Imbalanced brain connectome: A possible link behind functional somatic disorders, multiple chemical sensitivity, and long COVID patients

Background: Functional Somatic Disorders (FSDs) are characterized by persistent physical symptoms that cannot be explained by other somatic or psychiatric conditions. Multiple Chemical Sensitivity (MCS) is a non-allergic FSD characterized by odour intolerance and various somatic symptoms being attributed to the influence of toxic environmental chemicals in low, usually harmless doses. The pathophysiology of FSDs are still not clear. Smell and taste complaints were also among the notable symptoms characterizing the covid epidemic and the latest evidence suggests overlaps between long COVID and FSDs. Method(s): The study includes advanced analysis of MRI-derived functional and structural connectomes acquired on a 3 T MR scanner. Furthermore, it includes questionnaires and paraclinical tests, e.g. the Sniffin' Stick olfactory test, Mini-Mental State Examination, and Sino-Nasal Outcome test 22. The pilot part of the project included 6 MCS patients who were compared with 6 matched healthy participants. Later follow-up included analysis of 8 multiorgan FSD and 4 post-COVID patients. Result(s): The MCS group showed important brain structural connectivity differences in 34 tracts. Notably, for MCS patients, the olfactory cortex (especially in the right hemisphere) showed decreased connectivity with regions in the emotional system. Conclusion(s): We plan to extend these findings with whole-brain modelling of the functional connectivity in the patient groups. Long-term this could be used as a 'fingerprint' which could help with diagnosis and treatment monitoring in FSDs as well as with new diagnoses such as long-COVID.Copyright © 2023

Iteratively regularized Gauss-Newton type methods for approximating quasi-solutions of irregular nonlinear operator equations in Hilbert space with an application to COVID-19 epidemic dynamics.

We investigate a class of iteratively regularized methods for finding a quasi-solution of a noisy nonlinear irregular operator equation in Hilbert space. The iteration uses an a priori stopping rule involving the error level in input data. In assumptions that the Frechet derivative of the problem operator at the desired quasi-solution has a closed range, and that the quasi-solution fulfills the standard source condition, we establish for the obtained approximation an accuracy estimate linear with respect to the error level. The proposed iterative process is applied to the parameter identification problem for a SEIR-like model of the COVID-19 pandemic.