Prediction of trust propensity from intrinsic brain morphology and functional connectome

Background: During aging and in age-associated disorders, such as Alzheimer's Disease (AD), learning abilities decline. Probably, disturbances in signal transduction in brain cells underlie the cognitive decline. The phosphorylation/dephosphorylation imbalance occurring in degenerating neurons was recently related to abnormal activity of one or more signal transduction pathways. AD is known to be associated with altered neuronal Ca2+ homeostasis, as Ca2+ accumulates in affected neurons leading to functional impairment. It is becoming more and more evident the involvement of signal transduction pathways acting upon Ca2+ metabolism and phosphorylation regulation of proteins. A growing interest raised around the role of signal transduction systems in a number of human diseases including neurodegenerative diseases, with special regard to the systems related to the phosphoinositide (PI) pathway and AD. The PI signal transduction pathway plays a crucial role, being involved in a variety of cell functions, such as hormone secretion, neurotransmitter signal transduction, cell growth, membrane trafficking, ion channel activity, cytoskeleton regulation, cell cycle control, apoptosis, cell and tissue polarity, and contributes to regulate the Ca2+ levels in the nervous tissue. Conclusion: A number of observations indicated that PI-specific phospholipase C (PLC) enzymes might be involved in the alteration of neurotransmission. To understand the role and the timing of action of the signalling pathways recruited during the brain morphology changes during the AD progression might help to elucidate the aetiopathogenesis of the disease, paving the way to prognosis refinement and/or novel molecular therapeutic strategies.

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A Trait-State Model of Trust Propensity: Evidence From Two Career Transitions

Frontiers in Psychology ◽

10.3389/fpsyg.2019.02490 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 1

Author(s):

Lisa van der Werff ◽

Yseult Freeney ◽

Charles E. Lance ◽

Finian Buckley

Keyword(s):

State Model ◽

Career Transitions ◽

Trust Propensity

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Age and sex effects on brain morphology

Progress in Neuro-Psychopharmacology and Biological Psychiatry ◽

10.1016/s0278-5846(97)00160-7 ◽

1997 ◽

Vol 21 (8) ◽

pp. 1231-1237 ◽

Cited By ~ 73

Author(s):

Theodore J. Passe ◽

Pradeep Rajagopalan ◽

Larry A. Tupler ◽

Christopher E. Byrum ◽

James R. Macfall ◽

...

Keyword(s):

Brain Morphology ◽

Sex Effects ◽

Age And Sex

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Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot

Neurocomputing ◽

10.1016/j.neucom.2020.04.152 ◽

2021 ◽

Author(s):

Sukrit Gupta ◽

Yi Hao Chan ◽

Jagath C. Rajapakse

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Single Shot ◽

Functional Connectome

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Alcohol use and brain morphology in adolescence: a longitudinal study in three different cohorts

European Journal of Neuroscience ◽

10.1111/ejn.15411 ◽

2021 ◽

Author(s):

Hanan El Marroun ◽

Eduard T. Klapwijk ◽

Martijn Koevoets ◽

Rachel M. Brouwer ◽

Sabine Peters ◽

...

Keyword(s):

Longitudinal Study ◽

Alcohol Use ◽

Brain Morphology

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Development of the brain functional connectome follows puberty-dependent nonlinear trajectories

NeuroImage ◽

10.1016/j.neuroimage.2021.117769 ◽

2021 ◽

Vol 229 ◽

pp. 117769

Author(s):

Zeus Gracia-Tabuenca ◽

Martha Beatriz Moreno ◽

Fernando A. Barrios ◽

Sarael Alcauter

Keyword(s):

Functional Connectome ◽

The Brain

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Individual Uniqueness in the Neonatal Functional Connectome

Cerebral Cortex ◽

10.1093/cercor/bhab041 ◽

2021 ◽

Author(s):

Qiushi Wang ◽

Yuehua Xu ◽

Tengda Zhao ◽

Zhilei Xu ◽

Yong He ◽

...

Keyword(s):

Individual Differences ◽

Individual Identification ◽

Imaging Data ◽

Functional Connectome ◽

Middle Range ◽

Human Connectome Project ◽

The Individual ◽

And Behavior ◽

Identification Analysis ◽

The Brain

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

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Effects of eight neuropsychiatric copy number variants on human brain structure

Translational Psychiatry ◽

10.1038/s41398-021-01490-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Claudia Modenato ◽

Kuldeep Kumar ◽

Clara Moreau ◽

Sandra Martin-Brevet ◽

Guillaume Huguet ◽

...

Keyword(s):

Copy Number ◽

Copy Number Variants ◽

Principal Component ◽

Brain Morphology ◽

Brain Anatomy ◽

Brain Morphometry ◽

Supplementary Motor Cortex ◽

Psychiatric Conditions ◽

Latent Dimension ◽

Mirror Effects

AbstractMany copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions.

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