Browsing by Subject "Network analysis"

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  • Kangaslampi, Samuli; Garoff, Ferdinand; Golden, Shannon; Peltonen, Kirsi (2021)
    We analyzed the network structure of DSM-IV PTSD symptoms among 2792 help-seeking Central and East African refugees in Kenya exposed to multiple, severe traumatic events and on-going stressors. To some extent, our results reproduced structures identified among clinical populations in Europe, including strong links within traditional symptom clusters, such as between avoidance of thoughts and situations, and hypervigilance and startling. However, we found substantial differences in most central symptoms, with detachment and disinterest far less and emotional numbing and concentration problems more central in our analyses. Our networks did not reproduce the common finding of particularly low centrality of amnesia. We further noted substantive similarities in network structure, but also differences, between refugees living in an urban environment and in refugee camps. Concentration problems were most central among mainly Somali refugees at a refugee camp, and associated with amnesia and sense of foreshortened future, while emotional numbing was the most central symptom among majority Congolese refugees in Nairobi. Our findings highlight the importance of contextual and cultural factors for PTSD symptomatology, and are informative for assessment and treatment among help-seeking refugees.
  • Airaksinen, Jaakko; Gluschkoff, Kia; Kivimäki, Mika; Jokela, Markus (2020)
    Background: Many chronic diseases increase the risk of depressive symptoms, but few studies have examined whether these diseases also affect the composition of symptoms a person is likely to experience. As the risk and progression of depression may vary between chronic diseases, we used network analysis to examine how depression symptoms are connected before and after the diagnosis of diabetes, heart disease, stroke, and cancer. Methods: Participants (N = 7779) were from the longitudinal survey of the Health and Retirement Study. Participants were eligible if they had information on depression symptoms two and/or four years before and after the diagnosis of either diabetes, heart disease, cancer or stroke. We formed a control group with no chronic disease that was matched on age, sex and ethnic background to those with a disease. We constructed depression symptom networks and compared the overall connectivity of those networks, and depression symptom sum scores, for before and after the diagnosis of each disease. Results: Depression symptom sum scores increased with the diagnosis of each disease. The connectivity of depression symptoms remained unchanged for all the diseases, except for stroke, for which the connectivity decreased with the diagnosis. Limitations: Comorbidity with other chronic diseases was not controlled for as we focused on the onset of specific diseases. Conclusions: Our results suggest that although the mean level of depression symptoms increases after the diagnosis of chronic disease, with most chronic diseases, these changes are not reflected in the network structure of depression symptoms.
  • Saari, T. T.; Hallikainen, I.; Hintsa, T.; Koivisto, A. M. (2020)
    Background: Affective symptoms in Alzheimer's disease (AD) can be rated with both informantand self-ratings. Information from these two modalities may not converge. We estimated network structures of affective symptoms in AD with both rating modalities and assessed the longitudinal stability of the networks. Methods: Network analyses combining self-rated and informant-rated affective symptoms were conducted in 3198 individuals with AD at two time points (mean follow-up 387 days), drawn from the NACC database. Self rated symptoms were assessed by Geriatric Depression Scale, and informant-rated symptoms included depression, apathy and anxiety questions from Neuropsychiatric Inventory Questionnaire. Results: Informant-rated symptoms were mainly connected to symptoms expressing lack of positive affect, but not to the more central symptoms of self-rated worthlessness and helplessness. Networks did not differ in structure (p = .71), or connectivity (p = .92) between visits. Symptoms formed four clinically meaningful clusters of depressive symptoms and decline, lack of positive affect, informant-rated apathy and anxiety and informant-rated depression. Limitations: The symptom dynamics in our study could have been present before AD diagnosis. The lack of positive affect cluster may represent a methodological artefact rather than a theoretically meaningful subgroup. Requiring follow-up lead to a selection of patients with less cognitive decline. Conclusions: Informant rating may only capture the more visible affective symptoms, such as not being in good spirits, instead of more central and severe symptoms, such as hopelessness and worthlessness. Future research should continue to be mindful of differences between self- and informant-rated symptoms even in earlier stages of AD.
  • Zhang, Yuezhou; Xhaard, Henri; Ghemtio, Leo (2018)
    Betulin derivatives have been proven effective in vitro against Leishmania donovani amastigotes, which cause visceral leishmaniasis. Identifying the molecular targets and molecular mechanisms underlying their action is a currently an unmet challenge. In the present study, we tackle this problem using computational methods to establish properties essential for activity as well as to screen betulin derivatives against potential targets. Recursive partitioning classification methods were explored to develop predictive models for 58 diverse betulin derivatives inhibitors of L. donovani amastigotes. The established models were validated on a testing set, showing excellent performance. Molecular fingerprints FCFP_6 and ALogP were extracted as the physicochemical properties most extensively involved in separating inhibitors from non-inhibitors. The potential targets of betulin derivatives inhibitors were predicted by in silico target fishing using structure-based pharmacophore searching and compound-pharmacophore-targetpathway network analysis, first on PDB and then among L. donovani homologs using a PSI-BLAST search. The essential identified proteins are all related to protein kinase family. Previous research already suggested members of the cyclin-dependent kinase family and MAP kinases as Leishmania potential drug targets. The PSI-BLAST search suggests two L. donovani proteins to be especially attractive as putative betulin target, heat shock protein 83 and membrane transporter D1.
  • Zhang, Yuezhou; Xhaard, Henri; Ghemtio, Leo (Springer International Publishing, 2018)
    Abstract Betulin derivatives have been proven effective in vitro against Leishmania donovani amastigotes, which cause visceral leishmaniasis. Identifying the molecular targets and molecular mechanisms underlying their action is a currently an unmet challenge. In the present study, we tackle this problem using computational methods to establish properties essential for activity as well as to screen betulin derivatives against potential targets. Recursive partitioning classification methods were explored to develop predictive models for 58 diverse betulin derivatives inhibitors of L. donovani amastigotes. The established models were validated on a testing set, showing excellent performance. Molecular fingerprints FCFP_6 and ALogP were extracted as the physicochemical properties most extensively involved in separating inhibitors from non-inhibitors. The potential targets of betulin derivatives inhibitors were predicted by in silico target fishing using structure-based pharmacophore searching and compound-pharmacophore-target-pathway network analysis, first on PDB and then among L. donovani homologs using a PSI-BLAST search. The essential identified proteins are all related to protein kinase family. Previous research already suggested members of the cyclin-dependent kinase family and MAP kinases as Leishmania potential drug targets. The PSI-BLAST search suggests two L. donovani proteins to be especially attractive as putative betulin target, heat shock protein 83 and membrane transporter D1.
  • Hatlestad-Hall, C; Bruna, R; Syvertsen, MR; Erichsen, A; Andersson, V; Vecchio, F; Miraglia, F; Rossini, PM; Renvall, H; Tauboll, E; Maestu, F; Haraldsen, IH (2021)
    Objective: The hypersynchronous neuronal activity associated with epilepsy causes widespread functional network disruptions extending beyond the epileptogenic zone. This altered network topology is considered a mediator for non-seizure symptoms, such as cognitive impairment. The aim of this study was to investigate functional network alterations in focal epilepsy patients with good seizure control and high quality of life. Methods: We compared twenty-two focal epilepsy patients and sixteen healthy controls on graph metrics derived from functional connectivity of source-level resting-state EEG. Graph metrics were calculated over a range of network densities in five frequency bands. Results: We observed a significantly increased small world index in patients relative to controls. On the local level, two left-hemisphere regions displayed a shift towards greater alpha band "hubness". The findings were not mediated by age, sex or education, nor by age of epilepsy onset, duration or focus lateralisation. Conclusions: Widespread functional network alterations are evident in focal epilepsy, even in a cohort characterised by successful anti-seizure medication therapy and high quality of life. These findings might support the position that functional network analysis could hold clinical relevance for epilepsy. Significance: Focal epilepsy is accompanied by global and local functional network aberrancies which might be implied in the sustenance of non-seizure symptoms. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
  • Abraham, Gad; Bhalala, Oneil G.; de Bakker, Paul I. W.; Ripatti, Samuli; Inouye, Michael (2014)