Kuzniecky, R. | Search bioRxiv

bioRxiv2018

Resting state functional connectivity patterns associated with pharmacological treatment resistance in temporal lobe epilepsy

There are no functional imaging based biomarkers for pharmacological treatment response in temporal lobe epilepsy (TLE). In this study, we investigated whether there is an association between resting state functional brain connectivity (RsFC) and seizure control in TLE. We screened a large database containing resting state functional magnetic resonance imaging (Rs-fMRI) data from 286 epilepsy patients. Patient medical records were screened for seizure characterization, EEG reports for lateralization and location of seizure foci to establish uniformity of seizure localization within patient groups. Rs-fMRI data from patients with well-controlled left TLE, patients with treatment-resistant left TLE, and healthy controls were analyzed. Healthy controls and cTLE showed similar functional connectivity patterns, whereas trTLE exhibited a significant bilateral decrease in thalamo-hippocampal functional connectivity. This work is the first to demonstrate differences in neural network connectivity between well-controlled and treatment-resistant TLE. These differences are spatially highly focused and suggest sites for the etiology and possibly treatment of TLE. Altered thalamo-hippocampal RsFC thus is a potential new biomarker for TLE treatment resistance.\n\nSummaryResting State functional magnetic resonance imaging (Rs-fMRI), previously utilized to predict lateralization of seizure foci in temporal lobe epilepsy (TLE), is utilized to determine potential mechanisms and biomarkers for treatment-resistant and well-controlled unilateral TLE. We found significant differences in thalamo-hippocampal functional connectivity between treatment-resistant and well-controlled TLE patients. Differences in functional connectivity were focused to thalamo-hippocampal connections and more pronounced in the hemisphere ipsilateral to seizure foci. Aberrant functional connectivity patterns as measured by Rs-fMRI could thus serve as biomarkers for treatment response in TLE.

neuroscience↗

bioRxiv2017

NAPR: a cloud-based framework for neuroanatomical age prediction

This paper describes NAPR, a cloud-based framework for accessing age prediction models created using machine learning-based analysis of neuroimaging data. The NAPR service is provided at https://www.cloudneuro.org. The NAPR system allows external users to predict the age of individual subjects using their own MRI data. As a demonstration of the NAPR approach, age prediction models were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367). MRI scans were processed using Freesurfer v5.3. Age prediction models were trained using relevance vector machines and Gaussian processes machine learning techniques. NAPR will allow for rigorous and transparent out-of-sample assessment of age prediction model performance, and may therefore assist in the translation of neuroimaging-based modelling techniques to the clinic.

neuroscience↗