1
|
Huang H, Chen Z, Fan B, Huang D, Qiu Z, Luo C, Zheng J. Abnormal global and local connectivity in patients with anti-N-methyl-D-aspartate receptor encephalitis: A resting-state functional MRI study. Brain Res 2024; 1837:148985. [PMID: 38714228 DOI: 10.1016/j.brainres.2024.148985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/27/2024] [Accepted: 05/04/2024] [Indexed: 05/09/2024]
Abstract
OBJECTIVE We decided to investigate the changes of global and local connectivity in anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis patients based on eigenvector centrality (EC) and regional homogeneity (ReHo). We sought new biomarkers to identify the patients based on multivariate pattern analysis (MVPA). METHODS Functional MRI (fMRI) was performed on all participants. EC, ReHo and MVPA were used to analyze the fMRI images. The correlation between the global or local connectivity and neuropsychology tests was detected. RESULTS The MoCA scores of the patients were lower than those of the healthy controls (HCs), while the HAMD24 and HAMA scores of the patients were higher than those of the HCs. Increased EC values in the right calcarine (CAL.R) and decreased EC values in the right putamen (PUT.R) distinguished these subjects with anti-NMDAR encephalitis from HCs. The higher ReHo values in the left postcentral gyrus (PoCG.L) were detected in the patients. The correlation analysis showed that the EC values in the PUT.R were negatively correlated with HAMD24 and HAMA scores, while the ReHo values in the PoCG.L were negatively correlated with MoCA scores. Better classification performance was reached in the EC-based classifier (AUC = 0.80), while weaker classification performance was achieved in the ReHo-based classifier (AUC = 0.74) or the classifier based on EC and ReHo (AUC = 0.74). The brain areas with large weights were located in the frontal lobe, parietal lobe, cerebellum and basal ganglia. CONCLUSION Our findings suggest that abnormal global and local connectivity may play an important part in the pathophysiological mechanism of neuropsychiatric symptoms in the anti-NMDAR encephalitis patients. The EC-based classifier may be better than the ReHo-based classifier in identifying anti-NMDAR encephalitis patients.
Collapse
Affiliation(s)
- Huachun Huang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zexiang Chen
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Binglin Fan
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dongying Huang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhuoyan Qiu
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Cuimi Luo
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China.
| |
Collapse
|
2
|
Batta I, Abrol A, Calhoun VD. Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data. J Neurosci Methods 2024; 406:110109. [PMID: 38494061 PMCID: PMC11100582 DOI: 10.1016/j.jneumeth.2024.110109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 02/12/2024] [Accepted: 03/12/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces. NEW METHOD We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable. RESULTS Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis. COMPARISON WITH EXISTING METHOD(S) Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information. CONCLUSIONS As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD.
Collapse
Affiliation(s)
- Ishaan Batta
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA.
| | - Anees Abrol
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA
| | - Vince D Calhoun
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
| |
Collapse
|
3
|
Cheek CL, Lindner P, Grigorenko EL. Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods. Behav Genet 2024; 54:233-251. [PMID: 38336922 DOI: 10.1007/s10519-024-10177-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 01/24/2024] [Indexed: 02/12/2024]
Abstract
Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.
Collapse
Affiliation(s)
- Connor L Cheek
- Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA.
- Department of Physics, University of Houston, Houston, TX, USA.
| | - Peggy Lindner
- Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA
- Department of Information Science Technology, University of Houston, Houston, TX, USA
| | - Elena L Grigorenko
- Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA
- Department of Psychology, University of Houston, Houston, TX, USA
- Baylor College of Medicine, Houston, TX, USA
- Sirius University of Science and Technology, Sochi, Russia
| |
Collapse
|
4
|
Tong X, Xie H, Wu W, Keller CJ, Fonzo GA, Chidharom M, Carlisle NB, Etkin A, Zhang Y. Individual deviations from normative electroencephalographic connectivity predict antidepressant response. J Affect Disord 2024; 351:220-230. [PMID: 38281595 PMCID: PMC10923099 DOI: 10.1016/j.jad.2024.01.177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 01/15/2024] [Accepted: 01/18/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders. METHODS We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment. RESULTS We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses. CONCLUSIONS Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment. TRIAL REGISTRATION Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.
Collapse
Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA; George Washington University School of Medicine, Washington, DC, USA
| | - Wei Wu
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA; Veterans Affairs Palo Alto Healthcare System, Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, USA
| | - Gregory A Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | | | | | - Amit Etkin
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA; Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA.
| |
Collapse
|
5
|
Bayar Kapici O, Kapici Y, Tekın A, Şırık M. A novel diagnosis method for schizophrenia based on globus pallidus data. Psychiatry Res Neuroimaging 2023; 336:111732. [PMID: 37922672 DOI: 10.1016/j.pscychresns.2023.111732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/25/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023]
Abstract
This research aims to diagnose schizophrenia with machine learning-based algorithms. Bayesian neural network, logistic regression, decision tree, k-nearest neighbor, and gaussian kernel classification techniques are investigated to diagnose schizophrenia with data from 125 persons. This study showed that left lateral ventricles and left globus pallidus volumes and their percentages in the brain were significantly lower than HCs in FEP patients. Using brain volumes, we were able to diagnose FEP with an accuracy of 73.6 % via logistic regression and with an accuracy of 86.4 % using the SVM kernel classifier method. Therefore, brain volumes can be used to diagnose FEP with the SVM kernel classifier method.
Collapse
Affiliation(s)
- Olga Bayar Kapici
- Department of Radiology, Adıyaman Training and Research Hospital, Adıyaman, Turkey
| | - Yaşar Kapici
- Department of Psychiatry, Kahta State Hospital, Adıyaman, Turkey.
| | - Atilla Tekın
- Department of Psychiatry, Adıyaman University Faculty of Medicine, Adıyaman, Turkey
| | - Mehmet Şırık
- Department of Radiology, Adıyaman University Faculty of Medicine, Adıyaman, Turkey
| |
Collapse
|
6
|
Andrade SM, da Silva-Sauer L, de Carvalho CD, de Araújo ELM, Lima EDO, Fernandes FML, Moreira KLDAF, Camilo ME, Andrade LMMDS, Borges DT, da Silva Filho EM, Lindquist AR, Pegado R, Morya E, Yamauti SY, Alves NT, Fernández-Calvo B, de Souza Neto JMR. Identifying biomarkers for tDCS treatment response in Alzheimer's disease patients: a machine learning approach using resting-state EEG classification. Front Hum Neurosci 2023; 17:1234168. [PMID: 37859768 PMCID: PMC10582524 DOI: 10.3389/fnhum.2023.1234168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/18/2023] [Indexed: 10/21/2023] Open
Abstract
Background Transcranial direct current stimulation (tDCS) is a promising treatment for Alzheimer's Disease (AD). However, identifying objective biomarkers that can predict brain stimulation efficacy, remains a challenge. The primary aim of this investigation is to delineate the cerebral regions implicated in AD, taking into account the existing lacuna in comprehension of these regions. In pursuit of this objective, we have employed a supervised machine learning algorithm to prognosticate the neurophysiological outcomes resultant from the confluence of tDCS therapy plus cognitive intervention within both the cohort of responders and non-responders to antecedent tDCS treatment, stratified on the basis of antecedent cognitive outcomes. Methods The data were obtained through an interventional trial. The study recorded high-resolution electroencephalography (EEG) in 70 AD patients and analyzed spectral power density during a 6 min resting period with eyes open focusing on a fixed point. The cognitive response was assessed using the AD Assessment Scale-Cognitive Subscale. The training process was carried out through a Random Forest classifier, and the dataset was partitioned into K equally-partitioned subsamples. The model was iterated k times using K-1 subsamples as the training bench and the remaining subsample as validation data for testing the model. Results A clinical discriminating EEG biomarkers (features) was found. The ML model identified four brain regions that best predict the response to tDCS associated with cognitive intervention in AD patients. These regions included the channels: FC1, F8, CP5, Oz, and F7. Conclusion These findings suggest that resting-state EEG features can provide valuable information on the likelihood of cognitive response to tDCS plus cognitive intervention in AD patients. The identified brain regions may serve as potential biomarkers for predicting treatment response and maybe guide a patient-centered strategy. Clinical Trial Registration https://classic.clinicaltrials.gov/ct2/show/NCT02772185?term=NCT02772185&draw=2&rank=1, identifier ID: NCT02772185.
Collapse
Affiliation(s)
- Suellen Marinho Andrade
- Aging and Neuroscience Laboratory, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | - Leandro da Silva-Sauer
- Aging and Neuroscience Laboratory, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | | | | | - Eloise de Oliveira Lima
- Aging and Neuroscience Laboratory, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | - Fernanda Maria Lima Fernandes
- Center for Alternative and Renewable Energies (CEAR), Department of Electrical Engineering, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | | | - Maria Eduarda Camilo
- Laboratory of Ergonomics and Health, Department of Physiotherapy, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | | | - Daniel Tezoni Borges
- Department of Physiotherapy, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | | | - Ana Raquel Lindquist
- Department of Physiotherapy, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Rodrigo Pegado
- Department of Physiotherapy, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Edgard Morya
- Edmond and Lily Safra International Institute of Neurosciences (IIN-ELS), Macaíba, Rio Grande do Norte, Brazil
| | - Seidi Yonamine Yamauti
- Edmond and Lily Safra International Institute of Neurosciences (IIN-ELS), Macaíba, Rio Grande do Norte, Brazil
| | - Nelson Torro Alves
- Department of Psychology, Federal University of Paraíba, João Pessoa, Brazil
| | - Bernardino Fernández-Calvo
- Department of Psychology, Federal University of Paraíba, João Pessoa, Brazil
- Department of Psychology, Faculty of Educational Sciences and Psychology, University of Cordoba, Córdoba, Spain
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Córdoba, Spain
| | - José Maurício Ramos de Souza Neto
- Center for Alternative and Renewable Energies (CEAR), Department of Electrical Engineering, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| |
Collapse
|
7
|
Leming MJ, Bron EE, Bruffaerts R, Ou Y, Iglesias JE, Gollub RL, Im H. Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting. NPJ Digit Med 2023; 6:129. [PMID: 37443276 DOI: 10.1038/s41746-023-00868-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer's, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.
Collapse
Affiliation(s)
- Matthew J Leming
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, USA.
| | - Esther E Bron
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Rose Bruffaerts
- Computational Neurology, Experimental Neurobiology Unit (ENU), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | - Yangming Ou
- Boston Children's Hospital, 300 Longwood Ave, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Center for Medical Image Computing, University College London, London, UK
- Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Randy L Gollub
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hyungsoon Im
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, USA.
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
| |
Collapse
|
8
|
Tong X, Xie H, Wu W, Keller C, Fonzo G, Chidharom M, Carlisle N, Etkin A, Zhang Y. Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.24.23290434. [PMID: 37292874 PMCID: PMC10246152 DOI: 10.1101/2023.05.24.23290434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo. This modest efficacy is partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment - the approved antidepressants only benefit a portion of patients, calling for personalized psychiatry based on individual-level prediction of treatment responses. Normative modeling, a framework that quantifies individual deviations in psychopathological dimensions, offers a promising avenue for the personalized treatment for psychiatric disorders. In this study, we built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients. We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between treatment responses. Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective MDD treatment.
Collapse
Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA
- George Washington University School of Medicine, Washington, DC, USA
| | - Wei Wu
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Corey Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, USA
| | - Gregory Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | | | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | - Amit Etkin
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
| |
Collapse
|
9
|
Lim J, Kim S, Lee C, Park J, Yang G, Yook T. Verbenalin Reduces Amyloid-Beta Peptide Generation in Cellular and Animal Models of Alzheimer's Disease. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27248678. [PMID: 36557811 PMCID: PMC9781082 DOI: 10.3390/molecules27248678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/27/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022]
Abstract
Verbenalin, among the major constituents of Verbena officinalis, has been reported to exhibit sleep-promoting and antioxidant activities. This study demonstrates the effects of verbenalin on amyloid-beta (Aβ) peptide generation in Swedish mutant amyloid precursor protein (APP)-overexpressing Neuro2a cells (SweAPP/N2a) and in Alzheimer's disease (AD) animal models. We further performed molecular biological analyses of these in vitro and in vivo models of AD. The effects of verbenalin were assessed based on the expression of factors related to Aβ peptide production using Western blotting, enzyme-linked immunosorbent assay, and immunohistochemistry (IHC). The intracellular expression and release of APP protein were both decreased by verbenalin treatment in SweAPP/N2a cells. Thus, the production of Aβ peptides was decreased. Compared to those in AD transgenic (Tg) mice, IHC revealed that verbenalin-treated animals showed decreased Aβ and tau expression levels in the hippocampus. In addition, verbenalin restored the expression of brain-derived neurotrophic factor (BDNF) in the hippocampus of AD animal models. These findings suggest that verbenalin may decrease Aβ formation both in vitro and in vivo. Verbenalin may also help improve the pathological hallmarks of AD.
Collapse
Affiliation(s)
- Juhee Lim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Woosuk University, Wanju 55338, Republic of Korea
| | - Seokhee Kim
- College of Korean Medicine, Woosuk University, Jeonju 54986, Republic of Korea
| | - Changhyun Lee
- College of Korean Medicine, Woosuk University, Jeonju 54986, Republic of Korea
| | - Jeongwoo Park
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Gabsik Yang
- College of Korean Medicine, Woosuk University, Jeonju 54986, Republic of Korea
- Correspondence: (G.Y.); (T.Y.); Tel.: +82-63-220-8625 (T.Y.); Fax: +82-63-220-8400 (T.Y.)
| | - Taehan Yook
- College of Korean Medicine, Woosuk University, Jeonju 54986, Republic of Korea
- Correspondence: (G.Y.); (T.Y.); Tel.: +82-63-220-8625 (T.Y.); Fax: +82-63-220-8400 (T.Y.)
| |
Collapse
|
10
|
Lee DH, Kwon BI, Yu JS, Park SK, Kim JH. Neural mechanisms underlying peripheral facial nerve palsy: A protocol for systematic review and meta-analysis of neuroimaging studies. Medicine (Baltimore) 2022; 101:e32110. [PMID: 36482522 PMCID: PMC9726285 DOI: 10.1097/md.0000000000032110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Peripheral facial nerve palsy is a general term used to describe diseases that cause lower motor neuron lesions in the facial nerve. The most common cause of peripheral facial nerve palsy is idiopathic Bell's palsy (BP), accounting for 60% to 70% of all peripheral facial nerve palsy cases. Idiopathic BP is found in various medical conditions, such as ischemia, viral infection, and neuroinflammation; however, the specific pathophysiology remains unclear. Neuroimaging techniques help understand the neural mechanisms underlying various neurological disorders. In recent years, several studies have applied functional neuroimaging to investigate alterations of brain function in BP patients. However, the results have been inconsistent due to differences in methodology and quality. Moreover, systematic reviews of studies on functional neuroimaging for BP are insufficient. Therefore, the present study aims to verify the neural mechanisms of brain alterations and draw conclusions about the functional reorganization in patients with BP. METHODS This review includes all suitable studies published on or before October 31, 2022. A thorough search will be conducted using the following databases: MEDLINE, Cochrane Library, Excerpta Medica Database (EMBASE), China Knowledge Resource Integrated Database (CNKI), Korean Medical database (KMBASE), Korean Studies Information Service System (KISS). Clinical studies of BP using functional neuroimaging will be selected. We will apply a coordinate-based meta-analysis because most individual neuroimaging studies provide their results as coordinates in the standard space. The primary outcomes will include the types of functional neuroimaging methods and alterations of brain function in BP patients. The secondary outcomes will include the information about clinical measurement of the disease. Study selection, data extraction, and risk of bias assessment will be conducted. If possible, heterogeneity tests, data synthesis, and subgroup analyses will be conducted. RESULTS The study will analyze the alterations in brain activity and worsening of clinical symptoms caused by idiopathic BP. CONCLUSION The aim of this study is to investigate functional reorganization of brain alterations in patients with BP. This review will improve the understanding of the neural mechanisms of BP based on the most recent publications through extensive data retrieval. If sufficient data are collected, a sensitivity analysis is performed to verify the robustness of the conclusions.
Collapse
Affiliation(s)
- Dong Hyuk Lee
- Department of Anatomy, College of Korean Medicine, Sangji University, Wonju-si, Gangwon-do, Republic of Korea
- Research institute of Korean medicine, Sangji University, Wonju-si, Gangwon-do, Republic of Korea
| | - Bo-In Kwon
- Research institute of Korean medicine, Sangji University, Wonju-si, Gangwon-do, Republic of Korea
- Department of Pathology, College of Korean Medicine, Sangji University, Wonju-si, Gangwon-do, Republic of Korea
| | - Jun-Sang Yu
- Research institute of Korean medicine, Sangji University, Wonju-si, Gangwon-do, Republic of Korea
- Department of Sasang Constitutional Medicine, College of Korean Medicine, Sangji University, Wonju-si, Gangwon-do, Republic of Korea
| | - Sang Kyun Park
- Department of Meridian & Acupoints, College of Korean Medicine, Sangji University, Wonju-si, Gangwon-do, Republic of Korea
| | - Joo-Hee Kim
- Research institute of Korean medicine, Sangji University, Wonju-si, Gangwon-do, Republic of Korea
- Department of Acupuncture and Moxibustion Medicine, College of Korean Medicine, Sangji University, Wonju-si, Gangwon-do, Republic of Korea
- * Correspondence: Joo-Hee Kim, Department of Acupuncture and Moxibustion Medicine, College of Korean Medicine, Sangji University, 83 Sangjidae-gil, Wonju-si, Gangwon-do 26339, Republic of Korea (e-mail: )
| |
Collapse
|
11
|
Longitudinal changes in grey matter and cognitive performance over four years of healthy aging. NEUROIMAGE: REPORTS 2022. [DOI: 10.1016/j.ynirp.2022.100140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
12
|
Automatic detection of Alzheimer’s disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
13
|
Dong A, Zhang G, Liu J, Wei Z. Latent feature representation learning for Alzheimer's disease classification. Comput Biol Med 2022; 150:106116. [PMID: 36215848 DOI: 10.1016/j.compbiomed.2022.106116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/18/2022] [Accepted: 09/17/2022] [Indexed: 11/03/2022]
Abstract
Early detection and treatment of Alzheimer's Disease (AD) are significant. Recently, multi-modality imaging data have promoted the development of the automatic diagnosis of AD. This paper proposes a method based on latent feature fusion to make full use of multi-modality image data information. Specifically, we learn a specific projection matrix for each modality by introducing a binary label matrix and local geometry constraints and then project the original features of each modality into a low-dimensional target space. In this space, we fuse latent feature representations of different modalities for AD classification. The experimental results on Alzheimer's Disease Neuroimaging Initiative database demonstrate the proposed methods effectiveness in classifying AD.
Collapse
Affiliation(s)
- Aimei Dong
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
| | - Guodong Zhang
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
| | - Jian Liu
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
| | - Zhonghe Wei
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
| |
Collapse
|
14
|
Li P, Quan W, Wang Z, Liu Y, Cai H, Chen Y, Wang Y, Zhang M, Tian Z, Zhang H, Zhou Y. Early-stage differentiation between Alzheimer's disease and frontotemporal lobe degeneration: Clinical, neuropsychology, and neuroimaging features. Front Aging Neurosci 2022; 14:981451. [PMID: 36389060 PMCID: PMC9659748 DOI: 10.3389/fnagi.2022.981451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 10/10/2022] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) are the two most common forms of neurodegenerative dementia. Although both of them have well-established diagnostic criteria, achieving early diagnosis remains challenging. Here, we aimed to make the differential diagnosis of AD and FTLD from clinical, neuropsychological, and neuroimaging features. MATERIALS AND METHODS In this retrospective study, we selected 95 patients with PET-CT defined AD and 106 patients with PET-CT/biomarker-defined FTLD. We performed structured chart examination to collect clinical data and ascertain clinical features. A series of neuropsychological scales were used to assess the neuropsychological characteristics of patients. Automatic tissue segmentation of brain by Dr. Brain tool was used to collect multi-parameter volumetric measurements from different brain areas. All patients' structural neuroimage data were analyzed to obtain brain structure and white matter hyperintensities (WMH) quantitative data. RESULTS The prevalence of vascular disease associated factors was higher in AD patients than that in FTLD group. 56.84% of patients with AD carried at least one APOE ε4 allele, which is much high than that in FTLD patients. The first symptoms of AD patients were mostly cognitive impairment rather than behavioral abnormalities. In contrast, behavioral abnormalities were the prominent early manifestations of FTLD, and few patients may be accompanied by memory impairment and motor symptoms. In direct comparison, patients with AD had slightly more posterior lesions and less frontal atrophy, whereas patients with FTLD had more frontotemporal atrophy and less posterior lesions. The WMH burden of AD was significantly higher, especially in cortical areas, while the WMH burden of FTLD was higher in periventricular areas. CONCLUSION These results indicate that dynamic evaluation of cognitive function, behavioral and psychological symptoms, and multimodal neuroimaging are helpful for the early diagnosis and differentiation between AD and FTLD.
Collapse
Affiliation(s)
- Pan Li
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Wei Quan
- Department of Neurosurgery, General Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Injuries, Variations and Regeneration of Nervous System, Tianjin Neurological Institute, Tianjin, China
| | - Zengguang Wang
- Department of Neurosurgery, General Hospital of Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Injuries, Variations and Regeneration of Nervous System, Tianjin Neurological Institute, Tianjin, China
| | - Ying Liu
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Hao Cai
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Yuan Chen
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Yan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Miao Zhang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Zhiyan Tian
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Huihong Zhang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
- Department of Neurology, Tianjin Huanhu Hospital Affiliated to Tianjin Medical University, Tianjin Huanhu Hospital Affiliated to Nankai University, Tianjin University Huanhu Hospital, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgery Institute, Tianjin Huanhu Hospital, Tianjin, China
| |
Collapse
|
15
|
Tong X, Xie H, Carlisle N, Fonzo GA, Oathes DJ, Jiang J, Zhang Y. Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity. Transl Psychiatry 2022; 12:367. [PMID: 36068228 PMCID: PMC9448815 DOI: 10.1038/s41398-022-02134-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 08/18/2022] [Accepted: 08/22/2022] [Indexed: 11/22/2022] Open
Abstract
Medication and other therapies for psychiatric disorders show unsatisfying efficacy, in part due to the significant clinical/ biological heterogeneity within each disorder and our over-reliance on categorical clinical diagnoses. Alternatively, dimensional transdiagnostic studies have provided a promising pathway toward realizing personalized medicine and improved treatment outcomes. One factor that may influence response to psychiatric treatments is cognitive function, which is reflected in one's intellectual capacity. Intellectual capacity is also reflected in the organization and structure of intrinsic brain networks. Using a large transdiagnostic cohort (n = 1721), we sought to discover neuroimaging biomarkers by developing a resting-state functional connectome-based prediction model for a key intellectual capacity measure, Full-Scale Intelligence Quotient (FSIQ), across the diagnostic spectrum. Our cross-validated model yielded an excellent prediction accuracy (r = 0.5573, p < 0.001). The robustness and generalizability of our model was further validated on three independent cohorts (n = 2641). We identified key transdiagnostic connectome signatures underlying FSIQ capacity involving the dorsal-attention, frontoparietal and default-mode networks. Meanwhile, diagnosis groups showed disorder-specific biomarker patterns. Our findings advance the neurobiological understanding of cognitive functioning across traditional diagnostic categories and provide a new avenue for neuropathological classification of psychiatric disorders.
Collapse
Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | - Gregory A Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jing Jiang
- Departments of Pediatrics and Psychiatry, Carver College of Medicine, University of Iowa, Iowa, IA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
| |
Collapse
|
16
|
Ran X, Shi J, Chen Y, Jiang K. Multimodal neuroimage data fusion based on multikernel learning in personalized medicine. Front Pharmacol 2022; 13:947657. [PMID: 36059988 PMCID: PMC9428611 DOI: 10.3389/fphar.2022.947657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging has been widely used as a diagnostic technique for brain diseases. With the development of artificial intelligence, neuroimaging analysis using intelligent algorithms can capture more image feature patterns than artificial experience-based diagnosis. However, using only single neuroimaging techniques, e.g., magnetic resonance imaging, may omit some significant patterns that may have high relevance to the clinical target. Therefore, so far, combining different types of neuroimaging techniques that provide multimodal data for joint diagnosis has received extensive attention and research in the area of personalized medicine. In this study, based on the regularized label relaxation linear regression model, we propose a multikernel version for multimodal data fusion. The proposed method inherits the merits of the regularized label relaxation linear regression model and also has its own superiority. It can explore complementary patterns across different modal data and pay more attention to the modal data that have more significant patterns. In the experimental study, the proposed method is evaluated in the scenario of Alzheimer’s disease diagnosis. The promising performance indicates that the performance of multimodality fusion via multikernel learning is better than that of single modality. Moreover, the decreased square difference between training and testing performance indicates that overfitting is reduced and hence the generalization ability is improved.
Collapse
|
17
|
Qin Y, Cui J, Ge X, Tian Y, Han H, Fan Z, Liu L, Luo Y, Yu H. Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features. Front Aging Neurosci 2022; 14:935055. [PMID: 36034132 PMCID: PMC9399682 DOI: 10.3389/fnagi.2022.935055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 07/22/2022] [Indexed: 11/13/2022] Open
Abstract
Due to the clinical continuum of Alzheimer’s disease (AD), the accuracy of early diagnostic remains unsatisfactory and warrants further research. The objectives of this study were: (1) to develop an effective hierarchical multi-class framework for clinical populations, namely, normal cognition (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD, and (2) to explore the geometric properties of cognition-related anatomical structures in the cerebral cortex. A total of 1,670 participants were enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, comprising 985 participants (314 NC, 208 EMCI, 258 LMCI, and 205 AD) in the model development set and 685 participants (417 NC, 110 EMCI, 83 LMCI, and 75 AD) after 2017 in the temporal validation set. Four cortical geometric properties for 148 anatomical structures were extracted, namely, cortical thickness (CTh), fractal dimension (FD), gyrification index (GI), and sulcus depth (SD). By integrating these imaging features with Mini-Mental State Examination (MMSE) scores at four-time points after the initial visit, we identified an optimal subset of 40 imaging features using the temporally constrained group sparse learning method. The combination of selected imaging features and clinical variables improved the multi-class performance using the AdaBoost algorithm, with overall accuracy rates of 0.877 in the temporal validation set. Clinical Dementia Rating (CDR) was the primary clinical variable associated with AD-related populations. The most discriminative imaging features included the bilateral CTh of the dorsal part of the posterior cingulate gyrus, parahippocampal gyrus (PHG), parahippocampal part of the medial occipito-temporal gyrus, and angular gyrus, the GI of the left inferior segment of the insula circular sulcus, and the CTh and SD of the left superior temporal sulcus (STS). Our hierarchical multi-class framework underscores the utility of combining cognitive variables with imaging features and the reliability of surface-based morphometry, facilitating more accurate early diagnosis of AD in clinical practice.
Collapse
Affiliation(s)
- Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jing Cui
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Xiaoyan Ge
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yuling Tian
- Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Hongjuan Han
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Zhao Fan
- Center of Translational Medicine, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China
| | - Long Liu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yanhong Luo
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
- *Correspondence: Hongmei Yu,
| |
Collapse
|
18
|
Batta I, Abrol A, Calhoun VD, the Alzheimer’s Disease Neuroimaging Initiative. SVR-based Multimodal Active Subspace Analysis for the Brain using Neuroimaging Data.. [DOI: 10.1101/2022.07.28.501879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
ABSTRACTUnderstanding the patterns of changes in brain function and structure due to various disorders and diseases is of utmost importance. There have been numerous efforts toward successful biomarker discovery for complex brain disorders by evaluating neuroimaging datasets with novel analytical frameworks. However, due to the multi-faceted nature of the disorders involving a wide and overlapping range of symptoms as well as complex changes in structural and functional brain networks, it is increasingly important to devise computational frameworks that can consider the underlying patterns of heterogeneous changes with specific target assessments, at the same time producing a summarizing output from the high-dimensional neuroimaging data. While various machine learning approaches focus on diagnostic prediction, many learning frameworks analyze important features at the level of brain regions involved in prediction using supervised methods. Unsupervised learning methods have also been utilized to break down the neuroimaging features into lower dimensional components. However, most learning frameworks either do not consider the target assessment information while extracting brain subspaces, or can extract only higher dimensional importance associations as an ordered list of involved features, making manual interpretation at the level of subspaces difficult. We present a novel multimodal active subspace learning framework to understand various subspaces within the brain that are associated with changes in particular biological and cognitive traits. For a given cognitive or biological trait, our framework performs a decomposition of the feature importances to extract robust multimodal subspaces that define the most significant change in the given trait. Through a rigorous cross-validation procedure on an Alzheimer’s disease (AD) dataset, we show that our framework can extract subspaces covering both functional and structural modalities, which are specific to a given clinical assessment (like memory and other cognitive skills) and also retain predictive performance in standard machine learning algorithms. We show that our framework not only uncovers AD-related brain regions (e.g., hippocampus, entorhinal cortex) in the associated brain subspaces, but also enables an automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and cognitive skill proficiency related to brain disorders like AD.
Collapse
|
19
|
Erdoğan SB, Yükselen G. Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers. SENSORS (BASEL, SWITZERLAND) 2022; 22:5407. [PMID: 35891088 PMCID: PMC9322944 DOI: 10.3390/s22145407] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.
Collapse
|
20
|
Pavel DG, Henderson TA, DeBruin S, Cohen PF. The Legacy of the TTASAAN Report - Premature Conclusions and Forgotten Promises About SPECT Neuroimaging: A Review of Policy and Practice Part II. Front Neurol 2022; 13:851609. [PMID: 35655621 PMCID: PMC9152128 DOI: 10.3389/fneur.2022.851609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 02/28/2022] [Indexed: 11/29/2022] Open
Abstract
Brain perfusion single photon emission computed tomography (SPECT) scans were initially developed in 1970s. A key radiopharmaceutical, hexamethylpropyleneamine oxime (HMPAO), was not stabilized until 1993 and most early SPECT scans were performed on single-head gamma cameras. These early scans were of inferior quality. In 1996, the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology (TTASAAN) issued a report regarding the use of SPECT in the evaluation of neurological disorders. This two-part series explores the policies and procedures related to perfusion SPECT functional neuroimaging. In Part I, the comparison between the quality of the SPECT scans and the depth of the data for key neurological and psychiatric indications at the time of the TTASAAN report vs. the intervening 25 years were presented. In Part II, the technical aspects of perfusion SPECT neuroimaging and image processing will be explored. The role of color scales will be reviewed and the process of interpreting a SPECT scan will be presented. Interpretation of a functional brain scans requires not only anatomical knowledge, but also technical understanding on correctly performing a scan, regardless of the scanning modality. Awareness of technical limitations allows the clinician to properly interpret a functional brain scan. With this foundation, four scenarios in which perfusion SPECT neuroimaging, together with other imaging modalities and testing, lead to a narrowing of the differential diagnoses and better treatment. Lastly, recommendations for the revision of current policies and practices are made.
Collapse
Affiliation(s)
- Dan G Pavel
- PathFinder Brain SPECT, Deerfield, IL, United States.,The International Society of Applied Neuroimaging (ISAN), Denver, CO, United States
| | - Theodore A Henderson
- The International Society of Applied Neuroimaging (ISAN), Denver, CO, United States.,The Synaptic Space, Inc., Denver, CO, United States.,Neuro-Luminance, Inc., Denver, CO, United States.,Dr. Theodore Henderson, Inc., Denver, CO, United States.,Neuro-Laser Foundation, Denver, CO, United States
| | - Simon DeBruin
- The International Society of Applied Neuroimaging (ISAN), Denver, CO, United States.,Good Lion Imaging, Baltimore, MD, United States
| | - Philip F Cohen
- The International Society of Applied Neuroimaging (ISAN), Denver, CO, United States.,Nuclear Medicine, Lions Gate Hospital, Vancouver, BC, Canada.,Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
21
|
El-Sappagh S, Saleh H, Ali F, Amer E, Abuhmed T. Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07263-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
22
|
Senthilkumar T, Kumarganesh S, Sivakumar P, Periyarselvam K. Primitive detection of Alzheimer’s disease using neuroimaging: A progression model for Alzheimer’s disease: Their applications, benefits, and drawbacks. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Alzheimer’s disease (A.D.) is the most widespread type of Dementia, and it is not a curable neurodegenerative disease that affects millions of older people. Researchers were able to use their understanding of Alzheimer’s disease risk variables to develop enrichment processes for longitudinal imaging studies. Using this method, they reduced their sample size and study time. This paper describes the primitive detective of Alzheimer’s diseases using Neuroimaging techniques. Several preprocessing methods were used to ensure that the dataset was ready for subsequent feature extraction and categorization. The noise was reduced by converting and averaging many scan frames from real to DCT space. Both sides of the averaged image were filtered and combined into a single shot after being converted to real space. InceptionV3 and DenseNet201 are two pre-trained models used in the suggested model. The PCA approach was used to select the traits, and the resulting explained variance ratio was 0.99The Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC)—and UCI machine learning datasets showed that our method is faster and more successful at identifying complete long-risk patterns when compared to existing methods.
Collapse
Affiliation(s)
- T. Senthilkumar
- GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India
| | | | - P. Sivakumar
- GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India
| | - K. Periyarselvam
- GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India
| |
Collapse
|
23
|
Niu H, Li W, Wang G, Hu Q, Hao R, Li T, Zhang F, Cheng T. Performances of whole-brain dynamic and static functional connectivity fingerprinting in machine learning-based classification of major depressive disorder. Front Psychiatry 2022; 13:973921. [PMID: 35958666 PMCID: PMC9360427 DOI: 10.3389/fpsyt.2022.973921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Alterations in static and dynamic functional connectivity during resting state have been widely reported in major depressive disorder (MDD). The objective of this study was to compare the performances of whole-brain dynamic and static functional connectivity combined with machine learning approach in differentiating MDD patients from healthy controls at the individual subject level. Given the dynamic nature of brain activity, we hypothesized that dynamic connectivity would outperform static connectivity in the classification. METHODS Seventy-one MDD patients and seventy-one well-matched healthy controls underwent resting-state functional magnetic resonance imaging scans. Whole-brain dynamic and static functional connectivity patterns were calculated and utilized as classification features. Linear kernel support vector machine was employed to design the classifier and a leave-one-out cross-validation strategy was used to assess classifier performance. RESULTS Experimental results of dynamic functional connectivity-based classification showed that MDD patients could be discriminated from healthy controls with an excellent accuracy of 100% irrespective of whether or not global signal regression (GSR) was performed (permutation test with P < 0.0002). Brain regions with the most discriminating dynamic connectivity were mainly and reliably located within the default mode network, cerebellum, and subcortical network. In contrast, the static functional connectivity-based classifiers exhibited unstable classification performances, i.e., a low accuracy of 38.0% without GSR (P = 0.9926) while a high accuracy of 96.5% with GSR (P < 0.0002); moreover, there was a considerable variability in the distribution of brain regions with static connectivity most informative for classification. CONCLUSION These findings suggest the superiority of dynamic functional connectivity in machine learning-based classification of depression, which may be helpful for a better understanding of the neural basis of MDD as well as for the development of effective computer-aided diagnosis tools in clinical settings.
Collapse
Affiliation(s)
- Heng Niu
- Department of MRI, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Weirong Li
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Guiquan Wang
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Qiong Hu
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Rui Hao
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Tianliang Li
- Department of Ultrasound, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Fan Zhang
- Department of Medical Imaging, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China
| | - Tao Cheng
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| |
Collapse
|
24
|
Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc6010002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Neuroimaging refers to the techniques that provide efficient information about the neural structure of the human brain, which is utilized for diagnosis, treatment, and scientific research. The problem of classifying neuroimages is one of the most important steps that are needed by medical staff to diagnose their patients early by investigating the indicators of different neuroimaging types. Early diagnosis of Alzheimer’s disease is of great importance in preventing the deterioration of the patient’s situation. In this research, a novel approach was devised based on a digital subtracted angiogram scan that provides sufficient features of a new biomarker cerebral blood flow. The used dataset was acquired from the database of K.A.U.H hospital and contains digital subtracted angiograms of participants who were diagnosed with Alzheimer’s disease, besides samples of normal controls. Since each scan included multiple frames for the left and right ICA’s, pre-processing steps were applied to make the dataset prepared for the next stages of feature extraction and classification. The multiple frames of scans transformed from real space into DCT space and averaged to remove noises. Then, the averaged image was transformed back to the real space, and both sides filtered with Meijering and concatenated in a single image. The proposed model extracts the features using different pre-trained models: InceptionV3 and DenseNet201. Then, the PCA method was utilized to select the features with 0.99 explained variance ratio, where the combination of selected features from both pre-trained models is fed into machine learning classifiers. Overall, the obtained experimental results are at least as good as other state-of-the-art approaches in the literature and more efficient according to the recent medical standards with a 99.14% level of accuracy, considering the difference in dataset samples and the used cerebral blood flow biomarker.
Collapse
|
25
|
Probing depression, schizophrenia, and other psychiatric disorders using fNIRS and the verbal fluency test: A systematic review and meta-analysis. J Psychiatr Res 2021; 140:416-435. [PMID: 34146793 DOI: 10.1016/j.jpsychires.2021.06.015] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/07/2021] [Accepted: 06/09/2021] [Indexed: 11/21/2022]
Abstract
Accessible neuroimaging tools that can identify specific frontal lobe dysfunction associated with psychiatric disorders could be useful for improving disease diagnosis and prognosis and treatment development. Functional near-infrared spectroscopy (fNIRS), in conjunction with the verbal fluency test (VFT), has emerged as an inexpensive and convenient method for understanding psychiatric disorders. However, questions remain regarding the specificity and uniqueness of fNIRS measurements for different disorders and the soundness of the methods applied previously. To address these knowledge gaps, we conducted a systematic review and meta-analysis of fNIRS studies using the VFT to probe psychiatric disorders. A literature search was conducted using PubMed and PsycINFO on October 27, 2020. Overall, 82% and 49% of the 121 included studies reported significantly reduced changes in oxyhemoglobin concentrations (HbO) and significantly fewer produced words during the VFT in psychiatric patients compared with healthy controls, respectively. For most psychiatric disorders, changes in HbO are more sensitive than changes in deoxyhemoglobin concentrations and VFT performance to detect psychopathologies. In addition, meta-analyses based on the proportion of channels that exhibited significant differences in HbO changes between patients and controls and on the effect sizes of group differences consistently showed that for major depression and schizophrenia, hypoactivation could be found across the frontotemporal regions, but its topographical distribution is disorder-specific. Thus, the fNIRS-VFT paradigm holds promise for understanding, detecting, and differentiating psychiatric disorders, and has the potential for developing accessible neuroimaging biomarkers for different psychiatric disorders. The findings are discussed with regard to the strengths and weaknesses of the applied methods, following by recommendations.
Collapse
|
26
|
Ahammed MS, Niu S, Ahmed MR, Dong J, Gao X, Chen Y. DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network. Front Neuroinform 2021; 15:635657. [PMID: 34248531 PMCID: PMC8265393 DOI: 10.3389/fninf.2021.635657] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 04/26/2021] [Indexed: 12/17/2022] Open
Abstract
Non-invasive whole-brain scans aid the diagnosis of neuropsychiatric disorder diseases such as autism, dementia, and brain cancer. The assessable analysis for autism spectrum disorders (ASD) is rationally challenging due to the limitations of publicly available datasets. For diagnostic or prognostic tools, functional Magnetic Resonance Imaging (fMRI) exposed affirmation to the biomarkers in neuroimaging research because of fMRI pickup inherent connectivity between the brain and regions. There are profound studies in ASD with introducing machine learning or deep learning methods that have manifested advanced steps for ASD predictions based on fMRI data. However, utmost antecedent models have an inadequacy in their capacity to manipulate performance metrics such as accuracy, precision, recall, and F1-score. To overcome these problems, we proposed an avant-garde DarkASDNet, which has the competence to extract features from a lower level to a higher level and bring out promising results. In this work, we considered 3D fMRI data to predict binary classification between ASD and typical control (TC). Firstly, we pre-processed the 3D fMRI data by adopting proper slice time correction and normalization. Then, we introduced a novel DarkASDNet which surpassed the benchmark accuracy for the classification of ASD. Our model's outcomes unveil that our proposed method established state-of-the-art accuracy of 94.70% to classify ASD vs. TC in ABIDE-I, NYU dataset. Finally, we contemplated our model by performing evaluation metrics including precision, recall, F1-score, ROC curve, and AUC score, and legitimize by distinguishing with recent literature descriptions to vindicate our outcomes. The proposed DarkASDNet architecture provides a novel benchmark approach for ASD classification using fMRI processed data.
Collapse
Affiliation(s)
- Md Shale Ahammed
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Sijie Niu
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | | | - Jiwen Dong
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Xizhan Gao
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Yuehui Chen
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| |
Collapse
|
27
|
Bron EE, Klein S, Papma JM, Jiskoot LC, Venkatraghavan V, Linders J, Aalten P, De Deyn PP, Biessels GJ, Claassen JAHR, Middelkoop HAM, Smits M, Niessen WJ, van Swieten JC, van der Flier WM, Ramakers IHGB, van der Lugt A. Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease. NEUROIMAGE-CLINICAL 2021; 31:102712. [PMID: 34118592 PMCID: PMC8203808 DOI: 10.1016/j.nicl.2021.102712] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 11/18/2022]
Abstract
This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01). Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.
Collapse
Affiliation(s)
- Esther E Bron
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Janne M Papma
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Lize C Jiskoot
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Vikram Venkatraghavan
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Jara Linders
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Pauline Aalten
- Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Peter Paul De Deyn
- Department of Neurology and Alzheimer Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Huub A M Middelkoop
- Department of Neurology & Neuropsychology, Leiden University Medical Center, Leiden, The Netherlands; Institute of Psychology, Health, Medical and Neuropsychology Unit, Leiden University, The Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Imaging Physics, Applied Sciences, Delft University of Technology, The Netherlands
| | | | | | - Inez H G B Ramakers
- Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| |
Collapse
|
28
|
Taxali A, Angstadt M, Rutherford S, Sripada C. Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling. Cereb Cortex 2021; 31:2822-2833. [PMID: 33447841 PMCID: PMC8599720 DOI: 10.1093/cercor/bhaa390] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 11/08/2020] [Accepted: 11/08/2020] [Indexed: 08/17/2023] Open
Abstract
Recent studies found low test-retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the "good" range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.
Collapse
Affiliation(s)
- Aman Taxali
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Saige Rutherford
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
29
|
Ebrahimi A, Luo S. Convolutional neural networks for Alzheimer's disease detection on MRI images. J Med Imaging (Bellingham) 2021; 8:024503. [PMID: 33937437 DOI: 10.1117/1.jmi.8.2.024503] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 04/13/2021] [Indexed: 01/17/2023] Open
Abstract
Purpose: Detection of Alzheimer's disease (AD) on magnetic resonance imaging (MRI) using convolutional neural networks (CNNs), which is useful for detecting AD in its preliminary states. Approach: Our study implements and compares several deep models and configurations, including two-dimensional (2D) and three-dimensional (3D) CNNs and recurrent neural networks (RNNs). To use a 2D CNN on 3D MRI volumes, each MRI scan is split into 2D slices, neglecting the connection among 2D image slices in an MRI volume. Instead, a CNN model could be followed by an RNN in a way that the model of 2D CNN + RNN can understand the connection among sequences of 2D image slices for an MRI. The issue is that the feature extraction step in the 2D CNN is independent of classification in the RNN. To tackle this, 3D CNNs can be employed instead of 2D CNNs to make voxel-based decisions. Our study's main contribution is to introduce transfer learning from a dataset of 2D images to 3D CNNs. Results: The results on our MRI dataset indicate that sequence-based decisions improve the accuracy of slice-based decisions by 2% in classifying AD patients from healthy subjects. Also the 3D voxel-based method with transfer learning outperforms the other methods with 96.88% accuracy, 100% sensitivity, and 94.12% specificity. Conclusions: Several implementations and experiments using CNNs on MRI scans for AD detection demonstrated that the voxel-based method with transfer learning from ImageNet to MRI datasets using 3D CNNs considerably improved the results compared with the others.
Collapse
Affiliation(s)
- Amir Ebrahimi
- The University of Newcastle, School of Electrical Engineering and Computing, Callaghan, New South Wales, Australia
| | - Suhuai Luo
- The University of Newcastle, School of Electrical Engineering and Computing, Callaghan, New South Wales, Australia
| | | |
Collapse
|
30
|
Lamanna C. Task-sharing with artificial intelligence: a design hypothesis for an Emergency Unit in sub-Saharan Africa. Pan Afr Med J 2021; 38:387. [PMID: 34381531 PMCID: PMC8325458 DOI: 10.11604/pamj.2021.38.387.20557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Accepted: 04/08/2021] [Indexed: 11/30/2022] Open
Abstract
In sub-Saharan Africa, there is a significant unmet need for emergency care, with a shortage of trained providers. One model to increase the number of providers is to task-share: roles traditionally filled by clinicians are shared with lay workers who have received task-specific training. Separately, there has been much recent interest in the possible implications of artificial intelligence (AI) on healthcare. This paper proposes that, by combining the task-sharing model with AI, it is possible to design an Emergency Unit (EU) that shares the tasks currently undertaken by physicians and nurses with lay providers, with the activities of lay providers guided and supervised by AI. The proposed model would free emergency care clinicians to focus on higher-acuity and complex cases while AI-supervised routine care is provided by lay providers. The paper outlines the model for such an implementation and considers the potential benefits to patient care, as well as considering the risks, costs, effect on providers, and ethical questions. The paper concludes that AI and healthcare workers can operate as a team, with significant potential to augment human resources for health in sub-Saharan Africa.
Collapse
Affiliation(s)
- Camillo Lamanna
- University of New South Wales, Kirby Institute, Wallace Wurth Building, Kensington, New South Wales 2052, Sydney, Australia
| |
Collapse
|
31
|
Grosch M, Beyer L, Lindner M, Kaiser L, Ahmadi SA, Stockbauer A, Bartenstein P, Dieterich M, Brendel M, Zwergal A, Ziegler S. Metabolic connectivity-based single subject classification by multi-regional linear approximation in the rat. Neuroimage 2021; 235:118007. [PMID: 33831550 DOI: 10.1016/j.neuroimage.2021.118007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 03/15/2021] [Accepted: 03/18/2021] [Indexed: 10/21/2022] Open
Abstract
Metabolic connectivity patterns on the basis of [18F]-FDG positron emission tomography (PET) are used to depict complex cerebral network alterations in different neurological disorders and therefore may have the potential to support diagnostic decisions. In this study, we established a novel statistical classification method taking advantage of differential time-dependent states of whole-brain metabolic connectivity following unilateral labyrinthectomy (UL) in the rat and explored its classification accuracy. The dataset consisted of repeated [18F]-FDG PET measurements at baseline and 1, 3, 7, and 15 days (= maximum of 5 classes) after UL with 17 rats per measurement day. Classification in different stages after UL was performed by determining connectivity patterns for the different classes by Pearson's correlation between uptake values in atlas-based segmented brain regions. Connections were fitted with a linear function, with which different thresholds on the correlation coefficient (r = [0.5, 0.85]) were investigated. Rats were classified by determining the congruence of their PET uptake pattern with the fitted connectivity patterns in the classes. Overall, the classification accuracy with this method was 84.3% for 3 classes, 75.0% for 4 classes, and 54.1% for 5 classes and outperformed random classification as well as machine learning classification on the same dataset. The optimal classification thresholds of the correlation coefficient and distance-to-fit were found to be |r| > 0.65 and d = 4 when using Siegel's slope estimator for fitting. This connectivity-based classification method can compete with machine learning classification and may have methodological advantages when applied to support PET-based diagnostic decisions in neurological network disorders (such as neurodegenerative syndromes).
Collapse
Affiliation(s)
- Maximilian Grosch
- German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany; Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany.
| | - Leonie Beyer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany
| | - Magdalena Lindner
- German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany; Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany
| | - Seyed-Ahmad Ahmadi
- German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany
| | - Anna Stockbauer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany; Munich Cluster of Systems Neurology, SyNergy, Munich, Germany
| | - Marianne Dieterich
- German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany; Department of Neurology, University Hospital, LMU Munich, Munich, Germany; Munich Cluster of Systems Neurology, SyNergy, Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany; Munich Cluster of Systems Neurology, SyNergy, Munich, Germany
| | - Andreas Zwergal
- German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany; Department of Neurology, University Hospital, LMU Munich, Munich, Germany
| | - Sibylle Ziegler
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany
| |
Collapse
|
32
|
Lalousis PA, Wood SJ, Schmaal L, Chisholm K, Griffiths SL, Reniers RLEP, Bertolino A, Borgwardt S, Brambilla P, Kambeitz J, Lencer R, Pantelis C, Ruhrmann S, Salokangas RKR, Schultze-Lutter F, Bonivento C, Dwyer D, Ferro A, Haidl T, Rosen M, Schmidt A, Meisenzahl E, Koutsouleris N, Upthegrove R. Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach. Schizophr Bull 2021; 47:1130-1140. [PMID: 33543752 PMCID: PMC8266654 DOI: 10.1093/schbul/sbaa185] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Diagnostic heterogeneity within and across psychotic and affective disorders challenges accurate treatment selection, particularly in the early stages. Delineation of shared and distinct illness features at the phenotypic and brain levels may inform the development of more precise differential diagnostic tools. We aimed to identify prototypes of depression and psychosis to investigate their heterogeneity, with common, comorbid transdiagnostic symptoms. Analyzing clinical/neurocognitive and grey matter volume (GMV) data from the PRONIA database, we generated prototypic models of recent-onset depression (ROD) vs. recent-onset psychosis (ROP) by training support-vector machines to separate patients with ROD from patients with ROP, who were selected for absent comorbid features (pure groups). Then, models were applied to patients with comorbidity, ie, ROP with depressive symptoms (ROP+D) and ROD participants with sub-threshold psychosis-like features (ROD+P), to measure their positions within the affective-psychotic continuum. All models were independently validated in a replication sample. Comorbid patients were positioned between pure groups, with ROP+D patients being more frequently classified as ROD compared to pure ROP patients (clinical/neurocognitive model: χ2 = 14.874; P < .001; GMV model: χ2 = 4.933; P = .026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: χ2 = 1.956; P = 0.162; GMV model: χ2 = 0.005; P = .943). Clinical/neurocognitive and neuroanatomical models demonstrated separability of prototypic depression from psychosis. The shift of comorbid patients toward the depression prototype, observed at the clinical and biological levels, suggests that psychosis with affective comorbidity aligns more strongly to depressive rather than psychotic disease processes. Future studies should assess how these quantitative measures of comorbidity predict outcomes and individual responses to stratified therapeutic interventions.
Collapse
Affiliation(s)
- Paris Alexandros Lalousis
- Institute for Mental Health, University of Birmingham, Birmingham, UK
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
- To whom correspondence should be addressed; 52 Pritchatts Road, B15 2SA, Birmingham, UK; e-mail:
| | - Stephen J Wood
- Institute for Mental Health, University of Birmingham, Birmingham, UK
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia
- Centre for Youth Mental Health, The University of Melbourne, Parkville, Australia
| | - Lianne Schmaal
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia
- Centre for Youth Mental Health, The University of Melbourne, Parkville, Australia
| | - Katharine Chisholm
- Institute for Mental Health, University of Birmingham, Birmingham, UK
- Department of Psychology, Aston University, Birmingham, UK
| | - Sian Lowri Griffiths
- Institute for Mental Health, University of Birmingham, Birmingham, UK
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Renate L E P Reniers
- Institute for Mental Health, University of Birmingham, Birmingham, UK
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
- Institute of Clinical Sciences, University of Birmingham, Birmingham, UK
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Stefan Borgwardt
- Department of Psychiatry, University of Basel, Basel, Switzerland
- Department of Psychiatry and Psychotherapy, Center of Brain, Behavior and Metabolism, University of Lübeck, Germany
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Ludwig Maxmilians University, Munich, Germany
| | - Rebekka Lencer
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Australia
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | | | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Carolina Bonivento
- IRCCS “E. Medea” Scientific Institute, San Vito al Tagliamento (Pn), Italy
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig Maxmilians University, Munich, Germany
| | - Adele Ferro
- Department of Neurosciences and Mental Health, IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Theresa Haidl
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Andre Schmidt
- Department of Psychiatry, University of Basel, Basel, Switzerland
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maxmilians University, Munich, Germany
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
- Early Intervention Service, Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, UK
| | | |
Collapse
|
33
|
Busatto G, Rosa PG, Serpa MH, Squarzoni P, Duran FL. Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities. REVISTA BRASILEIRA DE PSIQUIATRIA (SAO PAULO, BRAZIL : 1999) 2021; 43:83-101. [PMID: 32520165 PMCID: PMC7861184 DOI: 10.1590/1516-4446-2019-0757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 02/03/2020] [Indexed: 11/23/2022]
Abstract
The last four decades have witnessed tremendous growth in research studies applying neuroimaging methods to evaluate pathophysiological and treatment aspects of psychiatric disorders around the world. This article provides a brief history of psychiatric neuroimaging research in Brazil, including quantitative information about the growth of this field in the country over the past 20 years. Also described are the various methodologies used, the wealth of scientific questions investigated, and the strength of international collaborations established. Finally, examples of the many methodological advances that have emerged in the field of in vivo neuroimaging are provided, with discussion of the challenges faced by psychiatric research groups in Brazil, a country of limited resources, to continue incorporating such innovations to generate novel scientific data of local and global relevance.
Collapse
Affiliation(s)
- Geraldo Busatto
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Pedro G. Rosa
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Mauricio H. Serpa
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Paula Squarzoni
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Fabio L. Duran
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| |
Collapse
|
34
|
Eitel F, Schulz MA, Seiler M, Walter H, Ritter K. Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Exp Neurol 2021; 339:113608. [PMID: 33513353 DOI: 10.1016/j.expneurol.2021.113608] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/07/2021] [Accepted: 01/09/2021] [Indexed: 12/13/2022]
Abstract
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.
Collapse
Affiliation(s)
- Fabian Eitel
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Marc-André Schulz
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Moritz Seiler
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Henrik Walter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany
| | - Kerstin Ritter
- Charité - Universitätsmedizin Berlin, Corporate Member Of Freie Universität Berlin, Humboldt-Universität zu Berlin; Department of Psychiatry and Psychotherapy, 10117 Berlin, Germany; Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany.
| |
Collapse
|
35
|
Projective parameter transfer based sparse multiple empirical kernel learning Machine for diagnosis of brain disease. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.008] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
36
|
Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
Collapse
Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| |
Collapse
|
37
|
Abstract
Magnetic resonance imaging (MRI) is a noninvasive imaging tool for neuroradiological diagnosis. Numerous concepts of automated MRI analysis and the use of machine learning have been proposed to assist diagnosis and prognosis. While these academic innovations have proven effective in principle within controlled environments, their application to clinical practice has faced unmet requirements, such as the ability to perform reliably across a heterogeneous population, to work robustly in the presence of comorbidities, and to be invariant to scanner hardware and image quality. The lack of realistic confidence bounds and the inability to handle missing data have also reduced the application of most of these methods outside of academic studies. Mastering the complex challenges in the diagnostic process may help researchers discover novel biological constructs in multimodal data and improve stratification for clinical trials, paving the way for precision medicine. This review presents the state of the art of computerized brain MRI analysis for diagnostic purposes. We critically evaluate the current clinical usefulness of the methods and highlight challenges and future perspectives of the field.
Collapse
Affiliation(s)
- Saima Rathore
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- University Hospital of of Old Age Psychiatry and Psychotherapy, University of Bern, 3008 Bern, Switzerland
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| |
Collapse
|
38
|
An Improved Multi-Modal based Machine Learning Approach for the Prognosis of Alzheimer’s disease. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2020. [DOI: 10.1016/j.jksuci.2020.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
39
|
Bachli MB, Sedeño L, Ochab JK, Piguet O, Kumfor F, Reyes P, Torralva T, Roca M, Cardona JF, Campo CG, Herrera E, Slachevsky A, Matallana D, Manes F, García AM, Ibáñez A, Chialvo DR. Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach. Neuroimage 2019; 208:116456. [PMID: 31841681 PMCID: PMC7008715 DOI: 10.1016/j.neuroimage.2019.116456] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 10/29/2019] [Accepted: 12/09/2019] [Indexed: 12/12/2022] Open
Abstract
Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions –Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)– across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.
Collapse
Affiliation(s)
- M Belen Bachli
- Center for Complex Systems & Brain Sciences (CEMSC(3)), Escuela de Ciencia y Tecnologia (ECyT), Universidad Nacional de San Martín, 25 de Mayo 1169, San Martín, (1650), Buenos Aires, Argentina
| | - Lucas Sedeño
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina.
| | - Jeremi K Ochab
- Marian Smoluchowski Institute of Physics, Mark Kac Complex Systems Research Center Jagiellonian University, Ul. Łojasiewicza 11, PL30-348, Kraków, Poland
| | - Olivier Piguet
- ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia; The University of Sydney, Brain and Mind Centre and School of Psychology, Sydney, Australia
| | - Fiona Kumfor
- ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia; The University of Sydney, Brain and Mind Centre and School of Psychology, Sydney, Australia
| | - Pablo Reyes
- Radiology, Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia; Medical School, Physiology Sciences, Psychiatry and Mental Health Pontificia Universidad Javeriana (PUJ) - Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia
| | - Teresa Torralva
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
| | - María Roca
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
| | | | - Cecilia Gonzalez Campo
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad Icesi, Cali, Colombia
| | - Andrea Slachevsky
- Gerosciences Center for Brain Health and Metabolism, Santiago, Chile; Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department, ICBM, Neurosciences Department, East Neuroscience Department, Faculty of Medicine, University of Chile, Avenida Salvador 486, Providencia, Santiago, Chile; Memory and Neuropsychiatric Clinic (CMYN) Neurology Department- Hospital del Salvador & University of Chile, Av. Salvador 364, Providencia, Santiago, Chile; Servicio de Neurología, Departamento de Medicina, Clínica Alemana-Universidad del Desarrollo, Chile
| | - Diana Matallana
- Medical School, Aging Institute, Psychiatry and Mental Health, Pontificia Universidad Javeriana (PUJ) - Centro de Memoria y Cognición Intellectus. Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia
| | - Facundo Manes
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina; ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia
| | - Adolfo M García
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina; Faculty of Education, National University of Cuyo (UNCuyo), Sobremonte 74, C5500, Mendoza, Argentina
| | - Agustín Ibáñez
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina; ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia; Universidad Autónoma del Caribe, Calle 90, No 46-112, C2754, Barranquilla, Colombia; Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Diagonal Las Torres, 2640, Santiago, Chile
| | - Dante R Chialvo
- Center for Complex Systems & Brain Sciences (CEMSC(3)), Escuela de Ciencia y Tecnologia (ECyT), Universidad Nacional de San Martín, 25 de Mayo 1169, San Martín, (1650), Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina
| |
Collapse
|
40
|
Chakravarty MM. Guest editorial: Special issue on machine learning in schizophrenia. Schizophr Res 2019; 214:1-2. [PMID: 31711732 DOI: 10.1016/j.schres.2019.10.044] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 10/20/2019] [Indexed: 11/16/2022]
Affiliation(s)
- M Mallar Chakravarty
- Computational Brain Anatomy (CoBrA Laboratory), Douglas Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.
| |
Collapse
|
41
|
Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:9108108. [PMID: 31781290 PMCID: PMC6875180 DOI: 10.1155/2019/9108108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 08/04/2019] [Accepted: 09/06/2019] [Indexed: 12/17/2022]
Abstract
In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect the structure of the constructed network and its topological properties. However, we still do not know how network scale differences affect the classification accuracy, performance of classification features, and effectiveness of the feature selection strategy using P values in terms of the machine learning method. This study used five scale parcellations, involving 90, 256, 497, 1003, and 1501 nodes. Three local properties of resting-state functional brain networks were selected (degree, betweenness centrality, and nodal efficiency), and the support vector machine method was used to construct classifiers to identify patients with major depressive disorder. We analyzed the impact of the five scales on classification accuracy. In addition, the effectiveness and redundancy of features obtained by the different scale parcellations were compared. Finally, traditional statistical significance (P value) was verified as a feature selection criterion. The results showed that the feature effectiveness of different scales was similar; in other words, parcellation with more regions did not provide more effective discriminative features. Nevertheless, parcellation with more regions did provide a greater quantity of discriminative features, which led to an improvement in the accuracy of the classification. However, due to the close distance between brain regions, the redundancy of parcellation with more regions was also greater. The traditional P value feature selection strategy is feasible with different scales, but our analysis showed that the traditional P < 0.05 threshold was too strict for feature selection. This study provides an important reference for the selection of network scales when applying topological properties of brain networks to machine learning methods.
Collapse
|
42
|
Mayer EA, Labus J, Aziz Q, Tracey I, Kilpatrick L, Elsenbruch S, Schweinhardt P, Van Oudenhove L, Borsook D. Role of brain imaging in disorders of brain-gut interaction: a Rome Working Team Report. Gut 2019; 68:1701-1715. [PMID: 31175206 PMCID: PMC6999847 DOI: 10.1136/gutjnl-2019-318308] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 03/18/2019] [Accepted: 03/24/2019] [Indexed: 12/12/2022]
Abstract
Imaging of the living human brain is a powerful tool to probe the interactions between brain, gut and microbiome in health and in disorders of brain-gut interactions, in particular IBS. While altered signals from the viscera contribute to clinical symptoms, the brain integrates these interoceptive signals with emotional, cognitive and memory related inputs in a non-linear fashion to produce symptoms. Tremendous progress has occurred in the development of new imaging techniques that look at structural, functional and metabolic properties of brain regions and networks. Standardisation in image acquisition and advances in computational approaches has made it possible to study large data sets of imaging studies, identify network properties and integrate them with non-imaging data. These approaches are beginning to generate brain signatures in IBS that share some features with those obtained in other often overlapping chronic pain disorders such as urological pelvic pain syndromes and vulvodynia, suggesting shared mechanisms. Despite this progress, the identification of preclinical vulnerability factors and outcome predictors has been slow. To overcome current obstacles, the creation of consortia and the generation of standardised multisite repositories for brain imaging and metadata from multisite studies are required.
Collapse
Affiliation(s)
- Emeran A Mayer
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Jennifer Labus
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Qasim Aziz
- Neurogastroenterology Group, Queen Mary University of London, London, UK
| | - Irene Tracey
- Departments of Anaesthetics and Clinical Neurology, Pembroke College, Oxford, UK
| | - Lisa Kilpatrick
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, Vatche and Tamar Manoukian Division of Digestive Diseases David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Sigrid Elsenbruch
- Institute of Medical Psychology & Behavioral Immunobiology, University Hospital Essen, University of Duisburg, Duisburg, Germany
| | | | - Lukas Van Oudenhove
- Translational Research in GastroIntestinal Disorders, KU Leuven Department of Clinical and Experimental Medicine, University of Leuven, Leuven, Belgium
| | - David Borsook
- Center for Pain and the Brain, Boston Children's, Massachusetts General and McLean Hospitals, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
43
|
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments. Neuroimage 2019; 198:255-270. [DOI: 10.1016/j.neuroimage.2019.05.040] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 05/14/2019] [Accepted: 05/15/2019] [Indexed: 01/06/2023] Open
|
44
|
Poldrack RA, Gorgolewski KJ, Varoquaux G. Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging. Annu Rev Biomed Data Sci 2019. [DOI: 10.1146/annurev-biodatasci-072018-021237] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data-sharing resources that have been developed for neuroimaging data, as well as the role of data standards (particularly the brain imaging data structure) in enabling the automated sharing, processing, and reuse of large neuroimaging data sets. We outline how the open source Python language has provided the basis for a data science platform that enables reproducible data analysis and visualization. We also discuss how new advances in software engineering, such as containerization, provide the basis for greater reproducibility in data analysis. The emergence of this new ecosystem provides an example for many areas of science that are currently struggling with reproducibility.
Collapse
Affiliation(s)
- Russell A. Poldrack
- Department of Psychology, Stanford University, Stanford, California 94305, USA
| | | | - Gaël Varoquaux
- Parietal Team, Inria and NeuroSpin/CEA (Atomic Energy Commission), 91191 Gif/-sur-Yvette, France
| |
Collapse
|
45
|
Wee CY, Liu C, Lee A, Poh JS, Ji H, Qiu A. Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations. Neuroimage Clin 2019; 23:101929. [PMID: 31491832 PMCID: PMC6627731 DOI: 10.1016/j.nicl.2019.101929] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/02/2019] [Accepted: 07/02/2019] [Indexed: 01/18/2023]
Abstract
Combining machine learning with neuroimaging data has a great potential for early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it remains unclear how well the classifiers built on one population can predict MCI/AD diagnosis of other populations. This study aimed to employ a spectral graph convolutional neural network (graph-CNN), that incorporated cortical thickness and geometry, to identify MCI and AD based on 3089 T1-weighted MRI data of the ADNI-2 cohort, and to evaluate its feasibility to predict AD in the ADNI-1 cohort (n = 3602) and an Asian cohort (n = 347). For the ADNI-2 cohort, the graph-CNN showed classification accuracy of controls (CN) vs. AD at 85.8% and early MCI (EMCI) vs. AD at 79.2%, followed by CN vs. late MCI (LMCI) (69.3%), LMCI vs. AD (65.2%), EMCI vs. LMCI (60.9%), and CN vs. EMCI (51.8%). We demonstrated the robustness of the graph-CNN among the existing deep learning approaches, such as Euclidean-domain-based multilayer network and 1D CNN on cortical thickness, and 2D and 3D CNNs on T1-weighted MR images of the ADNI-2 cohort. The graph-CNN also achieved the prediction on the conversion of EMCI to AD at 75% and that of LMCI to AD at 92%. The find-tuned graph-CNN further provided a promising CN vs. AD classification accuracy of 89.4% on the ADNI-1 cohort and >90% on the Asian cohort. Our study demonstrated the feasibility to transfer AD/MCI classifiers learned from one population to the other. Notably, incorporating cortical geometry in CNN has the potential to improve classification performance.
Collapse
Affiliation(s)
- Chong-Yaw Wee
- Department of Biomedical Engineering and Clinical Research Center, National University of Singapore, Singapore
| | - Chaoqiang Liu
- Department of Biomedical Engineering and Clinical Research Center, National University of Singapore, Singapore
| | - Annie Lee
- Department of Biomedical Engineering and Clinical Research Center, National University of Singapore, Singapore
| | - Joann S Poh
- Department of Biomedical Engineering and Clinical Research Center, National University of Singapore, Singapore
| | - Hui Ji
- Department of Mathematics, National University of Singapore, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering and Clinical Research Center, National University of Singapore, Singapore.
| |
Collapse
|
46
|
Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal 123I-FP-CIT indices. J Neurol 2019; 266:1771-1781. [PMID: 31037416 PMCID: PMC6586917 DOI: 10.1007/s00415-019-09330-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 04/10/2019] [Accepted: 04/21/2019] [Indexed: 12/30/2022]
Abstract
Objectives To provide an automated classification method for degenerative parkinsonian syndromes (PS) based on semiquantitative 123I-FP-CIT SPECT striatal indices and support-vector-machine (SVM) analysis. Methods 123I-FP-CIT SPECT was performed at a single-center level on 370 individuals with PS, including 280 patients with Parkinson’s disease (PD), 21 with multiple system atrophy-parkinsonian type (MSA-P), 41 with progressive supranuclear palsy (PSP) and 28 with corticobasal syndrome (CBS) (mean age 70.3 years, 47% female, mean disease duration at scan 1.4 year), as well as 208 age- and gender-matched control subjects. Striatal volumes-of-interest (VOIs) uptake, VOIs asymmetry indices (AIs) and caudate/putamen (C/P) ratio were used as input for SVM individual classification using fivefold cross-validation. Results Univariate analyses showed significantly lower VOIs uptake, higher striatal AI and C/P ratio for each PS in comparison to controls (all p < 0.001). Among PS, higher degree of striatal impairment was observed in MSA-P and PSP, while CBS showed moderate uptake reduction and higher AI. Binary SVM classification showed 92.9% accuracy in distinguishing PS from controls. Classification based on each binary combination of PS ranged 62.9–83.7% accuracy with the most satisfactory results when separating CBS from the other PS. Sensitivity and specificity values were high and balanced ranging from 60 to 80% for all analyses with > 70% accuracy. Overall, striatal AI and C/P ratio on the more affected side had the highest weighting factors. Conclusion Semiquantitative 123I-FP-CIT SPECT striatal evaluation combined with SVM represents a promising approach to disentangle PD from non-degenerative conditions and from atypical PS at the early stage.
Collapse
|
47
|
Deng Y, Hung KSY, Lui SSY, Chui WWH, Lee JCW, Wang Y, Li Z, Mak HKF, Sham PC, Chan RCK, Cheung EFC. Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals. Prog Neuropsychopharmacol Biol Psychiatry 2019; 88:66-73. [PMID: 29935206 DOI: 10.1016/j.pnpbp.2018.06.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 05/23/2018] [Accepted: 06/19/2018] [Indexed: 01/07/2023]
Abstract
BACKGROUND Schizophrenia has been characterized as a neurodevelopmental disorder of brain disconnectivity. However, whether disrupted integrity of white matter tracts in schizophrenia can potentially serve as individual discriminative biomarkers remains unclear. METHODS A random forest algorithm was applied to tractography-based diffusion properties obtained from a cohort of 65 patients with first-episode schizophrenia (FES) and 60 healthy individuals to investigate the machine-learning discriminative power of white matter disconnectivity. Recursive feature elimination was used to select the ultimate white matter features in the classification. Relationships between algorithm-predicted probabilities and clinical characteristics were also examined in the FES group. RESULTS The classifier was trained by 80% of the sample. Patients were distinguished from healthy individuals with an overall accuracy of 71.0% (95% confident interval: 61.1%, 79.6%), a sensitivity of 67.3%, a specificity of 75.0%, and the area under receiver operating characteristic curve (AUC) was 79.3% (χ2 p < 0.001). In validation using the held-up 20% of the sample, patients were distinguished from healthy individuals with an overall accuracy of 76.0% (95% confident interval: 54.9%, 90.6%), a sensitivity of 76.9%, a specificity of 75.0%, and an AUC of 73.1% (χ2 p = 0.012). Diffusion properties of inter-hemispheric fibres, the cerebello-thalamo-cortical circuits and the long association fibres were identified to be the most discriminative in the classification. Higher predicted probability scores were found in younger patients. CONCLUSIONS Our findings suggest that the widespread connectivity disruption observed in FES patients, especially in younger patients, might be considered potential individual discriminating biomarkers.
Collapse
Affiliation(s)
- Yi Deng
- Castle Peak Hospital, Hong Kong, China; Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Cognitive Analysis & Brain Imaging Laboratory, MIND Institute, University of California, Davis, CA, United States
| | | | - Simon S Y Lui
- Castle Peak Hospital, Hong Kong, China; Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | | | | | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Zhi Li
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Henry K F Mak
- Department of Radiology, The University of Hong Kong, Hong Kong, China
| | - Pak C Sham
- Center of Genomic Sciences, The University of Hong Kong, Hong Kong, China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Eric F C Cheung
- Castle Peak Hospital, Hong Kong, China; Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
| |
Collapse
|
48
|
Abstract
Mood disorders include all types of depression and bipolar disorder, and mood disorders are sometimes called affective disorders. We will discuss newly developing two issues in affective disorders in children and adolescents. Those are the new diagnostic challenges using neuroimaging techniques in affective disorders and the introduction of disruptive mood dysregulation disorder (DMDD). During the 1980s, mental health professionals began to recognize symptoms of mood disorders in children and adolescents, as well as adults. However, children and adolescents do not necessarily have or exhibit the same symptoms as adults. It is more difficult to diagnose mood disorders in children, especially because children are not always able to express how they feel. Child mental health professionals believe that mood disorders in children and adolescents remain one of the most underdiagnosed mental health problems. We are currently trying to introduce the new diagnostic technique-machine learning in children and adolescents with MDD. We will discuss the current progress in the clinical application of machine learning for MDD. After that, we would also discuss a new challenging diagnosis-DMDD. We are still suffering from a lack of evidence when trying to treat the patients with DMDD. In addition, there are some debates about the diagnostic validity of DMDD. We will explain the current situation of DMDD studies and the future directions in the study of DMDD.
Collapse
|
49
|
Tulay EE, Metin B, Tarhan N, Arıkan MK. Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases. Clin EEG Neurosci 2019; 50:20-33. [PMID: 29925268 DOI: 10.1177/1550059418782093] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.
Collapse
Affiliation(s)
| | | | - Nevzat Tarhan
- 1 Uskudar University, Istanbul, Turkey.,2 NPIstanbul Hospital, Istanbul, Turkey
| | | |
Collapse
|
50
|
Pinaya WHL, Mechelli A, Sato JR. Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study. Hum Brain Mapp 2018; 40:944-954. [PMID: 30311316 PMCID: PMC6492107 DOI: 10.1002/hbm.24423] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Revised: 09/25/2018] [Accepted: 10/02/2018] [Indexed: 11/11/2022] Open
Abstract
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain-based disorders which aim to overcome these limitations. We used an artificial neural network known as "deep autoencoder" to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.
Collapse
Affiliation(s)
- Walter H L Pinaya
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.,Center for Engineering, Modeling and Applied Social Sciences, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.,Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - João R Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| |
Collapse
|