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Journal articles on the topic 'Functional connectivity'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 September 2024

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1

Shi, Yuhu. "Dynamic Functional Connectivity Analysis of Seafarer’s Brain Functional Networks." International Journal of Pharma Medicine and Biological Sciences 9, no. 1 (January 2020): 33–37. http://dx.doi.org/10.18178/ijpmbs.9.1.33-37.

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2

Pillai, Jay J. "Functional Connectivity." Neuroimaging Clinics of North America 27, no. 4 (November 2017): i. http://dx.doi.org/10.1016/s1052-5149(17)30097-7.

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3

Pillai, Jay J. "Functional Connectivity." Neuroimaging Clinics of North America 27, no. 4 (November 2017): xvii. http://dx.doi.org/10.1016/j.nic.2017.08.001.

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4

Mukherji, Suresh K. "Functional Connectivity." Neuroimaging Clinics of North America 27, no. 4 (November 2017): xv. http://dx.doi.org/10.1016/j.nic.2017.08.002.

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5

Vogt, Peter, Joseph R. Ferrari, Todd R. Lookingbill, Robert H. Gardner, Kurt H. Riitters, and Katarzyna Ostapowicz. "Mapping functional connectivity." Ecological Indicators 9, no. 1 (January 2009): 64–71. http://dx.doi.org/10.1016/j.ecolind.2008.01.011.

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6

Ioannides, Andreas A. "Dynamic functional connectivity." Current Opinion in Neurobiology 17, no. 2 (April 2007): 161–70. http://dx.doi.org/10.1016/j.conb.2007.03.008.

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7

Höller, Yvonne, Viviana Versace, Eugen Trinka, and Raffaele Nardone. "Functional connectivity after hemispherectomy." Quantitative Imaging in Medicine and Surgery 10, no. 5 (May 2020): 1174–78. http://dx.doi.org/10.21037/qims.2020.03.17.

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8

Tomasi, D., and N. D. Volkow. "Functional connectivity density mapping." Proceedings of the National Academy of Sciences 107, no. 21 (May 10, 2010): 9885–90. http://dx.doi.org/10.1073/pnas.1001414107.

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9

Tang, Cheuk Ying, and Ramachandran Ramani. "Functional Connectivity and Anesthesia." International Anesthesiology Clinics 54, no. 1 (2016): 143–55. http://dx.doi.org/10.1097/aia.0000000000000083.

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10

Duff, Eugene P., Tamar Makin, Michiel Cottaar, Stephen M. Smith, and Mark W. Woolrich. "Disambiguating brain functional connectivity." NeuroImage 173 (June 2018): 540–50. http://dx.doi.org/10.1016/j.neuroimage.2018.01.053.

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11

van den Heuvel, Martijn P., René C. W. Mandl, Thomas Scheeuwe, Wiepke Kahn, René S. Kahn, and Hilleke E. Hulshoff Pol. "FUNCTIONAL CONNECTIVITY IN SCHIZOPHRENIA." Schizophrenia Research 117, no. 2-3 (April 2010): 475. http://dx.doi.org/10.1016/j.schres.2010.02.894.

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12

Lin, W., Q. Zhu, W. Gao, Y. Chen, C. H. Toh, M. Styner, G. Gerig, J. K. Smith, B. Biswal, and J. H. Gilmore. "Functional Connectivity MR Imaging Reveals Cortical Functional Connectivity in the Developing Brain." American Journal of Neuroradiology 29, no. 10 (September 10, 2008): 1883–89. http://dx.doi.org/10.3174/ajnr.a1256.

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13

Pascual-Marqui, Roberto D., M. Koukkou, D. Lehmann, and K. Kochi. "Functional Localization and Functional Connectivity with LORETA." Journal of Neurotherapy 4, no. 4 (July 17, 2001): 35–37. http://dx.doi.org/10.1300/j184v04n04_06.

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14

Honey, C. J., O. Sporns, L. Cammoun, X. Gigandet, J. P. Thiran, R. Meuli, and P. Hagmann. "Predicting human resting-state functional connectivity from structural connectivity." Proceedings of the National Academy of Sciences 106, no. 6 (February 2, 2009): 2035–40. http://dx.doi.org/10.1073/pnas.0811168106.

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15

Chen, Xue, and Yanjiang Wang. "Predicting resting-state functional connectivity with efficient structural connectivity." IEEE/CAA Journal of Automatica Sinica 5, no. 6 (November 2018): 1079–88. http://dx.doi.org/10.1109/jas.2017.7510880.

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16

Wang, Ying, Shuming Zhong, Guanmao Chen, Tao Liu, Lianping Zhao, Yao Sun, Yanbin Jia, and Li Huang. "Altered cerebellar functional connectivity in remitted bipolar disorder: A resting-state functional magnetic resonance imaging study." Australian & New Zealand Journal of Psychiatry 52, no. 10 (December 13, 2017): 962–71. http://dx.doi.org/10.1177/0004867417745996.

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Objectives: Several recent studies have reported a strong association between the cerebellar structural and functional abnormalities and psychiatric disorders. However, there are no studies to investigate possible changes in cerebellar functional connectivity in bipolar disorder. This study aimed to examine the whole-brain functional connectivity pattern of patients with remitted bipolar disorder II, in particular in the cerebellum. Methods: A total of 25 patients with remitted bipolar disorder II and 25 controls underwent resting-state functional magnetic resonance imaging and neuropsychological tests. Voxel-wise whole-brain connectivity was analyzed using a graph theory approach: functional connectivity strength. A seed-based resting-state functional connectivity analysis was further performed to investigate abnormal functional connectivity pattern of those regions with changed functional connectivity strength. Results: Remitted bipolar disorder II patients had significantly decreased functional connectivity strength in the bilateral posterior lobes of cerebellum (mainly lobules VIIb/VIIIa). The seed-based functional connectivity analyses revealed decreased functional connectivity between the right posterior cerebellum and the default mode network (i.e. right posterior cingulate cortex/precuneus and right superior temporal gyrus), bilateral hippocampus, right putamen, left paracentral lobule and bilateral posterior cerebellum and decreased functional connectivity between the left posterior cerebellum and the right inferior parietal lobule and bilateral posterior cerebellum in patients with remitted bipolar disorder II. Conclusion: Our results suggest that cerebellar dysconnectivity, in particular distributed cerebellar–cerebral functional connectivity, might be associated with the pathogenesis of bipolar disorder.

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17

Jui-Hong Chien, Vic, Teresa Wojtasiewicz, and William S. Anderson. "Analytical Tools for Functional Connectivity." Neurosurgery 79, no. 2 (August 2016): N16—N17. http://dx.doi.org/10.1227/01.neu.0000489887.02840.d7.

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18

Qi, Rongfeng, Long Jiang Zhang, Song Luo, Jun Ke, Xiang Kong, Qiang Xu, Chang Liu, Heng Lu, and Guang Ming Lu. "Default Mode Network Functional Connectivity." Medicine 93, no. 27 (December 2014): e227. http://dx.doi.org/10.1097/md.0000000000000227.

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19

Peltier, Scott J., and Yash Shah. "Biophysical Modulations of Functional Connectivity." Brain Connectivity 1, no. 4 (October 2011): 267–77. http://dx.doi.org/10.1089/brain.2011.0039.

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20

Quintero, A., E. Ichesco, R. Schutt, C. Myers, S. Peltier, and G. E. Gerstner. "Functional Connectivity of Human Chewing." Journal of Dental Research 92, no. 3 (January 25, 2013): 272–78. http://dx.doi.org/10.1177/0022034512472681.

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21

Niu, Haijing, and Yong He. "Resting-State Functional Brain Connectivity." Neuroscientist 20, no. 2 (September 10, 2013): 173–88. http://dx.doi.org/10.1177/1073858413502707.

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22

Zhou, Dongli, Wesley K. Thompson, and Greg Siegle. "MATLAB toolbox for functional connectivity." NeuroImage 47, no. 4 (October 2009): 1590–607. http://dx.doi.org/10.1016/j.neuroimage.2009.05.089.

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23

Sheffield, Julia M., Baxter P. Rogers, Jennifer Urbano Blackford, Stephan Heckers, and Neil D. Woodward. "Insula functional connectivity in schizophrenia." Schizophrenia Research 220 (June 2020): 69–77. http://dx.doi.org/10.1016/j.schres.2020.03.068.

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24

Wu, C. W., C. H. Hsieh, C. W. Li, and J. H. Chen. "Functional Connectivity at Meditation State." NeuroImage 47 (July 2009): S42. http://dx.doi.org/10.1016/s1053-8119(09)70006-0.

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25

McGuire, P. K., and C. D. Frith. "Disordered functional connectivity in schizophrenia." Psychological Medicine 26, no. 4 (July 1996): 663–67. http://dx.doi.org/10.1017/s0033291700037673.

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26

Jalilianhasanpour, Rozita, Elham Beheshtian, Ghazi Sherbaf, Sadaf Sahraian, and Haris I. Sair. "Functional Connectivity in Neurodegenerative Disorders." Topics in Magnetic Resonance Imaging 28, no. 6 (December 2019): 317–24. http://dx.doi.org/10.1097/rmr.0000000000000223.

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27

Kullmann, S., M. Heni, S. Frank, M. Hege, K. Linder, S. Zipfel, H. U. Haering, R. Veit, A. Fritsche, and H. Preissl. "P40: Hypothalamic functional connectivity networks." Clinical Neurophysiology 125 (June 2014): S59—S60. http://dx.doi.org/10.1016/s1388-2457(14)50203-9.

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28

Corbetta, Maurizio. "Functional connectivity and neurological recovery." Developmental Psychobiology 54, no. 3 (November 17, 2010): 239–53. http://dx.doi.org/10.1002/dev.20507.

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29

Almashaikhi, Talal, Sylvain Rheims, Karine Ostrowsky-Coste, Alexandra Montavont, Julien Jung, Julitta De Bellescize, Alexis Arzimanoglou, et al. "Intrainsular functional connectivity in human." Human Brain Mapping 35, no. 6 (September 12, 2013): 2779–88. http://dx.doi.org/10.1002/hbm.22366.

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30

Almashaikhi, Talal, Sylvain Rheims, Julien Jung, Karine Ostrowsky-Coste, Alexandra Montavont, Julitta De Bellescize, Alexis Arzimanoglou, et al. "Functional connectivity of insular efferences." Human Brain Mapping 35, no. 10 (May 19, 2014): 5279–94. http://dx.doi.org/10.1002/hbm.22549.

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31

Zwir, Igor, Javier Arnedo, Alberto Mesa, Coral Del Val, Gabriel De Erausquin, and C. Cloninger. "FUNCTIONAL CONNECTIVITY, PERSONALITY, & PSYCHOSIS." IBRO Neuroscience Reports 15 (October 2023): S912—S913. http://dx.doi.org/10.1016/j.ibneur.2023.08.1917.

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32

Zou, Yan, Weijun Tang, Xiangyang Qiao, and Ji Li. "Aberrant modulations of static functional connectivity and dynamic functional network connectivity in chronic migraine." Quantitative Imaging in Medicine and Surgery 11, no. 6 (June 2021): 2253–64. http://dx.doi.org/10.21037/qims-20-588.

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33

Cole, Michael W., Takuya Ito, Carrisa Cocuzza, and Ruben Sanchez-Romero. "The Functional Relevance of Task-State Functional Connectivity." Journal of Neuroscience 41, no. 12 (February 4, 2021): 2684–702. http://dx.doi.org/10.1523/jneurosci.1713-20.2021.

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34

Nan, Jiaofen, Jixin Liu, Guoying Li, Shiwei Xiong, Xuemei Yan, Qing Yin, Fang Zeng, et al. "Whole-Brain Functional Connectivity Identification of Functional Dyspepsia." PLoS ONE 8, no. 6 (June 17, 2013): e65870. http://dx.doi.org/10.1371/journal.pone.0065870.

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35

IMAI, NOBORU. "Dynamic Resting-State Functional Connectivity in Migraineurs." OBM Neurobiology 06, no. 04 (October 26, 2022): 1–11. http://dx.doi.org/10.21926/obm.neurobiol.2204143.

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Functional magnetic resonance imaging (fMRI) is widely used to detect changes in the resting-state brain networks of migraine patients. Functional connectivity fMRI analysis examines the functional organization of the brain based on temporal correlations of blood oxygen level-dependent signal changes in different brain regions. Most previous resting-state fMRI studies have assumed that functional connectivity between brain regions remains relatively stable over time. However, it is now known that the brain is a complex system that undergoes time-dependent dynamics. Therefore, functional connectivity may change over time. In recent years, resting-state fMRI analysis has evolved from the detection of static coupling to the study of dynamic connectivity. However, studies of dynamic functional connectivity in migraine patients are limited. Related studies have shown that dynamic functional connectivity analysis reveals significant changes in connectivity and abnormal networks not found in static functional connectivity analysis.

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36

Chen, Bo. "APPLICATION OF REGION EFFICIENCY INDEX IN FUNCTIONAL CONNECTIVITY ANALYSIS OF SCHIZOPHRENIA." Psychiatria Danubina 32, no. 2 (August 12, 2020): 159–67. http://dx.doi.org/10.24869/psyd.2020.159.

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37

Li, Hechun, Hongru Shi, Sisi Jiang, Changyue Hou, Hanxi Wu, Gang Yao, Dezhong Yao, and Cheng Luo. "Atypical Hierarchical Connectivity Revealed by Stepwise Functional Connectivity in Aging." Bioengineering 10, no. 10 (October 6, 2023): 1166. http://dx.doi.org/10.3390/bioengineering10101166.

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Hierarchical functional structure plays a crucial role in brain function. We aimed to investigate how aging affects hierarchical functional structure and to evaluate the relationship between such effects and molecular, microvascular, and cognitive features. We used resting-state functional magnetic resonance imaging (fMRI) data from 95 older adults (66.94 ± 7.23 years) and 44 younger adults (21.8 ± 2.53 years) and employed an innovative graph-theory-based analysis (stepwise functional connectivity (SFC)) to reveal the effects of aging on hierarchical functional structure in the brain. In the older group, an SFC pattern converged on the primary sensory—motor network (PSN) rather than the default mode network (DMN). Moreover, SFC decreased in the DMN and increased in the PSN at longer link-steps in aging, indicating a reconfiguration of brain hub systems during aging. Subsequent correlation analyses were performed between SFC values and molecular, microvascular features, and behavioral performance. Altered SFC patterns were associated with dopamine and serotonin, suggesting that altered hierarchical functional structure in aging is linked to the molecular fundament with dopamine and serotonin. Furthermore, increased SFC in the PSN, decreased SFC in the DMN, and accelerated convergence rate were all linked to poorer microvascular features and lower executive function. Finally, a mediation analysis among SFC features, microvascular features, and behavioral performance indicated that the microvascular state may influence executive function through SFC features, highlighting the interactive effects of SFC features and microvascular state on cognition.

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38

Edison, Paul. "Brain Connectivity: Disrupted Structural and Functional Connectivity—Cause or Effect?" Brain Connectivity 10, no. 5 (June 1, 2020): 200–201. http://dx.doi.org/10.1089/brain.2020.29011.ped.

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39

Chen, Jing, Dalong Sun, Yonghui Shi, Wei Jin, Yanbin Wang, Qian Xi, and Chuancheng Ren. "Alterations of static functional connectivity and dynamic functional connectivity in motor execution regions after stroke." Neuroscience Letters 686 (November 2018): 112–21. http://dx.doi.org/10.1016/j.neulet.2018.09.008.

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40

Lynch, Lauren K., Kun‐Han Lu, Haiguang Wen, Yizhen Zhang, Andrew J. Saykin, and Zhongming Liu. "Task‐evoked functional connectivity does not explain functional connectivity differences between rest and task conditions." Human Brain Mapping 39, no. 12 (August 24, 2018): 4939–48. http://dx.doi.org/10.1002/hbm.24335.

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41

Demirtaş, Murat, Cristian Tornador, Carles Falcón, Marina López‐Solà, Rosa Hernández‐Ribas, Jesús Pujol, José M. Menchón, et al. "Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder." Human Brain Mapping 37, no. 8 (April 28, 2016): 2918–30. http://dx.doi.org/10.1002/hbm.23215.

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42

Brennan, A., R. Naidoo, L. Greenstreet, Z. Mehrabi, N. Ramankutty, and C. Kremen. "Functional connectivity of the world’s protected areas." Science 376, no. 6597 (June 3, 2022): 1101–4. http://dx.doi.org/10.1126/science.abl8974.

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Global policies call for connecting protected areas (PAs) to conserve the flow of animals and genes across changing landscapes, yet whether global PA networks currently support animal movement—and where connectivity conservation is most critical—remain largely unknown. In this study, we map the functional connectivity of the world’s terrestrial PAs and quantify national PA connectivity through the lens of moving mammals. We find that mitigating the human footprint may improve connectivity more than adding new PAs, although both strategies together maximize benefits. The most globally important areas of concentrated mammal movement remain unprotected, with 71% of these overlapping with global biodiversity priority areas and 6% occurring on land with moderate to high human modification. Conservation and restoration of critical connectivity areas could safeguard PA connectivity while supporting other global conservation priorities.

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43

Roland, Jarod L., Abraham Z. Snyder, Carl D. Hacker, Anish Mitra, Joshua S. Shimony, David D. Limbrick, Marcus E. Raichle, Matthew D. Smyth, and Eric C. Leuthardt. "On the role of the corpus callosum in interhemispheric functional connectivity in humans." Proceedings of the National Academy of Sciences 114, no. 50 (November 28, 2017): 13278–83. http://dx.doi.org/10.1073/pnas.1707050114.

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Resting state functional connectivity is defined in terms of temporal correlations between physiologic signals, most commonly studied using functional magnetic resonance imaging. Major features of functional connectivity correspond to structural (axonal) connectivity. However, this relation is not one-to-one. Interhemispheric functional connectivity in relation to the corpus callosum presents a case in point. Specifically, several reports have documented nearly intact interhemispheric functional connectivity in individuals in whom the corpus callosum (the major commissure between the hemispheres) never develops. To investigate this question, we assessed functional connectivity before and after surgical section of the corpus callosum in 22 patients with medically refractory epilepsy. Section of the corpus callosum markedly reduced interhemispheric functional connectivity. This effect was more profound in multimodal associative areas in the frontal and parietal lobe than primary regions of sensorimotor and visual function. Moreover, no evidence of recovery was observed in a limited sample in which multiyear, longitudinal follow-up was obtained. Comparison of partial vs. complete callosotomy revealed several effects implying the existence of polysynaptic functional connectivity between remote brain regions. Thus, our results demonstrate that callosal as well as extracallosal anatomical connections play a role in the maintenance of interhemispheric functional connectivity.

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Messaritaki, Eirini, Sonya Foley, Simona Schiavi, Lorenzo Magazzini, Bethany Routley, Derek K. Jones, and Krish D. Singh. "Predicting MEG resting-state functional connectivity from microstructural information." Network Neuroscience 5, no. 2 (2021): 477–504. http://dx.doi.org/10.1162/netn_a_00187.

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Abstract Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

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45

Neudorf, Josh, Shaylyn Kress, and Ron Borowsky. "Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity." Brain Structure and Function 227, no. 1 (October 11, 2021): 331–43. http://dx.doi.org/10.1007/s00429-021-02403-8.

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AbstractAlthough functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).

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Wang, Sheng-Min, Dong Woo Kang, Yoo Hyun Um, Sunghwan Kim, Regina E. Y. Kim, Donghyeon Kim, Chang Uk Lee, and Hyun Kook Lim. "Cognitive Normal Older Adults with APOE-2 Allele Show a Distinctive Functional Connectivity Pattern in Response to Cerebral Aβ Deposition." International Journal of Molecular Sciences 24, no. 14 (July 8, 2023): 11250. http://dx.doi.org/10.3390/ijms241411250.

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The ε2 allele of apolipoprotein E (ε2) has neuroprotective effects against beta-amyloid (Aβ) pathology in Alzheimer’s disease (AD). However, its impact on the functional connectivity and hub efficiency in cognitively normal older adults (CN) with ε2 is unclear. We investigated the functional connectivity differences in the default mode network (DMN), salience network, and central executive network (CEN) between A-PET-negative (N = 29) and A-PET-positive (N = 15) CNs with ε2/ε2 or ε2/ε3 genotypes. The A-PET-positive CNs exhibited a lower anterior DMN functional connectivity, higher posterior DMN functional connectivity, and increased CEN functional connectivity compared to the A-PET-negative CNs. Cerebral Aβ retention was negatively correlated with anterior DMN functional connectivity and positively correlated with posterior DMN and anterior CEN functional connectivity. A graph theory analysis showed that the A-PET-positive CNs displayed a higher betweenness centrality in the middle frontal gyrus (left) and medial fronto-parietal regions (left). The betweenness centrality in the middle frontal gyrus (left) was positively correlated with Aβ retention. Our findings reveal a reversed anterior–posterior dissociation in the DMN functional connectivity and heightened CEN functional connectivity in A-PET-positive CNs with ε2. Hub efficiencies, measured by betweenness centrality, were increased in the DMN and CEN of the A-PET-positive CNs with ε2. These results suggest unique functional connectivity responses to Aβ pathology in CN individuals with ε2.

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47

Xia, Wenqing, Shaohua Wang, Andrea M. Spaeth, Hengyi Rao, Pin Wang, Yue Yang, Rong Huang, Rongrong Cai, and Haixia Sun. "Insulin Resistance-Associated Interhemispheric Functional Connectivity Alterations in T2DM: A Resting-State fMRI Study." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/719076.

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We aim to investigate whether decreased interhemispheric functional connectivity exists in patients with type 2 diabetes mellitus (T2DM) by using resting-state functional magnetic resonance imaging (rs-fMRI). In addition, we sought to determine whether interhemispheric functional connectivity deficits associated with cognition and insulin resistance (IR) among T2DM patients. We compared the interhemispheric resting state functional connectivity of 32 T2DM patients and 30 healthy controls using rs-fMRI. Partial correlation coefficients were used to detect the relationship between rs-fMRI information and cognitive or clinical data. Compared with healthy controls, T2DM patients showed bidirectional alteration of functional connectivity in several brain regions. Functional connectivity values in the middle temporal gyrus (MTG) and in the superior frontal gyrus were inversely correlated with Trail Making Test-B score of patients. Notably, insulin resistance (log homeostasis model assessment-IR) negatively correlated with functional connectivity in the MTG of patients. In conclusion, T2DM patients exhibit abnormal interhemispheric functional connectivity in several default mode network regions, particularly in the MTG, and such alteration is associated with IR. Alterations in interhemispheric functional connectivity might contribute to cognitive dysfunction in T2DM patients.

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48

Ke, Jun, Li Zhang, Rongfeng Qi, Qiang Xu, Yuan Zhong, Tao Liu, Jianjun Li, Guangming Lu, and Feng Chen. "Typhoon-Related Post-Traumatic Stress Disorder and Trauma Might Lead to Functional Integration Abnormalities in Intra- and Inter-Resting State Networks: a Resting-State Fmri Independent Component Analysis." Cellular Physiology and Biochemistry 48, no. 1 (2018): 99–110. http://dx.doi.org/10.1159/000491666.

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Background/Aims: Functional connectivity studies based on region of interest approach suggest altered functional connectivity of the default mode network (DMN), executive control network (ECN), and salience network (SN). The aim of this study is to determine whether intranetwork and internetwork brain connectivity are altered in both post-traumatic stress disorder (PTSD) patients and traumatized subjects without PTSD using a data-driven approach. Methods: Resting-state functional MRI data were acquired for 27 patients with typhoon-related PTSD, 33 trauma-exposed controls (TEC), and 30 healthy controls (HC). Functional connectivity within the DMN, ECN, and SN as well as functional and effective connectivity between these resting-state networks were examined with independent component analysis (ICA), and then compared between groups by conducting analysis of variance. Results: Within the DMN, the TEC group showed decreased and increased functional connectivity in the superior frontal gyrus compared with the PTSD group and the HC group, respectively. The TEC group showed increased angular functional connectivity within the DMN and decreased functional connectivity in the superior temporal gyrus/posterior insula within the SN relative to the HC group. Compared with the TEC group, the PTSD group showed increased functional connectivity in the middle frontal gyrus and supplementary motor area within the ECN as well as in the inferior frontal gyrus/anterior insula within the SN. The PTSD group showed decreased functional connectivity in the supplementary motor area within the SN relative to both control groups. Moreover, the PTSD showed increased excitatory influence from the ECN to DMN compared with both control groups, while the TEC group showed increased inhibitory influence from the DMN to ECN compared with the HC group. Intranetwork functional connectivity within the DMN and SN is altered in traumatized subjects irrespective of PTSD diagnosis. PTSD patients also showed altered intranetwork functional connectivity within the ECN. Conclusions: Distinct changes of effective connectivity between the DMN and ECN in the PTSD group and TEC group may reflect different compensatory mechanisms for rebalance of resting-state networks in the two groups.

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Peters, Henning, Valentin Riedl, Andrei Manoliu, Martin Scherr, Dirk Schwerthöffer, Claus Zimmer, Hans Förstl, Josef Bäuml, Christian Sorg, and Kathrin Koch. "Changes in extra-striatal functional connectivity in patients with schizophrenia in a psychotic episode." British Journal of Psychiatry 210, no. 1 (January 2017): 75–82. http://dx.doi.org/10.1192/bjp.bp.114.151928.

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Abstract:

BackgroundIn patients with schizophrenia in a psychotic episode, intra-striatal intrinsic connectivity is increased in the putamen but not ventral striatum. Furthermore, multimodal changes have been observed in the anterior insula that interact extensively with the putamen.AimsWe hypothesised that during psychosis, putamen extra-striatal functional connectivity is altered with both the anterior insula and areas normally connected with the ventral striatum (i.e. altered functional connectivity distinctiveness of putamen and ventral striatum).MethodWe acquired resting-state functional magnetic resonance images from 21 patients with schizophrenia in a psychotic episode and 42 controls.ResultsPatients had decreased functional connectivity: the putamen with right anterior insula and dorsal prefrontal cortex, the ventral striatum with left anterior insula. Decreased functional connectivity between putamen and right anterior insula was specifically associated with patients' hallucinations. Functional connectivity distinctiveness was impaired only for the putamen.ConclusionsResults indicate aberrant extra-striatal connectivity during psychosis and a relationship between reduced putamen–right anterior insula connectivity and hallucinations. Data suggest that altered intrinsic connectivity links striatal and insular pathophysiology in psychosis.

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Nikolaou, F., C. Orphanidou, P. Papakyriakou, K. Murphy, R. G. Wise, and G. D. Mitsis. "Spontaneous physiological variability modulates dynamic functional connectivity in resting-state functional magnetic resonance imaging." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2067 (May 13, 2016): 20150183. http://dx.doi.org/10.1098/rsta.2015.0183.

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Abstract:

It is well known that the blood oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is influenced—in addition to neuronal activity—by fluctuations in physiological signals, including arterial CO 2 , respiration and heart rate/heart rate variability (HR/HRV). Even spontaneous fluctuations of the aforementioned physiological signals have been shown to influence the BOLD fMRI signal in a regionally specific manner. Related to this, estimates of functional connectivity between different brain regions, performed when the subject is at rest, may be confounded by the effects of physiological signal fluctuations. Moreover, resting functional connectivity has been shown to vary with respect to time (dynamic functional connectivity), with the sources of this variation not fully elucidated. In this context, we examine the relation between dynamic functional connectivity patterns and the time-varying properties of simultaneously recorded physiological signals (end-tidal CO 2 and HR/HRV) using resting-state fMRI measurements from 12 healthy subjects. The results reveal a modulatory effect of the aforementioned physiological signals on the dynamic resting functional connectivity patterns for a number of resting-state networks (default mode network, somatosensory, visual). By using discrete wavelet decomposition, we also show that these modulation effects are more pronounced in specific frequency bands.

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