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Highlights
• Brain connectivity states identified by cofluctuation strength.
• CMEP as new method to robustly predict human traits from brain imaging data.
• Network-identifying connectivity ‘events’ are not predictive of cognitive ability.
• Sixteen temporally independent fMRI time frames allow for significant prediction.
• Neuroimaging-based assessment of cognitive ability requires sufficient scan lengths.
Abstract
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
A potential clinical and etiological overlap between schizophrenia (SZ) and bipolar disorder (BD) has long been a subject of discussion. Imaging studies imply functional and structural alterations of the hippocampus in both diseases. Thus, imaging this core memory region could provide insight into the pathophysiology of these disorders and the associated cognitive deficits. To examine possible shared alterations in the hippocampus, we conducted a multi-modal assessment, including functional and structural imaging as well as neurobehavioral measures of memory performance in BD and SZ patients compared with healthy controls. We assessed episodic memory performance, using tests of verbal and visual learning (HVLT, BVMT) in three groups of participants: BD patients (n = 21), SZ patients (n = 21) and matched (age, gender, education) healthy control subjects (n = 21). In addition, we examined hippocampal resting state functional connectivity, hippocampal volume using voxel-based morphometry (VBM) and fibre integrity of hippocampal connections using diffusion tensor imaging (DTI). We found memory deficits, changes in functional connectivity within the hippocampal network as well as volumetric reductions and altered white matter fibre integrity across patient groups in comparison with controls. However, SZ patients when directly compared with BD patients were more severely affected in several of the assessed parameters (verbal learning, left hippocampal volumes, mean diffusivity of bilateral cingulum and right uncinated fasciculus). The results of our study suggest a graded expression of verbal learning deficits accompanied by structural alterations within the hippocampus in BD patients and SZ patients, with SZ patients being more strongly affected. Our findings imply that these two disorders may share some common pathophysiological mechanisms. The results could thus help to further advance and integrate current pathophysiological models of SZ and BD.