Mapping the impact of white matter hyperintensities on the human connectome dysconnectivity
Poster No:
106
Submission Type:
Abstract Submission
Authors:
Bianca Mazini1, Caio Seguin2, Jinglei Lv3, Guray Erus4, Junhao Wen5, Christos Davatzikos4, Patric Hagmann6, Andrew Zalesky2, Ye Tian7
Institutions:
1Systems Lab, Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, 2Systems Lab, Department of Psychiatry, The University of Melbourne, Melbourne, Australia, 3The University of Sydney, Sydney, New South Wales, 4Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 5Columbia University, New York, NY, 6Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Vaud, 7Department of Psychiatry, The University of Melbourne, Melbourne, Australia
First Author:
Bianca Mazini
Systems Lab, Department of Psychiatry, The University of Melbourne
Melbourne, Victoria
Systems Lab, Department of Psychiatry, The University of Melbourne
Melbourne, Victoria
Co-Author(s):
Andrew Zalesky
Systems Lab, Department of Psychiatry, The University of Melbourne
Melbourne, Australia
Systems Lab, Department of Psychiatry, The University of Melbourne
Melbourne, Australia
Introduction:
White matter hyperintensities (WMH) of presumed vascular origin are the main imaging marker of cerebral small vessel disease(cSVD). Their prevalence increases with age, affecting 20–50%of the population in midlife and over 90%in older age. While some people with WMH develop cognitive impairment, others remain asymptomatic. Despite numerous studies showing associations between WMH and cognitive impairment, the mechanisms leading to dementia remain unclear. One hypothesis is that WMH disrupt the structural connectome (SC), impairing the efficiency of associative networks and contributing to cognitive decline. To explore this hypothesis, we conducted a large-scale study using the UK Biobank. Our goals are to identify differences in SC between asymptomatic individuals with WMH and patients with cognitive symptoms of vascular origin and to quantify the extent to which these connectome disruptions are driven by WMH.
Methods:
We studied a subset of UKB participants that included 1) asymptomatic subjects with WMH ( N=4030), 2) individuals with probable cSVD (WMH and hypertension or diabetes) but without dementia (group2,N=13508), and 3) individuals with dementia(N=108). A lesion mask indicating WMH was created for each individual based on FLAIR images. We first assessed whether the volume of WMH differed across groups. We then mapped SC between 84 brain regions using tractography, and tested for between-group differences in SC using the network-based statistic(NBS).We tested whether the extent of SC disruptions associate with WMH volume.
We also mapped a dysconnectivity matrix for each individual to identify regions between which connectivity was disrupted due to the presence of WMH. To achieve this, we generated an average reference tractogram mapped from 50 healthy subjects in the Human Connectome Project. The group-average streamlines were filtered through an individual-specific WMH lesion mask, yielding a dysconnectivity matrix for each individual. Group-average dysconnectivity matrices were computed and compared to the networks identified using the NBS, allowing us to investigate potential relationships between dysconnectivity due to WMH and network disruptions.
We also mapped a dysconnectivity matrix for each individual to identify regions between which connectivity was disrupted due to the presence of WMH. To achieve this, we generated an average reference tractogram mapped from 50 healthy subjects in the Human Connectome Project. The group-average streamlines were filtered through an individual-specific WMH lesion mask, yielding a dysconnectivity matrix for each individual. Group-average dysconnectivity matrices were computed and compared to the networks identified using the NBS, allowing us to investigate potential relationships between dysconnectivity due to WMH and network disruptions.
Results:
We first found progressively increased WMH volume from asymptomatic to dementia group (total: χ2= 1554.58 p= <0.001; periventricular: χ2= 1573.4 p= <0.001). We also found extensively affected networks in dementia compared to cSVD and asymptomatic groups as well as between the latter two groups (Fig 1a). Interestingly, increased WMH volume (periventricular, total) was associated with reduced SC strength (Fig 2). Similarly, the dysconnectivity matrix showed a gradient of dysconnectivity severity across groups, with the highest in dementia group and the lowest in asymptomatic individuals. Both methods revealed extensively impacted white matter tracts in cSVD and dementia, and consistently highlighted the key involvement of subcortical areas, frontoparietal and default mode networks (Fig 1b and 1c).
·Section 1a: affected networks with NBS; Section 1b: involvement of cortical and subcortical areas with dysconnectivity matrices; Section 1c: involvement of cortical and subcortical areas with NBS
·Correlation between disrupted networks (found with NBS) and WMH burden
Conclusions:
We found that individuals with WMH present extensive SC disruption. Importantly, we showed that the disruption is progressive and can be explained by the severity of periventricular WMH. These findings may explain the cognitive decline observed in individuals with symptomatic WMH, as the most affected regions - frontoparietal nework, default mode network and subcortical areas - are linked to executive function, commonly impaired in individuals with cSVD and dementia.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
White Matter Anatomy, Fiber Pathways and Connectivity
Keywords:
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - White matter hyperintensities; cerebral small vessel disease; dementia; structural connectivity;
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
2. Pantoni, L., & Simoni, M. (2003). Pathophysiology of Cerebral Small Vessels in Vascular Cognitive Impairment. International Psychogeriatrics, 15(S1), 59–65. https://doi.org/10.1017/s1041610203008974
3. Van Dijk, E. J., Prins, N. D., Vrooman, H. A., Hofman, A., Koudstaal, P. J., & Breteler, M. M. B. (2008). Progression of Cerebral Small Vessel Disease in Relation to Risk Factors and Cognitive Consequences. Stroke, 39(10), 2712–2719. https://doi.org/10.1161/strokeaha.107.513176
4. Smith, E. E., Salat, D. H., Jeng, J., McCreary, C. R., Fischl, B., Schmahmann, J. D., Dickerson, B. C., Viswanathan, A., Albert, M. S., Blacker, D., & Greenberg, S. M. (2011). Correlations between MRI white matter lesion location and executive function and episodic memory. Neurology, 76(17), 1492–1499. https://doi.org/10.1212/wnl.0b013e318217e7c8
5. Petersen, Steven E., & Sporns, O. (2015). Brain Networks and Cognitive Architectures. Neuron, 88(1), 207–219. https://doi.org/10.1016/j.neuron.2015.09.027
6. Griffanti, L., Zamboni, G., Khan, A., Li, L., Bonifacio, G., Sundaresan, V., Schulz, U. G., Kuker, W., Battaglini, M., Rothwell, P. M., & Jenkinson, M. (2016). BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities. NeuroImage, 141, 191–205. https://doi.org/10.1016/j.neuroimage.2016.07.018
7. Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: Identifying differences in brain networks. NeuroImage, 53(4), 1197–1207. https://doi.org/10.1016/j.neuroimage.2010.06.041
8. Taghvaei, M., Mechanic-Hamilton, D. J., Shokufeh Sadaghiani, Banafsheh Shakibajahromi, Sudipto Dolui, Das, S., Brown, C., Tackett, W., Khandelwal, P., Cook, P., Shinohara, R. T., Yushkevich, P., Bassett, D. S., Wolk, D. A., & Detre, J. A. (2023). Impact of white matter hyperintensities on structural connectivity and cognition in cognitively intact ADNI participants. Neurobiology of Aging, 135, 79-90.https://doi.org/10.1016/j.neurobiolaging.2023.10.012