Poster No:
1781
Submission Type:
Abstract Submission
Authors:
Ruhunur Özdemir1, Kai Lehtimäki1, Timo Möttönen1, Joonas Haapasalo2, Soila Jarvenpaa1, Eetu Siitama1, Jukka Peltola1
Institutions:
1Tampere University Hospital, Tampere, Finland, 2Tampere University Hospital, Tampere , Finland
First Author:
Co-Author(s):
Introduction:
Drug-resistant epilepsy (DRE) affects approximately one-third of individuals diagnosed with epilepsy, presenting a significant therapeutic challenge. For patients unresponsive to pharmacological therapies, deep brain stimulation (DBS) targeting the anterior nucleus of the thalamus (ANT) has emerged as a viable intervention. Despite its demonstrated efficacy, the mechanisms underlying DBS and the heterogeneity in clinical outcomes remain inadequately understood. Optimizing DBS requires a detailed understanding of ANT-related neural networks, which can be achieved through advancements in diffusion-weighted imaging (DWI). Traditional diffusion tensor imaging (DTI) has limitations in resolving complex fiber architectures, such as crossing, bending, and fanning fibers, commonly found in white matter. To address these challenges, this study employs high-angular-resolution diffusion imaging (HARDI) and multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) deterministic and probabilistic tractography to map the intricate white matter connectivity of the ANT.
Methods:
HARDI data were acquired from five healthy volunteers using a 3T Siemens MAGNETOM Skyra MRI scanner. A multi-shell imaging protocol with b-values of 1000, 2000, and 3000 s/mm² and 64 gradient directions was utilized. MSMT-CSD tractography was employed to generate deterministic and probabilistic fiber reconstructions. Regions of interest (ROIs) were manually delineated in the ANT, hippocampus, cingulate cortex, and amygdala, with additional ROIs mimicking DBS lead placements to simulate volumes of activated tissue (VAT). Preprocessing steps included denoising, motion correction, and bias field correction to ensure data integrity. Advanced tractography algorithms were applied to resolve the complex fiber orientations, enabling comprehensive mapping of the white matter pathways associated with the ANT.
Results:
The tractography analysis revealed five major fiber systems associated with the ANT: (1) the anterior thalamic radiation (ATR), (2) the mammillothalamic tract (MTT), (3) the thalamo-cingulate tract (TCT), (4) the inferior thalamic peduncle (ITP), and (5) the temporo-pulvinar tract (TPT). These pathways exhibited distinct connectivity patterns, depending on the spatial location of the ANT ROI. The ATR demonstrated robust connections to frontal cortical regions, including the orbitofrontal and medial prefrontal cortices, while the MTT connected the ANT to the mammillary bodies. The TCT bridged the ANT with the cingulate cortex, penetrating the corpus callosum en route to the cingulum bundle. The ITP exhibited remote connections to the amygdala, ventral tegmental area, and occipital cortex. Probabilistic tractography consistently demonstrated more widespread and distant connections compared to deterministic approaches. Variations in connectivity patterns between individuals highlighted the potential influence of inter-subject anatomical differences on DBS outcomes.
Conclusions:
This study demonstrates the utility of advanced neuroimaging techniques in elucidating the structural connectivity of the ANT and its implications for DBS targeting. The application of HARDI and MSMT-CSD based deterministic and probabilistic tractography enables high-resolution visualization of complex fiber systems, providing a framework for individualized DBS planning. By bridging the gap between anatomical precision and clinical outcomes, this work contributes to the optimization of DBS therapy for patients with DRE. Future research should focus on extending these methodologies to patient-specific datasets to further refine targeting strategies and improve therapeutic efficacy.
Brain Stimulation:
Deep Brain Stimulation 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Acquisition
Basal Ganglia
Cortex
Data analysis
Epilepsy
Neurological
STRUCTURAL MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
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.
Yes
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.
Not applicable
Please indicate which methods were used in your research:
Diffusion MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
AFNI
FSL
Provide references using APA citation style.
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Tournier, J.D., Yeh, C.H., Calamante, F., Cho, K.H., Connelly, A., Lin, C.P., 2008. Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data. Neuroimage 42 (2), 617–625. https://doi.org/10.1016/J.NEUROIMAGE.2008.05.002.
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chromatic_sharpening_of_FOD-based_DEC_maps_by_structural_T1_information/links /555968a908ae980ca6106a79/Panchromatic-sharpening-of-FOD-based-DEC-maps-by-str
No