New Graph-Theoretical-Multimodal Approach Using Temporal and Structural Correlations Reveals Disruption in the Thalamo-Cortical Network in Patients with Schizophrenia

Finotelli, Paolo; Forlim, Caroline Garcia; Klock, Leonie; Pini, Alessia; Bächle, Johanna; Stoll, Laura; Giemsa, Patrick; Fuchs, Marie; Schoofs, Nikola; Montag, Christiane; Dulio, Paolo; Gallinat, Jürgen; Kühn, Simone

doi:10.1089/brain.2018.0654

Cited by 8 publications

(3 citation statements)

References 48 publications

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“…Structural networks, on the other hand, have been mostly based on diffusion tensor imaging (DTI) data 26,27 , which allow estimating the physical connections among brain regions, using a technique known as tractography 28,29 . Nevertheless, there have also been a few studies presenting structural brain networks based on cortical thickness 30,31 , or volume 32,33 . The first structural network was discovered by He et al…”

Section: Introductionmentioning

confidence: 99%

Texture-based brain networks for characterization of healthy subjects from MRI

Silveira

Castellano

2023

Preprint

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Background: Brain networks have been widely used to study the relationships between brain regions based on their dynamics using, e.g. fMRI or EEG, and to characterize their real physical connections using DTI. However, few studies have investigated brain networks derived from structural properties; and those have been based on cortical thickness or gray matter volume. Purpose: The main objective of this work was to investigate the feasibility of obtaining useful information from brain networks derived from structural MRI, using texture features. We also wanted to verify if texture brain networks had any relation with established functional networks. Methods: T1-MR images were segmented using AAL and texture parameters from the gray-level co-occurrence matrix were computed for each region, for 760 subjects. Individual texture networks were used to evaluate the structural connections between regions of well-established functional networks; assess possible gender differences; investigate the dependence of texture network parameters with age; and single out brain regions with different texture-network characteristics. Results and discussion: Although around 70% of texture connections between regions belonging to the default mode, attention, and visual network were greater than the mean connection value, this effect was small (only between 7% and 15% of these connections were larger than one standard deviation), implying that texture-based morphology does not seem to subside function. This differs from cortical thickness-based morphology, which has been shown to relate to functional networks. Eighty out of 86 evaluated regions showed significant (ANCOVA, p<0.05) differences between genders. Forty-two out of 86 regions showed significant (ANCOVA, p<0.05) dependence with age; however, the R2 indicates that this is not a linear relation. Thalamus and putamen showed a very unique texture-wise structure compared to other analyzed regions. Conclusions: Texture networks were able to provide useful information regarding gender and age-related differences, as well as for singling out specific brain regions. We did not find a morphological texture-based subsidy for the evaluated functional brain networks. In the future, this approach will be extended to neurological patients to investigate the possibility of extracting biomarkers to help monitor disease evolution or treatment effectiveness.

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Section: Introductionmentioning

confidence: 99%

Texture-based brain networks for characterization of healthy subjects from MRI

Silveira

Castellano

2023

Preprint

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“…The topological features have been well defined, such as small-worldness [ 21 – 23 ], global and local efficiency [ 24 ], and highly connected hubs [ 25 ], which contain node degree, node betweenness, and so on. The brain network features have been characterized in AD [ 26 , 27 ], schizophrenia [ 28 ], multiple sclerosis [ 29 ], and other conditions. Moreover, several analyses have indicated that patients with PACG exhibit signs of changes in brain network properties that were identified in previous resting-state fMRI studies.…”

Section: Introductionmentioning

confidence: 99%

Brain Functional Network Analysis of Patients with Primary Angle-Closure Glaucoma

et al. 2022

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Objectives. Recent resting-state functional magnetic resonance imaging (fMRI) studies have focused on glaucoma-related neuronal degeneration in structural and spontaneous functional brain activity. However, there are limited studies regarding the differences in the topological organization of the functional brain network in patients with glaucoma. In this study, we aimed to assess both potential alterations and the network efficiency in the functional brain networks of patients with primary angle-closure glaucoma (PACG). Methods. We applied resting-state fMRI data to construct the functional connectivity network of 33 patients with PACG ( 54.21 ± 7.21 years ) and 33 gender- and age-matched healthy controls ( 52.42 ± 7.80 years ). The differences in the global and regional topological brain network properties between the two groups were assessed using graph theoretical analysis. Partial correlations between the altered regional values and clinical parameters were computed for patients with PACG. Results. No significant differences in global topological measures were identified between the two groups. However, significant regional alterations were identified in the patients with PACG, including differences within visual and nonvisual (somatomotor and cognition-emotion) regions. The normalized clustering coefficient and normalized local efficiency of the right superior parietal gyrus were significantly correlated with the retinal fiber layer thickness (RNFLT) and the vertical cup to disk ratio (V C/D). In addition, the normalized node betweenness of the left middle frontal gyrus (orbital portion) was significantly correlated with the V C/D in the patients with PACG. Conclusions. Our results suggest that regional inefficiency with decrease and compensatory increase in local functional properties of visual and nonvisual nodes preserved the brain network of the PACG at the global level.

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“…Structural networks, on the other hand, have been mostly based on diffusion tensor imaging (DTI) data (EKSTRAND et al, 2020;NENADIĆ et al, 2020), which allow estimating the physical connections among brain regions. Nevertheless, there have also been a few studies presenting structural brain networks based on cortical thickness (LERCH et al, 2006;SCHMITT et al, 2008) or volume (BASSETT et al, 2008;FINOTELLI et al, 2019). The first structural network was discovered by He et al using cortical thickness data obtained from magnetic resonance images CHEN;EVANS, 2007).…”

Section: Introductionmentioning

confidence: 99%

Characterization of healthy individuals using texture measurements extracted from magnetic resonance images

Vinicius da Silveira

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New Graph-Theoretical-Multimodal Approach Using Temporal and Structural Correlations Reveals Disruption in the Thalamo-Cortical Network in Patients with Schizophrenia

Cited by 8 publications

References 48 publications

Texture-based brain networks for characterization of healthy subjects from MRI

Texture-based brain networks for characterization of healthy subjects from MRI

Brain Functional Network Analysis of Patients with Primary Angle-Closure Glaucoma

Characterization of healthy individuals using texture measurements extracted from magnetic resonance images

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