Identification of a strategic brain network underlying processing speed deficits in vascular cognitive impairment
Abstract
Patients with vascular cognitive impairment (VCI) commonly exhibit deficits in processing speed. This has been attributed to a disruption of frontal-subcortical neuronal circuits by ischemic lesions, but the exact mechanisms and underlying anatomical structures are poorly understood. We set out to identify a strategic brain network for processing speed by applying graph-based data-mining techniques to MRI lesion maps from patients with small vessel disease. We studied 235 patients with CADASIL, a genetic small vessel disease causing pure VCI. Using a probabilistic atlas in standard space we first determined the regional volumes of white matter hyperintensities (WMH) and lacunar lesions (LL) within major white matter tracts. Conditional dependencies between the regional lesion volumes and processing speed were then examined using Bayesian network analysis. Exploratory analysis identified a network of five imaging variables as the best determinant of processing speed. The network included LL in the left anterior thalamic radiation and the left cingulum as well as WMH in the left forceps minor, the left parahippocampal white matter and the left corticospinal tract. Together these variables explained 34% of the total variance in the processing speed score. Structural equation modeling confirmed the findings obtained from the Bayesian models. In summary, using graph-based models we identified a strategic brain network having the highest predictive value for processing speed in our cohort of patients with pure small vessel disease. Our findings confirm and extend previous results showing a role of frontal-subcortical neuronal circuits, in particular dorsolateral prefrontal and cingulate circuits, in VCI.
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The regional distribution pattern of MRI lesions is an important factor in explaining observed deficits in CADASIL.
“For example, in two articles, Duering et al. proposed that the regional pattern of distribution of MRI lesions is an important factor in the explanation of observed deficits [21, 23].”
From Clinical and research applications of magnetic resonance imaging in the study of CADASIL (2019) · cites this paper as [21]
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Duering, M., Csanadi, E., Gesierich, B., Jouvent, E., Herve, D., Seiler, S., Belaroussi, B., Ropele, S., Schmidt, R., Chabriat, H., et al. (2013) · Brain · doi:10.1093/brain/awt184 · Cited by 183 · see citing works
Self-citationStrategic white matter tracts for processing speed deficits in age-related small vessel disease
Duering, M., Gesierich, B., Seiler, S., Pirpamer, L., Gonik, M., Hofer, E., Jouvent, E., Duchesnay, E., Chabriat, H., Ropele, S., et al. (2014) · Neurology · doi:10.1212/wnl.0000000000000475 · Cited by 143 · see citing works
Self-citationTuladhar, A. M., van Dijk, E., Zwiers, M. P., van Norden, A. G. W., de Laat, K. F., Shumskaya, E., Norris, D. G., & de Leeuw, F.-E. (2016) · Human Brain Mapping · doi:10.1002/hbm.23032 · Cited by 142 · see citing works
Righart, R., Duering, M., Gonik, M., Jouvent, E., Reyes, S., Hervé, D., Chabriat, H., & Dichgans, M. (2013) · NeuroImage: Clinical · doi:10.1016/j.nicl.2013.06.006 · Cited by 62 · see citing works
Self-citationClinical and research applications of magnetic resonance imaging in the study of CADASIL
Schoemaker, D., Quiroz, Y. T., Torrico-Teave, H., & Arboleda-Velasquez, J. F. (2019) · Neuroscience Letters · doi:10.1016/j.neulet.2019.01.014 · Cited by 36 · see citing works
“proposed that the regional pattern of distribution of MRI lesions is an important factor in the explanation of observed deficits [21, 23].”
BackgroundPrediction of 3-year clinical course in CADASIL
Jouvent, E., Duchesnay, E., Hadj-Selem, F., De Guio, F., Mangin, J.-F., Hervé, D., Duering, M., Ropele, S., Schmidt, R., Dichgans, M., et al. (2016) · Neurology · doi:10.1212/wnl.0000000000003252 · Cited by 30 · see citing works
Self-citationCamerino, I., Ferreira, J., Vonk, J. M., Kessels, R. P. C., de Leeuw, F.-E., Roelofs, A., Copland, D., & Piai, V. (2024) · Neuropsychology Review · doi:10.1007/s11065-022-09570-3 · Cited by 27 · see citing works
Cullen, B., Moreton, F. C., Stringer, M. S., Krishnadas, R., Kalladka, D., López-González, M. R., Santosh, C., Schwarzbauer, C., & Muir, K. W. (2016) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x16636395 · Cited by 12 · see citing works
- Authors
- Duering, M., Gonik, M., Malik, R., Zieren, N., Reyes, S., Jouvent, E., Hervé, D., Gschwendtner, A., Opherk, C., Chabriat, H., & Dichgans, M.
- Affiliations
- LMU Klinikum (DE)
- Assistance Publique – Hôpitaux de Paris (FR)
- German Center for Neurodegenerative Diseases (DE)
- Corresponding author
- Dichgans, Martin
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 66
- Pages
- 177-183
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2064722635
- Cited by
- 89
- References
- 60
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 6 · 2023: 6 · 2022: 7 · 2021: 5 · 2020: 5 · 2019: 10 · 2018: 5 · 2017: 7 · 2016: 9 · 2015: 4 · 2014: 9 · 2013: 7 · 2012: 1
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- DZNE Pub (submittedVersion)
- Topics
- Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
- Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- vascular cognitive impairment, processing speed, cerebral small vessel disease, CADASIL, white matter hyperintensities, brain networks, frontal-subcortical circuits, thalamic radiation, cingulum, forceps minor, corticospinal tract, Bayesian network analysis, structural equation modeling
- MeSH terms
- Adult, Aged, Bayes Theorem, Brain, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neural Pathways, CADASIL, Young Adult
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