Evidence map›Paper›PMID 38504361›Full record

ArticleAlzheimer's research & therapy2024

Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.

Parul Verma, Kamalini Ranasinghe, Janani Prasad, Chang Cai, Xihe Xie, Hannah Lerner, Danielle Mizuiri, Bruce Miller, Katherine Rankin, Keith Vossel and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alzheimer's research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 5 institutions in 1 country.

Parul VermaDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA. parulv.iitb@gmail.com.
Kamalini RanasingheMemory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Janani PrasadAmador Valley High School, Pleasanton, CA, USA.
Chang CaiDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Xihe XieDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Hannah LernerMemory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Danielle MizuiriDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Bruce MillerMemory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Katherine RankinMemory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Keith VosselMemory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Steven W CheungDepartment of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA.
Srikantan S NagarajanDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Ashish RajDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
University Memory and Aging Center · USUniversity of California System · USUniversity of California, San Francisco · USGreen Valley High School · USSan Francisco VA Medical Center · US

Funding

The Human Connectome Project (HCP)U01MH093765 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2010 to 2014
$12.1M
Project 4P41EB015896 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2012 to 2018
$9.8M
Predictive model of spread of Parkinson's pathology using network diffusionR01NS092802 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI GUPTA, AJAY, RAJ, ASHISH · 2016 to 2020
$2.8M
A Storage Area Network for Structural and Functional Image AnalysisS10RR023401 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2009 to 2009
$2.0M
Multimodal modeling framework for fusing structural and functional connectome dataR01EB022717 · NIBIB · WEILL MEDICAL COLL OF CORNELL UNIV · PI NAGARAJAN, SRIKANTAN S., RAJ, ASHISH · 2016 to 2018
$1.6M
SILICON GRAPHICS PRISM EXTREME 128P/1TBS10RR023043 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI SORENSEN, ALMA GREGORY · 2006 to 2006
$1.5M
Neurophysiological Assessments of Network Dysfunction in Alzheimer's DiseaseK08AG058749 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI RANASINGHE, KAMALINI GAYATHREE · 2019 to 2023
$859k
Computeserver Structural &Functional Image AnalysisS10RR019307 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2004 to 2004
$496k
Multimodal imaging measures to assess synaptic dysfunction in Alzheimer's diseaseR21AG077498 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI RANASINGHE, KAMALINI GAYATHREE · 2022 to 2022
$484k
Alzheimer's Association AARFD-22-923931NCRR NIH HHS S10 RR019307NCRR NIH HHS S10 RR023043NCRR NIH HHS S10 RR023401NIA NIH HHS K08 AG058749NIA NIH HHS R21 AG077498NIBIB NIH HHS P41 EB015896NIBIB NIH HHS R01 EB022717NIH HHS R01EB022717NIH HHS R01NS092802NIMH NIH HHS U01 MH093765NINDS NIH HHS R01 NS092802
6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM).

methodsSGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years).

resultsPatients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition.

conclusionsThese results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Indexed as

Alzheimer DiseaseCognition DisordersCognitive DysfunctionAgedBrainCognitionHumansMiddle AgedAlzheimer’s diseaseBrain activityCognitive declineMagnetoencephalographySpectral graph theory

Identifiers

PMID38504361
PMCPMC10953266
OpenAlexW4392948795

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.