Biology subjects

Alhazmi, F.

Publications and source records attributed to Alhazmi, F..

3 recordsLinked to original sources

bioRxiv2021

Parameter Sensitivity and Experimental Validation for Fractional-Order Dynamical Modeling of Neurovascular Coupling

GoalNeurovascular coupling is a fundamental mechanism linking neural activity to cerebral blood flow (CBF) response. Modeling this coupling is very important to understand brain functions, yet challenging due to the complexity of the involved phenomena. One key feature that different studies have reported is the time delay that is inherently present between the neural activity and cerebral blood flow, which has been described by adding a delay parameter in standard models. An alternative approach was recently proposed where the framework of fractional-order modeling is employed to characterize the complex phenomena underlying the neurovascular. Thanks to its nonlocal property, a fractional derivative is suitable for modeling delayed and power-law phenomena. MethodsIn this study, we analyzed and validated an effective fractional-order for the effective modeling and characterization of the neurovascular coupling mechanism. To show the added value of the fractional order parameters of the proposed model, we perform a parameter sensitivity analysis of the fractional model compared to its integer counterpart. Moreover, the model was validated using neural activity-CBF data related to both event and block design experiments that were acquired using electrophysiology and laser Doppler flowmetry recordings, respectively. ResultsThe validation results show the aptitude and flexibility of the fractional-order paradigm in fitting a more comprehensive range of well-shaped CBF response behaviors while maintaining a low model complexity. Comparison with the standard integer-order models shows the added value of the fractional-order parameters in capturing various key determinants of the cerebral hemodynamic response, e.g., post-stimulus undershoot. ConclusionsThis investigation authenticates the ability and adaptability of the fractional-order framework to characterize a wider range of well-shaped cerebral blood flow responses while preserving low model complexity through a series of unconstrained and constrained optimizations. Impact StatementThe present study proposes a novel fractional-order framework for modeling neurovascular coupling. A parameter sensitivity analysis demonstrates the potential flexibility, and effectiveness of the fractional-order paradigm in reconstructing the cerebral hemodynamics with manageable complexity; and a real experimental validation analysis demonstrates the ability of the model in modeling a wider range of well-shaped CBF responses.

bioengineering↗

bioRxiv2017

Multivariate genotypic analyses that identify specific genotypes to characterize disease and control groups in ADNI

INTRODUCTIONGenetic contributions to Alzheimers Disease (AD) are likely polygenic and not necessarily explained by uniformly applied linear and additive effects. In order to better understand the genetics of AD, we require statistical techniques to address both polygenic and possible non-additive effects.\n\nMETHODSWe used partial least squares-correspondence analysis (PLS-CA)--a method designed to detect multivariate genotypic effects. We used ADNI-1 (N = 756) as a discovery sample with two forms of PLS-CA: diagnosis-based and ApoE-based. We used ADNI-2 (N= 791) as a validation sample with a diagnosis-based PLS-CA.\n\nRESULTSWith PLS-CA we identified some expected genotypic effects (e.g., APOE/TOMM40, and APP) and a number of new effects that include, for examples, risk-associated genotypes in RBFOX1 and GPC6 and control-associated genotypes in PTPN14 and CPNE5.\n\nDISCUSSIONThrough the use of PLS-CA, we were able to detect complex (multivariate, genotypic) genetic contributions to AD, which included many non-additive and non-linear risk and possibly protective effects.

genomics↗

bioRxiv2017

The Latent Semantic Space and Corresponding Brain Regions of the Functional Neuroimaging Literature via NeuroSynth

The functional neuroimaging literature has become increasingly complex and thus difficult to navigate. This complexity arises from the rate at which new studies are published and from the terminology that varies widely from study-to-study and even more so from discipline-to-discipline. One way to investigate and manage this problem is to build a \"semantic space\" that maps the different vocabulary used in functional neuroimaging literature. Such a semantic space will also help identify the primary research domains of neuroimaging and their most commonly reported brain regions. In this work, we analyzed the multivariate semantic structure of abstracts in Neurosynth and found that there are six primary domains of the functional neuroimaging literature each with their own preferred reported brain regions. Our analyses also highlight possible semantic sources of reported brain regions within and across domains because some research topics (e.g., memory disorders, substance use disorder) use heterogeneous terminology. Furthermore, we highlight the growth and decline of the primary domains over time. Finally, we note that our techniques and results form the basis of a \"recommendation engine\" that could help readers better navigate the neuroimaging literature.

neuroscience↗