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      Local and Distributed fMRI Changes Induced by 40 Hz Gamma tACS of the Bilateral Dorsolateral Prefrontal Cortex: A Pilot Study

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          Abstract

          Over the past few years, the possibility of modulating fast brain oscillatory activity in the gamma ( γ) band through transcranial alternating current stimulation (tACS) has been discussed in the context of both cognitive enhancement and therapeutic scenarios. However, the effects of tACS targeting regions outside the motor cortex, as well as its spatial specificity, are still unclear. Here, we present a concurrent tACS-fMRI block design study to characterize the impact of 40 Hz tACS applied over the left and right dorsolateral prefrontal cortex (DLPFC) in healthy subjects. Results suggest an increase in blood oxygenation level-dependent (BOLD) activity in the targeted bilateral DLPFCs, as well as in surrounding brain areas affected by stimulation according to biophysical modeling, i.e., the premotor cortex and anterior cingulate cortex (ACC). However, off-target effects were also observed, primarily involving the visual cortices, with further effects on the supplementary motor areas (SMA), left subgenual cingulate, and right superior temporal gyrus. The specificity of 40 Hz tACS over bilateral DLPFC and the possibility for network-level effects should be considered in future studies, especially in the context of recently promoted gamma-induction therapeutic protocols for neurodegenerative disorders.

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          The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

          Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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            A multi-modal parcellation of human cerebral cortex

            Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.
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              Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.

              Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.
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                Author and article information

                Contributors
                Journal
                Neural Plast
                Neural Plast
                np
                Neural Plasticity
                Hindawi
                2090-5904
                1687-5443
                2022
                16 July 2022
                : 2022
                : 6197505
                Affiliations
                1Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Italy
                2Non-invasive Brain Stimulation Unit, Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
                3Unit of Neuroimaging and Neurointervention, “Santa Maria Alle Scotte” Medical Center, Siena, Italy
                4Neuroelectrics, Cambridge, MA, USA
                5Neuroelectrics, Barcelona, Spain
                6Human Physiology Section, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
                7Precision Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
                Author notes

                Academic Editor: nicoletta berardi

                Author information
                https://orcid.org/0000-0002-4006-4629
                https://orcid.org/0000-0002-9736-6564
                https://orcid.org/0000-0002-7202-5421
                https://orcid.org/0000-0001-8556-2376
                https://orcid.org/0000-0001-6155-9439
                https://orcid.org/0000-0003-1235-6533
                https://orcid.org/0000-0003-3084-0177
                https://orcid.org/0000-0003-4621-9537
                https://orcid.org/0000-0001-6697-9459
                https://orcid.org/0000-0002-6533-7427
                Article
                10.1155/2022/6197505
                9308536
                35880231
                ec94015f-f508-43c4-8046-6b7bb9edd6e3
                Copyright © 2022 Lucia Mencarelli et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 December 2021
                : 6 May 2022
                : 21 June 2022
                Funding
                Funded by: ADDF
                Award ID: GA201902–2017902
                Funded by: National Institutes of Health
                Award ID: R01 AG060981-01
                Award ID: R01 MH117063-01
                Award ID: P01 AG031720-06A1
                Funded by: Defense Advanced Research Projects Agency
                Award ID: HR001117S0030
                Categories
                Research Article

                Neurosciences
                Neurosciences

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