Age-related visual dynamics in HIV-infected adults with cognitive impairment

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Objective

To investigate whether aging differentially affects neural activity serving visuospatial processing in a large functional neuroimaging study of HIV-infected participants and to determine whether such aging effects are attributable to differences in the duration of HIV infection.

Methods

A total of 170 participants, including 93 uninfected controls and 77 HIV-infected participants, underwent neuropsychological assessment followed by neuroimaging with magnetoencephalography (MEG). Time-frequency analysis of the MEG data followed by advanced image reconstruction of neural oscillatory activity and whole-brain statistical analyses were used to examine interactions between age, HIV infection, and cognitive status. Post hoc testing for a mediation effect of HIV infection duration on the relationship between age and neural activity was performed using a quasi-Bayesian approximation for significance testing.

Results

Cognitively impaired HIV-infected participants were distinguished from unimpaired HIV-infected and control participants by their unique association between age and gamma oscillations in the parieto-occipital cortex. This relationship between age and gamma was fully mediated by the duration of HIV infection in cognitively impaired participants. Impaired HIV-infected participants were also distinguished by their atypical relationship between alpha oscillations and age in the superior parietal cortex.

Conclusions

Impaired HIV-infected participants exhibited markedly different relationships between age and neural responses in the parieto-occipital cortices relative to their peers. This suggests a differential effect of chronological aging on the neural bases of visuospatial processing in a cognitively impaired subset of HIV-infected adults. Some of these relationships were fully accounted for by differences in HIV infection duration, whereas others were more readily associated with aging.

Related collections

Most cited references 47

Record: found
Abstract: found
Article: not found

Alpha-band oscillations, attention, and controlled access to stored information

Wolfgang Klimesch (2012)

Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.

0 comments Cited 978 times – based on 0 reviews      Review now

Record: found
Abstract: found
Article: not found

Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements.

S Taulu, J Simola (2006)

Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal signal space separation method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic separation into brain-related and external interference signals is accomplished with signal space separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the method removes the artefacts without altering the field patterns of the brain signals.

0 comments Cited 599 times – based on 0 reviews      Review now

Record: found
Abstract: found
Article: not found

Dynamic imaging of coherent sources: Studying neural interactions in the human brain.

J. Gross, J. Kujala, M. Hämäläinen … (2001)

Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept ("binding") is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto- or electroencephalography (MEG/EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.

0 comments Cited 531 times – based on 0 reviews      Review now

All references

Author and article information

Journal

Journal ID (nlm-ta): Neurol Neuroimmunol Neuroinflamm

Journal ID (iso-abbrev): Neurol Neuroimmunol Neuroinflamm

Journal ID (hwp): nnn

Journal ID (publisher-id): NEURIMMINFL

Title: Neurology® Neuroimmunology & Neuroinflammation

Publisher: Lippincott Williams & Wilkins (Hagerstown, MD )

ISSN (Electronic): 2332-7812

Publication date Collection: May 2020

Publication date (Electronic): 26 February 2020

Publication date PMC-release: 26 February 2020

Volume: 7

Issue: 3

Electronic Location Identifier: e690

Affiliations

From the Center for Magnetoencephalography (B.R.G., A.I.W., T.W.W.), University of Nebraska Medical Center, Omaha, NE; Department of Neurological Sciences (A.I.W., M.T.R., T.W.W.), UNMC, Omaha; Department of Internal Medicine (J.O.N., S.S.), Division of Infectious Diseases, UNMC; Department of Neurology (K.R.R.), University of North Carolina School of Medicine, Chapel Hill, NC; and Department of Pharmacology and Experimental Neuroscience (H.S.F.), UNMC, Omaha, NE.

Author notes

Correspondence Dr. Wilson twwilson@ 123456unmc.edu

Go to Neurology.org/NN for full disclosures. Funding information is provided at the end of the article.

[*]

These authors contributed equally to this manuscript.

[†]

This author conducted statistical analysis of the data.

The Article Processing Charge was funded by the NIH.

Author information

Boman R. Groff http://orcid.org/0000-0002-2424-5913

Alex I. Wiesman http://orcid.org/0000-0003-0917-1570

Article

Publisher ID: NEURIMMINFL2019024158

DOI: 10.1212/NXI.0000000000000690

PMC ID: 7051212

PubMed ID: 32102916

SO-VID: b019f3ea-d73b-45bd-89ad-93b720d5b6fd

License:

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

History

Date received : 16 September 2019

Date accepted : 17 January 2020

Funding

Funded by: NIH

Award ID: R01-MH103220, R01-MH116782, R01-MH118013, P30-MH062261, F31-AG055332

Custom metadata

OPEN-ACCESS TRUE

Data availability:

Comments

Comment on this article

Cited by 16

See all cited by

Most referenced authors 463

See all reference authors