{'fraction_non_alphanumeric': 0.03916666666666667, 'fraction_numerical': 0.009166666666666667, 'mean_word_length': 5.982558139534884, '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}}
Exploring the functional connectome in white matter

A major challenge in neuroscience is understanding how brain function emerges from the connectome. Most current methods have focused on quantifying functional connectomes in gray‐matter (GM) signals obtained from functional magnetic resonance imaging (fMRI), while signals from white‐matter (WM) have generally been excluded as noise. In this study, we derived a functional connectome from WM resting‐state blood‐oxygen‐level‐dependent (BOLD)‐fMRI signals from a large cohort (n = 488). The WM functional connectome exhibited weak small‐world topology and nonrandom modularity. We also found a long‐term (i.e., over 10 months) topological reliability, with topological reproducibility within different brain parcellation strategies, spatial distance effect, global and cerebrospinal fluid signals regression or not. Furthermore, the small‐worldness was positively correlated with individuals' intelligence values (r = .17, pcorrected = .0009). The current findings offer initial evidence using WM connectome and present additional measures by which to uncover WM functional information in both healthy individuals and in cases of clinical disease.