Evidence map›Paper›PMID 30198220›Full record

ArticleJournal of clinical neurology (Seoul, Korea)2018

Multimodal Assessment of Neural Substrates in Computerized Cognitive Training: A Preliminary Study.

Hae Ri Na, Jae Sung Lim, Woo Jung Kim, Jae Won Jang, Min Jae Baek, Jeongeun Kim, Young Ho Park, So Young Park, SangYun Kim

Registry-linked trialOpen access · diamondAbstract read
In one paragraph

Article in Journal of clinical neurology (Seoul, Korea), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02480738 (Effectiveness of Computerized Cognitive Training Apparatus), which is not on this map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
0.4field-weighted citation impact, top 40% 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.

NCT02480738 nacompletednot on this map

Effectiveness of Computerized Cognitive Training Apparatus (CoCoTA) in the Elderly With Normal Cognition, Subjective Cognitive Impairment, Mild Cognitive Impairment

TypeinterventionalSponsorSeoul National University Bundang HospitalRan2012 to 2013Enrolled26ConditionsMild Cognitive ImpairmentArmsComputerized Cognitive Training Apparatus
3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Trial
  5. Trial
  6. Trial
  7. Article
  8. Review
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 4 institutions in 1 country.

Hae Ri Na *Department of Neurology, Bobath Memorial Hospital, Seongnam, Korea.
Jae Sung Lim *Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Korea.
Woo Jung KimDepartment of Neurology, Hallym University Sacred Heart Hospital, Anyang, Korea.
Jae Won JangDepartment of Neurology, Kangwon National University Hospital, Chuncheon, Korea.
Min Jae BaekDepartment of Neurology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Jeongeun KimDepartment of Neurology, Gangnam-gu Haengbok Convalescence Hospital, Seoul, Korea.
Young Ho ParkDepartment of Neurology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
So Young ParkDepartment of Neurology, Incheon Sarang General Hospital, Incheon, Korea.
SangYun KimDepartment of Neurology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea. neuroksy@snu.ac.kr.
Seoul National University Bundang Hospital · KRHallym University Sacred Heart Hospital · KRBobath memorial hospital · KRKangwon National University Hospital · KR

Funding

Ministry of Knowledge Economy 10035434
6 · The paper itself

Abstract

background and purposeSeveral studies have validated the clinical efficacy of computerized cognitive training applications. However, few studies have investigated the neural substrates of these training applications using simultaneous multimodal neuroimaging modalities. We aimed to determine the effectiveness of computerized cognitive training and corresponding neural substrates through a multimodal approach.

methodsTen patients with mild cognitive impairment (MCI), six patients with subjective memory impairment (SMI), and 10 normal controls received custom-developed computerized cognitive training in the memory clinic of a university hospital. All of the participants completed 24 sessions of computerized cognitive training, each lasting 40 minutes and performed twice weekly. They were assessed using neuropsychological tests (both computerized and conventional), electroencephalography, fluorodeoxyglucose positron-emission tomography (FDG-PET), volumetric magnetic resonance imaging (MRI), and diffusion-tensor imaging (DTI) at pre- and posttraining.

resultsThe patients with MCI exhibited significant improvements in the trail-making test-black & white-B, and memory domain of the computerized cognitive assessment. Subjects with normal cognition exhibited significant improvements in scores in the language and attention-/psychomotor-speed domains. There were no significant changes in subjects with SMI. In the pre- and posttraining evaluations of the MCI group, FDG-PET showed focal activation in the left anterior insula and anterior cingulate after training. Volumetric MRI showed a focal increase in the cortical thickness in the rostral anterior cingulate. DTI revealed increased fractional anisotropy in several regions, including the anterior cingulate.

conclusionsThe anterior cingulate and anterior insula, which are parts of the salience network, may be substrates for the improvements in cognitive function induced by computerized cognitive training.

Indexed as

cingulatecomputerized cognitive traininginsulamagnetic resonance imagingmild cognitive impairmentsalience network

Identifiers

PMID30198220
PMCPMC6172514
OpenAlexW2895750857

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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.