A Neuroscientific Perspective

This review investigates how recent neuroimaging findings on vulnerability for depression and the mechanisms of mindfulness may serve to inform and enhance the understanding that is guiding the use of mindfulness training in the prevention and treatment of recurrent and chronic depression. In particular, we review evidence suggesting that alterations in default-mode-network activity and connectivity represent a fundamental deficit underlying cognitive vulnerability for depression and explore the ways in which mindfulness meditation may reverse such alterations. Furthermore, we discuss findings from studies that have investigated the effects of mindfulness on emotion- regulatory capacities. These findings suggest mindful emotion regulation has a characteristic neural signature that is particularly conducive to therapeutic learning. We conclude that training in mindfulness has unique strengths for addressing neural mechanisms associated with cognitive vulnerabilities for recurrent and chronic depression and propose future lines of research to more effectively harness this potential

Decreased long‐range temporal correlations in the resting‐state functional magnetic resonance imaging blood‐oxygen‐level‐dependent signal reflect motor sequence learning up to 2 weeks following training

Decreased long-range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long-range temporal memory within resting-state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel-wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well-known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting-state and suggests that a cortical subset of sequence-specific regions may continue to represent a functional signature of learning reflected in decreased long-range temporal dependence after a period of inactivity

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

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

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

Eine grundlegende organisatorische Achse im menschlichen Kortex

Classical work in neuroanatomy suggests that the spatial arrangement of cortical areas in overarching gradients is a key organizational feature of the cerebral cortex. While studies performed in experimental animals provide strong evidence for spatial gradients in cortical microstructure and connectivity, similar research in humans has been obstructed by methodological challenges. In consequence, the significance of structural gradients for human cortical function remains unaddressed. The work presented in this dissertation capitalizes on recent advances in magnetic resonance imaging and novel analytic strategies to investigate spatial gradients in the human cerebral cortex in vivo. We first introduce a set of relevant tools and proceed to demonstrate a global gradient in cortical features that spans between sensorimotor and transmodal areas. This gradient is reflected in the distribution of intracortical myelin and captures the main axis of variance in functional connectivity patterns. It is spatially embedded in the intrinsic geometry of the cortex and tracks a functional spectrum of increasing abstraction. Finally, we propose that this gradient constitutes a core organizing axis of the human cerebral cortex, and describe an intrinsic cortical coordinate system on its basis. Studying the cortex with respect to its intrinsic dimensions can inform our understanding of how the spectrum of cortical function emerges from structural constraints.Klassische Arbeiten in der Neuroanatomie legen nahe, dass die Anordnung von Rindenfeldern in räumlichen Gradienten ein zentrales Organisationsmerkmal der Großhirnrinde darstellt. Räumliche Gradienten in kortikaler Mikrostruktur und Konnektivität konnten in Versuchstieren eindeutig nachgewiesen werden. Entsprechende Studien im menschlichen Gehirn waren hingegen bisher nicht praktikabel. Daher bleibt auch die Bedeutung struktureller Gradienten für den funktionellen Aufbau des menschlichen Kortex derzeit ungeklärt. Die vorliegende Dissertation macht sich aktuelle Fortschritte in der Magnetresonanztomographie und neue analytische Ansätze zunutze um räumliche Gradienten im menschlichen Kortex in vivo zu untersuchen. Wir führen zunächst einige sachdienliche Werkzeuge ein und weisen anschließend nach, dass verschiedene kortikale Eigenschaften in einem Gradienten zwischen sensomotorischen und transmodalen Regionen organisiert sind. Dieser Gradient findet in der Verteilung des intrakortikalen Myelingehalts Ausdruck und erfasst einen Großteil der Varianz funktioneller Konnektivitätsmuster. Er steht mit der spezifischen Geometrie des Kortex in enger Beziehung und spiegelt sich in einem funktionellen Spektrum zunehmender Abstraktion wider. Wir schlagen schließlich vor, dass dieser Gradient eine grundlegende Organisationsachse des menschlichen Kortex darstellt und arbeiten ein hierauf basierendes intrinsisches kortikales Koordinatensystem aus. Eine Erforschung des Kortex im Hinblick auf seine intrinsischen Dimensionen kann unser Verständnis davon befördern, wie die strukturellen Bedingungen des Kortex sein funktionelles Spektrum hervorbringen

Gradients of functional connectivity in the mouse cortex reflect neocortical evolution

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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

Loading and plotting of cortical surface representations in Nilearn

Processing neuroimaging data on the cortical surface traditionally requires dedicated heavy-weight software suites. Here, we present an initial support of cortical surfaces in Python within the neuroimaging data processing toolbox Nilearn. We provide loading and plotting functions for different surface data formats with minimal dependencies, along with examples of their application. Limitations of the current implementation and potential next steps are discussed