Top

Published in:

05-12-2022 | Research

Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects

Authors: Jodie P. Gray, Jordi Manuello, Aaron F. Alexander-Bloch, Cassandra Leonardo, Crystal Franklin, Ki Sueng Choi, Franco Cauda, Tommaso Costa, John Blangero, David C. Glahn, Helen S. Mayberg, Peter T. Fox

Published in: Neuroinformatics | Issue 2/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.

previous article Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion

next article NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Alexander-Bloch, A., Gogtay, N., Meunier, D., et al. (2010). Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia. Frontiers in Systems Neuroscience, 4, 147. https://doi.org/10.3389/fnsys.2010.00147CrossRefPubMedPubMedCentral

Alexander-Bloch, A., Lambiotte, R., Roberts, B., et al. (2012). The discovery of population differences in network community structure: New methods and applications to brain functional networks in schizophrenia. NeuroImage, 59(4), 388–3900. https://doi.org/10.1016/j.neuroimage.2011.11.035CrossRef

Appel, S. H. (1981). A unifying hypothesis for the cause of amyotrophic lateral sclerosis, parkinsonism, and alzheimer disease. Annals of Neurology, 10(6), 499–505. https://doi.org/10.1002/ana.410100602CrossRefPubMed

Baltruschat, S. A., Ventura-Campos, N., Cruz-Gómez, A. J., et al. (2015). Gray matter atrophy is associated with functional connectivity reorganization during the Paced Auditory Serial Addition Test (PASAT) execution in multiple sclerosis (MS). Journal of Neuroradiology, 42(3), 141–149. https://doi.org/10.1016/j.neurad.2015.02.006CrossRefPubMed

Barron, D. S., Fox, P. T., Pardoe, H., et al. (2015). Thalamic functional connectivity predicts seizure laterality in individual TLE patients: Application of a biomarker development strategy. Neuroimage Clinical, 7, 273–280. https://doi.org/10.1016/j.nicl.2014.08.002CrossRefPubMedPubMedCentral

Barron, D. S., Tandon, N., Lancaster, J. L., et al. (2014). Thalamic structural connectivity in medial temporal lobe epilepsy. Epilepsia, 55(6), e50-55. https://doi.org/10.1111/epi.12637CrossRefPubMedPubMedCentral

Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20, 352–362. https://doi.org/10.1038/nn.4502CrossRef

Beijers, L., Wardenaar, K. J., van Loo, H. M., & Schoevers, R. A. (2019). Dara-driven biological subtypes of depression: Systematic review of biological approaches to depression subtyping. Molecular Psychiatry, 24, 888–900. https://doi.org/10.1038/s41380-019-0385-5CrossRefPubMed

Betzel, R. F., Bertolero, M. A., Gordon, E. M., et al. (2019). The community structure of functional brain networks exhibits scale-specific patterns of variability across individuals and time. NeuroImage, 202, 115990. https://doi.org/10.1016/j.neuroimage.2019.07.003CrossRefPubMed

Botha, H., Utianski, R. L., Whitwell, J. L., et al. (2018). Disrupted functional connectivity in primary progressive apraxia of speech. Neuroimage Clinical, 18, 617–629. https://doi.org/10.1016/j.nicl.2018.02.036CrossRefPubMedPubMedCentral

Bremner, J. D., Vythilingam, M., Vermetten, E., et al. (2007). Effects of antidepressant treatment on neural correlates of emotional and neutral declarative verbal memory in depression. Journal of Affective Disorders, 101(1–3), 99–111. https://doi.org/10.1016/j.jad.2006.10.028CrossRefPubMed

Cauda, F., Nani, A., Costa, T., et al. (2018a). The morphometric co-atrophy networking of schizophrenia, autistic and obsessive spectrum disorders. Human Brain Mapping, 39(5), 1898–1928. https://doi.org/10.1002/hbm.23952CrossRefPubMedPubMedCentral

Cauda, F., Nani, A., Manuello, J., et al. (2018b). Brain structural alterations are distributed following functional, anatomic, and genetic connectivity. Brain, 141(11), 3211–3232. https://doi.org/10.1093/brain/awy252CrossRefPubMedPubMedCentral

Cauda, F., Nani, A., Manuello, J., et al. (2019). The alteration landscape of the cerebral cortex. NeuroImage, 184, 359–371. https://doi.org/10.1016/j.neuroimage.2018.09.036CrossRefPubMed

Chiang, F. L., Feng, M., Romero, R. S., et al. (2021). Disruption of the Atrophy-based Functional Network in Multiple Sclerosis is Associated with Clinical Disability: Validation of a Meta-Analytic Model in Resting-State Functional MRI. Radiology, 299(1), 159–166. https://doi.org/10.1148/radiol.2021203414CrossRefPubMed

Chiang, F. L., Wang, Q., Yu, F. F., et al. (2019). Localised grey matter atrophy in multiple sclerosis is network-based: A coordinate-based meta-analysis. Clinical Radiology, 74(10), 816–819. https://doi.org/10.1016/j.crad.2019.07.005CrossRefPubMedCentral

Crossley, N. A., Mechelli, A., Scott, J., et al. (2014). The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain, 137(8), 2382–2395. https://doi.org/10.1093/brain/awu132CrossRefPubMedPubMedCentral

Douaud, G., Smith, S., Jenkinson, M., et al. (2007). Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain, 130(Pt 9), 2375–2386. https://doi.org/10.1093/brain/awm184CrossRefPubMed

Drevets, W. C., Price, J. L., & Furey, M. L. (2008). Brain structural and functional abnormalities in mood disorders: Implications for neurocircuitry models of depression. Brain Structure and Function, 213, 93–118. https://doi.org/10.1007/s00429-008-0189-xCrossRefPubMed

Drevets, W. C., Price, J. L., Simpson, J. R., Jr., et al. (1997). Subgenual prefrontal cortex abnormalities in mood disorders. Nature, 386, 824–827. https://doi.org/10.1038/386824a0CrossRefPubMed

Dunlop, B. W., Binder, E. B., Cubells, J. F., et al. (2012). Predictors of remission in depression to individual and combined treatments (PReDICT): Study protocol for a randomized controlled trial. Trials, 13, 106. https://doi.org/10.1186/1745-6215-13-106CrossRefPubMedPubMedCentral

Eickhoff, S. B., Bzdok, D., Laird, A. R., et al. (2012). Activation likelihood estimation meta-analysis revisited. NeuroImage, 59(3), 2349–2361. https://doi.org/10.1016/j.neuroimage.2011.09.017CrossRefPubMed

Fox, P. T., & Lancaster, J. L. (2002). Opinion: Mapping context and content: The BrainMap model. Nature Reviews Neuroscience, 3(4), 319–321. https://doi.org/10.1038/nrn789CrossRefPubMed

Fu, H., Hardy, J., & Duff, K. E. (2018). Selective Neuronal Vulnerability in Neurodegenerative Diseases: From Stressor Thresholds to Degeneration. Nature Neuroscience, 21, 1350–1358. https://doi.org/10.1038/s41593-018-0221-2CrossRefPubMedPubMedCentral

Glerean, E. (2014). BRAMILA Matlab tools [Computer scripts]. Retrieved from https://users.aalto.fi/~eglerean/bramila.html

GingerALE [Software edition 3.0]. (2019). Retrieved from http://brainmap.org/ale/

Goedert, M., Clavaguera, F., & Tolnay, M. (2010). The propagation of prion-like protein inclusions in neurodegenerative diseases. Trends in Neurosciences, 33(7), 317–325. https://doi.org/10.1016/j.tins.2010.04.003CrossRefPubMed

González-Redondo, R., García-García, D., Clavero, P., et al. (2014). Grey matter hypometabolism and atrophy in Parkinson’s disease with cognitive impairment: A two-step process. Brain, 137, 2356–2367. https://doi.org/10.1093/brain/awu159CrossRefPubMedPubMedCentral

Good, C. D., Johnsrude, I. S., Ashburner, J., et al. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14(1 Pt 1), 21–36. https://doi.org/10.1006/nimg.2001.0786CrossRefPubMed

Goodkind, M., Eickhoff, S. B., Oathes, et al. (2015). Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry., 72(4), 305–315. https://doi.org/10.1001/jamapsychiatry.2014.2206CrossRefPubMedPubMedCentral

Gray, J.P., et al. (2020) ANIMA Database. Study data available at: https://anima.fz-juelich.de/studies/Gray_MMD_MA_2020

Gray, J. P., Müller, V. I., Eickhoff, S., et al. (2020). Multimodal Abnormalities of Brain Structure and Function in Major Depressive Disorder: A Meta-Analysis of Neuroimaging Studies. American Journal of Psychiatry, 177, 422–434. https://doi.org/10.1176/appi.ajp.2019.19050560CrossRefPubMed

Hamilton, J. P., Siemer, M., & Gotlib, I. H. (2008). Amygdala volume in Major Depressive Disorder: A meta-analysis of magnetic resonance imaging studies. Molecular Psychiatry, 13(11), 993–1000. https://doi.org/10.1038/mp.2008.57CrossRefPubMedPubMedCentral

Jack, C. R., Jr., Knopman, D. S., Jagust, W. J., et al. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. The Lancet Neurology, 9, 119–128. https://doi.org/10.1016/S1474-4422(09)70299-6CrossRefPubMedPubMedCentral

Jeub, L.G.S., Bazzi, M., Jutla, I.S., et al. (2011–2019). A generalized Louvain method for community detection implemented in MATLAB. Retrieved from https://github.com/GenLouvain/GenLouvain

Keedwell, P. A., Andrew, C., Williams, S. C., et al. (2005). The Neural Correlates of Anhedonia in Major Depressive Disorder. Biological Psychiatry, 58(11), 843–853. https://doi.org/10.1016/j.biopsych.2005.05.019CrossRefPubMed

Kim, J. M., Hamilton, J. P., & Gotlib, I. H. (2008). Reduced Caudate Gray Matter Volume in Women with Major Depressive Disorder. Psychiatry Research: Neuroimaging, 164(2), 114–122. https://doi.org/10.1016/j.pscychresns.2007.12.020CrossRef

Kotkowski, E., Price, L. R., Fox, P. M., et al. (2018). The hippocampal network model: A transdiagnostic metaconnectomic approach. Neuroimage Clinical, 18, 115–129. https://doi.org/10.1016/j.nicl.2018.01.002CrossRefPubMedPubMedCentral

Krishnan, K., McDonald, W. M., Escalona, P. R., et al. (1992). Magnetic Resonance Imaging of the Caudate Nuclei in Depression. Archives of General Psychiatry, 49(7), 533–557. https://doi.org/10.1001/archpsyc.1992.01820070047007CrossRef

Laird, A. R., Lancaster, J. L., & Fox, P. T. (2005). BrainMap: The social evolution of a functional neuroimaging database. Neuroinformatics, 3(1), 65–78. https://doi.org/10.1385/ni:3:1:065CrossRefPubMed

Lancaster, J. L., Laird, A. R., Eickhoff, S. B., et al. (2012). Automated regional behavioral analysis for human brain images. Frontiers in Neuroinformatics, 6, 23. https://doi.org/10.3389/fninf.2012.00023CrossRefPubMedPubMedCentral

Lawrie, S. M., McIntosh, A. M., Hall, J., et al. (2008). Brain structure and function changes during the development of schizophrenia: The evidence from studies of subjects at increased genetic risk. Schizophrenia Bulletin, 34(2), 330–340. https://doi.org/10.1093/schbul/sbm158CrossRefPubMedPubMedCentral

Li, K., Laird, A. R., Price, L. R., et al. (2016). Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades. Front in Aging Neuroscience, 8, 137. https://doi.org/10.3389/fnagi.2016.00137CrossRef

Liloia, D., Mancuso, L., Uddin, L. Q., et al. (2021). Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence. Neuroimage Clinical, 30, 102583. https://doi.org/10.1016/j.nicl.2021.102583CrossRefPubMedPubMedCentral

Lu, Y., Liang, H., Han, D., et al. (2016). The volumetric and shape changes of the putamen and thalamus in first episode, untreated major depressive disorder. Neuroimage Clinical, 11, 658–666. https://doi.org/10.1016/j.nicl.2016.04.008CrossRefPubMedPubMedCentral

Malhi, G. S., & Mann, J. J. (2018). Depression. The Lancet, 10161, 2299–2312. https://doi.org/10.1016/S0140-6736(18)31948-2CrossRef

Mancuso, L., Fornito, A., Costa, T., et al. (2020). A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders. NeuroImage, 222, 117220. https://doi.org/10.1016/j.neuroimage.2020.117220CrossRefPubMed

Mango [Software edition 4.1]. (2019). Retrieved from http://ric.uthscsa.edu/mango/

Manuello, J., Nani, A., Premi, E., et al. (2017). The Pathoconnectivity Profile of Alzheimer’s Disease: A Morphometric Coalteration Network Analysis. Frontiers in Neurology, 8, 379. https://doi.org/10.3389/fneur.2017.00739CrossRef

Marangell, L. B., Martinez, M., Jurdi, R. A., et al. (2007). Neurostimulation therapies in depression: A review of new modalities. Acta Psychiatrica Scandinavica, 116(3), 174–181. https://doi.org/10.1111/j.1600-0447.2007.01033.xCrossRefPubMed

Mayberg, H. S., Lozano, A. M., Voon, V., et al. (2005). Deep Brain Stimulation for Treatment-Resistant Depression. Neuron, 45(5), 651–660. https://doi.org/10.1016/j.neuron.2005.02.014CrossRefPubMed

McGrath, C. L., Kelley, M. E., Holtzheimer, P. E., et al. (2013). Toward a Neuroimaging Treatment Selection Biomarker for Major Depressive Disorder. JAMA Psychiatry, 70(8), 821–829. https://doi.org/10.1001/jamapsychiatry.2013.143CrossRefPubMedPubMedCentral

Müller, V. I., Cieslik, E. C., Serbanescu, I., et al. (2017). Altered Brain Activity in Unipolar Depression Revisited: Meta-analyses of Neuroimaging Studies. JAMA Psychiatry, 74(1), 47–55. https://doi.org/10.1001/jamapsychiatry.2016.2783CrossRefPubMedPubMedCentral

Olvera, R. L., Bearden, C. E., Velligan, D. I., et al. (2011). Common Genetic Influences on Depression, Alcohol and Substance Use Disorders in Mexican-American Families. American Journal of Medical Genetics Part b: Neuropsychiatric Genetics, 156B(5), 561–568. https://doi.org/10.1002/ajmg.b.31196CrossRef

Patel, R. S., Bowman, F. D., & Riling, J. K. (2006). A Bayesian approach to determining connectivity of the human brain. Human Brain Mapping, 27(3), 267–276. https://doi.org/10.1002/hbm.20182CrossRefPubMed

Reid, A.T., Bzdok, D., Genon, S., et al: (2015). ANIMA: A data-sharing initiative for neuroimaging meta-analyses. Neuroimage, 124(B), 1245–1253. https://doi.org/10.1016/j.neuroimage.2015.07.060

Rodrigue, A. L., Mastrovito, D., Esteban, O., et al. (2021). Searching for Imaging Biomarkers of Psychotic Dysconnectivity. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(12), 1135–1144. https://doi.org/10.1016/j.bpsc.2020.12.002CrossRefPubMed

Sacher, J., Neumann, J., Fünfstück, T., et al. (2012). Mapping the depressed brain: A meta-analysis of structural and functional alterations in major depressive disorder. Journal of Affective Disorders, 140(2), 142–148. https://doi.org/10.1016/j.jad.2011.08.001CrossRefPubMed

Sanchis-Segura, C., Cruz-Gómez, A. J., Belenguer, A., et al. (2016). Increased regional gray matter atrophy and enhanced functional connectivity in male multiple sclerosis patients. Neuroscience Letters, 630, 154–157. https://doi.org/10.1016/j.neulet.2016.07.028CrossRefPubMed

Seeley, W. W., Crawford, R. K., Zhou, J., et al. (2009). Neurodegenerative diseases target large-scale human brain networks. Neuron, 62(1), 42–52. https://doi.org/10.1016/j.neuron.2009.03.024CrossRefPubMedPubMedCentral

Shannon, P., Markiel, A., Ozier, O., et al. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504. https://doi.org/10.1101/gr.1239303CrossRefPubMedPubMedCentral

Smith, S. M., Fox, P. T., Miller, K. L., et al. (2009). Correspondence of the brain’s functional architecture during activation and rest. PNAS, 106(31), 13040–13045. https://doi.org/10.1073/pnas.0905267106CrossRefPubMedPubMedCentral

Smith, S. M., Jenkinson, M., Woolrich, M. W., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208-219. https://doi.org/10.1016/j.neuroimage.2004.07.051CrossRefPubMed

Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., et al. (2011). Network modelling methods for FMRI. NeuroImage, 54(2), 875–891. https://doi.org/10.1016/j.neuroimage.2010.08.063CrossRefPubMed

Sporns, O., & Bassett, D. S. (2018). Editorial: New Trends in Connectomics. Network Neuroscience, 2(2), 125–127. https://doi.org/10.1162/netn_e_00052CrossRefPubMedPubMedCentral

Sprooten, E., Rasgon, A., Goodman, M., et al. (2017). Addressing reverse inference in psychiatric neuroimaging: Meta-analyses of task-related brain activation in common mental disorders. Human Brain Mapping, 38(4), 1846–1864. https://doi.org/10.1002/hbm.23486CrossRefPubMedPubMedCentral

Turkeltaub, P. E., Eden, G. F., Jones, K. M., et al. (2002). Meta-analysis of the Functional Neuroanatomy of Single-Word Reading: Method and Validation. NeuroImage, 16, 765–780. https://doi.org/10.1006/nimg.2002.1131CrossRefPubMed

Vanasse, T., Fox, P. M., Barron, D. S., et al. (2018). BrainMap VBM: An Environment for Structural Meta-analysis. Human Brain Mapping, 39(8), 3308–3325. https://doi.org/10.1002/hbm.24078CrossRefPubMedPubMedCentral

Vanasse, T. J., Fox, P. T., Fox, P. M., et al. (2021). Brain pathology recapitulates physiology: A network meta-analysis. Communications Biology, 4, 301. https://doi.org/10.1038/s42003-021-01832-9CrossRefPubMedPubMedCentral

Victor, T. A., Furey, M. L., Fromm, S. J., et al. (2010). Relationship Between Amygdala Responses to Masked Faces and Mood State and Treatment in Major Depressive Disorder. Archives of General Psychiatry, 67(11), 1128–1138. https://doi.org/10.1001/archgenpsychiatry.2010.144CrossRefPubMedPubMedCentral

Wang, W., Zhao, Y., Hu, X., et al. (2017). Conjoint and dissociated structural and functional abnormalities in first-episode drug-naive patients with major depressive disorder: A multimodal meta-analysis. Scientific Reports, 7, 10401. https://doi.org/10.1038/s41598-017-08944-5CrossRefPubMedPubMedCentral

Warren, J. D., Rohrer, J. D., Schott, J. M., et al. (2013). Molecular nexopathies: A new paradigm of neurodegenerative disease. Trends in Neurosciences, 36(10), 561–569. https://doi.org/10.1016/j.tins.2013.06.007CrossRefPubMedPubMedCentral

Watts, D., & Strogatz, S. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442. https://doi.org/10.1038/30918CrossRefPubMed

Winter, N. R., Leenings, R., Ernsting, J., et al. (2022). Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities. JAMA Psychiatry, Online Edition. https://doi.org/10.1001/jamapsychiatry.2022.1780CrossRef

World Health Organization. (2021). Depression and Other Common Mental Disorders: Global Health Estimates. License: CC BY-NC-SA 3.0 IGO. https://www.who.int/news-room/fact-sheets/detail/depression

Yeo, B. T. T., Krienen, F. M., Sepulcre, J., et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011CrossRefPubMed

Zhou, J., Gennatas, E. D., Kramer, J. H., et al. (2012). Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome. Neuron, 73(6), 1216–1227. https://doi.org/10.1016/j.neuron.2012.03.004CrossRefPubMedPubMedCentral

Title: Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects
Authors: Jodie P. Gray
Jordi Manuello
Aaron F. Alexander-Bloch
Cassandra Leonardo
Crystal Franklin
Ki Sueng Choi
Franco Cauda
Tommaso Costa
John Blangero
David C. Glahn
Helen S. Mayberg
Peter T. Fox
Publication date: 05-12-2022
Publisher: Springer US
Published in: Neuroinformatics / Issue 2/2023
Print ISSN: 1539-2791
Electronic ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-022-09614-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 2/2023

Correction to: Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition

Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains

Multimodal 3D Mouse Brain Atlas Framework with the Skull-Derived Coordinate System

Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition

Scalable Query Answering Under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach

ABCD_Harmonizer: An Open-source Tool for Mapping and Controlling for Scanner Induced Variance in the Adolescent Brain Cognitive Development Study

Premium Partner