Research Article

Independent Component and Graph Theory Analyses Reveal Normalized Brain Networks on Resting-State Functional MRI After Working Memory Training in People With HIV

Corresponding Author

Chunying Jia MS

[email protected]

orcid.org/0000-0002-7941-0605

Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland, USA

Address reprint requests to: C.J., 370 ITE building, 1000 Hilltop Circle, Baltimore, Maryland 21250, USA. E-mail: [email protected], L.C., 670 W. Baltimore Street, Rm 1161, Bldg HSF III, Baltimore, Maryland 21201, USA. E-mail: [email protected], or T.A., 324 ITE building, 1000 Hilltop Circle, Baltimore, Maryland 21250, USA. E-mail: [email protected].

Search for more papers by this author

Qunfang Long PhD,

Qunfang Long PhD

Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland, USA

Search for more papers by this author

Thomas Ernst PhD,

Thomas Ernst PhD

Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA

Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA

Search for more papers by this author

Yuanqi Shang BS,

Yuanqi Shang BS

Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA

Search for more papers by this author

Linda Chang MD,

Corresponding Author

Linda Chang MD

[email protected]

Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA

Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA

Department of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, USA

Search for more papers by this author

Tülay Adali PhD,

Corresponding Author

Tülay Adali PhD

[email protected]

Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland, USA

Search for more papers by this author

Chunying Jia MS,

Corresponding Author

Chunying Jia MS

[email protected]

orcid.org/0000-0002-7941-0605

Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland, USA

Search for more papers by this author

Qunfang Long PhD,

Qunfang Long PhD

Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland, USA

Search for more papers by this author

Thomas Ernst PhD,

Thomas Ernst PhD

Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA

Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA

Search for more papers by this author

Yuanqi Shang BS,

Yuanqi Shang BS

Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA

Search for more papers by this author

Linda Chang MD,

Corresponding Author

Linda Chang MD

[email protected]

Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA

Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA

Department of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, USA

Search for more papers by this author

Tülay Adali PhD,

Corresponding Author

Tülay Adali PhD

[email protected]

Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland, USA

Search for more papers by this author

First published: 27 September 2022

https://doi.org/10.1002/jmri.28439

Citations: 1

Linda Chang and Tülay Adali are co-senior authors.

Share a link

Email
Wechat
Bluesky

Abstract

Background

Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown.

Purpose

To test whether WMT affects PWH brain functional connectivity in resting-state fMRI (rsfMRI).

Study Type

Prospective.

Population

A total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53 HIV-seronegative controls (SN, ages 49.5 ± 1.6 years, six women).

Field Strength/Sequence

Axial single-shot gradient-echo echo-planar imaging at 3.0 T was performed at baseline (TL1), at 1-month (TL2), and at 6-months (TL3), after WMT.

Assessment

All participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training, n = 58: 28 PWH, 30 SN; nonadaptive training, n = 48: 25 PWH, 23 SN), 25 sessions over 5–8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2.

Statistical Tests

Two-way analyses of variance (ANOVA) on GT metrics and two-sample t-tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) using Pearson's correlations. The significance level was set at P < 0.05 after false discovery rate correction.

Results

The ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (P = 0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r = −0.62 at LD = 50%) and vDMN eCent before training significantly correlated with memory performance changes (r = 0.53 at LD = 50%).

Data Conclusion

ICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH.

Evidence Level

Technical Efficacy

Conflict of Interest

No competing financial interests exist.

Supporting Information

References

1Gallo P, Frei K, Rordorf C, Lazdins J, Tavolato B, Fontana A. Human immunodeficiency virus type 1 (HIV-1) infection of the central nervous system: An evaluation of cytokines in cerebrospinal fluid. J Neuroimmunol 1989; 23: 109-116.
10.1016/0165-5728(89)90029-5
CAS PubMed Web of Science® Google Scholar
2McArthur JC, Steiner J, Sacktor N, Nath A. Human immunodeficiency virus-associated neurocognitive disorders: Mind the gap. Ann Neurol 2010; 67: 699-714.
10.1002/ana.22053
CAS PubMed Web of Science® Google Scholar
3Saylor D, Dickens AM, Sacktor N, et al. HIV-associated neurocognitive disorder—Pathogenesis and prospects for treatment. Nat Rev Neurol 2016; 12(4): 234-248.
10.1038/nrneurol.2016.27
PubMed Web of Science® Google Scholar
4Chang L, Løhaugen GC, Andres T, et al. Adaptive working memory training improved brain function in human immunodeficiency virus–seropositive patients. Ann Neurol 2017; 81: 17-34.
10.1002/ana.24805
CAS PubMed Web of Science® Google Scholar
5Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995; 34: 537-541.
10.1002/mrm.1910340409
CAS PubMed Web of Science® Google Scholar
6Biswal BB, Kylen JV, Hyde JS. Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps. NMR Biomed 1997; 10: 165-170.
10.1002/(SICI)1099-1492(199706/08)10:4/5<165::AID-NBM454>3.0.CO;2-7
CAS PubMed Web of Science® Google Scholar
7Harnett NG, van Rooij SJ, Ely TD, et al. Prognostic neuroimaging biomarkers of trauma-related psychopathology: Resting-state fMRI shortly after trauma predicts future PTSD and depression symptoms in the AURORA study. Neuropsychopharmacology 2021; 46(7): 1263-1271.
10.1038/s41386-020-00946-8
PubMed Web of Science® Google Scholar
8Ibrahim B, Suppiah S, Ibrahim N, et al. Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review. Hum Brain Mapp 2021; 42(9): 2941-2968.
10.1002/hbm.25369
PubMed Web of Science® Google Scholar
9Comon P, Jutten C. Handbook of blind source separation: Independent component analysis and applications. Cambridge, MA: Academic Press; 2010.
Google Scholar
10Bullmore E, Sporns O. Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009; 10: 186-198.
10.1038/nrn2575
CAS PubMed Web of Science® Google Scholar
11Laney J, Adalı T, Waller SM, Westlake KP. Quantifying motor recovery after stroke using independent vector analysis and graph-theoretical analysis. NeuroImage: Clinical 2015; 8: 298-304.
10.1016/j.nicl.2015.04.014
PubMed Web of Science® Google Scholar
12Chang L, Løhaugen G, Douet V, Miller EN, Skranes J, Ernst T. Neural correlates of working memory training in HIV patients: Study protocol for a randomized controlled trial. Trials 2016; 17: 62.
10.1186/s13063-016-1160-4
CAS PubMed Web of Science® Google Scholar
13Shin J, Ahn S, Hu X. Correction for the T1 effect incorporating flip angle estimated by Kalman filter in cardiac-gated functional MRI. Magn Reson Med 2013; 70: 1626-1633.
10.1002/mrm.24620
PubMed Web of Science® Google Scholar
14Yan C, Zang Y. DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 2010; 4: 13.
PubMed Google Scholar
15Calhoun VD, Adalı T. Group ICA of fMRI toolbox (GIFT). 2004. Accessed on January 18, 2020. Available from http://icatb.sourceforge.net.
Google Scholar
16Calhoun VD, Adali T, Pearlson GD, Pekar JJ. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 2001; 14: 140-151.
10.1002/hbm.1048
CAS PubMed Web of Science® Google Scholar
17Fu GS, Anderson M, Adalı T. Likelihood estimators for dependent samples and their application to order detection. IEEE Trans Signal Process 2014; 62: 4237-4244.
10.1109/TSP.2014.2333551
Web of Science® Google Scholar
18Bhinge S, Long Q, Calhoun VD, Adali T. Spatial dynamic functional connectivity analysis identifies distinctive biomarkers in schizophrenia. Front Neurosci 2019; 13: 1006.
10.3389/fnins.2019.01006
PubMed Web of Science® Google Scholar
19Li XL, Adali T. Blind spatiotemporal separation of second and/or higherorder correlated sources by entropy rate minimization. IEEE international conference on acoustics, speech and signal processing. Dallas, TX: IEEE; 2010. p 1934-1937.
10.1109/ICASSP.2010.5495311
Google Scholar
20Long Q, Bhinge S, Levin-Schwartz Y, Boukouvalas Z, Calhoun VD, Adalı T. The role of diversity in data-driven analysis of multi-subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics. Hum Brain Mapp 2019; 40: 489-504.
10.1002/hbm.24389
PubMed Web of Science® Google Scholar
21Long Q, Jia C, Boukouvalas Z, Gabrielson B, Emge D, Adali T. Consistent run selection for independent component analysis: Application to fMRI analysis. IEEE international conference on acoustics, speech and signal processing (ICASSP). Calgary, AB, Canada: IEEE; 2018. p 2581-2585.
10.1109/ICASSP.2018.8461646
Google Scholar
22Damaraju E, Allen EA, Belger A, et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage 2014; 5: 298-308.
10.1016/j.nicl.2014.07.003
CAS PubMed Web of Science® Google Scholar
23Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 2014; 24(3): 663-676.
10.1093/cercor/bhs352
PubMed Web of Science® Google Scholar
24Cox RW. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996; 29: 162-173.
10.1006/cbmr.1996.0014
CAS PubMed Web of Science® Google Scholar
25West DB. Introduction to graph theory. Vol 2. Upper Saddle River, NJ: Prentice Hall; 2001.
Google Scholar
26Rubinov M, Sporns O. Complex network measures of brain connectivity: Uses and interpretations. Neuroimage 2010; 52(3): 1059-1069.
10.1016/j.neuroimage.2009.10.003
PubMed Web of Science® Google Scholar
27Thomas JB, Brier MR, Snyder AZ, Vaida FF, Ances BM. Pathways to neurodegeneration: Effects of HIV and aging on resting-state functional connectivity. Neurology 2013; 80: 1186-1193.
10.1212/WNL.0b013e318288792b
PubMed Web of Science® Google Scholar
28Ortega M, Brier MR, Ances BM. Effects of HIV and combination antiretroviral therapy (cART) on cortico-striatal functional connectivity. AIDS (London, England) 2015; 29(6): 703-712.
10.1097/QAD.0000000000000611
CAS PubMed Web of Science® Google Scholar
29Zhuang Y, Qiu X, Wang L, et al. Combination antiretroviral therapy improves cognitive performance and functional connectivity in treatment-naïve HIV-infected individuals. J Neurovirol 2017; 23: 704-712.
10.1007/s13365-017-0553-9
CAS PubMed Web of Science® Google Scholar
30Liu D, Zhao C, Wang W, et al. Altered gray matter volume and functional connectivity in human immunodeficiency virus-infected adults. Front Neurosci 2020; 14: 1230.
10.3389/fnins.2020.601063
Web of Science® Google Scholar
31Samboju V, Philippi CL, Chan P, et al. Structural and functional brain imaging in acute HIV. NeuroImage 2018; 20: 327-335.
10.1016/j.nicl.2018.07.024
PubMed Web of Science® Google Scholar
32Thomas JB, Brier MR, Ortega M, Benzinger TL, Ances BM. Weighted brain networks in disease: Centrality and entropy in human immunodeficiency virus and aging. Neurobiol Aging 2015; 36: 401-412.
10.1016/j.neurobiolaging.2014.06.019
PubMed Web of Science® Google Scholar
33Eijlers AJ, Meijer KA, Wassenaar TM, et al. Increased default-mode network centrality in cognitively impaired multiple sclerosis patients. Neurology 2017; 88: 952-960.
10.1212/WNL.0000000000003689
PubMed Web of Science® Google Scholar
34Chang L, Speck O, Miller EN, et al. Neural correlates of attention and working memory deficits in HIV patients. Neurology 2001; 57(6): 1001-1007.
10.1212/WNL.57.6.1001
CAS PubMed Web of Science® Google Scholar
35Chang L, Tomasi D, Yakupov R, et al. Adaptation of the attention network in human immunodeficiency virus brain injury. Ann Neurol 2004; 56(2): 259-272.
10.1002/ana.20190
CAS PubMed Web of Science® Google Scholar
36Tomasi D, Chang L, de Castro CE, Telang F, Ernst T. The human immunodeficiency virus reduces network capacity: Acoustic noise effect. Ann Neurol 2006; 59(2): 419-423.
10.1002/ana.20766
CAS PubMed Web of Science® Google Scholar
37Ernst T, Yakupov R, Nakama H, et al. Declined neural efficiency in cognitively stable human immunodeficiency virus patients. Ann Neurol 2009; 65(3): 316-325.
10.1002/ana.21594
CAS PubMed Web of Science® Google Scholar
38Chang L, Holt JL, Yakupov R, Jiang CS, Ernst T. Lower cognitive reserve in the aging human immunodeficiency virus-infected brain. Neurobiol Aging 2013; 34(4): 1240-1253.
10.1016/j.neurobiolaging.2012.10.012
PubMed Web of Science® Google Scholar
39Schoonheim MM, Hulst HE, Landi D, et al. Gender-related differences in functional connectivity in multiple sclerosis. Mult Scler J 2012; 18(2): 164-173.
10.1177/1352458511422245
PubMed Web of Science® Google Scholar

Citing Literature

Volume57, Issue5

May 2023

Pages 1552-1564

Independent Component and Graph Theory Analyses Reveal Normalized Brain Networks on Resting-State Functional MRI After Working Memory Training in People With HIV

Abstract

Background

Purpose

Study Type

Population

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

Evidence Level

Technical Efficacy

Conflict of Interest

Supporting Information

References

Citing Literature

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

Independent Component and Graph Theory Analyses Reveal Normalized Brain Networks on Resting-State Functional MRI After Working Memory Training in People With HIV

Abstract

Background

Purpose

Study Type

Population

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

Evidence Level

Technical Efficacy

Conflict of Interest

Supporting Information

References

Citing Literature

References

Related

Information