Julia M. Huntenburg

Situating the default-mode network along a principal gradient of macroscale cortical organization

Significance We describe an overarching organization of large-scale connectivity that situates the default-mode network at the opposite end of a spectrum from primary sensory and motor regions. This topography, based on the differentiation of connectivity patterns, is also embedded in the spatial distance along the cortical surface between these respective systems. In addition, this connectivity gradient accounts for the respective positions of canonical networks and captures a functional spectrum from perception and action to more abstract cognitive functions. These results suggest that the default-mode network consists of regions at the top of a representational hierarchy that describe the current cognitive landscape in the most abstract terms. Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface—and are precisely equidistant—from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.

Individual variation in intentionality in the mind-wandering state is reflected in the integration of the default-mode, fronto-parietal, and limbic networks

Abstract Mind‐wandering has a controversial relationship with cognitive control. Existing psychological evidence supports the hypothesis that episodes of mind‐wandering reflect a failure to constrain thinking to task‐relevant material, as well the apparently alternative view that control can facilitate the expression of self‐generated mental content. We assessed whether this apparent contradiction arises because of a failure to consider differences in the types of thoughts that occur during mind‐wandering, and in particular, the associated level of intentionality. Using multi‐modal magnetic resonance imaging (MRI) analysis, we examined the cortical organisation that underlies inter‐individual differences in descriptions of the spontaneous or deliberate nature of mind‐wandering. Cortical thickness, as well as functional connectivity analyses, implicated regions relevant to cognitive control and regions of the default‐mode network for individuals who reported high rates of deliberate mind‐wandering. In contrast, higher reports of spontaneous mind‐wandering were associated with cortical thinning in parietal and posterior temporal regions in the left hemisphere (which are important in the control of cognition and attention) as well as heightened connectivity between the intraparietal sulcus and a region that spanned limbic and default‐mode regions in the ventral inferior frontal gyrus. Finally, we observed a dissociation in the thickness of the retrosplenial cortex/lingual gyrus, with higher reports of spontaneous mind‐wandering being associated with thickening in the left hemisphere, and higher repots of deliberate mind‐wandering with thinning in the right hemisphere. These results suggest that the intentionality of the mind‐wandering state depends on integration between the control and default‐mode networks, with more deliberation being associated with greater integration between these systems. We conclude that one reason why mind‐wandering has a controversial relationship with control is because it depends on whether the thoughts emerge in a deliberate or spontaneous fashion. HighlightsDeliberate and spontaneous mind‐wandering have unique structural and functional correlates.Reports of deliberate mind‐wandering correlated with regions in both default‐mode and fronto‐parietal networks.Spontaneous mind‐wandering was linked to less integrity in parietal and temporal regions.Intentionality during the mind‐wandering state may depend upon integration between the default‐mode and fronto‐parietal networks.These neurocognitive differences explain why mind‐wandering has a complex relationship with cognitive control.

2015 Brainhack Proceedings

Table of contentsI1 Introduction to the 2015 Brainhack ProceedingsR. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. PfannmöllerA1 Distributed collaboration: the case for the enhancement of Brainspell’s interfaceAmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto ToroA2 Advancing open science through NiDataBen Cipollini, Ariel RokemA3 Integrating the Brain Imaging Data Structure (BIDS) standard into C-PACDaniel Clark, Krzysztof J. Gorgolewski, R. Cameron CraddockA4 Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNIR. Cameron Craddock, Daniel J. ClarkA5 LORIS: DICOM anonymizerSamir Das, Cécile Madjar, Ayan Sengupta, Zia MohadesA6 Automatic extraction of academic collaborations in neuroimagingSebastien DeryA7 NiftyView: a zero-footprint web application for viewing DICOM and NIfTI filesWeiran DengA8 Human Connectome Project Minimal Preprocessing Pipelines to NipypeEric Earl, Damion V. Demeter, Kate Mills, Glad Mihai, Luka Ruzic, Nick Ketz, Andrew Reineberg, Marianne C. Reddan, Anne-Lise Goddings, Javier Gonzalez-Castillo, Krzysztof J. GorgolewskiA9 Generating music with resting-state fMRI dataCaroline Froehlich, Gil Dekel, Daniel S. Margulies, R. Cameron CraddockA10 Highly comparable time-series analysis in NitimeBen D. FulcherA11 Nipype interfaces in CBRAINTristan Glatard, Samir Das, Reza Adalat, Natacha Beck, Rémi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Étienne Rousseau, Alan C. EvansA12 DueCredit: automated collection of citations for software, methods, and dataYaroslav O. Halchenko, Matteo Visconti di Oleggio CastelloA13 Open source low-cost device to register dog’s heart rate and tail movementRaúl Hernández-Pérez, Edgar A. Morales, Laura V. CuayaA14 Calculating the Laterality Index Using FSL for Stroke Neuroimaging DataKaori L. Ito, Sook-Lei LiewA15 Wrapping FreeSurfer 6 for use in high-performance computing environmentsHans J. JohnsonA16 Facilitating big data meta-analyses for clinical neuroimaging through ENIGMA wrapper scriptsErik Kan, Julia Anglin, Michael Borich, Neda Jahanshad, Paul Thompson, Sook-Lei LiewA17 A cortical surface-based geodesic distance package for PythonDaniel S Margulies, Marcel Falkiewicz, Julia M HuntenburgA18 Sharing data in the cloudDavid O’Connor, Daniel J. Clark, Michael P. Milham, R. Cameron CraddockA19 Detecting task-based fMRI compliance using plan abandonment techniquesRamon Fraga Pereira, Anibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe MeneguzziA20 Self-organization and brain functionJörg P. Pfannmöller, Rickson Mesquita, Luis C.T. Herrera, Daniela DenticoA21 The Neuroimaging Data Model (NIDM) APIVanessa Sochat, B Nolan NicholsA22 NeuroView: a customizable browser-base utilityAnibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe MeneguzziA23 DIPY: Brain tissue classificationJulio E. Villalon-Reina, Eleftherios Garyfallidis

Predicting brain-age from multimodal imaging data captures cognitive impairment

Abstract The disparity between the chronological age of an individual and their brain‐age measured based on biological information has the potential to offer clinically relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain‐age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain‐imaging data improves age prediction. Using cortical anatomy and whole‐brain functional connectivity on a large adult lifespan sample (N=2354, age 19–82), we found that multimodal data improves brain‐based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain‐age measure was robust to confounding effects: head motion did not drive brain‐based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N=475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain‐age prediction to confounds, generalizability across sites, and sensitivity to clinically‐relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders. HighlightsBrain‐based age prediction is improved with multimodal neuroimaging data.Participants with cognitive impairment show increased brain aging.Age prediction models are robust to motion and generalize to independent datasets from other sites.

A Systematic Relationship Between Functional Connectivity and Intracortical Myelin in the Human Cerebral Cortex

Abstract Research in the macaque monkey suggests that cortical areas with similar microstructure are more likely to be connected. Here, we examine this link in the human cerebral cortex using 2 magnetic resonance imaging (MRI) measures: quantitative T1 maps, which are sensitive to intracortical myelin content and provide an in vivo proxy for cortical microstructure, and resting‐state functional connectivity. Using ultrahigh‐resolution MRI at 7 T and dedicated image processing tools, we demonstrate a systematic relationship between T1‐based intracortical myelin content and functional connectivity. This effect is independent of the proximity of areas. We employ nonlinear dimensionality reduction to characterize connectivity components and identify specific aspects of functional connectivity that are linked to myelin content. Our results reveal a consistent spatial pattern throughout different analytic approaches. While functional connectivity and myelin content are closely linked in unimodal areas, the correspondence is lower in transmodal areas, especially in posteromedial cortex and the angular gyrus. Our findings are in agreement with comprehensive reports linking histologically assessed microstructure and connectivity in different mammalian species and extend them to the human cerebral cortex in vivo.

Large-Scale Gradients in Human Cortical Organization

Recent advances in mapping cortical areas in the human brain provide a basis for investigating the significance of their spatial arrangement. Here we describe a dominant gradient in cortical features that spans between sensorimotor and transmodal areas. We propose that this gradient constitutes a core organizing axis of the human cerebral cortex, and describe an intrinsic coordinate system on its basis. Studying the cortex with respect to these intrinsic dimensions can inform our understanding of how the spectrum of cortical function emerges from structural constraints.

CD4 blockade directly inhibits mouse and human CD4(+) T cell functions independent of Foxp3(+) Tregs.

CD4(+) helper T cells orchestrate protective immunity against pathogens, yet can also induce undesired pathologies including allergies, transplant rejection and autoimmunity. Non-depleting CD4-specific antibodies such as clone YTS177.9 were found to promote long-lasting T cell tolerance in animal models. Thus, CD4 blockade could represent a promising therapeutic approach for human autoimmune diseases. However, the mechanisms underlying anti-CD4-induced tolerance are incompletely resolved. Particularly, multiple immune cells express CD4 including Foxp3(+) regulatory T cells (Tregs) and dendritic cells (DCs), both controlling the activation of CD4(+)Foxp3(-) helper T cells. Utilizing mixed leukocyte reactions (MLRs) reflecting physiological interactions between T cells and DCs, we report that anti-CD4 treatment inhibits CD4(+)Foxp3(-) T cell proliferation in an IL-2-independent fashion. Notably, YTS177.9 binding induces a rapid internalization of CD4 on both CD4(+)Foxp3(-) T cells and Foxp3(+) Tregs. However, no expansion or activation of immunosuppressive CD4(+)Foxp3(+) Tregs was observed following anti-CD4 treatment. Additionally, cytokine production, maturation and T cell priming capacity of DCs are not affected by anti-CD4 exposure. In line with these data, the selective ablation of Foxp3(+) Tregs from MLRs by the use of diphtheria toxin (DT)-treated bacterial artificial chromosome (BAC)-transgenic DEREG mice completely fails to abrogate the suppressive activity of multiple anti-CD4 antibodies. Instead, tolerization is associated with the defective expression of various co-stimulatory receptors including OX40 and CD30, suggesting altered signaling through the TCR complex. Consistent with our findings in mice, anti-CD4 treatment renders human CD4(+) T cells tolerant in the absence of Tregs. Thus, our results establish that anti-CD4 antibodies can directly tolerize pathogenic CD4(+)Foxp3(-) helper T cells. This has important implications for the treatment of human inflammatory diseases.

Higher body mass index is associated with reduced posterior default mode connectivity in older adults

Obesity is a complex neurobehavioral disorder that has been linked to changes in brain structure and function. However, the impact of obesity on functional connectivity and cognition in aging humans is largely unknown. Therefore, the association of body mass index (BMI), resting‐state network connectivity, and cognitive performance in 712 healthy, well‐characterized older adults of the Leipzig Research Center for Civilization Diseases (LIFE) cohort (60–80 years old, mean BMI 27.6 kg/m2 ± 4.2 SD, main sample: n = 521, replication sample: n = 191) was determined. Statistical analyses included a multivariate model selection approach followed by univariate analyses to adjust for possible confounders. Results showed that a higher BMI was significantly associated with lower default mode functional connectivity in the posterior cingulate cortex and precuneus. The effect remained stable after controlling for age, sex, head motion, registration quality, cardiovascular, and genetic factors as well as in replication analyses. Lower functional connectivity in BMI‐associated areas correlated with worse executive function. In addition, higher BMI correlated with stronger head motion. Using 3T neuroimaging in a large cohort of healthy older adults, independent negative associations of obesity and functional connectivity in the posterior default mode network were observed. In addition, a subtle link between lower resting‐state connectivity in BMI‐associated regions and cognitive function was found. The findings might indicate that obesity is associated with patterns of decreased default mode connectivity similar to those seen in populations at risk for Alzheimers disease. Hum Brain Mapp 38:3502–3515, 2017.

Aberrant Long-Range Temporal Correlations in Depression Are Attenuated after Psychological Treatment

The spontaneous oscillatory activity in the human brain shows long-range temporal correlations (LRTC) that extend over time scales of seconds to minutes. Previous research has demonstrated aberrant LRTC in depressed patients; however, it is unknown whether the neuronal dynamics normalize after psychological treatment. In this study, we recorded EEG during eyes-closed rest in depressed patients (N = 71) and healthy controls (N = 25), and investigated the temporal dynamics in depressed patients at baseline, and after attending either a brief mindfulness training or a stress reduction training. Compared to the healthy controls, depressed patients showed stronger LRTC in theta oscillations (4–7 Hz) at baseline. Following the psychological interventions both groups of patients demonstrated reduced LRTC in the theta band. The reduction of theta LRTC differed marginally between the groups, and explorative analyses of separate groups revealed noteworthy topographic differences. A positive relationship between the changes in LRTC, and changes in depressive symptoms was observed in the mindfulness group. In summary, our data show that aberrant temporal dynamics of ongoing oscillations in depressive patients are attenuated after treatment, and thus may help uncover the mechanisms with which psychotherapeutic interventions affect the brain.

Brief training in mindfulness may normalize a blunted error-related negativity in chronically depressed patients

The error-related negativity (ERN), an evoked-potential that arises in response to the commission of errors, is an important early indicator of self-regulatory capacities. In this study we investigated whether brief mindfulness training can reverse ERN deficits in chronically depressed patients. The ERN was assessed in a sustained attention task. Chronically depressed patients (n = 59) showed significantly blunted expression of the ERN in frontocentral and frontal regions, relative to healthy controls (n = 18). Following two weeks of training, the patients (n = 24) in the mindfulness condition showed a significantly increased ERN magnitude in the frontal region, but there were no significant changes in patients who had received a resting control (n = 22). The findings suggest that brief training in mindfulness may help normalize aberrations in the ERN in chronically depressed patients, providing preliminary evidence for the responsiveness of this parameter to mental training.