 Muehlboeck J-S, Westman E, Simmons A (2014) TheHiveDB image data management and analysis framework. Front neuroinformatics 7:49 Greve DN, Salat DH, Bowen SL et al (2016) Different partial volume correction methods lead to different conclusions: an 18F-FDG-PET study of aging. NeuroImage 132:334–343 Greve DN, Svarer C, Fisher PM et al (2014) Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data. NeuroImage 92:225–236 Falahati F, Ferreira D, Soininen H et al (2016) The effect of age correction on multivariate classification in Alzheimer’s disease, with a focus on the characteristics of incorrectly and correctly classified subjects. Brain Topogr 29:296–307 Voevodskaya O, Simmons A, Nordenskjöld R et al (2014) The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer's disease. Front Aging Neurosci 6:264 Nugent S, Croteau E, Potvin O et al (2020) Selection of the optimal intensity normalization region for FDG-PET studies of normal aging and Alzheimer’s disease. Sci Rep 10(1):9261 Kantarci K, Boeve BF, Przybelski SA et al (2021) FDG PET metabolic signatures distinguishing prodromal DLB and prodromal AD. NeuroImage: Clin 31:102754 Meyer P-F, Binette AP, Gonneaud J, Breitner JC, Villeneuve S, Investigators A (2020) Characterization of Alzheimer disease biomarker discrepancies using cerebrospinal fluid phosphorylated tau and AV1451 positron emission tomography. JAMA Neurol 77(4):508–516 Bucci M, Chiotis K, Nordberg A, Initiative ADN (2021) Alzheimer’s disease profiled by fluid and imaging markers: tau PET best predicts cognitive decline. Mol Psychiatry 26(10):5888–5898 Cairns NJ, Perrin RJ, Franklin EE et al (2015) Neuropathologic assessment of participants in two multi-center longitudinal observational studies: The A lzheimer D isease N euroimaging I nitiative (ADNI) and the D ominantly I nherited A lzheimer N etwork (DIAN). Neuropathology 35(4):390–400 Montine TJ, Phelps CH, Beach TG et al (2012) National Institute on Aging–Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease: a practical approach. Acta Neuropathol 123:1–11 McKeith IG, Dickson DW, Lowe J et al (2005) Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 65(12):1863–1872 Risacher SL, Anderson WH, Charil A et al (2017) Alzheimer disease brain atrophy subtypes are associated with cognition and rate of decline. Neurology 89(21):2176–2186 Whitwell JL, Dickson DW, Murray ME et al (2012) Neuroimaging correlates of pathologically defined subtypes of Alzheimer's disease: a case-control study. Lancet Neurol 11(10):868–877 Levin F, Grothe MJ, Dyrba M, Franzmeier N, Teipel SJ, Initiative ADN (2024) Longitudinal trajectories of cognitive reserve in hypometabolic subtypes of Alzheimer's disease. Neurobiol Aging 135:26–38 Poulakis K, Pereira JB, Mecocci P et al (2018) Heterogeneous patterns of brain atrophy in Alzheimer's disease. Neurobiol Aging 65:98–108 Ferreira D, Mohanty R, Murray ME, Nordberg A, Kantarci K, Westman E (2022) The hippocampal sparing subtype of Alzheimer’s disease assessed in neuropathology and in vivo tau positron emission tomography: a systematic review. Acta Neuropathol Commun 10(1):166 Mowinckel AM, Vidal-Piñeiro D (2020) Visualization of brain statistics with R packages ggseg and ggseg3d. Adv Methods Practices Psychol Sci 3(4):466–483 Mohanty R, Ferreira D, Frerich S et al (2022) Neuropathologic features of antemortem atrophy-based subtypes of Alzheimer disease. Neurology 99(4):e323–e333 Spina S, La Joie R, Petersen C et al (2021) Comorbid neuropathological diagnoses in early versus late-onset Alzheimer’s disease. Brain 144(7):2186–2198 Wisse L, De Flores R, Xie L et al (2021) Pathological drivers of neurodegeneration in suspected non-Alzheimer’s disease pathophysiology. Alzheimers Res Ther 13(1):100 Ferreira D, Shams S, Cavallin L et al (2018) The contribution of small vessel disease to subtypes of Alzheimer's disease: a study on cerebrospinal fluid and imaging biomarkers. Neurobiol Aging 70:18–29 Wheatley SH, Mohanty R, Poulakis K et al (2024) Divergent neurodegenerative patterns: Comparison of [(18)F] fluorodeoxyglucose-PET- and MRI-based Alzheimer's disease subtypes. Brain Commun 6(6):fcae426. 10.1093/braincomms/fcae426 Poulakis K, Pereira JB, Muehlboeck J-S et al (2022) Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease. Nat Commun 13(1):4566 Mohanty R, Mårtensson G, Poulakis K et al (2020) Comparison of subtyping methods for neuroimaging studies in Alzheimer’s disease: a call for harmonization. Brain Commun 2(2):fcaa192 Duong MT, Das SR, Khandelwal P et al (2024) Hypometabolic mismatch with atrophy and tau pathology in mixed Alzheimer and Lewy body disease. Brain. :awae352 Bergeret S, Queneau M, Rodallec M et al (2021) [18F] FDG PET may differentiate cerebral amyloid angiopathy from Alzheimer’s disease. Eur J Neurol 28(5):1511–1519 Bergeret S, Queneau M, Rodallec M et al (2021) Brain glucose metabolism in cerebral amyloid angiopathy: An FDG-PET study. Lippincott Williams & Wilkins Hagerstown, MD Fotiadis P, van Rooden S, van der Grond J et al (2016) Cortical atrophy in patients with cerebral amyloid angiopathy: a case-control study. Lancet Neurol 15(8):811–819 Samuraki M, Matsunari I, Yoshita M et al (2015) Cerebral amyloid angiopathy-related microbleeds correlate with glucose metabolism and brain volume in Alzheimer’s disease. J Alzheimers Dis 48(2):517–528 Mohanty R, Wheatley S, Chiotis K, Marseglia A, Westman E, Cohort ADNI (2025) Distinct cerebrovascular pathways underlying Alzheimer’s disease-related neurodegeneration. Acta Neuropathol 150(1):64 Woodworth DC, Nguyen HL, Khan Z, Kawas CH, Corrada MM, Sajjadi SA (2021) Utility of MRI in the identification of hippocampal sclerosis of aging. Alzheimer's Dement 17(5):847–855 Buciuc M, Botha H, Murray ME et al (2020) Utility of FDG-PET in diagnosis of Alzheimer-related TDP-43 proteinopathy. Neurology 95(1):e23–e34 Dawe RJ, Bennett DA, Schneider JA, Arfanakis K (2011) Neuropathologic correlates of hippocampal atrophy in the elderly: a clinical, pathologic, postmortem MRI study. PLoS ONE 6(10):e26286 Nelson PT, Dickson DW, Trojanowski JQ et al (2019) Limbic-predominant age-related TDP-43 encephalopathy (LATE): consensus working group report. Brain 142(6):1503–1527 Buciuc M, Martin PR, Tosakulwong N et al (2022) TDP-43-associated atrophy in brains with and without frontotemporal lobar degeneration. NeuroImage: Clin 34:102954 Ferreira D, Przybelski SA, Lesnick TG et al (2025) Longitudinal FDG-PET Metabolic Change Along the Lewy Body Continuum. JAMA Neurol 82(3):285–294 Abdelnour C, Ferreira D, Oppedal K et al (2020) The combined effect of amyloid-β and tau biomarkers on brain atrophy in dementia with Lewy bodies. NeuroImage: Clin 27:102333 Collij LE, Mastenbroek SE, Mattsson-Carlgren N et al (2024) Lewy body pathology exacerbates brain hypometabolism and cognitive decline in Alzheimer’s disease. Nat Commun 15(1):8061 Inguanzo A, Poulakis K, Mohanty R et al (2023) MRI data-driven clustering reveals different subtypes of Dementia with Lewy bodies. NPJ Parkinsons Dis Jan 20(1):5. 10.1038/s41531-023-00448-6 Josephs KA, Dickson DW, Tosakulwong N et al (2017) Rates of hippocampal atrophy and presence of post-mortem TDP-43 in patients with Alzheimer's disease: a longitudinal retrospective study. Lancet Neurol 16(11):917–924 Rodriguez-Oroz MC, Gago B, Clavero P, Delgado-Alvarado M, Garcia-Garcia D, Jimenez-Urbieta H (2015) The relationship between atrophy and hypometabolism: is it regionally dependent in dementias? Curr Neurol Neurosci Rep 15:1–12 Minoshima S, Foster NL, Kuhl DE (1994) Posterior cingulate cortex in Alzheimer's disease Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE (1997) Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Annals Neurology: Official J Am Neurol Association Child Neurol Soc 42(1):85–94 Mosconi L (2013) Glucose metabolism in normal aging and Alzheimer’s disease: methodological and physiological considerations for PET studies. Clin translational imaging 1:217–233 Heiss W-D, Zimmermann-Meinzingen S (2012) PET imaging in the differential diagnosis of vascular dementia. J Neurol Sci 322(1–2):268–273 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8910160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593379928,"identity":"cc4f6deb-3efb-4f41-8f76-afecdaf398b9","order_by":0,"name":"Sophia H. Wheatley","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYHACNoYEIGkAYj5gYJCTYCZBC2MDkGFMnBYGJC2JMwipl2/vPfbgQQ1D4nb25ucPEioOp89sZ2D+8AGPFoMz59INEo4xJO7sOWbYkHDmcO5sZgY2SXxWGUjkmEkksDHkbriRYNiQ2HY4dx5QCzMPPofNAGn5B9Ry//nHhsR/h9PlmBmYP//B55kbQC2JbSBbeIC2NBxOkAaGmDQ+HUC/pEkk9knUbziTUzgj4Vi64cxmxjbJHnwOA4aY5I9vNsYGx49v+PChxlpe4vzhwx9+4LOGAexTCRivmQEUP3g1QLXAQR0B1aNgFIyCUTASAQCOMlE+y5Vp3wAAAABJRU5ErkJggg==","orcid":"","institution":"Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden","correspondingAuthor":true,"prefix":"","firstName":"Sophia","middleName":"H.","lastName":"Wheatley","suffix":""},{"id":593379929,"identity":"5cbeae61-82ea-40d5-b93e-d41ad73e8201","order_by":1,"name":"Daniel Ferreira","email":"","orcid":"","institution":"Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas, España","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Ferreira","suffix":""},{"id":593379930,"identity":"83794b97-11cf-4a54-acf8-98def887e00a","order_by":2,"name":"Agneta Nordberg","email":"","orcid":"","institution":"Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden","correspondingAuthor":false,"prefix":"","firstName":"Agneta","middleName":"","lastName":"Nordberg","suffix":""},{"id":593379931,"identity":"9bde55ed-6300-494b-94e4-71c3530e5249","order_by":3,"name":"Rosaleena Mohanty","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYLCCB1CaGUyytzEcIKglAUmLBAPPMZK1SKThV23e3v7wQ0KFTR5/A3fi44KaO3UGN58lHmD4Y4NTi8yZM8YSCWfSiiUO8G42nnHsmYTB7bQDBxjbcFslIZHDIJHYdjix4QDvNmketsNALekNBxgbDuPRkv74R+K//4nzwVr+AbXcPN4AdNh/PFoSzCSAViRuAGnhbQNqucF24AAD2wHcWnjOmFkkHEtO3HgY6BfevsOSM8+kJRxIbEvGrYW9/fGNDzV2ifOO9258zPPtMD/f8WPGHz78scOpBQGYobQCyEkJRGhAAPkGkpSPglEwCkbBCAAAPtFcPVuCo/cAAAAASUVORK5CYII=","orcid":"","institution":"Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK","correspondingAuthor":true,"prefix":"","firstName":"Rosaleena","middleName":"","lastName":"Mohanty","suffix":""},{"id":593379932,"identity":"90a267f2-c388-4b09-9a1f-ea6d945ca0e0","order_by":4,"name":"Eric Westman","email":"","orcid":"","institution":"Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Westman","suffix":""}],"badges":[],"createdAt":"2026-02-18 14:14:30","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8910160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8910160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103050207,"identity":"3ee63078-7ee7-4c81-b605-74edde39a40e","added_by":"auto","created_at":"2026-02-20 07:48:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":446795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBrain maps of identified hypometabolism and atrophy patterns.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferences between regional FDG-PET SUVRs and MRI grey matter volume of in vivo based neurodegeneration patterns with reference to Aβ- cognitively normal reference group. W-scores calculated as z-scores adjusted for age at scan, sex, education and APOE ɛ4 allele carriership were plotted for regions in the Desikan-Killiany atlas. Higher w-scores (darker purples) have greater differences, i.e., higher hypometabolism or atrophy in the pattern compared to the reference group.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8910160/v1/841bce1a7c1cc9d2dfe66ccb.png"},{"id":106401550,"identity":"8be969c0-7def-408e-af07-6a5e06e16be2","added_by":"auto","created_at":"2026-04-08 09:06:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1340883,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFDG-PET and MRI patterns’ percentage overlap.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConfusion matrix of FDG-PET and MRI patterns. Percentage overlap of FDG-PET (y axis) and MRI (x axis) patterns calculated using the total number of each FDG-PET pattern (total percentage sum up row-wise). The unweighted kappa was 0.19 indicating only slight agreement between FDG-PET and MRI patterns.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8910160/v1/68babb6beefb618cdbed78e3.png"},{"id":102998562,"identity":"87b55370-b9ab-447a-b765-fe97f5a8f74a","added_by":"auto","created_at":"2026-02-19 12:44:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3072696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFrequency of postmortem pathologies in cognitively impaired individuals.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEach bar represents one individual. Groups in each plot show overlap between in vivo FDG-PET and MRI patterns (naming convention: \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eFDG-PET pattern_MRI pattern\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e). Non-AD pathologies assessed postmortem per individual are shown by stacked bars. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(A) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eAll cases; \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003ecases with no/low ADNC; \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003ecases with intermediate/high ADNC, i.e., AD pathologies (Aβ and neurofibrillary tangles) were present in all cases. Global burden for each non-AD pathology was defined as follows: moderate-to-severe cerebral amyloid angiopathy or arteriolosclerosis; TDP-43 involvement in one or more limbic regions (amygdala, hippocampus, and entorhinal or inferior temporal cortex); Lewy bodies in one or more of brainstem, limbic regions, neocortex, amygdala, or olfactory bulb; and hippocampal sclerosis affecting one or both hemispheres. Abbreviations: CP = Cortical Predominant; LP = Limbic Predominant; Diff = Diffuse; ADNC = Alzheimer’s disease neuropathologic change.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8910160/v1/175fe6c73006798224f72253.png"},{"id":102998560,"identity":"7957cef4-39e7-45c2-a56d-ddf73ac741ab","added_by":"auto","created_at":"2026-02-19 12:44:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":261238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLinear partial correlations of measures of typicality and severity with regional neuropathology.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdjustment for severity was performed for measures of typicality and vice versa as well as age at scan and additionally field strength for MRI measures. Rho correlation coefficients for typicality are presented in blue (limbic predominant pattern) and red (cortical-predominant pattern) and for severity in blue (low, less neurodegeneration) and red (high, more neurodegeneration). The pathologies with significant correlations, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001 were plotted with the rho coefficients. Abbreviations: AMYG = amygdala; Abeta (CP) = amyloid-beta (core plaques); CA = hippocampus at the level of lateral geniculate nucleus including cornu Ammonis 1 subfield; DG = hippocampus at the level of lateral geniculate nucleus including dentate gyrus; ENTX = entorhinal cortex; IPL = inferior parietal lobe (angular gyrus); MFG = middle frontal gyrus; PHG = hippocampus at the level of lateral geniculate nucleus including parahippocampal gyrus; STG = superior and middle temporal gyri; Tau = phosphorylated tau assessing neurofibrillary tangles; TDP-43 = phosphorylated TAR DNA-binding protein 43 neuronal cytoplasmic inclusion; α-syn = alpha-synuclein Lewy body pathology.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8910160/v1/09869f6f4b040bcdcdab5db3.png"},{"id":106405332,"identity":"24176925-56e9-4d19-89ac-b5e4ce9a5396","added_by":"auto","created_at":"2026-04-08 09:25:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6899547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8910160/v1/ba32d67a-61a3-45e7-a2bb-2e145bfa7238.pdf"},{"id":103049876,"identity":"14a3c16f-e8d1-4bea-8d6d-19c89fa885ca","added_by":"auto","created_at":"2026-02-20 07:47:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":312154,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material\u003c/p\u003e","description":"","filename":"NeuropathFDGMRISubtypessupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8910160/v1/f15be2f4c493398577b1dff2.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDistinct associations of corticolimbic hypometabolism and atrophy patterns correlate with postmortem neuropathologies in cognitively impaired individuals\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer's disease (AD) is characterized by the presence of amyloid-beta plaques and neurofibrillary tangles (NFTs) in the brain. The regional load of these pathologies can vary\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e from the common staging schemes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Amyloid-beta plaques and NFTs often lead to neuronal death or neurodegeneration, downstream markers which are not specific to AD\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo capture neurodegeneration \u003cem\u003ein vivo\u003c/em\u003e, the latest diagnostic framework for AD includes unspecific neuroimaging markers such as reduced glucose metabolism from [\u003csup\u003e18\u003c/sup\u003eF] fluorodeoxyglucose-PET (FDG-PET) and atrophy from magnetic resonance imaging (MRI) along with fluid-based biomarkers\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Neuroimaging markers in AD typically show medial-temporal atrophy\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and temporo-parietal hypometabolism\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, different neurodegeneration patterns have also been observed namely, Limbic Predominant, Cortical Predominant and Diffuse (including Typical AD)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Pattern-specific demographic and clinical differences highlight the need for a move towards precision medicine\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIncreasing evidence shows that the AD hallmarks, amyloid-beta plaques and NFTs, rarely present in isolation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Concomitant pathologies include transactive response DNA binding protein of 43 kDa (TDP-43)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, hippocampal sclerosis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, arteriolosclerosis\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, cerebral amyloid angiopathy (CAA) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003eand alpha-synuclein\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The number of concomitant pathologies increases with age\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and can contribute to the neurodegenerative processes in the brain\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDistinct neuroimaging patterns have also been linked to these pathologies. Regional glucose metabolism patterns from FDG-PET have been shown to correlate with TDP-43\u003csup\u003e19,20\u003c/sup\u003e, hippocampal sclerosis\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and aggregates composed from alpha-synuclein, Lewy bodies\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. MRI has also shown to be useful in distinguishing between postmortem-confirmed AD and other neurodegenerative diseases such as frontotemporal lobe dementia\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and hippocampal sclerosis\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA multimodal neuroimaging study showed that FDG-PET and MRI patterns in cognitively impaired individuals are not interchangeable at the individual-level\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Although, tau accumulation has been proposed to precede neurodegeneration\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, differences between both load and regional distribution have also been observed between tau-PET and MRI\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Importantly, even among measures of neurodegeneration, differences persist. Reasons for these differences between FDG-PET and MRI AD patterns remain unclear, and whether other pathologies could partly explain the differences have not been fully investigated.\u003c/p\u003e \u003cp\u003eTo address this gap, we investigate whether the differences in topography of \u003cem\u003ein vivo\u003c/em\u003e FDG-PET and MRI neurodegeneration patterns are linked to postmortem neuropathology in individuals with cognitive impairment. The hypothesis is that distinct combinations of AD and non-AD pathologies explain these differences \u0026ndash; specifically that cortical neurodegeneration patterns are associated with alpha-synuclein and/or cerebrovascular pathologies, whereas limbic neurodegeneration patterns are linked to TDP-43 pathology and/or hippocampal sclerosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eIn this study a total of 245 individuals were included, 69 individuals with cognitive impairment from the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) with both \u003cem\u003ein vivo\u003c/em\u003e neuroimaging (FDG-PET and MRI) and postmortem neuropathologic assessment. Further, 176 Aβ-negative cognitively unimpaired individuals with \u003cem\u003ein vivo\u003c/em\u003e neuroimaging (FDG-PET and MRI) were included as a reference group. Cognitively impaired individuals in ADNI were either classed as mild cognitively impaired (\u0026lsquo;MCI\u0026rsquo;) or \u0026lsquo;AD\u0026rsquo;, based on clinical assessment irrespective of biomarker status. ADNI inclusion criteria were: memory complaints, MMSE scores 24\u0026ndash;30 (MCI) or 20\u0026ndash;26 (AD), CDR global scores 0.5 (MCI) or 0.5\u0026ndash;1.0 (AD), and for AD participants only, NINCDS/ADRDA criteria for probable AD\u003csup\u003e27\u003c/sup\u003e. For determining Aβ positivity in the cognitively impaired group, florbetapir amyloid PET \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e was used with cutoff 1.11 standardised uptake ratio and if not available, cerebrospinal fluid Aβ 1\u0026ndash;42 with cutoff\u0026thinsp;\u0026le;\u0026thinsp;880 pg/ml was used\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. ADNI is a longitudinal multi-centre study aimed at collecting neuroimaging, genetic, clinical, psychological and neuropathologic measures. ADNI was launched in 2003 as a public-private partnership, led by principal investigator Michael W. Weiner, MD. The detailed inclusion and exclusion criteria for the participants can be found on the ADNI website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://adni.loni.usc.edu/help-faqs/adni-documentation/\u003c/span\u003e\u003cspan address=\"https://adni.loni.usc.edu/help-faqs/adni-documentation/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAntemortem Neuroimaging Biomarkers\u003c/h3\u003e\n\u003cp\u003eWe included the antemortem FDG-PET and MRI scans closest to postmortem evaluation. The mean interval between FDG-PET and MRI was 48 days. Mean interval between FDG-PET scan and death was 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 years and mean interval between MRI scan and death was 2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 years.\u003c/p\u003e \u003cp\u003eT1-weighted magnetisation-prepared rapid gradient-echo (MPRAGE) MRI scans were collected from various sites using multiple scanners (both 1.5 and 3T scanners), following the appropriate protocol (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://adni.loni.usc.edu/help-faqs/adni-documentation/\u003c/span\u003e\u003cspan address=\"https://adni.loni.usc.edu/help-faqs/adni-documentation/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Scans were pre-processed in-house using FreeSurfer (version 6.0.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://freesurfer.net\u003c/span\u003e\u003cspan address=\"https://freesurfer.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) through theHiveDB database\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Briefly, the preprocessing pipeline involved removal of artefacts, transformation to Talairach space and segmentation of cortical and subcortical regions from the Desikan-Killiany and Fischl atlases\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Cortical and subcortical grey matter volumes were extracted using automated segmentation labelling respectively. Grey matter volumes were adjusted for head size using the estimated total intracranial volumes from FreeSurfer\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFDG-PET from multiple scanners following the appropriate protocols were acquired (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://adni.loni.usc.edu/help-faqs/adni-documentation/\u003c/span\u003e\u003cspan address=\"https://adni.loni.usc.edu/help-faqs/adni-documentation/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). FDG-PET scans co-registered to the last MRI scan and regional standardised uptake ratios (SUVRs) were extracted using PETSurfer\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. SUVRs were extracted without partial volume effect (PVE) correction and scaled to the pons. The pons was chosen because it has been deemed optimal compared to other reference regions across ageing and neurodegeneration studies using FDG-PET\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003ePost-hoc Neuroimaging-based Measures\u003c/h3\u003e\n\u003cp\u003eBased on prior studies assessing non-AD pathologies, two surrogate measures were calculated, namely: cingulate island sign (FreeSurfer regions: posterior cingulate, precuneus, cuneus) and inferior temporal/medial temporal lobe ratio (IMT ratio based on FreeSurfer regions including inferiortemporal, middletemporal, hippocampus, amygdala). These two measures use regional FDG-PET; higher cingulate island sign indicates higher likelihood of Dementia with Lewy bodies\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and a higher IMT ratio is related with hippocampal sclerosis\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eAntemortem Fluid Biomarkers\u003c/h3\u003e\n\u003cp\u003eCerebrospinal fluid (CSF) biomarkers taken at the last assessment closest to the imaging data included Aβ 1\u0026ndash;42, total tau (t-tau) and phosphorylated tau (p-tau). The cutoff of 26.64 pg/ml in CSF p-tau was used for determining tau positivity\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eNeuropathologic Variables\u003c/h3\u003e\n\u003cp\u003eWe included postmortem neuropathologic assessment provided by the ADNI (Version 11: 12th April 2018 \u0026amp; 14th November 2022). Neuropathological data included pathological counts in all individuals (n\u0026thinsp;=\u0026thinsp;69), and regional neuropathologic assessment in a subsample (n\u0026thinsp;=\u0026thinsp;43). All participants consented to participate in the ADNI study according to Good Clinical Practice guidelines and the Declaration of Helsinki. Additional consent for postmortem brain collection and neuropathologic assessment was attained by a trained physician at the ADNI site\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the global level, AD pathology was assessed in terms of the amyloid plaque (staged A0-A3), tau tangle scores (staged B0-B3) and composite Alzheimer\u0026rsquo;s Disease Neuropathologic Change scores (not AD, low, intermediate, high) as per the National Institute on Aging\u0026ndash;Alzheimer\u0026rsquo;s Association guidelines for the neuropathologic assessment\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Non-AD pathologies assessed in the current study were alpha-synucleinopathy (presence of Lewy body pathology in brainstem, limbic region, neocortex, amygdala and olfactory bulb\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e), arteriolosclerosis (moderate-severe), cerebral amyloid angiopathy (CAA) (moderate-severe), limbic TDP-43 (presence in amygdala, hippocampus, entorhinal/inferior temporal cortex)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, overall TDP-43 pathology (presence in limbic regions and neocortex), and hippocampal sclerosis (presence in one or both hemispheres). At the regional level, we separately examined the same neuropathologic variables as above along with neuronal loss or gliosis using semi quantitative scores and/or regional counts in mediotemporal regions including amygdala, hippocampal subfields (CA1, dentate gyrus, parahippocampal gyrus), entorhinal cortex, as well as neocortical regions including inferior parietal lobe, superior temporal gyrus, and middle frontal gyrus.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePattern Identification and Characterization\u003c/h2\u003e \u003cp\u003eFollowing prior work on neuropathologically defined imaging patterns, we applied similar measures using neuroimaging data\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Using a \u003cem\u003ecategorical approach\u003c/em\u003e, cognitively impaired individuals were classified into distinct patterns based on the hippocampus-to-cortex ratio per modality\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e (\u003cem\u003eSuppl. Figure\u0026nbsp;1\u003c/em\u003e). Three patterns were labelled as \u0026lsquo;Limbic Predominant\u0026rsquo;, \u0026lsquo;Cortical Predominant\u0026rsquo; and \u0026lsquo;Diffuse\u0026rsquo;. We chose to use the term \u0026lsquo;Cortical Predominant\u0026rsquo; as it has been commonly used in the FDG-PET subtyping\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, and parallels the term \u0026lsquo;Hippocampal Sparing\u0026rsquo; in the neuropathologically-defined\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and MRI-based subtyping\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Additionally, using a \u003cem\u003econtinuous\u003c/em\u003e approach, we assessed corticolimbic neurodegenerative patterns along the two previously proposed axes of \u0026lsquo;typicality\u0026rsquo; and \u0026lsquo;severity\u0026rsquo;\u003csup\u003e8\u003c/sup\u003e. Typicality was measured by hippocampus-to-cortex ratio in each modality. Severity was measured by average cortical SUVR in FDG-PET; or total grey matter volume adjusted for estimated intracranial volume in MRI.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eFirstly, to assess differences in our \u003cem\u003ein vivo\u003c/em\u003e and postmortem variables between our cognitively impaired and cognitively unimpaired groups, we ran Mann-Whitney U and chi-square testing with multiple comparison adjustments using Benjamini-Hochberg false discovery rate (BH-FDR). To visualize the topographical patterns of regional neurodegeneration (hypometabolism in FDG-PET and atrophy in MRI), w-scores adjusted for age at scan, sex, education and \u003cem\u003eAPOE\u003c/em\u003e-ε4 allele carriership relative to the cognitively unimpaired reference group were calculated. These values were averaged across the cognitively impaired individuals in each pattern and reversed so that higher w-scores correspond to greater neurodegeneration. Brain maps were created using \u003cem\u003epython-ggseg\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. We assessed differences in \u003cem\u003ein vivo\u003c/em\u003e and postmortem variables using Kruskal-Wallis and chi-square tests adjusted for multiple comparisons. BH-FDR correction was applied within variable groups (demographic, genetic, cognitive, CSF biomarker, imaging and neuropathology measures).\u003c/p\u003e \u003cp\u003eIn the categorical approach of pattern identification, the percentage overlap between the FDG-PET and MRI patterns was visualized in a confusion matrix, which quantifies agreement between patterns. To assess whether specific pathologies may explain differences between FDG-PET and MRI patterns, we compared patterns pairwise by frequency of non-AD pathologies at an individual-level for the whole sample and stratified by AD Neuropathologic Change status.\u003c/p\u003e \u003cp\u003eIn the continuous approach of pattern identification, we examined whether region-specific pathologies may better explain differences between FDG-PET and MRI patterns based on Spearman\u0026rsquo;s partial correlations between typicality/severity and neuropathologic variables in mediotemporal and neocortical regions. Models were adjusted for age at scan, the complementary axis, i.e., severity for typicality models and vice versa, and additionally field strength for MRI. Significance values were uncorrected due to the use of small sample size and use of postmortem data (gold standard) which strengthen the results found\u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. For easier interpretation of the results, the severity values were reversed in the partial correlation heatmaps, so that positive values indicate higher severity, i.e. more neurodegeneration. All analyses were performed using Python (version 3.11.5).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDemographic, clinical, and postmortem variables of the groups used in this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The cognitively impaired cohort (median age at scan 79 years FDG PET and 80 years for MRI) was older than the cognitively unimpaired reference group (74 years, age at scan). Most of the individuals were males (78%), more than half were \u003cem\u003eAPOE-\u003c/em\u003eɛ4 carriers (59%), had median Mini-Mental State Examination (MMSE) scores of 21, in vivo Aβ+ (64%) and had high/intermediate AD Neuropathologic Change (82%). In the cognitively unimpaired reference group (\u003cem\u003ein vivo\u003c/em\u003e only cohort), half were male (52%), few were \u003cem\u003eAPOE-\u003c/em\u003eɛ4 carriers (19%), all were Aβ- and had median MMSE score of 29.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDemographic, clinical, and postmortem characteristics of the cohorts used in this study.\u003c/b\u003e Values are described either as median with standard deviations or number or percentage. The reference cohort was Aβ- cognitively unimpaired individuals without neuropathological data. All p-values are adjusted for multiple comparisons using Benjamini-Hochberg false discovery rate. Abbreviations: ADNC: Alzheimer\u0026rsquo;s Disease Neuropathologic Change, CDR: Clinical Dementia Rating, IMT ratio: inferior medial temporal ratio, MCI: Mild cognitive impairment, MMSE: Mini-Mental State examination, TDP-43: transactive response DNA binding protein 43 kDa\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCognitively Impaired\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCognitively Unimpaired\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(in vivo only)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAdjusted p-values\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at death (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at FDG-PET scan (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at MRI scan (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eField Strength (% 3T scans)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPOE ε4 carriership (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal CDR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePET or CSF Aβ+ (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbeta 1\u0026ndash;42 (pg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e593.6 (343.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1204 (333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSF t-tau (pg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e303.9 (152.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSF p-tau (pg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.2 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.8 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep-tau positive (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIMT ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCingulate Island Sign ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTDP-43 positive (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eADNC: Not AD/Low, Intermediate/High\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (18%), 56 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBrain weight (grams)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1211.5 (131.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFDG-PET and MRI patterns of neurodegeneration\u003c/h2\u003e \u003cp\u003eBrain maps were generated to visualize patterns of neurodegeneration (w-scores adjusted for age at scan, sex, education and APOE-ε4 allele carriership relative to cognitively unimpaired reference group) using FDG-PET and MRI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, each imaging modality showed the expected regional pattern of neurodegeneration: FDG-PET revealed temporo-parietal hypometabolism, while MRI demonstrated medial temporal atrophy. The overlap between the FDG-PET and MRI patterns from the categorical approach is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the Limbic Predominant pattern, both modalities showed greater neurodegeneration in deeper structures (such as the hippocampus and amygdala) with relative preservation of posterior cortical regions, including the cingulate.\u003c/p\u003e \u003cp\u003eThe Diffuse pattern showed widespread hypometabolism, most pronounced in the posterior cingulate \u0026mdash; an area commonly affected in AD. Diffuse atrophy followed a similarly distribution but with focal atrophy in the temporal lobe, hippocampus, and amygdala.\u003c/p\u003e \u003cp\u003eThe Cortical Predominant patterns reflected the greatest degree of cortical neurodegeneration. Cortical Predominant hypometabolism showed little to no involvement of deeper structures, whereas the corresponding atrophy pattern still exhibited relatively high levels of atrophy in these regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFDG-PET and MRI pattern characteristics\u003c/h2\u003e \u003cp\u003eDifferences in demographic and biological data in the FDG-PET and MRI patterns can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Due to the small sample size and irregular group sizes due to the nature of the group categorisation, only trends can be determined from these results. Limbic Predominant patterns showed the highest age at death. Limbic Predominant hypometabolism pattern was significantly older at FDG-PET scan. APOE ε4 carriership was the least frequent in Cortical Predominant patterns. Cortical Predominant patterns also had the lowest MMSE and highest Clinical Dementia Rating scores. CSF t-tau was lower in Limbic Predominant, higher in Cortical Predominant atrophy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDemographic, clinical, and postmortem variables of the FDG-PET and MRI-based patterns.\u003c/b\u003e Pattern identification methods in each modality were performed on the same sample (n\u0026thinsp;=\u0026thinsp;69). Variables are presented as median values with standard deviation in brackets, except for number, sex, tau positivity and pathologies which are stated with number of cases and percentage. All p-values are adjusted for multiple comparisons using Benjamini-Hochberg false discovery rate. Abbreviations: APOE: apolipoprotein E gene, CDR: Clinical Dementia Rating Scale; GMV: grey matter volume; ICV: intracranial volume; IMT ratio: inferior medial temporal ratio; MMSE: Mini Mental State examination; SUVR: standard uptake value ratio; TDP-43: transactive response DNA binding protein 43 kDa.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFDG-PET Patterns\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eMRI Patterns\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLimbic Predominant Hypometabolism\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;9 (13%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDiffuse Hypometabolism\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;51 (74%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCortical Predominant Hypometabolism\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;9 (13%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAdjusted p-values\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eLimbic Predominant Atrophy\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;8 (12%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eDiffuse\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAtrophy\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;54 (78%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCortical Predominant Atrophy\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;7 (10%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eAdjusted p-values\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at death (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.0 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.0 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.0 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84.5 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.0 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84.0 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at FDG-PET scan (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.0 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.0 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.0 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.5 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.5 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.0 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at MRI scan (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.0 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.0 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.0 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.0 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.0 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.0 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPOE ε4 carriership (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.5 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.0 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.0 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.0 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.0 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.0 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.0 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal CDR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.0 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbeta 1\u0026ndash;42 (pg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e519.7 (391.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e615.5 (352.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e470.9 (232.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e503.1 (463.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e617.7 (345.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e491.9 (149.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSF p-tau (pg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.3 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.3 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.7 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.2 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.6 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSF t-tau (pg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283.6 (156.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320.4 (154.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300.4 (166.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e270.9 (125.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e303.9 (159.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e424.6 (147.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSF of PET abeta positive (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSF p-tau positive (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFDG-PET Hippocampus-to-cortex ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMRI Hippocampus-to-cortex ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFDG-PET Average Total Cortical SUVR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMRI Total Gray Matter Volume, ICV adjusted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e523484.2 (42836.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e533837.1 (52513.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e502197.0 (53689.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e550845.2 (15637.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e528827.0 (54417.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e502197.0 (37882.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFDG-PET IMT ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFDG-PET Cingulate Island Sign ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntermediate or high ADNC (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArteriolosclerosis (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCerebral amyloid angiopathy (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLewy bodies (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHippocampal Sclerosis (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTDP-43 positive (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBrain weight (grams)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1278.5 (102.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1191.0 (134.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1170.0 (136.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1275.0 (100.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1200.0 (136.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1150.0 (99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the \u003cem\u003ein-vivo\u003c/em\u003e neuroimaging measures, hippocampus-to-cortex ratios were significantly greatest in Cortical Predominant and lowest in Limbic Predominant patterns. However, this is expected as the pattern identification was applied using these measures. Cortical Predominant patterns had the lowest total cortical SUVR and total gray matter volumes; Limbic Predominant had the highest values.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIn vivo\u003c/em\u003e FDG-PET IMT ratios were significantly highest in Limbic Predominant hypometabolism and lowest in Cortical Predominant hypometabolism pattern. All five hippocampal sclerosis cases occurred in Limbic Predominant or Diffuse patterns. Brain weight was lower in Cortical Predominant and higher in Limbic Predominant patterns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGlobal neuropathological differences between FDG-PET and MRI patterns\u003c/h2\u003e \u003cp\u003eWe examined the differences between \u003cem\u003ein vivo\u003c/em\u003e FDG-PET and MRI patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in relation to individual-level postmortem neuropathologic findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Beyond AD, we focused on three groups of pathologies: cerebrovascular disease (CAA, arteriolosclerosis), alpha-synucleinopathy (Lewy bodies), and limbic pathologies (TDP-43, hippocampal sclerosis) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and stratified by AD Neuropathologic Change status (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFDG-PET and MRI patterns were different at the individual-level only in cases with intermediate/high AD Neuropathologic Change, i.e., confirmed AD neuropathology (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), suggesting that non-AD pathologies may differentially modify AD-related hypometabolism and atrophy. Cerebrovascular pathologies were relatively more frequent in Cortical Predominant patterns (\u003cem\u003eCP\u003c/em\u003e in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC: \u003cem\u003eDiff_CP\u003c/em\u003e and \u003cem\u003eCP_Diff\u003c/em\u003e), whereas limbic pathologies (hippocampal sclerosis, TDP-43) were more frequent in Limbic Predominant patterns (\u003cem\u003eLP\u003c/em\u003e in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC: \u003cem\u003eLP_Diff\u003c/em\u003e). Diffuse patterns were more likely to have three or more pathologies. Alpha-synuclein pathology were frequent across all patterns irrespective of the AD Neuropathologic Change status.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRegional neuropathological differences between FDG-PET and MRI patterns\u003c/h2\u003e \u003cp\u003eBuilding on the group-level trends described above, we next evaluated FDG-PET and MRI patterns in relation to the continuous measures of \u003cb\u003etypicality\u003c/b\u003e and \u003cb\u003eseverity\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and investigated their associations with regional neuropathologies. Typicality characterizes the relative predominance of limbic versus cortical involvement, whereas severity indexes the extent of neurodegeneration. Across modalities, typicality and severity demonstrated distinct correlations with mediotemporal and neocortical pathologies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Hippocampal sclerosis was excluded from analysis due to its occurrence in only five cases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAssociations with typicality: Limbic predominant atrophy, but not hypometabolism, was associated with elevated amyloid, tau, and arteriolosclerosis in neocortical regions. Conversely, Limbic Predominant hypometabolism, but not atrophy, was associated with increased tau and arteriolosclerosis within mediotemporal regions. Limbic Predominant atrophy exhibited a stronger association with mediotemporal neuronal loss compared with hypometabolism, whereas Limbic Predominant hypometabolism additionally corresponded to neocortical neuronal loss. In both FDG-PET and MRI, cortical predominance was associated with increased neocortical alpha-synuclein pathology, whereas limbic predominance was associated with elevated TDP-43 pathology.\u003c/p\u003e \u003cp\u003eAssociations with severity: Greater atrophy, but not hypometabolism, was associated with increased neocortical tau burden. In contrast, greater hypometabolism, but not atrophy, was associated with heightened neocortical arteriolosclerosis. Atrophy showed a stronger association with neocortical neuronal loss than hypometabolism, while both measures demonstrated comparable associations with mediotemporal neuronal loss. In neither FDG-PET nor MRI did overall neurodegeneration severity correlate with amyloid, alpha-synuclein, or TDP-43 pathology.\u003c/p\u003e \u003cp\u003eSignificant values were not corrected for multiple testing, therefore should be interpreted as exploratory findings. All correlations (significant and non-significant) for the regional pathologies used in this study can be found in the Supplementary Material (\u003cem\u003eSuppl. Figure\u0026nbsp;2\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHeterogeneity in neurodegeneration in terms of corticolimbic patterns is captured well by both FDG-PET and MRI. However, factors underlying the differences between FDG and MRI patterns remain partly unresolved. In this study, we demonstrate that a differential involvement of core AD and non-AD pathologies partly account for these discrepancies. Specifically, differences between patterns reflects interactions between AD pathology and cerebrovascular, limbic and alpha-synuclein pathologies. Furthermore, FDG-PET and MRI patterns show selective vulnerability to various pathologies and neuronal loss in a region-specific manner (mediotemporal versus neocortical).\u003c/p\u003e \u003cp\u003eTopography of FDG-PET and MRI patterns showed neurodegeneration and clinical characteristics consistent with previous subtyping studies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. However, FDG-PET and MRI patterns did not align at an individual-level, a difference consistent with prior studies comparing FDG-PET and MRI patterns\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e as well as tau PET and MRI patterns\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. A key finding of our study is that differences between FDG-PET and MRI patterns were observed in relation to non-AD pathologies, but only in individuals with intermediate or high AD neuropathologic change (i.e., confirmed AD neuropathology). This finding suggests that interaction of core AD and non-AD pathologies may result in differential expressions of downstream functional (hypometabolism) and structural (atrophy) patterns. While differences between hypometabolism and atrophy in AD has been shown to vary by alpha-synuclein status\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, our study expands further by additionally accounting for cerebrovascular and limbic pathologies in different neurodegenerative patterns. In line with the revised framework for AD\u003csup\u003e5\u003c/sup\u003e, our findings support consideration of multiple proteinopathies in disentangling the manifestation of neurodegenerative patterns.\u003c/p\u003e \u003cp\u003eIn line with our hypotheses, cerebrovascular pathology (CAA, arteriolosclerosis) was relatively frequent in Cortical Predominant patterns and limbic pathology (hippocampal sclerosis, TDP-43) was relatively frequent in Limbic Predominant and Diffuse patterns. CAA contributes to cortical hypometabolism and cortical atrophy independently and in concomitance with AD pathology\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR57 CR58 CR59\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. In addition, both TDP-43 pathology and hippocampal sclerosis selectively affect limbic areas, so neurodegeneration in these regions is plausible\u003csup\u003e\u003cspan additionalcitationids=\"CR62 CR63 CR64\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. An unexpected yet interesting observation was that one individual showed limbic predominance in FDG-PET and cortical predominance in MRI \u0026ndash; presence of both limbic (TDP-43) and cerebrovascular (CAA) pathologies as well as AD pathology could explain such a manifestation. Regarding the diffuse pattern, over 50% of the cases with Diffuse pattern showed AD biomarker positivity in vivo which reached to over 80% with confirmed AD pathology postmortem. We further observed that diffuse neurodegeneration showed combinations of all of AD, cerebrovascular, limbic and alpha-synuclein pathologies postmortem. While concomitance of pathologies has been linked to widespread neurodegeneration in this pattern in MRI\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and our study extends a similar association to FDG-PET.\u003c/p\u003e \u003cp\u003eFDG-PET and MRI patterns based on typicality captured regional differences in amyloid-beta, tau, alpha-synuclein, cerebrovascular, limbic pathologies and neuronal loss whereas severity mainly captured differences in tau, cerebrovascular pathology and neuronal loss. This result shows that neurodegeneration-based typicality captures phenotypic variations based on AD and non-AD pathologies and neuronal loss at a regional level.\u003c/p\u003e \u003cp\u003eBased on typicality, FDG-PET and MRI patterns showed some shared findings in relation to mediotemporal and neocortical pathologies. Cortical predominance in both modalities was related to increased cortical alpha-synuclein. Cortical Predominant hypometabolism and atrophy is a common pattern in dementia with Lewy bodies\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, a synucleinopathy, both with and without AD pathology\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Our Cortical Predominant patterns showed neurodegeneration in frontal and posterior brain regions, consistent with the fronto-occipital pattern described in dementia with Lewy bodies with highest frequency of AD pathology\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Limbic predominance in both modalities was related to increased mediotemporal TDP-43, which is expected and consistent with prior reports in cases with concomitant AD and TDP-43\u003csup\u003e62,70\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, FDG-PET and MRI patterns also showed associations in relation to AD and cerebrovascular pathologies in the mediotemporal versus neocortical regions. Limbic Predominant atrophy was related to neocortical amyloid, tau, and arteriolosclerosis while hypometabolism was related to mediotemporal tau and arteriolosclerosis, demonstrating that similar patterns in FDG-PET and MRI can have different pathological correlates. This finding may indicate that limbic atrophy is linked to more distributed pathological processes, while limbic hypometabolism is linked to regionally proximal pathological processes. Both limbic hypometabolism and atrophy reflected mediotemporal neuronal loss while limbic hypometabolism additionally reflected more distant cortical neuronal loss, supporting the notion that hypometabolism exceeds atrophy in this pattern. Regional differences between FDG-PET and MRI have been shown to vary by the underlying pathology (AD, alpha-synuclein, TDP-43, etc.)\u003csup\u003e71\u003c/sup\u003e. Whether such differences occur in patterns has not been reported before. Taken together, our findings support that the relationship between hypometabolism and atrophy depends not only on regional pathologies but also on the pattern.\u003c/p\u003e \u003cp\u003eBased on severity, both global hypometabolism and atrophy correlated with mediotemporal and neocortical neuronal loss, which indicates that the diffuse pattern corresponds to greater neurodegeneration in vivo and postmortem, in alignment with prior literature \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Our current study additionally demonstrates that they differ in their pathological correlates. Specifically, greater overall hypometabolism correlated with temporal and frontal arteriolosclerosis, regions which have been implicated in vascular dementia\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Given that hypometabolism also correlated with temporal neuronal loss, it is possible that cerebrovascular pathology may partly explain neurodegeneration captured by FDG-PET. In contrast, greater atrophy has stronger association with frontal tau and neuronal loss, corresponding to the advanced Braak stages of tau pathology\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These findings may suggest that severity may better capture AD-unspecific (cerebrovascular) neuronal loss in FDG-PET and AD-specific (tau) neuronal loss in MRI.\u003c/p\u003e \u003cp\u003eWe acknowledge the limitations of this study. The sample size was modest, especially within the subsample with regional neuropathology but expected for a study including postmortem data. In addition, the sample was even smaller when looking at individuals with regional pathology data, so the results need to be replicated in a larger sample. Postmortem pathologies were evaluated for positivity or presence, which may not fully capture the different stages of each pathology. Future studies should consider incorporating insights from longitudinal neuroimaging to assess whether the pathologic correlates observed \u003cem\u003ein vivo\u003c/em\u003e can be detected even earlier.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides a head-to-head comparison of \u003cem\u003ein vivo\u003c/em\u003e FDG-PET and MRI patterns in individuals with cognitive impairment in relation to postmortem AD and non-AD pathologies. Despite capturing corticolimbic neurodegeneration, individual-level differences were observed between FDG-PET and MRI patterns only in presence of AD pathology and explained by cerebrovascular, limbic, alpha-synuclein pathologies postmortem. As hypothesised, cortical neurodegeneration patterns were correlated with alpha-synuclein pathology and limbic neurodegeneration patterns with hippocampal sclerosis and TDP-43 pathologies. Regional pathologies suggest that FDG-PET and MRI patterns may have comparable associations with alpha-synuclein and limbic pathologies but differential associations with AD (amyloid, tau) and cerebrovascular pathologies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosures:\u0026nbsp;\u003c/strong\u003eDF consults for BioArctic and has received honoraria from Esteve Pharmaceuticals S.A.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding/Support\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Swedish Research Council (VR) No. 2016-02282, 2021-01861, 2025-02405; the Center for Innovative Medicine (CIMED) No. FoUI-954459, FoUI-975174, FoUI-987392; the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet No. FoUI-952838, FoUI-954893; The Swedish Brain Foundation (Hj\u0026auml;rnfonden) No. FO2022-0084, FO2024-0239; The Swedish Alzheimer\u0026apos;s Foundation (Alzheimerfonden) No. AF-967495, AF-980387, AF-1031812; The Swedish Parkinson\u0026apos;s foundation (Parkinsonfonden) No. 1647/25, 1557/24, 1521/23;\u0026nbsp;\u003cstrong\u003eVINNOVA\u0026nbsp;\u003c/strong\u003e2025-03749; Olle Engkvists Foundation (Olle Engkvists Stiftelse) No. 186-0660, 224-0069; EU Innovative Health Initiative Joint Undertaking (IHI JU) AD-RIDDLE and ACCESS-AD; King Gustaf V:s and Queen Victorias Foundation; The Swedish Dementia Foundation (Demensfonden); The Strategic Research Programme in Neuroscience at Karolinska Institutet (StratNeuro); The Swedish Society for Medical Research (SSMF) PD21-0042; the \u0026Aring;ke Wiberg Foundation; Neurofonden; Karolinska Institutet Research Grants (Foundation for Geriatric Diseases at Karolinska Institutet, Loo and Hans Osterman Foundation for Medical Research); The Lars Hierta Memorial Foundation; Gun and Bertil Stohne\u0026rsquo;s Foundation; The Foundation for Old Maids\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eas well as Birgitta and Sten Westerberg for additional financial support. DF receives funding from the Swedish Research Council (Vetenskapsr\u0026aring;det, grants 2022-00916 and 2025-02984), the Center for Innovative Medicine (CIMED, grants 20200505 and FoUI-988826), the regional agreement on medical training and clinical research of Stockholm Region (ALF Medicine, grants FoUI-962240, FoUI-987534, and FoUI-1023640), the Swedish Brain Foundation (Hj\u0026auml;rnfonden FO2021-0131, FO2022-0175, FO2023-0261, and FO2025-0214), the Swedish Alzheimer Foundation (Alzheimerfonden AF-968032, AF-980580, AF-994058, AF-1010553, and AF-1031740), the Swedish Dementia Foundation (Demensfonden), the Gamla Tj\u0026auml;narinnor Foundation, the Gun och Bertil Stohnes Foundation, the \u0026Aring;ke Wiberg Foundation, the Strategic Research Programme in Neuroscience at Karolinska Institutet (StratNeuro) Bridging Grant, the Swedish Parkinson Foundation (Parkinsonfonden), the Hans-Gabriel and Alice Trolle-Wachtmeisters Foundation, the Greta and Johan Kocks Foundation, Funding for Research from Karolinska Institutet, Neurofonden, and the Foundation for Geriatric Diseases at Karolinska Institutet, contributions from private bequests and academic agreements with industry. The funding sources did not have any involvement in the study design, collection, analysis, and interpretation of data, writing of the report, and the decision to submit the article for publication\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection and sharing for this project was funded by the Alzheimer\u0026apos;s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer\u0026rsquo;s Association; Alzheimer\u0026rsquo;s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research \u0026amp; Development, LLC.; Johnson \u0026amp; Johnson Pharmaceutical Research \u0026amp; Development LLC.; Lumosity; Lundbeck; Merck \u0026amp; Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer\u0026rsquo;s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.\u0026nbsp;\u003c/p\u003e\n\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMurray ME, Graff-Radford NR, Ross OA, Petersen RC, Duara R, Dickson DW (2011) Neuropathologically defined subtypes of Alzheimer's disease with distinct clinical characteristics: a retrospective study. Lancet Neurol 10(9):785\u0026ndash;796\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82(4):239\u0026ndash;259\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThal DR, R\u0026uuml;b U, Orantes M, Braak H (2002) Phases of Aβ-deposition in the human brain and its relevance for the development of AD. Neurology 58(12):1791\u0026ndash;1800\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJack CR Jr, Bennett DA, Blennow K et al (2018) NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimer's Dement 14(4):535\u0026ndash;562\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJack CR Jr, Andrews JS, Beach TG et al (2024) Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimer's Dement 20(8):5143\u0026ndash;5169\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJack CR Jr, Petersen RC, Xu YC et al (1997) Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease. Neurology 49(3):786\u0026ndash;794\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinoshima S, Cross D, Thientunyakit T, Foster NL, Drzezga A (2022) 18F-FDG PET imaging in neurodegenerative dementing disorders: insights into subtype classification, emerging disease categories, and mixed dementia with copathologies. J Nucl Med 63(Supplement 1):2S\u0026ndash;12S\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira D, Nordberg A, Westman E (2020) Biological subtypes of Alzheimer disease: A systematic review and meta-analysis. Neurology Mar 10(10):436\u0026ndash;448. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/WNL.0000000000009058\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000009058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevin F, Ferreira D, Lange C et al (2021) Data-driven FDG-PET subtypes of Alzheimer\u0026rsquo;s disease-related neurodegeneration. Alzheimers Res Ther 13:1\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeTure MA, Dickson DW (2019) The neuropathological diagnosis of Alzheimer\u0026rsquo;s disease. Mol neurodegeneration 14(1):32\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovacs GG, Milenkovic I, W\u0026ouml;hrer A et al (2013) Non-Alzheimer neurodegenerative pathologies and their combinations are more frequent than commonly believed in the elderly brain: a community-based autopsy series. Acta Neuropathol 126:365\u0026ndash;384\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArvanitakis Z, Leurgans SE, Wang Z, Wilson RS, Bennett DA, Schneider JA (2011) Cerebral amyloid angiopathy pathology and cognitive domains in older persons. Ann Neurol 69(2):320\u0026ndash;327\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabinovici GD, Carrillo MC, Forman M et al (2017) Multiple comorbid neuropathologies in the setting of Alzheimer's disease neuropathology and implications for drug development. Alzheimer's dementia: translational Res Clin interventions 3(1):83\u0026ndash;91\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmador-Ortiz C, Lin WL, Ahmed Z et al (2007) TDP‐43 immunoreactivity in hippocampal sclerosis and Alzheimer's disease. Annals Neurology: Official J Am Neurol Association Child Neurol Soc 61(5):435\u0026ndash;445\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArvanitakis Z, Capuano AW, Leurgans SE, Bennett DA, Schneider JA (2016) Relation of cerebral vessel disease to Alzheimer's disease dementia and cognitive function in elderly people: a cross-sectional study. Lancet Neurol 15(9):934\u0026ndash;943\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAttems J, Jellinger KA (2014) The overlap between vascular disease and Alzheimer\u0026rsquo;s disease-lessons from pathology. BMC Med 12:1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyle PA, Yu L, Wilson RS, Leurgans SE, Schneider JA, Bennett DA (2018) Person-specific contribution of neuropathologies to cognitive loss in old age. Ann Neurol 83(1):74\u0026ndash;83\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton RL (2000) Lewy bodies in Alzheimer's disease: a neuropathological review of 145 cases using α-synuclein immunohistochemistry. Brain Pathol 10(3):378\u0026ndash;384\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeneses A, Koga S, O\u0026rsquo;Leary J, Dickson DW, Bu G, Zhao N (2021) TDP-43 pathology in Alzheimer\u0026rsquo;s disease. Mol neurodegeneration 16:1\u0026ndash;15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrothe MJ, Moscoso A, Silva-Rodr\u0026iacute;guez J et al (2023) Differential diagnosis of amnestic dementia patients based on an FDG‐PET signature of autopsy‐confirmed LATE‐NC. Alzheimer's Dement 19(4):1234\u0026ndash;1244\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBotha H, Mantyh WG, Murray ME et al (2018) FDG-PET in tau-negative amnestic dementia resembles that of autopsy-proven hippocampal sclerosis. Brain 141(4):1201\u0026ndash;1217\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva-Rodr\u0026iacute;guez J, Labrador-Espinosa MA, Moscoso A et al (2023) Differential effects of Tau Stage, Lewy Body Pathology, and substantia Nigra degeneration on 18F-FDG PET patterns in clinical Alzheimer disease. J Nucl Med 64(2):274\u0026ndash;280\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabinovici G, Seeley W, Kim E et al (2008) Distinct MRI atrophy patterns in autopsy-proven Alzheimer's disease and frontotemporal lobar degeneration. Am J Alzheimer's Disease Other Dementias\u0026reg; 22(6):474\u0026ndash;488\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNho K, Saykin AJ, Initiative ADN, Nelson PT (2016) Hippocampal sclerosis of aging, a common Alzheimer\u0026rsquo;s disease \u0026lsquo;mimic\u0026rsquo;: risk genotypes are associated with brain atrophy outside the temporal lobe. J Alzheimer\u0026rsquo;s Disease 52(1):373\u0026ndash;383\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWheatley SH, Mohanty R, Poulakis K et al (2024) Divergent neurodegenerative patterns: Comparison of [18F] fluorodeoxyglucose-PET-and MRI-based Alzheimer\u0026rsquo;s disease subtypes. Brain Commun 6(6):fcae426\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohanty R, Ferreira D, Nordberg A, Westman E, Initiative ADN (2023) Associations between different tau-PET patterns and longitudinal atrophy in the Alzheimer\u0026rsquo;s disease continuum: biological and methodological perspectives from disease heterogeneity. Alzheimers Res Ther 15(1):37\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984) Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 34(7):939\u0026ndash;939\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandau S, Thomas B, Thurfjell L et al (2014) Amyloid PET imaging in Alzheimer\u0026rsquo;s disease: a comparison of three radiotracers. Eur J Nucl Med Mol Imaging 41:1398\u0026ndash;1407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansson O, Seibyl J, Stomrud E et al (2018) CSF biomarkers of Alzheimer's disease concord with amyloid-β PET and predict clinical progression: a study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimer's Dement 14(11):1470\u0026ndash;1481\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuehlboeck J-S, Westman E, Simmons A (2014) TheHiveDB image data management and analysis framework. Front neuroinformatics 7:49\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreve DN, Salat DH, Bowen SL et al (2016) Different partial volume correction methods lead to different conclusions: an 18F-FDG-PET study of aging. NeuroImage 132:334\u0026ndash;343\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreve DN, Svarer C, Fisher PM et al (2014) Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data. NeuroImage 92:225\u0026ndash;236\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalahati F, Ferreira D, Soininen H et al (2016) The effect of age correction on multivariate classification in Alzheimer\u0026rsquo;s disease, with a focus on the characteristics of incorrectly and correctly classified subjects. Brain Topogr 29:296\u0026ndash;307\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoevodskaya O, Simmons A, Nordenskj\u0026ouml;ld R et al (2014) The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer's disease. Front Aging Neurosci 6:264\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNugent S, Croteau E, Potvin O et al (2020) Selection of the optimal intensity normalization region for FDG-PET studies of normal aging and Alzheimer\u0026rsquo;s disease. Sci Rep 10(1):9261\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKantarci K, Boeve BF, Przybelski SA et al (2021) FDG PET metabolic signatures distinguishing prodromal DLB and prodromal AD. NeuroImage: Clin 31:102754\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer P-F, Binette AP, Gonneaud J, Breitner JC, Villeneuve S, Investigators A (2020) Characterization of Alzheimer disease biomarker discrepancies using cerebrospinal fluid phosphorylated tau and AV1451 positron emission tomography. JAMA Neurol 77(4):508\u0026ndash;516\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBucci M, Chiotis K, Nordberg A, Initiative ADN (2021) Alzheimer\u0026rsquo;s disease profiled by fluid and imaging markers: tau PET best predicts cognitive decline. Mol Psychiatry 26(10):5888\u0026ndash;5898\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCairns NJ, Perrin RJ, Franklin EE et al (2015) Neuropathologic assessment of participants in two multi-center longitudinal observational studies: The A lzheimer D isease N euroimaging I nitiative (ADNI) and the D ominantly I nherited A lzheimer N etwork (DIAN). Neuropathology 35(4):390\u0026ndash;400\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontine TJ, Phelps CH, Beach TG et al (2012) National Institute on Aging\u0026ndash;Alzheimer\u0026rsquo;s Association guidelines for the neuropathologic assessment of Alzheimer\u0026rsquo;s disease: a practical approach. Acta Neuropathol 123:1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKeith IG, Dickson DW, Lowe J et al (2005) Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 65(12):1863\u0026ndash;1872\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRisacher SL, Anderson WH, Charil A et al (2017) Alzheimer disease brain atrophy subtypes are associated with cognition and rate of decline. Neurology 89(21):2176\u0026ndash;2186\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitwell JL, Dickson DW, Murray ME et al (2012) Neuroimaging correlates of pathologically defined subtypes of Alzheimer's disease: a case-control study. Lancet Neurol 11(10):868\u0026ndash;877\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevin F, Grothe MJ, Dyrba M, Franzmeier N, Teipel SJ, Initiative ADN (2024) Longitudinal trajectories of cognitive reserve in hypometabolic subtypes of Alzheimer's disease. Neurobiol Aging 135:26\u0026ndash;38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoulakis K, Pereira JB, Mecocci P et al (2018) Heterogeneous patterns of brain atrophy in Alzheimer's disease. Neurobiol Aging 65:98\u0026ndash;108\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira D, Mohanty R, Murray ME, Nordberg A, Kantarci K, Westman E (2022) The hippocampal sparing subtype of Alzheimer\u0026rsquo;s disease assessed in neuropathology and in vivo tau positron emission tomography: a systematic review. Acta Neuropathol Commun 10(1):166\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMowinckel AM, Vidal-Pi\u0026ntilde;eiro D (2020) Visualization of brain statistics with R packages ggseg and ggseg3d. Adv Methods Practices Psychol Sci 3(4):466\u0026ndash;483\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohanty R, Ferreira D, Frerich S et al (2022) Neuropathologic features of antemortem atrophy-based subtypes of Alzheimer disease. Neurology 99(4):e323\u0026ndash;e333\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpina S, La Joie R, Petersen C et al (2021) Comorbid neuropathological diagnoses in early versus late-onset Alzheimer\u0026rsquo;s disease. Brain 144(7):2186\u0026ndash;2198\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWisse L, De Flores R, Xie L et al (2021) Pathological drivers of neurodegeneration in suspected non-Alzheimer\u0026rsquo;s disease pathophysiology. Alzheimers Res Ther 13(1):100\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira D, Shams S, Cavallin L et al (2018) The contribution of small vessel disease to subtypes of Alzheimer's disease: a study on cerebrospinal fluid and imaging biomarkers. Neurobiol Aging 70:18\u0026ndash;29\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWheatley SH, Mohanty R, Poulakis K et al (2024) Divergent neurodegenerative patterns: Comparison of [(18)F] fluorodeoxyglucose-PET- and MRI-based Alzheimer's disease subtypes. Brain Commun 6(6):fcae426. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/braincomms/fcae426\u003c/span\u003e\u003cspan address=\"10.1093/braincomms/fcae426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoulakis K, Pereira JB, Muehlboeck J-S et al (2022) Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer\u0026rsquo;s disease. Nat Commun 13(1):4566\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohanty R, M\u0026aring;rtensson G, Poulakis K et al (2020) Comparison of subtyping methods for neuroimaging studies in Alzheimer\u0026rsquo;s disease: a call for harmonization. Brain Commun 2(2):fcaa192\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuong MT, Das SR, Khandelwal P et al (2024) Hypometabolic mismatch with atrophy and tau pathology in mixed Alzheimer and Lewy body disease. Brain. :awae352\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergeret S, Queneau M, Rodallec M et al (2021) [18F] FDG PET may differentiate cerebral amyloid angiopathy from Alzheimer\u0026rsquo;s disease. Eur J Neurol 28(5):1511\u0026ndash;1519\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergeret S, Queneau M, Rodallec M et al (2021) Brain glucose metabolism in cerebral amyloid angiopathy: An FDG-PET study. Lippincott Williams \u0026amp; Wilkins Hagerstown, MD\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFotiadis P, van Rooden S, van der Grond J et al (2016) Cortical atrophy in patients with cerebral amyloid angiopathy: a case-control study. Lancet Neurol 15(8):811\u0026ndash;819\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamuraki M, Matsunari I, Yoshita M et al (2015) Cerebral amyloid angiopathy-related microbleeds correlate with glucose metabolism and brain volume in Alzheimer\u0026rsquo;s disease. J Alzheimers Dis 48(2):517\u0026ndash;528\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohanty R, Wheatley S, Chiotis K, Marseglia A, Westman E, Cohort ADNI (2025) Distinct cerebrovascular pathways underlying Alzheimer\u0026rsquo;s disease-related neurodegeneration. Acta Neuropathol 150(1):64\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoodworth DC, Nguyen HL, Khan Z, Kawas CH, Corrada MM, Sajjadi SA (2021) Utility of MRI in the identification of hippocampal sclerosis of aging. Alzheimer's Dement 17(5):847\u0026ndash;855\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuciuc M, Botha H, Murray ME et al (2020) Utility of FDG-PET in diagnosis of Alzheimer-related TDP-43 proteinopathy. Neurology 95(1):e23\u0026ndash;e34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawe RJ, Bennett DA, Schneider JA, Arfanakis K (2011) Neuropathologic correlates of hippocampal atrophy in the elderly: a clinical, pathologic, postmortem MRI study. PLoS ONE 6(10):e26286\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson PT, Dickson DW, Trojanowski JQ et al (2019) Limbic-predominant age-related TDP-43 encephalopathy (LATE): consensus working group report. Brain 142(6):1503\u0026ndash;1527\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuciuc M, Martin PR, Tosakulwong N et al (2022) TDP-43-associated atrophy in brains with and without frontotemporal lobar degeneration. NeuroImage: Clin 34:102954\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira D, Przybelski SA, Lesnick TG et al (2025) Longitudinal FDG-PET Metabolic Change Along the Lewy Body Continuum. JAMA Neurol 82(3):285\u0026ndash;294\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdelnour C, Ferreira D, Oppedal K et al (2020) The combined effect of amyloid-β and tau biomarkers on brain atrophy in dementia with Lewy bodies. NeuroImage: Clin 27:102333\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollij LE, Mastenbroek SE, Mattsson-Carlgren N et al (2024) Lewy body pathology exacerbates brain hypometabolism and cognitive decline in Alzheimer\u0026rsquo;s disease. Nat Commun 15(1):8061\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInguanzo A, Poulakis K, Mohanty R et al (2023) MRI data-driven clustering reveals different subtypes of Dementia with Lewy bodies. NPJ Parkinsons Dis Jan 20(1):5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41531-023-00448-6\u003c/span\u003e\u003cspan address=\"10.1038/s41531-023-00448-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJosephs KA, Dickson DW, Tosakulwong N et al (2017) Rates of hippocampal atrophy and presence of post-mortem TDP-43 in patients with Alzheimer's disease: a longitudinal retrospective study. Lancet Neurol 16(11):917\u0026ndash;924\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez-Oroz MC, Gago B, Clavero P, Delgado-Alvarado M, Garcia-Garcia D, Jimenez-Urbieta H (2015) The relationship between atrophy and hypometabolism: is it regionally dependent in dementias? Curr Neurol Neurosci Rep 15:1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinoshima S, Foster NL, Kuhl DE (1994) Posterior cingulate cortex in Alzheimer's disease\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE (1997) Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Annals Neurology: Official J Am Neurol Association Child Neurol Soc 42(1):85\u0026ndash;94\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMosconi L (2013) Glucose metabolism in normal aging and Alzheimer\u0026rsquo;s disease: methodological and physiological considerations for PET studies. Clin translational imaging 1:217\u0026ndash;233\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeiss W-D, Zimmermann-Meinzingen S (2012) PET imaging in the differential diagnosis of vascular dementia. J Neurol Sci 322(1\u0026ndash;2):268\u0026ndash;273\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Karolinska Institutet","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"In vivo neuroimaging, FDG PET, MRI, heterogeneity, postmortem neuropathology","lastPublishedDoi":"10.21203/rs.3.rs-8910160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8910160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiological heterogeneity in cognitively impaired individuals has been described by distinct hypometabolic (FDG-PET) and atrophy (MRI) corticolimbic neurodegeneration patterns. However, the neuroimaging modalities can show different patterns at the individual-level. This study investigated whether postmortem neuropathologies may explain these differences.\u003c/p\u003e \u003cp\u003eThe study includes 245 individuals, 69 with cognitive impairment who underwent in vivo neuroimaging and neuropathological assessment and 176 cognitively unimpaired individuals as a reference group. Neurodegeneration patterns were identified in both in vivo FDG-PET and MRI, and their link to postmortem AD (amyloid-beta, tau) and non-AD (CAA, alpha-synuclein, TDP-43, and hippocampal sclerosis) pathologies was examined.\u003c/p\u003e \u003cp\u003eIn vivo individual-level differences of hypometabolic and atrophy neurodegeneration patterns could be associated to different AD and non-AD pathologies postmortem. The patterns significantly differed by neuropathology and neuronal loss at the regional-level but not global-level. Specifically, limbic predominant atrophy was related to distant (neocortical) amyloid, tau, and arteriolosclerosis while limbic predominant hypometabolism with local (mediotemporal) tau and arteriolosclerosis. Both limbic atrophy and hypometabolism reflected local (mediotemporal) TDP-43 and neuronal loss, with limbic hypometabolism additionally reflecting neocortical neuronal loss. Cortical predominant atrophy and hypometabolism were correlated with local (neocortical) alpha synuclein.\u003c/p\u003e \u003cp\u003eNeurodegeneration patterns differentially reflect underlying pathologies. Specifically, limbic predominant patterns were more frequently associated with TDP-43 pathology and hippocampal sclerosis, whereas cortical predominant patterns more often reflected cerebrovascular disease and alpha-synuclein pathology. Differences between corticolimbic hypometabolic and atrophy patterns were observed only in cases with postmortem-confirmed AD pathology, suggesting that non-AD pathologies (TDP-43, hippocampal sclerosis, cerebrovascular disease, and alpha-synuclein) may differentially modify AD-related neurodegeneration.\u003c/p\u003e","manuscriptTitle":"Distinct associations of corticolimbic hypometabolism and atrophy patterns correlate with postmortem neuropathologies in cognitively impaired individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 12:44:18","doi":"10.21203/rs.3.rs-8910160/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"839bf705-1892-4405-8731-d967b2458524","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63143679,"name":"Neurobiology of Disease"}],"tags":[],"updatedAt":"2026-02-19T12:44:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 12:44:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8910160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8910160","identity":"rs-8910160","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}