Individual-fMRI-approaches reveal cerebellum and visual communities to be functionally connected in obsessive compulsive disorder.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
14 01 2021
14 01 2021
Historique:
received:
04
05
2020
accepted:
11
12
2020
entrez:
15
1
2021
pubmed:
16
1
2021
medline:
10
8
2021
Statut:
epublish
Résumé
There is significant interest in understanding the pathophysiology of Obsessive-Compulsive Disorder (OCD) using resting-state fMRI (rsfMRI). Previous studies acknowledge abnormalities within and beyond the fronto-striato-limbic circuit in OCD that require further clarifications. However, limited information could be inferred from the conventional way of investigating the functional connectivity differences between OCD and healthy controls. Here, we identified altered brain organization in patients with OCD by applying individual-based approaches to maximize the identification of underlying network-based features specific to the OCD group. rsfMRI of 20 patients with OCD and 22 controls were preprocessed, and individual-fMRI-subspace was derived for each subject within each group. We evaluated group differences in functional connectivity using individual-fMRI-subspace and established its advantage over conventional-fMRI methodology. We applied prediction-based approaches to highlight the group differences by evaluating the differences in functional connections that predicted the clinical scores (namely, the Obsessive-Compulsive Inventory-Revised (OCI-R) and Hamilton Anxiety Rating Scale). Then, we explored the brain network organization of both groups by estimating the subject-specific communities within each group. Lastly, we evaluated associations between the inter-individual variation of nodes in the communities to clinical measures using linear regression. Functional connectivity analysis using individual-fMRI-subspace detected 83 connections that were different between OCD and control groups, compared to none found using conventional-fMRI methodology. Connectome-based prediction analysis did not show significant overlap between the two groups in the functional connections that predicted the clinical scores. This suggests that the functional architecture in patients with OCD may be different compared to controls. Seven communities were found in both groups. Interestingly, within the OCD group but not controls, we observed functional connectivity between cerebellar and visual regions, and lack of connectivity between striato-limbic and frontal areas. Inter-individual variations in the community-size of these two communities were also associated with the OCI-R score (p < .005). Due to our small sample size, we further validated our results by (i) accounting for head motion, (ii) applying global signal regression (GSR) in data processing, and (iii) using an alternate atlas for parcellation. While the main results were consistently observed with accounting for head motion and using another atlas, the key findings were not reproduced with GSR application. The study demonstrated the existence of disconnectedness in fronto-striato-limbic community and connectedness between cerebellar and visual areas in OCD patients, which was also related to the clinical symptomatology of OCD.
Identifiants
pubmed: 33446780
doi: 10.1038/s41598-020-80346-6
pii: 10.1038/s41598-020-80346-6
pmc: PMC7809273
doi:
Types de publication
Clinical Trial
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1354Références
Kessler, R. C., Petukhova, M., Sampson, N. A., Zaslavsky, A. M. & Wittchen, H.-U. Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. Int. J. Methods Psychiatr. Res. 21, 169–184 (2012).
pubmed: 22865617
pmcid: 4005415
doi: 10.1002/mpr.1359
Subramaniam, M., Abdin, E., Vaingankar, J. A. & Chong, S. A. Obsessive–compulsive disorder: prevalence, correlates, help-seeking and quality of life in a multiracial Asian population. Soc. Psychiatry Psychiatr. Epidemiol. 47, 2035–2043 (2012).
pubmed: 22526825
doi: 10.1007/s00127-012-0507-8
Organization, W. H. The World Health Report 2001: Mental Health : New Understanding, New Hope. (World Health Organization, 2001).
Robbins, T. W., Vaghi, M. M. & Banca, P. Obsessive-compulsive disorder: puzzles and prospects. Neuron 102, 27–47 (2019).
pubmed: 30946823
doi: 10.1016/j.neuron.2019.01.046
Saxena, S., Brody, A. L., Schwartz, J. M. & Baxter, L. R. Neuroimaging and frontal-subcortical circuitry in obsessive-compulsive disorder. Br. J. Psychiatry 173, 26–37 (1998).
doi: 10.1192/S0007125000297870
Saxena, S., Bota, R. G. & Brody, A. L. Brain-behavior relationships in obsessive-compulsive disorder. In Seminars in clinical neuropsychiatry, Vol. 6, No. 2. 82–101 (2001).
Moresco, R. M. et al. Fluvoxamine treatment and D 2 receptors: a pet study on OCD drug-naïve patients. Neuropsychopharmacology 32, 197 (2007).
pubmed: 17019408
doi: 10.1038/sj.npp.1301199
Mataix-Cols, D. et al. Distinct neural correlates of washing, checking, and hoarding symptomdimensions in obsessive-compulsive disorder. Arch. Gen. Psychiatry 61, 564–576 (2004).
pubmed: 15184236
doi: 10.1001/archpsyc.61.6.564
Mataix-Cols, D., do Rosario-Campos, M. C. & Leckman, J. F. A multidimensional model of obsessive-compulsive disorder. Am. J. Psychiatry 162, 228–238 (2005).
pubmed: 15677583
doi: 10.1176/appi.ajp.162.2.228
Lawrence, N. S. et al. Decision making and set shifting impairments are associated with distinct symptom dimensions in obsessive-compulsive disorder. Neuropsychology 20, 409 (2006).
pubmed: 16846259
doi: 10.1037/0894-4105.20.4.409
Menzies, L. et al. Integrating evidence from neuroimaging and neuropsychological studies of obsessive-compulsive disorder: the orbitofronto-striatal model revisited. Neurosci. Biobehav. Rev. 32, 525–549 (2008).
pubmed: 18061263
doi: 10.1016/j.neubiorev.2007.09.005
Simon, D., Kaufmann, C., Müsch, K., Kischkel, E. & Kathmann, N. Fronto-striato-limbic hyperactivation in obsessive-compulsive disorder during individually tailored symptom provocation. Psychophysiology 47, 728–738 (2010).
pubmed: 20158678
Posner, J. et al. Reduced functional connectivity within the limbic cortico-striato-thalamo-cortical loop in unmedicated adults with obsessive-compulsive disorder. Hum. Brain Mapp. 35, 2852–2860 (2014).
pubmed: 24123377
doi: 10.1002/hbm.22371
Anticevic, A. et al. Global resting-state functional magnetic resonance imaging analysis identifies frontal cortex, striatal, and cerebellar dysconnectivity in obsessive-compulsive disorder. Biol. Psychiat. 75, 595–605 (2014).
pubmed: 24314349
doi: 10.1016/j.biopsych.2013.10.021
Cheng, Y. et al. Abnormal resting-state activities and functional connectivities of the anterior and the posterior cortexes in medication-naive patients with obsessive-compulsive disorder. PLoS ONE 8, e67478 (2013).
pubmed: 23840714
pmcid: 3696097
doi: 10.1371/journal.pone.0067478
Hou, J. et al. Morphologic and functional connectivity alterations of corticostriatal and default mode network in treatment-naïve patients with obsessive-compulsive disorder. PLoS ONE 8, e83931 (2013).
pubmed: 24358320
pmcid: 3865285
doi: 10.1371/journal.pone.0083931
Tian, L. et al. Abnormal functional connectivity of brain network hubs associated with symptom severity in treatment-naive patients with obsessive–compulsive disorder: a resting-state functional MRI study. Prog. Neuropsychopharmacol. Biol. Psychiatry 66, 104–111 (2016).
pubmed: 26683173
doi: 10.1016/j.pnpbp.2015.12.003
Xu, T. et al. Altered resting-state cerebellar-cerebral functional connectivity in obsessive-compulsive disorder. Psychol. Med. 49, 1156–1165 (2019).
pubmed: 30058519
doi: 10.1017/S0033291718001915
Newman, M. E. Communities, modules and large-scale structure in networks. Nat. Phys. 8, 25–31 (2012).
doi: 10.1038/nphys2162
Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).
pubmed: 22099467
pmcid: 3222858
doi: 10.1016/j.neuron.2011.09.006
Crossley, N. A. et al. Cognitive relevance of the community structure of the human brain functional coactivation network. Proc. Natl. Acad. Sci. 110, 11583–11588 (2013).
pubmed: 23798414
doi: 10.1073/pnas.1220826110
pmcid: 3710853
Vaghi, M. M. et al. Specific frontostriatal circuits for impaired cognitive flexibility and goal-directed planning in obsessive-compulsive disorder: evidence from resting-state functional connectivity. Biol. Psychiat. 81, 708–717 (2017).
pubmed: 27769568
doi: 10.1016/j.biopsych.2016.08.009
Brennan, B. P. et al. Use of an individual-level approach to identify cortical connectivity biomarkers in obsessive-compulsive disorder. Biol. Psychiatry: Cognit. Neurosci. Neuroimaging 4, 27–38 (2019).
Kashyap, R. et al. Individual-specific fMRI-Subspaces improve functional connectivity prediction of behavior. NeuroImage 189, 804–812 (2019).
pubmed: 30711467
doi: 10.1016/j.neuroimage.2019.01.069
Yan, C.-G., Craddock, R. C., Zuo, X.-N., Zang, Y.-F. & Milham, M. P. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage 80, 246–262 (2013).
pubmed: 23631983
doi: 10.1016/j.neuroimage.2013.04.081
Patriat, R. et al. The effect of resting condition on resting-state fMRI reliability and consistency: a comparison between resting with eyes open, closed, and fixated. Neuroimage 78, 463–473 (2013).
pubmed: 23597935
doi: 10.1016/j.neuroimage.2013.04.013
Rondinoni, C., Amaro, E. Jr., Cendes, F., Dos Santos, A. C. & Salmon, C. E. G. Effect of scanner acoustic background noise on strict resting-state fMRI. Braz. J. Med. Biol. Res. 46, 359–367 (2013).
pubmed: 23579634
pmcid: 3854411
doi: 10.1590/1414-431X20132799
Kong, R. et al. Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb. Cortex 29, 2533–2551 (2019).
pubmed: 29878084
doi: 10.1093/cercor/bhy123
Stephan, K. E. & Friston, K. J. Functional connectivity. In Encyclopedia of Neuroscience (ed. Squire, L. R.) 391–397 (Academic Press, New York, 2009). https://doi.org/10.1016/B978-008045046-9.00308-9 .
doi: 10.1016/B978-008045046-9.00308-9
Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664 (2015).
pubmed: 26457551
pmcid: 5008686
doi: 10.1038/nn.4135
Rosenberg, M. D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci. 19, 165–171 (2016).
pubmed: 26595653
doi: 10.1038/nn.4179
Dubois, J. & Adolphs, R. Building a science of individual differences from fMRI. Trends Cognit. Sci. 20, 425–443 (2016).
doi: 10.1016/j.tics.2016.03.014
Marek, S., Tervo-Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S., Donohue, M. R., Foran, W., Miller, R. L., Feczko, E. & Miranda-Dominguez, O. Towards reproducible brain-wide association studies. BioRxiv https://doi.org/10.1101/2020.08.21.257758 (2020).
Betzel, R. F. et al. The community structure of functional brain networks exhibits scale-specific patterns of inter-and intra-subject variability. Neuroimage 202, 115990 (2019).
pubmed: 31291606
doi: 10.1016/j.neuroimage.2019.07.003
Li, N. et al. A unified connectomic target for deep brain stimulation in obsessive-compulsive disorder. Nat. Commun. 11, 1–12 (2020).
First, M. B., Spitzer, R. L., Gibbon, M. & Williams, J. B. User’s guide for the Structured Clinical Interview for DSM-IV Axis I Disorders SCID-I: Clinician Version (American Psychiatric Pub, Chicago, 1997).
The Yale-Brown Obsessive Compulsive Scale: I. Development, Use, and Reliability | JAMA Psychiatry | JAMA Network. https://jamanetwork.com/journals/jamapsychiatry/article-abstract/494743 .
Hamilton, M. A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 23, 56 (1960).
pubmed: 14399272
pmcid: 495331
doi: 10.1136/jnnp.23.1.56
Hamilton, M. A. X. The assessment of anxiety states by rating. Br. J. Med. Psychol. 32, 50–55 (1959).
pubmed: 13638508
doi: 10.1111/j.2044-8341.1959.tb00467.x
Foa, E. B. et al. The obsessive-compulsive inventory: development and validation of a short version. Psychol. Assess. 14, 485 (2002).
pubmed: 12501574
doi: 10.1037/1040-3590.14.4.485
Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J. & Nichols, T. E. Statistical Parametric Mapping: the Analysis of Functional Brain Images (Elsevier, Amsterdam, 2011).
Sladky, R. et al. Slice-timing effects and their correction in functional MRI. Neuroimage 58, 588–594 (2011).
pubmed: 21757015
doi: 10.1016/j.neuroimage.2011.06.078
Ashburner, J. A fast diffeomorphic image registration algorithm. Neuroimage 38, 95–113 (2007).
pubmed: 17761438
doi: 10.1016/j.neuroimage.2007.07.007
Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125–141 (2012).
pubmed: 22642651
doi: 10.1089/brain.2012.0073
Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002).
pubmed: 11771995
doi: 10.1006/nimg.2001.0978
Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90–101 (2007).
pubmed: 17560126
doi: 10.1016/j.neuroimage.2007.04.042
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
pubmed: 12377157
doi: 10.1006/nimg.2002.1132
Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B. & Bandettini, P. A. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?. Neuroimage 44, 893–905 (2009).
pubmed: 18976716
doi: 10.1016/j.neuroimage.2008.09.036
Kashyap, R., Bhattacharjee, S., Yeo, B. T. & Chen, S. A. Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality. Hum. Brain Mapp. 41(5), 1261–1273 (2019).
pubmed: 31773817
pmcid: 7267929
doi: 10.1002/hbm.24873
Zhou, G., Cichocki, A., Zhang, Y. & Mandic, D. P. Group component analysis for multiblock data: common and individual feature extraction. IEEE Trans. Neural Netw. Learn. Syst. 27, 2426–2439 (2016).
pubmed: 26529787
doi: 10.1109/TNNLS.2015.2487364
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1 (2010).
pubmed: 20808728
pmcid: 2929880
doi: 10.18637/jss.v033.i01
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67, 301–320 (2005).
doi: 10.1111/j.1467-9868.2005.00503.x
Scheinost, D. et al. Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage 193, 35–45 (2019).
pubmed: 30831310
doi: 10.1016/j.neuroimage.2019.02.057
Alexander, D. L., Tropsha, A. & Winkler, D. A. Beware of R 2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J. Chem. Inf. Model. 55, 1316–1322 (2015).
pubmed: 26099013
pmcid: 4530125
doi: 10.1021/acs.jcim.5b00206
Efron, B. Estimating the error rate of a prediction rule: improvement on cross-validation. J. Am. Stat. Assoc. 78, 316–331 (1983).
doi: 10.1080/01621459.1983.10477973
Jiang, W. & Simon, R. A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification. Stat. Med. 26, 5320–5334 (2007).
pubmed: 17624926
doi: 10.1002/sim.2968
Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap (CRC Press, Boca Raton, 1994).
doi: 10.1201/9780429246593
Poldrack, R. A., Huckins, G. & Varoquaux, G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiatry 77(5), 534–540 (2019).
doi: 10.1001/jamapsychiatry.2019.3671
Yamashita, M. et al. A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity. Elife 7, e38844 (2018).
pubmed: 30526859
pmcid: 6324880
doi: 10.7554/eLife.38844
Jutla, I. S., Jeub, L. G. & Mucha, P. J. A generalized Louvain method for community detection implemented in MATLAB. http://netwiki.amath.unc.edu/GenLouvain (2011).
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A. & Onnela, J.-P. Community structure in time-dependent, multiscale, and multiplex networks. Science 328, 876–878 (2010).
pubmed: 20466926
doi: 10.1126/science.1184819
Van Den Heuvel, M. P. & Pol, H. E. H. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20, 519–534 (2010).
pubmed: 20471808
doi: 10.1016/j.euroneuro.2010.03.008
Uddin, L. Q., Yeo, B. T. & Spreng, R. N. Towards a universal taxonomy of macro-scale functional human brain networks. Brain topography 32, 926–942 (2019).
Thomas Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).
doi: 10.1152/jn.00338.2011
pmcid: 3174820
Doucet, G. E., Lee, W. H. & Frangou, S. Evaluation of the spatial variability in the major resting-state networks across human brain functional atlases. Hum. Brain Mapp. 40, 4577–4587 (2019).
pubmed: 31322303
pmcid: 6771873
doi: 10.1002/hbm.24722
Ji, J. L. et al. Mapping the human brain’s cortical-subcortical functional network organization. Neuroimage 185, 35–57 (2019).
pubmed: 30291974
doi: 10.1016/j.neuroimage.2018.10.006
Parkes, L., Fulcher, B., Yücel, M. & Fornito, A. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage 171, 415–436 (2018).
pubmed: 29278773
doi: 10.1016/j.neuroimage.2017.12.073
Siegel, J. S. et al. Data quality influences observed links between functional connectivity and behavior. Cereb. Cortex 27, 4492–4502 (2017).
pubmed: 27550863
doi: 10.1093/cercor/bhw253
Li, J. et al. Global signal regression strengthens association between resting-state functional connectivity and behavior. NeuroImage 196, 126–141 (2019).
pubmed: 30974241
doi: 10.1016/j.neuroimage.2019.04.016
Fox, M. D., Zhang, D., Snyder, A. Z. & Raichle, M. E. The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101, 3270–3283 (2009).
pubmed: 19339462
pmcid: 2694109
doi: 10.1152/jn.90777.2008
Murphy, K. & Fox, M. D. Towards a consensus regarding global signal regression for resting state functional connectivity MRI. Neuroimage 154, 169–173 (2017).
pubmed: 27888059
doi: 10.1016/j.neuroimage.2016.11.052
Gotts, S. J. et al. The perils of global signal regression for group comparisons: a case study of autism spectrum disorders. Front. Hum. Neurosci. 7, 356 (2013).
pubmed: 23874279
pmcid: 3709423
doi: 10.3389/fnhum.2013.00356
Saad, Z. S. et al. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2, 25–32 (2012).
pubmed: 22432927
pmcid: 3484684
doi: 10.1089/brain.2012.0080
Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).
pubmed: 28981612
doi: 10.1093/cercor/bhx179
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
pubmed: 27437579
pmcid: 4990127
doi: 10.1038/nature18933
Blair, R. C. & Karniski, W. An alternative method for significance testing of waveform difference potentials. Psychophysiology 30, 518–524 (1993).
pubmed: 8416078
doi: 10.1111/j.1469-8986.1993.tb02075.x
Takagi, Y. et al. A neural marker of obsessive-compulsive disorder from whole-brain functional connectivity. Sci. Rep. 7, 1–10 (2017).
doi: 10.1038/s41598-017-07792-7
Nakao, T., Okada, K. & Kanba, S. Neurobiological model of obsessive–compulsive disorder: evidence from recent neuropsychological and neuroimaging findings. Psychiatry Clin. Neurosci. 68, 587–605 (2014).
pubmed: 24762196
doi: 10.1111/pcn.12195
Selya, A. S., Rose, J. S., Dierker, L. C., Hedeker, D. & Mermelstein, R. J. A practical guide to calculating Cohen’s f2, a measure of local effect size, from PROC MIXED. Front. Psychol. 3, 111 (2012).
pubmed: 22529829
pmcid: 3328081
doi: 10.3389/fpsyg.2012.00111
Hou, J.-M. et al. Resting-state functional connectivity abnormalities in patients with obsessive–compulsive disorder and their healthy first-degree relatives. J. Psychiatry Neurosci. (JPN) 39, 304 (2014).
doi: 10.1503/jpn.130220
Smith, S. M. et al. Correspondence of the brain’s functional architecture during activation and rest. PNAS 106, 13040–13045 (2009).
pubmed: 19620724
doi: 10.1073/pnas.0905267106
pmcid: 2722273
Tavor, I. et al. Task-free MRI predicts individual differences in brain activity during task performance. Science 352, 216–220 (2016).
pubmed: 27124457
pmcid: 6309730
doi: 10.1126/science.aad8127
Liang, P., Wang, Z., Yang, Y., Jia, X. & Li, K. Functional disconnection and compensation in mild cognitive impairment: evidence from DLPFC connectivity using resting-state fMRI. PLoS ONE 6, e22153 (2011).
pubmed: 21811568
pmcid: 3141010
doi: 10.1371/journal.pone.0022153
Medaglia, J. D. Functional neuroimaging in traumatic brain injury: From nodes to networks. Front. Neurol. 8, 407 (2017).
pubmed: 28883806
pmcid: 5574370
doi: 10.3389/fneur.2017.00407
Sha, Z. et al. Functional disruption of cerebello-thalamo-cortical networks in obsessive compulsive disorder. Biol.: Cognit. Neurosci. Neuroimaging 5(4), 438–447 (2019).
Hampshire, A. et al. Inhibition-related cortical hypoconnectivity as a candidate vulnerability marker for obsessive-compulsive disorder. Biolo. Psychiatry: Cognit. Neurosci. Neuroimaging 5(2), 222–230 (2019).
Goncalves, Ó. F. et al. Cognitive and emotional impairments in obsessive–compulsive disorder: evidence from functional brain alterations. Porto Biomed. J. 1, 92–105 (2016).
pubmed: 32258557
pmcid: 6806741
doi: 10.1016/j.pbj.2016.07.005
Moody, T. D. et al. Mechanisms of cognitive-behavioral therapy for obsessive-compulsive disorder involve robust and extensive increases in brain network connectivity. Transl. Psychiatry 7, e1230–e1230 (2017).
pubmed: 28872637
pmcid: 5639240
doi: 10.1038/tp.2017.192
Tepper, J. M., Abercrombie, E. D. & Bolam, J. P. Basal ganglia macrocircuits. In Progress in Brain Research (eds Tepper, J. M. et al.), vol. 160, 3–7 (Elsevier, Amsterdam, 2007).
Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain’s default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 1–38 (2008).
pubmed: 18400922
doi: 10.1196/annals.1440.011
Fan, J. et al. Altered connectivity within and between the default mode, central executive, and salience networks in obsessive-compulsive disorder. J. Affect. Disord. 223, 106–114 (2017).
pubmed: 28743059
doi: 10.1016/j.jad.2017.07.041
Grady, C. L., Rieck, J. R., Nichol, D., Rodrigue, K. M. & Kennedy, K. M. Influence of sample size and analytic approach on stability and interpretation of brain-behavior correlations in task-related fMRI data. Hum. Brain Mapp 42(1), 204–219 https://doi.org/10.1002/hbm.25217 (2020).
Schlösser, R. G. M. et al. Fronto-cingulate effective connectivity in obsessive compulsive disorder: a study with fMRI and dynamic causal modeling. Hum. Brain Mapp. 31, 1834–1850 (2010).
pubmed: 20162605
pmcid: 6871164
doi: 10.1002/hbm.20980
Kashyap, R., Bhattacharjee, S., Sommer, W. & Zhou, C. Repetition priming effects for famous faces through dynamic causal modelling of latency-corrected event-related brain potentials. Eur. J. Neurosci. 49, 1330–1347 (2019).
pubmed: 30549325