Individual‐specific functional connectome biomarkers predict schizophrenia positive symptoms during adolescent brain maturation

Abstract

Even with an overarching functional dysconnectivity model of adolescent‐onset schizophrenia (AOS), there have been no functional connectome (FC) biomarkers identified for predicting patients' specific symptom domains. Adolescence is a period of dramatic brain maturation, with substantial interindividual variability in brain anatomy. However, existing group‐level hypotheses of AOS lack precision in terms of neuroanatomical boundaries. This study aimed to identify individual‐specific FC biomarkers associated with schizophrenic symptom manifestation during adolescent brain maturation. We used a reliable individual‐level cortical parcellation approach to map functional brain regions in each subject, that were then used to identify FC biomarkers for predicting dimension‐specific psychotic symptoms in 30 antipsychotic‐naïve first‐episode AOS patients (recruited sample of 39). Age‐related changes in biomarker expression were compared between these patients and 31 healthy controls. Moreover, 29 antipsychotic‐naïve first‐episode AOS patients (analyzed sample of 25) were recruited from another center to test the generalizability of the prediction model. Individual‐specific FC biomarkers could significantly and better predict AOS positive‐dimension symptoms with a relatively stronger generalizability than at the group level. Specifically, positive symptom domains were estimated based on connections between the frontoparietal control network (FPN) and salience network and within FPN. Consistent with the neurodevelopmental hypothesis of schizophrenia, the FPN–SN connection exhibited aberrant age‐associated alteration in AOS. The individual‐level findings reveal reproducible FPN‐based FC biomarkers associated with AOS positive symptom domains, and highlight the importance of accounting for individual variation in the study of adolescent‐onset disorders.

Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder

“…Studying inter-individual differences in brain networks is essential for understanding complex psychiatric disorders [ 16 ]. For example, studies of inter-individual differences in functional brain networks have found that it can be used to predict schizophrenia positive symptoms [ 17 ]. Highly heterogeneous brain structures are reported in ASD, and there is a link between heterogeneity of brain structures and clinical symptoms, which together influence the ASD classification [ 18 ].…”

Two common and distinct forms of variation in human functional brain networks

“…At present, many resting-state and task-based fMRI studies aggregate or compare data across individuals based on spatial normalization, thus assuming that the same spatial layout of brain systems is conserved across individuals (Fedorenko, 2021). However, widespread individual differences in the localization of brain regions and systems may lead to detrimental effects when performing group-level analyses, including loss of sensitivity and functional resolution (Nieto-Castanon and Fedorenko, 2012), and prediction accuracy of task functional connectivity (Porter et al, 2021), task-evoked signals (Guntupalli et al, 2018; Haxby et al, 2020), and prediction of behavior from resting networks (Brennan et al, 2019; Fan et al, 2020; Kong et al, 2019; Kong et al, 2021). Together, these limitations lead group studies to fall short of providing comprehensive explanations of brain function and cognition as a whole.…”

“…While a number of studies have identified locations of individual differences in brain network organization [11][12][13][14][15][16][17][18][19][20] , there has not yet been substantial research into the different forms that these variants take and how they might link to sources and mechanisms observed in past comparative studies of cortical neuroanatomy in other species 2 . Many studies, including our past work, treat all forms of cortical variation uniformly, and often assume that variants are driven primarily by boundary shifts in the borders between systems: a functional region may expand, contract, or be slightly offset relative to the typical pattern (e.g., as has been documented for V1, which can FN1 Network variant locations are present even after surface-based normalization 63,64,101 that align data across people by large-scale sulcal features. Individual differences in fcMRI are not well related to variations in anatomical metrics (refs.…”

“… FN1 Network variant locations are present even after surface-based normalization 63, 64, 101 that align data across people by large-scale sulcal features. Individual differences in fcMRI are not well related to variations in anatomical metrics (refs.…”

Altered spontaneous brain activity during dobutamine challenge in healthy young adults: A resting-state functional magnetic resonance imaging study

“…Recent studies have also shown that the neural correlates of cardiac regulation are broader than previously thought, and from a brain side, executive function and information processing speed, and to a lesser extent memory, are also associated with cardiovascular factors (Beissner et al, 2013;Hooghiemstra et al, 2019;Valenza et al, 2019). Previous studies in animal models and patients with heart failure have confirmed the important potential of brainheart interactions for the treatment of cardiovascular disease (Choi et al, 2006;Vogels et al, 2007;Fan et al, 2021), the studies focus on patients with neurologically active diseases such as Alzheimer's disease and dementia have shown that it is associated with changes in cardiac function (Oh et al, 2012;Cermakova et al, 2017;Nonogaki et al, 2017). More studies have shown that there is a certain relationship between the risk of developing heart failure and cognitive impairment and that this risk has a tendency to transfer to young people (Cermakova et al, 2017).…”