From Default Mode Network to the Basal Configuration: Sex Differences in the Resting-State Brain Connectivity as a Function of Age and Their Clinical Correlates

Connectomics is a framework that models brain structure and function interconnectivity as a network, rather than narrowly focusing on select regions-of-interest. MRI-derived connectomes can be structural, usually based on diffusion-weighted MR imaging, or functional, usually formed by examining fMRI blood-oxygen-level-dependent (BOLD) signal correlations. Recently, we developed a novel method for assessing the hierarchical modularity of functional brain networks—the probability associated community estimation (PACE). PACE uniquely permits a dual formulation, thus yielding equivalent connectome modular structure regardless of whether positive or negative edges are considered. This method was rigorously validated using the 1,000 functional connectomes project data set (F1000, RRID:SCR_005361) (1) and the Human Connectome Project (HCP, RRID:SCR_006942) (2, 3) and we reported novel sex differences in resting-state connectivity not previously reported. (4) This study further examines sex differences in regard to hierarchical modularity as a function of age and clinical correlates, with findings supporting a basal configuration framework as a more nuanced and dynamic way of conceptualizing the resting-state connectome that is modulated by both age and sex. Our results showed that differences in connectivity between men and women in the 22–25 age range were not significantly different. However, these same non-significant differences attained significance in both the 26–30 age group (p = 0.003) and the 31–35 age group (p < 0.001). At the most global level, areas of diverging sex difference include parts of the prefrontal cortex and the temporal lobe, amygdala, hippocampus, inferior parietal lobule, posterior cingulate, and precuneus. Further, we identified statistically different self-reported summary scores of inattention, hyperactivity, and anxiety problems between men and women. These self-reports additionally divergently interact with age and the basal configuration between sexes.

[1]  A. Moss,et al.  Oral contraceptive use and the risk of cardiac events in patients with long QT syndrome. , 2014, Heart rhythm.

[2]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[3]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[4]  Brenton W. McMenamin,et al.  Network Organization Unfolds over Time during Periods of Anxious Anticipation , 2014, The Journal of Neuroscience.

[5]  Cédric Lemogne,et al.  Self-Referential Processing, Rumination, and Cortical Midline Structures in Major Depression , 2013, Front. Hum. Neurosci..

[6]  Dan Schonfeld,et al.  Investigating brain community structure abnormalities in bipolar disorder using path length associated community estimation , 2014, Human brain mapping.

[7]  J. Gross,et al.  The cognitive control of emotion , 2005, Trends in Cognitive Sciences.

[8]  G. Wilkinson,et al.  Gender differences in depression. Critical review. , 2000, The British journal of psychiatry : the journal of mental science.

[9]  C. N. Macrae,et al.  Finding the Self? An Event-Related fMRI Study , 2002, Journal of Cognitive Neuroscience.

[10]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[11]  B. T. Thomas Yeo,et al.  Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations , 2017, NeuroImage.

[12]  A. Aleman,et al.  Self-reflection and the brain: A theoretical review and meta-analysis of neuroimaging studies with implications for schizophrenia , 2010, Neuroscience & Biobehavioral Reviews.

[13]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[14]  Ryan L. Olson,et al.  Do sex differences in rumination explain sex differences in depression? , 2017, Journal of neuroscience research.

[15]  W. Rutz,et al.  Male depression and suicide. , 2001, International clinical psychopharmacology.

[16]  Richard F. Betzel,et al.  Modular Brain Networks. , 2016, Annual review of psychology.

[17]  N. Upadhayay,et al.  Comparison of cognitive functions between male and female medical students: a pilot study. , 2014, Journal of clinical and diagnostic research : JCDR.

[18]  E. Sibille,et al.  Sex differences in mood disorders: perspectives from humans and rodent models , 2014, Biology of Sex Differences.

[19]  G. Shulman,et al.  Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Alex R. Smith,et al.  Sex differences in the structural connectome of the human brain , 2013, Proceedings of the National Academy of Sciences.

[21]  Koene R. A. Van Dijk,et al.  Amygdala subnuclei resting-state functional connectivity sex and estrogen differences , 2016, Psychoneuroendocrinology.

[22]  Liang Zhan,et al.  The significance of negative correlations in brain connectivity , 2017, The Journal of comparative neurology.

[23]  Michael Breakspear,et al.  Graph analysis of the human connectome: Promise, progress, and pitfalls , 2013, NeuroImage.

[24]  A. Kretschmer,et al.  Evidence for Stress-like Alterations in the HPA-Axis in Women Taking Oral Contraceptives , 2017, Scientific Reports.

[25]  R. Gur,et al.  Gender differences in aging: cognition, emotions, and neuroimaging studies , 2002, Dialogues in clinical neuroscience.

[26]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[27]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[28]  H. Simon,et al.  Near decomposability and the speed of evolution , 2002 .

[29]  Paul M. Thompson,et al.  A Framework for Quantifying Node-Level Community Structure Group Differences in Brain Connectivity Networks , 2012, MICCAI.

[30]  Edward T. Bullmore,et al.  Modular and Hierarchically Modular Organization of Brain Networks , 2010, Front. Neurosci..

[31]  B. Fredrickson,et al.  Response styles and the duration of episodes of depressed mood. , 1993, Journal of abnormal psychology.

[32]  T. Achenbach Achenbach System of Empirically Based Assessment (ASEBA) , 2015 .

[33]  L. Cahill,et al.  Amygdala reactivity to negative stimuli is influenced by oral contraceptive use. , 2015, Social cognitive and affective neuroscience.

[34]  S. Resnick,et al.  Perimenopausal use of hormone therapy is associated with enhanced memory and hippocampal function later in life , 2011, Brain Research.

[35]  A. Toga,et al.  Mapping Continued Brain Growth and Gray Matter Density Reduction in Dorsal Frontal Cortex: Inverse Relationships during Postadolescent Brain Maturation , 2001, The Journal of Neuroscience.

[36]  Adam J. Schwarz,et al.  Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data , 2011, NeuroImage.

[37]  F. Craik,et al.  In search of the emotional self: an fMRI study using positive and negative emotional words. , 2003, The American journal of psychiatry.

[38]  Amy M. Jimenez,et al.  Alterations to task positive and task negative networks during executive functioning in Mild Cognitive Impairment , 2018, NeuroImage: Clinical.

[39]  M. Milad,et al.  Contribution of estradiol levels and hormonal contraceptives to sex differences within the fear network during fear conditioning and extinction , 2015, BMC Psychiatry.

[40]  Karen L. Siedlecki,et al.  Evaluation of prefrontal–hippocampal effective connectivity following 24 hours of estrogen infusion: An FDG-PET study , 2008, Psychoneuroendocrinology.

[41]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[42]  Nikos Makris,et al.  Relationship of DAT1 and adult ADHD to task-positive and task-negative working memory networks , 2011, Psychiatry Research: Neuroimaging.

[43]  G. Knyazev,et al.  Task-positive and task-negative networks in major depressive disorder: A combined fMRI and EEG study. , 2018, Journal of affective disorders.

[44]  Sterling C. Johnson,et al.  Self-appraisal decisions evoke dissociated dorsal—ventral aMPFC networks , 2006, NeuroImage.

[45]  Thomas E. Nichols,et al.  A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.

[46]  K. Luan Phan,et al.  Functional Neuroimaging Studies of Human Emotions , 2004, CNS Spectrums.

[47]  Stephan Eliez,et al.  Sex differences in thickness, and folding developments throughout the cortex , 2013, NeuroImage.

[48]  M. Weissman,et al.  Cross-national epidemiology of major depression and bipolar disorder. , 1996, JAMA.

[49]  Roger Guimerà,et al.  Missing and spurious interactions and the reconstruction of complex networks , 2009, Proceedings of the National Academy of Sciences.

[50]  C. Lebel,et al.  Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood , 2011, The Journal of Neuroscience.

[51]  Marcia K. Johnson,et al.  Dissociating medial frontal and posterior cingulate activity during self-reflection. , 2006, Social cognitive and affective neuroscience.