Using a similar approach as in the above subsection, we estimated the Pearson’s spatial correlation between the regional ΔICC values of the brain-fingerprints and each of the normative atlas maps (from neuromaps36 (link)) of 19 receptors and transporters from 9 neurotransmitter systems. These latter consisted of: dopamine (D1: 13 adults, [11C]SCH23390 PET; D2: 92, [11C]FLB-457, DAT: 174, [123I]-FP-CIT), serotonin (5-HT1a: 36, [11C]WAY-100635; 5-HT1b: 88, [11C]P943; 5-HT2a: 29, [11C]Cimbi-36; 5-HT4: 59, [11C]SB207145; 5-HT6: 30, [11C]GSK215083; 5-HTT: 100, [11C]DASB), acetylcholine (α4β2: 30, [18F]flubatine; M1: 24, [11C]LSN3172176; VAChT: 30, [18F]FEOBV), GABA (GABAa: 16, [11C]flumazenil), glutamate (NMDA: 29, [18F]GE-179; mGluR5: 123, [11C]ABP688), norepinephrine (NET: 77, [11C]MRB), histamine (H3: 8, [11 C]GSK189254), cannabinoid (CB1: 77, [11 C]OMAR), and opioid (MOR: 204, [11 C]carfentanil).
Each map was parcellated using the 68 regions of the Desikan-Killiany atlas38 (link). Statistical significance was determined after correction for multiple comparisons using a False Discovery Rate (FDR) as implemented in the R function p.adjust98 . We derived Bayes factors to quantify the evidence in favour of the alternative hypothesis (i.e., a spatial correlation does exist) using the correlationBF function in R. For each significant spatial correspondence observed, we also estimated p-values based on spatially constrained permutation tests96 (link),97 (link). We performed 1000 permutations of the labels of patients vs. controls, computed a corresponding null ICC matrix for the patients and controls groups, and estimated the correlation of the resulting random differences in ICC for these null models with each of the 19 neurotransmitter atlas maps.
Each map was parcellated using the 68 regions of the Desikan-Killiany atlas38 (link). Statistical significance was determined after correction for multiple comparisons using a False Discovery Rate (FDR) as implemented in the R function p.adjust98 . We derived Bayes factors to quantify the evidence in favour of the alternative hypothesis (i.e., a spatial correlation does exist) using the correlationBF function in R. For each significant spatial correspondence observed, we also estimated p-values based on spatially constrained permutation tests96 (link),97 (link). We performed 1000 permutations of the labels of patients vs. controls, computed a corresponding null ICC matrix for the patients and controls groups, and estimated the correlation of the resulting random differences in ICC for these null models with each of the 19 neurotransmitter atlas maps.
