Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning
Abstract
:1. Introduction
2. Methodological Overview of Resting-State fMRI (rs-fMRI) Studies
3. The Default Mode Network (DMN)
3.1. Overview of DMN
3.2. rs-fMRI Studies Associated with DMN in Late-Life Depression (LLD)
3.3. rs-fMRI Studies Associated with DMN in Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI)
4. The Executive Control Network (ECN)
4.1. Overview of ECN
4.2. rs-fMRI Studies Associated with ECN in LLD
4.3. rs-fMRI studies associated with the ECN in AD and MCI
5. The Salience Network (SN)
5.1. Overview of SN
5.2. rs-fMRI Studies Associated with the SN in LLD
5.3. rs-fMRI Studies Associated with SN in AD and MCI
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Association, A.P. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®); American Psychiatric Pub: Washington, DC, USA, 2013; ISBN 0890425574. [Google Scholar]
- World Health Organization. Dementia. Available online: https://www.who.int/news-room/fact-sheets/detail/dementia (accessed on 21 December 2020).
- Hebert, L.E.; Weuve, J.; Scherr, P.A.; Evans, D.A. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology 2013, 80, 1778–1783. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Petersen, R.C.; Stevens, J.C.; Ganguli, M.; Tangalos, E.G.; Cummings, J.L.; DeKosky, S.T. Practice parameter: Early detection of dementia: Mild cognitive impairment. Neurology 2001, 56, 1133–1142. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Association, A. 2018 Alzheimer’s disease facts and figures. Alzheimer’s Dement. 2018, 14, 367–429. [Google Scholar] [CrossRef]
- Greenberg, S.M.; Bacskai, B.J.; Hernandez-Guillamon, M.; Pruzin, J.; Sperling, R.; van Veluw, S.J. Cerebral amyloid angiopathy and Alzheimer disease—One peptide, two pathways. Nat. Rev. Neurol. 2020, 16, 30–42. [Google Scholar] [CrossRef]
- Ozben, T.; Ozben, S. Neuro-inflammation and anti-inflammatory treatment options for Alzheimer’s disease. Clin. Biochem. 2019, 72, 87–89. [Google Scholar] [CrossRef]
- Brookmeyer, R.; Johnson, E.; Ziegler-Grahamm, K.; Arrighi, H.M. Forecasting the global prevalence and burden of Alzheimer’s disease. Alzheimer’s Dement. 2007, 3, 186–191. [Google Scholar] [CrossRef] [Green Version]
- Baumgart, M.; Snyder, H.M.; Carrillo, M.C.; Fazio, S.; Kim, H.; Johns, H. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective. Alzheimer’s Dement. 2015, 11, 718–726. [Google Scholar] [CrossRef] [Green Version]
- Xu, W.; Tan, L.; Wang, H.-F.; Jiang, T.; Tan, M.-S.; Tan, L.; Zhao, Q.-F.; Li, J.-Q.; Wang, J.; Yu, J.-T. Meta-analysis of modifiable risk factors for Alzheimer’s disease. J. Neurol. 2015, 86, 1299–1306. [Google Scholar] [CrossRef]
- Clare, L.; Wu, Y.-T.; Teale, J.C.; MacLeod, C.; Matthews, F.; Brayne, C.; Woods, B.; Team, C.-W. study Potentially modifiable lifestyle factors, cognitive reserve, and cognitive function in later life: A cross-sectional study. PLoS Med. 2017, 14, e1002259. [Google Scholar] [CrossRef]
- Chi, S.; Wang, C.; Jiang, T.; Zhu, X.-C.; Yu, J.-T.; Tan, L. The Prevalence of Depression in Alzheimer’s Disease: A Systematic Review and Meta-Analysis. Curr. Alzheimer Res. 2015, 12, 189–198. [Google Scholar] [CrossRef]
- Fuchs, E. Neurogenesis in the adult brain: Is there an association with mental disorders? Eur. Arch. Psychiatry Clin. Neurosci. 2007, 257, 247–249. [Google Scholar] [CrossRef] [PubMed]
- Herbert, J.; Lucassen, P.J. Depression as a risk factor for Alzheimer’s disease: Genes, steroids, cytokines and neurogenesis—What do we need to know? Front. Neuroendocrinol. 2016, 41, 153–171. [Google Scholar] [CrossRef] [PubMed]
- Goldberg, D. The heterogeneity of “major depression”. World Psychiatry 2011, 10, 226–228. [Google Scholar] [CrossRef] [PubMed]
- Lynch, C.J.; Gunning, F.M.; Liston, C. Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes. Biol. Psychiatry 2020, 88, 83–94. [Google Scholar] [CrossRef]
- Riddle, M.; Potter, G.G.; McQuoid, D.R.; Steffens, D.C.; Beyer, J.L.; Taylor, W.D. Longitudinal Cognitive Outcomes of Clinical Phenotypes of Late-Life Depression. Am. J. Geriatr. Psychiatry 2017, 25, 1123–1134. [Google Scholar] [CrossRef]
- Leggett, A.; Zarit, S.H.; Hoang, C.N.; Nguyen, H.T. Correlates of cognitive impairment in older Vietnamese. Aging Ment. Health 2013, 17, 915–923. [Google Scholar] [CrossRef]
- Tedros, A.G.; Christopher, J.L.M. Global Burden of Disease Study 2017. Lancet 2017, 5, 1–27. [Google Scholar]
- Ownby, R.L.; Crocco, E.; Acevedo, A.; John, V.; Loewenstein, D. Depression and Risk for Alzheimer Disease: Systematic Review, Meta-analysis, and Metaregression Analysis. Arch. Gen. Psychiatry 2006, 63, 530–538. [Google Scholar] [CrossRef] [Green Version]
- Diniz, B.S.; Sibille, E.; Ding, Y.; Tseng, G.; Aizenstein, H.J.; Lotrich, F.; Becker, J.T.; Lopez, O.L.; Lotze, M.T.; Klunk, W.E.; et al. Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression. Mol. Psychiatry 2015, 20, 594–601. [Google Scholar] [CrossRef] [Green Version]
- Brailean, A.; Aartsen, M.J.; Muniz-Terrera, G.; Prince, M.; Prina, A.M.; Comijs, H.C.; Huisman, M.; Beekman, A. Longitudinal associations between late-life depression dimensions and cognitive functioning: A cross-domain latent growth curve analysis. Psychol. Med. 2017, 47, 690–702. [Google Scholar] [CrossRef] [Green Version]
- Ismail, Z.; Elbayoumi, H.; Fischer, C.E.; Hogan, D.B.; Millikin, C.P.; Schweizer, T.; Mortby, M.E.; Smith, E.E.; Patten, S.B.; Fiest, K.M. Prevalence of Depression in Patients with Mild Cognitive Impairment: A Systematic Review and Meta-analysis. JAMA Psychiatry 2017, 74, 58–67. [Google Scholar] [CrossRef] [PubMed]
- Almeida, O.P.; Hankey, G.J.; Yeap, B.B.; Golledge, J.; Flicker, L. Depression as a modifiable factor to decrease the risk of dementia. Transl. Psychiatry 2017, 7, e1117. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Brendel, M.; Pogarell, O.; Xiong, G.; Delker, A.; Bartenstein, P.; Rominger, A. Depressive symptoms accelerate cognitive decline in amyloid-positive MCI patients. Eur. J. Nucl. Med. Mol. Imaging 2015, 42, 716–724. [Google Scholar] [CrossRef] [PubMed]
- Mahgoub, N.; Alexopoulos, G.S. Amyloid Hypothesis: Is There a Role for Antiamyloid Treatment in Late-Life Depression? Am. J. Geriatr. Psychiatry 2016, 24, 239–247. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Do Nascimento, K.K.F.; Silva, K.P.; Malloy-Diniz, L.F.; Butters, M.A.; Diniz, B.S. Plasma and cerebrospinal fluid amyloid-β levels in late-life depression: A systematic review and meta-analysis. J. Psychiatr. Res. 2015, 69, 35–41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Butters, M.A.; Young, J.B.; Lopez, O.; Aizenstein, H.J.; Mulsant, B.H.; Reynolds, C.F., 3rd; DeKosky, S.T.; Becker, J.T. Pathways linking late-life depression to persistent cognitive impairment and dementia. Dialogues Clin. Neurosci. 2008, 10, 345–357. [Google Scholar]
- Geerlings, M.I.; Gerritsen, L. Late-Life Depression, Hippocampal Volumes, and Hypothalamic-Pituitary-Adrenal Axis Regulation: A Systematic Review and Meta-analysis. Biol. Psychiatry 2017, 82, 339–350. [Google Scholar] [CrossRef] [PubMed]
- Edwards Iii, G.A.; Gamez, N.; Escobedo, G., Jr.; Calderon, O.; Moreno-Gonzalez, I. Modifiable Risk Factors for Alzheimer’s Disease. Front. Aging Neurosci. 2019, 11, 146–163. [Google Scholar] [CrossRef] [Green Version]
- Epp, A.M.; Dobson, K.S.; Dozois, D.J.A.; Frewen, P.A. A systematic meta-analysis of the Stroop task in depression. Clin. Psychol. Rev. 2012, 32, 316–328. [Google Scholar] [CrossRef]
- Monteiro, S.; Monteiro, B.; Candida, M.; Adler, N.; Campos, C.; Rocha, N.B.F.; Paes, F.; Nardi, A.E.; Machado, S. Association between depression severity and executive functioning in late-life depression: A systematic review. Med. Express 2016, 3, 1–9. [Google Scholar] [CrossRef] [Green Version]
- Roca, M.; Vives, M.; López-Navarro, E.; García-Campayo, J.; Gili, M. Cognitive impairments and depression: A critical review. Actas Esp. Psiquiatr. 2015, 43, 187–193. [Google Scholar] [PubMed]
- Weisenbach, S.L.; Kumar, A. Current Understanding of the Neurobiology and Longitudinal Course of Geriatric Depression. Curr. Psychiatry Rep. 2014, 16, 463–471. [Google Scholar] [CrossRef] [PubMed]
- Rock, P.L.; Roiser, J.P.; Riedel, W.J.; Blackwell, A.D. Cognitive impairment in depression: A systematic review and meta-analysis. Psychol. Med. 2014, 44, 2029–2040. [Google Scholar] [CrossRef] [Green Version]
- Ahern, E.; Semkovska, M. Cognitive functioning in the first-episode of major depressive disorder: A systematic review and meta-analysis. Neuropsychology 2017, 31, 52–72. [Google Scholar] [CrossRef] [PubMed]
- Liao, W.; Zhang, X.; Shu, H.; Wang, Z.; Liu, D.; Zhang, Z. The characteristic of cognitive dysfunction in remitted late life depression and amnestic mild cognitive impairment. Psychiatry Res. 2017, 251, 168–175. [Google Scholar] [CrossRef]
- Chen, J.; Shu, H.; Wang, Z.; Zhan, Y.; Liu, D.; Liao, W.; Xu, L.; Liu, Y.; Zhang, Z. Convergent and divergent intranetwork and internetwork connectivity patterns in patients with remitted late-life depression and amnestic mild cognitive impairment. Cortex 2016, 83, 194–211. [Google Scholar] [CrossRef]
- Li, W.; Wang, Y.; Ward, B.D.; Antuono, P.G.; Li, S.-J.; Goveas, J.S. Intrinsic inter-network brain dysfunction correlates with symptom dimensions in late-life depression. J. Psychiatr. Res. 2017, 87, 71–80. [Google Scholar] [CrossRef] [Green Version]
- Yue, Y.; Jia, X.; Hou, Z.; Zang, Y.; Yuan, Y. Frequency-dependent amplitude alterations of resting-state spontaneous fluctuations in late-onset depression. Biomed. Res. Int. 2015, 2015, 1–9. [Google Scholar] [CrossRef]
- Alexopoulos, G.S.; Hoptman, M.J.; Kanellopoulos, D.; Murphy, C.F.; Lim, K.O.; Gunning, F.M. Functional connectivity in the cognitive control network and the default mode network in late-life depression. J. Affect. Disord. 2012, 139, 56–65. [Google Scholar] [CrossRef] [Green Version]
- Li, X.; Steffens, D.C.; Potter, G.G.; Guo, H.; Song, S.; Wang, L. Decreased between-hemisphere connectivity strength and network efficiency in geriatric depression. Hum. Brain Mapp. 2017, 38, 53–67. [Google Scholar] [CrossRef] [Green Version]
- Ozer, S.; Young, J.; Champ, C.; Burke, M. A systematic review of the diagnostic test accuracy of brief cognitive tests to detect amnestic mild cognitive impairment. Int. J. Geriatr. Psychiatry 2016, 31, 1139–1150. [Google Scholar] [CrossRef] [PubMed]
- Bai, F.; Shu, N.; Yuan, Y.; Shi, Y.; Yu, H.; Wu, D.; Wang, J.; Xia, M.; He, Y.; Zhang, Z. Topologically Convergent and Divergent Structural Connectivity Patterns between Patients with Remitted Geriatric Depression and Amnestic Mild Cognitive Impairment. J. Neurosci. 2012, 32, 4307–4318. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.; Jia, X.; Liang, P.; Qi, Z.; Yang, Y.; Zhou, W.; Li, K. Changes in thalamus connectivity in mild cognitive impairment: Evidence from resting state fMRI. Eur. J. Radiol. 2012, 81, 277–285. [Google Scholar] [CrossRef] [PubMed]
- Li, H.-J.; Hou, X.-H.; Liu, H.-H.; Yue, C.-L.; He, Y.; Zuo, X.-N. Toward systems neuroscience in mild cognitive impairment and Alzheimer’s disease: A meta-analysis of 75 fMRI studies. Hum. Brain Mapp. 2015, 36, 1217–1232. [Google Scholar] [CrossRef] [PubMed]
- Shimoda, K.; Kimura, M.; Yokota, M.; Okubo, Y. Comparison of regional gray matter volume abnormalities in Alzheimer’s disease and late life depression with hippocampal atrophy using VSRAD analysis: A voxel-based morphometry study. Psychiatry Res. Neuroimaging 2015, 232, 71–75. [Google Scholar] [CrossRef] [Green Version]
- Biswal, B.; Zerrin Yetkin, F.; Haughton, V.M.; Hyde, J.S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 1995, 34, 537–541. [Google Scholar] [CrossRef]
- Smitha, K.A.; Akhil Raja, K.; Arun, K.M.; Rajesh, P.G.; Thomas, B.; Kapilamoorthy, T.R.; Kesavadas, C. Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. Neuroradiol. J. 2017, 30, 305–317. [Google Scholar] [CrossRef]
- Rosenberg, M.D.; Finn, E.S.; Scheinost, D.; Papademetris, X.; Shen, X.; Constable, R.T.; Chun, M.M. A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci. 2016, 19, 165–171. [Google Scholar] [CrossRef] [Green Version]
- Hsu, W.-T.; Rosenberg, M.D.; Scheinost, D.; Constable, R.T.; Chun, M.M. Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Soc. Cogn. Affect. Neurosci. 2018, 13, 224–232. [Google Scholar] [CrossRef] [Green Version]
- Jiang, R.; Calhoun, V.D.; Zuo, N.; Lin, D.; Li, J.; Fan, L.; Qi, S.; Sun, H.; Fu, Z.; Song, M.; et al. Connectome-based individualized prediction of temperament trait scores. Neuroimage 2018, 183, 366–374. [Google Scholar] [CrossRef]
- Beaty, R.E.; Kenett, Y.N.; Christensen, A.P.; Rosenberg, M.D.; Benedek, M.; Chen, Q.; Fink, A.; Qiu, J.; Kwapil, T.R.; Kane, M.J.; et al. Robust prediction of individual creative ability from brain functional connectivity. Proc. Natl. Acad. Sci. USA 2018, 115, 1087–1092. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Siegel, J.S.; Ramsey, L.E.; Snyder, A.Z.; Metcalf, N.V.; Chacko, R.V.; Weinberger, K.; Baldassarre, A.; Hacker, C.D.; Shulman, G.L.; Corbetta, M. Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proc. Natl. Acad. Sci. USA 2016, 113, E4367–E4376. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jalbrzikowski, M.; Liu, F.; Foran, W.; Calabro, F.J.; Roeder, K.; Devlin, B.; Luna, B. Cognitive and default mode networks support developmental stability in functional connectome fingerprinting through adolescence. bioRxiv 2019, 812719. [Google Scholar] [CrossRef] [Green Version]
- Gratton, C.; Laumann, T.O.; Nielsen, A.N.; Greene, D.J.; Gordon, E.M.; Gilmore, A.W.; Nelson, S.M.; Coalson, R.S.; Snyder, A.Z.; Schlaggar, B.L.; et al. Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation. Neuron 2018, 98, 439–452.e5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kühn, S.; Vanderhasselt, M.-A.; De Raedt, R.; Gallinat, J. Why ruminators won’t stop: The structural and resting state correlates of rumination and its relation to depression. J. Affect. Disord. 2012, 141, 352–360. [Google Scholar] [CrossRef] [Green Version]
- Brakowski, J.; Spinelli, S.; Dörig, N.; Bosch, O.G.; Manoliu, A.; Holtforth, M.G.; Seifritz, E. Resting state brain network function in major depression—Depression symptomatology, antidepressant treatment effects, future research. J. Psychiatr. Res. 2017, 92, 147–159. [Google Scholar] [CrossRef]
- Takamura, T.; Hanakawa, T. Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders. J. Neural Transm. 2017, 124, 821–839. [Google Scholar] [CrossRef]
- Lee, M.H.; Smyser, C.D.; Shimony, J.S. Resting-state fMRI: A review of methods and clinical applications. Am. J. Neuroradiol. 2013, 34, 1866–1872. [Google Scholar] [CrossRef] [Green Version]
- Zang, Y.; Jiang, T.; Lu, Y.; He, Y.; Tian, L. Regional homogeneity approach to fMRI data analysis. Neuroimage 2004, 22, 394–400. [Google Scholar] [CrossRef]
- Mckeown, M.J.; Sejnowski, T.J. Independent Component Analysis of fMRI Data: Examining the Assumptions. Hum. Brain Mapp. 1998, 6, 368–372. [Google Scholar] [CrossRef]
- Van De Ven, V.G.; Formisano, E.; Prvulovic, D.; Roeder, C.H.; Linden, D.E.J. Functional Connectivity as Revealed by Spatial Independent Component Analysis of fMRI Measurements During Rest. Hum. Brain Mapp. 2004, 22, 165–178. [Google Scholar] [CrossRef] [PubMed]
- Bullmore, E.; Sporns, O. Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 2009, 10, 186–198. [Google Scholar] [CrossRef] [PubMed]
- Bassett, D.S.; Bullmore, E.T. Small-World Brain Networks Revisited. Neuroscientist 2016, 23, 499–516. [Google Scholar] [CrossRef] [Green Version]
- Friston, K.J. Functional and effective connectivity in neuroimaging: A synthesis. Hum. Brain Mapp. 1994, 2, 56–78. [Google Scholar] [CrossRef]
- Liang, P.; Li, Z.; Deshpande, G.; Wang, Z.; Hu, X.; Li, K. Altered causal connectivity of resting state brain networks in amnesic MCI. PLoS ONE 2014, 9, e88476. [Google Scholar] [CrossRef] [Green Version]
- Yu-Feng, Z.; Yong, H.; Chao-Zhe, Z.; Qing-Jiu, C.; Man-Qiu, S.; Meng, L.; Li-Xia, T.; Tian-Zi, J.; Yu-Feng, W. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 2007, 29, 83–91. [Google Scholar] [CrossRef]
- Zou, Q.-H.; Zhu, C.-Z.; Yang, Y.; Zuo, X.-N.; Long, X.-Y.; Cao, Q.-J.; Wang, Y.-F.; Zang, Y.-F. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. J. Neurosci. Methods 2008, 172, 137–141. [Google Scholar] [CrossRef] [Green Version]
- Geng, J.; Yan, R.; Shi, J.; Chen, Y.; Mo, Z.; Shao, J.; Wang, X.; Yao, Z.; Lu, Q. Altered regional homogeneity in patients with somatic depression: A resting-state fMRI study. J. Affect. Disord. 2019, 246, 498–505. [Google Scholar] [CrossRef]
- Peng, D.; Liddle, E.B.; Iwabuchi, S.J.; Zhang, C.; Wu, Z.; Liu, J.; Jiang, K.; Xu, L.; Liddle, P.F.; Palaniyappan, L.; et al. Dissociated large-scale functional connectivity networks of the precuneus in medication-naïve first-episode depression. Psychiatry Res. Neuroimaging 2015, 232, 250–256. [Google Scholar] [CrossRef]
- Liu, F.; Hu, M.; Wang, S.; Guo, W.; Zhao, J.; Li, J.; Xun, G.; Long, Z.; Zhang, J.; Wang, Y.; et al. Abnormal regional spontaneous neural activity in first-episode, treatment-naive patients with late-life depression: A resting-state fMRI study. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2012, 39, 326–331. [Google Scholar] [CrossRef]
- Gray, J.P.; Müller, V.I.; Eickhoff, S.B.; Fox, P.T. Multimodal Abnormalities of Brain Structure and Function in Major Depressive Disorder: A Meta-Analysis of Neuroimaging Studies. Am. J. Psychiatry 2020, 177, 422–434. [Google Scholar] [CrossRef] [PubMed]
- Müller, V.I.; Cieslik, E.C.; Serbanescu, I.; Laird, A.R.; Fox, P.T.; Eickhoff, S.B. Altered Brain Activity in Unipolar Depression Revisited: Meta-analyses of Neuroimaging Studies. JAMA Psychiatry 2017, 74, 47–55. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Marek, S.; Tervo-Clemmens, B.; Nielsen, A.N.; Wheelock, M.D.; Miller, R.L.; Laumann, T.O.; Earl, E.; Foran, W.W.; Cordova, M.; Doyle, O.; et al. Identifying reproducible individual differences in childhood functional brain networks: An ABCD study. Dev. Cogn. Neurosci. 2019, 40, 100706. [Google Scholar] [CrossRef] [PubMed]
- Mulders, P.C.; van Eijndhoven, P.F.; Schene, A.H.; Beckmann, C.F.; Tendolkar, I. Resting-state functional connectivity in major depressive disorder: A review. Neurosci. Biobehav. Rev. 2015, 56, 330–344. [Google Scholar] [CrossRef]
- Yuen, G.S.; Gunning-Dixon, F.M.; Hoptman, M.J.; AbdelMalak, B.; McGovern, A.R.; Seirup, J.K.; Alexopoulos, G.S. The salience network in the apathy of late-life depression. Int. J. Geriatr. Psychiatry 2014, 29, 1116–1124. [Google Scholar] [CrossRef] [Green Version]
- Hohenfeld, C.; Werner, C.J.; Reetz, K. Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker? NeuroImage Clin. 2018, 18, 849–870. [Google Scholar] [CrossRef]
- Greicius, M.D.; Ben, K.; Allan, L.R.; Vinod, M. Functional connectivity in the resting brain: A network analysis of the default mod hypothesis. Proc. Natl. Acad. Sci. USA 2003, 100, 253–258. [Google Scholar] [CrossRef] [Green Version]
- Buckner, R.L.; Andrews-Hanna, J.R.; Schacter, D.L. The Brain’s Default Network. Ann. N. Y. Acad. Sci. 2008, 1124, 1–38. [Google Scholar] [CrossRef] [Green Version]
- Mohan, A.; Roberto, A.J.; Mohan, A.; Lorenzo, A.; Jones, K.; Carney, M.J.; Liogier-Weyback, L.; Hwang, S.; Lapidus, K.A.B. The Significance of the Default Mode Network (DMN) in Neurological and Neuropsychiatric Disorders: A Review. Yale J. Biol. Med. 2016, 89, 49–57. [Google Scholar]
- Kyeong, S.; Kim, J.; Kim, J.; Kim, E.J.; Kim, H.E.; Kim, J.-J. Differences in the modulation of functional connectivity by self-talk tasks between people with low and high life satisfaction. Neuroimage 2020, 217, 116929. [Google Scholar] [CrossRef]
- Spreng, R.N.; Mar, R.A.; Kim, A.S.N. The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: A quantitative meta-analysis. J. Cogn. Neurosci. 2009, 21, 489–510. [Google Scholar] [CrossRef] [PubMed]
- Raichle, M.E. The Brain’s Default Mode Network. Annu. Rev. Neurosci. 2015, 38, 433–447. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Buckner, R.L.; Sepulcre, J.; Talukdar, T.; Krienen, F.M.; Liu, H.; Hedden, T.; Andrews-Hanna, J.R.; Sperling, R.A.; Johnson, K.A. Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer’s Disease. J. Neurosci. 2009, 29, 1860–1873. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Andrews-Hanna, J.R.; Reidler, J.S.; Sepulcre, J.; Poulin, R.; Buckner, R.L. Functional-Anatomic Fractionation of the Brain’s Default Network. Neuron 2010, 65, 550–562. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Andrews-Hanna, J.R.; Smallwood, J.; Spreng, R.N. The default network and self-generated thought: Component processes, dynamic control, and clinical relevance. Ann. N. Y. Acad. Sci. 2014, 1316, 29–52. [Google Scholar] [CrossRef] [PubMed]
- Leech, R.; Sharp, D.J. The role of the posterior cingulate cortex in cognition and disease. Brain 2014, 137, 12–32. [Google Scholar] [CrossRef] [Green Version]
- Menon, V. Large-scale brain networks and psychopathology: A unifying triple network model. Trends Cogn. Sci. 2011, 15, 483–506. [Google Scholar] [CrossRef]
- Scalabrini, A.; Vai, B.; Poletti, S.; Damiani, S.; Mucci, C.; Colombo, C.; Zanardi, R.; Benedetti, F.; Northoff, G. All roads lead to the default-mode network—global source of DMN abnormalities in major depressive disorder. Neuropsychopharmacology 2020, 45, 2058–2069. [Google Scholar] [CrossRef]
- Connolly, C.G.; Wu, J.; Ho, T.C.; Hoeft, F.; Wolkowitz, O.; Eisendrath, S.; Frank, G.; Hendren, R.; Max, J.E.; Paulus, M.P.; et al. Resting-State Functional Connectivity of Subgenual Anterior Cingulate Cortex in Depressed Adolescents. Biol. Psychiatry 2013, 74, 898–907. [Google Scholar] [CrossRef] [Green Version]
- Yin, Y.; He, X.; Xu, M.; Hou, Z.; Song, X.; Sui, Y.; Liu, Z.; Jiang, W.; Yue, Y.; Zhang, Y.; et al. Structural and functional connectivity of default mode network underlying the cognitive impairment in late-onset depression. Sci. Rep. 2016, 6, 1–10. [Google Scholar] [CrossRef] [Green Version]
- Posner, J.; Hellerstein, D.J.; Gat, I.; Mechling, A.; Klahr, K.; Wang, Z.; McGrath, P.J.; Stewart, J.W.; Peterson, B.S. Antidepressants Normalize the Default Mode Network in Patients with Dysthymia. JAMA Psychiatry 2013, 70, 373–382. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yuan, Y.; Zhang, Z.; Bai, F.; Yu, H.; Shi, Y.; Qian, Y.; Liu, W.; You, J.; Zhang, X.; Liu, Z. Abnormal neural activity in the patients with remitted geriatric depression: A resting-state functional magnetic resonance imaging study. J. Affect. Disord. 2008, 111, 145–152. [Google Scholar] [CrossRef] [PubMed]
- Chen, J.; Liu, F.; Xun, G.; Chen, H.; Hu, M.; Guo, X.; Xiao, C.; Wooderson, S.C.; Guo, W.; Zhao, J. Early and late onset, first-episode, treatment-naive depression: Same clinical symptoms, different regional neural activities. J. Affect. Disord. 2012, 143, 56–63. [Google Scholar] [CrossRef] [PubMed]
- Steffens, D.C.; Wang, L.; Manning, K.J.; Pearlson, G.D. Negative Affectivity, Aging, and Depression: Results from the Neurobiology of Late-Life Depression (NBOLD) Study. Am. J. Geriatr. Psychiatry 2017, 25, 1135–1149. [Google Scholar] [CrossRef]
- Guo, W.; Liu, F.; Zhang, J.; Zhang, Z.; Yu, L.; Liu, J.; Chen, H.; Xiao, C. Abnormal default-mode network homogeneity in first-episode, drug-naive major depressive disorder. PLoS ONE 2014, 9, e91102. [Google Scholar] [CrossRef] [Green Version]
- Zhu, X.; Wang, X.; Xiao, J.; Liao, J.; Zhong, M.; Wang, W.; Yao, S. Evidence of a Dissociation Pattern in Resting-State Default Mode Network Connectivity in First-Episode, Treatment-Naive Major Depression Patients. Biol. Psychiatry 2012, 71, 611–617. [Google Scholar] [CrossRef]
- Li, B.; Liu, L.; Friston, K.J.; Shen, H.; Wang, L.; Zeng, L.-L.; Hu, D. A Treatment-Resistant Default Mode Subnetwork in Major Depression. Biol. Psychiatry 2013, 74, 48–54. [Google Scholar] [CrossRef]
- Wu, M.; Andreescu, C.; Butters, M.A.; Tamburo, R.; Reynolds, C.F.; Aizenstein, H. Default-mode network connectivity and white matter burden in late-life depression. Psychiatry Res. Neuroimaging 2011, 194, 39–46. [Google Scholar] [CrossRef] [Green Version]
- Andreescu, C.; Tudorascu, D.L.; Butters, M.A.; Tamburo, E.; Patel, M.; Price, J.; Karp, J.F.; Reynolds, C.F.; Aizenstein, H. Resting state functional connectivity and treatment response in late-life depression. Psychiatry Res. Neuroimaging 2013, 214, 313–321. [Google Scholar] [CrossRef] [Green Version]
- Sheline, Y.I.; Price, J.L.; Yan, Z.; Mintun, M.A. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc. Natl. Acad. Sci. USA 2010, 107, 11020–11025. [Google Scholar] [CrossRef] [Green Version]
- Van Tol, M.J.; Li, M.; Metzger, C.D.; Hailla, N.; Horn, D.I.; Li, W.; Heinze, H.J.; Bogerts, B.; Steiner, J.; He, H.; et al. Local cortical thinning links to resting-state disconnectivity in major depressive disorder. Psychol. Med. 2014, 44, 2053–2065. [Google Scholar] [CrossRef] [PubMed]
- Braak, H.; Braak, E.; Bohl, J. Staging of Alzheimer-Related Cortical Destruction. Eur. Neurol. 1993, 33, 403–408. [Google Scholar] [CrossRef] [PubMed]
- Braak, H.; Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991, 82, 239–259. [Google Scholar] [CrossRef] [PubMed]
- Hanseeuw, B.J.; Schultz, A.P.; Betensky, R.A.; Sperling, R.A.; Johnson, K.A. Decreased hippocampal metabolism in high-amyloid mild cognitive impairment. Alzheimer’s Dement. 2016, 12, 1288–1296. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Serra, L.; Bozzali, M.; Fadda, L.; De Simone, M.S.; Bruschini, M.; Perri, R.; Caltagirone, C.; Carlesimo, G.A. The role of hippocampus in the retrieval of autobiographical memories in patients with amnestic Mild Cognitive Impairment due to Alzheimer’s disease. J. Neuropsychol. 2018, 14, 46–68. [Google Scholar] [CrossRef] [PubMed]
- Den Heijer, T.; van der Lijn, F.; Koudstaal, P.J.; Hofman, A.; van der Lugt, A.; Krestin, G.P.; Niessen, W.J.; Breteler, M.M.B. A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline. Brain 2010, 133, 1163–1172. [Google Scholar] [CrossRef]
- Steffens, D.C.; McQuoid, D.R.; Payne, M.E.; Potter, G.G. Change in Hippocampal Volume on Magnetic Resonance Imaging and Cognitive Decline Among Older Depressed and Nondepressed Subjects in the Neurocognitive Outcomes of Depression in the Elderly Study. Am. J. Geriatr. Psychiatry 2011, 19, 4–12. [Google Scholar] [CrossRef] [Green Version]
- Allen, G.; Barnard, H.; McColl, R.; Hester, A.L.; Fields, J.A.; Weiner, M.F.; Ringe, W.K.; Lipton, A.M.; Brooker, M.; McDonald, E.; et al. Reduced Hippocampal Functional Connectivity in Alzheimer Disease. Arch. Neurol. 2007, 64, 1482–1487. [Google Scholar] [CrossRef] [Green Version]
- Li, S.-J.; Li, Z.; Wu, G.; Zhang, M.-J.; Franczak, M.; Antuono, P.G. Alzheimer Disease: Evaluation of a Functional MR Imaging Index as a Marker. Radiology 2002, 225, 253–259. [Google Scholar] [CrossRef]
- Wang, L.; Zang, Y.; He, Y.; Liang, M.; Zhang, X.; Tian, L.; Wu, T.; Jiang, T.; Li, K. Changes in hippocampal connectivity in the early stages of Alzheimer’s disease: Evidence from resting state fMRI. Neuroimage 2006, 31, 496–504. [Google Scholar] [CrossRef]
- Das, S.R.; Pluta, J.; Mancuso, L.; Kliot, D.; Yushkevich, P.A.; Wolk, D.A. Anterior and posterior MTL networks in aging and MCI. Neurobiol. Aging 2015, 36, S141–S150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kenny, E.R.; Blamire, A.M.; Firbank, M.J.; O’Brien, J.T. Functional connectivity in cortical regions in dementia with Lewy bodies and Alzheimer’s disease. Brain 2012, 135, 569–581. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sohn, W.S.; Yoo, K.; Na, D.L.; Jeong, Y. Progressive Changes in Hippocampal Resting-state Connectivity Across Cognitive Impairment: A Cross-sectional Study from Normal to Alzheimer Disease. Alzheimer Dis. Assoc. Disord. 2014, 28, 239–246. [Google Scholar] [CrossRef] [PubMed]
- Tahmasian, M.; Pasquini, L.; Scherr, M.; Meng, C.; Förster, S.; Mulej Bratec, S.; Shi, K.; Yakushev, I.; Schwaiger, M.; Grimmer, T.; et al. The lower hippocampus global connectivity, the higher its local metabolism in Alzheimer disease. Neurology 2015, 84, 1956–1963. [Google Scholar] [CrossRef]
- Sun, Y.; Wang, Y.; Lu, J.; Liu, R.; Schwarz, C.G.; Zhao, H.; Zhang, Y.; Xu, L.; Zhu, B.; Zhang, B.; et al. Disrupted functional connectivity between perirhinal and parahippocampal cortices with hippocampal subfields in patients with mild cognitive impairment and Alzheimer’s disease. Oncotarget 2017, 8, 99112–99124. [Google Scholar] [CrossRef] [Green Version]
- Agosta, F.; Pievani, M.; Geroldi, C.; Copetti, M.; Frisoni, G.B.; Filippi, M. Resting state fMRI in Alzheimer’s disease: Beyond the default mode network. Neurobiol. Aging 2012, 33, 1564–1578. [Google Scholar] [CrossRef]
- Hafkemeijer, A.; Möller, C.; Dopper, E.G.P.; Jiskoot, L.C.; Schouten, T.M.; van Swieten, J.C.; van der Flier, W.M.; Vrenken, H.; Pijnenburg, Y.A.L.; Barkhof, F.; et al. Resting state functional connectivity differences between behavioral variant frontotemporal dementia and Alzheimer’s disease. Front. Hum. Neurosci. 2015, 9, 1–12. [Google Scholar] [CrossRef]
- Damoiseaux, J.S.; Prater, K.E.; Miller, B.L.; Greicius, M.D. Functional connectivity tracks clinical deterioration in Alzheimer’s disease. Neurobiol. Aging 2012, 33, 828.e19–828.e30. [Google Scholar] [CrossRef] [Green Version]
- Filippi, M.; Agosta, F.; Scola, E.; Canu, E.; Magnani, G.; Marcone, A.; Valsasina, P.; Caso, F.; Copetti, M.; Comi, G.; et al. Functional network connectivity in the behavioral variant of frontotemporal dementia. Cortex 2013, 49, 2389–2401. [Google Scholar] [CrossRef]
- Franciotti, R.; Falasca, N.W.; Bonanni, L.; Anzellotti, F.; Maruotti, V.; Comani, S.; Thomas, A.; Tartaro, A.; Taylor, J.-P.; Onofrj, M. Default network is not hypoactive in dementia with fluctuating cognition: An Alzheimer disease/dementia with Lewy bodies comparison. Neurobiol. Aging 2013, 34, 1148–1158. [Google Scholar] [CrossRef]
- Gili, T.; Cercignani, M.; Serra, L.; Perri, R.; Giove, F.; Maraviglia, B.; Caltagirone, C.; Bozzali, M. Regional brain atrophy and functional disconnection across Alzheimer’s disease evolution. J. Neurol. Neurosurg. Psychiatry 2011, 82, 58–66. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Griffanti, L.; Dipasquale, O.; Laganà, M.M.; Nemni, R.; Clerici, M.; Smith, S.M.; Baselli, G.; Baglio, F. Effective artifact removal in resting state fMRI data improves detection of DMN functional connectivity alteration in Alzheimer’s disease. Front. Hum. Neurosci. 2015, 9, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schwindt, G.C.; Chaudhary, S.; Crane, D.; Ganda, A.; Masellis, M.; Grady, C.L.; Stefanovic, B.; Black, S.E. Modulation of the Default-Mode Network Between Rest and Task in Alzheimer’s Disease. Cereb. Cortex 2013, 23, 1685–1694. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, Z.; Yan, C.; Zhao, C.; Qi, Z.; Zhou, W.; Lu, J.; He, Y.; Li, K. Spatial patterns of intrinsic brain activity in mild cognitive impairment and alzheimer’s disease: A resting-state functional MRI study. Hum. Brain Mapp. 2011, 32, 1720–1740. [Google Scholar] [CrossRef] [PubMed]
- Weiler, M.; Fukuda, A.; Massabki, L.; Lopes, T.; Franco, A.; Damasceno, B.; Cendes, F.; Balthazar, M. Default Mode, Executive Function, and Language Functional Connectivity Networks are Compromised in Mild Alzheimer’s Disease. Curr. Alzheimer Res. 2014, 11, 274–282. [Google Scholar] [CrossRef]
- Zhang, Z.; Liu, Y.; Jiang, T.; Zhou, B.; An, N.; Dai, H.; Wang, P.; Niu, Y.; Wang, L.; Zhang, X. Altered spontaneous activity in Alzheimer’s disease and mild cognitive impairment revealed by Regional Homogeneity. Neuroimage 2012, 59, 1429–1440. [Google Scholar] [CrossRef]
- Zhou, W.; Xia, Z.; Bi, Y.; Shu, H. Altered connectivity of the dorsal and ventral visual regions in dyslexic children: A resting-state fMRI study. Front. Hum. Neurosci. 2015, 9, 495–504. [Google Scholar] [CrossRef] [Green Version]
- Balthazar, M.L.F.; Pereira, F.R.S.; Lopes, T.M.; da Silva, E.L.; Coan, A.C.; Campos, B.M.; Duncan, N.W.; Stella, F.; Northoff, G.; Damasceno, B.P.; et al. Neuropsychiatric symptoms in Alzheimer’s disease are related to functional connectivity alterations in the salience network. Hum. Brain Mapp. 2014, 35, 1237–1246. [Google Scholar] [CrossRef]
- Binnewijzend, M.A.A.; Schoonheim, M.M.; Sanz-Arigita, E.; Wink, A.M.; van der Flier, W.M.; Tolboom, N.; Adriaanse, S.M.; Damoiseaux, J.S.; Scheltens, P.; van Berckel, B.N.M.; et al. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol. Aging 2012, 33, 2018–2028. [Google Scholar] [CrossRef]
- Brier, M.R.; Thomas, J.B.; Snyder, A.Z.; Benzinger, T.L.; Zhang, D.; Raichle, M.E.; Holtzman, D.M.; Morris, J.C.; Ances, B.M. Loss of Intranetwork and Internetwork Resting State Functional Connections with Alzheimer’s Disease Progression. J. Neurosci. 2012, 32, 8890–8899. [Google Scholar] [CrossRef]
- Badhwar, A.; Tam, A.; Dansereau, C.; Orban, P.; Hoffstaedter, F.; Bellec, P. Resting-state network dysfunction in Alzheimer’s disease: A systematic review and meta-analysis. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 2017, 8, 73–85. [Google Scholar] [CrossRef] [PubMed]
- Koch, K.; Myers, N.E.; Göttler, J.; Pasquini, L.; Grimmer, T.; Förster, S.; Manoliu, A.; Neitzel, J.; Kurz, A.; Förstl, H.; et al. Disrupted Intrinsic Networks Link Amyloid-β Pathology and Impaired Cognition in Prodromal Alzheimer’s Disease. Cereb. Cortex 2015, 25, 4678–4688. [Google Scholar] [CrossRef] [PubMed]
- Jones, D.T.; Vemuri, P.; Murphy, M.C.; Gunter, J.L.; Senjem, M.L.; Machulda, M.M.; Przybelski, S.A.; Gregg, B.E.; Kantarci, K.; Knopman, D.S.; et al. Non-Stationarity in the “Resting Brain’s” Modular Architecture. PLoS ONE 2012, 7, e39731. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Song, J.; Qin, W.; Liu, Y.; Duan, Y.; Liu, J.; He, X.; Li, K.; Zhang, X.; Jiang, T.; Yu, C. Aberrant Functional Organization within and between Resting-State Networks in AD. PLoS ONE 2013, 8, e63727. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guo, Z.; Liu, X.; Hou, H.; Wei, F.; Liu, J.; Chen, X. Abnormal degree centrality in Alzheimer’s disease patients with depression: A resting-state functional magnetic resonance imaging study. Exp. Gerontol. 2016, 79, 61–66. [Google Scholar] [CrossRef] [PubMed]
- Brier, M.R.; Thomas, J.B.; Fagan, A.M.; Hassenstab, J.; Holtzman, D.M.; Benzinger, T.L.; Morris, J.C.; Ances, B.M. Functional connectivity and graph theory in preclinical Alzheimer’s disease. Neurobiol. Aging 2014, 35, 757–768. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sun, Y.; Yin, Q.; Fang, R.; Yan, X.; Wang, Y.; Bezerianos, A. Disrupted Functional Brain Connectivity and Its Association to Structural Connectivity in Amnestic Mild Cognitive Impairment and Alzheimer’s Disease. PLoS ONE 2014, 9, e96505. [Google Scholar] [CrossRef] [Green Version]
- Toussaint, P.-J.; Maiz, S.; Coynel, D.; Doyon, J.; Messé, A.; de Souza, L.C.; Sarazin, M.; Perlbarg, V.; Habert, M.-O.; Benali, H. Characteristics of the default mode functional connectivity in normal ageing and Alzheimer’s disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements. Neuroimage 2014, 101, 778–786. [Google Scholar] [CrossRef]
- Sanz-arigita, E.J.; Schoonheim, M.M.; Damoiseaux, J.S.; Rombouts, S.A.R.B. Loss of ‘Small-World’ Networks in Alzheimer’s Disease: Graph Analysis of fMRI Resting-State Functional Connectivity. PLoS ONE 2010, 5, e13788. [Google Scholar] [CrossRef] [Green Version]
- Wang, J.; Zuo, X.; Dai, Z.; Xia, M.; Zhao, Z.; Zhao, X.; Jia, J.; Han, Y. Disrupted Functional Brain Connectome in Individuals at Risk for Alzheimer’s Disease. Biol. Psychiatry 2013, 73, 472–481. [Google Scholar] [CrossRef]
- Zhao, X.; Liu, Y.; Wang, X.; Liu, B.; Xi, Q.; Guo, Q.; Jiang, H.; Jiang, T.; Wang, P. Disrupted Small-World Brain Networks in Moderate Alzheimer’s Disease: A Resting-State fMRI Study. PLoS ONE 2012, 7, e33540. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, J.; Wang, X.; He, Y.; Yu, X.; Wang, H.; He, Y. Apolipoprotein E ε4 modulates functional brain connectome in Alzheimer’s disease. Hum. Brain Mapp. 2015, 36, 1828–1846. [Google Scholar] [CrossRef] [PubMed]
- Quiroz, Y.T.; Schultz, A.P.; Chen, K.; Protas, H.D.; Brickhouse, M.; Fleisher, A.S.; Langbaum, J.B.; Thiyyagura, P.; Fagan, A.M.; Shah, A.R.; et al. Brain Imaging and Blood Biomarker Abnormalities in Children with Autosomal Dominant Alzheimer Disease: A Cross-Sectional Study. JAMA Neurol. 2015, 72, 912–919. [Google Scholar] [CrossRef] [PubMed]
- Chhatwal, J.P.; Schultz, A.P.; Johnson, K.; Benzinger, T.L.S.; Jack, C.; Ances, B.M.; Sullivan, C.A.; Salloway, S.P.; Ringman, J.M.; Koeppe, R.A.; et al. Impaired default network functional connectivity in autosomal dominant Alzheimer disease. Neurology 2013, 81, 736–744. [Google Scholar] [CrossRef] [PubMed]
- Sala-Llonch, R.; Fortea, J.; Bartrés-Faz, D.; Bosch, B.; Lladó, A.; Peña-Gómez, C.; Antonell, A.; Castellanos-Pinedo, F.; Bargalló, N.; Molinuevo, J.L.; et al. Evolving Brain Functional Abnormalities in PSEN1 Mutation Carriers: A Resting and Visual Encoding fMRI Study. J. Alzheimer’s Dis. 2013, 36, 165–175. [Google Scholar] [CrossRef] [PubMed]
- Adriaanse, S.M.; Sanz-Arigita, E.J.; Binnewijzend, M.A.A.; Ossenkoppele, R.; Tolboom, N.; van Assema, D.M.E.; Wink, A.M.; Boellaard, R.; Yaqub, M.; Windhorst, A.D.; et al. Amyloid and its association with default network integrity in Alzheimer’s disease. Hum. Brain Mapp. 2014, 35, 779–791. [Google Scholar] [CrossRef]
- Thomas, J.B.; Brier, M.R.; Bateman, R.J.; Snyder, A.Z.; Benzinger, T.L.; Xiong, C.; Raichle, M.; Holtzman, D.M.; Sperling, R.A.; Mayeux, R.; et al. Functional Connectivity in Autosomal Dominant and Late-Onset Alzheimer Disease. JAMA Neurol. 2014, 71, 1111–1122. [Google Scholar] [CrossRef] [Green Version]
- Li, X.; Westman, E.; Thordardottir, S.; Ståhlbom, A.K.; Almkvist, O.; Blennow, K.; Wahlund, L.-O.; Graff, C. The Effects of Gene Mutations on Default Mode Network in Familial & Alzheimer’s Disease. J. Alzheimer’s Dis. 2017, 56, 327–334. [Google Scholar] [CrossRef]
- Matura, S.; Prvulovic, D.; Butz, M.; Hartmann, D.; Sepanski, B.; Linnemann, K.; Oertel-Knöchel, V.; Karakaya, T.; Fußer, F.; Pantel, J.; et al. Recognition memory is associated with altered resting-state functional connectivity in people at genetic risk for Alzheimer’s disease. Eur. J. Neurosci. 2014, 40, 3128–3135. [Google Scholar] [CrossRef]
- Sheline, Y.I.; Morris, J.C.; Snyder, A.Z.; Price, J.L.; Yan, Z.; D’Angelo, G.; Liu, C.; Dixit, S.; Benzinger, T.; Fagan, A.; et al. APOE4 Allele Disrupts Resting State fMRI Connectivity in the Absence of Amyloid Plaques or Decreased CSF Aβ42. J. Neurosci. 2010, 30, 17035–17040. [Google Scholar] [CrossRef] [Green Version]
- Su, Y.Y.; Liang, X.; Schoepf, U.J.; Varga-Szemes, A.; West, H.C.; Qi, R.; Kong, X.; Chen, H.J.; Lu, G.M.; Zhang, L.J. APOE Polymorphism Affects Brain Default Mode Network in Healthy Young Adults: A STROBE Article. Medicine 2015, 94, e1734. [Google Scholar] [CrossRef] [PubMed]
- Filippini, N.; MacIntosh, B.J.; Hough, M.G.; Goodwin, G.M.; Frisoni, G.B.; Smith, S.M.; Matthews, P.M.; Beckmann, C.F.; Mackay, C.E. Distinct patterns of brain activity in young carriers of the APOE-ε4 allele. Proc. Natl. Acad. Sci. USA 2009, 106, 7209–7214. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Song, H.; Long, H.; Zuo, X.; Yu, C.; Liu, B.; Wang, Z.; Wang, Q.; Wang, F.; Han, Y.; Jia, J. APOE effects on default mode network in Chinese cognitive normal elderly: Relationship with clinical cognitive performance. PLoS ONE 2015, 10, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Goveas, J.S.; Xie, C.; Chen, G.; Li, W.; Ward, B.D.; Franczak, M.B.; Jones, J.L.; Antuono, P.G.; Li, S.J. Functional Network Endophenotypes Unravel the Effects of Apolipoprotein E Epsilon 4 in Middle-Aged Adults. PLoS ONE 2013, 8, 1–10. [Google Scholar] [CrossRef]
- Westlye, E.T.; Lundervold, A.; Rootwelt, H.; Lundervold, A.J.; Westlye, L.T. Increased hippocampal default mode synchronization during rest in middle-aged and elderly APOE ε4 carriers: Relationships with memory performance. J. Neurosci. 2011, 31, 7775–7783. [Google Scholar] [CrossRef]
- Zhang, H.-Y.; Wang, S.-J.; Xing, J.; Liu, B.; Ma, Z.-L.; Yang, M.; Zhang, Z.-J.; Teng, G.-J. Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer’s disease. Behav. Brain Res. 2009, 197, 103–108. [Google Scholar] [CrossRef]
- Han, S.D.; Arfanakis, K.; Fleischman, D.A.; Leurgans, S.E.; Tuminello, E.R.; Edmonds, E.C.; Bennett, D.A. Functional connectivity variations in mild cognitive impairment: Associations with cognitive function. J. Int. Neuropsychol. Soc. 2012, 18, 39–48. [Google Scholar] [CrossRef] [Green Version]
- Bai, F.; Watson, D.R.; Yu, H.; Shi, Y.; Yuan, Y.; Zhang, Z. Abnormal resting-state functional connectivity of posterior cingulate cortex in amnestic type mild cognitive impairment. Brain Res. 2009, 1302, 167–174. [Google Scholar] [CrossRef]
- Qureshi, M.N.I.; Ryu, S.; Song, J.; Lee, K.H.; Lee, B. Evaluation of Functional Decline in Alzheimer’s Dementia Using 3D Deep Learning and Group ICA for rs-fMRI Measurements. Front. Aging Neurosci. 2019, 11, 8–16. [Google Scholar] [CrossRef] [Green Version]
- Goveas, J.S.; Xie, C.; Ward, B.D.; Wu, Z.; Li, W.; Franczak, M.; Jones, J.L.; Antuono, P.G.; Li, S. Recovery of Hippocampal Network Connectivity Correlates with Cognitive Improvement in Mild Alzheimer’s Disease Patients Treated with Donepezil Assessed by Resting-State fMRI. J. Magn. Reson. Imaging 2011, 34, 764–773. [Google Scholar] [CrossRef] [Green Version]
- Li, W.; Antuono, P.G.; Xie, C.; Chen, G.; Jones, J.L.; Ward, B.D.; Franczak, M.B.; Goveas, J.S.; Li, S. NeuroImage Changes in regional cerebral blood fl ow and functional connectivity in the cholinergic pathway associated with cognitive performance in subjects with mild Alzheimer’s disease after 12-week donepezil treatment. Neuroimage 2012, 60, 1083–1091. [Google Scholar] [CrossRef] [Green Version]
- Ferreira, L.K.; Busatto, G.F. Resting-state functional connectivity in normal brain aging. Neurosci. Biobehav. Rev. 2013, 37, 384–400. [Google Scholar] [CrossRef] [PubMed]
- Wang, L.; Brier, M.R.; Snyder, A.Z.; Thomas, J.B.; Fagan, A.M.; Xiong, C.; Benzinger, T.L.; Holtzman, D.M.; Morris, J.C.; Ances, B.M. Cerebrospinal Fluid Aβ42, Phosphorylated Tau181, and Resting-State Functional Connectivity. JAMA Neurol. 2013, 70, 1242–1248. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Elman, J.A.; Madison, C.M.; Baker, S.L.; Vogel, J.W.; Marks, S.M.; Crowley, S.; O’Neil, J.P.; Jagust, W.J. Effects of Beta-Amyloid on Resting State Functional Connectivity Within and Between Networks Reflect Known Patterns of Regional Vulnerability. Cereb. Cortex 2016, 26, 695–707. [Google Scholar] [CrossRef] [Green Version]
- Hafkemeijer, A.; Möller, C.; Dopper, E.G.P.; Jiskoot, L.C.; van den Berg-Huysmans, A.A.; van Swieten, J.C.; van der Flier, W.M.; Vrenken, H.; Pijnenburg, Y.A.L.; Barkhof, F.; et al. A Longitudinal Study on Resting State Functional Connectivity in Behavioral Variant Frontotemporal Dementia and Alzheimer’s Disease. J. Alzheimer’s Dis. 2017, 55, 521–537. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hart, B.; Cribben, I.; Fiecas, M. A longitudinal model for functional connectivity networks using resting-state fMRI. Neuroimage 2018, 178, 687–701. [Google Scholar] [CrossRef] [Green Version]
- Lau, W.K.-W.; Leung, P.P.-Y.; Chung, C.L.-P. Effects of the Satir Model on Mental Health: A Randomized Controlled Trial. Res. Soc. Work Pract. 2018, 29, 775–785. [Google Scholar] [CrossRef]
- Zhou, J.; Greicius, M.D.; Gennatas, E.D.; Growdon, M.E.; Jang, J.Y.; Rabinovici, G.D.; Kramer, J.H.; Weiner, M.; Miller, B.L.; Seeley, W.W. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease. Brain 2010, 133, 1352–1367. [Google Scholar] [CrossRef] [Green Version]
- Fox, M.D.; Corbetta, M.; Snyder, A.Z.; Vincent, J.L.; Raichle, M.E. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc. Natl. Acad. Sci. USA 2006, 103, 10046–10051. [Google Scholar] [CrossRef] [Green Version]
- Dosenbach, N.U.F.; Fair, D.A.; Miezin, F.M.; Cohen, A.L.; Wenger, K.K.; Dosenbach, R.A.T.; Fox, M.D.; Snyder, A.Z.; Vincent, J.L.; Raichle, M.E.; et al. Distinct brain networks for adaptive and stable task control in humans. Proc. Natl. Acad. Sci. USA 2007, 104, 11073–11078. [Google Scholar] [CrossRef] [Green Version]
- Sylvester, C.M.; Shulman, G.L.; Jack, A.I.; Corbetta, M. Anticipatory and Stimulus-Evoked Blood Oxygenation Level-Dependent Modulations Related to Spatial Attention Reflect a Common Additive Signal. J. Neurosci. 2009, 29, 10671–10682. [Google Scholar] [CrossRef] [PubMed]
- Challis, E.; Hurley, P.; Serra, L.; Bozzali, M.; Oliver, S.; Cercignani, M. Gaussian process classification of Alzheimer’s disease and mild cognitive impairment from resting-state fMRI. Neuroimage 2015, 112, 232–243. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Khazaee, A.; Ebrahimzadeh, A.; Babajani-Feremi, A. Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clin. Neurophysiol. 2015, 126, 2132–2141. [Google Scholar] [CrossRef] [PubMed]
- Shen, K.; Welton, T.; Lyon, M.; McCorkindale, A.N.; Sutherland, G.T.; Burnham, S.; Fripp, J.; Martins, R.; Grieve, S.M. Structural core of the executive control network: A high angular resolution diffusion MRI study. Hum. Brain Mapp. 2020, 41, 1226–1236. [Google Scholar] [CrossRef]
- Niendam, T.A.; Laird, A.R.; Ray, K.L.; Dean, Y.M.; Glahn, D.C.; Carter, C.S. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn. Affect. Behav. Neurosci. 2012, 12, 241–268. [Google Scholar] [CrossRef]
- Reineberg, A.E.; Banich, M.T. Functional connectivity at rest is sensitive to individual differences in executive function: A network analysis. Hum. Brain Mapp. 2016, 37, 2959–2975. [Google Scholar] [CrossRef]
- Zhu, Z.; Johnson, N.F.; Kim, C.; Gold, B.T. Reduced frontal cortex efficiency is associated with lower white matter integrity in aging. Cereb. Cortex 2015, 25, 138–146. [Google Scholar] [CrossRef] [Green Version]
- Rosenberg-Katz, K.; Herman, T.; Jacob, Y.; Mirelman, A.; Giladi, N.; Hendler, T.; Hausdorff, J.M. Fall risk is associated with amplified functional connectivity of the central executive network in patients with Parkinson’s disease. J. Neurol. 2015, 262, 2448–2456. [Google Scholar] [CrossRef]
- Cai, S.; Peng, Y.; Chong, T.; Zhang, Y.; von Deneen, K.M.; Huang, L. Differentiated Effective Connectivity Patterns of the Executive Control Network in Progressive MCI: A Potential Biomarker for Predicting AD. Curr. Alzheimer Res. 2017, 14. [Google Scholar] [CrossRef]
- Zhao, Q.; Lu, H.; Metmer, H.; Li, W.X.Y.; Lu, J. Evaluating functional connectivity of executive control network and frontoparietal network in Alzheimer’s disease. Brain Res. 2018, 1678, 262–272. [Google Scholar] [CrossRef]
- Cieri, F.; Esposito, R.; Cera, N.; Pieramico, V.; Tartaro, A.; Di Giannantonio, M. Late-life depression: Modifications of brain resting state activity. J. Geriatr. Psychiatry Neurol. 2017, 30, 140–150. [Google Scholar] [CrossRef] [PubMed]
- Respino, M.; Hoptman, M.J.; Victoria, L.W.; Alexopoulos, G.S.; Solomonov, N.; Stein, A.T.; Coluccio, M.; Morimoto, S.S.; Blau, C.J.; Abreu, L.; et al. Cognitive Control. Network Homogeneity and Executive Functions in Late-Life Depression. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2020, 5, 213–221. [Google Scholar] [CrossRef] [PubMed]
- Manning, K.; Wang, L.; Steffens, D. Recent advances in the use of imaging in psychiatry: Functional magnetic resonance imaging of large-scale brain networks in late-life depression. F1000Research 2019, 8, 1–9. [Google Scholar] [CrossRef]
- Alalade, E.; Denny, K.; Potter, G.; Steffens, D.; Wang, L. Altered Cerebellar-Cerebral Functional Connectivity in Geriatric Depression. PLoS ONE 2011, 6, e20035. [Google Scholar] [CrossRef] [PubMed]
- Yin, Y.; Hou, Z.; Wang, X.; Sui, Y.; Yuan, Y. Association between altered resting-state cortico-cerebellar functional connectivity networks and mood/cognition dysfunction in late-onset depression. J. Neural Transm. 2015, 122, 887–896. [Google Scholar] [CrossRef]
- Yue, Y.; Yuan, Y.; Hou, Z.; Jiang, W.; Bai, F.; Zhang, Z. Abnormal Functional Connectivity of Amygdala in Late- Onset Depression Was Associated with Cognitive Deficits. PLoS ONE 2013, 8, e75058. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, Z.; Yuan, Y.; Bai, F.; Shu, H.; You, J.; Li, L.; Zhang, Z. Altered functional connectivity networks of hippocampal subregions in remitted late-onset depression: A longitudinal resting-state study. Neurosci. Bull. 2015, 31, 13–21. [Google Scholar] [CrossRef] [Green Version]
- Lockwood, K.A.; Alexopoulos, G.S.; van Gorp, W.G. Executive dysfunction in geriatric depression. Am. J. Psychiatry 2002, 159, 1119–1126. [Google Scholar] [CrossRef]
- Manning, K.J.; Alexopoulos, G.S.; Mcgovern, A.R.; Morimoto, S.S.; Yuen, G.; Kanellopoulos, T.; Gunning, F.M. Executive functioning in late-life depression. Psychiatr. Ann. 2014, 44, 143–146. [Google Scholar] [CrossRef]
- Gandelman, J.A.; Albert, K.; Boyd, B.D.; Park, J.W.; Riddle, M.; Woodward, N.D.; Kang, H.; Landman, B.A.; Taylor, W.D. Intrinsic Functional Network Connectivity Is Associated with Clinical Symptoms and Cognition in Late-Life Depression. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2019, 4, 160–170. [Google Scholar] [CrossRef]
- Alexopoulos, G.S.; Kiosses, D.N.; Klimstra, S.; Kalayam, B.; Bruce, M.L. Clinical Presentation of the “Depression–Executive Dysfunction Syndrome” of Late Life. Am. J. Geriatr. Psychiatry 2002, 10, 98–106. [Google Scholar] [CrossRef] [PubMed]
- Alexopoulos, G.S.; Kiosses, D.N.; Heo, M.; Murphy, C.F.; Shanmugham, B.; Gunning-Dixon, F. Executive Dysfunction and the Course of Geriatric Depression. Biol. Psychiatry 2005, 58, 204–210. [Google Scholar] [CrossRef] [PubMed]
- Manning, K.J.; Alexopoulos, G.S.; Banerjee, S.; Morimoto, S.S.; Seirup, J.K.; Klimstra, S.A.; Yuen, G.; Kanellopoulos, T.; Gunning-Dixon, F. Executive functioning complaints and escitalopram treatment response in late-life depression. Am. J. Geriatr. Psychiatry 2015, 23, 440–445. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Morimoto, S.S.; Kanellopoulos, D.; Manning, K.J.; Alexopoulos, G.S. Diagnosis and treatment of depression and cognitive impairment in late life. Ann. N. Y. Acad. Sci. 2015, 1345, 36–46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Castellazzi, G.; Palesi, F.; Casali, S.; Vitali, P.; Sinforiani, E.; Wheeler-Kingshott, C.A.M.; D’Angelo, E. A comprehensive assessment of resting state networks: Bidirectional modification of functional integrity in cerebro-cerebellar networks in dementia. Front. Neurosci. 2014, 8, 223–250. [Google Scholar] [CrossRef] [PubMed]
- Gour, N.; Ranjeva, J.-P.; Ceccaldi, M.; Confort-Gouny, S.; Barbeau, E.; Soulier, E.; Guye, M.; Didic, M.; Felician, O. Basal functional connectivity within the anterior temporal network is associated with performance on declarative memory tasks. Neuroimage 2011, 58, 687–697. [Google Scholar] [CrossRef] [PubMed]
- Firbank, M.; Kobeleva, X.; Cherry, G.; Killen, A.; Gallagher, P.; Burn, D.J.; Thomas, A.J.; O’Brien, J.T.; Taylor, J.P. Neural correlates of attention-executive dysfunction in lewy body dementia and Alzheimer’s disease. Hum. Brain Mapp. 2016, 37, 1254–1270. [Google Scholar] [CrossRef] [Green Version]
- Levine, M.E.; Lu, A.T.; Bennett, D.A.; Horvath, S. Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning. Aging 2015, 7, 1198–1211. [Google Scholar] [CrossRef]
- Liu, Y.; Yu, J.T.; Wang, H.F.; Han, P.R.; Tan, C.C.; Wang, C.; Meng, X.F.; Risacher, S.L.; Saykin, A.J.; Tan, L. APOE genotype and neuroimaging markers of Alzheimer’s disease: Systematic review and meta-analysis. J. Neurol. Neurosurg. Psychiatry 2015, 86, 127–134. [Google Scholar] [CrossRef] [Green Version]
- Menon, V.; Uddin, L.Q. Saliency, switching, attention and control: A network model of insula function. Brain Struct. Funct. 2010, 214, 655–667. [Google Scholar] [CrossRef] [Green Version]
- Seeley, X.W.W. The Salience Network: A Neural System for Perceiving and Responding to Homeostatic Demands. J. Neurosci. 2019, 39, 9878–9882. [Google Scholar] [CrossRef] [PubMed]
- Downar, J.; Crawley, A.P.; Mikulis, D.J.; Davis, K.D. A multimodal cortical network for the detection of changes in the sensory environment. Nat. Neurosci. 2000, 3, 277–283. [Google Scholar] [CrossRef] [PubMed]
- Touroutoglou, A.; Hollenbeck, M.; Dickerson, B.C.; Feldman Barrett, L. Dissociable large-scale networks anchored in the right anterior insula subserve affective experience and attention. Neuroimage 2012, 60, 1947–1958. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Seeley, W.W.; Menon, V.; Schatzberg, A.F.; Keller, J.; Glover, G.H.; Kenna, H.; Reiss, A.L.; Greicius, M.D. Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control. J. Neurosci. 2007, 27, 2349–2356. [Google Scholar] [CrossRef] [PubMed]
- Chand, G.B.; Wu, J.; Hajjar, I.; Qiu, D. Interactions of the Salience Network and Its Subsystems with the Default-Mode and the Central-Executive. Brain Connect. 2017, 7, 401–412. [Google Scholar] [CrossRef] [PubMed]
- Elton, A.; Gao, W. Divergent task-dependent functional connectivity of executive control and salience networks. Cortex 2014, 51, 56–66. [Google Scholar] [CrossRef]
- Bonnelle, V.; Ham, T.E.; Leech, R.; Kinnunen, K.M.; Mehta, M.A.; Greenwood, R.J.; Sharp, D.J. Salience network integrity predicts default mode network function after traumatic brain injury. Proc. Natl. Acad. Sci. USA 2012, 109, 4690–4695. [Google Scholar] [CrossRef] [Green Version]
- Dai, L.; Zhou, H.; Xu, X.; Zuo, Z. Brain structural and functional changes in patients with major depressive disorder: A literature review. PeerJ 2019, 7, e8170. [Google Scholar] [CrossRef]
- Cullen, K.R.; Westlund, M.K.; Klimes-Dougan, B.; Mueller, B.A.; Houri, A.; Eberly, L.E.; Lim, K.O. Abnormal Amygdala Resting-State Functional Connectivity in Adolescent Depression. JAMA Psychiatry 2014, 71, 1138–1147. [Google Scholar] [CrossRef]
- Luking, K.R.; Repovs, G.; Belden, A.C.; Gaffrey, M.S.; Botteron, K.N.; Luby, J.L.; Barch, D.M. Functional Connectivity of the Amygdala in Early-Childhood-Onset Depression. J. Am. Acad. Child Adolesc. Psychiatry 2011, 50, 1027–1041.e3. [Google Scholar] [CrossRef] [Green Version]
- Davey, C.G.; Whittle, S.; Harrison, B.J.; Simmons, J.G.; Byrne, M.L.; Schwartz, O.S.; Allen, N.B. Functional brain-imaging correlates of negative affectivity and the onset of first-episode depression. Psychol. Med. 2015, 45, 1001–1009. [Google Scholar] [CrossRef] [PubMed]
- Zhang, H.; Li, L.; Wu, M.; Chen, Z.; Hu, X.; Chen, Y.; Zhu, H.; Jia, Z.; Gong, Q. Brain gray matter alterations in first episodes of depression: A meta-analysis of whole-brain studies. Neurosci. Biobehav. Rev. 2016, 60, 43–50. [Google Scholar] [CrossRef] [PubMed]
- Wang, L.; Chou, Y.H.; Potter, G.G.; Steffens, D.C. Altered synchronizations among neural networks in geriatric depression. Biomed. Res. Int. 2015, 2015. [Google Scholar] [CrossRef] [Green Version]
- Steffens, D.C.; Wang, L.; Pearlson, G.D. Functional connectivity predictors of acute depression treatment outcome. Int. Psychogeriatr. 2019, 31, 1831–1835. [Google Scholar] [CrossRef] [PubMed]
- Fredericks, C.A.; Sturm, V.E.; Brown, J.A.; Hua, A.Y.; Bilgel, M.; Wong, D.F.; Resnick, S.M.; Seeley, W.W. Early affective changes and increased connectivity in preclinical Alzheimer’s disease. Alzheimer’s Dement. 2018, 10, 471–479. [Google Scholar] [CrossRef]
- Machulda, M.M.; Jones, D.T.; Vemuri, P.; McDade, E.; Avula, R.; Przybelski, S.; Boeve, B.F.; Knopman, D.S.; Petersen, R.C.; Jack, C.R., Jr. Effect of APOE ε4 status on intrinsic network connectivity in cognitively normal elderly subjects. Arch. Neurol. 2011, 68, 1131–1136. [Google Scholar] [CrossRef] [Green Version]
- He, X.; Qin, W.; Liu, Y.; Zhang, X.; Duan, Y.; Song, J.; Li, K.; Jiang, T.; Yu, C. Abnormal salience network in normal aging and in amnestic mild cognitive impairment and Alzheimer’s disease. Hum. Brain Mapp. 2014, 35, 3446–3464. [Google Scholar] [CrossRef]
- Rami, L.; Sala-Llonch, R.; Solé-Padullés, C.; Fortea, J.; Olives, J.; Lladó, A.; Peña-Gómez, C.; Balasa, M.; Bosch, B.; Antonell, A.; et al. Distinct Functional Activity of the Precuneus and Posterior Cingulate Cortex During Encoding in the Preclinical Stage of Alzheimer’s Disease. J. Alzheimer’s Dis. 2012, 31, 517–526. [Google Scholar] [CrossRef] [Green Version]
- Scheff, S.W.; Price, D.A.; Ansari, M.A.; Roberts, K.N.; Schmitt, F.A.; Ikonomovic, M.D.; Mufson, E.J. Synaptic Change in the Posterior Cingulate Gyrus in the Progression of Alzheimer’s Disease. J. Alzheimer’s Dis. 2015, 43, 1073–1090. [Google Scholar] [CrossRef] [Green Version]
- Mutlu, J.; Landeau, B.; Tomadesso, C.; de Flores, R.; Mézenge, F.; de La Sayette, V.; Eustache, F.; Chételat, G. Connectivity Disruption, Atrophy, and Hypometabolism within Posterior Cingulate Networks in Alzheimer’s Disease. Front. Neurosci. 2016, 10, 582–591. [Google Scholar] [CrossRef]
- Jamieson, A.; Goodwill, A.M.; Termine, M.; Campbell, S.; Szoeke, C. Depression related cerebral pathology and its relationship with cognitive functioning: A systematic review. J. Affect. Disord. 2019, 250, 410–418. [Google Scholar] [CrossRef] [PubMed]
- Dong, X.; Yan, L.; Huang, L.; Guan, X.; Dong, C.; Tao, H.; Wang, T.; Qin, X.; Wan, Q. Repetitive transcranial magnetic stimulation for the treatment of Alzheimer’s disease: A systematic review and meta-analysis of randomized controlled trials. PLoS ONE 2018, 13, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Crossley, N.A.; Mechelli, A.; Scott, J.; Carletti, F.; Fox, P.T.; McGuire, P.; Bullmore, E.T. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 2014, 137, 2382–2395. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sepulcre, J.; Schultz, A.P.; Sabuncu, M.; Gomez-Isla, T.; Chhatwal, J.; Becker, A.; Sperling, R.; Johnson, K.A. In Vivo Tau, Amyloid, and Gray Matter Profiles in the Aging Brain. J. Neurosci. 2016, 36, 7364–7374. [Google Scholar] [CrossRef] [Green Version]
- Curado, M.; Escolano, F.; Lozano, M.A.; Hancock, E.R. Early Detection of Alzheimer’s Disease: Detecting Asymmetries with a Return Random Walk Link Predictor. Entropy 2020, 22, 465. [Google Scholar] [CrossRef] [Green Version]
- Dachena, C.; Casu, S.; Fanti, A.; Lodi, M.B.; Mazzarella, G. Combined Use of MRI, fMRIand Cognitive Data for Alzheimer’s Disease: Preliminary Results. Appl. Sci. 2019, 9, 3156. [Google Scholar] [CrossRef] [Green Version]
Summary of Key Findings | Key Studies |
---|---|
Relative increase in DMN functional connectivity | [92] |
Dissociation within DMN network - decreased posterior DMN functional connectivity - elevation anterior DMN functional connectivity | [94,95,96] |
Restoration of dissociation within DMN network was associated with antidepressant treatment | [100,101] |
Summary of Key Findings | Key Studies |
---|---|
Decreased in DMN functional connectivity | [112,118,123,126,127,128,131] |
Dissociation within DMN network; - decreased posterior DMN functional connectivity - elevation anterior DMN functional connectivity | [134,135] |
DMN networks had longer distances with the loss of edges | [138,141,142] |
Altered DMN functional connectivity was associated with decline of cognition | [143,158,160] |
Altered DMN functional connectivity was associated with genetic mutation | [146,149,152,154,157,163] |
Summary of Key Findings | Key Studies |
---|---|
Decreased in ECN functional connectivity | [186,188] |
Restoration of ECN functional connectivity after remission | [189] |
Decreased in ECN functional connectivity was associated with executive dysfunction | [192] |
Summary of Key Findings | Key Studies |
---|---|
Decreased in ECN functional connectivity | [197] |
Inconclusive result was also reported (increased ECN functional connectivity in AD) | [198] |
ECN functional connectivity was associated with AD progression | [181] |
Summary of Key Findings | Key Studies |
---|---|
Decreased SN functional connectivity | [39] |
Increased functional connectivity between SN and DMN | [77,215] |
Disrupted SN pattern was associated with worse treatment response | [216] |
Summary of Key Findings | Key Studies |
---|---|
Intensified SN functional connectivity was observed in AD patients | [170] |
Increased SN functional connectivity was associated with - elevation of amyloid level, Apo ε4 carriers, and elevation of tau | [166,217,218, 219] |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kim, J.; Kim, Y.-K. Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning. Biomedicines 2021, 9, 82. https://doi.org/10.3390/biomedicines9010082
Kim J, Kim Y-K. Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning. Biomedicines. 2021; 9(1):82. https://doi.org/10.3390/biomedicines9010082
Chicago/Turabian StyleKim, Junhyung, and Yong-Ku Kim. 2021. "Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning" Biomedicines 9, no. 1: 82. https://doi.org/10.3390/biomedicines9010082
APA StyleKim, J., & Kim, Y. -K. (2021). Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning. Biomedicines, 9(1), 82. https://doi.org/10.3390/biomedicines9010082