Transient effects of tumor location on the functional architecture at rest in glioblastoma patients: three longitudinal case studies

BackgroundThe cognitive function of brain tumor patients is affected during the treatment. There is evidence that gliomas and surgery alter the functional brain connectivity but studies on the longitudinal effects are lacking.MethodsWe acquired longitudinal (pre- and post-radiotherapy) resting-state functional magnetic resonance imaging on three selected glioblastoma patients. These cases were selected to study three models: a lesion involving a functional hub within a central system, a lesion involving a peripheral node within a central system and a lesion involving a peripheral node of a non-central system.ResultsWe found that, as expected, the tumor lesion affects connections in close vicinity, but when the lesion relates to a functional hub, these changes involve long-range connections leading to diverse connectivity profiles pre- and post-radiotherapy. In particular, a global but temporary improvement in the post-radiotherapy connectivity was obtained when treating a lesion close to a network hub, such as the posterior Cingulate Cortex.ConclusionsThis suggests that this node re-establishes communication to nodes further away in the network. Eventually, these observed effects seem to be transient and on the long-term the tumor burden leads to an overall decline of connectivity following the course of the pathology. Furthermore, we obtained that the link between hubs, such as the Supplementary Motor Area and posterior Cingulate Cortex represents an important backbone by means of which within and across network communication is handled: the disruption of this connection seems to imply a strong decrease in the overall connectivity.

Protocol publication

[…] The data preprocessing pipeline consists of several steps performed using a combination of tools including dcm2nii [], ITK-SNAP [], the FMRIB’s Software Library (FSL) [] and custom homemade MATLAB scripts []. Semiautomatic tumor segmentations were done with ITK-SNAP based on anatomical images and reviewed by an expert radiologist. Preprocessing of the functional data consisted of excluding the first two volumes of each experimental run to allow the fMRI signal to reach steady state and scrubbing of the volumes with motion related artifacts. FEAT tool was used for preprocessing (motion and slice time correction, brain extraction, 5 mm smoothing, intensity normalization) and registration to T1-weighted image (6° of freedom) and 2 mm MNI152-template (12° of freedom) were based on FSL’s FLIRT and FNIRT. […]