Complex systems representing effective connectivity in patients with Type One diabetes mellitus

Guàrdia‐Olmos, Joan; Gudayol-Ferré, Esteve; Gallardo-Moreno, Geisa B.; Martínez-Ricart, Mar; Peró‐Cebollero, Maribel; González-Garrido, Andrés A.

doi:10.1371/journal.pone.0208247

Cited by 5 publications

(2 citation statements)

References 55 publications

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“…Besides, elevated HbA1c at diagnosis was found correlated with decreased volume in cerebellar white matter (33). Except for structure differences, some studies also found activation differences between T1DM and healthy controls in cerebellum (41,42).…”

Section: Discussionmentioning

confidence: 93%

Altered Gray Matter Volume in Patients With Type 1 Diabetes Mellitus

et al. 2020

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Background and Purpose: Many imaging studies have reported structure alterations in patients with type 1 diabetes mellitus (T1DM) by using voxel-based morphometry (VBM). Nevertheless, the results reported were inconsistent and had not been reviewed quantitatively. Accordingly, the quantitative meta-analysis which including VBM studies of patients with T1DM was conducted. Materials and Methods: The gray matter volume alterations in patients with T1DM was estimated by using the software seed-based d mapping. Meantime, the meta-regression was applied to detect the effects of some demographics and clinical characteristics. Results: Six studies were finally included, which with 6 datasets comprising 414 T1DM patients and 216 healthy controls. The pooled meta-analyses detected that patients with T1DM showed robustly increased gray matter volume in the left dorsolateral superior frontal gyrus and middle frontal gyrus and a decreased gray matter volume in the right lingual gyrus, cerebellum, precuneus, the left inferior temporal gyrus, and middle temporal gyrus. The meta-regression showed that the mean age, the female patient's ratio, duration of illness and HbAlc% for T1DM patients were not linearly related with gray matter alterations. Conclusion: This meta-analysis demonstrates that gray matter volume decreases in T1DM patients were mainly locates in the cortical regions and cerebellum.

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Section: Discussionmentioning

confidence: 93%

Altered Gray Matter Volume in Patients With Type 1 Diabetes Mellitus

et al. 2020

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“…In addition, and in accordance with previous studies, the analysis of functional connectivity networks will allow the identification of complex behavior patterns that can be interpreted as a reflection of compensatory mechanisms associated with the characteristics of the diagnosed pathology. For example, a study of the patterns of connectivity networks has found that the pattern of complexity in the effective connectivity network was much denser in patients diagnosed with type I diabetes than in a group of healthy people matched by age and years of formal education [44]. This type of statistical approach should show its effectiveness in identifying different patterns of connectivity between subjects diagnosed with MCI and their healthy controls.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Brain Functional Connectivity in Patients with Mild Cognitive Impairment

et al. 2019

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Mild cognitive impairment is defined as greater cognitive decline than expected for a person at a particular age and is sometimes considered a stage between healthy aging and Alzheimers disease or other dementia syndromes. It is known that functional connectivity patterns change in people with this diagnosis. We studied functional connectivity patterns and functional segregation in a resting-state fMRI paradigm comparing 10 MCI patients and 10 healthy controls matched by education level, age and sex. Ninety ROIs from the automated anatomical labeling (AAL) atlas were selected for functional connectivity analysis. A correlation matrix was created for each group, and a third matrix with the correlation coefficient differences between the two matrices was created. Functional segregation was analyzed with the 3-cycle method, which is novel in studies of this topic. Finally, cluster analyses were also performed. Our results showed that the two correlation matrices were visually similar but had many differences related to different cognitive functions. Differences were especially apparent in the anterior default mode network (DMN), while the visual resting-state network (RSN) showed no differences between groups. Differences in connectivity patterns in the anterior DMN should be studied more extensively to fully understand its role in the differentiation of healthy aging and an MCI diagnosis.

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