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      Gamma neuromodulation improves episodic memory and its associated network in amnestic mild cognitive impairment: a pilot study

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          Abstract

          Amnestic mild cognitive impairment (aMCI) is a predementia stage of Alzheimer’s disease associated with dysfunctional episodic memory and limited treatment options. We aimed to characterize feasibility, clinical, and biomarker effects of noninvasive neurostimulation for aMCI. 13 individuals with aMCI received eight 60-minute sessions of 40-Hz (gamma) transcranial alternating current stimulation (tACS) targeting regions related to episodic memory processing. Feasibility, episodic memory, and plasma Alzheimer’s disease bio-markers were assessed. Neuroplastic changes were characterized by resting-state functional connectivity (RSFC) and neuronal excitatory/inhibitory balance. Gamma tACS was feasible and aMCI participants demonstrated improvement in multiple metrics of episodic memory, but no changes in biomarkers. Improvements in episodic memory were most pronounced in participants who had the highest modeled tACS-induced electric fields and exhibited the greatest changes in RSFC. Increased RSFC was also associated with greater hippocampal excitability and higher baseline white matter integrity. This study highlights initial feasibility and the potential of gamma tACS to rescue episodic memory in an aMCI population by modulating connectivity and excitability within an episodic memory network.

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          The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.

          Ziad S Nasreddine, Natalie Phillips, Valérie Bédirian … (2005)
          To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia. Validation study. A community clinic and an academic center. Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score > or =17), and 90 healthy elderly controls (NC). The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD. Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively). MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.
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            FSL.

            Mark Jenkinson, Christian F Beckmann, Timothy E J Behrens … (2012)
            FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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              FreeSurfer.

              Bruce Fischl, Chuhua Fu (2012)
              FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 8100437
                Journal ID (pubmed-jr-id): 6047
                Journal ID (nlm-ta): Neurobiol Aging
                Journal ID (iso-abbrev): Neurobiol Aging
                Title: Neurobiology of aging
                ISSN (Print): 0197-4580
                ISSN (Electronic): 1558-1497
                Publication date Nihms-submitted: 7 October 2023
                Publication date (Print): September 2023
                Publication date (Electronic): 20 April 2023
                Publication date PMC-release: 18 October 2023
                Volume: 129
                Pages: 72-88
                Affiliations
                [a ] Department of Neurology, University of California-San Francisco, San Francisco, CA
                [b ] Neuroscape, University of California-San Francisco, San Francisco, CA
                [c ] Weill Institute for Neurosciences, Memory and Aging Center, University of California-San Francisco, San Francisco, CA
                [d ] Departments of Ophthalmology and Vision Science and Dermatology, Institute for Regenerative Cures, University of California-Davis, Davis, CA
                [e ] Departments of Physiology and Psychiatry, University of California-San Francisco, San Francisco, CA
                Author notes

                Author contributions

                T.Z., K.J., P.W., M.Z., J.K., and A.G. designed the protocol. T.Z., M.Z., A.G., J.K., and A.B. secured funding. J.R., J.R-M., and A.O. recruited participants. A.O. and K.J. collected the behavioral and neuroimaging data. C.L.G. processed and analyzed the resting-state fMRI data. J.R-M., B.C., and A.L.L. processed the biomarker data. K.J. and T.Z. conducted EF modeling and analyzed biomarkers. K.J. analyzed behavioral and MRS data. C.L.G., T.Z., and K.J. drafted the manuscript.

                [* ] Corresponding authors at: 675 Nelson Rising Lane, San Francisco, CA 94158, USA. KevJones22@ 123456gmail.com (K.T. Jones), Theodore.Zanto@ 123456ucsf.edu (T.P. Zanto).
                Article
                Manuscript ID: NIHMS1934076
                DOI: 10.1016/j.neurobiolaging.2023.04.005
                PMC ID: 10583532
                PubMed ID: 37276822
                SO-VID: c2ddc72c-3c54-4646-bfde-c35d2c2f3e83
                License:

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Subject: Article

                ScienceOpen disciplines: Neurosciences
                Keywords: amnestic mild cognitive impairment,neurostimulation,gamma stimulation,episodic memory,resting-state functional connectivity
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: amnestic mild cognitive impairment, neurostimulation, gamma stimulation, episodic memory, resting-state functional connectivity

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