{'fraction_non_alphanumeric': 0.037520391517128875, 'fraction_numerical': 0.008156606851549755, 'mean_word_length': 5.321305841924398, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
GWSPM : A toolbox for graph wavelet-based statistical parametric mapping

Submission Authors: Hamid Behjat1, Martin Larsson1, David Abramian Petrosian1, Leif Sörnmo1, Dimitri Van De Ville2 Institutions: 1Lund University, Lund, Sweden, 2EPFL, Geneva, Switzerland First Author: Hamid Behjat Lecture Information | Contact Me Lund University Lund, Sweden Introduction: In fMRI studies with evoked activity, brain activity is detected by voxel-wise GLM fitting, followed by statistical hypothesis testing. Statistical parametric mapping (SPM) [1], one of the most popular classical methods, relies upon Gaussian smoothing to deal with the multiple-comparison correction. As an alternative, we have recently introduced a graph-based framework for fMRI brain activation mapping [2]. The graph is designed such that it encodes the delicate topology of gray matter (GM) where brain activity is expected. Wavelets tailored to the convoluted boundaries of GM are constructed through exploiting the spectral graph wavelet (SGW) transform [3] and are then used to define an anatomically-informed, multi-scale spatial transformation for fMRI data. The use of spatial wavelet transforms has the benefit of providing a compact representation of activation patterns [4]. The framework extends wavelet-based SPM (WSPM) [5], which is a scheme that combines wavelet processing of non-smoothed data with voxel-wise statistical testing while guaranteeing strong FP control. Here, we present the next-generation implementation of the proposed framework as a toolbox that enables both single-subject (fixed-effects) and multi-subject (random-effects) analysis. Methods: The analysis pipeline consists of 3 main steps: (1) GM graph design, (2) SGW design and data transformation and (3) integrated wavelet denoising and spatial domain statistical inference. Steps (1) and (2) differ based on the analysis type. For single-subject analysis, the brain graph is constructed based on the GM topology of the subject, using a 26-neighborhood connectivity scheme. To prevent adjacent GM across banks of sulci being encoded as connected, pial surfaces extracted using FreeSurfer are exploited. fMRI volumes are then mapped as graph signals and decomposed through transformation to the graph's spectral domain. A desired GLM is then fitted to the time course of each wavelet coefficient [6]. For multi-subject analysis, a GM graph consisting of two subgraphs is constructed; one subgraph encodes the cerebral topology using a DARTEL [7] generated group GM template, and the other encodes the cerebellar structure based on the SUIT template [8]-[9]. First-level contrast maps are then mapped as graph signals, decomposed through transformation to the graph's spectral domain and the resulting coefficients are then subjected to GLM fitting across subjects [2]. In step (3), for both single and multi-subject analysis, integrated wavelet denoising and statistical inference is performed based on the WSPM theory [5]. The implementation takes advantage of multiple options and features from the SPM package. As an improvement to the theory in [2], we overcome the need to construct each and every graph wavelet atom by deriving an upper bound on the expected t-value associated to each graph node; only atoms associated to nodes with a t-value upper bound above the statistical threshold are computed. Results: Snapshots of sample user interaction steps provided in the toolbox are presented. The extraction of the cerebrum and cerebellum GM templates is visually guided (Figure 1). Graph wavelets, realized at different scales and centered at different locations in GM can be constructed and visualized (Figure 2).