Resting-state Functional Connectivity and Deception: Exploring Individualized Deceptive Propensity by Machine Learning

Individual differences in pain perception are of interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual sensitivity to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Here, we identify and validate a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature allows assessing the individual sensitivity to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have implications for translational research and the development and assessment of analgesic treatment strategies.

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“…refs. [32][33][34][35] ). However, the majority of previously used fMRIbased predictive models were only internally validated (i.e.…”

Section: Discussionmentioning

confidence: 99%

Pain-free resting-state functional brain connectivity predicts individual pain sensitivity

Spisák¹,

Kincses

Schlitt³

et al. 2020

Nat Commun

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“…Relevance vector regression method which showed better performances than other prediction methods was applied to predict the age (Cui & Gong, 2018 ; Cui et al, 2016 ). A 10‐fold cross‐validation strategy was used to estimate the generalization ability of the prediction mode (Tang et al, 2018 ). Pearson correlation coefficient between the real age and predicted age was calculated to depict the prediction performance.…”

Section: Methodsmentioning

confidence: 99%

Group‐guided individual functional parcellation of the hippocampus and application to normal aging

Zhang

Cui

et al. 2021

Human Brain Mapping

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Aging is closely associated with cognitive decline affecting attention, memory and executive functions. The hippocampus is the core brain area for human memory, learning, and cognition processing. To delineate the individual functional patterns of hippocampus is pivotal to reveal the neural basis of aging. In this study, we developed a group-guided individual parcellation approach based on semisupervised affinity propagation clustering using the resting-state functional magnetic resonance imaging to identify individual functional subregions of hippocampus and to identify the functional patterns of each subregion during aging. A three-way group parcellation was yielded and was taken as prior information to guide individual parcellation of hippocampus into head, body, and tail in each subject. The superiority of individual parcellation of hippocampus is validated by higher intraregional functional similarities by compared to group-level parcellation results. The individual variations of hippocampus were associated with coactivation patterns of three typical functions of hippocampus. Moreover, the individual functional connectivities of hippocampus subregions with predefined target regions could better predict age than group-level functional connectivities. Our study provides a novel framework for individual brain functional parcellations, which may facilitate the future individual researches for brain cognitions and brain disorders and directing accurate neuromodulation.

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“…Noninvasive neuroimaging techniques have contributed to search for quantitative brain-based measurements of psychiatric disorders [79,80]. Nevertheless, neuroimaging markers in psychiatry still lack the level of precision for clinical practice [81]. As pointed out by Etkin, one of the major factors that limited the impact of neuroimaging efforts is the failure in embracing fully data-driven analyses [80].…”

Section: Discussionmentioning

confidence: 99%

Decoding fear of negative evaluation from brain morphology: A machine‐learning study on structural neuroimaging data

Feng

Luo

2022

Quant. Biol.

Self Cite

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Background: Fear of negative evaluation (FNE), referring to negative expectation and feelings toward other people's social evaluation, is closely associated with social anxiety that plays an important role in our social life. Exploring the neural markers of FNE may be of theoretical and practical significance to psychiatry research (e.g., studies on social anxiety). Methods: To search for potentially relevant biomarkers of FNE in human brain, the current study applied multivariate relevance vector regression, a machine-learning and data-driven approach, on brain morphological features (e.g., cortical thickness) derived from structural imaging data; further, we used these features as indexes to predict self-reported FNE score in each participant. Results: Our results confirm the predictive power of multiple brain regions, including those engaged in negative emotional experience (e.g., amygdala, insula), regulation and inhibition of emotional feeling (e.g., frontal gyrus, anterior cingulate gyrus), and encoding and retrieval of emotional memory (e.g., posterior cingulate cortex, parahippocampal gyrus). Conclusions: The current findings suggest that anxiety represents a complicated construct that engages multiple brain systems, from primitive subcortical mechanisms to sophisticated cortical processes.

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Resting-state Functional Connectivity and Deception: Exploring Individualized Deceptive Propensity by Machine Learning

Cited by 19 publications

References 71 publications

Pain-free resting-state functional brain connectivity predicts individual pain sensitivity

Pain-free resting-state functional brain connectivity predicts individual pain sensitivity

Group‐guided individual functional parcellation of the hippocampus and application to normal aging

Decoding fear of negative evaluation from brain morphology: A machine‐learning study on structural neuroimaging data

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