rs-fMRI network-based TMS targeting
The present data are collected from 8 Alzheimer's disease patients to prove the feasibility of a tailored network-based transcranial magnetic stimulation (TMS) targeting approach. Based on resting state functional imaging, the procedure allows to extract individual optimal targets of two networks affected by Alzheimer's disease: the default mode (DMN) and the fronto-parietal network (FPN). The dataset includes: - The raw independent components maps (melodic_IC.nii.gz) in native space extracted from individual rs-fMRI with Melodic independent component analysis (Beckman and Smith 2004) - A table with the demographic and clinical characteristics of the sample (n=8) together with the individual coordinates of the target in native space.
Steps to reproduce
ICA spatial maps were extracted from individual rs-fMRI scans using independent component analysis (ICA) with Melodic (Beckmann and Smith, 2004). The networks of interest (DMN and FPN) were identified using a template matching procedure with published templates (Shirer et al., 2012). The selected DMN and FPN spatial maps were then back-transformed to subjects’ native T1 space and FSL’s cluster routine was used to decompose each network into clusters, which are provided with information on their size, coordinates, and maximum intensity. For each subject, DMN and FPN candidate targets were defined as the peak (local maxima) within the largest cluster located, respectively, in the left IPL and left DLPFC (defined by visual inspection). The local maxima were overlaid onto the native T1 scan and the final target was selected according to the following criteria: (i) location specific to the network of interest (i.e., coordinates falling within the spatial maps of both DMN and FPN were excluded); (ii) being on a cortical gyrus and not on a sulcus (i.e., overlap with GM); (iii) representing the shortest perpendicular path between scalp and cortex.