Spatial patterns of atrophy, hypometabolism, and amyloid deposition in Alzheimer's disease correspond to dissociable functional brain networks: Network-Specificity of AD Pathology

Grothe, Michel J.; Teipel, Stefan J.; for the Alzheimer's Disease Neuroimaging Initiative

doi:10.1002/hbm.23018

Cited by 150Dementia and Cognitive Impairment ResearchFunctional Brain Connectivity StudiesAlzheimer's disease research and treatments

Spatial patterns of atrophy, hypometabolism, and amyloid deposition in Alzheimer's disease correspond to dissociable functional brain networks: Network-Specificity of AD Pathology

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Grothe, Michel J., Teipel, Stefan J., for the Alzheimer's Disease Neuroimaging Initiative·2016·Human Brain Mapping

DOI 10.1002/hbm.23018PMC PMC4715545

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Abstract

Recent neuroimaging studies of Alzheimer's disease (AD) have emphasized topographical similarities between AD-related brain changes and a prominent cortical association network called the default-mode network (DMN). However, the specificity of distinct imaging abnormalities for the DMN compared to other intrinsic connectivity networks (ICNs) of the limbic and heteromodal association cortex has not yet been examined systematically. We assessed regional amyloid load using AV45-PET, neuronal metabolism using FDG-PET, and gray matter volume using structural MRI in 473 participants from the Alzheimer's Disease Neuroimaging Initiative, including preclinical, predementia, and clinically manifest AD stages. Complementary region-of-interest and voxel-based analyses were used to assess disease stage- and modality-specific changes within seven principle ICNs of the human brain as defined by a standardized functional connectivity atlas. Amyloid deposition in AD dementia showed a preference for the DMN, but high effect sizes were also observed for other neocortical ICNs, most notably the frontoparietal-control network. Atrophic changes were most specific for an anterior limbic network, followed by the DMN, whereas other neocortical networks were relatively spared. Hypometabolism appeared to be a mixture of both amyloid- and atrophy-related profiles. Similar patterns of modality-dependent network specificity were also observed in the predementia and, for amyloid deposition, in the preclinical stage. These quantitative data confirm a high vulnerability of the DMN for multimodal imaging abnormalities in AD. However, rather than being selective for the DMN, imaging abnormalities more generally affect higher order cognitive networks and, importantly, the vulnerability profiles of these networks markedly differ for distinct aspects of AD pathology.

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Discordance between imaging biomarkers has been noted in sporadic AD.
“Discordance between imaging biomarkers has been noted in sporadic AD 37–44.”
From Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study (2018) · cites this paper as [39]
There is an increased Aβ-PET signal within the central executive network and salience network in the progression of AD.
“There is an increased Aβ-PET signal within the CEN and the SN in the progression of AD.244,245”
From Amyloid β-based therapy for Alzheimer’s disease: Challenges, successes and future (2023) · cites this paper as [245]

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Alzheimer's Disease and Amnestic Mild Cognitive Impairment Weaken Connections Within the Default-Mode Network: A Multi-Modal Imaging Study
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Covarying alterations in Aβ deposition, glucose metabolism, and gray matter volume in cognitively normal elderly
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Link between hippocampus' raised local and eased global intrinsic connectivity in AD
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Detecting hippocampal hypometabolism in Mild Cognitive Impairment using automatic voxel-based approaches
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Voxel-based analysis of Alzheimer's disease PET imaging using a triplet of radiotracers: PIB, FDDNP, and FDG
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Anterior and posterior MTL networks in aging and MCI
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Mosconi, L., Initiative, A. D. N., Andrews, R. D., & Matthews, D. C. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-121867 · Cited by 43 · see citing works
Posterior cingulate hypometabolism in early Alzheimer's disease: what is the contribution of local atrophy versus disconnection?
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The Default Mode Network and Related Right Hemisphere Structures may be the Key Substrates of Dementia
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Reduced glucose uptake and Aβ in brain regions with hyperintensities in connected white matter
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Greater Default-Mode Network Abnormalities Compared to High Order Visual Processing Systems in Amnestic Mild Cognitive Impairment: An Integrated Multi-Modal MRI Study
Sala‐Llonch, R., Bosch, B., Arenaza‐Urquijo, E. M., Rami, L., Bargalló, N., Junqué, C., Molinuevo, J. L., & David Bartrés-faz, A. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-101038 · Cited by 24 · see citing works
The Roots of Alzheimer's Disease: Are High-Expanding Cortical Areas Preferentially Targeted?
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Are AD-Typical Regions the Convergence Point of Multiple Pathologies?
Villeneuve, S., Wirth, M., & Joie, R. L. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00042 · Cited by 19 · see citing works

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The Amyloid-β Pathway in Alzheimer’s Disease
Hampel, H., Hardy, J., Blennow, K., Chen, C., Perry, G., Kim, S. H., Villemagne, V. L., Aisen, P., Vendruscolo, M., Iwatsubo, T., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01249-0 · Cited by 1,952 · see citing works
Amyloid β-based therapy for Alzheimer’s disease: Challenges, successes and future
Zhang, Y., Chen, H., Li, R., Sterling, K., & Song, W. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01484-7 · Cited by 988 · see citing works
Imaging the evolution and pathophysiology of Alzheimer disease
Jagust, W. (2018) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0067-3 · Cited by 604 · see citing works
Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study
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Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Morris, J. C., et al. (2017) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.11.007 · Cited by 314 · see citing works
In vivo staging of regional amyloid deposition
Grothe, M. J., Barthel, H., Sepulcre, J., Dyrba, M., Sabri, O., Teipel, S. J., Stern, Y., & Initiative, A. D. N. (2022) · DZNE Pub · doi:10.7916/0vj1-4c02 · Cited by 294 · see citing works
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Biomarkers for tau pathology
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Networks of tau distribution in Alzheimer’s disease
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Hypergraph based multi-task feature selection for multimodal classification of Alzheimer's disease
Shao, W., Peng, Y., Zu, C., Wang, M., & Zhang, D. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101663 · Cited by 167 · see citing works
Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight
Vogel, J. W., Corriveau‐Lecavalier, N., Franzmeier, N., Pereira, J. B., Brown, J. A., Maaß, A., Botha, H., Seeley, W. W., Bassett, D. S., Jones, D. T., et al. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00731-8 · Cited by 160 · see citing works
Long-term running exercise improves cognitive function and promotes microglial glucose metabolism and morphological plasticity in the hippocampus of APP/PS1 mice
Zhang, S., Zhu, L., Peng, Y., Zhang, L., Chao, F., Jiang, L. H., Xiao, Q., Liang, X., Tang, J., Yang, H., et al. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02401-5 · Cited by 143 · see citing works
Molecular properties underlying regional vulnerability to Alzheimer’s disease pathology
Grothe, M. J., Sepulcre, J., Gonzalez-Escamilla, G., Jelistratova, I., Schöll, M., Hansson, O., Teipel, S. J., & Initiative, A. D. N. (2018) · Brain · doi:10.1093/brain/awy189 · Cited by 142 · see citing works
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Focal temporal pole atrophy and network degeneration in semantic variant primary progressive aphasia
Collins, J. A., Montal, V., Hochberg, D., Quimby, M., Mandelli, M. L., Makris, N., Seeley, W. W., Gorno‐Tempini, M. L., & Dickerson, B. C. (2016) · Brain · doi:10.1093/brain/aww313 · Cited by 139 · see citing works
Functional Connectivity Disruption in Subjective Cognitive Decline and Mild Cognitive Impairment: A Common Pattern of Alterations
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Modulating Gamma Oscillations Promotes Brain Connectivity to Improve Cognitive Impairment
Liu, C., Han, T., Xu, Z., Liu, J., Zhang, M., Du, J., Zhou, Q., Duan, Y., Li, Y., Wang, J., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab371 · Cited by 125 · see citing works
Neuronal network disintegration: common pathways linking neurodegenerative diseases
Ahmed, R. M., Devenney, E. M., Irish, M., Ittner, A., Naismith, S., Ittner, L. M., Rohrer, J. D., Halliday, G. M., Eisen, A., Hodges, J. R., et al. (2016) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2014-308350 · Cited by 128 · see citing works
Alzheimer Disease and Mild Cognitive Impairment: Integrated Pulsed Arterial Spin-Labeling MRI and 18 F-FDG PET
Riederer, I., Bohn, K. P., Preibisch, C., Wiedemann, E., Zimmer, C., Alexopoulos, P., & Förster, S. (2018) · Radiology · doi:10.1148/radiol.2018170575 · Cited by 101 · see citing works
Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks
Marzban, E. N., Eldeib, A. M., Yassine, I. A., Kadah, Y. M., & Initiative, A. D. N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230409 · Cited by 102 · see citing works
Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer’s dementia
Wang, M., Jiang, J., Yan, Z., Alberts, I., Ge, J., Zhang, H., Zuo, C., Yu, J., Rominger, A., Shi, K., et al. (2020) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-04814-x · Cited by 98 · see citing works
Tau Pathology Distribution in Alzheimer's disease Corresponds Differentially to Cognition-Relevant Functional Brain Networks
Hansson, O., Grothe, M. J., Strandberg, T. O., Ohlsson, T. G., Hägerström, D., Jögi, J., Smith, R. A., & Schöll, M. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00167 · Cited by 98 · see citing works
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Cortical hypometabolism reflects local atrophy and tau pathology in symptomatic Alzheimer’s disease
Strom, A., Iaccarino, L., Edwards, L., Lesman-Segev, O. H., Soleimani-Meigooni, D. N., Pham, J., Baker, S. L., Landau, S. M., Jagust, W. J., Miller, B. L., et al. (2022) · Brain · doi:10.1093/brain/awab294 · Cited by 91 · see citing works
Medial Temporal Lobe Disconnection and Hyperexcitability Across Alzheimer’s Disease Stages
Pasquini, L., Rahmani, F., Balajoo, S. M., Joie, R. L., Zarei, M., Sorg, C., Drzezga, A. E., & Tahmasian, M. (2019) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-190121 · Cited by 79 · see citing works
A novel joint HCPMMP method for automatically classifying Alzheimer’s and different stage MCI patients
Sheng, J., Wang, B., Zhang, Q., Liu, Q., Ma, Y., Liu, W., Shao, M., & Chen, B. (2019) · Behavioural Brain Research · doi:10.1016/j.bbr.2019.03.004 · Cited by 77 · see citing works
Evaluating functional connectivity of executive control network and frontoparietal network in Alzheimer’s disease
Zhao, Q., Lu, H., Metmer, H., Li, X., & Lu, J. (2017) · Brain Research · doi:10.1016/j.brainres.2017.10.025 · Cited by 77 · see citing works
Disrupted Regional Cerebral Blood Flow, Functional Activity and Connectivity in Alzheimer’s Disease: A Combined ASL Perfusion and Resting State fMRI Study
Zheng, W., Cui, B., Han, Y., Song, H., Li, K., Yong He, A., & Wang, Z. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00738 · Cited by 75 · see citing works
Effective connectivity in the default mode network is distinctively disrupted in Alzheimer's disease—A simultaneous resting‐state FDG‐PET/fMRI study
Scherr, M., Utz, L., Tahmasian, M., Pasquini, L., Grothe, M. J., Rauschecker, J. P., Grimmer, T., Drzezga, A. E., Sorg, C., & Riedl, V. (2019) · Human Brain Mapping · doi:10.1002/hbm.24517 · Cited by 73 · see citing works
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Differential diagnosis of amnestic dementia patients based on an FDG‐PET signature of autopsy‐confirmed LATE‐NC
Grothe, M. J., Moscoso, A., Silva‐Rodríguez, J., Lange, C., Nho, K. T., Saykin, A. J., Nelson, P. T., Schöll, M., Buchert, R., Teipel, S. J., et al. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12763 · Cited by 70 · see citing works
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The corticotopic organization of the human basal forebrain as revealed by regionally selective functional connectivity profiles
Fritz, H.-C. J., Ray, N. J., Dyrba, M., Sorg, C., Teipel, S., & Grothe, M. (2018) · Human Brain Mapping · doi:10.1002/hbm.24417 · Cited by 71 · see citing works
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Utility of FDG-PET in diagnosis of Alzheimer-related TDP-43 proteinopathy
Buciuc, M., Botha, H., Murray, M. E., Schwarz, C. G., Senjem, M. L., Jones, D. T., Knopman, D. S., Boeve, B. F., Petersen, R. C., Jack, C. R., et al. (2020) · Neurology · doi:10.1212/wnl.0000000000009722 · Cited by 70 · see citing works
Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease
Dyrba, M., Hanzig, M., Altenstein, S., Bader, S., Ballarini, T., Brosseron, F., Büerger, K., Cantré, D., Dechent, P., Dobisch, L., et al. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00924-2 · Cited by 66 · see citing works
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Grab‐AD : Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease
Jin, D., Wang, P., Zalesky, A., Liu, B., Song, C., Wang, D., Xu, K., Yang, H., Zhang, Z., Yao, H., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25023 · Cited by 59 · see citing works
Dementia with Lewy bodies: association of Alzheimer pathology with functional connectivity networks
Schumacher, J., Gunter, J. L., Przybelski, S. A., Jones, D. T., Graff‐Radford, J., Savica, R., Schwarz, C. G., Senjem, M. L., Jack, C. R., Lowe, V. J., et al. (2021) · Brain · doi:10.1093/brain/awab218 · Cited by 57 · see citing works
Spatial Relationships between Molecular Pathology and Neurodegeneration in the Alzheimer’s Disease Continuum
Iaccarino, L., Joie, R. L., Edwards, L., Strom, A., Schonhaut, D. R., Ossenkoppele, R., Pham, J. Q., Mellinger, T. J., Janabi, M., Baker, S. L., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa184 · Cited by 55 · see citing works
Brain properties predict proximity to symptom onset in sporadic Alzheimer’s disease
Vogel, J. W., Vachon‐Presseau, É., Binette, A. P., Tam, A., Orban, P., Joie, R. L., Savard, M., Picard, C., Poirier, J., Bellec, P., et al. (2018) · Brain · doi:10.1093/brain/awy093 · Cited by 50 · see citing works
Cognitive and Neuropathophysiological Outcomes of Gamma-tACS in Dementia: A Systematic Review
Manippa, V., Palmisano, A., Nitsche, M. A., Filardi, M., Vilella, D., Logroscino, G., & Rivolta, D. (2023) · Neuropsychology Review · doi:10.1007/s11065-023-09589-0 · Cited by 55 · see citing works
Perfusion Neuroimaging Abnormalities Alone Distinguish National Football League Players from a Healthy Population
Amen, D., Willeumier, K. C., Omalu, B. I., Newberg, A. B., Raghavendra, C. S., & Raji, C. A. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160207 · Cited by 53 · see citing works
Causal structural covariance network revealing atrophy progression in Alzheimer's disease continuum
Zhao, Q., Chen, F., Lu, J., Lv, P., Li, W., Liang, X., Wang, M., Wang, Z., Zhang, X., Zhang, B., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25531 · Cited by 52 · see citing works
Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression
Teipel, S. J., Grothe, M. J., Metzger, C. D., Grimmer, T., Sorg, C., Ewers, M., Franzmeier, N., Meisenzahl, E. M., Klöppel, S., Borchardt, V., et al. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00318 · Cited by 48 · see citing works
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Functional signature of conversion of patients with mild cognitive impairment
Pizzi, S. D., Punzi, M., Sensi, S. L., & Initiative, F. T. A. D. N. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.10.004 · Cited by 50 · see citing works
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Processing speed and attention training modifies autonomic flexibility: A mechanistic intervention study
Lin, F. V., Tao, Y., Chen, Q., Anthony, M., Zhang, Z., Tadin, D., & Heffner, K. L. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116730 · Cited by 48 · see citing works
Amyloid-beta modulates the association between neurofilament light chain and brain atrophy in Alzheimer’s disease
Kang, M. S., Aliaga, A., Shin, M., Mathotaarachchi, S. S., Benedet, A. L., Pascoal, T. A., Therriault, J., Chamoun, M., Savard, M., Devenyi, G. A., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0818-1 · Cited by 48 · see citing works
Gaussian Graphical Models Reveal Inter-Modal and Inter-Regional Conditional Dependencies of Brain Alterations in Alzheimer's Disease
Dyrba, M., Mohammadi, R., Grothe, M. J., Kirste, T., & Teipel, S. J. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00099 · Cited by 47 · see citing works
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Gray Matter Network Disruptions and Regional Amyloid Beta in Cognitively Normal Adults
ten Kate, M., Visser, P. J., Bakardjian, H., Barkhof, F., Sikkes, S. A. M., van der Flier, W. M., Scheltens, P., Hampel, H., Habert, M.-O., Dubois, B., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00067 · Cited by 41 · see citing works
Cognitive and neuroimaging correlates of financial exploitation vulnerability in older adults without dementia: Implications for early detection of Alzheimer’s disease
Fenton, L. E., Weissberger, G. H., Boyle, P. A., Mosqueda, L. A., Yassine, H. N., Nguyen, A. L., Lim, A. C., & Han, S. D. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104773 · Cited by 42 · see citing works
The Relationship of Brain Amyloid Load and APOE Status to Regional Cortical Thinning and Cognition in the ADNI Cohort
Li, C., Initiative, A. D. N., Loewenstein, D., Duara, R., Cabrerizo, M., Barker, W., & Adjouadi, M. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170286 · Cited by 41 · see citing works
Deep Brain Stimulation for Alzheimer’s Disease: Tackling Circuit Dysfunction
Lam, J., Lee, J., Liu, C. Y., Lozano, A. M., & Lee, D. J. (2020) · Neuromodulation Technology at the Neural Interface · doi:10.1111/ner.13305 · Cited by 42 · see citing works
Functional connectivity change between posterior cingulate cortex and ventral attention network relates to the impairment of orientation for time in Alzheimer’s disease patients
Yamashita, K.-I., Uehara, T., Prawiroharjo, P., Yamashita, K., Togao, O., Hiwatashi, A., Taniwaki, Y., Utsunomiya, H., Matsushita, T., Yamasaki, R., et al. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9860-x · Cited by 37 · see citing works
Regional patterns of gray matter volume, hypometabolism, and beta-amyloid in groups at risk of Alzheimer's disease
Wirth, M., Bejanin, A., Joie, R. L., Arenaza‐Urquijo, E. M., Gonneaud, J., Landeau, B., Perrotin, A., Mézenge, F., Sayette, V. D. L., Desgranges, B., et al. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.10.023 · Cited by 37 · see citing works
Metabolic brain networks in aging and preclinical Alzheimer's disease
Arnemann, K. L., Stöber, F., Narayan, S., Rabinovici, G. D., & Jagust, W. J. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.12.037 · Cited by 38 · see citing works
Individual Correspondence of Amyloid-β and Intrinsic Connectivity in the Posterior Default Mode Network Across Stages of Alzheimer’s Disease
Pasquini, L., Initiative, A. D. N., Benson, G. S., Grothe, M. J., Utz, L., Myers, N. E., Yakushev, I. Y., Grimmer, T., Scherr, M., & Sorg, C. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170096 · Cited by 37 · see citing works
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Distinct Interplay Between Atrophy and Hypometabolism in Alzheimer’s Versus Semantic Dementia
Bejanin, A., Joie, R. L., Landeau, B., Belliard, S., Sayette, V. D. L., Eustache, F., Desgranges, B., & Chételat, G. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy069 · Cited by 35 · see citing works
Age-related vulnerability of the human brain connectome
Filippi, M., Cividini, C., Basaia, S., Spinelli, E. G., Castelnovo, V., Leocadi, M., Canu, E., & Agosta, F. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02157-1 · Cited by 34 · see citing works
Cerebral glucose metabolism in Alzheimer's disease
Salmon, É., Collette, F., & Bastin, C. (2024) · Cortex · doi:10.1016/j.cortex.2024.07.004 · Cited by 33 · see citing works
ASAF: altered spontaneous activity fingerprinting in Alzheimer's disease based on multisite fMRI.
Li, J.-C., Jin, D., Li, A., Liu, B., Song, C., Wang, P., Wang, D., Xu, K.-B., Yang, H.-W., Yao, H.-X., et al. (2019) · Science Bulletin · doi:10.1016/j.scib.2019.04.034 · Cited by 30 · see citing works
Dynamic Cortico-hippocampal Networks Underlying Memory and Cognition: The PMAT Framework
Inhoff, M. C., & Ranganath, C. (2017) · Springer eBooks · doi:10.1007/978-3-319-50406-3_18 · Cited by 31 · see citing works
Connectomics and molecular imaging in neurodegeneration
Bischof, G. N., Ewers, M., Franzmeier, N., Grothe, M. J., Hoenig, M. C., Kocagöncü, E., Neitzel, J., Rowe, J. B., Strafella, A. P., Drzezga, A. E., et al. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04394-5 · Cited by 30 · see citing works
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Beyond the amyloid cascade: An update of Alzheimer's disease pathophysiology
Gouilly, D., Rafiq, M., Nogueira, L., Salabert, A., Payoux, P., Péran, P., & Pariente, J. (2023) · Revue Neurologique · doi:10.1016/j.neurol.2022.12.006 · Cited by 27 · see citing works
The Effects of Amyloid and Tau on Functional Network Connectivity in Older Populations
Wales, R. M., & Leung, H.-C. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0902 · Cited by 26 · see citing works
Cholinergic dysfunction, neurodegeneration, and amyloid-beta pathology in neurodegenerative diseases
Petrova, T., Orellana, C., Jelić, V., Oeksengaard, A., Snædal, J., Høgh, P., Andersen, B. B., Naik, M., Engedal, K., Wahlund, L., et al. (2020) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2020.111099 · Cited by 26 · see citing works
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P1‐268: NETWORK‐SPECIFICITY OF MULTIMODAL IMAGING ABNORMALITIES IN ALZHEIMER'S DISEASE
Grothe, M. J., & Teipel, S. J. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2014.05.508
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F2‐02‐04: NETWORK‐SPECIFICITY OF MULTIMODAL IMAGING ABNORMALITIES IN ALZHEIMER'S DISEASE
Grothe, M. J., & Teipel, S. J. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2014.04.144
Self-citation
IC‐01‐02: The IMAP* Project: Relative Degrees of Regional Grey Matter Atrophy, Hypometabolism and B‐Amyloid Deposition in Alzheimer's Disease Assessed Through Multimodal Neuroimaging
Joie, R. L., Tauber, C., Mézenge, F., Camus, V., Perrotin, A., Ribeiro, M. J., Landeau, B., Ibazizène, M., Guilloteau, D., Barré, L., et al. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.05.004
P4‐061: The IMAP* project: Relative degrees of regional grey matter atrophy, hypometabolism and β‐amyloid deposition in Alzheimer's disease assessed through multimodal neuroimaging
Joie, R. L., Tauber, C., Mézenge, F., Camus, V., Perrotin, A., Ribeiro, M. J., Landeau, B., Ibazizène, M., Guilloteau, D., Barré, L., et al. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.05.2081
O2‐04‐08: Progression of metabolic decline and amyloid deposition in Alzheimer's disease (AD) and their regional relation to a functional connectivity network in healthy controls
Förster, S., Grimmer, T., Miederer, I., Henriksen, G., Yousefi, B. H., Greve, D. N., Graner, P., Wester, H., Förstl, H., Kurz, A., et al. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.05.329

Authors: Grothe, M. J., Teipel, S. J., & Initiative, F. T. A. D. N.
Affiliations: German Center for Neurodegenerative Diseases (DE)
ORCID: Grothe, Michel J.
Teipel, Stefan J.
Venue: Human Brain Mapping
Publisher: Wiley
Published: 2015-10-06
Volume: 37
Issue: 1
Pages: 35-53
ISSN: 1065-9471 (ISSN-L)
DOI: 10.1002/hbm.23018
PMC: PMC4715545
OpenAlex: W1913607034
Cited by: 150
References: 83
Citations by year: 2026: 8 · 2025: 10 · 2024: 13 · 2023: 14 · 2022: 12 · 2021: 26 · 2020: 16 · 2019: 11 · 2018: 18 · 2017: 15 · 2016: 7
FWCI: 10.81
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Human Brain Mapping (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
DZNE Pub (submittedVersion)
Funders: National Institutes of Health (U01 AG024904)
Department of Defense (W81XWH-12-2-0012.)
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords: Alzheimer's disease, default mode network, resting-state networks, amyloid deposition, hypometabolism, gray matter atrophy, frontoparietal network, FDG-PET, preclinical Alzheimer's disease
MeSH terms: Aged, Aged, 80 and over, Alzheimer Disease, Amyloid, Atrophy, Brain, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Mental Status Schedule, Metabolic Diseases, Middle Aged, Models, Neurological, Neural Pathways, Radiopharmaceuticals, Fluorodeoxyglucose F18, Positron-Emission Tomography

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Spatial patterns of atrophy, hypometabolism, and amyloid deposition in Alzheimer's disease correspond to dissociable functional brain networks: Network-Specificity of AD Pathology

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