Daniela Dentico

Experienced Mindfulness Meditators Exhibit Higher Parietal-Occipital EEG Gamma Activity during NREM Sleep

Over the past several years meditation practice has gained increasing attention as a non-pharmacological intervention to provide health related benefits, from promoting general wellness to alleviating the symptoms of a variety of medical conditions. However, the effects of meditation training on brain activity still need to be fully characterized. Sleep provides a unique approach to explore the meditation-related plastic changes in brain function. In this study we performed sleep high-density electroencephalographic (hdEEG) recordings in long-term meditators (LTM) of Buddhist meditation practices (approximately 8700 mean hours of life practice) and meditation naive individuals. We found that LTM had increased parietal-occipital EEG gamma power during NREM sleep. This increase was specific for the gamma range (25–40 Hz), was not related to the level of spontaneous arousal during NREM and was positively correlated with the length of lifetime daily meditation practice. Altogether, these findings indicate that meditation practice produces measurable changes in spontaneous brain activity, and suggest that EEG gamma activity during sleep represents a sensitive measure of the long-lasting, plastic effects of meditative training on brain function.

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

Reduced mediodorsal thalamic volume and prefrontal cortical spindle activity in schizophrenia

BACKGROUND We recently found marked deficits in sleep spindles, non-rapid eye movement (NREM) sleep oscillations that are generated within the thalamus and then amplified and sustained in the cortex, in patients with schizophrenia compared to both healthy and psychiatric controls. Here, we investigated the thalamic and cortical contributions to these sleep spindle deficits. METHODS Anatomical volume of interest analysis (i.e., thalamic volumes) and electroencephalogram (EEG) source modeling (i.e., spindle-related cortical currents) were performed in patients with schizophrenia and healthy comparison subjects. FINDINGS Schizophrenia patients had reduced mediodorsal (MD) thalamic volumes, especially on the left side, compared to healthy controls, whereas whole thalami and lateral geniculate nuclei did not differ between groups. Furthermore, left MD volumes were strongly correlated with the number of scalp-recorded spindles in an anterior frontal region, and cortical currents underlying these anterior frontal spindles were localized in the prefrontal cortex, in Brodmann area (BA) 10. Finally, prefrontal currents at the peak of spindle activity were significantly reduced in schizophrenia patients and correlated with their performance in an abstraction/working memory task. CONCLUSION Altogether, these findings point to deficits in a specific thalamo-cortical circuitry in schizophrenia, which is associated with some cognitive deficits commonly reported in those patients.

Reversal of cortical information flow during visual imagery as compared to visual perception.

The role of bottom-up and top-down connections during visual perception and the formation of mental images was examined by analyzing high-density EEG recordings of brain activity using two state-of-the-art methods for assessing the directionality of cortical signal flow: state-space Granger causality and dynamic causal modeling. We quantified the directionality of signal flow in an occipito-parieto-frontal cortical network during perception of movie clips versus mental replay of the movies and free visual imagery. Both Granger causality and dynamic causal modeling analyses revealed an increased top-down signal flow in parieto-occipital cortices during mental imagery as compared to visual perception. These results are the first direct demonstration of a reversal of the predominant direction of cortical signal flow during mental imagery as compared to perception.

Short Meditation Trainings Enhance Non-REM Sleep Low-Frequency Oscillations

Study Objectives We have recently shown higher parietal-occipital EEG gamma activity during sleep in long-term meditators compared to meditation-naive individuals. This gamma increase was specific for NREM sleep, was present throughout the entire night and correlated with meditation expertise, thus suggesting underlying long-lasting neuroplastic changes induced through prolonged training. The aim of this study was to explore the neuroplastic changes acutely induced by 2 intensive days of different meditation practices in the same group of practitioners. We also repeated baseline recordings in a meditation-naive cohort to account for time effects on sleep EEG activity. Design High-density EEG recordings of human brain activity were acquired over the course of whole sleep nights following intervention. Setting Sound-attenuated sleep research room. Patients or Participants Twenty-four long-term meditators and twenty-four meditation-naïve controls. Interventions Two 8-h sessions of either a mindfulness-based meditation or a form of meditation designed to cultivate compassion and loving kindness, hereafter referred to as compassion meditation. Measurements and Results We found an increase in EEG low-frequency oscillatory activities (1–12 Hz, centered around 7–8 Hz) over prefrontal and left parietal electrodes across whole night NREM cycles. This power increase peaked early in the night and extended during the third cycle to high-frequencies up to the gamma range (25–40 Hz). There was no difference in sleep EEG activity between meditation styles in long-term meditators nor in the meditation naïve group across different time points. Furthermore, the prefrontal-parietal changes were dependent on meditation life experience. Conclusions This low-frequency prefrontal-parietal activation likely reflects acute, meditation-related plastic changes occurring during wakefulness, and may underlie a top-down regulation from frontal and anterior parietal areas to the posterior parietal and occipital regions showing chronic, long-lasting plastic changes in long-term meditators.

Neurobiological correlates of impulsivity in healthy adults: Lower prefrontal gray matter volume and spontaneous eye-blink rate but greater resting-state functional connectivity in basal ganglia-thalamo-cortical circuitry

&NA; Studies consistently implicate aberrance of the brains reward‐processing and decision‐making networks in disorders featuring high levels of impulsivity, such as attention‐deficit hyperactivity disorder, substance use disorder, and psychopathy. However, less is known about the neurobiological determinants of individual differences in impulsivity in the general population. In this study of 105 healthy adults, we examined relationships between impulsivity and three neurobiological metrics – gray matter volume, resting‐state functional connectivity, and spontaneous eye‐blink rate, a physiological indicator of central dopaminergic activity. Impulsivity was measured both by performance on a task of behavioral inhibition (go/no‐go task) and by self‐ratings of attentional, motor, and non‐planning impulsivity using the Barratt Impulsiveness Scale (BIS‐11). Overall, we found that less gray matter in medial orbitofrontal cortex and paracingulate gyrus, greater resting‐state functional connectivity between nodes of the basal ganglia‐thalamo‐cortical network, and lower spontaneous eye‐blink rate were associated with greater impulsivity. Specifically, less prefrontal gray matter was associated with higher BIS‐11 motor and non‐planning impulsivity scores, but was not related to task performance; greater correlated resting‐state functional connectivity between the basal ganglia and thalamus, motor cortices, and prefrontal cortex was associated with worse no‐go trial accuracy on the task and with higher BIS‐11 motor impulsivity scores; lower spontaneous eye‐blink rate was associated with worse no‐go trial accuracy and with higher BIS‐11 motor impulsivity scores. These data provide evidence that individual differences in impulsivity in the general population are related to variability in multiple neurobiological metrics in the brains reward‐processing and decision‐making networks. HighlightsDifferences in impulsivity are linked to variability in multiple metrics.Greater impulsivity is associated with less prefrontal gray matter volume.Greater impulsivity is associated with increased functional connectivity.Greater impulsivity is associated with lower spontaneous eye‐blink rate.

Local Low Frequency fMRI Synchrony Is A Proxy For The Spatial Extent Of Functional Connectivity

Functional connectivity Magnetic Resonance Imaging (fcMRI) has assumed a central role in neuroimaging efforts to understand the changes underlying brain disorders. Current models of the spatial and temporal structure of fcMRI based connectivity contain strong a priori assumptions. We report that low temporal frequency fMRI signal synchrony within the local (3 mm radius) neighborhood of a location on the cortical surface strongly predicts the scale of its global functional connectivity. This relationship is tested vertex-wise across the cortex using Spearman’s rank order correlation on an individual subject basis. Furthermore, this relationship is shown to be dynamically preserved across repeated within session scans. These results provide a model free data driven method to visualize and quantitatively analyze patterns of connectivity at the imaging voxel resolution across the entire cortex on an individual subject basis. The procedure thus provides a tool to check directly the validity of spatial and temporal prior assumptions incorporated in the analysis of fcMRI data.

Topological Network Analysis of Electroencephalographic Power Maps

Meditation practice as a non-pharmacological intervention to provide health related benefits has generated much neuroscientific interest in its effects on brain activity. Electroencephalogram (EEG), an imaging modality known for its inexpensive procedure and excellent temporal resolution, is often utilized to investigate the neuroplastic effects of meditation under various experimental conditions. In these studies, EEG signals are routinely mapped on a topographic layout of channels to visualize variations in spectral powers within certain frequency ranges. Topological data analysis (TDA) of the topographic power maps modeled as graphs can provide different insight to EEG signals than standard statistical methods. A highly effective TDA technique is persistent homology, which reveals topological characteristics of a power map by tracking feature changes throughout a filtration process on the graph structure of the map. In this paper, we propose a novel inference procedure based on filtrations induced by sublevel sets of the power maps of high-density EEG signals. We apply the pipeline to simulated and real data, where we compare the persistent homological features of topographic maps of spectral powers in high-frequency bands of EEG signals recorded on long-term meditators and meditation-naive practitioners.