Special Issue "Advancements in Neuroimaging"

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 16 December 2022 | Viewed by 7628

Special Issue Editor

Dr. Semen A. Kurkin
E-Mail Website
Guest Editor
Neuroscience and Cognitive Technology Lab, Innopolis University, Kazan, Russia
Interests: neuroscience; neuroimaging; EEG and MEG; brain-computer interfaces; neurodegenerative diseases; neurorehabilitation
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Special Issue Information

Dear Colleagues,

The active development of neuroimaging systems, especially non-invasive ones (EEG, MEG, fMRI, PET, fNIRS, and others), makes it possible to obtain various information about the processes occurring in the human brain, both in a normal state and in pathologies. In particular, information on hemodynamics, electrical and magnetic activity, and structural features of the brain is available. The development of computer systems for storing and processing the obtained data has led to the accumulation of a massive amount of biomedical data (Big Data) on the functioning of the human brain. Consequently, the development and application of modern methods for analyzing neuroimaging Big Data to identify patterns in these data (for example, biomarkers of developing neurodegenerative or neurological diseases or patterns associated with a specific cognitive activity of a person) are hot topics in neuroscience and neuroimaging. Here, interdisciplinary approaches based on the principles of information theory, the methods of statistics, machine learning, nonlinear dynamics, and the theory of complex networks seem to be especially promising. Such methods are in demand for the development of brain–computer interfaces, clinical decision support systems, and the diagnostics of various brain diseases and disorders. Thus, the Special Issue on “Advancements in Neuroimaging” aims to attract high-quality research studies from scientists and specialists that advance the application of state-of-the-art neuroimaging techniques and post-processing methods in scientific and clinical contexts.

Potential areas of interest include but are not limited to the following directions:

  • New and substantive developments in neuroimage acquisition techniques of various modalities;
  • Innovative methods for neuroimaging data and biomedical Big Data processing and analysis;
  • Development of brain–computer interfaces and clinical decision support systems.

The speakers' submissions of "Baltic Forum: Neuroscience, Artificial Intelligence and Complex Systems" and other accepted manuscripts contributing to our objective for advancements in neuroimaging are strongly welcomed for the Special Issue.

Dr. Semen A. Kurkin
Guest Editor

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. Diagnostics 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 1800 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

  • Neuroscience;
  • Neuroimaging techniques;
  • EEG analysis;
  • MEG analysis;
  • Functional near-infrared spectroscopy (fNIRS);
  • Functional magnetic resonance imaging (fMRI);
  • Brain–computer interfaces;
  • Clinical decision support systems;
  • Brain diseases;
  • Neurorehabilitation;
  • Brain functional networks;
  • Biomedical Big Data;
  • Machine learning methods.

Published Papers (10 papers)

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Research

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Article
Diagnostic Performance for Differential Diagnosis of Atypical Parkinsonian Syndromes from Parkinson’s Disease Using Quantitative Indices of 18F-FP-CIT PET/CT
Diagnostics 2022, 12(6), 1402; https://doi.org/10.3390/diagnostics12061402 - 06 Jun 2022
Viewed by 350
Abstract
We are aimed to evaluate the diagnostic performances of quantitative indices obtained from dual-phase 18F-FP-CIT PET/CT for differential diagnosis of atypical parkinsonian syndromes (APS) from Parkinson’s disease (PD). We analyzed 172 subjects, including 105 non-Parkinsonism, 26 PD, 8 PSP, 1 CBD, 8 [...] Read more.
We are aimed to evaluate the diagnostic performances of quantitative indices obtained from dual-phase 18F-FP-CIT PET/CT for differential diagnosis of atypical parkinsonian syndromes (APS) from Parkinson’s disease (PD). We analyzed 172 subjects, including 105 non-Parkinsonism, 26 PD, 8 PSP, 1 CBD, 8 MSA-P, 9 MSA-C, and 15 DLB retrospectively. Two sequential PET/CT scans were acquired at 5 min and 3 h. We compared subregional binding potentials, putamen-to-caudate nucleus ratio of the binding potential, asymmetry index, and degree of washout. To differentiate APS, all BPs in both early and late phases (except late BPbrainstem) and all factors of the percent change except for putamen in APS significantly differed from PD. When a cut-off for early BPcerebellum was set as 0.79, the sensitivity, specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and accuracy for differentiating APS 73.2%, 91.7%, 93.8%, 66.7%, and 80.0%. The early BPcerebellum showed significantly greater SP and PPV than the late quantitative indices. Combined criteria regarding both early and late indices exhibited only greater NPV. The quantitative indices showed high diagnostic performances in differentiating APS from PD. Our findings provide the dual-phase 18F-FP-CIT PET/CT would be useful for differentiating APS from PD. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Article
Cut-Out Towne-View Whole-Brain 320-Row Four-Dimensional Computed Tomography Angiography for Assessing the Anterior Intracranial Collateral Status: A Retrospective Study
Diagnostics 2022, 12(6), 1336; https://doi.org/10.3390/diagnostics12061336 - 27 May 2022
Viewed by 428
Abstract
Whole-brain four-dimensional computed tomography angiography (W4D-CTA) using a 320-row area detector CT (320r-ADCT) has been applied before thrombectomy. Endovascular physicians require images with high interrater reliability (IRR) for making appropriate decisions. However, the 320r-ADCT gantry cannot be tilted, and the patient’s head position [...] Read more.
Whole-brain four-dimensional computed tomography angiography (W4D-CTA) using a 320-row area detector CT (320r-ADCT) has been applied before thrombectomy. Endovascular physicians require images with high interrater reliability (IRR) for making appropriate decisions. However, the 320r-ADCT gantry cannot be tilted, and the patient’s head position influences the anteroposterior (AP)-view W4D-CTA images. This study aimed to determine which W4D-CTA images are appropriate pre-thrombectomy, whether the unedited AP view or cut-out Towne view. This study included the W4D-CTA images of acute stroke patients with occlusion of the internal carotid artery or the middle cerebral artery (MCA) from April to July 2021. Images produced by 320r-ADCT were transferred to a workstation. Unedited AP-view images were automatically generated. Towne-view images were cut out for this study. Collateral status was evaluated as poor, intermediate, or good based on the visualization of the MCA peripheral branches. In addition, the IRR was assessed using intraclass correlation coefficients (ICC) (2,1). Fifteen patients were analyzed. In the unedited AP-view and cut-out Towne-view W4D-CTA images, the ICC (2,1) were 0.147 and 0.796, respectively. Cut-out Towne-view W4D-CTA images with substantial IRR are superior to the unedited AP-view images for assessing the anterior intracranial collateral status. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Article
Clinical Evaluation of an Innovative Metal-Artifact-Reduction Algorithm in FD-CT Angiography in Cerebral Aneurysms Treated by Endovascular Coiling or Surgical Clipping
Diagnostics 2022, 12(5), 1140; https://doi.org/10.3390/diagnostics12051140 - 04 May 2022
Viewed by 378
Abstract
Treated cerebral aneurysms (IA) require follow-up imaging to ensure occlusion. Metal artifacts complicate radiologic assessment. Our aim was to evaluate an innovative metal-artifact-reduction (iMAR) algorithm for flat-detector computed tomography angiography (FD-CTA) regarding image quality (IQ) and detection of aneurysm residua/reperfusion in comparison to [...] Read more.
Treated cerebral aneurysms (IA) require follow-up imaging to ensure occlusion. Metal artifacts complicate radiologic assessment. Our aim was to evaluate an innovative metal-artifact-reduction (iMAR) algorithm for flat-detector computed tomography angiography (FD-CTA) regarding image quality (IQ) and detection of aneurysm residua/reperfusion in comparison to 2D digital subtraction angiography (DSA). Patients with IAs treated by endovascular coiling or clipping underwent both FD-CTA and DSA. FD-CTA datasets were postprocessed with/without iMAR algorithm (MAR+/MAR−). Evaluation of all FD-CTA and DSA datasets regarding qualitative (IQ, MAR) and quantitative (coil package diameter/CPD) parameters was performed. Aneurysm occlusion was assessed for each dataset and compared to DSA findings. In total, 40 IAs were analyzed (ncoiling = 24; nclipping = 16). All iMAR+ datasets demonstrated significantly better IQ (pIQ coiling < 0.0001; pIQ clipping < 0.0001). iMAR significantly reduced the metal-artifact burden but did not affect the CPD. iMAR significantly improved the detection of aneurysm residua/reperfusion with excellent agreement with DSA (naneurysm detection MAR+/MAR−/DSA = 22/1/26). The iMAR algorithm significantly improves IQ by effective reduction of metal artifacts in FD-CTA datasets. The proposed algorithm enables reliable detection of aneurysm residua/reperfusion with good agreement to DSA. Thus, iMAR can help to reduce the need for invasive follow-up in treated IAs. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Article
Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression
Diagnostics 2022, 12(2), 469; https://doi.org/10.3390/diagnostics12020469 - 12 Feb 2022
Viewed by 526
Abstract
We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left [...] Read more.
We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Article
Experimental Diagnostics of the Emotional State of Individuals Using External Stimuli and a Model of Neurocognitive Brain Activity
Diagnostics 2022, 12(1), 125; https://doi.org/10.3390/diagnostics12010125 - 06 Jan 2022
Viewed by 333
Abstract
In this paper, we study ways and methods to diagnose the emotional state of individuals using external audiovisual stimuli and heart telemetry tools. We apply a mathematical model of neurocognitive brain activity developed specifically for this study to interpret the experimental scheme and [...] Read more.
In this paper, we study ways and methods to diagnose the emotional state of individuals using external audiovisual stimuli and heart telemetry tools. We apply a mathematical model of neurocognitive brain activity developed specifically for this study to interpret the experimental scheme and its results. This experimental technique is based on monitoring and analyzing the dynamics of heart rate variability (HRV), taking into account the particular context and events occurring around the subject of the study. In addition, we provide a brief description of the theory of information images/representations used for the paradigm and interpretation of the experiment. For this study, we viewed the human mind as a one-dimensional potential hole with finite walls of different sizes and an internal potential barrier modeling the border between consciousness and subconsciousness. We also provided the foundations of the mathematical apparatus for this particular view. This experiment allowed us to identify the characteristic markers of influencing external stimuli, which form a foundation for diagnosing the emotional state of an individual. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Article
MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability
Diagnostics 2022, 12(1), 84; https://doi.org/10.3390/diagnostics12010084 - 30 Dec 2021
Viewed by 663
Abstract
Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability [...] Read more.
Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort (CamCAN, baseline: n = 613; mean 16-month follow-up: n = 245) and a chronic symptomatic TBI cohort (TEAM-TBI, baseline: n = 62; mean 6-month follow-up: n = 40). The MEG-derived neuroelectric measures were corrected for the empty-room contribution using a random forest classifier. The mean 16-month correlation between baseline and 16-month follow-up CamCAN measures was 0.67; test-retest reliability was markedly improved in this study compared with previous work. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index and was assessed via adjudication for six clinical syndromes: chronic pain, psychological health, and oculomotor, vestibular, cognitive, and sleep dysfunction. Linear classifiers constructed from the 136 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, p < 0.0003 for each, i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms and clinical syndromes. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful, i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Article
Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm
Diagnostics 2022, 12(1), 24; https://doi.org/10.3390/diagnostics12010024 - 23 Dec 2021
Viewed by 1054
Abstract
Pathological changes in the cortical lamina can cause several mental disorders. Visualization of these changes in vivo would enhance their diagnostics. Recently a framework for visualizing cortical structures by magnetic resonance imaging (MRI) has emerged. This is based on mathematical modeling of multi-component [...] Read more.
Pathological changes in the cortical lamina can cause several mental disorders. Visualization of these changes in vivo would enhance their diagnostics. Recently a framework for visualizing cortical structures by magnetic resonance imaging (MRI) has emerged. This is based on mathematical modeling of multi-component T1 relaxation at the sub-voxel level. This work proposes a new approach for their estimation. The approach is validated using simulated data. Sixteen MRI experiments were carried out on healthy volunteers. A modified echo-planar imaging (EPI) sequence was used to acquire 105 individual volumes. Data simulating the images were created, serving as the ground truth. The model was fitted to the data using a modified Trust Region algorithm. In single voxel experiments, the estimation accuracy of the T1 relaxation times depended on the number of optimization starting points and the level of noise. A single starting point resulted in a mean percentage error (MPE) of 6.1%, while 100 starting points resulted in a perfect fit. The MPE was <5% for the signal-to-noise ratio (SNR) ≥ 38 dB. Concerning multiple voxel experiments, the MPE was <5% for all components. Estimation of T1 relaxation times can be achieved using the modified algorithm with MPE < 5%. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Article
A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change
Diagnostics 2021, 11(12), 2281; https://doi.org/10.3390/diagnostics11122281 - 06 Dec 2021
Viewed by 696
Abstract
To evaluate single- and multiparametric MRI models to differentiate recurrent glioblastoma (GBM) and treatment-related changes (TRC) in clinical routine imaging. Selective and unselective apparent diffusion coefficient (ADC) and minimum, mean, and maximum cerebral blood volume (CBV) measurements in the lesion were performed. Minimum, [...] Read more.
To evaluate single- and multiparametric MRI models to differentiate recurrent glioblastoma (GBM) and treatment-related changes (TRC) in clinical routine imaging. Selective and unselective apparent diffusion coefficient (ADC) and minimum, mean, and maximum cerebral blood volume (CBV) measurements in the lesion were performed. Minimum, mean, and maximum ratiosCBV (CBVlesion to CBVhealthy white matter) were computed. All data were tested for lesion discrimination. A multiparametric model was compiled via multiple logistic regression using data demonstrating significant difference between GBM and TRC and tested for its diagnostic strength in an independent patient cohort. A total of 34 patients (17 patients with recurrent GBM and 17 patients with TRC) were included. ADC measurements showed no significant difference between both entities. All CBV and ratiosCBV measurements were significantly higher in patients with recurrent GBM than TRC. A minimum CBV of 8.5, mean CBV of 116.5, maximum CBV of 327 and ratioCBV minimum of 0.17, ratioCBV mean of 2.26 and ratioCBV maximum of 3.82 were computed as optimal cut-off values. By integrating these parameters in a multiparametric model and testing it in an independent patient cohort, 9 of 10 patients, i.e., 90%, were classified correctly. The multiparametric model further improves radiological discrimination of GBM from TRC in comparison to single-parameter approaches and enables reliable identification of recurrent tumors. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Article
Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
Diagnostics 2021, 11(11), 2103; https://doi.org/10.3390/diagnostics11112103 - 13 Nov 2021
Cited by 2 | Viewed by 969
Abstract
Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not [...] Read more.
Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Review

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Review
Vessel Wall Magnetic Resonance Imaging in Cerebrovascular Diseases
Diagnostics 2022, 12(2), 258; https://doi.org/10.3390/diagnostics12020258 - 20 Jan 2022
Cited by 2 | Viewed by 664
Abstract
Cerebrovascular diseases are a leading cause of disability and death worldwide. The definition of stroke etiology is mandatory to predict outcome and guide therapeutic decisions. The diagnosis of pathological processes involving intracranial arteries is especially challenging, and the visualization of intracranial arteries’ vessel [...] Read more.
Cerebrovascular diseases are a leading cause of disability and death worldwide. The definition of stroke etiology is mandatory to predict outcome and guide therapeutic decisions. The diagnosis of pathological processes involving intracranial arteries is especially challenging, and the visualization of intracranial arteries’ vessel walls is not possible with routine imaging techniques. Vessel wall magnetic resonance imaging (VW-MRI) uses high-resolution, multiparametric MRI sequences to directly visualize intracranial arteries walls and their pathological alterations, allowing a better characterization of their pathology. VW-MRI demonstrated a wide range of clinical applications in acute cerebrovascular disease. Above all, it can be of great utility in the differential diagnosis of atherosclerotic and non-atherosclerotic intracranial vasculopathies. Additionally, it can be useful in the risk stratification of intracranial atherosclerotic lesions and to assess the risk of rupture of intracranial aneurysms. Recent advances in MRI technology made it more available, but larger studies are still needed to maximize its use in daily clinical practice. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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