Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient’s scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.
Parkinson disease, Alzheimer disease and other neurodegenerative disorders are characterized by specific disease-related functional topographies (brain networks) that can be identified and validated using metabolic PET or resting-state functional MRI.
Brain network activity can be quantified on an individual patient basis, and the resulting network expression levels can be used in research and clinical settings.
Expression levels for multiple disease-related topographies can be entered into computational algorithms used to classify patients according to the diagnostic likelihood of these diseases.
Expression levels for abnormal disease networks correlate with clinical symptom severity and can be modulated by effective treatment.
Network expression levels increase over time and can be used to predict the likelihood of transition from preclinical to symptomatic disease in at-risk individuals.
The characterization of treatment-induced networks opens the door to their future use as objective outcome measures in clinical trials.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
Feigin, V. L. et al. Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18, 459–480 (2019).
Vos, T. et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet, 396, 1204–1222 (2020).
Nichols, E. et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health 7, e105–e125 (2022).
Ou, Z. et al. Global trends in the incidence, prevalence, and years lived with disability of Parkinson’s disease in 204 countries/territories from 1990 to 2019. Front. Public Health 9, 776847 (2021).
Wanneveich, M., Moisan, F., Jacqmin-Gadda, H., Elbaz, A. & Joly, P. Projections of prevalence, lifetime risk, and life expectancy of Parkinson’s disease (2010–2030) in France. Mov. Disord. 33, 1449–1455 (2018).
Nichols, E. et al. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18, 88–106 (2019).
Dorsey, E. R. et al. Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 17, 939–953 (2018).
Cummings, J., Lee, G., Zhong, K., Fonseca, J. & Taghva, K. Alzheimer’s disease drug development pipeline: 2021. Alzheimers Dement. 7, e12179 (2021).
McFarthing, K. et al. Parkinson’s disease drug therapies in the clinical trial pipeline: 2020. J. Parkinsons Dis. 10, 757–774 (2020).
Beach, T. G., Monsell, S. E., Phillips, L. E. & Kukull, W. Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005–2010. J. Neuropathol. Exp. Neurol. 71, 266–273 (2012).
Rizzo, G. et al. Accuracy of clinical diagnosis of dementia with Lewy bodies: a systematic review and meta-analysis. J. Neurol. Neurosurg. Psychiatry 89, 358–366 (2018).
Adler, C. H. et al. Low clinical diagnostic accuracy of early vs advanced Parkinson disease: clinicopathologic study. Neurology 83, 406–412 (2014).
Joutsa, J., Gardberg, M., Röyttä, M. & Kaasinen, V. Diagnostic accuracy of parkinsonism syndromes by general neurologists. Parkinsonism Relat. Disord. 20, 840–844 (2014).
Rizzo, G. et al. Accuracy of clinical diagnosis of Parkinson disease. Neurology 86, 566–576 (2016).
Jack, C. R. et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14, 535–562 (2018).
Schindlbeck, K. A. & Eidelberg, D. Network imaging biomarkers: insights and clinical applications in Parkinson’s disease. Lancet Neurol. 17, 629–640 (2018).
Woo, C. W., Chang, L. J., Lindquist, M. A. & Wager, T. D. Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 20, 365–377 (2017). This Review provides a valuable summary of multivariate models of brain imaging data as potential biomarkers.
Kragel, P. A., Koban, L., Barrett, L. F. & Wager, T. D. Representation, pattern information, and brain signatures: from neurons to neuroimaging. Neuron 99, 257–273 (2018).
Peng, S. et al. Dynamic 18F-FPCIT PET: quantification of Parkinson disease metabolic networks and nigrostriatal dopaminergic dysfunction in a single imaging session. J. Nucl. Med. 62, 1775–1782 (2021).
Christie, I. N., Wells, J. A., Kasparov, S., Gourine, A. V. & Lythgoe, M. F. Volumetric spatial correlations of neurovascular coupling studied using single pulse opto-fMRI. Sci. Rep. 7, 41583 (2017).
Agarwal, S., Sair, H. I., Yahyavi-Firouz-Abadi, N., Airan, R. & Pillai, J. J. Neurovascular uncoupling in resting state fMRI demonstrated in patients with primary brain gliomas. J. Magn. Reson. Imaging 43, 620–626 (2016).
Chen, J., Venkat, P., Zacharek, A. & Chopp, M. Neurorestorative therapy for stroke. Front. Hum. Neurosci. 8, 382 (2014).
Østergaard, L. et al. Capillary transit time heterogeneity and flow-metabolism coupling after traumatic brain injury. J. Cereb. Blood Flow. Metab. 34, 1585–1598 (2014).
Hirano, S. et al. Dissociation of metabolic and neurovascular responses to levodopa in the treatment of Parkinson’s disease. J. Neurosci. 28, 4201–4209 (2008).
Jourdain, V. A. et al. Flow-metabolism dissociation in the pathogenesis of levodopa-induced dyskinesia. JCI Insight 1, e86615 (2016).
Guedj, E. et al. EANM procedure guidelines for brain PET imaging using 18F FDG, version 3. Eur. J. Nucl. Med. Mol. Imaging 49, 632–651 (2022).
Spetsieris, P. G. & Eidelberg, D. Scaled subprofile modeling of resting state imaging data in Parkinson’s disease: methodological issues. Neuroimage 54, 2899–2914 (2011). This paper provides a comprehensive presentation of computational procedures to identify and validate disease-related metabolic covariance patterns.
Habeck, C. & Stern, Y. Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease. Cell Biochem. Biophys. 58, 53–67 (2010). An oustanding introduction to the multivariate analyses used to characterize disease-related network topographies.
Eidelberg, D. Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci. 32, 548–557 (2009).
Alexander, G. E. & Moeller, J. R. Application of the scaled subprofile model to functional imaging in neuropsychiatric disorders: a principal component approach to modeling brain function in disease. Hum. Brain Mapp. 2, 79–94 (1994).
Sala, A. & Perani, D. Brain molecular connectivity in neurodegenerative diseases: recent advances and new perspectives using positron emission tomography. Front. Neurosci. 13, 617 (2019).
Yakushev, I., Drzezga, A. & Habeck, C. Metabolic connectivity: methods and applications. Curr. Opin. Neurol. 30, 677–685 (2017).
Spetsieris, P. G. & Eidelberg, D. Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson’s disease. Neuroimage 226, 117568 (2021). This paper facilitates the biological interpretion of disease networks by visualizing relevant node-to-node connections using graphical displays.
Jollife, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374, 20150202 (2016).
Habeck, C. et al. A new approach to spatial covariance modeling of functional brain imaging data: ordinal trend analysis. Neural Comput. 17, 1602–1645 (2005).
Mure, H. et al. Parkinson’s disease tremor-related metabolic network: characterization, progression, and treatment effects. Neuroimage 54, 1244–1253 (2011).
Mure, H. et al. Improved sequence learning with subthalamic nucleus deep brain stimulation: evidence for treatment-specific network modulation. J. Neurosci. 32, 2804–2813 (2012).
Tang, C. C. et al. Metabolic network as a progression biomarker of premanifest Huntington’s disease. J. Clin. Invest. 123, 4076–4088 (2013).
Niethammer, M. et al. Gene therapy reduces Parkinson’s disease symptoms by reorganizing functional brain connectivity. Sci. Transl. Med. 10, eaau0713 (2018). A study that shows how subthalamic gene therapy for advanced PD induces a unique and more-efficient metabolic network that correlates with treatment outcome.
Brakedal, B. et al. The NADPARK study: a randomized phase I trial of nicotinamide riboside supplementation in Parkinson’s disease. Cell Metab. 34, 396–407 (2022). This study uses similar methods to those in the preceding paper to identify a treatment-related network induced by a supplement that boosts mitochondrial respiration in early PD.
Li, B. & Freeman, R. D. Neurometabolic coupling between neural activity, glucose, and lactate in activated visual cortex. J. Neurochem. 135, 742–754 (2015).
Stoessl, A. J. Glucose utilization: still in the synapse. Nat. Neurosci. 20, 382–384 (2017).
Patel, A. B. et al. Direct evidence for activity-dependent glucose phosphorylation in neurons with implications for the astrocyte-to-neuron lactate shuttle. Proc. Natl Acad. Sci. USA 111, 5385–5390 (2014).
Xiang, X. et al. Microglial activation states drive glucose uptake and FDG-PET alterations in neurodegenerative diseases. Sci. Transl. Med. 13, eabe5640 (2021).
Savio, A. et al. Resting-state networks as simultaneously measured with functional MRI and PET. J. Nucl. Med. 58, 1314–1317 (2017).
Marchitelli, R. et al. Simultaneous resting-state FDG-PET/fMRI in Alzheimer disease: relationship between glucose metabolism and intrinsic activity. Neuroimage 176, 246–258 (2018).
Jamadar, S. D. et al. Metabolic and hemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study. Cereb. Cortex 31, 2855–2867 (2021).
Sala, A., Lizarraga, A., Ripp, I., Cumming, P. & Yakushev, I. Static versus functional PET: making sense of metabolic connectivity. Cereb. Cortex 32, 1125–1129 (2021).
Watabe, T. & Hatazawa, J. Evaluation of functional connectivity in the brain using positron emission tomography: a mini-review. Front. Neurosci. 13, 775 (2019).
Cao, J. & Worsley, K. The geometry of correlation fields with an application to functional connectivity of the brain. Ann. Appl. Probab. 9, 1021–1057 (1999).
Sun, F. T., Miller, L. M. & D’Esposito, M. Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. Neuroimage 21, 647–658 (2004).
Hyvärinen, A. Independent component analysis: recent advances. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 371, 20110534 (2013).
Baggio, H.-C. et al. Cognitive impairment and resting-state network connectivity in Parkinson’s disease. Hum. Brain Mapp. 36, 199–212 (2015).
Calhoun, V. D., Liu, J. & Adalı, T. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage 45, S163–S172 (2009).
Vo, A. et al. Parkinson’s disease-related network topographies characterized with resting state functional MRI. Hum. Brain Mapp. 38, 617–630 (2017). This study shows how rs-fMRI can be used to identify disease-related topographies that are similar to their PET counterparts.
Rommal, A. et al. Parkinson’s disease-related pattern (PDRP) identified using resting-state functional MRI: validation study. Neuroimage Rep. 1, 100026 (2021).
Greuel, A. et al. GBA variants in Parkinson’s disease: clinical, metabolomic, and multimodal neuroimaging phenotypes. Mov. Disord. 35, 2201–2210 (2020).
Steidel, K. et al. Dopaminergic pathways and resting-state functional connectivity in Parkinson’s disease with freezing of gait. Neuroimage Clin. 32, 102899 (2021).
Meles, S. K. et al. The cerebral metabolic topography of spinocerebellar ataxia type 3. Neuroimage Clin. 19, 90–97 (2018).
Sporns, O. Graph theory methods: applications in brain networks. Dialogues Clin. Neurosci. 20, 111–121 (2018). Overview of graph theory as applied to the study of brain networks.
Muskulus, M., Houweling, S., Verduyn-Lunel, S. & Daffertshofer, A. Functional similarities and distance properties. J. Neurosci. Methods 183, 31–41 (2009).
Newman, M. Networks (Oxford Univ. Press, 2010).
Fortunato, S. Community detection in graphs. Phys. Rep. 486, 75–174 (2010).
Agosta, F. et al. Brain network connectivity assessed using graph theory in frontotemporal dementia. Neurology 81, 134–143 (2013).
Imai, M. et al. Metabolic network topology of Alzheimer’s disease and dementia with Lewy bodies generated using fluorodeoxyglucose positron emission tomography. J. Alzheimers Dis. 73, 197–207 (2020).
Sala, A. et al. Altered brain metabolic connectivity at multiscale level in early Parkinson’s disease. Sci. Rep. 7, 4256 (2017).
Yao, Z. et al. A FDG-PET study of metabolic networks in apolipoprotein E ε4 allele carriers. PLoS ONE 10, e0132300 (2015).
Sporns, O. & Betzel, R. F. Modular brain networks. Annu. Rev. Psychol. 67, 613–640 (2016).
Ko, J. H., Spetsieris, P. G. & Eidelberg, D. Network structure and function in Parkinson’s disease. Cereb. Cortex 28, 4121–4135 (2018).
Schindlbeck, K. A. et al. Metabolic network abnormalities in drug-naïve Parkinson’s disease. Mov. Disord. 35, 587–594 (2020).
Vo, A. et al. Adaptive and pathological connectivity responses in Parkinson’s disease brain networks. Cereb. Cortex https://doi.org/10.1093/cercor/bhac110 (2022). This study shows that connectivity patterns within the network space distinguish maladaptive changes from beneficial adaptations in PD.
Adler, C. H. et al. Unified staging system for Lewy body disorders: clinicopathologic correlations and comparison to Braak staging. J. Neuropathol. Exp. Neurol. 78, 891–899 (2019).
Hawkes, C. H., Del Tredici, K. & Braak, H. A timeline for Parkinson’s disease. Parkinsonism Relat. Disord. 16, 79–84 (2010).
Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson’s disease. Mov. Disord. 30, 1591–1601 (2015).
Aarsland, D. et al. Parkinson disease-associated cognitive impairment. Nat. Rev. Dis. Prim. 7, 47 (2021).
Niethammer, M. & Eidelberg, D. Metabolic brain networks in translational neurology: concepts and applications. Ann. Neurol. 72, 635–647 (2012).
Meles, S. K., Teune, L. K., de Jong, B. M., Dierckx, R. A. & Leenders, K. L. Metabolic imaging in Parkinson disease. J. Nucl. Med. 58, 23–28 (2017).
Stamelou, M. & Bhatia, K. P. Atypical parkinsonism. Neurol. Clin. 33, 39–56 (2015).
Kovacs, G. G. et al. Distribution patterns of tau pathology in progressive supranuclear palsy. Acta Neuropathol. 140, 99–119 (2020).
Briggs, M. et al. Validation of the new pathology staging system for progressive supranuclear palsy. Acta Neuropathol. 141, 787–789 (2021).
Brettschneider, J. et al. Progression of α-synuclein pathology in multiple system atrophy of the cerebellar type. Neuropathol. Appl. Neurobiol. 43, 315–329 (2017).
Rus, T. et al. Stereotyped relationship between motor and cognitive metabolic networks in Parkinson’s disease. Mov. Disord. 37, 2247–2256 (2022).
Ma, Y., Tang, C., Spetsieris, P. G., Dhawan, V. & Eidelberg, D. Abnormal metabolic network activity in Parkinson’s disease: test–retest reproducibility. J. Cereb. Blood Flow. Metab. 27, 597–605 (2007).
Tomše, P. et al. Abnormal metabolic brain network associated with Parkinson’s disease: replication on a new European sample. Neuroradiology 59, 507–515 (2017).
Wu, P. et al. Metabolic brain network in the Chinese patients with Parkinson’s disease based on 18F-FDG PET imaging. Parkinsonism Relat. Disord. 19, 622–627 (2013).
Meles, S. K. et al. Abnormal pattern of brain glucose metabolism in Parkinson’s disease: replication in three European cohorts. Eur. J. Nucl. Med. Mol. Imaging 47, 437–450 (2020).
Matthews, D. C. et al. FDG PET Parkinson’s disease-related pattern as a biomarker for clinical trials in early stage disease. Neuroimage Clin. 20, 572–579 (2018).
Teune, L. K. et al. Validation of parkinsonian disease-related metabolic brain patterns. Mov. Disord. 28, 547–551 (2013).
Lin, T. P. et al. Metabolic correlates of subthalamic nucleus activity in Parkinson’s disease. Brain 131, 1373–1380 (2008).
Helmich, R. C., Hallett, M., Deuschl, G., Toni, I. & Bloem, B. R. Cerebral causes and consequences of parkinsonian resting tremor: a tale of two circuits? Brain 135, 3206–3226 (2012).
Zach, H. et al. Dopamine-responsive and dopamine-resistant resting tremor in Parkinson disease. Neurology 95, e1461–e1470 (2020).
Ko, J. H., Spetsieris, P., Ma, Y., Dhawan, V. & Eidelberg, D. Quantifying significance of topographical similarities of disease-related brain metabolic patterns. PLoS ONE 9, e88119 (2014).
Tang, C. C. et al. Hemispheric network expression in Parkinson’s disease: relationship to dopaminergic asymmetries. J. Parkinsons Dis. 10, 1737–1749 (2020).
Ma, Y. et al. Parkinson’s disease spatial covariance pattern: noninvasive quantification with perfusion MRI. J. Cereb. Blood Flow. Metab. 30, 505–509 (2010).
Ma, Y. & Eidelberg, D. Functional imaging of cerebral blood flow and glucose metabolism in Parkinson’s disease and Huntington’s disease. Mol. Imaging Biol. 9, 223–233 (2007).
Liu, C. et al. Brain functional and structural signatures in Parkinson’s disease. Front. Aging Neurosci. 12, 125 (2020).
Melzer, T. R. et al. Arterial spin labelling reveals an abnormal cerebral perfusion pattern in Parkinson’s disease. Brain 134, 845–855 (2011).
Rane, S. et al. Arterial spin labeling detects perfusion patterns related to motor symptoms in Parkinson’s disease. Parkinsonism Relat. Disord. 76, 21–28 (2020).
Stam, C. J. Modern network science of neurological disorders. Nat. Rev. Neurosci. 15, 683–695 (2014).
Correa, C., Crnovrsanin, T. & Kwan-Liu, M. Visual reasoning about social networks using centrality sensitivity. IEEE Trans. Vis. Comput. Graph. 18, 106–120 (2012).
Schindlbeck, K. A. et al. LRRK2 and GBA variants exert distinct influences on parkinson’s disease-specific metabolic networks. Cereb. Cortex 30, 2867–2878 (2020).
Davis, M. Y. et al. Association of GBA mutations and the E326K polymorphism with motor and cognitive progression in Parkinson disease. JAMA Neurol. 73, 1217–1224 (2016).
Saunders-Pullman, R. et al. Progression in the LRRK2-associated Parkinson disease population. JAMA Neurol. 75, 312–319 (2018).
Wolters, A. F. et al. Resting-state fMRI in Parkinson’s disease patients with cognitive impairment: a meta-analysis. Parkinsonism Relat. Disord. 62, 16–27 (2019).
Spetsieris, P. G. et al. Metabolic resting-state brain networks in health and disease. Proc. Natl Acad. Sci. USA 112, 2563–2568 (2015). This study identifies the metabolic DMN in healthy individuals and describes the effects of neurodegeneration on expression of this pattern in patients with PD and Alzheimer disease.
Ruppert, M. C. et al. The default mode network and cognition in Parkinson’s disease: a multimodal resting-state network approach. Hum. Brain Mapp. 42, 2623–2641 (2021).
Huang, C. et al. Metabolic brain networks associated with cognitive function in Parkinson’s disease. Neuroimage 34, 714–723 (2007).
Mattis, P. J., Tang, C. C., Ma, Y., Dhawan, V. & Eidelberg, D. Network correlates of the cognitive response to levodopa in Parkinson disease. Neurology 77, 858–865 (2011).
Mattis, P. J. et al. Distinct brain networks underlie cognitive dysfunction in Parkinson and Alzheimer diseases. Neurology 87, 1925–1933 (2016).
Schindlbeck, K. A. et al. Cognition-related functional topographies in Parkinson’s disease: localized loss of the ventral default mode network. Cereb. Cortex 31, 5139–5150 (2021). This study uses rs-fMRI to explore the topographic relationship between the PDCP and DMN.
Hirano, S. Clinical implications for dopaminergic and functional neuroimage research in cognitive symptoms of Parkinson’s disease. Mol. Med. 27, 40 (2021).
Huang, C. et al. Metabolic abnormalities associated with mild cognitive impairment in Parkinson disease. Neurology 70, 1470–1477 (2008).
Meles, S. K. et al. Abnormal metabolic pattern associated with cognitive impairment in Parkinson’s disease: a validation study. J. Cereb. Blood Flow. Metab. 35, 1478–1484 (2015).
Trošt, M. et al. Metabolic brain changes related to specific cognitive impairment in non-demented Parkinson’s disease patients [abstract #1306]. Presented at 2016 International Congress, International Parkinson and Movement Disorder Society. https://www.mdsabstracts.org/abstract/metabolic-brain-changes-related-to-specific-cognitive-impairment-in-non-demented-parkinsons-disease-patients/ (2016).
Smallwood, J. et al. The default mode network in cognition: a topographical perspective. Nat. Rev. Neurosci. 22, 503–513 (2021). In this paper, the authors attribute the integrative role of the DMN in higher-order cognitive functions to its position at the end of the cortical processing stream.
Meles, S. K., et al. in PET and SPECT in Neurology. 73–104 (Springer International, 2021).
Högl, B., Stefani, A. & Videnovic, A. Idiopathic REM sleep behaviour disorder and neurodegeneration — an update. Nat. Rev. Neurol. 14, 40–56 (2018).
Holtbernd, F. et al. Abnormal metabolic network activity in REM sleep behavior disorder. Neurology 82, 620–627 (2014).
Kogan, R. V. et al. Four-year follow-up of 18F fluorodeoxyglucose positron emission tomography-based Parkinson’s disease-related pattern expression in 20 patients with isolated rapid eye movement sleep behavior disorder shows prodromal progression. Mov. Disord. 36, 230–235 (2021).
Ge, J. et al. Assessing cerebral glucose metabolism in patients with idiopathic rapid eye movement sleep behavior disorder. J. Cereb. Blood Flow. Metab. 35, 2062–2069 (2015).
Shin, J. H. et al. Parkinson disease-related brain metabolic patterns and neurodegeneration in isolated REM sleep behavior disorder. Neurology 97, e378–e388 (2021).
Meles, S. K. et al. The metabolic pattern of idiopathic REM sleep behavior disorder reflects early-stage Parkinson disease. J. Nucl. Med. 59, 1437–1444 (2018).
Yoon, E. J. et al. A new metabolic network correlated with olfactory and executive dysfunctions in idiopathic rapid eye movement sleep behavior disorder. J. Clin. Neurol. 15, 175–183 (2019).
Wu, P. et al. Consistent abnormalities in metabolic network activity in idiopathic rapid eye movement sleep behaviour disorder. Brain 137, 3122–3128 (2014).
Huang, C. et al. Changes in network activity with the progression of Parkinson’s disease. Brain 130, 1834–1846 (2007).
Tang, C. C., Poston, K. L., Dhawan, V. & Eidelberg, D. Abnormalities in metabolic network activity precede the onset of motor symptoms in Parkinson’s disease. J. Neurosci. 30, 1049–1056 (2010).
Braak, H. et al. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol. Aging 24, 197–211 (2003).
Ko, J. H., Lerner, R. P. & Eidelberg, D. Effects of levodopa on regional cerebral metabolism and blood flow. Mov. Disord. 30, 54–63 (2015).
Ge, J. et al. Metabolic network as an objective biomarker in monitoring deep brain stimulation for Parkinson’s disease: a longitudinal study. EJNMMI Res. 10, 131 (2020).
Asanuma, K. et al. Network modulation in the treatment of Parkinson’s disease. Brain 129, 2667–2678 (2006).
Trošt, M. et al. Network modulation by the subthalamic nucleus in the treatment of Parkinson’s disease. Neuroimage 31, 301–307 (2006).
Rommelfanger, K. S. & Wichmann, T. Extrastriatal dopaminergic circuits of the basal ganglia. Front. Neuroanat. 4, 139 (2010).
Jourdain, V. A. et al. Increased putamen hypercapnic vasoreactivity in levodopa-induced dyskinesia. JCI Insight 2, e96411 (2017).
Ntetsika, T., Papathoma, P.-E. & Markaki, I. Novel targeted therapies for Parkinson’s disease. Mol. Med. 27, 17 (2021).
Niethammer, M. et al. Long-term follow-up of a randomized AAV2-GAD gene therapy trial for Parkinson’s disease. JCI Insight 2, e90133 (2017).
Ko, J. H. et al. Network modulation following sham surgery in Parkinson’s disease. J. Clin. Invest. 124, 3656–3666 (2014). This study shows that the clinical response to sham surgery in patients with PD is mediated by a specific metabolic brain network that is active only in patients who are blinded to treatment.
Prasuhn, J. & Brüggemann, N. Genotype-driven therapeutic developments in Parkinson’s disease. Mol. Med. 27, 42 (2021).
Filippi, M., Balestrino, R., Basaia, S. & Agosta, F. Neuroimaging in glucocerebrosidase‐associated parkinsonism: a systematic review. Mov. Disord. 37, 1375–1393 (2022).
Meles, S. K., Oertel, W. H. & Leenders, K. L. Circuit imaging biomarkers in preclinical and prodromal Parkinson’s disease. Mol. Med. 27, 111 (2021).
Tolosa, E., Vila, M., Klein, C. & Rascol, O. LRRK2 in Parkinson disease: challenges of clinical trials. Nat. Rev. Neurol. 16, 97–107 (2020).
Blauwendraat, C., Nalls, M. A. & Singleton, A. B. The genetic architecture of Parkinson’s disease. Lancet Neurol. 19, 170–178 (2020).
Beach, T. G. & Adler, C. H. Importance of low diagnostic accuracy for early Parkinson’s disease. Mov. Disord. 33, 1551–1554 (2018).
Rus, T. et al. Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated-metabolic brain patterns’ based approach. Eur. J. Nucl. Med. Mol. Imaging 47, 2901–2910 (2020). This study supports the utility of automated pattern-based differential diagnosis of parkinsonism in a real-world clinical setting.
Rus, T. et al. Atypical clinical presentation of pathologically proven Parkinson’s disease: the role of Parkinson’s disease related metabolic pattern. Parkinsonism Relat. Disord. 78, 1–3 (2020).
Tang, C. C. et al. Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol. 9, 149–158 (2010).
Papathoma, P. E. et al. A replication study, systematic review and meta-analysis of automated image-based diagnosis in parkinsonism. Sci. Rep. 12, 2763 (2022).
Eckert, T. et al. Abnormal metabolic networks in atypical parkinsonism. Mov. Disord. 23, 727–733 (2008).
Ge, J. et al. Reproducible network and regional topographies of abnormal glucose metabolism associated with progressive supranuclear palsy: multivariate and univariate analyses in American and Chinese patient cohorts. Hum. Brain Mapp. 39, 2842–2858 (2018).
Shen, B. et al. Reproducible metabolic topographies associated with multiple system atrophy: network and regional analyses in Chinese and American patient cohorts. Neuroimage Clin. 28, 102416 (2020).
Tomše, P. et al. Abnormal metabolic covariance patterns associated with multiple system atrophy and progressive supranuclear palsy. Phys. Med. 98, 131–138 (2022).
Poston, K. L. et al. Network correlates of disease severity in multiple system atrophy. Neurology 78, 1237–1244 (2012).
Martí‐Andrés, G. et al. Multicenter validation of metabolic abnormalities related to PSP according to the MDS‐PSP criteria. Mov. Disord. 35, 2009–2018 (2020). This study shows the robustness and reproducibility of the PSPRP across different populations and clinical phenotypes.
Ko, J. H., Lee, C. S. & Eidelberg, D. Metabolic network expression in parkinsonism: clinical and dopaminergic correlations. J. Cereb. Blood Flow. Metab. 37, 683–693 (2016).
Niethammer, M. et al. A disease-specific metabolic brain network associated with corticobasal degeneration. Brain 137, 3036–3046 (2014).
Schindlbeck, K. A. et al. Neuropathological correlation supports automated image-based differential diagnosis in parkinsonism. Eur. J. Nucl. Med. Mol. Imaging 48, 3522–3529 (2021). This study compares the results of an automated pattern-based diagnostic algorithm with autopsy findings in patients with parkinsonism of uncertain cause.
Tripathi, M. et al. Automated differential diagnosis of early parkinsonism using metabolic brain networks: a validation study. J. Nucl. Med. 57, 60–66 (2016).
Eckert, T. et al. FDG PET in the differential diagnosis of parkinsonian disorders. Neuroimage 26, 912–921 (2005).
Meyer, P. T., Frings, L., Rücker, G. & Hellwig, S. 18F-FDG PET in parkinsonism: differential diagnosis and evaluation of cognitive impairment. J. Nucl. Med. 58, 1888–1898 (2017).
Gu, S.-C., Ye, Q. & Yuan, C.-X. Metabolic pattern analysis of 18F-FDG PET as a marker for Parkinson’s disease: a systematic review and meta-analysis. Rev. Neurosci. 30, 743–756 (2019).
Manzanera, O. M. et al. Scaled subprofile modeling and convolutional neural networks for the identification of Parkinson’s disease in 3D nuclear imaging data. Int. J. Neural Syst. 29, 1950010 (2019).
Mudali, D., Teune, L. K., Renken, R. J., Leenders, K. L. & Roerdink, J. B. T. M. Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features. Comput. Math. Methods Med. 2015, 136921 (2015).
Vázquez-Mojena, Y., León-Arcia, K., González-Zaldivar, Y., Rodríguez-Labrada, R. & Velázquez-Pérez, L. Gene therapy for polyglutamine spinocerebellar ataxias: advances, challenges, and perspectives. Mov. Disord. 36, 2731–2744 (2021).
Fields, E. et al. Gene targeting techniques for Huntington’s disease. Ageing Res. Rev. 70, 101385 (2021).
van der Horn, H. J. et al. A resting-state fMRI pattern of spinocerebellar ataxia type 3 and comparison with 18F-FDG PET. Neuroimage Clin. 34, 103023 (2022).
Alzheimer’s Association. 2018 Alzheimer’s disease facts and figures. Alzheimers Dement. 14, 367–429 (2018).
Hansson, O. Biomarkers for neurodegenerative diseases. Nat. Med. 27, 954–963 (2021).
Yu, M., Sporns, O. & Saykin, A. J. The human connectome in Alzheimer disease — relationship to biomarkers and genetics. Nat. Rev. Neurol. 17, 545–563 (2021).
Pievani, M., Filippini, N., Van Den Heuvel, M. P., Cappa, S. F. & Frisoni, G. B. Brain connectivity in neurodegenerative diseases — from phenotype to proteinopathy. Nat. Rev. Neurol. 10, 620–633 (2014).
Pievani, M., de Haan, W., Wu, T., Seeley, W. W. & Frisoni, G. B. Functional network disruption in the degenerative dementias. Lancet Neurol. 10, 829–843 (2011).
Raj, A., Kuceyeski, A. & Weiner, M. A network diffusion model of disease progression in dementia. Neuron 73, 1204–1215 (2012). Landmark paper relating network progression in neurodegenerative processes to pathological spread from one anatomical layer to the next.
Knopman, D. S. et al. Alzheimer disease. Nat. Rev. Dis. Prim. 7, 33 (2021).
Karikari, T. K. et al. Blood phospho-tau in Alzheimer disease: analysis, interpretation, and clinical utility. Nat. Rev. Neurol. 42, 400–418 (2022).
Badhwar, A. et al. Resting-state network dysfunction in Alzheimer’s disease: a systematic review and meta-analysis. Alzheimers Dement. 8, 73–85 (2017).
Scarmeas, N. et al. Covariance PET patterns in early Alzheimer’s disease and subjects with cognitive impairment but no dementia: utility in group discrimination and correlations with functional performance. Neuroimage 23, 35–45 (2004).
Devanand, D. P. et al. PET network abnormalities and cognitive decline in patients with mild cognitive impairment. Neuropsychopharmacology 31, 1327–1334 (2006).
Teune, L. K. et al. The Alzheimer’s disease-related glucose metabolic brain pattern. Curr. Alzheimer Res. 11, 725–732 (2014).
Katako, A. et al. Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia. Sci. Rep. 8, 13236 (2018).
Perovnik, M. et al. Identification and validation of Alzheimer’s disease-related metabolic brain pattern in biomarker confirmed Alzheimer’s dementia patients. Sci. Rep. 12, 11752 (2022). This study shows the diagnostic robustness of the ADRP across independent populations of patients with biologically confirmed Alzheimer disease.
Peretti, D. E. et al. Feasibility of pharmacokinetic parametric PET images in scaled subprofile modelling using principal component analysis. Neuroimage Clin. 30, 102625 (2021).
Meles, S. K. et al. The Alzheimer’s disease metabolic brain pattern in mild cognitive impairment. J. Cereb. Blood Flow. Metab. 37, 3643–3648 (2017).
Blazhenets, G. et al. Principal components analysis of brain metabolism predicts development of Alzheimer dementia. J. Nucl. Med. 60, 837–843 (2019).
Spetsieris, P. G., Ma, Y., Dhawan, V. & Eidelberg, D. Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features. Neuroimage 45, 1241–1252 (2009).
Sörensen, A., Blazhenets, G., Schiller, F., Meyer, P. T. & Frings, L. Amyloid biomarkers as predictors of conversion from mild cognitive impairment to Alzheimer’s dementia: a comparison of methods. Alzheimers Res. Ther. 12, 155 (2020).
Blazhenets, G. et al. Predictive value of 18F-florbetapir and 18F-FDG PET for conversion from mild cognitive impairment to Alzheimer dementia. J. Nucl. Med. 61, 597–603 (2020). This study shows the value of the ADCRP cross-pattern as a predictor of dementia in patients with MCI.
Blazhenets, G. et al. Validation of the Alzheimer disease dementia conversion-related pattern as an ATN biomarker of neurodegeneration. Neurology 96, e1358–e1368 (2021). This study shows that the ADCRP outperforms fluid biomarkers of neurodegeneration as a predictor of dementia in patients with MCI.
Blum, D. et al. Controls-based denoising, a new approach for medical image analysis, improves prediction of conversion to Alzheimer’s disease with FDG-PET. Eur. J. Nucl. Med. Mol. Imaging 46, 2370–2379 (2019).
Blazhenets, G., Frings, L., Sörensen, A. & Meyer, P. T. Principal-component analysis–based measures of PET data closely reflect neuropathologic staging schemes. J. Nucl. Med. 62, 855–860 (2021).
Li, T. R. et al. Exploring brain glucose metabolic patterns in cognitively normal adults at risk of Alzheimer’s disease: a cross-validation study with Chinese and ADNI cohorts. Neuroimage Clin. 33, 102900 (2022).
Tai, H. et al. The neuropsychological correlates of brain perfusion and gray matter volume in Alzheimer’s disease. J. Alzheimers Dis. 78, 1639–1652 (2020).
Ferreira, D., Nordberg, A. & Westman, E. Biological subtypes of Alzheimer disease. Neurology 94, 436–448 (2020).
Jellinger, K. A. Pathobiological subtypes of Alzheimer disease. Dement. Geriatr. Cogn. Disord. 49, 321–333 (2020).
Outeiro, T. F. et al. Dementia with Lewy bodies: an update and outlook. Mol. Neurodegener. 14, 5 (2019).
Arnaoutoglou, N. A., O’Brien, J. T. & Underwood, B. R. Dementia with Lewy bodies — from scientific knowledge to clinical insights. Nat. Rev. Neurol. 15, 103–112 (2019).
Iizuka, T. & Kameyama, M. Spatial metabolic profiles to discriminate dementia with Lewy bodies from Alzheimer disease. J. Neurol. 267, 1960–1969 (2020).
Kang, S. W. et al. Implication of metabolic and dopamine transporter PET in dementia with Lewy bodies. Sci. Rep. 11, 14394 (2021).
Perovnik, M. et al. Metabolic brain pattern in dementia with Lewy bodies: relationship to Alzheimer’s disease topography. Neuroimage Clin. 35, 103080 (2022).
Lu, J. et al. Consistent abnormalities in metabolic patterns of Lewy body dementias. Mov. Disord. 37, 1861–1871 (2022).
McKeith, I. G. et al. Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB Consortium. Neurology 89, 88–100 (2017).
Hepp, D. H. et al. Distribution and load of amyloid-β pathology in Parkinson disease and dementia with Lewy bodies. J. Neuropathol. Exp. Neurol. 75, 936–945 (2016).
Lau, A. et al. Alzheimer’s disease-related metabolic pattern in diverse forms of neurodegenerative diseases. Diagnostics 11, 2023 (2021).
Ingram, M. et al. Spatial covariance analysis of FDG-PET and HMPAO-SPECT for the differential diagnosis of dementia with Lewy bodies and Alzheimer’s disease. Psychiatry Res. Neuroimaging 322, 111460 (2022).
Bang, J., Spina, S. & Miller, B. L. Frontotemporal dementia. Lancet 386, 1672–1682 (2015).
Nazem, A. et al. A multivariate metabolic imaging marker for behavioral variant frontotemporal dementia. Alzheimers Dement. 10, 583–594 (2018).
Rus, T. et al. Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia. Hum. Brain Mapp. https://doi.org/10.1002/hbm.26140 (2022).
Filippi, M. et al. Functional network connectivity in the behavioral variant of frontotemporal dementia. Cortex 49, 2389–2401 (2013).
Shlens, J. A tutorial on independent component analysis. Preprint at arXiv https://doi.org/10.48550/arXiv.1404.2986 (2014).
Myszczynska, M. A. et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat. Rev. Neurol. 16, 440–456 (2020). Review of current applications of machine learning in the study of neurodegenerative diseases.
Young, A. L. et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with subtype and stage inference. Nat. Commun. 9, 4273 (2018).
Franzmeier, N. et al. Predicting sporadic Alzheimer’s disease progression via inherited Alzheimer’s disease‐informed machine‐learning. Alzheimers Dement. 16, 501–511 (2020). This study proposes a machine learning model to predict cognitive decline in individuals with autosomal dominant Alzheimer disease and amyloid-positive individuals with MCI using a combination of fluid and imaging biomarkers.
Davatzikos, C. Machine learning in neuroimaging: progress and challenges. Neuroimage 197, 652–656 (2019).
Borchert, R. et al. Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review. Preprint at medRxiv https://doi.org/10.1101/2021.12.12.21267677 (2021).
M.P. and T.R. were supported by the Slovenian Research Agency (ARRS) through grant P1-0389 and projects J7-2600 and J7-3150. T.R. is a recipient of the Fulbright Foreign Student Program sponsored by the US Department of State’s Bureau of Educational and Cultural Affairs. The authors thank Yoon Young Choi for her invaluable editorial assistance in preparing the manuscript.
D.E. declares that he receives funding from the NIH and The Michael J. Fox Foundation for Parkinson’s Research.
Peer review information
Nature Reviews Neurology thanks Yoshikazu Nakano, who co-reviewed with Shigeki Hirano; Meichen Yu; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Correlation coefficient between the degree of all nodes on two opposite ends of a link; a measure of the diversity of connections in a graph that provides an index of overall network stability.
- Characteristic path length
The average number of edges in the shortest paths connecting the nodes of the network; a measure of the integration of information processing and the global efficiency of the network.
- Clustering coefficient
The likelihood that the nearest neighbours of a given network node are themselves connected; an index of the segregation of information processing within the network.
- Covariance topographies
Patterns of co-varying regional activity identified by principal component analysis of the individual’s scan data.
- Degree centrality
The number of connections divided by the number of nodes in the same graph; a measure of the overall connectivity of nodes.
- Dimensionality reduction
Mathematical procedures to identify one or more smaller matrices that contain information the same as or similar to the original large data matrix; this approach is used to extract relevant properties of the data (such as specific disease-related topographies) and remove extraneous effects.
- Expression levels
Also termed subject scores. The principal component scalar, which quantifies the extent to which a given topographic pattern is represented in a specific individual’s scan.
- Functional connectivity
Connections between brain regions defined by the magnitude of correlations in spontaneous signal fluctuations (resting-state functional MRI), local metabolic activity ([18F]fluorodeoxyglucose PET), electrical signals (electro-encephalography) or magnetic fields produced by electrical activity (magneto-encephalography).
- Functional neuroimaging
Technique to map regional changes in neuronal activity, based typically on blood oxygenation, cerebral metabolism or other physiological signals.
- Graph theory
Mathematical approach to studying properties of network structure (nodal organization) and function (information flow).
The relationship of the number of edges linking the nodes within a community (module or subgraph) to those linking the different communities for the network as a whole; a means of partitioning the network into organizationally discrete nodal clusters.
- Network patterns
Topographical patterns of neural activity in which interconnected brain regions form discrete networks.
- Predictive models
Mathematical constructs that predict the likelihood of future events or outcomes based on a set of input data.
Ratio of clustering coefficient to characteristic path length, normalized to corresponding values from an equivalent random graph; a measure of the balance between segregation and integration of information processing in the network space.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Perovnik, M., Rus, T., Schindlbeck, K.A. et al. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 19, 73–90 (2023). https://doi.org/10.1038/s41582-022-00753-3