Evidence map›Paper›PMID 32125614›Full record

ArticleBrain imaging and behavior2021

Default mode network integrity changes contribute to cognitive deficits in subcortical vascular cognitive impairment, no dementia.

Qi Qin, Yi Tang, Xuejiao Dou, Yida Qu, Yi Xing, Jianwei Yang, Tianshu Chu, Yong Liu, Jianping Jia

Abstract read
PubMed Publisher
In one paragraph

Article in Brain imaging and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.6field-weighted citation impact, top 14% 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

18 citing papers in PubMed, 23 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 6 institutions in 2 countries.

Qi QinDepartment of Neurology, Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, 45 Changchun Street, Beijing, 100053, China.
Yi TangDepartment of Neurology, Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, 45 Changchun Street, Beijing, 100053, China. tangyixw@163.com.
Xuejiao DouBrainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Yida QuBrainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Yi XingDepartment of Neurology, Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, 45 Changchun Street, Beijing, 100053, China.
Jianwei YangDepartment of Neurology, Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, 45 Changchun Street, Beijing, 100053, China.
Tianshu ChuCenter for Data Science, Courant, New York University, New York, NY, USA.
Yong LiuBrainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Jianping JiaDepartment of Neurology, Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, 45 Changchun Street, Beijing, 100053, China.
Capital Medical University · CNBeijing Academy of Artificial Intelligence · CNCenter for Excellence in Brain Science and Intelligence Technology · CNChinese Academy of Sciences · CNCourant Institute of Mathematical Sciences · USMinistry of Education of the People's Republic of China · CN

Funding

Beijing Municipal Science and Technology Commission Z171100000117001National Key R&D Program of China 2017YFC1310102National Natural Science Foundation of China 81671040Strategic Priority Research Program (B) of the Chinese Academy of Sciences XDB32020200
6 · The paper itself

Abstract

Vascular cognitive impairment, no dementia (VCIND) refers to cognitive deficits associated with underlying vascular causes that are insufficient to confirm a diagnosis of dementia. The default mode network (DMN) is a large-scale brain network of interacting brain regions involved in attention, working memory and executive function. The role of DMN white matter integrity in cognitive deficits of VCIND patients is unclear. Using diffusion tensor imaging (DTI), this study was carried out to investigate white matter microstructural changes in the DMN in VCIND patients and their contributions to cognitive deficits. Thirty-one patients with subcortical VCIND and twenty-two healthy elderly subjects were recruited. All patients underwent neuropsychological assessments and DTI examination. Voxel-based analyses were performed to extract fractional anisotropy (FA) and mean diffusivity (MD) measures in the DMN. Compared with the healthy elderly subjects, patients diagnosed with subcortical VCIND presented with abnormal white matter integrity in several key hubs of the DMN. The severity of damage in the white matter microstructure in the DMN significantly correlated with cognitive dysfunction. Mediation analyses demonstrated that DTI values could account for attention, executive and language impairments, and partly mediated global cognitive dysfunction in the subcortical VCIND patients. DMN integrity is significantly impaired in subcortical VCIND patients. The disrupted DMN connectivity could explain the attention, language and executive dysfunction, which indicates that the white matter integrity of the DMN may be a neuroimaging marker for VCIND.

Indexed as

Cognitive DysfunctionWhite MatterAgedBrainCognitionDefault Mode NetworkDiffusion Tensor ImagingHumansMagnetic Resonance ImagingNeuropsychological TestsCognitive functionDefault mode networkDiffusion tensor imagingVascular cognitive impairment, no dementia

Identifiers

PMID32125614
OpenAlexW3009380668

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.