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Whole brain resting-state analysis reveals decreased functional connectivity in major depression
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Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within 6 months before inclusion) and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxel-wise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: (1) decreased bilateral amygdala and left anterior insula connectivity in an affective network, (2) reduced connectivity of the left frontal pole in a network associated with attention and working memory, and (3) decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or gray matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder.
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“Conversely, other studies in MDD reported decreased functional connectivity between more dorsal areas of the frontolimbic network compared with subcortical and limbic areas within the frontolimbic (57,60-62).”
From Relation Between Structural and Functional Connectivity in Major Depressive Disorder (2013) · cites this paper as [62]
In the citing paper
“The abnormality of the restingstate functional connectivity related to the primary visual area, including the occipital cortex and calcarine gyrus and extending into the lingual gyrus, has been reported before (Veer et al., 2010) and may be related to impaired selective attention and working memory in major depression (Borkowska and Rybakowski, 2001;Phillips et al., 2003;Desseilles et al., 2009).”
From Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis (2012) · cites this paper as Veer et al., 2010
In the citing paper
“Aberrant connectivity of the visual cortical areas with regions known to be involved in visual processing, such as the lingual gyrus, fusiform gyrus, inferior occipital gyrus and calcarine gyrus, has been demonstrated in this study (Beckmann et al., 2005;Damoiseaux et al., 2006;Veer et al., 2010).”
From Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis (2012) · cites this paper as Veer et al., 2010
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Diagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. B. (2013) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,845
A new depression scale designed to be sensitive to change
Montgomery, S. A., & Åsberg, M. (1979) · British Journal of Psychiatry · doi:10.1192/bjp.134.4.382 · Cited by 13,962
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
Fast robust automated brain extraction
Smith, S. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10062 · Cited by 10,496
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
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Quidé, Y., Norman‐Nott, N., Hesam‐Shariati, N., McAuley, J. H., & Gustin, S. M. (2023) · BJPsych Open · doi:10.1192/bjo.2023.61 · Cited by 18
Zhang, S., She, S., Qiu, Y., Li, Z., Wu, X., Hu, H., Zheng, W., Huang, R., & Wu, H. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103468 · Cited by 16
Effects of Duration and Midpoint of Sleep on Corticolimbic Circuitry in Youth
Hehr, A., Marusak, H. A., Huntley, E. D., & Rabinak, C. A. (2019) · Chronic Stress · doi:10.1177/2470547019856332 · Cited by 17
Chung, M. K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316882610 · Cited by 18
Rakesh, D., Lv, J., Zalesky, A., Allen, N. B., Lubman, D. I., Yücel, M., & Whittle, S. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.10.051 · Cited by 16
Bergé, D., Lesh, T. A., Smucny, J., & Carter, C. S. (2020) · Psychological Medicine · doi:10.1017/s0033291720004808 · Cited by 16
Evaluating the Strength of Structural Connectivity Underlying Brain Functional Networks
Kemmer, P. B., Wang, Y., Bowman, F. D., Mayberg, H. S., & Guo, Y. (2018) · Brain Connectivity · doi:10.1089/brain.2018.0615 · Cited by 18
Peng, Y., Zhang, S., Zhou, Y., Song, Y., Yang, G., Hao, K., Yang, Y., Li, W., Lv, L., & Zhang, Y. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2020.113578 · Cited by 16
Subcortical structures and visual divergent thinking: a resting-state functional MRI analysis
Gao, Z., Liu, X., Zhang, D., Liu, M., & Hao, N. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02355-z · Cited by 14
Gao, M., Shao, R., Huang, C., Liu, H., Chen, Y.-L., Lee, S., Lin, C., & Lee, T. M. C. (2020) · Behavioural Brain Research · doi:10.1016/j.bbr.2020.112776 · Cited by 16
You, B., Wen, H., & Jackson, T. (2021) · Psychophysiology · doi:10.1111/psyp.13921 · Cited by 15
Cellular physiology of olfactory learning in the honeybee brain
Himmelreich, S., & Grünewald, B. (2012) · Apidologie · doi:10.1007/s13592-012-0135-z · Cited by 15
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Kenett, Y. N., Beaty, R. E., & Medaglia, J. D. (2018) · Personality Neuroscience · doi:10.1017/pen.2018.15 · Cited by 15
Krohne, L. G., Wang, Y., Hinrich, J. L., Mørup, M., Chan, R. C. K., & Madsen, K. H. (2019) · Human Brain Mapping · doi:10.1002/hbm.24751 · Cited by 13
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Scangos, K. W., Khambhati, A. N., Daly, P. M., Owen, L. W., Manning, J. R., Ambrose, J. B., Austin, E., Dawes, H. E., Krystal, A. D., & Chang, E. F. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.746499 · Cited by 15
Sun, H., Yan, R., Chen, Z., Wang, X., Xia, Y. X., Hua, L., Shen, N., Huang, Y., Xia, Q., Yao, Z., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03282-x · Cited by 14
Favaro, A., Manara, R., Pievani, M., Clementi, M., Forzan, M., Bruson, A., Tenconi, E., Degortes, D., Pinato, C., Giannunzio, V., et al. (2014) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2014.05.006 · Cited by 15
Na, X., Glasier, C. M., Andres, A., Bellando, J. B., Chen, H., Gao, W., Livingston, L. W., Badger, T. M., & Ou, X. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad176 · Cited by 18
Kafetzopoulos, V., Kokras, N., Katsaitis, F., Sousa, N., Leite‐Almeida, H., Sotiropoulos, I., & Dalla, C. (2024) · Psychopharmacology · doi:10.1007/s00213-024-06667-w · Cited by 14
Cheng, H., Wu, H. Q., & Yong Fan, A. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.09.004 · Cited by 14
James, G. M., Gryglewski, G., Vanicek, T., Berroterán-Infante, N., Philippe, C., Kautzky, A., Nics, L., Vraka, C., Godbersen, G. M., Unterholzner, J., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy249 · Cited by 14
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Miyauchi, E., Ide, M., Tachikawa, H., Nemoto, K., Arai, T., & Kawasaki, M. (2019) · Scientific Reports · doi:10.1038/s41598-018-36317-z · Cited by 14
Yang, Q., Zhang, X., Song, Y., Liu, F., Qin, W., Yu, C., & Liang, M. (2021) · Human Brain Mapping · doi:10.1002/hbm.25373 · Cited by 14
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Fahrbach, S. E., Nambu, J. R., & Schwartz, L. M. (2011) · Elsevier eBooks · doi:10.1016/b978-0-12-384747-8.10012-1 · Cited by 13
Martín‐Vega, D., Wicklein, M., Simonsen, T. J., Garbout, A., Ahmed, F., & Hall, M. J. R. (2021) · Zoologischer Anzeiger · doi:10.1016/j.jcz.2021.03.007 · Cited by 14
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Deng, S., & Gu, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08278 · Cited by 17
Hou, X., Mei, B., Wang, F., Guo, H., Li, S., Wu, G., Zang, C., & Cao, B. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.1028518 · Cited by 14
Wiafe, S.-L., Asante, N. O., Calhoun, V. D., & Faghiri, A. (2025) · Brain Connectivity · doi:10.1177/21580014251376733 · Cited by 13
Ribary, U., Doesburg, S. M., & Ward, L. M. (2014) · Springer eBooks · doi:10.1007/978-3-642-33045-2_19 · Cited by 13
Balkenius, A., & Hansson, B. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032133 · Cited by 12
Oh, J., Chun, J., Kim, E., Park, H., Lee, B., & Kim, J. (2016) · Brain and Behavior · doi:10.1002/brb3.602 · Cited by 12
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İçer, S., Benli, Ş. G., & Özmen, S. (2019) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22348 · Cited by 13
Guo, M., Wang, T., Zhang, Z., Chen, N., Li, Y., Wang, Y., Yao, Z., & Hu, B. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abbc28 · Cited by 13
Yu, R., Tan, H. L., Wang, E.-D., Huang, J., Wang, P.-J., Li, X., Zheng, H.-H., Lv, F., & Hu, H. (2022) · World Journal of Psychiatry · doi:10.5498/wjp.v12.i8.1016 · Cited by 17
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Moustafa, A. A., Phillips, J. R., Kéri, S., Misiak, B., & Frydecka, D. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00016 · Cited by 15
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Caravaggio, F., Plitman, E., Chung, J. K., Gerretsen, P., Kim, J., Iwata, Y., Chakravarty, M. M., Remington, G. J., & Graff‐Guerrero, A. (2017) · European Journal of Neuroscience · doi:10.1111/ejn.13661 · Cited by 14
Lindbergh, C. A., Zhao, Y., Lv, J., Mewborn, C. M., Puente, A. N., Terry, D. P., Renzi‐Hammond, L. M., Hammond, B. R., Liu, T., & Miller, L. S. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.02.014 · Cited by 12
Jaja, A. A., Grahn, J. A., Herrmann, B., & MacDonald, P. A. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.01.002 · Cited by 13
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Frequency-dependent circuits anchored in the dorsal and ventral left anterior insula
Wang, Y., Zou, Q., Ao, Y., Liu, Y., Ouyang, Y., Wang, X., Biswal, B. B., Cui, Q., & Chen, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-73192-z · Cited by 15
Wesarg, C., Veer, I. M., Oei, N. Y. L., Daedelow, L. S., Lett, T. A. P., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Quinlan, E. B., Desrivières, S., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25433 · Cited by 17
Self-citationLi, G., Feng, Q., Wang, X., Sun, J., Tang, S., Jia, H., Li, Y., & Qiu, J. (2025) · Psychological Medicine · doi:10.1017/s0033291725101001 · Cited by 13
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Wang, L., Chou, Y., Potter, G. G., & Steffens, D. C. (2015) · BioMed Research International · doi:10.1155/2015/343720 · Cited by 12
Baijot, J., Denissen, S., Costers, L., Gielen, J., Cambron, M., D’haeseleer, M., D'hooghe, M. B., Vanbinst, A., Mey, J. D., Nagels, G., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-86792-0 · Cited by 12
Molecular Mechanisms of Depression
Varidaki, A., Mohammad, H., & Coffey, E. T. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802456-0.00005-4 · Cited by 13
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Zhou, H., Shi, L., Shen, Y., Fang, Y.-M., He, Y., Li, H., Luo, X., Cheung, E. F. C., & Chan, R. C. K. (2021) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2021.111344 · Cited by 12
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Demenescu, L. R., Colic, L., Li, M., Safron, A., Biswal, B., Metzger, C. D., Li, S., & Walter, M. (2017) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-016-0726-1 · Cited by 11
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Császár-Nagy, N., Kapócs, G., & Bókkon, I. (2019) · Reviews in the Neurosciences · doi:10.1515/revneuro-2018-0092 · Cited by 12
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Sawalha, J., Yousefnezhad, M., Selvitella, A., Cao, B., Greenshaw, A. J., & Greiner, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-95987-4 · Cited by 13
Jung, J., Cho, S., Kim, N., Kang, C., & Kang, S. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.925823 · Cited by 10
Lee, S., Bijsterbosch, J. D., Alfaro‐Almagro, F., Elliott, L. T., McCarthy, P., Taschler, B., Sala‐Llonch, R., Beckmann, C. F., Duff, E. P., Smith, S. M., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119779 · Cited by 13
Das, P., & Fadamiro, H. Y. (2013) · Cell and Tissue Research · doi:10.1007/s00441-013-1568-z · Cited by 11
Niazy, R. K., Cole, D. M., Beckmann, C. F., & Smith, S. M. (2015) · Biological magnetic resonance · doi:10.1007/978-1-4899-7591-1_14 · Cited by 11
King, J. B., & Anderson, J. S. (2018) · Human Brain Mapping · doi:10.1002/hbm.24336 · Cited by 10
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Zhuo, C., Li, G., Lin, X., Jiang, D., Xu, Y. J., Tian, H., Wang, W., & Song, X. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00284-9 · Cited by 11
Hirst, R. J., Setti, A., Looze, C. D., Akuffo, K. O., Pető, T., Kenny, R. A., & Newell, F. N. (2020) · Cortex · doi:10.1016/j.cortex.2020.08.030 · Cited by 11
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Li, J. (2020) · Brain Science Advances · doi:10.26599/bsa.2020.9050020 · Cited by 11
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Ludwiczak, A., Jahanshahi, M., & Osman, M. (2014) · Neurodegenerative Disease Management · doi:10.2217/nmt.14.23 · Cited by 11
Čolić, L., Demenescu, L. R., Li, M., Kaufmann, J., Krause, A. L., Metzger, C., & Walter, M. (2015) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv110 · Cited by 12
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Wang, J., Hu, Z., & Wang, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46675-0_15 · Cited by 11
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Caldwell, H. W. (2015) · ScholarsArchive (Brigham Young University) · Cited by 10
Hsu, L., Lane, T. J., Wu, C. W., Lin, C.-Y. E., Yeh, C.-B., Kao, H., & Lin, C. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00364-w · Cited by 10
Garzón, B., Lövdén, M., de Boer, L., Axelsson, J., Riklund, K., Bäckman, L., Nyberg, L. H., & Guitart‐Masip, M. (2021) · Brain Structure and Function · doi:10.1007/s00429-020-02205-4 · Cited by 10
Kawakami, S., Okada, N., Satomura, Y., Shoji, E., Mori, S., Kiyota, M., Omileke, F., Hamamoto, Y., Morita, S., Koshiyama, D., et al. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae284 · Cited by 10
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Hou, J., Chen, A. C. N., Song, B., Sun, C., & Beauchaine, T. P. (2016) · Musicae Scientiae · doi:10.1177/1029864916662903 · Cited by 10
Liu, W., Hu, X., An, D., Zhou, D., & Gong, Q. (2019) · Scientific Reports · doi:10.1038/s41598-019-55002-3 · Cited by 10
Elseoud, A. A. (2013) · Cited by 9
Goldstein‐Piekarski, A. N., & Williams, L. M. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813333-0.00021-4 · Cited by 10
Altunkaya, S., Huang, S., Hsu, Y., Yang, J., Lin, C.-Y. E., Kuo, L., & Tu, M.-C. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.717037 · Cited by 9
Chaudhari, A., Wang, X., Wu, A., & Liu, H. (2023) · Bioengineering · doi:10.3390/bioengineering10091043 · Cited by 9
Narsude, M., van der Zwaag, W., Kober, T., Rolf Gruetter, A., & Marques, J. P. (2013) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24975 · Cited by 9
Spatial variability of low frequency brain signal differentiates brain states
Wang, Y., Ao, Y., Yang, Q., Liu, Y., Ouyang, Y., Jing, X., Pang, Y., Cui, Q., & Chen, H. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242330 · Cited by 11
Postpartum anhedonia: Emergent patterns in bipolar and unipolar depression
Gollan, J. K., Yang, A., Ciolino, J. D., Sit, D. K. Y., & Wisner, K. L. (2021) · Psychiatry Research · doi:10.1016/j.psychres.2021.114274 · Cited by 9
Reduced cortical complexity in patients with thyroid-associated ophthalmopathy
Zhou, J., Chen, W., Wu, Q., Chen, L., Chen, H., Liu, H., Xu, X., Wu, F., & Hu, H. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00683-0 · Cited by 9
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Watanabe, K., & Funahashi, S. (2014) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2014.07.004 · Cited by 9
Burke, M. R., Poyser, C. A., & Schießl, I. (2014) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbu027 · Cited by 10
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Mulders, P. C. R., van Eijndhoven, P., & Beckmann, C. F. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802456-0.00007-8 · Cited by 11
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Wang, W., Zhang, X., & Li, L. (2019) · Biometrics · doi:10.1111/biom.13099 · Cited by 9
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Standage, D., & Paré, M. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00778.2017 · Cited by 10
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Bowyer, S. M. (2014) · Current topics in behavioral neurosciences · doi:10.1007/7854_2014_348 · Cited by 9
Kang, Y., Jung, J., Han, K., Auer, D. P., & Ham, B. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120607 · Cited by 7
Ju, N., & Du, Y. (2023) · Radiology Science · doi:10.15212/radsci-2023-0008 · Cited by 8
Golbabaei, S., Vahid, A., Hatami, J., & Hamid Soltanian-zadeh, A. (2016) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme.2016.7890925 · Cited by 8
Neitzel, J., Nuttall, R., & Sorg, C. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00026 · Cited by 7
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Metadata
- Authors
- Veer, I. M.
- Affiliations
- Leiden University (NL)
- Corresponding author
- Veer, Ilya M.
- ORCID
- Veer, Ilya M.
- Venue
- Frontiers in Systems Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2010
- Volume
- 4
- ISSN
- 1662-5137 (ISSN-L)
- PMC
- PMC2950744
- OpenAlex
- W2169793721
- Cited by
- 753
- References
- 63
- Citations by year
- 2026: 6 · 2025: 24 · 2024: 16 · 2023: 15 · 2022: 31 · 2021: 75 · 2020: 49 · 2019: 59 · 2018: 53 · 2017: 52 · 2016: 69 · 2015: 74 · 2014: 73 · 2013: 72 · 2012: 56
- FWCI
- 30.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Systems Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- ZonMw
- Vrije Universiteit Amsterdam
- GGZ inGeest
- Rivierduinen
- GGZ Friesland
- GGZ Drenthe
- Nederlands Instituut voor Onderzoek van de Gezondheidszorg
- Leids Universitair Medisch Centrum
- Universitair Medisch Centrum Groningen
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- major depressive disorder, resting-state functional connectivity, whole-brain analysis, independent component analysis, dual regression, amygdala connectivity, anterior insula, frontal pole, lingual gyrus, affective networks, visual network, emotion regulation, cognitive impairment, medication-free patients, symptom severity
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