Reduced Global Efficiency and Random Network Features in Patients with Relapsing-Remitting Multiple Sclerosis with Cognitive Impairment

Hawkins, Randall A.; Shatil, Anwar S.; Lee, Liesly; Sengupta, Anwesha; Zhang, L.; Morrow, Sarah A.; Aviv, Richard I.

doi:10.3174/ajnr.a6435

Cited by 13 publications

(11 citation statements)

References 30 publications

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“…Other studies selected a fixed penalty term for all individual networks which is why this approach can also be seen as a special case of weight-based thresholding [ 34 , 37 ]. Group-level edge elimination for each individual-specific network was carried out by univariate testing of the edge weights ( N = 17), either by permutation testing to control the probability of including spurious connections at 0.05 or by only including edges with weights significantly different from zero [ 13 , 15 – 19 , 79 – 81 ]. Then all those edges that were characterized as spurious at the group level were removed from all individual-specific networks.…”

Section: Resultsmentioning

confidence: 99%

“…In some studies, graph-theoretical features were normalized by dividing them by the same metric computed from a randomly generated network of identical size, density and/or degree distribution to account for differences in network size and density, introducing additional computational complexity. Largely, studies ( N = 31, 23.8%) examined the normalized clustering coefficient and characteristic path length obtained by dividing the CC and CPL by the CC and CPL of multiple randomly generated networks [ 18 , 20 , 79 , 81 , 84 , 88 ]. For instance, Imms et al [ 89 ] highlighted the use of normalized CC and CPL as diagnostic biomarkers to differentiate between controls and patients with traumatic brain injury.…”

Section: Resultsmentioning

confidence: 99%

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Individual-specific networks for prediction modelling – A scoping review of methods

Gregorich

Melograna

Sunqvist

et al. 2022

BMC Med Res Methodol

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Background Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space. Methods We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase. Results Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction. Conclusion The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Individual-specific networks for prediction modelling – A scoping review of methods

Gregorich

Melograna

Sunqvist

et al. 2022

BMC Med Res Methodol

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“…Also, previous studies have described a decreased global efficiency on PwMS compared to healthy volunteers, suggesting a disrupted topological organization of the WM networks due to impaired structural connections (38). Besides, abnormalities of global efficiency have been associated with negative consequences on cognition impacting different cognitive domains such as memory and attention performance (39)(40)(41). As the compensation and adaptation of brain mechanisms probably deteriorate with age and with brain damage, it would appear that brain network dysfunction leads to CI (16).…”

Section: Discussionmentioning

confidence: 98%

Impact of Cognitive Reserve and Structural Connectivity on Cognitive Performance in Multiple Sclerosis

et al. 2020

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Background: Cognitive reserve (CR) could attenuate the impact of the brain burden on the cognition in people with multiple sclerosis (PwMS).Objective: To explore the relationship between CR and structural brain connectivity and investigate their role on cognition in PwMS cognitively impaired (PwMS-CI) and cognitively preserved (PwMS-CP).Methods: In this study, 181 PwMS (71% female; 42.9 ± 10.0 years) were evaluated using the Cognitive Reserve Questionnaire (CRQ), Brief Repeatable Battery of Neuropsychological tests, and MRI. Brain lesion and gray matter volumes were quantified, as was the structural network connectivity. Patients were classified as PwMS-CI (z scores = −1.5 SD in at least two tests) or PwMS-CP. Linear and multiple regression analyses were run to evaluate the association of CRQ and structural connectivity with cognition in each group. Hedges's effect size was used to compute the strength of associations.Results: We found a very low association between CRQ scores and connectivity metrics in PwMS-CP, while in PwMS-CI, this relation was low to moderate. The multiple regression model, adjusted for age, gender, mood, lesion volume, and graph metrics (local and global efficiency, and transitivity), indicated that the CRQ (β = 0.26, 95% CI: 0.17-0.35) was associated with cognition (adj R 2 = 0.34) in PwMS-CP (55%). In PwMS-CI, CRQ (β = 0.18, 95% CI: 0.07-0.29), age, and network global efficiency were independently associated with cognition (adj R 2 = 0.55). The age-and gender-adjusted association between CRQ score and global efficiency on having an impaired cognitive status was −0.338 (OR: 0.71, p = 0.036) and −0.531 (OR: 0.59, p = 0.002), respectively.Lopez-Soley et al. Cognitive Reserve in Multiple SclerosisConclusions: CR seems to have a marginally significant effect on brain structural connectivity, observed in patients with more severe clinical impairment. It protects PwMS from cognitive decline regardless of their cognitive status, yet once cognitive impairment has set in, brain damage and aging are also influencing cognitive performance.

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“…However, there are two causes that may affect signal quality. First, we used a non-linear co-registration method (SPM Lesion Normalization with Tissue Probability Maps 13 as used before 5 , 27 and recommended 14 , 28 ). This algorithm can cause a general inflation of voxels, as smaller MS brains are co-registered to the same standard MNI space as non-atrophied healthy brains.…”

Section: Discussionmentioning

confidence: 99%

“…Lesion filling can to some extent be used to counter these effects 28 , 29 . We did not apply lesion filling in our study as we wanted to be able to compare our results to previously published papers on graph theoretical measures in MS patients 5 – 7 , 27 .…”

Section: Discussionmentioning

confidence: 99%

Signal quality as Achilles’ heel of graph theory in functional magnetic resonance imaging in multiple sclerosis

Baijot

Denissen

Gielen

et al. 2021

Sci Rep

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Graph-theoretical analysis is a novel tool to understand the organisation of the brain.We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.

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Reduced Global Efficiency and Random Network Features in Patients with Relapsing-Remitting Multiple Sclerosis with Cognitive Impairment

Cited by 13 publications

References 30 publications

Individual-specific networks for prediction modelling – A scoping review of methods

Individual-specific networks for prediction modelling – A scoping review of methods

Impact of Cognitive Reserve and Structural Connectivity on Cognitive Performance in Multiple Sclerosis

Signal quality as Achilles’ heel of graph theory in functional magnetic resonance imaging in multiple sclerosis

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