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Brain Sciences
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Multimodal Data Fusion on Patients with Cognitive Impairment
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Multimodal Data Fusion on Patients with Cognitive Impairment

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 13097

Special Issue Editors

Prof. Dr. Gopikrishna Deshpande

E-Mail Website
Guest Editor
MRI Research Center, Department of Electrical and Computer Engineering, Auburn University; 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA
Interests: signal and image processing; functional magnetic resonance imaging (fMRI); multimodal imaging and data fusion (e.g. electroencephalography (EEG) and fMRI); network modeling of brain function (autoregressive, bayesian, wavelet and state-space models); ultra-high field (7T) MRI: sub-mm layer fMRI and multinuclear MR spectroscopy; big data analytics, pattern recognition and deep learning in neuroimaging; neurofeedback; sensory, motor and cognitive neuroscience; social and affective neuroscience; consciousness; complementary medicine, specifically meditation; clinical populations such as autism, Alzheimer’s, developmental psychopathologies, PTSD; neuroeconomics and neuromarketing; fMRI in awake dogs
Dr. Peipeng Liang

E-Mail Website
Guest Editor
School of Psychology, Capital Normal University, Beijing 100048, China
Interests: cognitive neuroscience; neuroimaging; cognitive neuropsychology; behavioral neuroscience; executive function; brain imaging; brain functional neuroimaging; fMRI; brain connectivity

Special Issue Information

Dear Colleagues,

Different types of cognitive impairment present a major health challenge worldwide. The topic of this Special Issue focuses on the scientific study of the neural mechanisms underlying cognitive impairments of the human brain. Its mission is to provide researchers and scientists with outstanding articles that seek to develop new ways of diagnosing patients with cognitive impairment and ultimately develop novel treatments.

Advances in neuroimaging, neuropsychological, neurophysiological, neuropharmacological, cognitive neuroscientific, and computational approaches have offered important insights into the study of patients with cognitive impairments. This topic is at the forefront of communicating scientific knowledge and discoveries to researchers, academics, and clinicians worldwide from the perspectives of multimodal data fusion and intelligent data integration.

The subjects of the manuscripts may cover all aspects including, but not limited to, behavior, psychology, neuropsychology, neurology, psychiatry, geriatrics, pharmacology, and neuroimaging. Research articles and review articles are all welcomed.

Prof. Dr. Gopikrishna Deshpande
Dr. Peipeng Liang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Multimodal Neuroimaging
  • Multimodal Data Fusion
  • Cognitive Impairment
  • Neuropsychiatric Diseases

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Published Papers (4 papers)

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Research

12 pages, 1676 KiB  
Article
Aberrant Amplitude of Low-Frequency Fluctuation and Degree Centrality within the Default Mode Network in Patients with Vascular Mild Cognitive Impairment
by Haoyuan Li, Xiuqin Jia, Yingying Li, Xuejia Jia and Qi Yang
Brain Sci. 2021, 11(11), 1534; https://doi.org/10.3390/brainsci11111534 - 19 Nov 2021
Cited by 20 | Viewed by 2524
Abstract
This study aimed to investigate whole-brain spontaneous activities changes in patients with vascular mild cognitive impairment (VaMCI), and to evaluate the relationships between these brain alterations and their neuropsychological assessments. Thirty-one patients with VaMCI and thirty-one healthy controls (HCs) underwent structural MRI and [...] Read more.
This study aimed to investigate whole-brain spontaneous activities changes in patients with vascular mild cognitive impairment (VaMCI), and to evaluate the relationships between these brain alterations and their neuropsychological assessments. Thirty-one patients with VaMCI and thirty-one healthy controls (HCs) underwent structural MRI and resting-state functional MRI (rs-fMRI) and neuropsychological assessments. The functional alterations were determined by the amplitude of low-frequency fluctuation (ALFF) and degree centrality (DC). The gray matter volume (GMV) changes were analyzed using voxel-based morphometry (VBM). Linear regression analysis was used to evaluate the relationships between the structural and functional changes of brain regions and neuropsychological assessments. The VaMCI group had significantly lower scores in the Montreal Cognitive Assessment (MoCA), and higher scores on the Hamilton Anxiety Rating Scale (HAMA) and Hamilton Depression Rating Scale (HAMD). Compared to the HCs, the VaMCI group exhibited GM atrophy in the right precentral gyrus (PreCG) and right inferior temporal gyrus (ITG). VaMCI patients further exhibited significantly decreased brain activity within the default mode network (DMN), including the bilateral precuneus (PCu), angular gyrus (AG), and medial frontal gyrus (medFG). Linear regression analysis revealed that the decreased ALFF was independently associated with lower MoCA scores, and the GM atrophy was independently associated with higher HAMD scores. The current finding suggested that aberrant spontaneous brain activity in the DMN might subserve as a potential biomarker of VaMCI, which may highlight the underlying mechanism of cognitive decline in cerebral small vessel disease. Full article
(This article belongs to the Special Issue Multimodal Data Fusion on Patients with Cognitive Impairment)
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10 pages, 1700 KiB  
Article
Magnetic Resonance Imaging Measurement of Entorhinal Cortex in the Diagnosis and Differential Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease
by Qianqian Li, Junkai Wang, Jianghong Liu, Yumeng Wang and Kuncheng Li
Brain Sci. 2021, 11(9), 1129; https://doi.org/10.3390/brainsci11091129 - 26 Aug 2021
Cited by 9 | Viewed by 2542
Abstract
Several magnetic resonance imaging studies have shown that the entorhinal cortex (ERC) is the first brain area related to pathologic changes in Alzheimer’s disease (AD), even before atrophy of the hippocampus (HP). However, change in ERC morphology (thickness, surface area and volume) in [...] Read more.
Several magnetic resonance imaging studies have shown that the entorhinal cortex (ERC) is the first brain area related to pathologic changes in Alzheimer’s disease (AD), even before atrophy of the hippocampus (HP). However, change in ERC morphology (thickness, surface area and volume) in the progression from aMCI to AD, especially in the subtypes of aMCI (single-domain and multiple-domain: aMCI-s and aMCI-m), however, is still unclear. ERC thickness, surface area and volume were measured in 29 people with aMCI-s, 22 people with aMCI-m, 18 patients with AD and 26 age-/sex-matched healthy controls. Group comparisons of the ERC geometry measurements (including thickness, volume and surface area) were performed using analyses of covariance (ANCOVA). Furthermore, receiver operator characteristic (ROC) analyses and the area under the curve (AUC) were employed to investigate classification ability (HC, aMCI-s, aMCI-m and AD from each other). There was a significant decreasing tendency in ERC thickness from HC to aMCI-s to aMCI-m to finally AD in both the left and the right hemispheres (left hemisphere: HC > aMCI-s > AD; right hemisphere: aMCI-s > aMCI-m > AD). For ERC volume, both the AD group and the aMCI-m group showed significantly decreased volume on both sides compared with the HC group. In addition, the AD group also had significantly decreased volume on both sides compared with the aMCI-s group. As for the ERC surface area, no significant difference was identified among the four groups. Furthermore, the AUC results demonstrate that combined ERC parameters (thickness and volume) can better discriminate the four groups from each other than ERC thickness alone. Finally, and most importantly, relative to HP volume, the capacity of combined ERC parameters was better at discriminating between HC and aMCI-s, as well as aMCI-m and AD. ERC atrophy, particularly the combination of ERC thickness and volume, might be regarded as a promising candidate biomarker in the diagnosis and differential diagnosis of aMCI and AD. Full article
(This article belongs to the Special Issue Multimodal Data Fusion on Patients with Cognitive Impairment)
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16 pages, 2186 KiB  
Article
The Effect of Light Sedation with Midazolam on Functional Connectivity of the Dorsal Attention Network
by Junkai Wang, Yachao Xu, Gopikrishna Deshpande, Kuncheng Li, Pei Sun and Peipeng Liang
Brain Sci. 2021, 11(8), 1107; https://doi.org/10.3390/brainsci11081107 - 22 Aug 2021
Cited by 6 | Viewed by 2855
Abstract
Altered connectivity within and between the resting-state networks (RSNs) brought about by anesthetics that induce altered consciousness remains incompletely understood. It is known that the dorsal attention network (DAN) and its anticorrelations with other RSNs have been implicated in consciousness. However, the role [...] Read more.
Altered connectivity within and between the resting-state networks (RSNs) brought about by anesthetics that induce altered consciousness remains incompletely understood. It is known that the dorsal attention network (DAN) and its anticorrelations with other RSNs have been implicated in consciousness. However, the role of DAN-related functional patterns in drug-induced sedative effects is less clear. In the current study, we investigated altered functional connectivity of the DAN during midazolam-induced light sedation. In a placebo-controlled and within-subjects experimental study, fourteen healthy volunteers received midazolam or saline with a 1-week interval. Resting-state fMRI data were acquired before and after intravenous drug administration. A multiple region of interest-driven analysis was employed to investigate connectivity within and between RSNs. It was found that functional connectivity was significantly decreased by midazolam injection in two regions located in the left inferior parietal lobule and the left middle temporal area within the DAN as compared with the saline condition. We also identified three clusters in anticorrelation between the DAN and other RSNs for the interaction effect, which included the left medial prefrontal cortex, the right superior temporal gyrus, and the right superior frontal gyrus. Connectivity between all regions and DAN was significantly decreased by midazolam injection. The sensorimotor network was minimally affected. Midazolam decreased functional connectivity of the dorsal attention network. These findings advance the understanding of the neural mechanism of sedation, and such functional patterns might have clinical implications in other medical conditions related to patients with cognitive impairment. Full article
(This article belongs to the Special Issue Multimodal Data Fusion on Patients with Cognitive Impairment)
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21 pages, 2366 KiB  
Article
Unsupervised Machine Learning to Identify Separable Clinical Alzheimer’s Disease Sub-Populations
by Jayant Prakash, Velda Wang, Robert E. Quinn III and Cassie S. Mitchell
Brain Sci. 2021, 11(8), 977; https://doi.org/10.3390/brainsci11080977 - 23 Jul 2021
Cited by 10 | Viewed by 4017
Abstract
Heterogeneity among Alzheimer’s disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the [...] Read more.
Heterogeneity among Alzheimer’s disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the ADNIMERGE dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Patient sociodemographics, brain imaging, biomarkers, cognitive tests, and medication usage were included for analysis. Four AD clinical sub-populations were identified using between-cluster mean fold changes [cognitive performance, brain volume]: cluster-1 represented least severe disease [+17.3, +13.3]; cluster-0 [−4.6, +3.8] and cluster-3 [+10.8, −4.9] represented mid-severity sub-populations; cluster-2 represented most severe disease [−18.4, −8.4]. ARM assessed frequently occurring pharmacologic substances within the 4 sub-populations. No drug class was associated with the least severe AD (cluster-1), likely due to lesser antecedent disease. Anti-hyperlipidemia drugs associated with cluster-0 (mid-severity, higher volume). Interestingly, antioxidants vitamin C and E associated with cluster-3 (mid-severity, higher cognition). Anti-depressants like Zoloft associated with most severe disease (cluster-2). Vitamin D is protective for AD, but ARM identified significant underutilization across all AD sub-populations. Identification and feature characterization of four distinct AD sub-population “clusters” using standard clinical features enhances future clinical trial selection criteria and cross-study comparative analysis. Full article
(This article belongs to the Special Issue Multimodal Data Fusion on Patients with Cognitive Impairment)
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