Differential Effects of Confluent and Nonconfluent White Matter Hyperintensities on Functional Connectivity in Mild Cognitive Impairment
Abstract
Background: White matter hyperintensities (WMHs) indicate active small vessel disease. Emerging evidence suggests that confluent WMH (C-WMH) results in greater cognitive impairment compared with nonconfluent WMH (NC-WMH) visualized as punctate lesions. However, the mechanism linking C-WMH and early cognitive impairment is not clearly understood. Aims: To investigate the effects of C-WMH and NC-WMH on whole-brain functional connectivity (FC) across 138 regions of interest (ROIs) and cognition in 63 subjects with mild cognitive impairment (MCI). Methods: MCI subjects were classified as C-WMH or NC-WMH using the Staals criteria on the Fazekas WMH scale. Group-level ROI-to-ROI FC trends and differences based on WMH subtypes were computed using standard resting-state functional magnetic resonance imaging analysis. Global cognitive performance was measured with mini-mental state examination (MMSE). Results: Subjects with C-WMH exhibited increased inter-regional FC in the fronto-parietal, fronto-occipital, parieto-occipital, and temporo-parietal regions of the salience, dorsal-attention, default-mode, and visual networks compared with NC-WMH. Increased intra-regional FC was also observed within the frontal and parietal lobes in C-WMH. In addition to widespread increased FC in C-WMH, a few regions in the fronto-temporal and intra-frontal areas demonstrated reduced FC in C-WMH compared with NC-WMH. Analyses of cognitive correlates demonstrated increased parieto-occipital FC to be negatively associated with MMSE in the C-WMH. The increased parieto-occipital FC may be related to loss of higher order inhibitory control in the parieto-occipital regions induced by C-WMH or alternatively a compensatory mechanism to FC alterations induced by C-WMH. Conclusion: C-WMH in subjects with MCI is associated with widespread increase in intra- and inter-regional FC. These findings provide novel insights into divergent FC related to confluence of WMH in MCI. Impact statement White matter hyperintensities (WMHs) have been demonstrated to be a major risk factor for progressive cognitive decline. However, the relationship between structural and functional brain changes related to different types of WMH lesions as well as different stages of WMH progression remains unclear. In this study, we demonstrate that confluent WMH is significantly associated with divergent functional connectivity changes in patients with mild cognitive impairment (MCI). This finding may allow identification of MCI subjects who are adversely affected by WMH and thus provides a window of opportunity to introduce pharmacological and nonpharmacological interventional measures.
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Executive function performance is dependent on both intact white matter pathway connectivity and pathologic integrity of the cortical regions themselves.
“Indeed, the present findings support the hypothesis that executive function performance is dependent on both intact white matter pathway connectivity, as well as pathologic integrity of the cortical regions themselves [12,45,46]”
Cites
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152 · cited 1× · see citing works
“Diagnosis of MCI was made by cognitive neurologists based on the NIA-AA and Petersen criteria (Albert et al., 2011; Petersen, 2004).”
Mild cognitive impairment as a diagnostic entity
Petersen, R. C. (2004) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2004.01388.x · Cited by 7,864 · see citing works
MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging
Fazekas, F., Chawluk, J. B., Alavi, A. A., Hurtig, H. I., & Zimmerman, R. A. (1987) · American Journal of Roentgenology · doi:10.2214/ajr.149.2.351 · Cited by 5,327 · see citing works
<i>Conn</i> : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0073 · Cited by 4,885 · see citing works
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.042 · Cited by 4,502 · see citing works
The Mini-Mental State Examination
Folstein, M. F. (1983) · Archives of General Psychiatry · doi:10.1001/archpsyc.1983.01790060110016 · Cited by 1,976 · see citing works
Pathogenesis of Leukoaraiosis: A Review
Pantoni, L., & Garcia, J. H. (1997) · Stroke · doi:10.1161/01.str.28.3.652 · Cited by 1,225 · see citing works
An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis
Schmidt, P. J., Christian Gaser, B., Arsić, M., Buck, D., Förschler, A., Berthele, A., Hoshi, M., Ilg, R., Schmid, V. J., Zimmer, C., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.032 · Cited by 1,219 · see citing works
Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden
Staals, J., Makin, S., Doubal, F., Dennis, M. S., & Wardlaw, J. M. (2014) · Neurology · doi:10.1212/wnl.0000000000000837 · Cited by 1,058 · see citing works
He, B. J., Snyder, A. Z., Vincent, J. L., Epstein, A. A., Shulman, G. L., & Corbetta, M. (2007) · Neuron · doi:10.1016/j.neuron.2007.02.013 · Cited by 916 · see citing works
Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations
Gouw, A. A., Seewann, A., van der Flier, W. M., Barkhof, F., Rozemuller, A. M., Scheltens, P., & Geurts, J. J. G. (2011) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.2009.204685 · Cited by 766 · see citing works
Celone, K., Calhoun, V. D., Dickerson, B. C., Atri, A., Chua, E. F., Miller, S. L., DePeau, K. M., Rentz, D., Selkoe, D. J., Blacker, D., et al. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.2250-06.2006 · Cited by 733 · see citing works
Brier, M. R., Thomas, J. B., Snyder, A. Z., Benzinger, T. L., Zhang, D., Raichle, M. E., Holtzman, D. M., Morris, J. C., & Ances, B. M. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5698-11.2012 · Cited by 595 · see citing works
Functional Alterations in Memory Networks in Early Alzheimer’s Disease
Sperling, R. A., Dickerson, B. C., Pihlajamaki, M. M., Vannini, P., LaViolette, P. S., Vitolo, O. V., Hedden, T., Becker, J. A., Rentz, D. M., Selkoe, D. J., et al. (2010) · NeuroMolecular Medicine · doi:10.1007/s12017-009-8109-7 · Cited by 587 · see citing works
van Dijk, E. J., Prins, N. D., Vrooman, H. A., Hofman, A., Koudstaal, P. J., & Breteler, M. M. B. (2008) · Stroke · doi:10.1161/strokeaha.107.513176 · Cited by 580 · see citing works
Resting state functional connectivity in preclinical Alzheimer’s disease
Sheline, Y. I., & Raichle, M. E. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.11.028 · Cited by 509 · see citing works
Identification of MCI individuals using structural and functional connectivity networks
Chong-yaw Wee, A., Yap, P., Zhang, D., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., Wang, L., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.015 · Cited by 378 · see citing works
Chand, G. B., Wu, J., Hajjar, I. M., & Qiu, D. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0509 · Cited by 189 · see citing works
Left frontal cortex connectivity underlies cognitive reserve in prodromal Alzheimer disease
Franzmeier, N., Duering, M., Weiner, M. W., Dichgans, M., Ewers, M., & Initiative, A. D. N. (2017) · Neurology · doi:10.1212/wnl.0000000000003711 · Cited by 148 · see citing works
Wang, P., Zhou, B., Yao, H., Zhan, Y., Zhang, Z., Cui, Y. J., Xu, K., Ma, J., Wang, L., An, N., et al. (2015) · Scientific Reports · doi:10.1038/srep14824 · Cited by 145 · see citing works
Trajectory of white matter hyperintensity burden preceding mild cognitive impairment
Silbert, L. C., Dodge, H. H., Perkins, L. N., Sherbakov, L. O., Lahna, D. L., Erten‐Lyons, D., Woltjer, R. L., Shinto, L. H., & Kaye, J. A. (2012) · Neurology · doi:10.1212/wnl.0b013e3182661f2b · Cited by 125 · see citing works
Predicting Aggressive Decline in Mild Cognitive Impairment
Tosto, G., Zimmerman, M. E., Carmichael, O. T., & Brickman, A. M. (2014) · JAMA Neurology · doi:10.1001/jamaneurol.2014.667 · Cited by 116 · see citing works
for the members of the Alzheimer’s Disease Cooperative Study Group*, van Straaten, E. C. W., Harvey, D., Scheltens, P., Barkhof, F., Petersen, R. C., Thal, L. J., Jack, C. R., & DeCarli, C. S. (2008) · Journal of Neurology · doi:10.1007/s00415-008-0874-y · Cited by 110 · see citing works
Franzmeier, N., Caballero, M. Á. A., Taylor, A. N. W., Simon-Vermot, L., Buerger, K., Ertl-Wagner, B., Mueller, C., Catak, C., Janowitz, D., Baykara, E., et al. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-016-9599-1 · Cited by 103 · see citing works
Reijmer, Y. D., Schultz, A. P., Leemans, A., O'Sullivan, M. J., Gurol, M. E., Sperling, R. A., Greenberg, S. M., Viswanathan, A., & Hedden, T. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.054 · Cited by 83 · see citing works
Tract-specific white matter hyperintensities disrupt neural network function in Alzheimer’s disease
Taylor, A. N. W., Kambeitz-Ilankovic, L., Gesierich, B., Simon-Vermot, L., Franzmeier, N., Araque Caballero, M. Á., Müller, S., Hesheng, L., Ertl-Wagner, B., Bürger, K., et al. (2017) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.06.2358 · Cited by 73 · see citing works
Chen, X., Huang, L., Ye, Q., Yang, D., Qin, R., Luo, C., Li, M., Zhang, B., & Xu, Y. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102088 · Cited by 72 · see citing works
Benson, G. S., Hildebrandt, A., Lange, C., Schwarz, C., Köbe, T., Sommer, W., Flöel, A., & Wirth, M. (2018) · Alzheimer s Research & Therapy · doi:10.1186/s13195-018-0434-3 · Cited by 70 · see citing works
Franzmeier, N., Hartmann, J., Taylor, A. N. W., Caballero, M. Á. A., Lee, S.-V., Buerger, K., Kambeitz-Ilankovic, L., Ertl-Wagner, B., Mueller, C., Catak, C., et al. (2017) · Journal of Alzheimer's Disease · doi:10.3233/jad-170360 · Cited by 69 · see citing works
White Matter Hyperintensities as Early and Independent Predictors of Alzheimer's Disease Risk
Mortamais, M., Artéro, S., & Ritchie, K. A. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-141473 · Cited by 67 · see citing works
Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer’s disease
Chong, J. S. X., Liu, S., Loke, Y. M., Hilal, S., Ikram, M. K., Xu, X., Tan, B. Y., Venketasubramanian, N., Chen, C., & Zhou, J. H. (2017) · Brain · doi:10.1093/brain/awx224 · Cited by 63 · see citing works
The association between lacunes and white matter hyperintensity features on MRI: The SMART-MR study
Ghaznawi, R., Geerlings, M. I., Jaarsma‐Coes, M. G., Zwartbol, M. H. T., Kuijf, H. J., van der Graaf, Y., Witkamp, T. D., Hendrikse, J., & de Bresser, J. (2018) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x18800463 · Cited by 53 · see citing works
Inaba, M., White, L. R., Bell, C., Chen, R., Petrovitch, H., Launer, L. J., Abbott, R. D., Ross, G. W., & Masaki, K. (2011) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2011.03490.x · Cited by 52 · see citing works
Levy-Cooperman, N., Ramirez, J., Lobaugh, N. J., & Black, S. E. (2008) · Stroke · doi:10.1161/strokeaha.107.498196 · Cited by 52 · see citing works
Vipin, A., Loke, Y. M., Liu, S., Hilal, S., Shim, H. Y., Xu, X., Tan, B. Y., Venketasubramanian, N., Chen, C., & Zhou, J. H. (2018) · Alzheimer s Research & Therapy · doi:10.1186/s13195-018-0413-8 · Cited by 48 · see citing works
The role of cerebrovascular disease when there is concomitant Alzheimer disease
Vemuri, P., & Knopman, D. S. (2015) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2015.09.013 · Cited by 47 · see citing works
Impaired Cross-Modal Inhibition in Alzheimer Disease
Drzezga, A. E., Grimmer, T., Peller, M., Wermke, M., Siebner, H. R., Rauschecker, J. P., Schwaiger, M., & Kurz, A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020288 · Cited by 47 · see citing works
Ding, W., Cao, W., Wang, Y., Sun, Y., Xue, C., Zhou, Y., Xu, Q., & Xu, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138180 · Cited by 38 · see citing works
Marco, M. D., Manca, R., Mitolo, M., & Venneri, A. (2017) · Neural Plasticity · doi:10.1155/2017/4050536 · Cited by 37 · see citing works
Lockhart, S. N., Luck, S. J., Geng, J. J., Beckett, L. A., Disbrow, E. A., Carmichael, O. T., & DeCarli, C. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122445 · Cited by 33 · see citing works
Prins, N. D., van der Flier, W. M., Brashear, H. R., Knol, D. L., van de Pol, L. A., Barkhof, F., & Scheltens, P. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-122233 · Cited by 25 · see citing works
Ye, Q., Chen, X., Qin, R., Huang, L., Yang, D., Liu, R., Zhang, B., Bai, F., & Xu, Y. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00695 · Cited by 22 · see citing works
Shi, L., Miao, X., Lou, W., Liu, K., Abrigo, J., Wong, A., Chu, C. W., Wang, D., & Mok, V. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00593 · Cited by 14 · see citing works
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Schulz, M., Malherbe, C., Cheng, B., Thomalla, G., & Schlemm, E. (2021) · BMC Medicine · doi:10.1186/s12916-021-01962-1 · Cited by 70 · see citing works
Dementia in Southeast Asia: influence of onset-type, education, and cerebrovascular disease
Vipin, A., Satish, V., Saffari, S. E., Koh, W. Z. H., Lim, L., da Silva, E. F., Mei, N. M., Choong, T. Y. O., Chua, E. V., Lim, L. L. H., et al. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00936-y · Cited by 26 · see citing works
Chen, J. X. Y., Vipin, A., Sandhu, G. K., Leow, Y. J., Zailan, F. Z., Tanoto, P., Lee, E. S., Lee, K. L., Cheung, C., & Kandiah, N. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2024.100029 · Cited by 22 · see citing works
Kumar, D., Yatawara, C., Wang, B., Wong, B., Tan, Y. J., Zailan, F. Z., Ng, K. P., & Kandiah, N. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215556 · Cited by 12 · see citing works
Brain structural changes in COPD patients with cognitive impairment
Jia-kai, H., Yun-sheng, T., Xin-yu, H., Shuai, Z., Wang, Z., Ze-hao, C., Yu-feng, M., Yi, L., Zi-ang, Y., Hong-tao, W., et al. (2026) · Journal of Neurology · doi:10.1007/s00415-025-13602-2 · Cited by 11 · see citing works
Ali, D. G., Abner, E. L., Bahrani, A. A., El Khouli, R., Gold, B. T., Jiang, Y., Wilcock, D. M., & Jicha, G. A. (2023) · Brain Sciences · doi:10.3390/brainsci13020218 · Cited by 7 · see citing works
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Huang, C., Zhou, X., Ren, M., Zhang, W., Wan, K., Yin, J., Li, M., Li, Z., Zhu, X., & Sun, Z. (2023) · Journal of Neuroscience Research · doi:10.1002/jnr.25230 · Cited by 4 · see citing works
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Schulz, M., Malherbe, C., Cheng, B., Thomalla, G., & Schlemm, E. (2021) · Figshare · doi:10.6084/m9.figshare.14538908
Cheng, K., Qin, R., Wang, X., Wang, W., Li, H., Xiang, L., Ni, L., Zhang, D., Zhang, J., & Wang, L. (2026) · Journal of Integrative Neuroscience · doi:10.31083/jin46727
Schmidt, T., Salzmann, R., Montagnese, M., Chan, D., Bernal, J., Pfister, M., Arndt, P., Peters, O., Hellmann‐Regen, J., Preis, L., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.24.740551
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- Authors
- Kumar, D., Vipin, A., Wong, B., Ng, K. P., & Kandiah, N.
- Affiliations
- National Neuroscience Institute (SG)
- Duke-NUS Medical School (SG)
- Nanyang Technological University (SG)
- Corresponding author
- Kandiah, Nagaendran
- Venue
- Brain Connectivity
- Publisher
- SAGE Publications
- Published
- 2020-12
- Volume
- 10
- Issue
- 10
- Pages
- 547-554
- ISSN
- 2158-0014 (ISSN-L)
- 2158-0022
- OpenAlex
- W3092259481
- Cited by
- 14
- References
- 43
- Citations by year
- 2026: 4 · 2025: 1 · 2023: 3 · 2022: 2 · 2021: 4
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Brain Connectivity (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- white matter hyperintensities, functional connectivity, mild cognitive impairment, cerebral small vessel disease, resting-state fMRI, cognitive decline, compensatory mechanisms, salience network, default mode network, dorsal attention network, visual network, inhibitory control
- MeSH terms
- Aged, Brain, Cognition, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neuropsychological Tests, Disease Progression, Cognitive Dysfunction, White Matter
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